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Archive for the ‘Image Processing/Computing’ Category


Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: The Davids vs. the Cancer Goliath Part 2

8:40 – 9:25 AM 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

Speakers:
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

 

3Derm Systems

3Derm Systems is an image analysis firm for dermatologic malignancies.  They use a tele-medicine platform to accurately triage out benign malignancies observed from the primary care physician, expediate those pathology cases if urgent to the dermatologist and rapidly consults with you over home or portable device (HIPAA compliant).  Their suite also includes a digital dermatology teaching resource including digital training for students and documentation services.

 

CNS Pharmaceuticals

developing drugs against CNS malignancies, spun out of research at MD Anderson.  They are focusing on glioblastoma and Berubicin, an anthracycline antiobiotic (TOPOII inhibitor) that can cross the blood brain barrier.  Berubicin has good activity in a number of animal models.  Phase I results were very positive and Phase II is scheduled for later in the year.  They hope that the cardiotoxicity profile is less severe than other anthracyclines.  The market opportunity will be in temazolamide resistant glioblastoma.

Cubismi

They are using machine learning and biomarker based imaging to visualize tumor heterogeneity. “Data is the new oil” (Intel CEO). We need prediction machines so they developed a “my body one file” system, a cloud based data rich file of a 3D map of human body.

CUBISMI IS ON A MISSION TO HELP DELIVER THE FUTURE PROMISE OF PRECISION MEDICINE TO CURE DISEASE AND ASSURE YOUR OPTIMAL HEALTH.  WE ARE BUILDING A PATIENT-DOCTOR HEALTH DATA EXCHANGE PLATFORM THAT WILL LEVERAGE REVOLUTIONARY MEDICAL IMAGING TECHNOLOGY AND PUT THE POWER OF HEALTH DATA INTO THE HANDS OF YOU AND YOUR DOCTORS.

 

CytoSavvy

CytoSavvy is a digital pathology company.  They feel AI has a fatal flaw in that no way to tell how a decision was made. Use a Shape Based Model Segmentation algorithm which uses automated image analysis to provide objective personalized pathology data.  They are partnering with three academic centers (OSU, UM, UPMC) and pool data and automate the rule base for image analysis.

CytoSavvy’s patented diagnostic dashboards are intuitive, easy–to-use and HIPAA compliant. Our patented Shape-Based Modeling Segmentation (SBMS) algorithms combine shape and color analysis capabilities to increase reliability, save time, and improve decisions. Specifications and capabilities for our web-based delivery system follow.

link to their white paper: https://www.cytosavvy.com/resources/healthcare-ai-value-proposition.pdf

PotentialMetrics

They were developing a diagnostic software for cardiology epidemiology measuring outcomes however when a family member got a cancer diagnosis felt there was a need for outcomes based models for cancer treatment/care.  They deliver real world outcomes for persoanlized patient care to help patients make decisions on there care by using a socioeconomic modeling integrated with real time clinical data.

Featured in the Wall Street Journal, using the informed treatment decisions they have generated achieve a 20% cost savings on average.  There research was spun out of Washington University St. Louis.

They have concentrated on urban markets however the CEO had mentioned his desire to move into more rural areas of the country as there models work well for patients in the rural setting as well.

Please follow on Twitter using the following #hash tags and @pharma_BI 

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

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Sperm Analysis by Smart Phone

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

 

Low sperm count and motility are markers for male infertility, a condition that is actually a neglected health issue worldwide, according to the World Health Organization. Researchers at Harvard Medical School have developed a very low cost device that can attach to a cell phone and provides a quick and easy semen analysis. The device is still under development, but a study of the machine’s capabilities concludes that it is just as accurate as the elaborate high cost computer-assisted semen analysis machines costing tens of thousands of dollars in measuring sperm concentration, sperm motility, total sperm count and total motile cells.

 

The Harvard team isn’t the first to develop an at-home fertility test for men, but they are the first to be able to determine sperm concentration as well as motility. The scientists compared the smart phone sperm tracker to current lab equipment by analyzing the same semen samples side by side. They analyzed over 350 semen samples of both infertile and fertile men. The smart phone system was able to identify abnormal sperm samples with 98 percent accuracy. The results of the study were published in the journal named Science Translational Medicine.

 

The device uses an optical attachment for magnification and a disposable microchip for handling the semen sample. With two lenses that require no manual focusing and an inexpensive battery, it slides onto the smart phone’s camera. Total cost for manufacturing the equipment: $4.45, including $3.59 for the optical attachment and 86 cents for the disposable micro-fluidic chip that contains the semen sample.

 

The software of the app is designed with a simple interface that guides the user through the test with onscreen prompts. After the sample is inserted, the app can photograph it, create a video and report the results in less than five seconds. The test results are stored on the phone so that semen quality can be monitored over time. The device is under consideration for approval from the Food and Drug Administration within the next two years.

 

With this device at home, a man can avoid the embarrassment and stress of providing a sample in a doctor’s clinic. The device could also be useful for men who get vasectomies, who are supposed to return to the urologist for semen analysis twice in the six months after the procedure. Compliance is typically poor, but with this device, a man could perform his own semen analysis at home and email the result to the urologist. This will make sperm analysis available in the privacy of our home and as easy as a home pregnancy test or blood sugar test.

 

The device costs about $5 to make in the lab and can be made available in the market at lower than $50 initially. This low cost could help provide much-needed infertility care in developing or underdeveloped nations, which often lack the resources for currently available diagnostics.

 

References:

 

https://www.nytimes.com/2017/03/22/well/live/sperm-counts-via-your-cellphone.html?em_pos=small&emc=edit_hh_20170324&nl=well&nl_art=7&nlid=65713389&ref=headline&te=1&_r=1

 

http://www.npr.org/sections/health-shots/2017/03/22/520837557/a-smartphone-can-accurately-test-sperm-count

 

https://www.ncbi.nlm.nih.gov/pubmed/28330865

 

http://www.sciencealert.com/new-smartphone-microscope-lets-men-check-the-health-of-their-own-sperm

 

https://www.newscientist.com/article/2097618-are-your-sperm-up-to-scratch-phone-microscope-lets-you-check/

 

https://www.dezeen.com/2017/01/19/yo-fertility-kit-men-test-sperm-count-smartphone-design-technology-apps/

 

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cancerandoncologyseriesccover

Series C: e-Books on Cancer & Oncology

Series C Content Consultant: Larry H. Bernstein, MD, FCAP

 

