Archive for the ‘Evidence-based decision-making’ Category

Reporter: Stephen J. Williams, PhD

10:00-10:45 AM The Davids vs. the Cancer Goliath Part 1

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
10:00 10:08 Belong.Life
10:09 10:17 Care+Wear
10:18 10:26 OncoPower
10:27 10:35 PolyAurum LLC
10:36 10:44 Seeker Health

Karthik Koduru, MD, Co-Founder and Chief Oncologist, OncoPower
Eliran Malki, Co-Founder and CEO, Belong.Life
Chaitenya Razdan, Co-founder and CEO, Care+Wear @_crazdan
Debra Shipley Travers, President & CEO, PolyAurum LLC @polyaurum
Sandra Shpilberg, Founder and CEO, Seeker Health @sandrashpilberg

Belong Life

  • 10,000 cancer patients a month helping patients navigate cancer care with Belong App
  • Belong Eco system includes all their practitioners and using a trigger based content delivery (posts, articles etc)
  • most important taking unstructured health data (images, social activity, patient compilance) and converting to structured data


personally design picc line cover for oncology patients

partners include NBA Major league baseball, Oscar de la Renta,

designs easy access pic line gowns and shirts

OncoPower :Digital Health in a Blockchain Ecosystem

problems associated with patient adherence and developed a product to address this

  1. OncoPower Blockchain: HIPAA compliant using the coin Oncopower security token to incentiavize patients and oncologists to consult with each other or oncologists with tumor boards; this is not an initial coin offering


  • spinout from UPENN; developing a nanoparticle based radiation therapy; glioblastoma muse model showed great response with gold based nanoparticle and radiation
  • they see enhanced tumor penetration, and retention of the gold nanoparticles
  • however most nanoparticles need to be a large size greater than 5 nm to see effect so they used a polymer based particle; see good uptake but excretion past a week so need to re-dose with Au nanoparticles
  • they are looking for capital and expect to start trials in 2020

Seeker Health

  • tying to improve the efficiency of clinical trial enrollment
  • using social networks to find the patients to enroll in clinical trials
  • steps they use 1) find patients on Facebook, Google, Twitter 2) engage patient screen 3) screening at clinical sites
  • Seeker Portal is a patient management system: patients referred to a clinical site now can be tracked

11:00- 11:45 AM Breakout: How to Scale Precision Medicine

The potential for precision medicine is real, but is limited by access to patient datasets. How are government entities, hospitals and startups bringing the promise of precision medicine to the masses of oncology patients

Moderator: Sandeep Burugupalli, Senior Manager, Real World Data Innovation, Pfizer @sandeepburug
Ingo ​Chakravarty, President and CEO, Navican @IngoChakravarty
Eugean Jiwanmall, Senior Research Analyst for Medical Policy & Technology Evaluation , Independence Blue Cross @IBX
Andrew Norden, M.D., Chief Medical Officer, Cota @ANordenMD
Ankur Parikh M.D, Medical Director of Precision Medicine, Cancer Treatment Centers of America @CancerCenter

Ingo: data is not ordered, only half of patients are tracked in some database, reimbursement a challenge

Eugean: identifying mutations as patients getting more comprehensive genomic coverage, clinical trials are expanding more rapidly as seen in 2018 ASCO

Ingo: general principals related to health outcomes or policy or reimbursement.. human studies are paramount but payers may not allowing for general principals (i.e. an Alk mutation in lung cancer and crizotanib treatment may be covered but maybe not for glioblastoma or another cancer containing similar ALK mutation; payers still depend on clinical trial results)

Andrew: using gene panels and NGS but only want to look for actionable targets; they establish an expert panel which reviews these NGS sequence results to determine actionable mutations

Ankur:  they have molecular tumor boards but still if want to prescribe off label and can’t find a clinical trial there is no reimbursement

Andrew: going beyond actionable mutations, although many are doing WES (whole exome sequencing) can we use machine learning to see if there are actionable data from a WES

Ingo: we forget in datasets is that patients have needs today and we need those payment systems and structures today

Eugean: problem is the start from cost (where the cost starts at and was it truly medically necessary)

Norden: there are not enough data sharing to make a decision; an enormous amount of effort to get businesses and technical limitations in data sharing; possibly there are policies needed to be put in place to assimilate datasets and promote collaborations

Ingo: need to take out the middle men between sequencing of patient tumor and treatment decision; middle men are taking out value out of the ‘supply chain’;

Andrew: PATIENTS DON’T OWN their DATA but MOST clinicians agree THEY SHOULD

Ankur: patients are willing to share data but the HIPAA compliance is a barrier


11:50- 12:30 AM Fireside Chat with Michael Pellini, M.D.

Building a Precision Medicine Business from the Ground Up: An Operating and Venture Perspective

Dr. Pellini has spent more than 20 years working on the operating side of four companies, each of which has pushed the boundaries of the standard of care. He will describe his most recent experience at Foundation Medicine, at the forefront of precision medicine, and how that experience can be leveraged on the venture side, where he now evaluates new healthcare technologies.

Michael Pellini, M.D., Managing Partner, Section 32 and Chairman, Foundation Medicine @MichaelPellini

Roche just bought Foundation Medicine for $2.5 billion.  They negotiated over 7 months but aside from critics they felt it was a great deal because it gives them, as a diagnostic venture, the international reach and biotech expertise.  Foundation Medicine offered Roche expertise on the diagnostic space including ability to navigate payers and regulatory aspects of the diagnostic business.  He feels it benefits all aspects of patient care and the work they do with other companies.

Moderatore: Roche is doing multiple deals to ‘own’ a disease state.

Dr. Pellini:  Roche is closing a deal with Flatiron just like how Merck closed deals with genomics companies.  He feels best to build the best company on a stand alone basis and provide for patients, then good things will happen.  However the problem of achieving scale for Precision Medicine is reimbursement by payers.  They still have to keep collecting data and evolving services to suit pharma.  They didn’t know if there model would work but when he met with FDA in 2011 they worked with Precision Medicine, said collect the data and we will keep working with you,

However the payers aren’t contributing to the effort.  They need to assist some of the young companies that can’t raise the billion dollars needed for all the evidence that payers require.  Precision Medicine still have problems, even though they have collected tremendous amounts of data and raised significant money.  From the private payer perspective there is no clear roadmap for success.

They recognized that the payers would be difficult but they had a plan but won’t invest in companies that don’t have a plan for getting reimbursement from payers.

Moderator: What is section 32?

Pellini:  Their investment arm invests in the spectrum of precision healtcare companies including tech companies.  They started with a digital path imaging system that went from looking through a scope and now looking at a monitor with software integrated with medical records. Section 32 has $130 million under management and may go to $400 Million but they want to stay small.

Pellini: we get 4-5 AI pitches a week.

Moderator: Are you interested in companion diagnostics?

Pellini:  There may be 24 expected 2018 drug approvals and 35% of them have a companion diagnostic (CDX) with them.  however going out ten years 70% may have a CDX associated with them.  Payers need to work with companies to figure out how to pay with these CDXs.




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Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

Stephen J. Williams: Reporter

MedCity CONVERGE is a two-day executive summit that gathers innovative thought leaders from across all healthcare sectors to provide actionable insight on where oncology innovation is heading.

On July 11-12, 2018 in Philadelphia, MedCity CONVERGE will gather technology disruptors, payers, providers, life science companies, venture capitalists and more to discuss how AI, Big Data and Precision Medicine are changing the game in cancer. See agenda.

The conference highlights innovation and best practices across the continuum—from research to technological innovation to transformations of treatment and care delivery, and most importantly, patient empowerment—from some of the country’s most innovative healthcare organizations managing the disease.

Meaningful networking opportunities abound, with executives driving the innovation from diverse entities: leading hospital systems, medical device firms, biotech, pharma, emerging technology startups and health IT, as well as the investment community.

Day 1: Wednesday, July 11, 2018

7:30 AM

2nd Floor – Paris Foyer

Registration + Breakfast

8:15 AM–8:30 AM

Paris Ballroom

Welcome Remarks: Arundhati Parmar, VP and Editor-in-Chief, MedCity News

8:30 AM–9:15 AM

Paris Ballroom

Practical Applications of AI in Cancer

We are far from machine learning dictating clinical decision making, but AI has important niche applications in oncology. Hear from a panel of innovative startups and established life science players about how machine learning and AI can transform different aspects in healthcare, be it in patient recruitment, data analysis, drug discovery or care delivery.

Moderator: Ayan Bhattacharya, Advanced Analytics Specialist Leader, Deloitte Consulting LLP
Wout Brusselaers, CEO and Co-Founder, Deep 6 AI @woutbrusselaers ‏
Tufia Haddad, M.D., Chair of Breast Medical Oncology and Department of Oncology Chair of IT, Mayo Clinic
Carla Leibowitz, Head of Corporate Development, Arterys @carlaleibowitz
John Quackenbush, Ph.D., Professor and Director of the Center for Cancer Computational Biology, Dana-Farber Cancer Institute

9:15 AM–9:45 AM

Paris Ballroom

Opening Keynote: Dr. Joshua Brody, Medical Oncologist, Mount Sinai Health System

The Promise and Hype of Immunotherapy

Immunotherapy is revolutionizing oncology care across various types of cancers, but it is also necessary to sort the hype from the reality. In his keynote, Dr. Brody will delve into the history of this new therapy mode and how it has transformed the treatment of lymphoma and other diseases. He will address the hype surrounding it, why so many still don’t respond to the treatment regimen and chart the way forward—one that can lead to more elegant immunotherapy combination paths and better outcomes for patients.

Joshua Brody, M.D., Assistant Professor, Mount Sinai School of Medicine @joshuabrodyMD

9:45 AM–10:00 AM

Paris Foyer

Networking Break + Showcase

10:00 AM–10:45 AM

Paris Ballroom

The Davids vs. the Cancer Goliath Part 1

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
10:00 10:08 Belong.Life
10:09 10:17 Care+Wear
10:18 10:26 OncoPower
10:27 10:35 PolyAurum LLC
10:36 10:44 Seeker Health

Karthik Koduru, MD, Co-Founder and Chief Oncologist, OncoPower
Eliran Malki, Co-Founder and CEO, Belong.Life
Chaitenya Razdan, Co-founder and CEO, Care+Wear @_crazdan
Debra Shipley Travers, President & CEO, PolyAurum LLC @polyaurum
Sandra Shpilberg, Founder and CEO, Seeker Health @sandrashpilberg

10:45 AM–11:00 AM

Paris Foyer

Networking Break + Showcase

11:00 AM–11:45 AM

Montpellier – 3rd Floor

Breakout: Biopharma Gets Its Feet Wet in Digital Health

In the last few years, biotech and pharma companies have been leveraging digital health tools in everything from oncology trials, medication adherence to patient engagement. What are the lessons learned?

