ProtChat: An AI Multi-Agent for Automated Protein Analysis Leveraging GPT-4 and Protein Language Model.

Large language models (LLMs) have transformed natural language processing, enabling advanced human-machine communication. Similarly, in computational biology, protein sequences are interpreted as natural language, facilitating the creation of protein large language models (PLLMs). However, applying PLLMs requires specialized preprocessing and script development, increasing the complexity of their use.

Potentially inappropriate prescribing among older adults with hypertension in China: prevalence and related comorbidities across different outpatient settings.

Purpose: Potentially inappropriate prescribing (PIP) is commonly encountered in older adults; yet, there is limited information on the occurrence of PIP among older adults with hypertension. This study aims to determine and compare the prevalence of PIP and its association with comorbidities in older adult outpatients with hypertension across hospitals and community health centers (CHCs).

Methods: This 3-year (2015-2017) repeated cross-sectional study used electronic medical records from Shenzhen, China, involving 62 hospitals and 678 primary medical institutions.

HybAVPnet: A Novel Hybrid Network Architecture for Antiviral Peptides Prediction.

Viruses pose a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, it is critical to use computational methods to predict antiviral peptides accurately and rapidly.

An Albumin-Holliday Junction Biomolecular Modular Design for Programmable Multifunctionality and Prolonged Circulation.

Combinatorial properties such as long-circulation and site- and cell-specific engagement need to be built into the design of advanced drug delivery systems to maximize drug payload efficacy. This work introduces a four-stranded oligonucleotide Holliday Junction (HJ) motif bearing functional moieties covalently conjugated to recombinant human albumin (rHA) to give a "plug-and-play" rHA-HJ multifunctional biomolecular assembly with extended circulation. Electrophoretic gel-shift assays show successful functionalization and purity of the individual high-performance liquid chromatography-purified modules as well as efficient assembly of the rHA-HJ construct.

Associated adverse health outcomes of polypharmacy and potentially inappropriate medications in community-dwelling older adults with diabetes.

This study aimed to identify the association of chronic polypharmacy and potentially inappropriate medications (PIMs) with adverse health outcomes (AHOs) in community-dwelling older adults with diabetes in China. A 2-year retrospective cohort study was conducted using 11,829 community-followed older adults with diabetes and medical records from 83 hospitals and 702 primary care centers in Shenzhen, China. Chronic polypharmacy and PIMs were identified from prescription records using Beers' criteria, and their associated AHO was analyzed using multivariable logistic regression analysis.

Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses.

Stereoelectroencephalography (SEEG) offers unique neural data from in-depth brain structures with fine temporal resolutions to better investigate the origin of epileptic brain activities. Although oscillatory patterns from different frequency bands and functional connectivity computed from the SEEG datasets are employed to study the epileptic zones, direct electrical stimulation-evoked electrophysiological recordings of synaptic responses, namely cortical-cortical evoked potentials (CCEPs), from the same SEEG electrodes are not explored for the localization of epileptic zones. Here we proposed a two-stream model with unsupervised learning and graph convolutional network tailored to the SEEG and CCEP datasets in individual patients to perform localization of epileptic zones.

Identification and Validation of Key Gene Modules and Pathways in Coronary Artery Disease Development and Progression.

The development and progression of atherosclerosis represent a chronic process involving complex molecular interactions. Therefore, identifying the potential hub genes and pathways contributing to coronary artery disease (CAD) development is essential for understanding its underlying molecular mechanisms. To this end, we performed transcriptome analysis of peripheral venous blood collected from 100 patients who were divided into four groups according to disease severity, including 27 patients in the atherosclerosis group, 22 patients in the stable angina group, 35 patients in the acute myocardial infarction group, and 16 controls.

Gut microbiome biomarkers in adolescent obesity: a regional study.

Purpose: This study aimed to characterize the gut microbiota in obese adolescents from Shenzhen (China), and evaluate influence of gender on BMI-related differences in the gut microbiome.

