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Artificial Intelligence (AI) Used to Successfully Determine Most Likely Repurposed Antibiotic Against Deadly Superbug Acinetobacter baumanni

Posted in Antibiotic resistance, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Drug Development Process, drug repurposing, Evidence-based decision-making, Infectious Disease & New Antibiotic Targets, Inflammatory Pathophysiology, Pharmaceutical Drug Discovery, Transformative Technologies in Healthcare, tagged Acinetobacter baumanni, AI, Antibiotic resistance, Artificial intelligence, Artificial Intelligence ( AI), artificial intelligence in drug design, drug screens, in silico, rational drug design, superbug, WHO on May 26, 2023| Leave a Comment »

Artificial Intelligence (AI) Used to Successfully Determine Most Likely Repurposed Antibiotic Against Deadly Superbug Acinetobacter baumanni

Reporter: Stephen J. Williams, Ph.D.

The World Health Organization has identified 3 superbugs, or infective micororganisms displaying resistance to common antibiotics and multidrug resistance, as threats to humanity:

Three bacteria were listed as critical:

• Acinetobacter baumannii bacteria that are resistant to important antibiotics called carbapenems. Acinetobacter baumannii are highly-drug resistant bacteria that can cause a range of infections for hospitalized patients, including pneumonia, wound, or blood infections.
• Pseudomonas aeruginosa, which are resistant to carbapenems. Pseudomonas aeruginosa can cause skin rashes and ear infectious in healthy people but also severe blood infections and pneumonia when contracted by sick people in the hospital.
• Enterobacteriaceae — a family of bacteria that live in the human gut — that are resistant to both carbepenems and another class of antibiotics, cephalosporins.

 

It has been designated critical need for development of  antibiotics to these pathogens.  Now researchers at Mcmaster University and others in the US had used artificial intelligence (AI) to screen libraries of over 7,000 chemicals to find a drug that could be repurposed to kill off the pathogen.

Liu et. Al. (1) published their results of an AI screen to narrow down potential chemicals that could work against Acinetobacter baumanii in Nature Chemical Biology recently.

Abstract

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Schematic workflow for incorporation of AI for antibiotic drug discovery for A. baumannii from 1. Liu, G., Catacutan, D.B., Rathod, K. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

Figure source: https://www.nature.com/articles/s41589-023-01349-8

Article Source: https://www.nature.com/articles/s41589-023-01349-8

1. Liu, G., Catacutan, D.B., Rathod, K. et al.Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

 

 

For reference to WHO and lists of most pathogenic superbugs see https://www.scientificamerican.com/article/who-releases-list-of-worlds-most-dangerous-superbugs/

The finding was first reported by the BBC.

Source: https://www.bbc.com/news/health-65709834

By James Gallagher

Health and science correspondent

Scientists have used artificial intelligence (AI) to discover a new antibiotic that can kill a deadly species of superbug.

The AI helped narrow down thousands of potential chemicals to a handful that could be tested in the laboratory.

The result was a potent, experimental antibiotic called abaucin, which will need further tests before being used.

The researchers in Canada and the US say AI has the power to massively accelerate the discovery of new drugs.

It is the latest example of how the tools of artificial intelligence can be a revolutionary force in science and medicine.

• AI breakthrough could spark medical revolution

Stopping the superbugs

Antibiotics kill bacteria. However, there has been a lack of new drugs for decades and bacteria are becoming harder to treat, as they evolve resistance to the ones we have.

More than a million people a year are estimated to die from infections that resist treatment with antibiotics.The researchers focused on one of the most problematic species of bacteria – Acinetobacter baumannii, which can infect wounds and cause pneumonia.

You may not have heard of it, but it is one of the three superbugs the World Health Organization has identified as a “critical” threat.

It is often able to shrug off multiple antibiotics and is a problem in hospitals and care homes, where it can survive on surfaces and medical equipment.

Dr Jonathan Stokes, from McMaster University, describes the bug as “public enemy number one” as it’s “really common” to find cases where it is “resistant to nearly every antibiotic”.

 

Artificial intelligence

To find a new antibiotic, the researchers first had to train the AI. They took thousands of drugs where the precise chemical structure was known, and manually tested them on Acinetobacter baumannii to see which could slow it down or kill it.

This information was fed into the AI so it could learn the chemical features of drugs that could attack the problematic bacterium.

The AI was then unleashed on a list of 6,680 compounds whose effectiveness was unknown. The results – published in Nature Chemical Biology – showed it took the AI an hour and a half to produce a shortlist.

The researchers tested 240 in the laboratory, and found nine potential antibiotics. One of them was the incredibly potent antibiotic abaucin.

Laboratory experiments showed it could treat infected wounds in mice and was able to kill A. baumannii samples from patients.

However, Dr Stokes told me: “This is when the work starts.”

The next step is to perfect the drug in the laboratory and then perform clinical trials. He expects the first AI antibiotics could take until 2030 until they are available to be prescribed.

Curiously, this experimental antibiotic had no effect on other species of bacteria, and works only on A. baumannii.

Many antibiotics kill bacteria indiscriminately. The researchers believe the precision of abaucin will make it harder for drug-resistance to emerge, and could lead to fewer side-effects.