Moderator: Anthony Green, Ph.D., Vice President, Technology Commercialization Group, Ben Franklin Technology Partners
Derek Bowen, VP of Business Development & Strategy, Blackfynn, Inc.
Gyan Kapur, Vice President, Activate Venture Partners
Tom Kottler, Co-Founder & CEO, HealthPrize Technologies @HealthPrize

11:00 AM–11:45 AM

Paris Ballroom

Breakout: How to Scale Precision Medicine

The potential for precision medicine is real, but is limited by access to patient datasets. How are government entities, hospitals and startups bringing the promise of precision medicine to the masses of oncology patients

Moderator: Sandeep Burugupalli, Senior Manager, Real World Data Innovation, Pfizer @sandeepburug
Ingo ​Chakravarty, President and CEO, Navican @IngoChakravarty
Eugean Jiwanmall, Senior Research Analyst for Medical Policy & Technology Evaluation , Independence Blue Cross @IBX
Andrew Norden, M.D., Chief Medical Officer, Cota @ANordenMD
Ankur Parikh M.D, Medical Director of Precision Medicine, Cancer Treatment Centers of America @CancerCenter

11:50 AM–12:30 PM

Paris Ballroom

Fireside Chat with Michael Pellini, M.D.

Building a Precision Medicine Business from the Ground Up: An Operating and Venture Perspective

Dr. Pellini has spent more than 20 years working on the operating side of four companies, each of which has pushed the boundaries of the standard of care. He will describe his most recent experience at Foundation Medicine, at the forefront of precision medicine, and how that experience can be leveraged on the venture side, where he now evaluates new healthcare technologies.

Michael Pellini, M.D., Managing Partner, Section 32 and Chairman, Foundation Medicine @MichaelPellini

12:30 PM–1:30 PM

Chez Colette Restaurant – Lobby

Lunch Reception

1:30 PM–2:15 PM

Paris Ballroom

Clinical Trials 2.0

The randomized, controlled clinical trial is the gold standard, but it may be time for a new model. How can patient networks and new technology be leveraged to boost clinical trial recruitment and manage clinical trials more efficiently?

Moderator: John Reites, Chief Product Officer, Thread @johnreites
Andrew Chapman M.D., Chief of Cancer Services , Sidney Kimmel Cancer Center, Thomas Jefferson University Hospital
Michelle Longmire, M.D., Founder, Medable @LongmireMD
Sameek Roychowdhury MD, PhD, Medical Oncologist and Researcher, Ohio State University Comprehensive Cancer Center @OSUCCC_James

2:20 PM–3:00 PM

Paris Ballroom

CONVERGEnce on Steroids: Why Comcast and Independence Blue Cross?

This year has seen a great deal of convergence in health care.  One of the most innovative collaborations announced was that of Cable and Media giant Comcast Corporation and health plan Independence Blue Cross.  This fireside chat will explore what the joint venture is all about, the backstory of how this unlikely partnership came to be, and what it might mean for our industry.

sponsored by Independence Blue Cross

Moderator: Tom Olenzak, Managing Director Strategic Innovation Portfolio, Independence Blue Cross @IBX
Marc Siry, VP, Strategic Development, Comcast
Michael Vennera, SVP, Chief Information Officer, Independence Blue Cross

3:00 PM–3:15 PM

Paris Foyer

Networking Break + Showcase

3:15 PM–4:00 PM

Montpellier – 3rd Floor

Breakout: Charting the Way Forward in Gene and Cell Therapy

There is a boom underway in cell and gene therapies that are being wielded to tackle cancer and other diseases at the cellular level. FDA has approved a few drugs in the space. These innovations raise important questions about patient access, patient safety, and personalized medicine. Hear from interesting startups and experts about the future of gene therapy.

Moderator: Alaric DeArment, Senior Reporter, MedCity News
Amy DuRoss, CEO, Vineti
Andre Goy, M.D., Chairman and Director of John Theurer Cancer Center , Hackensack University Medical Center

3:15 PM–4:00 PM

Paris Ballroom

Breakout: What’s A Good Model for Value-Based Care in Oncology?

How do you implement a value-based care model in oncology? Medicare has created a bundled payment model in oncology and there are lessons to be learned from that and other programs. Listen to two presentations from experts in the field.

Moderator: Mahek Shah, M.D., Senior Researcher, Harvard Business School @Mahek_MD
Charles Saunders M.D., CEO, Integra Connect
Mari Vandenburgh, Director of Value-Based Reimbursement Operations, Highmark @Highmark

4:00 PM–4:10 PM

Paris Foyer

Networking Break + Showcase

4:10 PM–4:55 PM

Montpellier – 3rd Floor

Breakout: Trends in Oncology Investing

A panel of investors interested in therapeutics, diagnostics, digital health and emerging technology will discuss what is hot in cancer investing.

Moderator: Stephanie Baum, Director of Special Projects, MedCity News @StephLBaum
Karen Griffith Gryga, Chief Investment Officer, Dreamit Ventures @karengg 
Stacey Seltzer, Partner, Aisling Capital
David Shaywitz, M.D., Ph.D., Senior Partner, Takeda Ventures

4:10 PM–4:55 PM

Paris Ballroom

Breakout: What Patients Want and Need On Their Journey

Cancer patients are living with an existential threat every day. A panel of patients and experts in oncology care management will discuss what’s needed to make the journey for oncology patients a bit more bearable.

sponsored by CEO Council for Growth

Moderator: Amanda Woodworth, M.D., Director of Breast Health, Drexel University College of Medicine
Kezia Fitzgerald, Chief Innovation Officer & Co-Founder, CareAline® Products, LLC
Sara Hayes, Senior Director of Community Development, Health Union @SaraHayes_HU
Katrece Nolen, Cancer Survivor and Founder, Find Cancer Help @KatreceNolen
John Simpkins, Administrative DirectorService Line Director of the Cancer Center, Children’s Hospital of Philadelphia

5:00 PM–5:45 PM

Paris Ballroom

Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

5:45 PM–7:00 PM

Paris Foyer

Networking Reception

Day 2: Thursday, July 12, 2018

7:30 AM

Paris Foyer

Breakfast + Registration

8:30 AM–8:40 AM

Paris Ballroom

Opening Remarks: Arundhati Parmar, VP and Editor-in-Chief, MedCity News

8:40 AM–9:25 AM

Paris Ballroom

The Davids vs. the Cancer Goliath Part 2

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
8:40 8:48 3Derm
8:49 8:57 CNS Pharmaceuticals
8:58 9:06 Cubismi
9:07 9:15 CytoSavvy
9:16 9:24 PotentiaMetrics

Liz Asai, CEO & Co-Founder, 3Derm Systems, Inc. @liz_asai
John M. Climaco, CEO, CNS Pharmaceuticals @cns_pharma 
John Freyhof, CEO, CytoSavvy
Robert Palmer, President & CEO, PotentiaMetrics @robertdpalmer 
Moira Schieke M.D., Founder, Cubismi, Adjunct Assistant Prof UW Madison @cubismi_inc

9:30 AM–10:15 AM

Paris Ballroom

Liquid Biopsy and Gene Testing vs. Reimbursement Hurdles

Genetic testing, whether broad-scale or single gene-testing, is being ordered by an increasing number of oncologists, but in many cases, patients are left to pay for these expensive tests themselves. How can this dynamic be shifted? What can be learned from the success stories?

Moderator: Shoshannah Roth, Assistant Director of Health Technology Assessment and Information Services , ECRI Institute @Ecri_Institute
Rob Dumanois, Manager – reimbursement strategy, Thermo Fisher Scientific
Eugean Jiwanmall, Senior Research Analyst for Medical Policy & Technology Evaluation , Independence Blue Cross @IBX
Michael Nall, President and Chief Executive Officer, Biocept

10:15 AM–10:25 AM

Paris Foyer

Networking Break + Showcase

10:25 AM–11:10 AM

Paris Ballroom

Promising Drugs, Pricing and Access

The drug pricing debate rages on. What are the solutions to continuing to foster research and innovation, while ensuring access and affordability for patients? Can biosimilars and generics be able to expand market access in the U.S.?

Moderator: Bunny Ellerin, Director, Healthcare and Pharmaceutical Management Program, Columbia Business School
Patrick Davish, AVP, Global & US Pricing/Market Access, Merck
Robert Dubois M.D., Chief Science Officer and Executive Vice President, National Pharmaceutical Council
Gary Kurzman, M.D., Senior Vice President and Managing Director, Healthcare, Safeguard Scientifics
Steven Lucio, Associate Vice President, Pharmacy Services, Vizient

11:10 AM–11:20 AM

Networking Break + Showcase

11:20 AM–12:05 PM

Paris Ballroom

Breaking Down Silos in Research

“Silo” is healthcare’s four-letter word. How are researchers, life science companies and others sharing information that can benefit patients more quickly? Hear from experts at institutions that are striving to tear down the walls that prevent data from flowing.

Moderator: Vini Jolly, Executive Director, Woodside Capital Partners
Ardy Arianpour, CEO & Co-Founder, Seqster @seqster
Lauren Becnel, Ph.D., Real World Data Lead for Oncology, Pfizer
Rakesh Mathew, Innovation, Research, & Development Lead, HealthShareExchange
David Nace M.D., Chief Medical Officer, Innovaccer

12:10 PM–12:40 PM

Paris Ballroom

Closing Keynote: Anne Stockwell, Cancer Survivor, Founder, Well Again

Finding Your Well Again
Anne Stockwell discusses her mission to help cancer survivors heal their emotional trauma and regain their balance after treatment. A multi-skilled artist as well as a three-time cancer survivor, Anne learned through experience that the emotional impact of cancer often strikes after treatment, isolating a survivor rather than lighting the way forward. Anne realized that her well-trained imagination as an artist was key to her successful reentry after cancer. Now she helps other survivors develop their own creative tools to help them find their way forward with joy.

Anne Stockwell, Founder and President, Well Again @annewellagain

12:40 PM–12:45 PM

Closing Remarks


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Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

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Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center



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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


Anti-Müllerian Hormone (AMH), is secreted by growing follicles that contains the egg or ovum. According to regular practice low AMH and high Follicle Stimulating Hormone (FSH) are generally considered as indicators of diminished egg quantity in a female. But, there are several cases the female conceived absolutely normally without any support even after low AMH was reported.


Therefore, a new research published in the Journal of the American Medical Association declares that AMH doesn’t dictate a woman’s reproductive potential. Although AMH testing is one of the most common ways that doctors assess a woman’s fertility. Present research says that all it takes is one egg each cycle and AMH is not a marker of whether a female can or cannot become pregnant. So, for women who haven’t yet tried to get pregnant and who are wondering whether they are fertile, an AMH value isn’t going to be helpful in that context. In addition, AMH is not necessarily a good marker to predict that whether one has to cryopreserve her eggs. So, practically doctors don’t yet have a way to definitively predict egg quality or a woman’s long-term ability to conceive, but age is obviously one of the most important factors.


