Demographic disparities in access to COVID-19 clinical trial sites across the United States: a geospatial analysis.

Throughout the COVID-19 pandemic, underserved populations, such as racial and ethnic minority communities, were disproportionately impacted by illness and death. Ensuring people from diverse backgrounds have the ability to participate in clinical trials is key to advancing health equity. We sought to analyze the spatial variability in locations of COVID-19 trials sites and to test associations with demographic correlates.

Revealing the Water Structure at Neutral and Charged Graphene/Water Interfaces through Quantum Simulations of Sum Frequency Generation Spectra.

The structure and dynamics of water at charged graphene interfaces fundamentally influence molecular responses to electric fields with implications for applications in energy storage, catalysis, and surface chemistry. Leveraging the realism of the MB-pol data-driven many-body potential and advanced path-integral quantum dynamics, we analyze the vibrational sum frequency generation (vSFG) spectrum of graphene/water interfaces under varying surface charges. Our quantum simulations reveal a distinctive dangling OH peak in the vSFG spectrum at neutral graphene, consistent with recent experimental findings yet markedly different from those of earlier studies.

Ten simple rules for good model-sharing practices.

Computational models are complex scientific constructs that have become essential for us to better understand the world. Many models are valuable for peers within and beyond disciplinary boundaries. However, there are no widely agreed-upon standards for sharing models.

Identification of Cannabis Product Characteristics and Pricing on Dark Web Markets.

Growing cannabis use has made it the most widely cultivated and trafficked illicit drug globally according to the World Health Organization, with 147 million people consuming cannabis-derived products (CDPs) in various product forms and constituency. Despite restrictions in certain countries, unregulated access can still be found on the dark web which specializes in trafficking of illicit goods. The objective was to systematically collect data from multiple marketplaces to identify types of cannabis products offered for sale.

Dissecting the Molecular Structure of the Air/Ice Interface from Quantum Simulations of the Sum-Frequency Generation Spectrum.

Ice interfaces are pivotal in mediating key chemical and physical processes such as heterogeneous chemical reactions in the environment, ice nucleation, and cloud microphysics. At the ice surface, water molecules form a quasi-liquid layer (QLL) with properties distinct from those of the bulk. Despite numerous experimental and theoretical studies, a molecular-level understanding of the QLL has remained elusive.

Selective diagnostics of Amyotrophic Lateral Sclerosis, Alzheimer's and Parkinson's Diseases with machine learning and miRNA.

The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of diseases with similar motor symptoms. However, the dysregulation of miRNAs can be used to create a robust machine-learning model for a reliable diagnosis of neurological diseases. We used miRNA sequence descriptors and gene target data to create machine-learning models that can be used as diagnostic tools.

Anelloviruses and Cancer.

Anelloviruses are among the most prevalent viruses within the human virome, detected in over 90% of healthy individuals. Despite their ubiquity, the role of anelloviruses in human health remains elusive. This review examines the potential associations of anelloviruses torque teno virus (TTV), torque teno midi virus (TTMDV), and torque teno mini virus (TTMV) with various cancers, highlighting the mixed conclusions from current epidemiological studies.

Prediction of peptide structural conformations with AlphaFold2.

Protein structure prediction artificial intelligence/machine learning (AI/ML) approaches has sparked substantial research interest in structural biology and adjacent disciplines. More recently, AlphaFold2 (AF2) has been adapted for the prediction of multiple structural conformations in addition to single-state structures. This novel avenue of research has focused on proteins (typically 50 residues in length or greater), while multi-conformation prediction of shorter peptides has not yet been explored in this context.

Unleashing quantum algorithms with Qinterpreter: bridging the gap between theory and practice across leading quantum computing platforms.

Quantum computing is a rapidly emerging and promising field with the potential to transform various research domains including drug design, network technologies, and sustainable energy solutions. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries enable quantum simulations on classical computers and execution on corresponding quantum hardware, such as Qiskit programs on IBM quantum computers.

Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank.

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, RCSB.org), the US Worldwide Protein Data Bank (wwPDB, wwPDB.org) data center for the global PDB archive, provides access to the PDB data via its RCSB.

Protein data bank: From two epidemics to the global pandemic to mRNA vaccines and Paxlovid.