VOLUME ONE 

Cancer Biology and Genomics

for

Disease Diagnosis

2015

http://www.amazon.com/dp/B013RVYR2K

Stephen J. Williams, PhD, Senior Editor

sjwilliamspa@comcast.net

Tilda Barliya, PhD, Editor

tildabarliya@gmail.com

Ritu Saxena, PhD, Editor

ritu.uab@gmail.com

Leaders in Pharmaceutical Business Intelligence 

Part I

Historical Perspective of Cancer Demographics, Etiology, and Progress in Research

Chapter 1:  The Occurrence of Cancer in World Populations

1.1   Understanding Cancer

Prabodh Kandala, PhD

1.2  Cancer Metastasis

Tilda Barliya, PhD

1.3      2013 Perspective on “War on Cancer” on December 23, 1971

Aviva Lev-Ari, PhD, RN

1.4   Global Burden of Cancer Treatment & Women Health: Market Access & Cost Concerns

Aviva Lev-Ari, PhD, RN

1.5    The Importance of Cancer Prevention Programs: New Perspectives for Fighting Cancer

Ziv Raviv, PhD

1.6      The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953,  

Larry H Bernstein, MD, FCAP

1.7      New Ecosystem of Cancer Research: Cross Institutional Team Science

Aviva Lev-Ari, PhD, RN

1.8       Cancer Innovations from across the Web

Larry H Bernstein, MD, FCAP

1.9         Exploring the role of vitamin C in Cancer therapy

Ritu Saxena PhD

1.10        Relation of Diet and Cancer

Sudipta Saha, PhD

1.11      Association between Non-melanoma Skin Cancer and subsequent Primary Cancers in White Population 

Aviva Lev-Ari, PhD, RN

1.12       Men With Prostate Cancer More Likely to Die from Other Causes

Prabodh Kandala, PhD

1.13      Battle of Steve Jobs and Ralph Steinman with Pancreatic Cancer: How we Lost

Ritu Saxena, PhD

Chapter 2.  Rapid Scientific Advances Changes Our View on How Cancer Forms

2.1     All Cancer Cells Are Not Created Equal: Some Cell Types Control Continued Tumor Growth, Others Prepare the Way for Metastasis 

Prabodh Kandala, PhD

2.2      Hold on. Mutations in Cancer do Good

Prabodh Kandala, PhD

2.3       Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

Larry H Bernstein, MD, FCAP

2.4          Naked Mole Rats Cancer-Free

Larry H Bernstein, MD, FCAP

2.5           Zebrafish—Susceptible to Cancer

Larry H Bernstein, MD, FCAP

2.6         Demythologizing Sharks, Cancer, and Shark Fins,

Larry H Bernstein, MD, FCAP

2.7       Tumor Cells’ Inner Workings Predict Cancer Progression

Prabodh Kandala, PhD

2.8      In Focus: Identity of Cancer Stem Cells

Ritu Saxena, PhD

2.9      In Focus: Circulating Tumor Cells

Ritu Saxena, PhD

2.10     Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers 

Stephen J. Williams, PhD

2.11     Role of Primary Cilia in Ovarian Cancer

Aashir Awan, PhD

Chapter 3:  A Genetic Basis and Genetic Complexity of Cancer Emerges

3.1       The Binding of Oligonucleotides in DNA and 3-D Lattice Structures

Larry H Bernstein, MD, FCAP

3.2      How Mobile Elements in “Junk” DNA Promote Cancer. Part 1: Transposon-mediated Tumorigenesis. 

Stephen J. Williams, PhD

3.3      DNA: One Man’s Trash is another Man’s Treasure, but there is no JUNK after all

Demet Sag, PhD

3.4 Issues of Tumor Heterogeneity

3.4.1    Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

Stephen J. Williams, PhD

3.4.2       Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Stephen J. Williams, PhD

3.5        arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data

Aviva Lev-Ari, PhD, RN

3.6        HBV and HCV-associated Liver Cancer: Important Insights from the Genome

Ritu Saxena, PhD

3.7      Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center

Aviva Lev-Ari, PhD, RN

3.8         Gastric Cancer: Whole-genome Reconstruction and Mutational Signatures

Aviva Lev-Ari, PhD, RN

3.9        Missing Gene may Drive more than a quarter of Breast Cancers

Aviva Lev-Ari, PhD, RN

3.10     Critical Gene in Calcium Reabsorption: Variants in the KCNJ and SLC12A1 genes – Calcium Intake and Cancer Protection

Aviva Lev-Ari,PhD, RN

Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth

4.1    Epigenetics

4.1.1     The Magic of the Pandora’s Box : Epigenetics and Stemness with Long non-coding RNAs (lincRNA)

Demet Sag, PhD, CRA, GCP

4.1.2     Stomach Cancer Subtypes Methylation-based identified by Singapore-Led Team

Aviva Lev-Ari, PhD, RN

4.1.3     The Underappreciated EpiGenome

Demet Sag, Ph.D., CRA, GCP

4.1.4     Differentiation Therapy – Epigenetics Tackles Solid Tumors

Stephen J. Williams, PhD

4.1.5      “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA

Demet Sag, Ph.D., CRA, GCP

4.1.6      DNA Methyltransferases – Implications to Epigenetic Regulation and Cancer Therapy Targeting: James Shen, PhD

Aviva Lev-Ari, PhD, RN

4.2   Metabolism

4.2.1      Mitochondria and Cancer: An overview of mechanisms

Ritu Saxena, PhD

4.2.2     Bioenergetic Mechanism: The Inverse Association of Cancer and Alzheimer’s

Aviva Lev-Ari, PhD, RN

4.2.3      Crucial role of Nitric Oxide in Cancer

Ritu Saxena, PhD

4.2.4      Nitric Oxide Mitigates Sensitivity of Melanoma Cells to Cisplatin

Stephen J. Williams, PhD

4.2.5      Increased risks of obesity and cancer, Decreased risk of type 2 diabetes: The role of Tumor-suppressor phosphatase and tensin homologue (PTEN)

Aviva Lev-Ari, PhD, RN

4.2.6      Lipid Profile, Saturated Fats, Raman Spectrosopy, Cancer Cytology

Larry H Bernstein, MD, FCAP

4.3     Other Factors Affecting Tumor Growth

4.3.1      Squeezing Ovarian Cancer Cells to Predict Metastatic Potential: Cell Stiffness as Possible Biomarker

Prabodh Kandala, PhD

4.3.2      Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease

Aviva Lev-Ari, PhD, RN

Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure

5.1 Breast Cancer

5.1.1      Cell Movement Provides Clues to Aggressive Breast Cancer

Prabodh Kandala, PhD

5.1.2    Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

Prabodh Kandala, PhD

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

Aviva Lev-Ari, PhD, RN

5.1.4       BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

Sudipta Saha, PhD

5.1.5      Breast Cancer and Mitochondrial Mutations

Larry H Bernstein, MD, FCAP

5.1.6      MIT Scientists Identified Gene that Controls Aggressiveness in Breast Cancer Cells

Aviva Lev-Ari PhD RN

5.1.7       “The Molecular pathology of Breast Cancer Progression”

Tilda Barliya, PhD

5.1.8       In focus: Triple Negative Breast Cancer

Ritu Saxena, PhD

5.1.9       Automated Breast Ultrasound System (‘ABUS’) for full breast scanning: The beginning of structuring a solution for an acute need!