The above mentioned study followed 750 women between the ages of 30 and 44 who had been trying to conceive for three months or less. During the 12-month observation period, those with low AMH values of less than 0.7 were not less likely to conceive than those who had normal AMH values. The study had various limitations, however, that are worth noting. The researchers only included women who did not have a history of infertility. Women who sought fertility treatments (about 6 percent) were withdrawn. And only 12 percent of the women were in the 38-to-44 age range. In addition, the number of live births was unavailable.


Among women aged 30 to 44 years without a history of infertility who had been trying to conceive for 3 months or less, biomarkers indicating diminished ovarian reserve compared with normal ovarian reserve were not associated with reduced fertility. These findings do not support the use of urinary or blood FSH tests or AMH levels to assess natural fertility for women with these characteristics. The researchers’ next want to see whether low AMH is associated with a higher risk of miscarriage among the women who conceived.


Although AMH testing isn’t designed to be an overall gauge of a woman’s fertility, it can still provide valuable information, especially for women who are infertile and seeking treatment. It can assist in diagnosing polycystic ovarian syndrome, and identify when a woman is getting closer to menopause. Previous research also showed that AMH is good predictor of a woman’s response to ovarian stimulation for in vitro fertilization and therefore it can predict the probability of conceiving via in vitro fertilization (I.V.F.).




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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Researchers have classified a brand-new organ inside human body. Known as the mesentery, the new organ is found in our digestive systems, and was long thought to be made up of fragmented, separate structures. But recent research has shown that it’s actually one, continuous organ. The evidence for the organ’s reclassification is now published in The Lancet Gastroenterology & Hepatology. Although we now know about the structure of this new organ, its function is still poorly understood, and studying it could be the key to better understanding and treatment of abdominal and digestive disease.


J Calvin Coffey, a researcher from the University Hospital Limerick in Ireland, who first discovered that the mesentery was an organ. In 2012, Coffey and his colleagues showed through detailed microscopic examinations that the mesentery is actually a continuous structure. Over the past four years, they’ve gathered further evidence that the mesentery should actually be classified as its own distinct organ, and the latest paper makes it official. Mesentery is a double fold of peritoneum – the lining of the abdominal cavity – that holds our intestine to the wall of our abdomen. It was described by the Italian polymath Leanardo da Vinci in 1508, but it has been ignored throughout the centuries, until now. Although there are generally considered to be five organs in the human body, there are in fact now 79, including the mesentery. The heart, brain, liver, lungs and kidneys are the vital organs, but there are another 74 that play a role in keeping us healthy. The distinctive anatomical and functional features of mesentery have been revealed that justify designation of the mesentery as an organ. Accordingly, the mesentery should be subjected to the same investigatory focus that is applied to other organs and systems. This provides a platform from which to direct future scientific investigation of the human mesentery in health and disease.


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From the NIH Website

New NIH breast cancer research to focus on prevention

A new phase of the Breast Cancer and the Environment Research Program (BCERP), focused on prevention, is being launched at the National Institutes of Health. Grant-funded researchers will now work across scientific disciplines, involve new racially and ethnically diverse communities, and expand the study of risk factors that precede breast cancer, such as breast density.

These new directions reflect recommendations made by the Interagency Breast Cancer and Environmental Research Coordinating Committee (IBCERCC) in 2013. IBCERCC was congressionally mandated to review the state of the science around breast cancer and environmental influences by the Breast Cancer and Environmental Research Act. Recommendations included prioritizing prevention, involving transdisciplinary research teams, engaging public stakeholders, collaborating across federal agencies, and communicating the science to the public.

This broadened research focus will add to the growing knowledge of environmental and genetic factors that may influence breast cancer risk across the lifespan. The six new BCERP projects, plus a new coordinating center promoting cross-project collaboration, are jointly funded by the National Institute of Environmental Health Sciences (NIEHS) and the National Cancer Institute. All projects involve strong partnerships between researchers and organizations focused on breast cancer prevention or environmental health.

The new research will be conducted at the following institutions

  • Brigham and Women’s Hospital, Boston
  • City of Hope/Beckman Research Institute, Duarte, California
  • Columbia University, New York City
  • Georgetown Lombardi Comprehensive Cancer Center, Washington, D.C.
  • Michigan State University, Lansing
  • University of Massachusetts, Amherst
  • University of Wisconsin – Madison (Coordinating Center)

“The beauty of this research is that scientific discoveries and community observations inform each other, in order to dive deeper into the complex causes of breast cancer,” said Gwen Collman, Ph.D., director of NIEHS Division of Extramural Research and Training.

The focus on minority and socio-economically disadvantaged women is an important step in addressing disparities in breast cancer outcomes. Although African-American women are diagnosed with breast cancer less often than white women, more aggressive cancers and breast cancer deaths are more common among African-American women.

Another new direction for BCERP is research on the role of breast density as a possible intermediate risk factor for breast cancer. Dense breast tissue is one of the most common risk factors for breast cancer. Identifying links between environmental exposures and high breast density may provide new insights into prevention.

“These priorities reflect our continued commitment to breast cancer prevention,” noted Caroline Dilworth, Ph.D., BCERP program lead at NIEHS. “Our goal is to build on the high quality science we’ve been funding for more than a decade, while also being responsive to the expert recommendations of the IBCERCC report.”

Grant Numbers: U01ES026130, U01ES026137, U01ES026122, U01ES026132, U01ES026119, U01ES026140, U01ES026127

NIEHS supports research to understand the effects of the environment on human health and is part of NIH. For more information on environmental health topics, visit Subscribe to one or more of the NIEHS news lists to stay current on NIEHS news, press releases, grant opportunities, training, events, and publications.

The National Cancer Institute leads the National Cancer Program and the NIH’s efforts to dramatically reduce the prevalence of cancer and improve the lives of cancer patients and their families, through research into prevention and cancer biology, the development of new interventions, and the training and mentoring of new researchers. For more information about cancer, please visit the NCI website at or call NCI’s Cancer Information Service at 1-800-4-CANCER.

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit

Other posts on this site on  Cancer and Early Detection  include

Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline

Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment

Warning signs may lead to better early detection of ovarian cancer

‘Mosaicism’ is Associated with Aging and Chronic Diseases like Cancer: detection of genetic mosaicism could be an early marker for detecting cancer.

CDC Findings: Due to Aging Population, Actual Number of Cancer Deaths is Rising while Risk of Dying From Cancer is Falling in the US

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Treatments other than Chemotherapy for Leukemias and Lymphomas

Author, Curator, Editor: Larry H. Bernstein, MD, FCAP

2.5.1 Radiation Therapy

Radiation therapy, also called radiotherapy or irradiation, can be used to treat leukemia, lymphoma, myeloma and myelodysplastic syndromes. The type of radiation used for radiotherapy (ionizing radiation) is the same that’s used for diagnostic x-rays. Radiotherapy, however, is given in higher doses.

Radiotherapy works by damaging the genetic material (DNA) within cells, which prevents them from growing and reproducing. Although the radiotherapy is directed at cancer cells, it can also damage nearby healthy cells. However, current methods of radiotherapy have been improved upon, minimizing “scatter” to nearby tissues. Therefore its benefit (destroying the cancer cells) outweighs its risk (harming healthy cells).

When radiotherapy is used for blood cancer treatment, it’s usually part of a treatment plan that includes drug therapy. Radiotherapy can also be used to relieve pain or discomfort caused by an enlarged liver, lymph node(s) or spleen.

Radiotherapy, either alone or with chemotherapy, is sometimes given as conditioning treatment to prepare a patient for a blood or marrow stem cell transplant. The most common types used to treat blood cancer are external beam radiation (see below) and radioimmunotherapy.
External Beam Radiation

External beam radiation is the type of radiotherapy used most often for people with blood cancers. A focused radiation beam is delivered outside the body by a machine called a linear accelerator, or linac for short. The linear accelerator moves around the body to deliver radiation from various angles. Linear accelerators make it possible to decrease or avoid skin reactions and deliver targeted radiation to lessen “scatter” of radiation to nearby tissues.

The dose (total amount) of radiation used during treatment depends on various factors regarding the patient, disease and reason for treatment, and is established by a radiation oncologist. You may receive radiotherapy during a series of visits, spread over several weeks (from two to 10 weeks, on average). This approach, called dose fractionation, lessens side effects. External beam radiation does not make you radioactive.

2.5.2  Bone marrow (BM) transplantation

There are three kinds of bone marrow transplants:

Autologous bone marrow transplant: The term auto means self. Stem cells are removed from you before you receive high-dose chemotherapy or radiation treatment. The stem cells are stored in a freezer (cryopreservation). After high-dose chemotherapy or radiation treatments, your stems cells are put back in your body to make (regenerate) normal blood cells. This is called a rescue transplant.

Allogeneic bone marrow transplant: The term allo means other. Stem cells are removed from another person, called a donor. Most times, the donor’s genes must at least partly match your genes. Special blood tests are done to see if a donor is a good match for you. A brother or sister is most likely to be a good match. Sometimes parents, children, and other relatives are good matches. Donors who are not related to you may be found through national bone marrow registries.

Umbilical cord blood transplant: This is a type of allogeneic transplant. Stem cells are removed from a newborn baby’s umbilical cord right after birth. The stem cells are frozen and stored until they are needed for a transplant. Umbilical cord blood cells are very immature so there is less of a need for matching. But blood counts take much longer to recover.

Before the transplant, chemotherapy, radiation, or both may be given. This may be done in two ways:

Ablative (myeloablative) treatment: High-dose chemotherapy, radiation, or both are given to kill any cancer cells. This also kills all healthy bone marrow that remains, and allows new stem cells to grow in the bone marrow.

Reduced intensity treatment, also called a mini transplant: Patients receive lower doses of chemotherapy and radiation before a transplant. This allows older patients, and those with other health problems to have a transplant.

A stem cell transplant is usually done after chemotherapy and radiation is complete. The stem cells are delivered into your bloodstream usually through a tube called a central venous catheter. The process is similar to getting a blood transfusion. The stem cells travel through the blood into the bone marrow. Most times, no surgery is needed.

Donor stem cells can be collected in two ways:

  • Bone marrow harvest. This minor surgery is done under general anesthesia. This means the donor will be asleep and pain-free during the procedure. The bone marrow is removed from the back of both hip bones. The amount of marrow removed depends on the weight of the person who is receiving it.
  • Leukapheresis. First, the donor is given 5 days of shots to help stem cells move from the bone marrow into the blood. During leukapheresis, blood is removed from the donor through an IV line in a vein. The part of white blood cells that contains stem cells is then separated in a machine and removed to be later given to the recipient. The red blood cells are returned to the donor.

Why the Procedure is Performed

A bone marrow transplant replaces bone marrow that either is not working properly or has been destroyed (ablated) by chemotherapy or radiation. Doctors believe that for many cancers, the donor’s white blood cells can attach to any remaining cancer cells, similar to when white cells attach to bacteria or viruses when fighting an infection.

