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Archive for the ‘Cardiovascular Tissue’ Category


Artificial Intelligence and Cardiovascular Disease

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

 

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

 

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

 

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

 

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

 

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

 

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

 

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

 

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

 

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.

 

References:

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.bhf.org.uk/informationsupport/heart-matters-magazine/research/artificial-intelligence

 

https://www.medicaldevice-network.com/news/heart-attack-artificial-intelligence/

 

https://www.nature.com/articles/s41569-019-0158-5

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711980/

 

www.j-pcs.org/article.asp

http://www.onlinejacc.org/content/71/23/2668

http://www.scielo.br/pdf/ijcs/v30n3/2359-4802-ijcs-30-03-0187.pdf

 

https://www.escardio.org/The-ESC/Press-Office/Press-releases/How-artificial-intelligence-is-tackling-heart-disease-Find-out-at-ICNC-2019

 

https://clinicaltrials.gov/ct2/show/NCT03877614

 

https://www.europeanpharmaceuticalreview.com/news/82870/artificial-intelligence-ai-heart-disease/

 

https://www.frontiersin.org/research-topics/10067/current-and-future-role-of-artificial-intelligence-in-cardiac-imaging

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.sciencedaily.com/releases/2019/05/190513104505.htm

 

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Regulatory MicroRNAs in Aberrant Cholesterol Transport and Metabolism

Curator: Marzan Khan, B.Sc

Aberrant levels of lipids and cholesterol accumulation in the body lead to cardiometabolic disorders such as atherosclerosis, one of the leading causes of death in the Western World(1). The physical manifestation of this condition is the build-up of plaque along the arterial endothelium causing the arteries to constrict and resist a smooth blood flow(2). This obstructive deposition of plaque is merely the initiation of atherosclerosis and is enriched in LDL cholesterol (LDL-C) as well foam cells which are macrophages carrying an overload of toxic, oxidized LDL(2). As the condition progresses, the plaque further obstructs blood flow and creates blood clots, ultimately leading to myocardial infarction, stroke and other cardiovascular diseases(2). Therefore, LDL is referred to as “the bad cholesterol”(2).

Until now, statins are most widely prescribed as lipid-lowering drugs that inhibit the enzyme 3-hydroxy-3methylgutaryl-CoA reductase (HMGCR), the rate-limiting step in de-novo cholesterol biogenesis (1). But some people cannot continue with the medication due to it’s harmful side-effects(1). With the need to develop newer therapeutics to combat cardiovascular diseases, Harvard University researchers at Massachusetts General Hospital discovered 4 microRNAs that control cholesterol, triglyceride, and glucose homeostasis(3)

MicroRNAs are non-coding, regulatory elements approximately 22 nucleotides long, with the ability to control post-transcriptional expression of genes(3). The liver is the center for carbohydrate and lipid metabolism. Stringent regulation of endogenous LDL-receptor (LDL-R) pathway in the liver is crucial to maintain a minimal concentration of LDL particles in blood(3). A mechanism whereby peripheral tissues and macrophages can get rid of their excess LDL is mediated by ATP-binding cassette, subfamily A, member 1 (ABCA1)(3). ABCA1 consumes nascent HDL particles- dubbed as the “good cholesterol” which travel back to the liver for its contents of triglycerides and cholesterol to be excreted(3).

Genome-wide association studies (GWASs) meta-analysis carried out by the researchers disclosed 4 microRNAs –(miR-128-1, miR-148a, miR-130b, and miR-301b) to lie close to single-nucleotide polymorphisms (SNPs) associated with abnormal metabolism and transport of lipids and cholesterol(3) Experimental analyses carried out on relevant cell types such as the liver and macrophages have proven that these microRNAs bind to the 3’ UTRs of both LDL-R and ABCA1 transporters, and silence their activity. Overexpression of miR-128-1 and miR148a in mice models caused circulating HDL-C to drop. Corroborating the theory under investigation further, their inhibition led to an increased clearance of LDL from the blood and a greater accumulation in the liver(3).