Structural biologists and the open-access Protein Data Bank (PDB) played decisive roles in combating the COVID-19 pandemic. Global biostructure data were turned into global knowledge, allowing scientists and engineers to understand the inner workings of coronaviruses and develop effective countermeasures. Two mRNA vaccines, initially designed with guidance from PDB structures of the SARS-CoV-1 and MERS-CoV spike proteins, prevented infections entirely or reduced the likelihood of morbidity and mortality for more than five billion individual recipients worldwide.

BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data.

BindingDB (bindingdb.org) is a public, web-accessible database of experimentally measured binding affinities between small molecules and proteins, which supports diverse applications including medicinal chemistry, biochemical pathway annotation, training of artificial intelligence models and computational chemistry methods development. This update reports significant growth and enhancements since our last review in 2016.

PD-1 and CD73 on naive CD4 T cells synergistically limit responses to self.

Vaccination with self- and foreign peptides induces weak and strong expansion of antigen-specific CD4 T cells, respectively, but the mechanism is not known. In the present study, we used computational analysis of the entire mouse major histocompatibility complex class II peptidome to test how much of the naive CD4 T cell repertoire specific for self-antigens was shaped by negative selection in the thymus and found that negative selection only partially explained the difference between responses to self and foreign. In naive uninfected and unimmunized mice, we identified higher expression of programmed cell death protein 1 (PD-1) and CD73 mRNA and protein on self-specific CD4 T cells compared with foreign-specific CD4 T cells.

Variations in Adverse Events Typology Following E-Cigarette Use: The Role of Preexisting Medical Conditions.

Background: Electronic cigarettes (e-cigarettes) are often marketed as safer alternatives to traditional smoking, yet evidence suggests potential health risks, especially among vulnerable populations. This study examines the immediate adverse events following e-cigarette use in individuals with preexisting health conditions to better understand these risks.

Methods: We conducted a STROBE-compliant observational study to identify specific preexisting medical conditions that may increase the risk of adverse events following e-cigarette use.

miRNA in Machine-Learning-Based Diagnostics of Oral Cancer.

Background: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives.

Cannabis-Derived Product Types, Flavors, and Compound Types From an E-Commerce Website.

Importance: Cannabis-derived products (CDPs) are widely available and diverse. A classification of product and flavor types is necessary to establish a foundation for comparative research, although research aiming to classify cannabis in its variety of products, flavors, and cannabinoid compounds based on public online e-commerce data is lacking.

Objective: To analyze data from a large cannabis e-commerce marketplace to identify and characterize cannabis product types, flavors, and compound types.

The molecular chronology of mammary epithelial cell fate switching.

The adult mammary gland is maintained by lineage-restricted progenitor cells through pregnancy, lactation, involution, and menopause. Injury resolution, transplantation-associated mammary gland reconstitution, and tumorigenesis are unique exceptions, wherein mammary basal cells gain the ability to reprogram to a luminal state. Here, we leverage newly developed cell-identity reporter mouse strains, and time-resolved single-cell epigenetic and transcriptomic analyses to decipher the molecular programs underlying basal-to-luminal fate switching .

Current Status of the MB-pol Data-Driven Many-Body Potential for Predictive Simulations of Water Across Different Phases.

Developing a molecular-level understanding of the properties of water is central to numerous scientific and technological applications. However, accurately modeling water through computer simulations has been a significant challenge due to the complex nature of the hydrogen-bonding network that water molecules form under different thermodynamic conditions. This complexity has led to over five decades of research and many modeling attempts.

piRNA in Machine-Learning-Based Diagnostics of Colorectal Cancer.

Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs-piwi-RNAs (piRNAs)-and their transcripts, we sought to identify if these piRNAs could be used as biomarkers for colorectal cancer (CRC). Using previously published data from serum samples of patients with CRC, 13 differently expressed piRNAs were selected as potential biomarkers.

Implementation of Machine Learning-Based System for Early Diagnosis of Feline Mammary Carcinomas through Blood Metabolite Profiling.

Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unspayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration.

Biomedical knowledge graph-optimized prompt generation for large language models.

Motivation: Large language models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains such as biomedicine. Solutions such as pretraining and domain-specific fine-tuning add substantial computational overhead, requiring further domain-expertise. Here, we introduce a token-optimized and robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE) with LLMs such as Llama-2-13b, GPT-3.

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.

Background: The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.

Objective: We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use.

Methods: We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022.

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasive diagnostic tests are greatly needed. Recently, circulating miRNAs have been reported to be altered in PDAC.