 

In principle, the AI could screen tens of millions of potential compounds – something that would be impractical to do manually.

“AI enhances the rate, and in a perfect world decreases the cost, with which we can discover these new classes of antibiotic that we desperately need,” Dr Stokes told me.

The researchers tested the principles of AI-aided antibiotic discovery in E. coli in 2020, but have now used that knowledge to focus on the big nasties. They plan to look at Staphylococcus aureus and Pseudomonas aeruginosa next.

“This finding further supports the premise that AI can significantly accelerate and expand our search for novel antibiotics,” said Prof James Collins, from the Massachusetts Institute of Technology.

He added: “I’m excited that this work shows that we can use AI to help combat problematic pathogens such as A. baumannii.”

Prof Dame Sally Davies, the former chief medical officer for England and government envoy on anti-microbial resistance, told Radio 4’s The World Tonight: “We’re onto a winner.”

She said the idea of using AI was “a big game-changer, I’m thrilled to see the work he (Dr Stokes) is doing, it will save lives”.

Other related articles and books published in this Online Scientific Journal include the following:

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology

(3 book series: Volume 1, 2&3, 4)

https://www.amazon.com/gp/product/B08VVWTNR4?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks

 

 

 

 

 

 

 

 

 

 

• The Immune System, Stress Signaling, Infectious Diseases and Therapeutic Implications:

 

• Series D, VOLUME 2

Infectious Diseases and Therapeutics

and

• Series D, VOLUME 3

The Immune System and Therapeutics

(Series D: BioMedicine & Immunology) Kindle Edition.

On Amazon.com since September 4, 2017

(English Edition) Kindle Edition – as one Book

https://www.amazon.com/dp/B075CXHY1B $115

 

Bacterial multidrug resistance problem solved by a broad-spectrum synthetic antibiotic

The Journey of Antibiotic Discovery

FDA cleared Clever Culture Systems’ artificial intelligence tech for automated imaging, analysis and interpretation of microbiology culture plates speeding up Diagnostics

Artificial Intelligence: Genomics & Cancer

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The Vibrant Philly Biotech Scene: Proteovant Therapeutics Using Artificial Intelligence and Machine Learning to Develop PROTACs

Posted in Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Autophagy, Autophagy-Modulating Proteins, BioIT: BioInformatics, Cell Biology, Signaling & Cell Circuits, Cell death pathways, Chemical Biology and its relations to Metabolic Disease, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Investment in Technological Breakthrough, Machine Learning, Proteolysis, Proteosome, Signaling, Signaling & Cell Circuits, Technology Transfer: Biotech and Pharmaceutical, Ubiquitin, Ubiquitinylation, Unfolded Protein Response (UPR), tagged Artificial intelligence, biotech startup, early ventures, Machine Learning, philadelphia, PROTAC, Proteovant, Therapeutic innovations, ubiquitin-proteosome, University of Michigan, Venture capital on October 26, 2021| Leave a Comment »

The Vibrant Philly Biotech Scene: Proteovant Therapeutics Using Artificial Intelligence and Machine Learning to Develop PROTACs

Reporter: Stephen J. Williams, Ph.D.

It has been a while since I have added to this series but there have been a plethora of exciting biotech startups in the Philadelphia area, and many new startups combining technology, biotech, and machine learning. One such exciting biotech is Proteovant Therapeutics, which is combining the new PROTAC (Proteolysis-Targeting Chimera) technology with their in house ability to utilize machine learning and artificial intelligence to design these types of compounds to multiple intracellular targets.

PROTACs (which actually is under a trademark name of Arvinus Operations, but is also refered to as Protein Degraders. These PROTACs take advantage of the cell protein homeostatic mechanism of ubiquitin-mediated protein degradation, which is a very specific targeted process which regulates protein levels of various transcription factors, protooncogenes, and receptors. In essence this regulated proteolyic process is needed for normal cellular function, and alterations in this process may lead to oncogenesis, or a proteotoxic crisis leading to mitophagy, autophagy and cellular death. The key to this technology is using chemical linkers to associate an E3 ligase with a protein target of interest. E3 ligases are the rate limiting step in marking the proteins bound for degradation by the proteosome with ubiquitin chains.

Structure-Based PROTAC Design: Project Overview

A review of this process as well as PROTACs can be found elsewhere in articles (and future articles) on this Open Access Journal.

Protevant have made two important collaborations:

1. Oncopia Therapeutics: came out of University of Michigan Innovation Hub and lab of Shaomeng Wang, who developed a library of BET and MDM2 based protein degraders. In 2020 was aquired by Riovant Sciences.
2. Riovant Sciences: uses computer aided design of protein degraders

Proteovant Company Description:

Proteovant is a newly launched development-stage biotech company focusing on discovery and development of disease-modifying therapies by harnessing natural protein homeostasis processes. We have recently acquired numerous assets at discovery and development stages from Oncopia, a protein degradation company. Our lead program is on track to enter IND in 2021. Proteovant is building a strong drug discovery engine by combining deep drugging expertise with innovative platforms including Roivant’s AI capabilities to accelerate discovery and development of protein degraders to address unmet needs across all therapeutic areas. The company has recently secured $200M funding from SK Holdings in addition to investment from Roivant Sciences. Our current therapeutic focus includes but is not limited to oncology, immunology and neurology. We remain agnostic to therapeutic area and will expand therapeutic focus based on opportunity. Proteovant is expanding its discovery and development teams and has multiple positions in biology, chemistry, biochemistry, DMPK, bioinformatics and CMC at many levels. Our R&D organization is located close to major pharmaceutical companies in Eastern Pennsylvania with a second site close to biotech companies in Boston area.