That the antisense inhibition of miRNA-128-1 increased insulin signaling in mice, propels us to hypothesize that abnormal expression of miR-128-1 might cause insulin resistance in metabolic syndrome, and defective insulin signaling in hepatic steatosis and dyslipidemia(3)

Further examination of miR-148 established that Liver-X-Receptor (LXR) activation of the Sterol regulatory element-binding protein 1c (SREBP1c), the transcription factor responsible for controlling  fatty acid production and glucose metabolism, also mediates the expression of miR-148a(4,5) That the promoter region of miR-148 contained binding sites for SREBP1c was shown by chromatin immunoprecipitation combined with massively parallel sequencing (ChIP-seq)(4). More specifically, SREBP1c attaches to the E-box2, E-box3 and E-box4 elements on miR-148-1a promoter sites to control its expression(4).

Earlier, the same researchers- Andres Naars and his team had found another microRNA called miR-33 to block HDL generation, and this blockage to reverse upon antisense targeting of miR-33(6).

These experimental data substantiate the theory of miRNAs being important regulators of lipoprotein receptors and transporter proteins as well as underscore the importance of employing antisense technologies to reverse their gene-silencing effects on LDL-R and ABCA1(4). Such a therapeutic approach, that will consequently lower LDL-C and promote HDL-C seems to be a promising strategy to treat atherosclerosis and other cardiovascular diseases(4).

References:

1.Goedeke L1,Wagschal A2,Fernández-Hernando C3, Näär AM4. miRNA regulation of LDL-cholesterol metabolism. Biochim Biophys Acta. 2016 Dec;1861(12 Pt B):. Biochim Biophys Acta. 2016 Dec;1861(12 Pt B):2047-2052

https://www.ncbi.nlm.nih.gov/pubmed/26968099

2.MedicalNewsToday. Joseph Nordgvist. Atherosclerosis:Causes, Symptoms and Treatments. 13.08.2015

http://www.medicalnewstoday.com/articles/247837.php

3.Wagschal A1,2, Najafi-Shoushtari SH1,2, Wang L1,2, Goedeke L3, Sinha S4, deLemos AS5, Black JC1,6, Ramírez CM3, Li Y7, Tewhey R8,9, Hatoum I10, Shah N11, Lu Y11, Kristo F1, Psychogios N4, Vrbanac V12, Lu YC13, Hla T13, de Cabo R14, Tsang JS11, Schadt E15, Sabeti PC8,9, Kathiresan S4,6,8,16, Cohen DE7, Whetstine J1,6, Chung RT5,6, Fernández-Hernando C3, Kaplan LM6,10, Bernards A1,6,16, Gerszten RE4,6, Näär AM1,2. Genome-wide identification of microRNAs regulating cholesterol and triglyceride homeostasis. . Nat Med.2015 Nov;21(11):1290

https://www.ncbi.nlm.nih.gov/pubmed/26501192

4.Goedeke L1,2,3,4, Rotllan N1,2, Canfrán-Duque A1,2, Aranda JF1,2,3, Ramírez CM1,2, Araldi E1,2,3,4, Lin CS3,4, Anderson NN5,6, Wagschal A7,8, de Cabo R9, Horton JD5,6, Lasunción MA10,11, Näär AM7,8, Suárez Y1,2,3,4, Fernández-Hernando C1,2,3,4. MicroRNA-148a regulates LDL receptor and ABCA1 expression to control circulating lipoprotein levels. Nat Med. 2015 Nov;21(11):1280-9.

https://www.ncbi.nlm.nih.gov/pubmed/26437365

5.Eberlé D1, Hegarty B, Bossard P, Ferré P, Foufelle F. SREBP transcription factors: master regulators of lipid homeostasis. Biochimie. 2004 Nov;86(11):839-48.

https://www.ncbi.nlm.nih.gov/pubmed/15589694

6.Harvard Medical School. News. MicoRNAs and Metabolism.