Your doctor may recommend a bone marrow transplant if you have:

Certain cancers, such as leukemia, lymphoma, and multiple myeloma

A disease that affects the production of bone marrow cells, such as aplastic anemia, congenital neutropenia, severe immunodeficiency syndromes, sickle cell anemia, thalassemia

Had chemotherapy that destroyed your bone

2.5.3 Autologous stem cell transplantation

Phase II trial of 131I-B1 (anti-CD20) antibody therapy with autologous stem cell transplantation for relapsed B cell lymphomas

O.W Press,  F Appelbaum,  P.J Martin, et al.

25 patients with relapsed B-cell lymphomas were evaluated with trace-labelled doses (2·5 mg/kg, 185-370 MBq [5-10 mCi]) of 131I-labelled anti-CD20 (B1) antibody in a phase II trial. 22 patients achieved 131I-B1 biodistributions delivering higher doses of radiation to tumor sites than to normal organs and 21 of these were treated with therapeutic infusions of 131I-B1 (12·765-29·045 GBq) followed by autologous hemopoietic stem cell reinfusion. 18 of the 21 treated patients had objective responses, including 16 complete remissions. One patient died of progressive lymphoma and one died of sepsis. Analysis of our phase I and II trials with 131I-labelled B1 reveal a progression-free survival of 62% and an overall survival of 93% with a median follow-up of 2 years. 131I-anti-CD20 (B1) antibody therapy produces complete responses of long duration in most patients with relapsed B-cell lymphomas when given at maximally tolerated doses with autologous stem cell rescue.

Autologous (Self) Transplants

An autologous transplant (or rescue) is a type of transplant that uses the person’s own stem cells. These cells are collected in advance and returned at a later stage. They are used to replace stem cells that have been damaged by high doses of chemotherapy, used to treat the person’s underlying disease.

In most cases, stem cells are collected directly from the bloodstream. While stem cells normally live in your marrow, a combination of chemotherapy and a growth factor (a drug that stimulates stem cells) called Granulocyte Colony Stimulating Factor (G-CSF) is used to expand the number of stem cells in the marrow and cause them to spill out into the circulating blood. From here they can be collected from a vein by passing the blood through a special machine called a cell separator, in a process similar to dialysis.

Most of the side effects of an autologous transplant are caused by the conditioning therapy used. Although they can be very unpleasant at times it is important to remember that most of them are temporary and reversible.

Procedure of Hematopoietic Stem Cell Transplantation

Hematopoietic stem cell transplantation (HSCT) is the transplantation of multipotent hematopoietic stem cells, usually derived from bone marrow, peripheral blood, or umbilical cord blood. It may be autologous (the patient’s own stem cells are used) or allogeneic (the stem cells come from a donor).

Hematopoietic Stem Cell Transplantation

Author: Ajay Perumbeti, MD, FAAP; Chief Editor: Emmanuel C Besa, MD

Hematopoietic stem cell transplantation (HSCT) involves the intravenous (IV) infusion of autologous or allogeneic stem cells to reestablish hematopoietic function in patients whose bone marrow or immune system is damaged or defective.

The image below illustrates an algorithm for typically preferred hematopoietic stem cell transplantation cell source for treatment of malignancy.

An algorithm for typically preferred hematopoietic stem cell transplantation cell source for treatment of malignancy: If a matched sibling donor is not available, then a MUD is selected; if a MUD is not available, then choices include a mismatched unrelated donor, umbilical cord donor(s), and a haploidentical donor.

Supportive Therapies

2.5.4  Blood transfusions – risks and complications of a blood transfusion

  • Allogeneic transfusion reaction (acute or delayed hemolytic reaction)
  • Allergic reaction
  • Viruses Infectious Diseases

The risk of catching a virus from a blood transfusion is very low.

HIV. Your risk of getting HIV from a blood transfusion is lower than your risk of getting killed by lightning. Only about 1 in 2 million donations might carry HIV and transmit HIV if given to a patient.

Hepatitis B and C. The risk of having a donation that carries hepatitis B is about 1 in 205,000. The risk for hepatitis C is 1 in 2 million. If you receive blood during a transfusion that contains hepatitis, you’ll likely develop the virus.

Variant Creutzfeldt-Jakob disease (vCJD). This disease is the human version of Mad Cow Disease. It’s a very rare, yet fatal brain disorder. There is a possible risk of getting vCJD from a blood transfusion, although the risk is very low. Because of this, people who may have been exposed to vCJD aren’t eligible blood donors.

  • Fever
  • Iron Overload
  • Lung Injury
  • Graft-Versus-Host Disease

Graft-versus-host disease (GVHD) is a condition in which white blood cells in the new blood attack your tissues.

2.5.5 Erythropoietin

Erythropoietin, (/ɨˌrɪθrɵˈpɔɪ.ɨtɨn/UK /ɛˌrɪθr.pˈtɪn/) also known as EPO, is a glycoprotein hormone that controls erythropoiesis, or red blood cell production. It is a cytokine (protein signaling molecule) for erythrocyte (red blood cell) precursors in the bone marrow. Human EPO has a molecular weight of 34 kDa.

Also called hematopoietin or hemopoietin, it is produced by interstitial fibroblasts in the kidney in close association with peritubular capillary and proximal convoluted tubule. It is also produced in perisinusoidal cells in the liver. While liver production predominates in the fetal and perinatal period, renal production is predominant during adulthood. In addition to erythropoiesis, erythropoietin also has other known biological functions. For example, it plays an important role in the brain’s response to neuronal injury.[1] EPO is also involved in the wound healing process.[2]

Exogenous erythropoietin is produced by recombinant DNA technology in cell culture. Several different pharmaceutical agents are available with a variety ofglycosylation patterns, and are collectively called erythropoiesis-stimulating agents (ESA). The specific details for labelled use vary between the package inserts, but ESAs have been used in the treatment of anemia in chronic kidney disease, anemia in myelodysplasia, and in anemia from cancer chemotherapy. Boxed warnings include a risk of death, myocardial infarction, stroke, venous thromboembolism, and tumor recurrence.[3]

2.5.6  G-CSF (granulocyte-colony stimulating factor)

Granulocyte-colony stimulating factor (G-CSF or GCSF), also known as colony-stimulating factor 3 (CSF 3), is a glycoprotein that stimulates the bone marrow to produce granulocytes and stem cells and release them into the bloodstream.

There are different types, including

  • Lenograstim (Granocyte)
  • Filgrastim (Neupogen, Zarzio, Nivestim, Ratiograstim)
  • Long acting (pegylated) filgrastim (pegfilgrastim, Neulasta) and lipegfilgrastim (Longquex)

Pegylated G-CSF stays in the body for longer so you have treatment less often than with the other types of G-CSF.

2.5.7  Plasma Exchange (plasmapheresis)

Plasmapheresis is a term used to refer to a broad range of procedures in which extracorporeal separation of blood components results in a filtered plasma product.[1, 2] The filtering of plasma from whole blood can be accomplished via centrifugation or semipermeable membranes.[3] Centrifugation takes advantage of the different specific gravities inherent to various blood products such as red cells, white cells, platelets, and plasma.[4] Membrane plasma separation uses differences in particle size to filter plasma from the cellular components of blood.[3]

Traditionally, in the United States, most plasmapheresis takes place using automated centrifuge-based technology.[5] In certain instances, in particular in patients already undergoing hemodialysis, plasmapheresis can be carried out using semipermeable membranes to filter plasma.[4]

In therapeutic plasma exchange, using an automated centrifuge, filtered plasma is discarded and red blood cells along with replacement colloid such as donor plasma or albumin is returned to the patient. In membrane plasma filtration, secondary membrane plasma fractionation can selectively remove undesired macromolecules, which then allows for return of the processed plasma to the patient instead of donor plasma or albumin. Examples of secondary membrane plasma fractionation include cascade filtration,[6] thermofiltration, cryofiltration,[7] and low-density lipoprotein pheresis.

The Apheresis Applications Committee of the American Society for Apheresis periodically evaluates potential indications for apheresis and categorizes them from I to IV based on the available medical literature. The following are some of the indications, and their categorization, from the society’s 2010 guidelines.[2]

  • The only Category I indication for hemopoietic malignancy is Hyperviscosity in monoclonal gammopathies

2.5.8  Platelet Transfusions

Indications for platelet transfusion in children with acute leukemia

Scott Murphy, Samuel Litwin, Leonard M. Herring, Penelope Koch, et al.
Am J Hematol Jun 1982; 12(4): 347–356;jsessionid=A6001D9D865EA1EBC667EF98382EF20C.f03t01

In an attempt to determine the indications for platelet transfusion in thrombocytopenic patients, we randomized 56 children with acute leukemia to one of two regimens of platelet transfusion. The prophylactic group received platelets when the platelet count fell below 20,000 per mm3 irrespective of clinical events. The therapeutic group was transfused only when significant bleeding occurred and not for thrombocytopenia alone. The time to first bleeding episode was significantly longer and the number of bleeding episodes were significantly reduced in the prophylactic group. The survival curves of the two groups could not be distinguished from each other. Prior to the last month of life, the total number of days on which bleeding was present was significantly reduced by prophylactic therapy. However, in the terminal phase (last month of life), the duration of bleeding episodes was significantly longer in the prophylactic group. This may have been due to a higher incidence of immunologic refractoriness to platelet transfusion. Because of this terminal bleeding, comparison of the two groups for total number of days on which bleeding was present did not show a significant difference over the entire study period.

Clinical and Laboratory Aspects of Platelet Transfusion Therapy
Yuan S, Goldfinger D

INTRODUCTION — Hemostasis depends on an adequate number of functional platelets, together with an intact coagulation (clotting factor) system. This topic covers the logistics of platelet use and the indications for platelet transfusion in adults. The approach to the bleeding patient, refractoriness to platelet transfusion, and platelet transfusion in neonates are discussed elsewhere.

Pooled Platelets – A single unit of platelets can be isolated from every unit of donated blood, by centrifuging the blood within the closed collection system to separate the platelets from the red blood cells (RBC). The number of platelets per unit varies according to the platelet count of the donor; a yield of 7 x 1010 platelets is typical [1]. Since this number is inadequate to raise the platelet count in an adult recipient, four to six units are pooled to allow transfusion of 3 to 4 x 1011 platelets per transfusion [2]. These are called whole blood-derived or random donor pooled platelets.

Advantages of pooled platelets include lower cost and ease of collection and processing (a separate donation procedure and pheresis equipment are not required). The major disadvantage is recipient exposure to multiple donors in a single transfusion and logistic issues related to bacterial testing.