Methods: Evaluation of physical examination, blood pressure measurement, serological assay and body composition were conducted in 205 adolescent subjects at Shenzhen. Fecal microbiome composition was profiled via high-throughput sequencing of the V3-V4 regions of the 16S rRNA gene.

Traffic Vibration Signal Analysis of DAS Fiber Optic Cables with Different Coupling Based on an Improved Wavelet Thresholding Method.

Distributed Acoustic Sensing (DAS) is a novel technology that uses fiber optics to sense and monitor vibrations. It has demonstrated immense potential for various applications, including seismology research, traffic vibration detection, structural health inspection, and lifeline engineering. DAS technology transforms long sections of fiber optic cables into a high-density array of vibration sensors, providing exceptional spatial and temporal resolution for real-time monitoring of vibrations.

AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein-protein interaction network.

Identifying unknown protein functional modules, such as protein complexes and biological pathways, from protein-protein interaction (PPI) networks, provides biologists with an opportunity to efficiently understand cellular function and organization. Finding complex nonlinear relationships in underlying functional modules may involve a long-chain of PPI and pose great challenges in a PPI network with an unevenly sparse and dense node distribution. To overcome these challenges, we propose AdaPPI, an adaptive convolution graph network in PPI networks to predict protein functional modules.

Advanced Stretchable Photodetectors: Strategies, Materials and Devices.

Past decades have witnessed the generation of new stretchable photodetectors in electronic eyes, health sensing, wearable devices, intelligent monitoring and other fields. Stretchable devices require not only outstanding performance but also excellent flexibility, adaptability and stability. Innovative strategies have been proposed to realize the stretchability of devices.

Prevalence of potentially inappropriate medications and association with comorbidities in older adults with diabetes in an outpatient visitation setting.

Aims: Potentially inappropriate medications had been found associated with adverse drug events such as falls, emergency department admissions and hospital readmissions. There is lack of information about the prevalence of potentially inappropriate medications and associated chronic conditions in older patients with diabetes in China. This study aimed to assess the prevalence of potentially inappropriate medications in older adults with diabetes in an outpatient visitation setting and the association with polypharmacy due to comorbidities.

Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study.

Background: Stroke risk assessment is an important means of primary prevention, but the applicability of existing stroke risk assessment scales in the Chinese population has always been controversial. A prospective study is a common method of medical research, but it is time-consuming and labor-intensive. Medical big data has been demonstrated to promote disease risk factor discovery and prognosis, attracting broad research interest.

Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization.

Molecular optimization, which transforms a given input molecule X into another Y with desired properties, is essential in molecular drug discovery. The traditional approaches either suffer from sample-inefficient learning or ignore information that can be captured with the supervised learning of optimized molecule pairs. In this study, we present a novel molecular optimization paradigm, Graph Polish.

Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering.

Clustering techniques attempt to group objects with similar properties into a cluster. Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention. Graph convolutional networks (GCNs) represent an effective approach for integrating the two complementary factors of node attributes and structural information for attributed graph clustering.

Aircraft Detection Using Phase-Sensitive Optical-Fiber OTDR.

Aircraft detection plays a vital role in aviation management and safe operation in the aviation system. Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR) technology is a prevailing sensing method in geophysics research, structure inspection, transportation detection, etc. Compared with existing video- or radio-based detection methods, Φ-OTDR is cost-effective, suitable for long-distance detection, and resistant to severe weather conditions.

Self-Encapsulation of Biomacromolecule Drugs in Porous Microscaffolds with Aqueous Two-Phase Systems.

At present, the most commonly used methods of microencapsulation of protein drugs such as spray drying, multiple emulsification, and phase separation, can easily cause the problem of protein instability, which leads to low bioavailability and uncontrolled release of protein drugs. Herein, a novel method to encapsulate protein drugs into porous microscaffolds effectively and stably was described. Ammonium hydrogen carbonate (NHHCO) was employed to prepare porous microscaffolds.

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction.

Due to the cost and complexity of biological experiments, many computational methods have been proposed to predict potential miRNA-disease associations by utilizing known miRNA-disease associations and other related information. However, there are some challenges for these computational methods. First, the relationships between miRNAs and diseases are complex.