https://hms.harvard.edu/news/micrornas-and-metabolism

7. MGH – Four microRNAs identified as playing key roles in cholesterol, lipid metabolism

http://www.massgeneral.org/about/pressrelease.aspx?id=1862

 

Other related articles published in this Open Access Online Scientific Journal include the following:

 

  • Cardiovascular Diseases, Volume Three: Etiologies of Cardiovascular Diseases: Epigenetics, Genetics and Genomics,

on Amazon since 11/29/2015

http://www.amazon.com/dp/B018PNHJ84

 

HDL oxidation in type 2 diabetic patients

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/11/27/hdl-oxidation-in-type-2-diabetic-patients/

 

HDL-C: Target of Therapy – Steven E. Nissen, MD, MACC, Cleveland Clinic vs Peter Libby, MD, BWH

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2014/11/07/hdl-c-target-of-therapy-steven-e-nissen-md-macc-cleveland-clinic-vs-peter-libby-md-bwh/

 

High-Density Lipoprotein (HDL): An Independent Predictor of Endothelial Function & Atherosclerosis, A Modulator, An Agonist, A Biomarker for Cardiovascular Risk

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/03/31/high-density-lipoprotein-hdl-an-independent-predictor-of-endothelial-function-artherosclerosis-a-modulator-an-agonist-a-biomarker-for-cardiovascular-risk/

 

Risk of Major Cardiovascular Events by LDL-Cholesterol Level (mg/dL): Among those treated with high-dose statin therapy, more than 40% of patients failed to achieve an LDL-cholesterol target of less than 70 mg/dL.

Reporter: Aviva Lev-Ari, PhD., RN

https://pharmaceuticalintelligence.com/2014/07/29/risk-of-major-cardiovascular-events-by-ldl-cholesterol-level-mgdl-among-those-treated-with-high-dose-statin-therapy-more-than-40-of-patients-failed-to-achieve-an-ldl-cholesterol-target-of-less-th/

 

LDL, HDL, TG, ApoA1 and ApoB: Genetic Loci Associated With Plasma Concentration of these Biomarkers – A Genome-Wide Analysis With Replication

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/12/18/ldl-hdl-tg-apoa1-and-apob-genetic-loci-associated-with-plasma-concentration-of-these-biomarkers-a-genome-wide-analysis-with-replication/

 

Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/04/15/two-mutations-in-a-pcsk9-gene-eliminates-a-protein-involve-in-controlling-ldl-cholesterol/

Artherogenesis: Predictor of CVD – the Smaller and Denser LDL Particles

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2012/11/15/artherogenesis-predictor-of-cvd-the-smaller-and-denser-ldl-particles/

 

A Concise Review of Cardiovascular Biomarkers of Hypertension

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/04/25/a-concise-review-of-cardiovascular-biomarkers-of-hypertension/

 

Triglycerides: Is it a Risk Factor or a Risk Marker for Atherosclerosis and Cardiovascular Disease ? The Impact of Genetic Mutations on (ANGPTL4) Gene, encoder of (angiopoietin-like 4) Protein, inhibitor of Lipoprotein Lipase

Reporters, Curators and Authors: Aviva Lev-Ari, PhD, RN and Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/03/13/triglycerides-is-it-a-risk-factor-or-a-risk-marker-for-atherosclerosis-and-cardiovascular-disease-the-impact-of-genetic-mutations-on-angptl4-gene-encoder-of-angiopoietin-like-4-protein-that-in/

 

Excess Eating, Overweight, and Diabetic

Larry H Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/11/15/excess-eating-overweight-and-diabetic/

 

Obesity Issues

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/11/12/obesity-issues/

 

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3D Printing for Surgical Planning: The Clinical and Economic Promise using Quantitative Clinical Evidence

Reporter: Aviva Lev-Ari, PhD, RN

The Clinical and Economic Promise of 3D Printing for Surgical Planning

M A K I N G  T H E  C A S E  T H R O U G H  Q U A N T I TAT I V E CLINICAL EVIDENCE

Stratasys engaged Quorum Consulting, experts in health economics and outcomes research, to conduct a comprehensive analysis of the clinical and economic evidence on 3D printing for surgical planning. This white paper, authored by Quorum Consulting, summarizes the result of that analysis.