Apheresis (single donor) Platelets – Platelets can also be collected from volunteer donors in the blood bank, in a one- to two-hour pheresis procedure. Platelets and some white blood cells are removed, and red blood cells and plasma are returned to the donor. A typical apheresis platelet unit provides the equivalent of six or more units of platelets from whole blood (ie, 3 to 6 x 1011 platelets) [2]. In larger donors with high platelet counts, up to three units can be collected in one session. These are called apheresis or single donor platelets.

Advantages of single donor platelets are exposure of the recipient to a single donor rather than multiple donors, and the ability to match donor and recipient characteristics such as HLA type, cytomegalovirus (CMV) status, and blood type for certain recipients.

Both pooled and apheresis platelets contain some white blood cells (WBC) that were collected along with the platelets. These WBC can cause febrile non-hemolytic transfusion reactions (FNHTR), alloimmunization, and transfusion-associated graft-versus-host disease (ta-GVHD) in some patients.

Platelet products also contain plasma, which can be implicated in adverse reactions including transfusion-related acute lung injury (TRALI) and anaphylaxis. (See ‘Complications of platelet transfusion’ .)

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Classification of Microbiota –

An Overview of Clinical Microbiology, Classification, and Antimicrobial Resistance

Author and Curator: Larry H. Bernstein, MD, FCAP

Classification of Microbiota

Introduction to Overview of Microbiology

This is a contribution to a series of pieces on the history of biochemistry, molecular biology, physiology and medicine in the 20th century.  Here I describe the common microbial organisms encountered in the clinical laboratory, the method of their collection, plating, culture and identification, and antibiotic sensitivity testing and resistant strains.

I may begin with the recognition that there are common strains in the environment that are not pathogenic, and there are pathogenic bacteria.
In addition, there are bacteria that coexist in the body habitat under specific conditions so that we are able to map the types expected to location, such as, skin, mouth and nasal cavities, the colon, the vagina and urinary system.  Meningitides occur as a result of extension from the nasal cavity to the brain.  When bacteria invade the circulation, it is referred to as septicemia, and the bacteria can cause valvular heart damage.

Bacteriology can be traced to origins in the 19th century.  The clinical features of localized infection are classically referred to as redness, heat, a raised lesion (pustule), and exudate (serous or purulent – watery or cellular).  This not only holds for a focal lesion (as skin), but also for pneumonia, urinary infection, and genital. It may be accompanied by cough, or bloody cough and wheezing, or by an unclear urine. In the case of septicemia, there is fever, and there may be seizures or delirium.

Collection and handling of specimens

Specimens are collected by sterile technique by a nurse or physician and sent to a lab as a swab, or as a blood specimen.  In the case of a febrile illness, blood cultures may be obtained from opposite arms, and another an hour later.  This is related to the possible cyclical seeding of bacteria into the circulation.  If the specimen is collected from a site of infection, a swab may be put onto a glass slide for gram staining.  The specimen collected is sent to the laboratory.

We may consider syphilis and tuberculosis special cases that I’ll set aside.  I shall not go into virology either, although I may referred to smallpox, influenza, polio, HIV under epidemic.  The first step in identification is the Gram stain, developed in the 19th century.  Organisms of the skin are Gram positive and appear blue on staining.  They are cocci, or circular, organized in characteristic clusters (staphylococcus, streptococcus) or in pairs (diplococci, eg. Pneumococcus), and if from the intestine (enterococcus).  If they are elongated rods, they might be coliform.  If they stain red, they are Gram negative.  Gram negative rods are coliform, and are enterobacteriaceae. Meningococci are Gram negative cocci.  So we have certain information about these organisms before we plate them for growth.

Laboratory growth characteristics

The specimen is applied to an agar plate with a metal rod applicator, or perhaps onto more than one agar plate.  The agar plate contains a growth media or a growth inhibitor that is more favorable to certain species than to others.  The bacteria are grown at 37o C in an incubator and colonies develop that are white or nonwhite, and they are smooth or wrinkled.  The appearance of the colonies is characteristic for certain strains.  If there is no contamination, all of the colonies look the same.  The next step is to:

  • Gram stain from a colony
  • Transfer samples from the colony to a series of growth media that identify presence or absence of specific nutrient requirements for growth (which is presumed from the prior findings).

In addition, the colony samples are grown on an agar to which is applied antibiotic tabs.  The tabs either allow or repress growth.  It wa some 50 years ago that the infectious disease physician and microbiologist Abraham Braude would culture the bacteria on agar plates that had a gradient of antibiotic to check for concentration that would inhibit growth.

Principles of Diagnosis (Extracts)

By John A. Washington

The clinical presentation of an infectious disease reflects the interaction between the host and the microorganism. This interaction is affected by the host immune status and microbial virulence factors. Signs and symptoms vary according to the site and severity of infection. Diagnosis requires a composite of information, including history, physical examination, radiographic findings, and laboratory data.

Microbiologic Examination

Direct Examination and Techniques: Direct examination of specimens reveals gross pathology. Microscopy may identify microorganisms. Immunofluorescence, immuno-peroxidase staining, and other immunoassays may detect specific microbial antigens. Genetic probes identify genus- or species-specific DNA or RNA sequences.

Culture: Isolation of infectious agents frequently requires specialized media. Nonselective (noninhibitory) media permit the growth of many microorganisms. Selective media contain inhibitory substances that permit the isolation of specific types of microorganisms.

Microbial Identification: Colony and cellular morphology may permit preliminary identification. Growth characteristics under various conditions, utilization of carbohydrates and other substrates, enzymatic activity, immunoassays, and genetic probes are also used.

Serodiagnosis: A high or rising titer of specific IgG antibodies or the presence of specific IgM antibodies may suggest or confirm a diagnosis.

Antimicrobial Susceptibility: Microorganisms, particularly bacteria, are tested in vitro to determine whether they are susceptible to antimicrobial agents.

Diagnostic medical microbiology is the discipline that identifies etiologic agents of disease. The job of the clinical microbiology laboratory is to test specimens from patients for microorganisms that are, or may be, a cause of the illness and to provide information (when appropriate) about the in vitro activity of antimicrobial drugs against the microorganisms identified (Fig. 1).

Laboratory procedures used in confirming a clinical diagnosis of infectious disease with a bacterial etiology

A variety of microscopic, immunologic, and hybridization techniques have been developed for rapid diagnosis

techniques have been developed for rapid diagnosis

techniques have been developed for rapid diagnosis

From: Chapter 10, Principles of Diagnosis
Medical Microbiology. 4th edition.
Baron S, editor.
Galveston (TX): University of Texas Medical Branch at Galveston; 1996.

For immunologic detection of microbial antigens, latex particle agglutination, coagglutination, and enzyme-linked immunosorbent assay (ELISA) are the most frequently used techniques in the clinical laboratory. Antibody to a specific antigen is bound to latex particles or to a heat-killed and treated protein A-rich strain of Staphylococcus aureus to produce agglutination (Fig. 10-2). There are several approaches to ELISA; the one most frequently used for the detection of microbial antigens uses an antigen-specific antibody that is fixed to a solid phase, which may be a latex or metal bead or the inside surface of a well in a plastic tray. Antigen present in the specimen binds to the antibody as inFig. 10-2. The test is then completed by adding a second antigen-specific antibody bound to an enzyme that can react with a substrate to produce a colored product. The initial antigen antibody complex forms in a manner similar to that shown inFigure 10-2. When the enzyme-conjugated antibody is added, it binds to previously unbound antigenic sites, and the antigen is, in effect, sandwiched between the solid phase and the enzyme-conjugated antibody. The reaction is completed by adding the enzyme substrate.

agglutination test ch10f2

agglutination test ch10f2

Figure 2 Agglutination test in which inert particles (latex beads or heat-killed S aureus Cowan 1 strain with protein A) are coated with antibody to any of a variety of antigens and then used to detect the antigen in specimens or in isolated bacteria

Genetic probes are based on the detection of unique nucleotide sequences with the DNA or RNA of a microorganism. Once such a unique nucleotide sequence, which may represent a portion of a virulence gene or of chromosomal DNA, is found, it is isolated and inserted into a cloning vector (plasmid), which is then transformed into Escherichia coli to produce multiple copies of the probe. The sequence is then reisolated from plasmids and labeled with an isotope or substrate for diagnostic use. Hybridization of the sequence with a complementary sequence of DNA or RNA follows cleavage of the double-stranded DNA of the microorganism in the specimen.

The use of molecular technology in the diagnoses of infectious diseases has been further enhanced by the introduction of gene amplication techniques, such as the polymerase chain reaction (PCR) in which DNA polymerase is able to copy a strand of DNA by elongating complementary strands of DNA that have been initiated from a pair of closely spaced oligonucleotide primers. This approach has had major applications in the detection of infections due to microorganisms that are difficult to culture (e.g. the human immunodeficiency virus) or that have not as yet been successfully cultured (e.g. the Whipple’s disease bacillus).

Solid media, although somewhat less sensitive than liquid media, provide isolated colonies that can be quantified if necessary and identified. Some genera and species can be recognized on the basis of their colony morphologies.

In some instances one can take advantage of differential carbohydrate fermentation capabilities of microorganisms by incorporating one or more carbohydrates in the medium along with a suitable pH indicator. Such media are called differential media (e.g., eosin methylene blue or MacConkey agar) and are commonly used to isolate enteric bacilli. Different genera of the Enterobacteriaceae can then be presumptively identified by the color as well as the morphology of colonies.

Culture media can also be made selective by incorporating compounds such as antimicrobial agents that inhibit the indigenous flora while permitting growth of specific microorganisms resistant to these inhibitors. One such example is Thayer-Martin medium, which is used to isolate Neisseria gonorrhoeae. This medium contains vancomycin to inhibit Gram-positive bacteria, colistin to inhibit most Gram-negative bacilli, trimethoprim-sulfamethoxazole to inhibit Proteus species and other species that are not inhibited by colistin and anisomycin to inhibit fungi. The pathogenic Neisseria species, N gonorrhoeae and N meningitidis, are ordinarily resistant to the concentrations of these antimicrobial agents in the medium.

Infection of the bladder (cystitis) or kidney (pyelone-phritis) is usually accompanied by bacteriuria of about ≥ 104 CFU/ml. For this reason, quantitative cultures (Fig. 10-3) of urine must always be performed. For most other specimens a semiquantitative streak method (Fig. 10-3) over the agar surface is sufficient. For quantitative cultures, a specific volume of specimen is spread over the agar surface and the number of colonies per milliliter is estimated.

Identification of bacteria (including mycobacteria) is based on growth characteristics (such as the time required for growth to appear or the atmosphere in which growth occurs), colony and microscopic morphology, and biochemical, physiologic, and, in some instances, antigenic or nucleotide sequence characteristics. The selection and number of tests for bacterial identification depend upon the category of bacteria present (aerobic versus anaerobic, Gram-positive versus Gram-negative, cocci versus bacilli) and the expertise of the microbiologist examining the culture. Gram-positive cocci that grow in air with or without added CO2 may be identified by a relatively small number of tests. The identification of most Gram-negative bacilli is far more complex and often requires panels of 20 tests for determining biochemical and physiologic characteristics.