Protein degradation

Source: Protevant

The ubiquitin proteasome system (UPS) is responsible for maintaining protein homeostasis. Targeted protein degradation by the UPS is a cellular process that involves marking proteins and guiding them to the proteasome for destruction. We leverage this physiological cellular machinery to target and destroy disease-causing proteins.

Unlike traditional small molecule inhibitors, our approach is not limited by the classic “active site” requirements. For example, we can target transcription factors and scaffold proteins that lack a catalytic pocket. These classes of proteins, historically, have been very difficult to drug. Further, we selectively degrade target proteins, rather than isozymes or paralogous proteins with high homology. Because of the catalytic nature of the interactions,  it is possible to achieve efficacy at lower doses with prolonged duration while decreasing dose-limiting toxicities.

Biological targets once deemed “undruggable” are now within reach.

About Riovant Sciences: from PRNewsWire https://www.prnewswire.com/news-releases/roivant-unveils-targeted-protein-degradation-platform-301186928.html

Roivant develops transformative medicines faster by building technologies and developing talent in creative ways, leveraging the Roivant platform to launch “Vants” – nimble and focused biopharmaceutical and health technology companies. These Vants include Proteovant but also Dermovant, ImmunoVant,as well as others.

Roivant’s drug discovery capabilities include the leading computational physics-based platform for in silico drug design and optimization as well as machine learning-based models for protein degradation.

The integration of our computational and experimental engines enables the rapid design of molecules with high precision and fidelity to address challenging targets for diseases with high unmet need.

Our current modalities include small molecules, heterobifunctionals and molecular glues.

Roivant Unveils Targeted Protein Degradation Platform

– First therapeutic candidate on track to enter clinical studies in 2021

– Computationally-designed degraders for six targets currently in preclinical development

– Acquisition of Oncopia Therapeutics and research collaboration with lab of Dr. Shaomeng Wang at the University of Michigan to add diverse pipeline of current and future compounds

– Clinical-stage degraders will provide foundation for multiple new Vants in distinct disease areas

– Platform supported by $200 million strategic investment from SK Holdings

Other articles in this Vibrant Philly Biotech Scene on this Online Open Access Journal include:

The Vibrant Philly Biotech Scene: PCCI Meeting Announcement, BioDetego Presents Colon Cancer Diagnostic Tool

The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

The Vibrant Philly Biotech Scene: Focus on Vaccines and Philimmune, LLC

The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

Philly Biotech Scene: Biobots and 3D BioPrinting (Now called Allevi)

Philly Biotech Scene: November 2015 PCCI Meeting Showcasing ViFant (Penn Center For Innovation)

Spark Therapeutics’ $4.8Billion deal Confirmed as Biggest VC-backed Exit in Philadelphia

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Science Policy Forum: Should we trust healthcare explanations from AI predictive systems?

Posted in AI Models in Healthcare, AI-assisted Cardiac MRI, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Bio-Ethics, BioIT: BioInformatics, Biological Networks, FDA, FDA Regulatory Affairs, Health Economics and Outcomes Research, Health Law Policy, Image Processing/Computing, Machine Learning, Personal Health Applications: Tech Innovations serves HealhCare, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, tagged AAAS, AI medical imaging, AI/ML, Artificial intelligence, explainability, explanatory AI, FDA, healthcare, image-interpretation training, interpretable AI, Machine Learning models, medical decision-making, medical decisionmaking, Medical imaging, Safety on July 31, 2021| Leave a Comment »

Science Policy Forum: Should we trust healthcare explanations from AI predictive systems?

Some in industry voice their concerns

Curator: Stephen J. Williams, PhD

Post on AI healthcare and explainable AI

   In a Policy Forum article in Science “Beware explanations from AI in health care”, Boris Babic, Sara Gerke, Theodoros Evgeniou, and Glenn Cohen discuss the caveats on relying on explainable versus interpretable artificial intelligence (AI) and Machine Learning (ML) algorithms to make complex health decisions.  The FDA has already approved some AI/ML algorithms for analysis of medical images for diagnostic purposes.  These have been discussed in prior posts on this site, as well as issues arising from multi-center trials.  The authors of this perspective article argue that choice of type of algorithm (explainable versus interpretable) algorithms may have far reaching consequences in health care.