Wade Aubry1,2, Raj Stewart1 , Chance Scott1 , Jeffrey Chu1

The modern emphasis on evidence-based medicine centers on three core tenets: • Best available research findings • Clinical expertise • Patient value Incorporating cutting-edge technology alongside these principles – often delicately balancing material innovation against scientific rigor, state-of-the-art professional training and experience, and attempts to provide the best care while respecting patient perspectives – is a challenge. 3D printing, however, aligns with the first two tenets, and when appropriately employed, may inform and indirectly influence the third.1

1 Quorum Consulting, Inc., San Francisco, CA, USA

2 University of California, San Francisco; San Francisco, CA, USA

 

3D printing was used in surgical planning applications in a wide range of specialties including cardiothoracic, orthopedic, neurological, reconstructive and transplant surgeries, as well as gastroenterology and surgical oncology. When examining these use cases, five general benefits emerge in association with 3D printing for surgical planning:

  • Patient communication
  • Anatomic familiarity
  • Procedure practice
  • Procedure selection
  • Patient selection / rule-out

 

INDICATION-SPECIFIC UTILIZATION AND EVIDENCEBASED EFFECTIVENESS DATA / RESULTS

  • Cardiothoracic surgery
  • Neurosurgery
  • Reconstructive surgeries

 

CONCLUSION

In a healthcare environment continuing to shift towards value- and outcome-contingent systems that penalize providers for inefficiencies and suboptimal outcomes in rendered care, 3D printed models for surgical planning – with their ability to facilitate procedural efficiency, improve treatment outcomes, and reduce downstream re-intervention costs – offer high potential value. Patients, clinicians and hospitals all have a vested interest in quality, affordable patient care and service, and surgical planning with 3D models appeals to each of these stakeholders.

Accordingly, results and trends from published literature and healthcare data support the effectiveness of 3D printing for surgical planning. As shown for several surgical procedures, clinicians with access to 3D printed models are able to provide better, more efficient care likely to improve patient outcomes and reduce the need for additional surgical interventions. Procedures that would most justify the financial and resource cost in creating 3D printed patient models are those with long operating times, high Relative Value Units (RVUs), greater risk and uncertainty, and risk of complications. Concurrently, this quality care is also potentially less costly and more profitable to providers. Amidst the growing commercial market for 3D printers and related technologies, there are some key differentiators when evaluating utility for surgical planning. As reflected in clinician surveys, the most effective 3D models should capably depict complex, fine anatomy with high fidelity to actual patient physiologies. This degree of fidelity crosses several characteristics:

  • Accurate depiction of a variety of colors
  • Simulation of multiple textures
  • Manipulability,

including the ability to be dissected or probed with surgical instruments.22 Given these real-world requirements, next generation multi-material and multi-color 3D printers likely represent the best option for facilities and clinicians. Viewed objectively, additional data addressing the quantitative impact of 3D printed models is needed. Preferably, this data will be generated from well-designed, patient outcome-oriented studies. However, in the interim, the tide of evidence favors 3D printed models for surgical planning, particularly for leading-edge clinicians and healthcare administrators who are able to recognize its value.

A Brief RVU Primer:

Relative Value Units (RVUs) are used by Medicare to determine reimbursement rates for a given service:

• For each service, Medicare determines the cost value of three primary components – physician’s work, practice expenses and malpractice insurance.

• These three components are then adjusted based on differences in living and business costs nationwide, using a factor called the Geographic Practice Cost Index (GPCI).

• The adjusted values are multiplied by an annual conversion factor, established by the U.S. Congress, and totaled to calculate final reimbursement rates.