Antimicrobial susceptibility tests are performed by either disk diffusion or a dilution method. In the former, a standardized suspension of a particular microorganism is inoculated onto an agar surface to which paper disks containing various antimicrobial agents are applied. Following overnight incubation, any zone diameters of inhibition about the disks are measured. An alternative method is to dilute on a log2 scale each antimicrobial agent in broth to provide a range of concentrations and to inoculate each tube or, if a microplate is used, each well containing the antimicrobial agent in broth with a standardized suspension of the microorganism to be tested. The lowest concentration of antimicrobial agent that inhibits the growth of the microorganism is the minimal inhibitory concentration.

Classification Principles

This Week’s Citation Classic®_______ Sneath P H A & Sokal R R.
Numerical taxonomy: the principles and practice of
numerical classification. San Francisco: Freeman, 1973. 573 p.
[Medical Research Council Microbial Systematics Unit, Univ. Leicester, England
and Dept. Ecology and Evolution, State Univ. New York, Stony Brook, NY]
Numerical taxonomy establishes classification
of organisms based on their similarities. It utilizes
many equally weighted characters and employs
clustering and similar algorithms to yield
objective groupings. It can beextended to give
phylogenetic or diagnostic systems and can be
applied to many other fields of endeavour.

Mathematical Foundations of Computer Science 1998
Lecture Notes in Computer Science Volume 1450, 1998, pp 474-482
Date: 28 May 2006
Positive Turing and truth-table completeness for NEXP are incomparable 1998
Levke Bentzien

The truth-table method [matrix method] is one of the decision procedures for sentence logic (q.v., §3.2). The method is based on the fact that the truth value of a compound formula of sentence logic, construed as a truth-function, is determined by the truth values of its arguments (cf. “Sentence logic” §2.2). To decide whether a formula A is a tautology or not, we list all possible combinations of truth values to the variables in A: A is a tautology if it takes the value truth under each assignment.

Using ideas introduced by Buhrman et al. ([2], [3]) to separate various completeness notions for NEXP = NTIME (2poly), positive Turing complete sets for NEXP are studied. In contrast to many-one completeness and bounded truth-table completeness with norm 1 which are known to coincide on NEXP ([3]), whence any such set for NEXP is positive Turing complete, we give sets A and B such that

A is ≤ bT(2) P -complete but not ≤ posT P -complete for NEXP

B is ≤ posT P -complete but not ≤ tt P -complete for NEXP. These results come close to optimality since a further strengthening of (1), as was done by Buhrman in [1] for EXP = DTIME(2poly), seems to require the assumption NEXP = co-NEXP.

Computability and Models
The University Series in Mathematics 2003, pp 1-10
Truth-Table Complete Computably Enumerable Sets
Marat M. Arslanov

We prove a truth-table completeness criterion for computably enumerable sets.
The authors research was partially supported by Russian Foundation of Basic Research, Project 99-01-00830, and RFBR-INTAS, Project 97-91-71991.

Department of Microbiology, Lovelace Foundation for Medical Education and Research,
Albuquerque, N.M. 87108, U.S.A.
Space life sciences 1971-12-1; 3(2): pp 135-156
(Received 15 July, 1971)
Abstract. A logical basis for classification is that elements grouped together and higher categories of elements should have a high degree of similarity with the provision that all groups and categories be disjoint to some degree. A methodology has been developed for constructing classifications automatically that gives
nearly instantaneous correlations of character patterns of organisms with time and clusters with apparent similarity. This means that automatic numerical identification will always construct schemes from which disjoint answers can be obtained if test sensitivities for characters are correct. Unidentified organisms are recycled through continuous classification with reconstruction of identification schemes. This process is
cyclic and self-correcting. The method also accumulates and analyzes data which updates and presents a more accurate biological picture.

Syndromic classification: A process for amplifying information using S-clustering

Eugene W. Rypka, PHD

Optimal classification/Rypka < Optimal classification>


1 Rypka’s Method

1.1 Equations

1.2 Examples

2 Notes and References

Rypka’s Method

Rypka’s[1] method[2] utilizes the theoretical and empirical separatory equations shown below to perform the task of optimal classification. The method finds the optimal order of the fewest attributes, which in combination define a bounded class of elements.

Application of the method begins with construction of an attribute-valued system in truth table[3] or spreadsheet form with elements listed in the left most column beginning in the second row. Characteristics[4] are listed in the first row beginning in the second column with the code name of the data in the upper left most cell. The values which connect each characteristic with each element are placed in the intersecting cells. Selecting appropriate characteristics to universally define the class of elements may be the most difficult part for the classifier of utilizing this method.

The elements are first sorted in descending order according to their truth table value, which is calculated from the existing sequence and value of characteristics for each element. Duplicate truth table values or multisets for the entire bounded class reveal either the need to eliminate duplicate elements or the need to include additional characteristics.

An empirical separatory value is calculated for each characteristic in the set and the characteristic with the greatest empirical separatory value is exchanged with the characteristic which occupies the most significant attribute position.

Next the second most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first characteristic. The characteristic which produces the greatest separatory value is then exchanged with the characteristic which occupies the second most significant attribute position.

Next the third most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first and second characteristics. The characteristic which produces the greatest empirical separatory value is then exchanged with the characteristic which occupies the third most significant attribute position. This procedure may continue until all characteristics have been processed or until one hundred percent separation of the elements has been achieved.

A larger radix will allow faster identification by excluding a greater percentage of elements per characteristic. A binary radix for instance excludes only fifty percent of the elements per characteristic whereas a five-valued radix excludes eighty percent of the elements per characteristic.[5] What follows is an elucidation of the matrix and separatory equations.[6]

Computational Example
Bounded Class Data

bounded class data

Bounded Class Dimensions

G = 28 – 28 elements – i = 0…G-1[1]

C = 10 – 10 characteristics or attributes – j = 0…C-1

V = 5 – 5 valued logic – l = 0…V-1

Order of Elements

order of elements

Count multisets

count multisets

Squared multiset Counts

squared multiset counts

Separatory Values

separatory values


max(T) = 309 = S8 = highest initial separatory value


Mathcad’s ORIGIN function applies to all arrays such that if more than one array is being used and one array requires a zero origin then the other arrays must use a zero origin with all variables being adapted as well.

Rypka’s Method Edit

Rypka’s[1] method[2] utilizes the theoretical and empirical separatory equations shown below to perform the task of optimal classification. The method finds the optimal order of the fewest attributes, which in combination define a bounded class of elements.

Application of the method begins with construction of an attribute-valued system in truth table[3] or spreadsheet form with elements listed in the left most column beginning in the second row. Characteristics[4] are listed in the first row beginning in the second column with the title of the attributes in the upper left most cell. Normally the file name of the data is given the title of the element class. The values which connect each characteristic with each element are placed in the intersecting cells. Selecting characteristics which all elements share may be the most difficult part of creating a database which can utilizing this method.

The elements are first sorted in descending order according to their truth table value, which is calculated from the existing sequence and value of characteristics for each element. Duplicate truth table values or multisets for the entire bounded class reveal either the need to eliminate duplicate elements or the need to include additional characteristics.

An empirical separatory value is calculated for each characteristic in the set and the characteristic with the greatest empirical separatory value is exchanged with the characteristic which occupies the most significant attribute position.

Next the second most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first characteristic. The characteristic which produces the greatest separatory value is then exchanged with the characteristic which occupies the second most significant attribute position.

Next the third most significant characteristic is found by calculating an empirical separatory value for each remaining characteristic in combination with the first and second characteristics. The characteristic which produces the greatest empirical separatory value is then exchanged with the characteristic which occupies the third most significant attribute position. This procedure may continue until all characteristics have been processed or until one hundred percent separation of the elements has been achieved.

A larger radix will allow faster identification by excluding a greater percentage of elements per characteristic. A binary radix for instance excludes only fifty percent of the elements per characteristic whereas a five-valued radix excludes eighty percent of the elements per characteristic.[5] What follows is an elucidation of the matrix and separatory equations.[6]

Syndromic Classification: A Process for Amplifying Information Using S-Clustering

Eugene W. Rypka, PhD
University of New Mexico, Albuquerque, New Mexico, USA
Statistics Editor: Marcello Pagano, PhD
Harvard School of Public Health, Boston, Massachusetts, USA
Nutrition 1996; 12(11/12): 827-829

In a previous issue of Nutrition, Drs. Bernstein and Pleban’ use the method of S-clustering to aid in nutritional classification of patients directly on-line. Classification of this type is called primary or syndromic classification.* It is created by a process called separatory (S-) clustering (E. Rypka, unpublished observations). The authors use S-clustering in Table I.  S-clustering extracts features (analytes, variables) from endogenous data that amplify or maximize structural information to create classes of patients (pathophysiologic events) which are the most disjointed or separable. S-clustering differs from other classificatory methods because it finds in a database a theoretic- or more- number of variables with the required variety that map closest to an ideal, theoretic, or structural information standard. In Table I of their article, Bernstein and Pleban’ indicate there would have to be 3 ’ = 243 rows to show all possible patterns. In Table II of this article, I have used a 33 = 27 row truth table to convey the notion of mapping amplified information to an ideal, theoretic standard using just the first three columns. Variables are scaled for use in S-clustering.

A Survey of Binary Similarity and Distance Measures
Seung-Seok Choi, Sung-Hyuk Cha, Charles C. Tappert
The binary feature vector is one of the most common
representations of patterns and measuring similarity and
distance measures play a critical role in many problems
such as clustering, classification, etc. Ever since Jaccard
proposed a similarity measure to classify ecological
species in 1901, numerous binary similarity and distance
measures have been proposed in various fields. Applying
appropriate measures results in more accurate data
analysis. Notwithstanding, few comprehensive surveys
on binary measures have been conducted. Hence we
collected 76 binary similarity and distance measures used
over the last century and reveal their correlations through
the hierarchical clustering technique.

This paper is organized as follows. Section 2 describes
the definitions of 76 binary similarity and dissimilarity
measures. Section 3 discusses the grouping of those
measures using hierarchical clustering. Section 4
concludes this work.

Historically, all the binary measures observed above have
had a meaningful performance in their respective fields.
The binary similarity coefficients proposed by Peirce,
Yule, and Pearson in 1900s contributes to the evolution
of the various correlation based binary similarity
measures. The Jaccard coefficient proposed at 1901 is
still widely used in the various fields such as ecology and
biology. The discussion of inclusion or exclusion of
negative matches was actively arisen by Sokal & Sneath
in during 1960s and by Goodman & Kruskal in 1970s.

