Association of Computed Tomography Scan-Assessed Body Composition with Immune and PI3K/AKT Pathway Proteins in Distinct Breast Cancer Tumor Components.

This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified into four body composition types: adequate (lowest two tertiles of total adipose tissue [TAT]) and highest two tertiles of total skeletal muscle [TSM] areas); high adiposity (highest tertile of TAT and highest two tertiles of TSM); low muscle (lowest tertile of TSM and lowest two tertiles of TAT); and high adiposity with low muscle (highest tertile of TAT and lowest tertile of TSM). Immune and PI3K/AKT pathway proteins were profiled in tumor epithelium and the leukocyte-enriched stromal microenvironment using GeoMx (NanoString).

Me-LLaMA: Medical Foundation Large Language Models for Comprehensive Text Analysis and Beyond.

Recent advancements in large language models (LLMs) like ChatGPT and LLaMA have shown significant potential in medical applications, but their effectiveness is limited by a lack of specialized medical knowledge due to general-domain training. In this study, we developed Me-LLaMA, a new family of open-source medical LLMs that uniquely integrate extensive domain-specific knowledge with robust instruction-following capabilities. Me-LLaMA comprises foundation models (Me-LLaMA 13B and 70B) and their chat-enhanced versions, developed through comprehensive continual pretraining and instruction tuning of LLaMA2 models using both biomedical literature and clinical notes.

Disparities in Utilization of Immune Checkpoint Inhibitor Therapy Among Older Patients With Advanced Non-Small Cell Lung Cancer: A SEER-Medicare Analysis.

Purpose: In the United States, there are disparities in access to care for patients with non-small cell lung cancer (NSCLC) on the basis of socioeconomic and racial/ethnic factors. This study investigates the association between race/ethnicity and the utilization of immune checkpoint inhibitor (ICI) therapy among older patients with advanced NSCLC (aNSCLC).

Methods: This retrospective study used data from the SEER-Medicare-linked database.

Multi-Task Deep Neural Networks for Irregularly Sampled Multivariate Clinical Time Series.

Multivariate clinical time series data, such as those contained in Electronic Health Records (EHR), often exhibit high levels of irregularity, notably, many missing values and varying time intervals. Existing methods usually construct deep neural network architectures that combine recurrent neural networks and time decay mechanisms to model variable correlations, impute missing values, and capture the impact of varying time intervals. The complete data matrices thus obtained from the imputation task are used for downstream risk prediction tasks.

Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing.

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms.

Assessing the benefit-risk profile of newer glucose-lowering drugs: A systematic review and network meta-analysis of randomized outcome trials.

Aim: To comprehensively evaluate the benefits and risks of glucagon-like peptide-1 receptor agonists (GLP-1RA), dipeptidyl peptidase 4 inhibitors (DPP4i), and sodium-glucose cotransporter 2 inhibitors (SGLT2i).

Materials And Methods: A systematic search of PubMed, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) from inception to November 2023 to identify randomized cardiovascular and kidney outcome trials that enrolled adults with type 2 diabetes, heart failure, or chronic kidney disease and compared DPP4i, GLP-1RAs, or SGLT2i to placebo. Twenty-one outcomes (e.

A method for the rapid detection of heavy metal mercury ions based on a novel mercury chelator N,N'-bis (2-mercaptoethyl) isophthalamide.

Mercury has caused severe harm to the environment and human health. A novel biological screen method was developed and identified a Hg chelator BDTH2. Both biological and chemical methods demonstrated BDTH2 displayed a high specificity and strong binding capacity for Hg.

Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks.

Predictive analytics using Electronic Health Records (EHRs) have become an active research area in recent years, especially with the development of deep learning techniques. A popular EHR data analysis paradigm in deep learning is patient representation learning, which aims to learn a condensed mathematical representation of individual patients. However, EHR data are often inherently irregular, i.

Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023.

In 2023, the CO growth rate was 3.37 ± 0.11 ppm at Mauna Loa, which was 86% above that of the previous year and hit a record high since observations began in 1958, while global fossil fuel CO emissions only increased by 0.

A Scoping Review of Observational Research on Cannabis Use for Symptom Management in HIV and Cancer: Implications for Cannabis Nursing.

Background: People with HIV have a higher risk of developing non-AIDS-defining cancers in older age, leading to a significant population living with two conditions, HIV and cancer. There is an increasing interest in cannabis use for symptom management in people with chronic conditions; in 2023, the American Nurses Association officially recognised cannabis nursing as a specialty nursing practice focusing on the care of individuals seeking education/guidance in the therapeutic use of cannabis, supporting the urgency of its research. However, the scientific literature lacks a synthesised review in the focused populations.