SOURCE

http://s3.amazonaws.com/engineering.whitepapers/Stratasys/SurgicalPlanningPromise_Quorum_WP.pdf

From: Medical Design & Outsourcing <newsletters@e.medicaldesignandoutsourcing.com>

Reply-To: <newsletters@e.medicaldesignandoutsourcing.com>

Date: Wednesday, February 15, 2017 at 2:00 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: The Clinical and Economic Promise of Surgical Planning Using 3D Printing

Other related articles published in this Open Access Online Scientific Journal including the following:

Curator: Aviva Lev-Ari, PhD, RN

 

Technologies for Patient-centered Medicine: From R&D in Biologics to New Medical Devices

 

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Medical MEMS, BioMEMS and Sensor Applications

Curator and Reporter: Aviva Lev-Ari, PhD, RN

 

Contents for Chapter 11

Medical MEMS, BioMEMS and Sensors Applications

Curators: Justin D. Pearlman, MD, PhD, FACC, LPBI Group, Danut Dragoi, PhD, LPBI Group and William H. Zurn, Alpha IP

FOR

Series E: Patient-centered Medicine

Volume 4:  Medical 3D BioPrinting – The Revolution in Medicine

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

https://pharmaceuticalintelligence.com/biomed-e-books/series-e-titles-in-the-strategic-plan-for-2014-1015/volume-four-medical-3d-bioprinting-the-revolution-in-medicine/

Work-in-Progress

ContactLens

Image Source

http://www.memsjournal.com/2010/05/medical-applications-herald-third-wave-of-mems.html

Image is courtesy of Google Images

 

WirelessPressure

Image Source

Stanford Engineering Team Invents Pressure Sensor That Uses Radio Waves | CytoFluidix

Image is courtesy of Google Images

 

Introduction by Dr. Pearlman

 

Chapter 1: Blood Glucose Sensors

1.1       MINIATURIZED GLUCOSE SENSOR – Google

  • Tiny wireless chip and miniaturized glucose sensor
  • Embedded between two layers of soft contact lens material
  • Accurate glucose monitoring for diabetics
  • Using bodily fluids, i.e. tears
  • Prototypes can generate one reading per second
  • Experimenting with LEDs
  • Early warning for the wearer

 

Chapter 2: Blood Chemistry Tests – up to 100 Samples

2.1       NON-INVASIVE BLOOD MONITOR- UCSD

  • Digital tattoo monitors blood below the skin
  • Tattoos are needle-less
    • Sensor-laden transdermal patch
  • Painless for the user Tiny sensors “ink”
  • Can read blood levels of:
    • Sodium, glucose, kidney function
  • Prototypes contain probes
  • Wireless, battery-powered chip
  • Continually test up to a hundred different samples

 

2.3       CELLPHONE-BASED RAPID-DIAGNOSTIC-TEST (RDT) READER – UCLA

  • Lateral flow immuno-chromatographic assays
  • Sense the presence of a target analyte in a sample
  • Device connects to the camera on a cell phone
  • Weighs only 65 grams

 

2.4       IMPLANTABLE BLOOD ANALYZER CHIP – EPFL

  • Implantable device for instantaneous blood analysis
  • Wireless data transmission to a doctor
  • Applications include monitoring general health
  • Tailor drug delivery to a patient’s unique needs
  • Includes five sensors and a radio transmitter
  • Powered via inductive coupling from a battery patch
  • Worn outside the body

 

Chapter 3: Motion Sensors for Head-Impact

3.1       HEAD-IMPACT MONITORING PATCH – STMicro & X2Biosystems

  • Wearable electronic contains MEMS motion sensors
  • Microcontroller, low-power radio transmitter, and power management circuitry
  • Cloud-based system combines athlete concussion history
  • Pre-season neurocognitive function, balance, and coordinate-performance data
  • Creates a baseline for comparison after a suspected injury event

 

Chapter 4: Drug Delivery & Drug Compliance Monitoring Systems

4.1       Smart Pill delivers Therapeutic Agent Load to target – ELECTRONIC PILL – Phillips

  • Electronic pill to treat gastrointestinal cancer
  • An ingestible pill is swallowed by the patient, finds its way to the tumor, dispenses the drugs and passes harmlessly from the body
  • Smart pill contains reservoir for drug supply, fluid pump for drug delivery, pH sensor (for navigation), thermometer, microprocessor, communication