Dror Nir, PhD

5.1.10       State of the art in oncologic imaging of breast.

Dror Nir, PhD

 

5.2 Gastrointestinal Cancer

5.2.1         Colon Cancer

Tilda Barliya, PhD

5.2.2      PIK3CA mutation in Colorectal Cancer may serve as a Predictive Molecular Biomarker for adjuvant Aspirin therapy

Aviva Lev-Ari, PhD, RN

5.2.3     State of the art in oncologic imaging of colorectal cancers.

Dror Nir, PhD

5.2.4     Pancreatic Cancer: Genetics, Genomics and Immunotherapy

Tilda Barliya, PhD

5.2.5     Pancreatic cancer genomes: Axon guidance pathway genes – aberrations revealed

Aviva Lev-Ari, PhD, RN

Part II

Advent of Translational Medicine, “omics”, and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and Advances in Drug Development

Chapter 6:  Treatment Strategies

6.1 Marketed and Novel Drugs

Breast Cancer                                   

6.1.1     Treatment for Metastatic HER2 Breast Cancer

Larry H Bernstein MD, FCAP

6.1.2          Aspirin a Day Tied to Lower Cancer Mortality

Aviva Lev-Ari, PhD, RN

6.1.3       New Anti-Cancer Drug Developed

Prabodh Kandala, Ph.D.

6.1.4         Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)

Aviva Lev-Ari ,PhD, RN

6.1.5     “To Die or Not To Die” – Time and Order of Combination drugs for Triple Negative Breast Cancer cells: A Systems Level Analysis

Anamika Sarkar, PhD. and Ritu Saxena, PhD

Melanoma

6.1.6    “Thymosin alpha1 and melanoma”

Tilda Barliya, PhD

Leukemia

6.1.7    Acute Lymphoblastic Leukemia and Bone Marrow Transplantation

Tilda Barliya PhD

6.2 Natural agents

Prostate Cancer                 

6.2.1      Scientists use natural agents for prostate cancer bone metastasis treatment

Ritu Saxena, PhD

Breast Cancer

6.2.2        Marijuana Compound Shows Promise In Fighting Breast Cancer

Prabodh Kandala, PhD

Ovarian Cancer                  

6.2.3        Dimming ovarian cancer growth

Prabodh Kandala, PhD

6.3 Potential Therapeutic Agents

Gastric Cancer                 

6.3.1       β Integrin emerges as an important player in mitochondrial dysfunction associated Gastric Cancer

Ritu Saxena, PhD

6.3.2      Arthritis, Cancer: New Screening Technique Yields Elusive Compounds to Block Immune-Regulating Enzyme

Prabodh Kandala, PhD

Pancreatic Cancer                                   

6.3.3    Usp9x: Promising therapeutic target for pancreatic cancer

Ritu Saxena, PhD

Breast Cancer                 

6.3.4       Breast Cancer, drug resistance, and biopharmaceutical targets

Larry H Bernstein, MD, FCAP

Prostate Cancer

6.3.5        Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition

Stephen J. Williams, PhD

Glioblastoma

6.3.6      Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma

Raphael Nir, PhD, MSM, MSc

6.3.7   Akt inhibition for cancer treatment, where do we stand today?

Ziv Raviv, PhD

Chapter 7:  Personalized Medicine and Targeted Therapy

7.1.1        Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders

Aviva Lev-Ari, PhD, RN

7.1.2      Personalized medicine-based cure for cancer might not be far away

Ritu Saxena, PhD

7.1.3      Personalized medicine gearing up to tackle cancer

Ritu Saxena, PhD

7.1.4       Cancer Screening at Sourasky Medical Center Cancer Prevention Center in Tel-Aviv

Ziv Raviv, PhD

7.1.5       Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics

Aviva Lev-Ari, PhD, RN

7.1.6       Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

Aviva Lev-Ari, PhD, RN

7.2 Personalized Medicine and Genomics

7.2.1       Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

Aviva Lev-Ari, PhD, RN

7.2.2       Whole exome somatic mutations analysis of malignant melanoma contributes to the development of personalized cancer therapy for this disease

Ziv Raviv, PhD

7.2.3       Genotype-based Analysis for Cancer Therapy using Large-scale Data Modeling: Nayoung Kim, PhD(c)

Aviva Lev-Ari, PhD, RN

7.2.4         Cancer Genomic Precision Therapy: Digitized Tumor’s Genome (WGSA) Compared with Genome-native Germ Line: Flash-frozen specimen and Formalin-fixed paraffin-embedded Specimen Needed

Aviva Lev-Ari, PhD, RN

7.2.5         LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

Aviva Lev-Ari, PhD, RN

7.2.6       Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

Stephen J. Williams, PhD

7.3  Personalized Medicine and Targeted Therapy

7.3.1     The Development of siRNA-Based Therapies for Cancer

Ziv Raviv, PhD

7.3.2       mRNA interference with cancer expression

Larry H Bernstein, MD, FCAP

7.3.3       CD47: Target Therapy for Cancer

Tilda Barliya, PhD

7.3.4      Targeting Mitochondrial-bound Hexokinase for Cancer Therapy

Ziv Raviv, PhD

7.3.5       GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”

Aviva Lev-Ari, PhD, RN

7.3.6         Personalized Pancreatic Cancer Treatment Option

Aviva Lev-Ari, PhD, RN

7.3.7        New scheme to routinely test patients for inherited cancer genes

Stephen J. Williams, PhD

7.3.8        Targeting Untargetable Proto-Oncogenes

Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

7.3.9        The Future of Translational Medicine with Smart Diagnostics and Therapies: PharmacoGenomics 

Demet Sag, PhD

7.4 Personalized Medicine in Specific Cancers

7.4.1      Personalized medicine and Colon cancer

Tilda Barliya, PhD

7.4.2      Comprehensive Genomic Characterization of Squamous Cell Lung Cancers

Aviva Lev-Ari, PhD, RN

7.4.3        Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4

Sudipta Saha, PhD

7.4.4        Cancer and Bone: low magnitude vibrations help mitigate bone loss

Ritu Saxena, PhD

7.4.5         New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests

Prabodh Kandala, PhD

Part III

Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection

Diagnosis, Detection And Biomarkers

Chapter 8:  Diagnosis Diagnosis: Prostate Cancer

8.1        Prostate Cancer Molecular Diagnostic Market – the Players are: SRI Int’l, Genomic Health w/Cleveland Clinic, Myriad Genetics w/UCSF, GenomeDx and BioTheranostics