Summary

Artificial intelligence and machine learning (AI/ML) algorithms are increasingly developed in health care for diagnosis and treatment of a variety of medical conditions (1). However, despite the technical prowess of such systems, their adoption has been challenging, and whether and how much they will actually improve health care remains to be seen. A central reason for this is that the effectiveness of AI/ML-based medical devices depends largely on the behavioral characteristics of its users, who, for example, are often vulnerable to well-documented biases or algorithmic aversion (2). Many stakeholders increasingly identify the so-called black-box nature of predictive algorithms as the core source of users’ skepticism, lack of trust, and slow uptake (3, 4). As a result, lawmakers have been moving in the direction of requiring the availability of explanations for black-box algorithmic decisions (5). Indeed, a near-consensus is emerging in favor of explainable AI/ML among academics, governments, and civil society groups. Many are drawn to this approach to harness the accuracy benefits of noninterpretable AI/ML such as deep learning or neural nets while also supporting transparency, trust, and adoption. We argue that this consensus, at least as applied to health care, both overstates the benefits and undercounts the drawbacks of requiring black-box algorithms to be explainable.

Source: https://science.sciencemag.org/content/373/6552/284?_ga=2.166262518.995809660.1627762475-1953442883.1627762475

Types of AI/ML Algorithms: Explainable and Interpretable algorithms

1.  Interpretable AI: A typical AI/ML task requires constructing algorithms from vector inputs and generating an output related to an outcome (like diagnosing a cardiac event from an image).  Generally the algorithm has to be trained on past data with known parameters.  When an algorithm is called interpretable, this means that the algorithm uses a transparent or “white box” function which is easily understandable. Such example might be a linear function to determine relationships where parameters are simple and not complex.  Although they may not be as accurate as the more complex explainable AI/ML algorithms, they are open, transparent, and easily understood by the operators.
2. Explainable AI/ML:  This type of algorithm depends upon multiple complex parameters and takes a first round of predictions from a “black box” model then uses a second algorithm from an interpretable function to better approximate outputs of the first model.  The first algorithm is trained not with original data but based on predictions resembling multiple iterations of computing.  Therefore this method is more accurate or deemed more reliable in prediction however is very complex and is not easily understandable.  Many medical devices that use an AI/ML algorithm use this type.  An example is deep learning and neural networks.

The purpose of both these methodologies is to deal with problems of opacity, or that AI predictions based from a black box undermines trust in the AI.

For a deeper understanding of these two types of algorithms see here:

https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html

or https://www.bmc.com/blogs/machine-learning-interpretability-vs-explainability/

(a longer read but great explanation)

From the above blog post of Jonathan Johnson

• How interpretability is different from explainability
• Why a model might need to be interpretable and/or explainable
• Who is working to solve the black box problem—and how

What is interpretability?

Does Chipotle make your stomach hurt? Does loud noise accelerate hearing loss? Are women less aggressive than men? If a machine learning model can create a definition around these relationships, it is interpretable.

All models must start with a hypothesis. Human curiosity propels a being to intuit that one thing relates to another. “Hmm…multiple black people shot by policemen…seemingly out of proportion to other races…something might be systemic?” Explore.

People create internal models to interpret their surroundings. In the field of machine learning, these models can be tested and verified as either accurate or inaccurate representations of the world.

Interpretability means that the cause and effect can be determined.

What is explainability?

ML models are often called black-box models because they allow a pre-set number of empty parameters, or nodes, to be assigned values by the machine learning algorithm. Specifically, the back-propagation step is responsible for updating the weights based on its error function.

To predict when a person might die—the fun gamble one might play when calculating a life insurance premium, and the strange bet a person makes against their own life when purchasing a life insurance package—a model will take in its inputs, and output a percent chance the given person has at living to age 80.

Below is an image of a neural network. The inputs are the yellow; the outputs are the orange. Like a rubric to an overall grade, explainability shows how significant each of the parameters, all the blue nodes, contribute to the final decision.

In this neural network, the hidden layers (the two columns of blue dots) would be the black box.

For example, we have these data inputs:

• Age
• BMI score
• Number of years spent smoking
• Career category

If this model had high explainability, we’d be able to say, for instance:

• The career category is about 40% important
• The number of years spent smoking weighs in at 35% important
• The age is 15% important
• The BMI score is 10% important

Explainability: important, not always necessary

Explainability becomes significant in the field of machine learning because, often, it is not apparent. Explainability is often unnecessary. A machine learning engineer can build a model without ever having considered the model’s explainability. It is an extra step in the building process—like wearing a seat belt while driving a car. It is unnecessary for the car to perform, but offers insurance when things crash.

The benefit a deep neural net offers to engineers is it creates a black box of parameters, like fake additional data points, that allow a model to base its decisions against. These fake data points go unknown to the engineer. The black box, or hidden layers, allow a model to make associations among the given data points to predict better results. For example, if we are deciding how long someone might have to live, and we use career data as an input, it is possible the model sorts the careers into high- and low-risk career options all on its own.

Perhaps we inspect a node and see it relates oil rig workers, underwater welders, and boat cooks to each other. It is possible the neural net makes connections between the lifespan of these individuals and puts a placeholder in the deep net to associate these. If we were to examine the individual nodes in the black box, we could note this clustering interprets water careers to be a high-risk job.

In the previous chart, each one of the lines connecting from the yellow dot to the blue dot can represent a signal, weighing the importance of that node in determining the overall score of the output.

• If that signal is high, that node is significant to the model’s overall performance.
• If that signal is low, the node is insignificant.