 

4.2       Drug Compliance Monitoring Systems

4.2.1    INGESTIBLE BIOMEDICAL SENSOR – Proteus Digital Health

  • Biomedical sensor that monitors medication adherence
  • Embedded into a pill, the sensor is activated by stomach fluid
  • Transmits a signal through the body to a skin patch
  • Indicates whether a patient has ingested material

 

4.2.2    MICROPUMP DEVICES – Purdue University

  • Device based on skin contact actuation for drug delivery
  • Actuation mechanism only requires body heat
  • Induced actuation can result to a gradient of 100 Pa/oC
  • Sufficient to drive liquid drug through micro-needle arrays
  • Prototypes exhibit low fabrication costs, employment of biocompatible materials and battery-less operation Suitable for single- or multiple-use transdermal drug dispensers

 

4.2.3    IMPLANTABLE MEMS DRUG DELIVERY SYSTEM – MIT

  • Device can deliver a vasoconstrictor agent
  • On demand to injured soldiers to prevent hemorrhagic shock
  • Other applications include medical implants
  • For cancer detection and monitoring
  • Implant can provide physicians and patients
  • Real-time information on the efficacy of treatment

 

Chapter 5: Remove Monitoring of Food-related Diseases

5.1       LASER-DRIVEN, HANDHELD SPECTROMETER

  • For analyzing food scanned
  • Information to a cloud-based application
  • Examines the results Data is accumulated from many users
  • Used to develop warning algorithms
  • For Allergies, Bacteria

 

Chapter 6: Skin Protection and Photo-Sensitivity Management

6.1       WEARABLE-UVEXPOSURESENSOR – Gizmag

  • Wristband for monitoring UV exposure
  • Allows user to maximize vitamin D production
  • Reducing the risk of sun
  • Over-exposure and skin cancer
  • LED indicators light up as UV exposure accumulates
  • Flashes once the safe UV limit has been reached

 

6.2       WEARABLE SKIN SENSOR KTH – Chemistry 2011

  • Bio-patch for measuring and collecting vital information through the skin
  • Inexpensive, versatile and comfortable to wear
  • User Data being gathered depends on where it is placed on the body

 

Chapter 7: Ophthalmic Applications

7.1       INTRAOCULAR PRESSURE SENSOR – Sensimed & ST Microelectronics

  • Smart contact lens called Triggerfish
  • Contact lens can measure, monitor, and control
  • Intra-ocular pressure levels for patients
  • Catch early cases of glaucoma
  • MEMS strain gage pressure sensor
  • Mounted on a flexible substrate MEMS

 

7.2       MICRO-MIRRORS ENABLING HANDHELD OPHTHALMIC – OCT News

  • Swept source OCT model for retinal 3D imaging
  • Replaces bulky galvanometer scanners in a handheld OCT probe for primary care physicians
  • Ultrahigh-speed two-axis optical beam steering gimbal-less MEMS mirrors
  • MEMS Actuator with a 2.4 mm bonded mirror and an angular reach of +6°
  • Low power consumption of <100mW including the MEMS actuator driver Retinal 3D Imaging

 

Chapter 8: Hearing Assist Technologies

8.1       MEMS TECHNOLOGY FOR HEARING RESTORATION – University of Utah

  • Eliminates electronics outside the ear
  • Associated with reliability issues and social stigma
  • Accelerometer-based microphone
  • Successfully tested in cadaver ear canals
  • Prototype measures 2.5 x 6.2mm, weighs 25mg

 

Chapter 9: Lab-on-a-Chip

9.1       ORGAN-ON-A-CHIP – Johns Hopkins University

  • Silicon substrate for living human cells
  • Controlled environment
  • Emulate how cells function inside a living human body
  • Replace controversial and costly animal testing
  • Lab-on-a-chip: a cost effective end to animal testing