Aviva Lev-Ari PhD RN

8.2         Today’s fundamental challenge in Prostate cancer screening

Dror Nir, PhD

Diagnosis & Guidance: Prostate Cancer

8.3      Prostate Cancers Plunged After USPSTF Guidance, Will It Happen Again?

Aviva Lev-Ari, PhD, RN

Diagnosis, Guidance and Market Aspects: Prostate Cancer

8.4       New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests

Prabodh Kandala, PhD

Diagnossis: Lung Cancer

8.5      Diagnosing lung cancer in exhaled breath using gold nanoparticles

Tilda Barliya PhD

Chapter 9:  Detection

Detection: Prostate Cancer

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

Dror Nir, PhD

Detection: Breast & Ovarian Cancer

9.2       Testing for Multiple Genetic Mutations via NGS for Patients: Very Strong Family History of Breast & Ovarian Cancer, Diagnosed at Young Ages, & Negative on BRCA Test

Aviva Lev-Ari, PhD, RN

Detection: Aggressive Prostate Cancer

9.3     A Blood Test to Identify Aggressive Prostate Cancer: a Discovery @ SRI International, Menlo Park, CA

Aviva Lev-Ari, PhD, RN

Diagnostic Markers & Screening as Diagnosis Method

9.4      Combining Nanotube Technology and Genetically Engineered Antibodies to Detect Prostate Cancer Biomarkers

Stephen J. Williams, PhD

Detection: Ovarian Cancer

9.5      Warning signs may lead to better early detection of ovarian cancer

Prabodh Kandala, PhD

9.6       Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?

Dror Nir, PhD

Chapter 10:  Biomarkers

                                                Biomarkers: Pancreatic Cancer

10.1        Mesothelin: An early detection biomarker for cancer (By Jack Andraka)

Tilda Barliya, PhD

Biomarkers: All Types of Cancer, Genomics and Histology

10.2                  Stanniocalcin: A Cancer Biomarker

Aashir Awan, PhD

10.3         Breast Cancer: Genomic Profiling to Predict Survival: Combination of Histopathology and Gene Expression Analysis

Aviva Lev-Ari, PhD, RN

Biomarkers: Pancreatic Cancer

10.4         Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University

Aviva Lev-Ari, PhD, RN

10.5     Early Biomarker for Pancreatic Cancer Identified

Prabodh Kandala, PhD

Biomarkers: Head and Neck Cancer

10.6        Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing

Aviva Lev-Ari, PhD, RN

10.7      Opens Exome Service for Rare Diseases & Advanced Cancer @Mayo Clinic’s OncoSpire

Aviva Lev-Ari, PhD, RN

Diagnostic Markers and Screening as Diagnosis Methods

10.8         In Search of Clarity on Prostate Cancer Screening, Post-Surgical Followup, and Prediction of Long Term Remission

Larry H Bernstein, MD, FCAP

Chapter 11  Imaging In Cancer

11.1  Introduction by Dror Nir, PhD

11.2  Ultrasound

11.2.1        2013 – YEAR OF THE ULTRASOUND

Dror Nir, PhD

11.2.2      Imaging: seeing or imagining? (Part 1)

Dror Nir, PhD

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

Dror Nir, PhD

11.2.4        Today’s fundamental challenge in Prostate cancer screening

Dror Nir, PhD

11.2.5       State of the art in oncologic imaging of Prostate

Dror Nir, PhD

11.2.6        From AUA 2013: “HistoScanning”- aided template biopsies for patients with previous negative TRUS biopsies

Dror Nir, PhD

11.2.7     On the road to improve prostate biopsy

Dror Nir, PhD

11.2.8       Ultrasound imaging as an instrument for measuring tissue elasticity: “Shear-wave Elastography” VS. “Strain-Imaging”

Dror Nir, PhD

11.2.9       What could transform an underdog into a winner?

Dror Nir, PhD

11.2.10        Ultrasound-based Screening for Ovarian Cancer

Dror Nir, PhD

11.2.11        Imaging Guided Cancer-Therapy – a Discipline in Need of Guidance

Dror Nir, PhD

11.3   MRI & PET/MRI

11.3.1     Introducing smart-imaging into radiologists’ daily practice

Dror Nir, PhD

11.3.2     Imaging: seeing or imagining? (Part 2)

[Part 1 is included in the ultrasound section above]

Dror Nir, PhD

11.3.3    Imaging-guided biopsies: Is there a preferred strategy to choose?

Dror Nir, PhD

11.3.4     New clinical results support Imaging-guidance for targeted prostate biopsy

Dror Nir, PhD

11.3.5      Whole-body imaging as cancer screening tool; answering an unmet clinical need?

Dror Nir, PhD

11.3.6        State of the art in oncologic imaging of Lymphoma

Dror Nir, PhD

11.3.7      A corner in the medical imaging’s ECO system

Dror Nir, PhD

11.4  CT, Mammography & PET/CT 

11.4.1      Causes and imaging features of false positives and false negatives on 18F-PET/CT in oncologic imaging

Dror Nir, PhD

11.4.2     Minimally invasive image-guided therapy for inoperable hepatocellular carcinoma

Dror Nir, PhD

11.4.3        Improving Mammography-based imaging for better treatment planning

Dror Nir, PhD

11.4.4       Closing the Mammography gap

Dror Nir, PhD

11.4.5       State of the art in oncologic imaging of lungs

Dror Nir, PhD

11.4.6       Ovarian Cancer and fluorescence-guided surgery: A report

Tilda Barliya, PhD

11.5  Optical Coherent Tomography (OCT)

11.5.1       Optical Coherent Tomography – emerging technology in cancer patient management

Dror Nir, PhD

11.5.2     New Imaging device bears a promise for better quality control of breast-cancer lumpectomies – considering the cost impact