With this understanding, we can define explainability as:

Knowledge of what one node represents and how important it is to the model’s performance.

So how does choice of these two different algorithms make a difference with respect to health care and medical decision making?

The authors argue: 

“Regulators like the FDA should focus on those aspects of the AI/ML system that directly bear on its safety and effectiveness – in particular, how does it perform in the hands of its intended users?”

A suggestion for

• Enhanced more involved clinical trials
• Provide individuals added flexibility when interacting with a model, for example inputting their own test data
• More interaction between user and model generators
• Determining in which situations call for interpretable AI versus explainable (for instance predicting which patients will require dialysis after kidney damage)

Other articles on AI/ML in medicine and healthcare on this Open Access Journal include

Applying AI to Improve Interpretation of Medical Imaging

Real Time Coverage @BIOConvention #BIO2019: Machine Learning and Artificial Intelligence #AI: Realizing Precision Medicine One Patient at a Time

LIVE Day Three – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 10, 2019

Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package

 

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Al is on the way to lead critical ED decisions on CT

Posted in AI Models in Healthcare, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Cancer Screening, Crohn's disease, CT, Gastroenterology, Liver & Digestive Diseases Research, Machine Learning, tagged Artificial intelligence, Crohn's disease, CT scans, Emergency department, Gastroenterology, Machine Learning (ML), Tel Aviv University on July 31, 2021| Leave a Comment »

Al is on the way to lead critical ED decisions on CT

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Artificial intelligence (AI) has infiltrated many organizational processes, raising concerns that robotic systems will eventually replace many humans in decision-making. The advent of AI as a tool for improving health care provides new prospects to improve patient and clinical team’s performance, reduce costs, and impact public health. Examples include, but are not limited to, automation; information synthesis for patients, “fRamily” (friends and family unpaid caregivers), and health care professionals; and suggestions and visualization of information for collaborative decision making.

In the emergency department (ED), patients with Crohn’s disease (CD) are routinely subjected to Abdomino-Pelvic Computed Tomography (APCT). It is necessary to diagnose clinically actionable findings (CAF) since they may require immediate intervention, which is typically surgical. Repeated APCTs, on the other hand, results in higher ionizing radiation exposure. The majority of APCT performance guidance is clinical and empiric. Emergency surgeons struggle to identify Crohn’s disease patients who actually require a CT scan to determine the source of acute abdominal distress.

Aid seems to be on the way. Researchers employed machine learning to accurately distinguish these sufferers from Crohn’s patients who appear with the same complaint but may safely avoid the recurrent exposure to contrast materials and ionizing radiation that CT would otherwise wreak on them.

The study entitled “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department” was published on July 9 in Digestive and Liver Disease by gastroenterologists and radiologists at Tel Aviv University in Israel.

Retrospectively, Jacob Ollech and his fellow researcher have analyzed 101 emergency treatments of patients with Crohn’s who underwent abdominopelvic CT.

They were looking for examples where a scan revealed clinically actionable results. These were classified as intestinal blockage, perforation, intra-abdominal abscess, or complex fistula by the researchers.

On CT, 44 (43.5 %) of the 101 cases reviewed had such findings.

Ollech and colleagues utilized a machine-learning technique to design a decision-support tool that required only four basic clinical factors to test an AI approach for making the call.

The approach was successful in categorizing patients into low- and high-risk groupings. The researchers were able to risk-stratify patients based on the likelihood of clinically actionable findings on abdominopelvic CT as a result of their success.

Ollech and co-authors admit that their limited sample size, retrospective strategy, and lack of external validation are shortcomings.

Moreover, several patients fell into an intermediate risk category, implying that a standard workup would have been required to guide CT decision-making in a real-world situation anyhow.

Consequently, they generate the following conclusion:

We believe this study shows that a machine learning-based tool is a sound approach for better-selecting patients with Crohn’s disease admitted to the ED with acute gastrointestinal complaints about abdominopelvic CT: reducing the number of CTs performed while ensuring that patients with high risk for clinically actionable findings undergo abdominopelvic CT appropriately.

Main Source:

Konikoff, Tom, Idan Goren, Marianna Yalon, Shlomit Tamir, Irit Avni-Biron, Henit Yanai, Iris Dotan, and Jacob E. Ollech. “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department.” Digestive and Liver Disease (2021). https://www.sciencedirect.com/science/article/abs/pii/S1590865821003340

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Al App for People with Digestive Disorders

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/24/ai-app-for-people-with-digestive-disorders/

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

Al System Used to Detect Lung Cancer

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Artificial Intelligence: Genomics & Cancer

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

Yet another Success Story: Machine Learning to predict immunotherapy response

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/07/06/yet-another-success-story-machine-learning-to-predict-immunotherapy-response/

Systemic Inflammatory Diseases as Crohn’s disease, Rheumatoid Arthritis and Longer Psoriasis Duration May Mean Higher CVD Risk

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/10/09/systemic-inflammatory-diseases-as-crohns-disease-rheumatoid-arthritis-and-longer-psoriasis-duration-may-mean-higher-cvd-risk/