 

Chapter 10: Intra-Cranial Studies: Pressure Measurement, Monitoring and Adaptation

10.1:   CEREBRAL PRESSURE SENSOR – Fraunhofer Institute

  • Sensor to monitor cerebral pressure that can lead to dementia
  • Pressure changes in the brain can be measured and transmitted
  • Reading device outside the patient’s body
  • Operating at very low power, the sensor module
  • Powered wirelessly by the reading device

 

10.2    WIRELESS, IMPLANTABLE BRAIN SENSOR – National Institute of Biomedical Imaging and Bioengineering

  • Fully implantable within the brain
  • Allow natural studies of brain activity
  • Cord-free control of advanced prosthetics

Wireless charging Prototypes transmitted brain activity data

 

Chapter 11: Cardiac and Cardiovascular Monitoring System

11.1    IMPLANTABLE MICRO DEVICE FOR MONITORING AND TREATING ANEURISMS – Electronic Design

  • RF-addressed wireless pressure sensor are powered by inductive coupling
  • Do not need batteries MEMS pressure sensor
  • Wireless antenna are inserted near the heart
  • With a catheter, Blood-pressure readings
  • Are sent to a wireless scanner for monitoring Pressure changes
  • Deflect the transducer’s diaphragm
  • Change the LC circuit’s resonant

 

11.2    CUSTOM- FITTED, IMPLANTABLE DEVICE FOR TREATMENT AND PREDICTION OF CARDIAC DISORDERS – Washington University

  • Working prototypes were developed on inexpensive 3D printers
  • The 3D elastic membrane is made of a soft, flexible, silicon material
  • Precisely shaped to match the outer layer of the heart

 

Chapter 12: microfluidic chips

12.1    MICROFLUIDIC MEMS FOR DIABETES TREATMENT – Micronews

  • Watertight pump mounted on a disposable skin patch
  • Provides continuous insulin infusion
  • Controlled by a dedicated smart phone device
  • Incorporating a BGM (blood- glucose meter)

 

12.2    ACOUSTIC RECEIVER ANTENNA/SENSOR PDMS MEMBRANE – Purdue

POLY-DI-METHYL-SILOXANE (PDMS)

Polydimethylsiloxane called PDMS or dimethicone is a polymer widely used for the fabrication and prototyping of microfluidic chips.

It is a mineral-organic polymer (a structure containing carbon and silicon) of the siloxane family (word derived from silicon, oxygen and alkane). Apart from microfluidics, it is used as a food additive (E900), in shampoos, and as an anti-foaming agent in beverages or in lubricating oils.

For the fabrication of microfluidic devices, PDMS (liquid) mixed with a cross-linking agent is poured into a microstructured mold and heated to obtain a elastomeric replica of the mold (PDMS cross-linked).

 

Why Use PDMS for Microfluidic Device Fabrication?

 

PDMS was chosen to fabricate microfluidic chips primarily for those reasons:

Human alveolar epithelial and pulmonary microvascular endothelial cells cultured in a PDMS chip to mimick lung functions

  • It is transparent at optical frequencies (240 nM – 1100 nM), which facilitates the observation of contents in micro-channels visually or through a microscope.
  • It has a low autofluorescence [2]
  • It is considered as bio-compatible (with some restrictions).

The PDMS bonds tightly to glass or another PDMS layer with asimple plasma treatment. This allows the production of multilayers PDMS devices and enables to take advantage of technological possibilities offered by glass substrates, such as the use of metal deposition, oxide deposition or surface functionalisation.

PDMS, during cross-linking, can be coated with a controlled thickness on a substrate using a simple spincoat. This allows the fabrication of multilayer devices and the integration of micro valves.

It is deformable, which allows the integration of microfluidic valves using the deformation of PDMS micro-channels, the easy connection of leak-proof fluidic connections and its use to detect very low forces like biomechanics interactions from cells.