Dror Nir, PhD

11.5.3        Virtual Biopsy – is it possible?

Dror Nir, PhD

11.5.4      New development in measuring mechanical properties of tissue

Dror Nir, PhD

Chapter 12. Nanotechnology Imparts New Advances in Cancer Treatment,  Detection, and Imaging  

12.1     DNA Nanotechnology

Tilda Barliya, PhD

12.2     Nanotechnology, personalized medicine and DNA sequencing

Tilda Barliya, PhD       

12.3     Nanotech Therapy for Breast Cancer

Tilda Barliya, PhD

12.4     Prostate Cancer and Nanotecnology

Tilda Barliya, PhD

12.5     Nanotechnology: Detecting and Treating metastatic cancer in the lymph node

Tilda Barliya, PhD

12.6     Nanotechnology Tackles Brain Cancer

Tilda Barliya, PhD

12.7     Lung Cancer (NSCLC), drug administration and nanotechnology

Tilda Barliya, PhD

Volume Epilogue by Larry H. Bernstein, MD, FACP

Epilogue: Envisioning New Insights in Cancer Translational Biology

Larry H. Berstein, MD, FACP

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Imaging  Living Cells and Devices, Presentation by Danut Dragoi, PhD, LPBI group

Imaging living cells is for a good number of years a hot place in Biology, Physics, Chemistry as well as Engineering and Technology for producing specific devices to visualize living cells. In this presentation is shown my opinion on this topic regarding actual status of applied technology for visualizing living cells as well as small small areas of interest.

Slide #1

Slide1

Slide #2

Slide2

As an overview, slide #2 describes: higher resolution imaging of living cells based on advanced CT and MicroCT scanners, and their actual technological trend,  advanced optical microscopy, optical magnetic imaging of living cells, and conclusion.

Slide #3

Slide3

Slide #3 describes a schematic of a computing tomography applied to a single cell, see the inside URL address. The work is in progress as a SBIR application of a group of researchers from Arizona State.The partial section of the cell is supposed to  reveal the contents of the cell, which is very important in Biology and Medicine.

Slide #4

Slide4

Slide #4 describes the principle of computed tomography for relative small objects that are expose by a soft x-ray source on the left, an x-ray detector screen that takes the x-ray projection radiography for the sample on the right. The sample is rotated discretely a small number of degrees and pictures recorded. Depending on the absorption of the sample, the reconstructed 3D object is possible. The resolution of the reconstructed object is a function of the number of pixels as well as the pitch distance d (in the slide 0.127 microns). Because the sample is rotated, the precision of the axis of rotation is very important and becomes a challenging task for small objects.

Slide #5

Slide5

Slide #5 shows a sample taken from the URL address given below the picture. It represents an insect and the future CT development is expected to produce similar images for mono living cells.

Slide #6

Slide6

For many Bio-labs the reverse optical microscope is the working horse. The slide above shows a such microscope with a culture cell inside a transparent box. The picture can be found at the address shown inside the slide.

Slide #7

Slide7

Slide #7 describes an innovative digital microscope from Keyence in which we can observe any object entirely in focus, a 20x greater depth-of-field than an optical microscope, we can view objects from any angle, and measure lengths directly on screen.

Slide #8

Slide8

Slide #8 shows an actual innovative digital microscope from Keyence, see the website address at the bottom of the slide.

Slide #9

Slide9

As we know, the samples visualized by a common optical microscope have to be flat on the surface to be visualized because there is no clear image above and below the focal plane, which is the surface of the sample. For a con-focal microscope the situation is changed. Objects can be visualized at different depths and image files recorded can reconstruct as 3D image object.

Slide #10

Slide10

Slide #10 describes the principle of a con-focal microscope, in which a green laser on left side excites molecules of the specimen at a given depth of focusing, the molecules emit on red light (less energetic than green light) that go all way to the photo-multiplier, which has a small pinhole aperture in front of it that limits the entrance of red rays (parasitic light) from out of range area. More details can be found at the URL address given at the bottom of the slide.

Slide #11

Confocal microscopy Leica

Slide #11 shows a sample of a living specimen taken with a Leica micro-system, see the website address inside the slide.

Slide #12

Slide12

Slide #12 shows the principle of fluorescent microscope and how it works. A light source is filtered to allow blue light (energetic photons for excitation of the molecules of the specimen), the green light emitted is going through objective and ocular lenses and further to the photo-multiplier or digital camera.

Slide #13

Slide13

For their discovery of fluorescent microscopy, Eric Betzig, William Moerner and Stefan Hell won the Nobel Prize in Chemistry on 2014,  for “the development of super-resolved fluorescence microscopy,” which brings optical microscopy into the nano-dimension.

Slide #14

Slide14

Slide #14 introduces the improvement on micro CT scanners for imaging living cells which now is on R & D under heavy development.  The goal is to visualize the interior of living cells. Challenging tasks are: miniaturization, respond to customer needs, low cost, and versatility.

Slide #15

Slide15

Slide #15 shows the schematic for an optical magnetic imaging microscope for visualizing living cells with one dimension less than 500 nm. The website address gven describes in details the working principle.

Slide #16

Slide16

Slide #16 shows the picture of a hand held microscope that is useful on finding spot cancer in moments, ses the website.

Slide #17

Slide17

Slide #17 shows a hand held MRI that connects to an iPhone. It is useful device for detecting cancer cells.

Slide #18

Slide18

Slide #18 shows in comparison a portable NMR device, left side, and a Lab NMR instrument whose height is greater than 5 Ft. The spectrum in the left side is that of Toluene and a capillary sample holder is shown also next to the magnetic device.

Slide #19

Slide19

Slide #19 shows that the hand held MRI can recognize complex molecules,  can diagnose cancer faster, can be connected to a smartphone, and be accurate on precise measurements.

Slide #20

Slide20

An optical dental camera is shown in slide #20. It is less then $100 and a USB cable can connect to a computer. It is very useful for every family in checking the status of the teeth and gums.

Slide #21

Slide21

For detecting dental cavities the x-ray source packaged as a camera and the sensor that connects to a computer are very useful tools in a dental office.