Autoimmune Inflammatory Bowel Diseases: Crohn’s Disease & Ulcerative Colitis: Potential Roles for Modulation of Interleukins 17 and 23 Signaling for Therapeutics

Curators: Larry H Bernstein, MD FCAP and Aviva Lev-Ari, PhD, RN https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/

Inflammatory Disorders: Inflammatory Bowel Diseases (IBD) – Crohn’s and Ulcerative Colitis (UC) and Others

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

https://pharmaceuticalintelligence.com/gama-delta-epsilon-gde-is-a-global-holding-company-absorbing-lpbi/subsidiary-5-joint-ventures-for-ip-development-jvip/drug-discovery-with-3d-bioprinting/ibd-inflammatory-bowl-diseases-crohns-and-ulcerative-colitis/

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This AI Just Evolved From Companion Robot To Home-Based Physician Helper

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Mobile Healthcare, Transformative Technologies in Healthcare, Voices of Patients and Healthcare Providers, tagged Artificial intelligence, Conditions and Diseases, ElliQ, healthcare, Intuition Robotics, Personalized medicine on July 6, 2021| Leave a Comment »

This AI Just Evolved From Companion Robot To Home-Based Physician Helper

Reporter: Ethan Coomber, Research Assistant III, Data Science and Podcast Library Development 

Article Author: Gil Press Senior Contributor Enterprise & Cloud @Forbes 

Twitter: @GilPress I write about technology, entrepreneurs and innovation.

Intuition Robotics announced today that it is expanding its mission of improving the lives of older adults to include enhancing their interactions with their physicians. The Israeli startup has developed the AI-based, award-winning proactive social robot ElliQ which has spent over 30,000 days in older adults’ homes over the past two years. Now ElliQ will help increase patient engagement while offering primary care providers continuous actionable data and insights for early detection and intervention.

The very big challenge Intuition Robotics set up to solve was to “understand how to create a relationship between a human and a machine,” says co-founder and CEO Dor Skuler. Unlike a number of unsuccessful high-profile social robots (e.g., Pepper) that tried to perform multiple functions in multiple settings, ElliQ has focused exclusively on older adults living alone. Understanding empathy and how to grow a trusting relationship were the key objectives of Intuition Robotics’ research project, as well as how to continuously learn the specific (and changing) behavioral characteristics, habits, and preferences of the older adults participating in the experiment.

The results are impressive: 90% of users engage with ElliQ every day, without deterioration in engagement over time. When ElliQ proactively initiates deep conversational interactions with its users, there’s 70% response rate. Most important, the participants share something personal with ElliQ almost every day. “She has picked up my attitude… she’s figured me out,” says Deanna Dezern, an ElliQ user who describes her robot companion as “my sister from another mother.”

The very big challenge Intuition Robotics set up to solve was to “understand how to create a relationship between a human and a machine,” says co-founder and CEO Dor Skuler. Unlike a number of unsuccessful high-profile social robots (e.g., Pepper) that tried to perform multiple functions in multiple settings, ElliQ has focused exclusively on older adults living alone. Understanding empathy and how to grow a trusting relationship were the key objectives of Intuition Robotics’ research project, as well as how to continuously learn the specific (and changing) behavioral characteristics, habits, and preferences of the older adults participating in the experiment.

The results are impressive: 90% of users engage with ElliQ every day, without deterioration in engagement over time. When ElliQ proactively initiates deep conversational interactions with its users, there’s 70% response rate. Most important, the participants share something personal with ElliQ almost every day. “She has picked up my attitude… she’s figured me out,” says Deanna Dezern, an ElliQ user who describes her robot companion as “my sister from another mother.”

Higher patient engagement leads to lower costs of delivering care and the quality of the physician-patient relationship is positively associated with improved functional health, studies have found. Typically, however, primary care physicians see their patients anywhere from once a month to once a year, even though about 85% of seniors in the U.S. have at least one chronic health condition. ElliQ, with the consent of its users, can provide data on the status of patients in between office visits and facilitate timely and consistent communications between physicians and their patients.

Supporting the notion of a home-based physician assistant robot is the transformation of healthcare delivery in the U.S. More and more primary care physicians are moving from a fee-for-service business model, where doctors are paid according to the procedures used to treat a patient, to “capitation,” where doctors are paid a set amount for each patient they see. This shift in how doctors are compensated is gaining momentum as a key solution for reducing the skyrocketing costs of healthcare: “…inadequate, unnecessary, uncoordinated, and inefficient care and suboptimal business processes eat up at least 35%—and maybe over 50%—of the more than $3 trillion that the country spends annually on health care. That suggests more than $1 trillion is being squandered,” states “The Case for Capitation,” a Harvard Business Review article.

Under this new business model, physicians have a strong incentive to reduce or eliminate visits to the ER and hospitalization, so ElliQ’s assistance in early intervention and support of proactive and preventative healthcare is highly valuable. ElliQ’s “new capabilities provide physicians with visibility into the patient’s condition at home while allowing seamless communication… can assist me and my team in early detection and mitigation of health issues, and it increases patients’ involvement in their care through more frequent engagement and communication,” says in a statement Dr. Peter Barker of Family Doctors, a Mass General Brigham-affiliated practice in Swampscott, MA, that is working with Intuition Robotics.