SOURCE

http://www.elveflow.com/microfluidic-tutorials/microfluidic-reviews-and-tutorials/the-poly-di-methyl-siloxane-pdms-and-microfluidics/

 

  • Ferrite RF radiation Acoustic wave Rectifier
  • Buried in PDMS Implantable miniature pressure sensor
  • Powered by an acoustically actuated cantilever
  • No battery required
  • Acoustic waves in the 200-500 hertz range
  • Cause cantilever to vibrate
  • Scavenging energy to power pressure sensor

 

Chapter 13: Peropheral Neuropathy Management

13.1    WIRELESS SHOE INSERT – Mobile Health News

  • WIRELESS SHOE INSERT – Mobile Health News
  • Help diabetics manage peripheral nerve damage
  • Insole collects data of where wearers
  • Putting pressure on their feet
  • Transmits wirelessly to a wristwatch-type display
  • Prevent amputations that often stem from diabetic foot ulcers

 

Chapter 14: Endoscopic Diagnostics Tools

14.1    ENDOSCOPE USING MEMS SCANNING MIRROR

  • For gastrointestinal and urological imaging
  • Alternative to biopsies in cancer detection
  • A laser beam pointed at the mirror is precisely deflected
  • Steered by the scanning mirror to reach a target

 

Chapter 15: MEMS guided Surgical Tools

15.1    MICROMACHINED SURGICAL TOOLS; SILICON MEMS TWEEZERS – ElectrolQ Used for minimally invasive surgical (MIS)

  • Procedures where diagnosis, monitoring, or treatment of diseases are performed
  • Performing with very small incisions MEMS
  • Based microsurgical tools is a key enabling technology for angioplasty, catheterization, endoscopy, laparoscopy, and neurosurgery

 

Summary by Dr. Pearlman

  • Multiple projects by Academia & Industry
  • Multiple MEMS devices for measuring body activities.
  • Many patch type devices attached to the skin
  • Devices attached to the eye
  • Smaller is better, lower footprint, lower power

 

 

 

 

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Mass-producing stem cells to satisfy the demands of regenerative medicine

Reported by: Irina Robu, PhD

Instead of culturing cell on round, flat Petri dishes, Steve Oh from A*STAR Bioprocessing Technology Institute he grew them in a tiny polystyrene beads known as microcarriers floating in a nutritional brew. The standard Petri dish fits fewer than 100,000 cells, a minuscule amount when stacked against the 2 billion muscle cells  that make up the heart or 100 billion red blood cells needed to fill a bag of blood. 

The average Petri dish fits fewer than 100,000 cells, a miniscule amount when stacked against the 2 billion muscle cells that make up the heart or the 100 billion red blood cells needed to fill a bag of blood. The approach Reuveny suggested potentially could produce cells in much vaster numbers to make them more practical for therapy.
 
Dr. Oh first tried the approach on human embryonic stem cells, because they have the potential to mature into any type of cell in the body and struggled to develop a coating that would make the stem cells stick to the microcarriers and formulate the right mixture of nutrients for cell to grow. After a year, one line of human embryonic stem cells survived past the 20 week mark of stability and found out that these cells were two to four times times more densely packed than grown in petri dishes.

However after six years of refining the processes, they were able to achieve three times higher cell densities than petri dishes approach by modifying the feeding strategy.  Their success started with cardiomyocytes wich are known as the fastest cell type to differentiate. The researchers developed a strategy to grow pure batches of cariomyocytes without adding growth factors but instead use small molecules to first inhibit and then activate a key cell differentiation pathway known as Wnt signaling. Then they apply the small molecule approach to grow and differentiate cardiomyocytes from embryonic stem cells directly on microcarriers. And according to Dr. Oh their method had beat the Petri dish methods on purity, yield, cost of cells and simplicity of process.

The main  goal of the research is to grow enough cells inexpensively in order to patch up one square-centimeter of damaged heart muscle following a heart attack.

Source

http://phys.org/news/2015-06-mass-producing-stem-cells-demands-regenerative.html

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