The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.

Objectives: In vitro fertilization (IVF) has the potential to give babies to millions more people globally, yet it continues to be underutilized. We established a globally applicable and locally adaptable IVF prognostics report and framework to support patient-provider counseling and enable validated, data-driven treatment decisions. This study investigates the IVF utilization rates associated with the usage of machine learning, center-specific (MLCS) prognostic reports (the Univfy® report) in provider-patient pre-treatment and IVF counseling. Methods: We used a retrospective cohort comprising 24,238 patients with new patient visits (NPV) from 2016 to 2022 across seven fertility centers in 17 locations in seven US states and Ontario, Canada. We tested the association of Univfy report usage and first intra-uterine insemination (IUI) and/or first IVF usage (a.k.a. conversion) within 180 days, 360 days, and \"Ever\" of NPV as primary outcomes. Results: Univfy report usage was associated with higher direct IVF conversion (without prior IUI), with odds ratios (OR) 3.13 (95% CI 2.83, 3.46), 2.89 (95% CI 2.63, 3.17), and 2.04 (95% CI 1.90, 2.20) and total IVF conversion (with or without prior IUI), OR 3.41 (95% CI 3.09, 3.75), 3.81 (95% CI 3.49, 4.16), and 2.78 (95% CI 2.59, 2.98) in 180-day, 360-day, and Ever analyses, respectively; p < 0.05. Among patients with Univfy report usage, after accounting for center as a factor, older age was a small yet independent predictor of IVF conversion. Conclusions: Usage of a patient-centric, MLCS-based prognostics report was associated with increased IVF conversion among new fertility patients. Further research to study factors influencing treatment decision making and real-world optimization of patient-centric workflows utilizing the MLCS reports is warranted.

Artificial intelligence (AI)-enabled technologies in the MedTech sector hold the promise to transform healthcare delivery by improving access, quality, and outcomes. As the regulatory contours of these technologies are being defined, there is a notable lack of literature on the key stakeholders such as the organizations and interest groups that have a significant input in shaping the regulatory framework. This article explores the perspectives and contributions of these stakeholders in shaping the regulatory paradigm of AI-enabled medical technologies. The formation of an AI regulatory framework requires the convergence of ethical, regulatory, technical, societal, and practical considerations. These multiple perspectives contribute to the various dimensions of an evolving regulatory paradigm. From the global governance guidelines set by the World Health Organization (WHO) to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.

MASH is a prevalent liver disease that can progress to fibrosis, cirrhosis, hepatocellular carcinoma (HCC), and ultimately death, but there are no approved therapies. Leukotriene B4 (LTB4) is a potent pro-inflammatory chemoattractant that drives macrophage and neutrophil chemotaxis, and genetic loss or inhibition of its high affinity receptor, leukotriene B4 receptor 1 (BLT1), results in improved insulin sensitivity and decreased hepatic steatosis. To validate the therapeutic efficacy of BLT1 inhibition in an inflammatory and pro-fibrotic mouse model of MASH and fibrosis, mice were challenged with a choline-deficient, L-amino acid defined high fat diet and treated with a BLT1 antagonist at 30 or 90 mg/kg for 8 weeks. Liver function, histology, and gene expression were evaluated at the end of the study. Treatment with the BLT1 antagonist significantly reduced plasma lipids and liver steatosis but had no impact on liver injury biomarkers or histological endpoints such as inflammation, ballooning, or fibrosis compared to control. Artificial intelligence-powered digital pathology analysis revealed a significant reduction in steatosis co-localized fibrosis in livers treated with the BLT1 antagonist. Liver RNA-seq and pathway analyses revealed significant changes in fatty acid, arachidonic acid, and eicosanoid metabolic pathways with BLT1 antagonist treatment, however, these changes were not sufficient to impact inflammation and fibrosis endpoints. Targeting this LTB4-BLT1 axis with a small molecule inhibitor in animal models of chronic liver disease should be considered with caution, and additional studies are warranted to understand the mechanistic nuances of BLT1 inhibition in the context of MASH and liver fibrosis.