With the new stage in its evolution, ElliQ becomes “a conversational agent for self-reported data on how people are doing based on what the doctor is telling us to look for and, at the same time, a super-simple communication channel between the physician and the patient,” says Skuler. As only 20% of the individual’s health has to do with the administration of healthcare, Skuler says the balance is already taken care of by ElliQ—encouraging exercise, watching nutrition, keeping mentally active, connecting to the outside world, and promoting a sense of purpose.

A recent article in The Communication of the ACM pointed out that “usability concerns have for too long overshadowed questions about the usefulness and acceptability of digital technologies for older adults.” Specifically, the authors challenge the long-held assumption that accessibility and aging research “fall under the same umbrella despite the fact that aging is neither an illness nor a disability.”

For Skuler, a “pyramid of value” is represented in Intuition Robotics offering. At the foundation is the physical product, easy to use and operate and doing what it is expected to do. Then there is the layer of “building relationships based on trust and empathy,” with a lot of humor and social interaction and activities for the users. On top are specific areas of value to older adults, and the first one is healthcare. There will be more in the future, anything that could help older adults live better lives, such as direct connections to the local community. ”Healthcare is an interesting experiment and I’m very much looking forward to see what else the future holds for ElliQ,” says Skuler.

Original. Reposted with permission, 7/7/2021.

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

The Future of Speech-Based Human-Computer Interaction
Reporter: Ethan Coomber
https://pharmaceuticalintelligence.com/2021/06/23/the-future-of-speech-based-human-computer-interaction/

Deep Medicine: How Artificial Intelligence Can Make Health Care Human Again
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2020/11/11/deep-medicine-how-artificial-intelligence-can-make-health-care-human-again/

Supporting the elderly: A caring robot with ‘emotions’ and memory
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https://pharmaceuticalintelligence.com/2015/02/10/supporting-the-elderly-a-caring-robot-with-emotions-and-memory/

Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves
Reporter: Abhisar Anand, Research Assistant I
https://pharmaceuticalintelligence.com/2021/06/22/developing-deep-learning-models-dl-for-classifying-emotions-through-brainwaves/

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Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Posted in Artificial Intelligence in CANCER, Cancer - General, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Screening, Clinical Genomics, CRISPR/Cas9 & Gene Editing, Genetics & Innovations in Treatment, Machine Learning, Mutant Gene Expression, Personalized and Precision Medicine & Genomic Research, Precision Cancer Medicine, tagged Artificial intelligence, Cancer, Cancer prognosis prediction, cancer progression, computational algorithm, driver gene, Machine Learning on May 4, 2021| Leave a Comment »

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low-risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) and Artificial Intelligence (AI) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions by predicting new algorithms.

In the majority of human cancers, heritable loss of gene function through cell division may be mediated as often by epigenetic as by genetic abnormalities. Epigenetic modification occurs through a process of interrelated changes in CpG island methylation and histone modifications. Candidate gene approaches of cell cycle, growth regulatory and apoptotic genes have shown epigenetic modification associated with loss of cognate proteins in sporadic pituitary tumors.

On 11th November 2020, researchers from the University of California, Irvine, has established the understanding of epigenetic mechanisms in tumorigenesis and publicized a previously undetected repertoire of cancer driver genes. The study was published in “Science Advances”

Researchers were able to identify novel tumor suppressor genes (TSGs) and oncogenes (OGs), particularly those with rare mutations by using a new prediction algorithm, called DORGE (Discovery of Oncogenes and tumor suppressor genes using Genetic and Epigenetic features) by integrating the most comprehensive collection of genetic and epigenetic data.

The senior author Wei Li, Ph.D., the Grace B. Bell chair and professor of bioinformatics in the Department of Biological Chemistry at the UCI School of Medicine said

Existing bioinformatics algorithms do not sufficiently leverage epigenetic features to predict cancer driver genes, even though epigenetic alterations are known to be associated with cancer driver genes.

The Study

This study demonstrated how cancer driver genes, predicted by DORGE, included both known cancer driver genes and novel driver genes not reported in current literature. In addition, researchers found that the novel dual-functional genes, which DORGE predicted as both TSGs and OGs, are highly enriched at hubs in protein-protein interaction (PPI) and drug/compound-gene networks.

• 

Prof. Li explained that the DORGE algorithm, successfully leveraged public data to discover the genetic and epigenetic alterations that play significant roles in cancer driver gene dysregulation and could be instrumental in improving cancer prevention, diagnosis and treatment efforts in the future.

Another new algorithmic prediction for the identification of cancer genes by Machine Learning has been carried out by a team of researchers at the Max Planck Institute for Molecular Genetics (MPIMG) in Berlin and the Institute of Computational Biology of Helmholtz Zentrum München combining a wide variety of data analyzed it with “Artificial Intelligence” and identified numerous cancer genes. They termed the algorithm as EMOGI (Explainable Multi-Omics Graph Integration). EMOGI can predict which genes cause cancer, even if their DNA sequence is not changed. This opens up new perspectives for targeted cancer therapy in personalized medicine and the development of biomarkers. The research was published in Nature Machine Intelligence on 12th April 2021.