Polyphasic Taxonomy of the Genus Vibrio: Numerical Taxonomy of Vibrio cholerae, Vibrio
parahaemolyticus, and Related Vibrio Species
JOURNAL OF BACTERIOLOGY, Oct. 1970;  104(1): 410-433
A set of 86 bacterial cultures, including 30 strains of Vibrio cholerae, 35 strains of
V. parahaemolyticus, and 21 representative strains of Pseudomonas, Spirillum,
Achromobacter, Arthrobacter, and marine Vibrio species were tested for a total of 200
characteristics. Morphological, physiological, and biochemical characteristics were
included in the analysis. Overall deoxyribonucleic acid (DNA) base compositions
and ultrastructure, under the electron microscope, were also examined. The taxonomic
data were analyzed by computer by using numerical taxonomy programs
designed to sort and cluster strains related phenetically. The V. cholerae strains
formed an homogeneous cluster, sharing overall S values of >75%. Two strains,
V. cholerae NCTC 30 and NCTC 8042, did not fall into the V. cholerae species
group when tested by the hypothetical median organism calculation. No separation
of “classic” V. cholerae, El Tor vibrios, and nonagglutinable vibrios was observed.
These all fell into a single, relatively homogeneous, V. cholerae species cluster.
PJ. parahaemolyticus strains, excepting 5144, 5146, and 5162, designated members
of the species V. alginolyticus, clustered at S >80%. Characteristics uniformly
present in all the Vibrio species examined are given, as are also characteristics and
frequency of occurrence for V. cholerae and V. parahaemolyticus. The clusters formed
in the numerical taxonomy analyses revealed similar overall DNA base compositions,
with the range for the Vibrio species of 40 to 48% guanine plus cytosine. Generic
level of relationship of V. cholerae and V. parahaemolyticus is considered
dubious. Intra- and intergroup relationships obtained from the numerical taxonomy
studies showed highly significant correlation with DNA/DNA reassociation data.

A Numerical Classification of the Genus Bacillus
Journal of General Microbiology (1988), 134, 1847-1882.

Three hundred and sixty-eight strains of aerobic, endospore-forming bacteria which included type and reference cultures of Bacillus and environmental isolates were studied. Overall similarities of these strains for 118 unit characters were determined by the SSMS,, and Dp coefficients and clustering achieved using the UPGMA algorithm. Test error was within acceptable limits. Six cluster-groups were defined at 70% SSM which corresponded to 69% Sp and 48-57% SJ.G roupings obtained with the three coefficients were generally similar but there were some changes in the definition and membership of cluster-groups and clusters, particularly with the SJ coefficient. The Bacillus strains were distributed among 31 major (4 or more strains), 18 minor (2 or 3 strains) and 30 single-member clusters at the 83% SsMle vel. Most of these clusters can be regarded as taxospecies. The heterogeneity of several species, including Bacillus breuis, B. circulans, B. coagulans, B. megateriun, B . sphaericus and B . stearothermophilus, has been indicated  and the species status of several taxa of hitherto uncertain validity confirmed. Thus on the basis of the numerical phenetic and appropriate (published) molecular genetic data, it is proposed
that the following names be recognized; BacillusJlexus (Batchelor) nom. rev., Bacillus fusiformis (Smith et al.) comb. nov., Bacillus kaustophilus (Prickett) nom. rev., Bacilluspsychrosaccharolyticus (Larkin & Stokes) nom. rev. and Bacillus simplex (Gottheil) nom. rev. Other phenetically well-defined taxospecies included ‘ B. aneurinolyticus’, ‘B. apiarius’, ‘B. cascainensis’, ‘B. thiaminolyticus’ and three clusters of environmental isolates related to B . firmus and previously described as ‘B. firmus-B. lentus intermediates’. Future developments in the light of the numerical phenetic data are discussed.

Numerical Classification of Bacteria
Part II. * Computer Analysis of Coryneform Bacteria (2)
Comparison of Group-Formations Obtained on Two
Different Methods of Scoring Data
By Eitaro MASUOan d Toshio NAKAGAWA
[Agr. Biol. Chem., 1969; 33(8): 1124-1133.
Sixty three organisms selected from 12 genera of bacteria were subjected to numerical analysis. The purpose of this work is to examine the relationships among 38 coryneform bacteria included in the test organisms by two coding methods-Sneath’s and Lockhart’s systems-, and to compare the results with conventional classification. In both cases of codification, five groups and one or two single item(s) were found in the resultant classifications. Different codings brought, however, a few distinct differences in some groups , especially in a group of sporogenic bacilli or lactic-acid bacteria. So far as the present work concerns, the result obtained on Lockhart’s coding rather than that obtained on Sneath’s coding resembled the conventional classification. The taxonomic positions of corynebacteria were quite different from those of the conventional classification, regardless
of which coding method was applied.
Though animal corynebacteria have conventionally been considered to occupy the
taxonomic position neighboring to genera Arthrobacter and Cellulornonas and regarded to be the nucleus of so-called “coryneform bacteria,’ the present work showed that many of the corynebacteria are akin to certain mycobacteria rather than to the organisms belonging to the above two genera.

Numerical Classification of Bacteria
Part III. Computer Analysis of “Coryneform Bacteria” (3)
Classification Based on DNA Base Compositions
By EitaroM ASUaOnd ToshioN AKAGAWA
Agr. Biol. Chem., 1969; 33(11): 1570-1576
It has been known that the base compositions of deoxyribonucleic acids (DNA) are
quite different from organism to organism. A pertinent example of this diversity is
found in bacterial species. The base compositions of DNA isolated from a wide variety
of bacteria are distributed in a range from 25 to 75 GC mole-percent (100x(G+C)/
(A+T+G+C)).1) The usefulness of the information of DNA base composition for
the taxonomy of bacteria has been emphasized by several authors. Lee et al.,” Sueoka,” and Freese) have speculated on the evolutionary significance of microbial DNA base composition. They pointed out that closely related microorganisms generally showed similar base compositions of DNA, and suggested that phylogenetic relationship should be reflected in the GC content.
In the present paper are compared the results of numerical classifications of 45
bacteria based on the two different similarity matrices: One representing the overall
similarities of phenotypic properties, the other representing the similarities of GC contents.

Advanced computational algorithms for microbial community analysis using massive 16S rRNA
sequence data
Y Sun, Y Cai, V Mai, W Farmerie, F Yu, J Li and S Goodison
Nucleic Acids Research, 2010; 38(22): e205

With the aid of next-generation sequencing technology, researchers can now obtain millions of microbial signature sequences for diverse applications ranging from human epidemiological studies to global ocean surveys. The development of advanced computational strategies to maximally extract pertinent information from massive nucleotide data has become a major focus of the bioinformatics community. Here, we describe a novel analytical strategy including discriminant and topology analyses that enables researchers to deeply investigate the hidden world of microbial communities, far beyond basic microbial diversity estimation. We demonstrate the utility of our
approach through a computational study performed on a previously published massive human gut 16S rRNA data set. The application of discriminant and
topology analyses enabled us to derive quantitative disease-associated microbial signatures and describe microbial community structure in far more detail than previously achievable. Our approach provides rigorous statistical tools for sequence based studies aimed at elucidating associations between known or unknown organisms and a variety of physiological or environmental conditions.

What is Drug Resistance?

Antimicrobial resistance is the ability of microbes, such as bacteria, viruses, parasites, or fungi, to grow in the presence of a chemical (drug) that would normally kill it or limit its growth.

Diagram showing the difference between non-resistant bacteria and drug resistant bacteria.

Credit: NIAID

DrugResistance difference between non-resistant bacteria and drug resistant bacteria

DrugResistance difference between non-resistant bacteria and drug resistant bacteria

Diagram showing the difference between non-resistant bacteria and drug resistant bacteria. Non-resistant bacteria multiply, and upon drug treatment, the bacteria die. Drug resistant bacteria multiply as well, but upon drug treatment, the bacteria continue to spread.

Between 5 and 10 percent of all hospital patients develop an infection. About 90,000 of these patients die each year as a result of their infection, up from 13,300 patient deaths in 1992.

According to the Centers for Disease Control and Prevention (April 2011), antibiotic resistance in the United States costs an estimated $20 billion a year in excess health care costs, $35 million in other societal costs and more than 8 million additional days that people spend in the hospital.

Resistance to Antibiotics: Are We in the Post-Antibiotic Era?

Alfonso J. Alanis
Archives of Medical Research 36 (2005) 697–705

Serious infections caused by bacteria that have become resistant to commonly used antibiotics have become a major global healthcare problem in the 21st century. They not only are more severe and require longer and more complex treatments, but they are also significantly more expensive to diagnose and to treat. Antibiotic resistance, initially a problem of the hospital setting associated with an increased number of hospital acquired infections usually in critically ill and immunosuppressed patients, has now extended into the community causing severe infections difficult to diagnose and treat. The molecular mechanisms by which bacteria have become resistant to antibiotics are diverse and complex. Bacteria have developed resistance to all different classes of antibiotics discovered to date. The most frequent type of resistance is acquired and transmitted horizontally via the conjugation of a plasmid. In recent times new mechanisms of resistance have resulted in the simultaneous development of resistance to several antibiotic classes creating very dangerous multidrug-resistant (MDR) bacterial strains, some also known as ‘‘superbugs’’. The indiscriminate and inappropriate use of antibiotics in outpatient clinics, hospitalized patients and in the food industry is the single largest factor leading to antibiotic resistance. The pharmaceutical industry, large academic institutions or the government are not investing the necessary resources to produce the next generation of newer safe and effective antimicrobial drugs. In many cases, large pharmaceutical companies have terminated their anti-infective research programs altogether due to economic reasons. The potential negative consequences of all these events are relevant because they put society at risk for the spread of potentially serious MDR bacterial infections.

Targeting the Human Macrophage with Combinations of Drugs and Inhibitors of Ca2+ and K+ Transport to Enhance the Killing of Intracellular Multi-Drug Resistant M. tuberculosis (MDR-TB) – a Novel, Patentable Approach to Limit the Emergence of XDR-TB

Marta Martins
Recent Patents on Anti-Infective Drug Discovery, 2011, 6, 000-000

The emergence of resistance in Tuberculosis has become a serious problem for the control of this disease. For that reason, new therapeutic strategies that can be implemented in the clinical setting are urgently needed. The design of new compounds active against mycobacteria must take into account that Tuberculosis is mainly an intracellular infection of the alveolar macrophage and therefore must maintain activity within the host cells. An alternative therapeutic approach will be described in this review, focusing on the activation of the phagocytic cell and the subsequent killing of the internalized bacteria. This approach explores the combined use of antibiotics and phenothiazines, or Ca2+ and K+ flux inhibitors, in the infected macrophage. Targeting the infected macrophage and not the internalized bacteria could overcome the problem of bacterial multi-drug resistance. This will potentially eliminate the appearance of new multi-drug resistant tuberculosis (MDR-TB) cases and subsequently prevent the emergence of extensively-drug resistant tuberculosis (XDR-TB). Patents resulting from this novel and innovative approach could be extremely valuable if they can be implemented in the clinical setting. Other patents will also be discussed such as the treatment of TB using immunomodulator compounds (for example: betaglycans).