Slide #22

Slide22

Slide #22 shows the conclusions of the presentation, in which we summarize: the automated con-focal microscope partially satisfies the actual needs for imaging of living cells, the optical magnetic imaging microscope for living cells is a promising technique,
a higher resolution is needed on all actual microscopes,  the advanced CT and Micro CT scanners provide a new avenue on the investigation of living cells, more research needed
on hand-held MRI, which is a new solution for complex molecules recognition including cancer

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3D Imaging of Cancer Cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

3D Imaging of Cancer Cells Could Lead to Improved Ability of Pathologists and Radiologists to Plan Cancer Treatments and Monitor Cell Interactions

Dark Daily Apr 8th 2016        Jon Stone

https://www.linkedin.com/pulse/3d-imaging-cancer-cells-could-lead-improved-ability-plan-joseph-colao

 

3D Imaging of Cancer Cells Could Lead to Improved Ability of Pathologists and Radiologists to Plan Cancer Treatments and Monitor Cell Interactions.

New technology from researchers at the University of Texas Southwestern Medical Center enables the ability to study cancer cells in their native microenvironments.

Imaging research is one step closer to giving clinicians a way to do high-resolution scans of malignant cells in order to diagnose cancer and help identify useful therapies. If this technology were to prove successful in clinical studies, it might change how anatomic pathologists and radiologists diagnose and treat cancer.

Researchers at the University of Texas Southwestern Medical Center developed a way to create near-isotropic, high-resolution scans of cells within their microenvironments. The process involves utilizing a combination of two-photonBessel beams and specialized filtering.

New Imaging Approach Could be Useful to Both Pathologists and Radiologists

In a recent press release, senior author Reto Fiolka, PhD, said “there is clear evidence that the environment strongly affects cellular behavior—thus, the value of cell culture experiments on glass must at least be questioned. Our microscope is one tool that may bring us a deeper understanding of the molecular mechanisms that drive cancer cell behavior, since it enables high-resolution imaging in more realistic tumor.”

In a study in Developmental Cell, Erik S. Welf, PhD, et al, described the new microenvironmental selective plane illumination microscopy (meSPIM). When developing the technology, the team outlined three goals:

1. The microscope design must not prohibitively constrain microenvironmental properties.

2. Spatial and temporal resolution must match the cellular features of interest.

3. Spatial resolution must be isotropic to avoid spatial bias in quantitative measurements.

This new technology offers pathologists and medical laboratory scientists a new look at cancer cells and other diseases. The study notes that meSPIM eliminates the influence of stiff barriers, such as glass slide covers, while also allowing a level of control over both mechanical and chemical influences that was previously impossible.

Early meSPIM Research Reveals New Cell Behaviors

Early use of meSPIM in observing melanoma cells is already offering new insights into the relationship between the cell behavior of cellular- and subcellular-scale mechanisms and the microenvironment in which these cells exist. The study notes, “The ability to image fine cellular details in controllable microenvironments revealed morphodynamic features not commonly observed in the narrow range of mechanical environments usually studied in vitro.”

One such difference is the appearance of blebbing. Created by melanoma cells and lines, these small protrusions are thought to aid in cell mobility and survival. Using meSPIM, observers could follow the blebbing process in real-time. Formation of blebs on slides and within an extracellular matrix (ECM) showed significant differences in both formation and manipulation of the surrounding microenvironment.

The team is also using meSPIM to take a look at membrane-associated biosensor and cytosolic biosensor signals in 3D. They hope that investigation of proteins such as phosphatidylinositol 3-kinase (PI3K) and protein kinase C will help to further clarify the roles these signals play in reorientation of fibroblasts.

meSPIM combined with computer vision enables imaging, visualization, and quantification of how cells alter collagen fibers over large distances within an image volume measuring 100 mm on each side. (Photo Copyright: Welf and Driscoll et al.)

The research team believes this opens new possibilities for studying diseases at a subcellular level, saying, “Cell biology is necessarily restricted to studying what we can measure. Accordingly, while the last hundred years have yielded incredible insight into cellular processes, unfortunately most of these studies have involved cells plated onto flat, stiff surfaces that are drastically different from the in vivo microenvironment …

“Here, we introduce an imaging platform that enables detailed subcellular observations without compromising microenvironmental control and thus should open a window for addressing these fundamental questions of cell biology.”

Limitations of meSPIM

One significant issue associated with the use of meSPIM is the need to process the large quantity of data into useful information. Algorithms currently allow for automatic bleb detection. However, manual marking, while time consuming, still provides increased accuracy. Researchers believe the next step in improving the quality of meSPIM scans lie in computer platforms designed to extract and process the scan data.

Until this process is automated, user bias, sample mounting, and data handling will remain risks for introducing errors into the collected data. Yet, even in its early stages, meSPIM offers new options for assessing the state of cancer cells and may eventually provide pathologists and radiologists with additional information when creating treatment plans or assessments.

 

Seeing cancer cells in 3-D (w/ Video)

http://phys.org/news/2016-02-cancer-cells-d-video.html

 

Cancer in 3-D

http://cdn.phys.org/newman/csz/news/800/2016/cancerin3d.png

Extracted surfaces of two cancer cells. (Left) A lung cancer cell colored by actin intensity near the cell surface. Actin is a structural molecule that is integral to cell movement. (Right) A melanoma cell colored by PI3-kinase activity near the cell surface. PI3K is a signaling molecule that is key to many cell processes. Credit: Welf and Driscoll et al.

Cancer cells don’t live on glass slides, yet the vast majority of images related to cancer biology come from the cells being photographed on flat, two-dimensional surfaces—images that are sometimes used to make conclusions about the behaviour of cells that normally reside in a more complex environment. But a new high-resolution microscope, presented February 22 in Developmental Cell, now makes it possible to visualize cancer cells in 3D and record how they are signaling to other parts of their environment, revealing previously unappreciated biology of how cancer cells survive and disperse within living things.

“There is clear evidence that the environment strongly affects cellular behavior—thus, the value of cell culture experiments on glass must at least be questioned,” says senior author Reto Fiolka, an optical scientist at the University of Texas Southwestern Medical Center. “Our is one tool that may bring us a deeper understanding of the molecular mechanisms that drive cancer cell behavior, since it enables high-resolution imaging in more realistic tumor environments.”

Read more at: http://phys.org/news/2016-02-cancer-cells-d-video.html#jCp

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Nanophotonics Applications

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Copper Plasmonics Explored for Nanophotonics Applications

http://www.photonics.com/Article.aspx?AID=58484

MOSCOW, March 22, 2016 — Experimental demonstration of copper components has expanded the list of potential materials suited to nanophotonic devices beyond gold and silver.

According to researchers from the Moscow Institute of Physics and Technology (MIPT), copper components are not only just as good as components based on noble metals, such as gold and silver, they can be easily implemented in integrated circuits using industry-standard fabrication processes. Gold and silver, as noble metals, may not enter into the requisite chemical reactions to create nanostructures readily and require expensive, difficult processing steps.