In cancer, cells get out of control. They proliferate and push their way into tissues, destroying organs and thereby impairing essential vital functions. This unrestricted growth is usually induced by an accumulation of DNA changes in cancer genes—i.e. mutations in these genes that govern the development of the cell. But some cancers have only very few mutated genes, which means that other causes lead to the disease in these cases.

The Study

The aim of the study has been represented in 4 main headings

• Additional targets for personalized medicine
• Better results by combination
• In search of hints for further studies
• Suitable for other types of diseases as well

The team was headed by Annalisa Marsico. The team used the algorithm to identify 165 previously unknown cancer genes. The sequences of these genes are not necessarily altered-apparently, already a dysregulation of these genes can lead to cancer. All of the newly identified genes interact closely with well-known cancer genes and be essential for the survival of tumor cells in cell culture experiments. The EMOGI can also explain the relationships in the cell’s machinery that make a gene a cancer gene. The software integrates tens of thousands of data sets generated from patient samples. These contain information about DNA methylations, the activity of individual genes and the interactions of proteins within cellular pathways in addition to sequence data with mutations. In these data, a deep-learning algorithm detects the patterns and molecular principles that lead to the development of cancer.

Marsico says

Ideally, we obtain a complete picture of all cancer genes at some point, which can have a different impact on cancer progression for different patients

Unlike traditional cancer treatments such as chemotherapy, personalized treatments are tailored to the exact type of tumor. “The goal is to choose the best treatment for each patient, the most effective treatment with the fewest side effects. In addition, molecular properties can be used to identify cancers that are already in the early stages.

Roman Schulte-Sasse, a doctoral student on Marsico’s team and the first author of the publication says

To date, most studies have focused on pathogenic changes in sequence, or cell blueprints, at the same time, it has recently become clear that epigenetic perturbation or dysregulation gene activity can also lead to cancer.

This is the reason, researchers merged sequence data that reflects blueprint failures with information that represents events in cells. Initially, scientists confirmed that mutations, or proliferation of genomic segments, were the leading cause of cancer. Then, in the second step, they identified gene candidates that are not very directly related to the genes that cause cancer.

Clues for future directions

The researcher’s new program adds a considerable number of new entries to the list of suspected cancer genes, which has grown to between 700 and 1,000 in recent years. It was only through a combination of bioinformatics analysis and the newest Artificial Intelligence (AI) methods that the researchers were able to track down the hidden genes.

Schulte-Sasse says “The interactions of proteins and genes can be mapped as a mathematical network, known as a graph.” He explained by giving an example of a railroad network; each station corresponds to a protein or gene, and each interaction among them is the train connection. With the help of deep learning—the very algorithms that have helped artificial intelligence make a breakthrough in recent years – the researchers were able to discover even those train connections that had previously gone unnoticed. Schulte-Sasse had the computer analyze tens of thousands of different network maps from 16 different cancer types, each containing between 12,000 and 19,000 data points.

Many more interesting details are hidden in the data. Patterns that are dependent on particular cancer and tissue were seen. The researchers were also observed this as evidence that tumors are triggered by different molecular mechanisms in different organs.

Marsico explains

The EMOGI program is not limited to cancer, the researchers emphasize. In theory, it can be used to integrate diverse sets of biological data and find patterns there. It could be useful to apply our algorithm for similarly complex diseases for which multifaceted data are collected and where genes play an important role. An example might be complex metabolic diseases such as diabetes.

Main Source

New prediction algorithm identifies previously undetected cancer driver genes

https://advances.sciencemag.org/content/6/46/eaba6784  

Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms

https://www.nature.com/articles/s42256-021-00325-y#citeas

Other Related Articles published in this Open Access Online Scientific Journal include the following:

AI System Used to Detect Lung Cancer

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Deep Learning extracts Histopathological Patterns and accurately discriminates 28 Cancer and 14 Normal Tissue Types: Pan-cancer Computational Histopathology Analysis

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/10/28/deep-learning-extracts-histopathological-patterns-and-accurately-discriminates-28-cancer-and-14-normal-tissue-types-pan-cancer-computational-histopathology-analysis/

Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

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

https://pharmaceuticalintelligence.com/2019/12/08/evolution-of-the-human-cell-genome-biology-field-of-gene-expression-gene-regulation-gene-regulatory-networks-and-application-of-machine-learning-algorithms-in-large-scale-biological-data-analysis/

Cancer detection and therapeutics

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/05/02/cancer-detection-and-therapeutics/

Free Bio-IT World Webinar: Machine Learning to Detect Cancer Variants

Reporter: Stephen J. Williams, PhD

https://pharmaceuticalintelligence.com/2016/05/04/free-bio-it-world-webinar-machine-learning-to-detect-cancer-variants/

Artificial Intelligence: Genomics & Cancer

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

Premalata Pati, PhD, PostDoc in Biological Sciences, Medical Text Analysis with Machine Learning

https://pharmaceuticalintelligence.com/2021-medical-text-analysis-nlp/premalata-pati-phd-postdoc-in-pharmaceutical-sciences-medical-text-analysis-with-machine-learning/

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