Six Epigenetic Faces of Streptococcus

Kevin Mayer

Medical illustration of Streptococcus pneumonia. [CDC]

Streptococcus pneumonia

Streptococcus pneumonia

It appears that S. pneumoniae has even more personalities, each associated with a different proclivity toward invasive, life-threatening disease. In fact, any of six personalities may emerge depending on the action of a single genetic switch.

To uncover the switch, an international team of scientists conducted a study in genomics, but they looked beyond nucleotide polymorphisms or accessory regions as possible phenotype-shifting mechanisms. Instead, they focused on the potential of restriction-modification (RM) systems to mediate gene regulation via epigenetic changes.

Scientists representing the University of Leicester, Griffith University’s Institute for Glycomics, theUniversity of Adelaide, and Pacific Biosciences realized that the S. pneumoniae genome contains two Type I, three Type II, and one Type IV RM systems. Of these, only the DpnI Type II RM system had been described in detail. Switchable Type I systems had been described previously, but these reports did not provide evidence for differential methylation or for phenotypic impact.

As it turned out, the Type I system embodied a mechanism capable of randomly changing the bacterium’s characteristics into six alternative states. The mechanism’s details were presented September 30 in Nature Communications, in an article entitled, “A random six-phase switch regulates pneumococcal virulence via global epigenetic changes.”

“The underlying mechanism for such phase variation consists of genetic rearrangements in a Type I restriction-modification system (SpnD39III),” wrote the authors. “The rearrangements generate six alternative specificities with distinct methylation patterns, as defined by single-molecule, real-time (SMRT) methylomics.”

Eradication of multidrug-resistant A. baumanniii in burn wounds by antiseptic pulsed electric field.

A Golberg, GF Broelsch, D Vecchio,S Khan, MR Hamblin, WG Austen, Jr, RL Sheridan,  ML Yarmush.

Emerging bacterial resistance to multiple drugs is an increasing problem in burn wound management. New non-pharmacologic interventions are needed for wound disinfection. Here we report on a novel physical method for disinfection: antiseptic pulsed electric field (PEF) applied externally to the infected wounds.  In an animal model, we show that PEF can reduce the load of multidrug resistant Acinetobacter baumannii present in a full thickness burn wound by more than four orders of magnitude, as detected by bioluminescence imaging. Furthermore, using a finite element numerical model, we demonstrate that PEF provides non-thermal, homogeneous, full thickness treatment for the burn wound, thus, overcoming the limitation of treatment depth for many topical antimicrobials. These modeling tools and our in vivo results will be extremely useful for further translation of the PEF technology to the clinical setting. We believe that PEF, in combination with systemic antibiotics, will synergistically eradicate multidrug-resistant burn wound infections, prevent biofilm formation and restore natural skin microbiome. PEF provides a new platform for infection combat in patients, therefore it has a potential to significantly decreasing morbidity and mortality.

Golberg, A. & Yarmush, M. L. Nonthermal irreversible electroporation: fundamentals, applications, and challenges. IEEE Trans Biomed Eng 60, 707-14 (2013).

Mechanisms Of Antibiotic Resistance In Salmonella: Efflux Pumps, Genetics, Quorum Sensing And Biofilm Formation.

Martins M, McCusker M, Amaral L, Fanning S
Perspectives in Drug Discovery and Design 02/2011; 8:114-123.

In Salmonella the main mechanisms of antibiotic resistance are mutations in target genes (such as DNA gyrase and topoisomerase IV) and the over-expression of efflux pumps. However, other mechanisms such as changes in the cell envelope; down regulation of membrane porins; increased lipopolysaccharide (LPS) component of the outer cell membrane; quorum sensing and biofilm formation can also contribute to the resistance seen in this microorganism. To overcome this problem new therapeutic approaches are urgently needed. In the case of efflux-mediated multidrug resistant isolates, one of the treatment options could be the use of efflux pump inhibitors (EPIs) in combination with the antibiotics to which the bacteria is resistant. By blocking the efflux pumps resistance is partly or wholly reversed, allowing antibiotics showing no activity against the MDR strains to be used to treat these infections. Compounds that show potential as an EPI are therefore of interest, as well as new strategies to target the efflux systems. Quorum sensing (QS) and biofilm formation are systems also known to be involved in antibiotic resistance. Consequently, compounds that can disrupt or inhibit these bacterial “communication systems” will be of use in the treatment of these infections.

Role of Phenothiazines and Structurally Similar Compounds of Plant Origin in the Fight against Infections by Drug Resistant Bacteria

SG Dastidar, JE Kristiansen, J Molnar and L Amaral
Antibiotics 2013, 2, 58-71;

Phenothiazines have their primary effects on the plasma membranes of prokaryotes and eukaryotes. Among the components of the prokaryotic plasma membrane affected are efflux pumps, their energy sources and energy providing enzymes, such as ATPase, and genes that regulate and code for the permeability aspect of a bacterium. The response of multidrug and extensively drug resistant tuberculosis to phenothiazines shows an alternative therapy for its treatment. Many phenothiazines have shown synergistic activity with several antibiotics thereby lowering the doses of antibiotics administered for specific bacterial infections. Trimeprazine is synergistic with trimethoprim. Flupenthixol (Fp) has been found to be synergistic with penicillin and chlorpromazine (CPZ); in addition, some antibiotics are also synergistic. Along with the antibacterial action described in this review, many phenothiazines possess plasmid curing activities, which render the bacterial carrier of the plasmid sensitive to antibiotics. Thus, simultaneous applications of a phenothiazine like TZ would not only act as an additional antibacterial agent but also would help to eliminate drug resistant plasmid from the infectious bacterial cells.

Multidrug Efflux Pumps Described for Staphylococcus aureus

Efflux Pump  Family Regulator(s) Substrate Specificity  References 
Chromosomally-encoded Efflux Systems 
NorA MFS MgrA, NorG(?) Hydrophilic fluoroquinolones (ciprofloxacin, norfloxacin)QACs (tetraphenylphosphonium, benzalkonium chloride)

Dyes (e.g. ethidium bromide, rhodamine)

NorB MFS MgrA, NorG Fluoroquinolones (e.g. hydrophilic: ciprofloxacin, norfloxacin and hydrophobic: moxifloxacin,
sparfloxacin)TetracyclineQACs (e.g. tetraphenylphosphonium, cetrimide)Dyes (e.g. ethidium bromide)
NorC MFS MgrA(?), NorG Fluoroquinolones (e.g. hydrophilic: ciprofloxacin and hydrophobic: moxifloxacin)Dyes (e.g. rhodamine) [35,36]
MepA MATE MepR Fluoroquinolones (e.g. hydrophilic: ciprofloxacin, norfloxacin and hydrophobic: moxifloxacin,
sparfloxacin)Glycylcyclines (e.g. tigecycline)QACs (e.g. tetraphenylphosphonium, cetrimide, benzalkonium chloride)Dyes (e.g. ethidium bromide)
MdeA MFS n.i. Hydrophilic fluoroquinolones (e.g. ciprofloxacin, norfloxacin)Virginiamycin, novobiocin, mupirocin, fusidic acid

QACs (e.g. tetraphenylphosphonium, benzalkonium chloride, dequalinium)

Dyes (e.g. ethidium bromide)

SepA n.d. n.i. QACs (e.g. benzalkonium chloride)Biguanidines (e.g. chlorhexidine)

Dyes (e.g. acriflavine)

SdrM MFS n.i. Hydrophilic fluoroquinolones (e.g. norfloxacin)Dyes (e.g. ethidium bromide, acriflavine) [42]
LmrS MFS n.i. Oxazolidinone (linezolid)Phenicols (e.g. choramphenicol, florfenicol)

Trimethoprim, erythromycin, kanamycin, fusidic acid

QACs (e.g. tetraphenylphosphonium)

Detergents (e.g. sodium docecyl sulphate)

Dyes (e.g. ethidium bromide)


Plasmid-encoded Efflux Systems

QacA MFS QacR QACs (e.g. tetraphenylphosphonium, benzalkonium chloride, dequalinium)Biguanidines (e.g. chlorhexidine)

Diamidines (e.g. pentamidine)

Dyes (e.g. ethidium bromide, rhodamine, acriflavine)

QacB MFS QacR QACs (e.g. tetraphenylphosphonium, benzalkonium chloride)Dyes (e.g. ethidium bromide, rhodamine, acriflavine) [53]
Smr SMR n.i. QACs (e.g. benzalkonium chloride, cetrimide)Dyes (e.g. ethidium bromide) [58,61]
QacG SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [67]
QacH SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [68]
QacJ SMR n.i. QACs (e.g. benzalkonium chloride, cetyltrymethylammonium)Dyes (e.g. ethidium bromide) [69]

a n.d.: The family of transporters to which SepA belongs is not elucidated to date.
b n.i.: The transporter has no regulator identified to date.
QACs: quaternary ammonium compounds
The importance of efflux pumps in bacterial antibiotic resistance

  1. A. Webber and L. J. V. Piddock
    Journal of Antimicrobial Chemotherapy (2003) 51, 9–11 pumps are transport proteins involved in the extrusion of toxic substrates (including virtually all classes of clinically relevant antibiotics) from within cells into the external environment. These proteins are found in both Gram-positive and -negative bacteria as well as in eukaryotic organisms. Pumps may be specific for one substrate or may transport a range of structurally dissimilar compounds (including antibiotics of multiple classes); such pumps can be associated with multiple drug resistance (MDR). In the prokaryotic kingdom there are five major families of efflux transporter: MF (major facilitator), MATE (multidrug and toxic efflux), RND (resistance-nodulation-division), SMR (small multidrug resistance) and ABC (ATP binding cassette). All these systems utilize the proton motive force as an energy source. Advances in DNA technology have led to the identification of members of the above families. Transporters that efflux multiple substrates, including antibiotics, have not evolved in response to the stresses of the antibiotic era. All bacterial genomes studied contain efflux pumps that indicate their ancestral origins. It has been estimated that ∼5–10% of all bacterial genes are involved in transport and a large proportion of these encode efflux pumps.
The efflux pump

The efflux pump

Multidrug-resistance efflux pumps — not just for resistance

Laura J. V. Piddock
Nature Reviews | Microbiology | Aug 2006; 4: 629

It is well established that multidrug-resistance efflux pumps encoded by bacteria can confer clinically relevant resistance to antibiotics. It is now understood that these efflux pumps also have a physiological role(s). They can confer resistance to natural substances produced by the host, including bile, hormones and host defense molecules. In addition, some efflux pumps of the resistance nodulation division (RND) family have been shown to have a role in the colonization and the persistence of bacteria in the host. Here, I present the accumulating evidence that multidrug-resistance efflux pumps have roles in bacterial pathogenicity and propose that these pumps therefore have greater clinical relevance than is usually attributed to them.

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