Nanoscale copper plasmonic waveguides on a silicon chip in a scanning near-field optical microscope (left) and their image obtained using electron microscopy (right).

Nanoscale copper plasmonic waveguides on a silicon chip in a scanning near-field optical microscope (left) and their image obtained using electron microscopy (right). Courtesy of MIPT.

In nanophotonics, the diffraction limit of light is overcome by using metal-dielectric structures. Light may be converted into surface plasmon polaritons, surface waves propagating along the surface of a metal, which make it possible to switch from conventional 3D photonics to 2D surface plasmon photonics, also known as plasmonics. This allows control of light at the 100-nm scale, far beyond the diffraction limit.

Now researchers from MIPT’s Laboratory of Nanooptics and Plasmonics have found a solution to the problems posed by noble metals. Based on a generalization of the theory for so-called plasmonic metals, in 2012 they found that copper as an optical material is not only able to compete with gold, but it can also be a better alternative. Unlike gold, copper can be easily structured using wet or dry etching. This gives a possibility to make nanoscale components that are easily integrated into silicon photonic or electronic integrated circuits.

Silicon chip with nanoscale copper plasmonic components.

Silicon chip with nanoscale copper plasmonic components. Courtesy of MIPT.

It took more than two years for the researchers to purchase the required equipment, develop the fabrication process, produce samples, conduct several independent measurements and confirm their hypothesis experimentally.

“As a result, we succeeded in fabricating copper chips with optical properties that are in no way inferior to gold-based chips,” says the research leader Dmitry Fedyanin. “Furthermore, we managed to do this in a fabrication process compatible with the CMOS technology, which is the basis for all modern integrated circuits, including microprocessors. It’s a kind of revolution in nanophotonics.”

The researchers said that the optical properties of thin polycrystalline copper films were determined by their internal structure, and that controlling this structure to achieve and consistently reproduce the required parameters in technological cycles was the most difficult task.

Having demonstrated copper’s suitable material characteristics, as well as nanoscale manufacturing capability, the researchers believe the devices could be integrated with both silicon nanoelectronics and silicon nanophotonics. Such technologies could enable LEDs, nanolasers, highly sensitive sensors and transducers for mobile devices, and high-performance optoelectronic processors with several tens of thousands of cores for graphics cards, personal computers and supercomputers.

“We conducted ellipsometry of the copper films and then confirmed these results using near-field scanning optical microscopy of the nanostructures. This proves that the properties of copper are not impaired during the whole process of manufacturing nanoscale plasmonic components,” says Dmitry Fedyanin.

The research was published in Nano Letters (doi: 10.1021/acs.nanolett.5b03942).

 

Ultralow-Loss CMOS Copper Plasmonic Waveguides

Surface plasmon polaritons can give a unique opportunity to manipulate light at a scale well below the diffraction limit reducing the size of optical components down to that of nanoelectronic circuits. At the same time, plasmonics is mostly based on noble metals, which are not compatible with microelectronics manufacturing technologies. This prevents plasmonic components from integration with both silicon photonics and silicon microelectronics. Here, we demonstrate ultralow-loss copper plasmonic waveguides fabricated in a simple complementary metal-oxide semiconductor (CMOS) compatible process, which can outperform gold plasmonic waveguides simultaneously providing long (>40 μm) propagation length and deep subwavelength (∼λ2/50, where λ is the free-space wavelength) mode confinement in the telecommunication spectral range. These results create the backbone for the development of a CMOS plasmonic platform and its integration in future electronic chips.

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Chromatography and Mass Spectroscopy

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Optimization of Chromatography in the Lab

Sanji Bhal & Karim Kassam; ACD/Labs

While analytical laboratories may still rely to some extent on trial-and-error approaches, there is agreement that this is increasingly less effective as systems become more complex. Regulatory bodies are putting increasing pressure on pharmaceutical companies to incorporate Quality by Design (QbD) approaches throughout the drug development process. QbD is defined in the ICH Q8 guideline as “A systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.”

Developing effective and robust separations methods can be a very time-consuming process. A comprehensive approach to method development would be thorough investigation of the design space for any given mixture or sample including buffer, column, solvent, time, temperature, etc. Given the time constraints and limited resources in any R&D laboratory, however, this type of broad scope investigation is unrealistic.

Modeling for the optimization of chromatographic separations of small molecules has been successfully used for approximately 30 years. A large number of articles have been published on this topic by L. Snyder, P. Janderra, P. Schoenmakers et al. Modeling of chromatographic separations continues to be of interest because as the science of chromatographic separations continues to evolve, modeling techniques must evolve with them to support the needs of the community.

New types of chromatographic techniques (UHPLC, HILIC, ion exchange chromatography, etc.) have demanded the need for new modeling tools. This also led to the need for translation of methods from one technique to newer techniques (HPLC to UHPLC, for example). Furthermore, as pharmaceutical R&D has expanded investigation of new drugs from small molecules to proteins and bio-molecules, many of the old rules no longer apply.

The ability to model the behavior of a sample in silico provides chromatographers with a number of advantages:

Greater efficiency in method development—it is difficult to estimate the number of hours required to identify a suitable method for separation of a mixture. An experienced scientist will rely on their knowledge while an inexperienced colleague may struggle with the same separation. As the number of experienced chromatographers decreases across organizations, and the existing scientists are retiring, software to assist those less experienced becomes more attractive.

With such a large number of variables (temperature, gradient, pH, salt concentration, etc.) it is advantageous to use all available knowledge and tools to ‘get ahead’. Increased efficiency can be realized not only in identifying an optimal method faster but also increasing throughput and decreasing scale-up time.

Risk mitigation through robust methods—this is the ideal result of a method development project. By applying QbD principles and understanding the analytical design space of a sample, the chromatographer can understand, reduce, and control sources of variability; and use this information to create a method that is reliable and robust. Simulation of methods provides scientists the luxury of thoroughly investigating method development space with limited consumption of resources and time, for the best result.

Economic considerations—while there is a cost in man hours and time spent on method development, there is also unrecoverable expenditure on consumables (solvents, columns, etc.). In being able to investigate chromatographic space in silico, this time and expenditure can be greatly reduced.

Green chemistry—the ability to model separations not only reduces the volume of waste, it may also help us reduce environmental impact. Consider the case of acetonitrile shortages in recent years. The ability to use alternative methods, i.e., replacing acetonitrile with methanol, not only lead to reduced cost but also has the side effect of more environmentally safe waste.