Comparative Study on the Prevention and Control Effects of Rockburst Between Hydraulic Fracturing Sections and Blank Sections.

Influenced by various factors such as the complex environment and high key layers in coal mines, hydraulic fracturing technology has gradually become the main means of controlling the hard roof strata to prevent and control rockburst in recent years, which can effectively release the stress on the roof, reduce the intensity of pressure, and ensure the safe and efficient mining of the working face in coal mines. However, the current research on hydraulic fracturing to prevent and control rockburst is mostly limited to optimizing fracturing parameters and monitoring and evaluating fracturing effects, and there are few studies on blank sections, which cannot guarantee the overall prevention and control effect of rockburst, or increase unnecessary construction costs. In this paper, for the directional long borehole staged hydraulic fracturing project, triangular-type blank sections and regular-type blank sections are defined, and the rockburst prevention and control effects of fracturing sections and triangular-type blank sections during fracturing are compared and analyzed by the underground-ground integrated microseismic monitoring technology and transient electromagnetic detection technology, and the rockburst prevention and control effects of fracturing sections and regular-type blank sections during the coal extraction period are compared and analyzed by the underground-ground integrated microseismic monitoring data such as microseismic energy level and frequency as well as the online stress monitoring data.

Sinomenine Ameliorated Microglial Activation and Neuropathic Pain After Chronic Constriction Injury Via TGF-β1/ALK5/Smad3 Signalling Pathway.

Sinomenine (SIN), a bioactive isoquinoline alkaloid extracted from the roots and stems of Sinomenium acutum, is efficacious against various chronic pain conditions. Inhibition of microglial activation at the spinal level contributes to the analgesic effects of SIN. Microglial activation in the spinal dorsal horn is key to sensitising neuropathic pain.

Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations - an ISPOR Working Group Report.

Objective: To provide an introduction to the uses of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), in the field of health technology assessment (HTA).

Methods: We reviewed applications of generative AI in three areas: systematic literature reviews, real world evidence (RWE) and health economic modeling.

Results: (1) Literature reviews: generative AI has the potential to assist in automating aspects of systematic literature reviews by proposing search terms, screening abstracts, extracting data and generating code for meta-analyses; (2) Real World Evidence (RWE): generative AI can facilitate automating processes and analyze large collections of real-world data (RWD) including unstructured clinical notes and imaging; (3) Health economic modeling: generative AI can aid in the development of health economic models, from conceptualization to validation.

pH-Responsive Modified HAMA Microspheres Regulate the Inflammatory Microenvironment of Intervertebral Discs.

Currently, intervertebral disc (IVD) degeneration is believed to lead to local accumulation of lactic acid in the IVD, a decrease in pH, activation of the inflammatory pathway, and continued destruction of homeostasis of the IVD. To address these issues, the intelligent and accurate release of drugs is particularly important. In this study, acid-sensitive release methacrylated hyaluronic acid (HAMA) microspheres were constructed by using microfluidic technology, which can be used as a targeted drug delivery system for intervertebral disc degeneration (IVDD) through Schiff base chemical bonding on the surface of the microspheres to achieve intelligent drug release.

Effect of Material and Structure of Ultra-High-Molecular-Weight Polyethylene Body Armor on Ballistic Limit Velocity: Numerical Simulation.

The material properties and structural characteristics of ballistic composites are crucial to their ballistic performance. A numerical model of a 1.1 g FSP penetrating a UHMWPE target plate was established in this paper.

CLCAP: Contrastive learning improves antigenicity prediction for influenza A virus using convolutional neural networks.

Influenza viruses are detected year-round over the world and the viruses will usually circulate during fall and winter, causing the seasonal flu. The growing novel variants of influenza viruses pose a significant concern to public health annually. However, the rapid mutation of the influenza viruses makes it challenging to timely track their evolution.

The Deficiency of the ASD-Related Gene CHD8 Disrupts Behavioral Patterns and Inhibits Hippocampal Neurogenesis in Mice.

Chromodomain helicase DNA-binding 8 (CHD8) is a gene that poses a high risk for autism spectrum disorder (ASD) and neurological development delay. Nevertheless, the impact of CHD8 haploinsufficiency on both hippocampus neurogenesis and behavior remains uncertain. Here, we performed behavioral assessments on male and female CHD8 heterozygous mice.

Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings.

Background: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system.

Methods: We used user-centered design methods to integrate the ML algorithm into the EHR system.