Improving Hospital Length of Stay Prediction through Heterogeneous Data Integration from MIMIC-III Records.

Accurate prediction of hospital length of stay (LoS) is a vital component in optimizing clinical workflows, resource allocation, and patient care. This study presents a comprehensive evaluation of machine learning models for both binary and multi-class LoS classification tasks using structured clinical variables, physiological measurements, and unstructured clinical notes. Seven data configurations were constructed from combinations of structured features (Z), including diagnoses, procedures, medications, laboratory tests, and microbiology results; MeSH-based symptoms (S); physiological signals (F); and textual representations (E): Z, F, E, ZS, ZSF, ZSE, and ZSEF.

Advancing infection profiling under data uncertainty through contagion potential.

During the COVID-19 pandemic, the prevalence of asymptomatic cases challenged the reliability of epidemiological statistics in policymaking. To address this, we introduced contagion potential (CP) as a continuous metric derived from sociodemographic and epidemiological data to quantify the infection risk posed by the asymptomatic within a region. However, CP estimation is hindered by incomplete or biased incidence data, where underreporting and testing constraints make direct estimation infeasible.

Network and modeling analysis of MAPK signaling cascade uncovers EGR1 regulation through ERK2 protein in breast cancer.

The study explores the therapeutic relevance of Cardiac glycosides (CGs), including lanatoside C (LC), peruvoside (PS), and strophanthidin (STR) in treating breast cancer, using network pharmacology studies and bioinformatics approaches. Building on our prior in vitro studies and transcriptome profiling, we aimed to explore protein expression alterations influenced by the selected compounds in the present study. The methodology was structured and directed to delineate the active protein targets and their molecular mechanism of action in controlling cancer progression.

GramSeq-DTA: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information.

Drug-target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug-target affinity prediction.

Physics-informed machine learning for automatic model reduction in chemical reaction networks.

Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational costs of detailed mechanistic models while leveraging the predictive power of machine learning. This study applies this fusion to the biomedical challenge of Aβ fibril aggregation, a key factor in Alzheimer's disease.

Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection.

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation-maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense.

Bone Marrow Stromal Cells Generate a Pro-Healing Inflammasome When Cultured on Titanium-Aluminum-Vanadium Surfaces with Microscale/Nanoscale Structural Features.

The surface topography and chemistry of titanium-aluminum-vanadium (Ti6Al4V) implants play critical roles in the osteoblast differentiation of human bone marrow stromal cells (MSCs) and the creation of an osteogenic microenvironment. To assess the effects of a microscale/nanoscale (MN) topography, this study compared the effects of MN-modified, anodized, and smooth Ti6Al4V surfaces on MSC response, and for the first time, directly contrasted MN-induced osteoblast differentiation with culture on tissue culture polystyrene (TCPS) in osteogenic medium (OM). Surface characterization revealed distinct differences in microroughness, composition, and topography among the Ti6Al4V substrates.

CORTADO: Hill Climbing Optimization for Cell-Type Specific Marker Gene Discovery.

The advent of single-cell RNA sequencing (scRNA-seq) has greatly enhanced our ability to explore cellular heterogeneity with high resolution. Identifying subpopulations of cells and their associated molecular markers is crucial in understanding their distinct roles in tissues. To address the challenges in marker gene selection, we introduce CORTADO, a computational framework based on hill-climbing optimization for the efficient discovery of cell-type-specific markers.

A Survey on Data-Driven Approaches for Reliability, Robustness, and Energy Efficiency in Wireless Body Area Networks.

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability.

COFFEE: consensus single cell-type specific inference for gene regulatory networks.

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data.

An effective drift-diffusion model for pandemic propagation and uncertainty prediction.

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages.

CHAI: consensus clustering through similarity matrix integration for cell-type identification.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering.

Neuron enriched extracellular vesicles' MicroRNA expression profiles as a marker of early life alcohol consumption.

Alcohol consumption may impact and shape brain development through perturbed biological pathways and impaired molecular functions. We investigated the relationship between alcohol consumption rates and neuron-enriched extracellular vesicles' (EVs') microRNA (miRNA) expression to better understand the impact of alcohol use on early life brain biology. Neuron-enriched EVs' miRNA expression was measured from plasma samples collected from young people using a commercially available microarray platform while alcohol consumption was measured using the Alcohol Use Disorders Identification Test.

CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell type identification), a wisdom of crowds approach for scRNAseq clustering.

COFFEE: Consensus Single Cell-Type Specific Inference for Gene Regulatory Networks.

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared to individual algorithm implementations on bulk RNA-seq and microarray data.

A comparison of bone microarchitectural and transcriptomic changes in murine long bones in response to hindlimb unloading and aging.

Age- and disuse-related bone loss both result in decreases in bone mineral density, cortical thickness, and trabecular thickness and connectivity. Disuse induces changes in the balance of bone formation and bone resorption like those seen with aging. There is a need to experimentally compare these two mechanisms at a structural and transcriptomic level to better understand how they may be similar or different.

Transcriptome Profiling of Cardiac Glycoside Treatment Reveals EGR1 and Downstream Proteins of MAPK/ERK Signaling Pathway in Human Breast Cancer Cells.

Cardiac glycosides (CGs) constitute a group of steroid-like compounds renowned for their effectiveness in treating cardiovascular ailments. In recent times, there has been growing recognition of their potential use as drug leads in cancer treatment. In our prior research, we identified three highly promising CG compounds, namely lanatoside C (LC), peruvoside (PS), and strophanthidin (STR), which exhibited significant antitumor effects in lung, liver, and breast cancer cell lines.

Examining indicators of complex network vulnerability across diverse attack scenarios.

Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends insight into the critical nodes, key pathways, and potential points of failure that may impact the connectivity and operational integrity of the underlying system. In this work, we investigate the topological properties or indicators, such as shortest path length, modularity, efficiency, graph density, diameter, assortativity, and clustering coefficient, that determine the vulnerability to (or robustness against) diverse attack scenarios.

Hindlimb Unloading Induces Bone Microarchitectural and Transcriptomic Changes in Murine Long Bones in an Age-Dependent Manner.

Unlabelled: Age and disuse-related bone loss both result in decreases in bone mineral density, cortical thickness, and trabecular thickness and connectivity. Disuse induces physiological changes in bone like those seen with aging. Bone microarchitecture and biomechanical properties were compared between 6- and 22-month-old C57BL/6J male control mice and 6-month-old mice that were hindlimb unloaded (HLU) for 3 weeks.

Infrastructure tools to support an effective Radiation Oncology Learning Health System.

Purpose: Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address.

Methods: We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts.

Determining the rate of infectious disease testing through contagion potential.

The emergence of new strains, varying in transmissibility, virulence, and presentation, makes the existing epidemiological statistics an inadequate representation of COVID-19 contagion. Asymptomatic individuals continue to act as carriers for the elderly and immunocompromised, making the timing and extent of vaccination and testing extremely critical in curbing contagion. In our earlier work, we proposed contagion potential (CP) as a measure of the infectivity of an individual in terms of their contact with other infectious individuals.

Neuron Enriched Exosomal MicroRNA Expression Profiles as a Marker of Early Life Alcohol Consumption.

Background: Alcohol consumption may impact and shape brain development through perturbed biological pathways and impaired molecular functions. We investigated the relationship between alcohol consumption rates and neuron-enriched exosomal microRNA (miRNA) expression to better understand the impact of alcohol use on early life brain biology.

Methods: Neuron-enriched exosomal miRNA expression was measured from plasma samples collected from young people using a commercially available microarray platform while alcohol consumption was measured using the Alcohol Use Disorders Identification Test.

Hierarchical Vaccine Allocation Based on Epidemiological and Behavioral Considerations.

Vaccines have proven useful in curbing contagion from new strains of the SARS-CoV-2 virus. However, equitable vaccine allocation continues to be a significant challenge worldwide, necessitating a comprehensive allocation strategy incorporating heterogeneity in epidemiological and behavioral considerations. In this paper, we present a hierarchical allocation strategy that assigns vaccines to zones and their constituent neighborhoods cost-effectively, based on their population density, susceptibility, infected count, and attitude towards vaccinations.

Identifying accurate link predictors based on assortativity of complex networks.

Link prediction algorithms in complex networks, such as social networks, biological networks, drug-drug interactions, communication networks, and so on, assign scores to predict potential links between two nodes. Link prediction (LP) enables researchers to learn unknown, new as well as future interactions among the entities being modeled in the complex networks. In addition to measures like degree distribution, clustering coefficient, centrality, etc.