Software provided with chromatographic instruments delivers many useful capabilities to execute experimental runs and control instruments. Simulation software, however, is typically purchased separately. Several commercial software packages are available, i.e., DryLab, ACD/LC Simulator (from ACD/Labs), ChromSword, and Osiris, each of which provides different advantages and limitations (an exhaustive list is outside the scope of this article).

Commercially available method optimization software is typically built on one of three models—simulations based on molecular structure, retention based modeling, and statistical modeling. Each has its pros and cons with details in their implementation that appeal to different applications.

Data input—flexibility of data import into a system from the instrument is an important consideration when dealing with multiple experiments under varying conditions. Lack of standardization of chromatographic data formats today, however, means that unless data from separations is transferred into Excel or similar software, scientists are left to transcribe information from one system to another. Direct data import from chromatographic runs into third-party modeling software, in the instrument format, is ideal since it avoids transcription errors and saves time in data input. ACD/Labs provides the only software (ACD/LC Simulator and ACD/GC Simulator) with instrument vendor-agnostic support of analytical data at this time.

Data visualization—the ability to review and interrogate data is of utmost importance in method development and optimization, and software vendors implement various tools to meet chromatographers’ requirements. While 3D modeling, offered by DryLab, has enjoyed popularity in the community, the question of applicability still remains. A significant amount of data input (upwards of 45 injections is not unreasonable for simultaneous optimization of 3 factors) is required for effective 3D modeling, which in itself is counter-intuitive if time and resource efficiency is the ultimate goal.

Automation—ACD/AutoChrom (from ACD/Labs) and ChromSword both provide automation through instrument control. AutoChrom provides automation of the most popular Waters Empower and Agilent ChemStation systems and keeps the scientist in control by allowing user input at key stages of the method development process. This software is best suited for challenging separations such as stability indicating methods and forced degradation studies.

Custom Modeling—while third-party modeling software may cover a broad range of structure and method development space, there is nothing better than the ability for scientists to create their own models. ACD/LC Simulator was the only software known to the authors at the time of publication that offers this capability. Work published by world class chromatographers Patrik Pettersson and Mel Eureby demonstrates the use of ACD/LC Simulator in successfully modelling protein and HILIC separations.

Reverse phase HPLC, temperature/gradient optimization as modeled in ACD/LC Simulator. (Credit:  ACD/Labs )

Reverse phase HPLC, temperature/gradient optimization as modeled in ACD/LC Simulator. (Credit: ACD/Labs )

Physicochemical property predictions such as logD and pKa can also help in method development and optimization. In a general sense, being able to predict behavior with respect to pH can offer insights into method development challenges. ChromSword and ACD/Labs software both provide property predictions, and the latter have been leaders in this field for almost two decades with applications across various areas of research and industries.

As the science of separations evolves and the compounds of interest change, the software to support scientific research and development will need to develop alongside. Software vendors need to satisfy the needs of their customer organizations in releasing the time of valuable scientists for innovation thus releasing them from monotonous and tedious tasks. If your organization has yet to invest in software for modeling separations, it will likely come in the future and many of the topics raised here should be kept in mind to ensure you get the best return on investment.

 

Tissue Imaging Mass Spec Detects Early Lipid Changes in Acute Kidney Injury

University of Alabama at Birmingham researchers have made a microscopic snapshot of the early renal lipid changes in acute kidney injury, using a laser-scanning method called MALDI tissue imaging to localize the changes.
These disease-model results, recently published in American Journal of Physiology’s Renal Physiology, show an example of the power of MALDI tissue imaging. MALDI tissue imaging is now available at UAB, and it will be able to aid basic and clinical biomedical research across the campus, said corresponding author Janusz Kabarowski, Ph.D., associate professor of microbiology.
“I think the opportunity to integrate this into existing UAB research centers to facilitate grants is immense,” Kabarowski said. “It can be utilized for any tissue damage. For drugs that can be imaged with MALDI imaging mass spectrometry, you can tell where in a slice of tissue the drugs get to, with obvious implications for testing candidate therapeutic agents in cancer research too. We can capture—at the molecular level—a moment in time.”
The imaging has the power to reveal spatial distribution of complex biochemical processes in an organism, showing where changes in proteins or small molecules take place. Unlike chemical stains, immunohistochemical tags or radioactive labels, it does not require a priori knowledge of the target compounds.
Acute kidney injury is a leading cause of hospital illness or death in critically ill patients. In a mouse model of the injury used by Kabarowski and colleagues, kidneys were made ischemic for 30 minutes. Six hours after reperfusion, and before gross kidney damage was seen, the kidneys were removed and cut in half. The lipids were extracted from one of the halves; the other was flash frozen and cut into thin sections that were mounted on specially coated slides.
Extracted lipids were analyzed using SWATH mass spectrometry, and the UAB researchers found that four were significantly changed at six hours (all were increases). Three of the lipids were ether-linked phospholipids, including a plasmalogen, a type of ether phospholipid thought to have protective anti-oxidant properties. They also found that the levels of these ether-linked phospholipids correlated with levels of plasma creatinine, a marker of acute kidney injury. This suggests a causal or a protective role for them in acute kidney injury, and also suggests they may be an effective early biomarker for injury.
The researchers then used MALDI tissue imaging to find where the most abundant of the ether-linked phospholipids was concentrated. In MALDI, a powerful laser scans the thin tissue section after application of a matrix material by vacuum sublimation, knocking the lipid ions off from the surface of the tissue. The MALDI time-of-flight mass spectrometry and ion fragmentation then allowed identification of the proximal tubules of the kidney as the place where the ether-linked phospholipids were concentrated. The proximal tubules are known to be most prone to developing ischemia-related injury.
Besides Kabarowski, authors of “Early lipid changes in acute kidney injury using SWATH lipidomics coupled with MALDI tissue imaging” are co-first authors Sangeetha Rao, M.D., fellow in the UAB Pediatric Critical Care Medicine, and Kelly B. Walters, UAB departments of Chemistry and Microbiology; Landon Wilson and Stephen Barnes, Ph.D., UAB Department of Pharmacology and Toxicology, Targeted Metabolomics and Proteomics Laboratory; Bo Chen, Ph.D., Subhashini Bolisetty, Ph.D., and Anupam Agarwal, M.D., UAB Division of Nephrology and the Nephrology Research and Training Center; and David Graves, UAB Department of Chemistry.
MALDI imaging mass spectrometry stands for “matrix-assisted laser desorption ionization” imaging mass spectrometry. SWATH mass spectrometry stands for “sequential window acquisition of all theoretical spectra” mass spectrometry.

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