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a \"dataset\" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

There is a rising concern related to the possible risk of human exposure to nanoparticles (NPs). Several studies have reported on the transport behavior of NPs in the porous media under varying conditions. Thus, there is a scope to use this information in a predictive model so that the transport behavior of any un-explored NPs could be predicted. The main focus of his study, therefore, is to apply different machine learning (ML) based models to predict the transport efficiency of a wide range of NPs and to identify the important features. To achieve the objective, first, the dataset is prepared by extracting data from published papers for selected NPs [i.e., silver (nAg), titanium dioxide (nTiO2), zinc oxide (nZnO), graphene oxide (nGO), and etc.]. Then, random forest, XGBoost, and CatBoost algorithms combined with synthetic minority oversampling technique (SMOTE) were applied where retention fraction (RF) is considered as the target feature and particle characteristics (i.e., surface charge, size, concentration), solution chemistry [pH, ionic strength (IS]), porous media properties (grain size, porosity) and flow rate are considered as the training features. The outcome of the study indicates that CatBoost combined with SMOTE performed the best in predicting RF for the entire range of NPs (R2 > 0.89 and MSE < 0.007) as well as for individual NPs. Feature importance analysis indicates four features, namely zeta potential, IS, pH, and particle diameter (the entire range of NPs, nGO, nZnO) or grain size (nAg, nTiO2) have significant weightage (>75%). The result suggests that the features overrule the prediction of transport behavior rather than the types of individual NPs. The relative importance of the features depends on the range of the parameter used. The identified important features are in accordance with the underlying physical process, which makes the prediction model more reliable.

The second most frequent cancer in the world and the most common malignancy in women is breast cancer. Breast cancer is a significant health concern in India with a high mortality-to-incidence ratio and presentation at a younger age.
Recent studies have identified gut microbiota as a significant factor that can have an influence on the development, treatment, and prognosis of breast cancer. This review article aims to describe the influence of microbial dysbiosis on breast cancer occurrence and the possible interactions between oncobiome and specific breast cancer molecular subtypes. The review further also discusses the role of epigenetics and diet/nutrition in the regulation of the gut and breast microbiome and its association with breast cancer prevention, therapy, and recurrence. Additionally, the recent technological advances in microbiome research, including next-generation sequencing (NGS) technologies, genome sequencing, single-cell sequencing, and microbial metabolomics along with recent advances in artificial intelligence (AI) have also been reviewed. This is an attempt to present a comprehensive status of the microbiome as a key cancer biomarker.
We believe that correlating microbiome and carcinogenesis is important as it can provide insights into the mechanisms by which microbial dysbiosis can influence cancer development and progression, leading to the potential use of the microbiome as a tool for prognostication and personalized therapy.

In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

UNASSIGNED: Mathematical modeling, probability estimation, and statistical inference represent core elements of modern artificial intelligence (AI) approaches for data-driven prediction, forecasting, classification, risk-estimation, and prognosis. Currently there are many tools that help calculate and visualize univariate probability distributions, however, very few resources venture beyond into multivariate distributions, which are commonly used in advanced statistical inference and AI decision-making. This article presents a new web-calculator that enables some calculation and visualization of bivariate and trivariate probability distributions.
UNASSIGNED: Several methods are explored to compute the joint bivariate and trivariate probability densities, including the optimal multivariate modeling using Gaussian copula. We developed an interactive webapp to visually illustrate the parallels between the mathematical formulation, computational implementation, and graphical depiction of multivariate probability density and cumulative distribution functions. To ensure the interface and functionality are hardware platform independent, scalable, and functional, the app and its component widgets are implemented using HTML5 and JavaScript.
UNASSIGNED: We validated the webapp by testing the multivariate copula models under different experimental conditions and inspecting the performance in terms of accuracy and reliability of the estimated multivariate probability densities and distribution function values.
UNASSIGNED: This article demonstrates the construction, implementation, and utilization of multivariate probability calculators. The proposed webapp implementation is freely available online (https://socr.umich.edu/HTML5/BivariateNormal/BVN2/) and can be used to assist with education and research of a diverse array of data scientists, STEM instructors, and AI learners.

UNASSIGNED: To introduce developers to medical device regulatory processes and data considerations in artificial intelligence and machine learning (AI/ML) device submissions and to discuss ongoing AI/ML-related regulatory challenges and activities.
UNASSIGNED: AI/ML technologies are being used in an increasing number of medical imaging devices, and the fast evolution of these technologies presents novel regulatory challenges. We provide AI/ML developers with an introduction to U.S. Food and Drug Administration (FDA) regulatory concepts, processes, and fundamental assessments for a wide range of medical imaging AI/ML device types.
UNASSIGNED: The device type for an AI/ML device and appropriate premarket regulatory pathway is based on the level of risk associated with the device and informed by both its technological characteristics and intended use. AI/ML device submissions contain a wide array of information and testing to facilitate the review process with the model description, data, nonclinical testing, and multi-reader multi-case testing being critical aspects of the AI/ML device review process for many AI/ML device submissions. The agency is also involved in AI/ML-related activities that support guidance document development, good machine learning practice development, AI/ML transparency, AI/ML regulatory research, and real-world performance assessment.
UNASSIGNED: FDA\'s AI/ML regulatory and scientific efforts support the joint goals of ensuring patients have access to safe and effective AI/ML devices over the entire device lifecycle and stimulating medical AI/ML innovation.