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. However, due to limited data, accurate prediction of antiviral peptides is still challenging and extracting effective feature representations from sequences is crucial for creating accurate models. This study introduces a novel two-step approach, named HybAVPnet, to predict antiviral peptides with a hybrid network architecture based on neural networks and traditional machine learning methods. We adopted a stacking-like structure to capture both the long-term dependencies and local evolution information to achieve a comprehensive and diverse prediction using the predicted labels and probabilities. Using an ensemble technique with the different kinds of features can reduce the variance without increasing the bias. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability.

Exploring the causal role of immune cells in cerebral aneurysm through single-cell transcriptomics and Mendelian randomization analysis.

Cerebral aneurysm (CA) represents a significant clinical challenge, characterized by pathological dilation of cerebral arteries. Recent evidence underscores the crucial involvement of immune cells in CA pathogenesis. This study aims to explore the complex interplay between immune cells and CA formation. We analyzed single-cell RNA sequencing data from the GSE193533 dataset, focusing on unruptured CA and their controls. Comprehensive cell-type identification and pseudo-time trajectory analyses were conducted to delineate the dynamic shifts in immune cell populations. Additionally, a two-sample Mendelian randomization (MR) approach was employed to investigate the causal influence of various immunophenotypes on CA susceptibility and the reciprocal effect of CA formation on immune phenotypes. Single-cell transcriptomic analysis revealed a progressive loss of vascular smooth muscle cells (VSMCs) and an increase in monocytes/macrophages (Mo/MΦ) and other immune cells, signifying a shift from a structural to an inflammatory milieu in CA evolution. MR analysis identified some vital immunophenotypes, such as CD64 on CD14+ CD16+ monocytes (OR: 1.236, 95% CI: 1.064-1.435, P = 0.006), as potential risk factors for CA development, while others, like CD28- CD8br %CD8br (OR: 0.883, 95% CI: 0.789-0.988, P = 0.030), appeared protective. Reverse MR analysis demonstrated that CA formation could modulate specific immunophenotypic expressions, highlighting a complex bidirectional interaction between CA pathology and immune response. This study underscores the pivotal role of immune cells in this process through the integration of single-cell transcriptomics with MR analysis, offering a comprehensive perspective on CA pathogenesis, and potentially guiding future therapeutic strategies targeting specific immune pathways.

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. Inclusion of an epidermal growth factor receptor (EGFR)-targeting nanobody module facilitates specific binding to EGFR-expressing cells resulting in approximately 150-fold increased fluorescence intensity determined by flow cytometric analysis compared to assemblies absent of nanobody inclusion. A cellular recycling assay demonstrated retained albumin-neonatal Fc receptor (FcRn) binding affinity and accompanying FcRn-driven cellular recycling. This translated to a 4-fold circulatory half-life extension (2.2 and 0.55 h, for the rHA-HJ and HJ, respectively) in a double transgenic humanized FcRn/albumin mouse. This work introduces a novel biomolecular albumin-nucleic acid construct with extended circulatory half-life and programmable multifunctionality due to its modular design.

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. We compared our localization results with the clinically marked electrode sites determined for surgical resections. Our model had good classification capability when compared to other state-of-the-art methods. Furthermore, based on our prediction results we performed group-level brain-area mapping analysis for temporal, frontal and parietal epilepsy patients and found that epileptic and non-epileptic brain networks were distinct in patients with different types of focal epilepsy. Our unsupervised data-driven model provides personalized localization analysis for the epileptic zones. The epileptic and non-epileptic brain areas disclosed by the prediction model provide novel insights into the network-level pathological characteristics of epilepsy.

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

Aim: 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. Methods: 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. Results: The prevalence of chronic polypharmacy and at least one PIM exposure was 46.37% and 55.09%, respectively. The top five PIMs were diuretics, benzodiazepines, first-generation antihistamines, sulfonylureas, and insulin (sliding scale). Chronic polypharmacy was positively associated with all-cause hospital admission, admission for coronary heart disease, admission for stroke, admission for dementia, and emergency department visits. Exposure to PIMs was positively associated with all-cause hospital admission, admission for heart failure (PIMs ≥2), admission for stroke (PIMs ≥3), emergency department visits, bone fracture, constipation, and diarrhea. Conclusion: Chronic polypharmacy and PIMs were prevalent in older adults with diabetes in Chinese communities. Iatrogenic exposure to chronic polypharmacy and PIMs is associated with a higher incidence of different AHOs. This observational evidence highlights the necessity of patient-centered medication reviews for chronic polypharmacy and PIMs use in older patients with diabetes in primary care facilities in China and draws attention to the caution of polypharmacy, especially PIM use in older adults with diabetes in clinical practice.

LncRNA SNHG15 regulates autophagy and prevents cerebral ischaemia-reperfusion injury through mediating miR-153-3p/ATG5 axis.

Ischaemic stroke is a common cerebrovascular disease. Long non-coding RNA (lncRNA) of small nucleolar RNA host gene (SNHG15) has been supposedly performed a regulatory role in many diseases. Nonetheless, the function of SNHG15 in cerebral ischaemia-reperfusion injury has not been clarified. The OGD/R of Neuro2A cells simulated the ischaemic and reperfused states of the brain. Neuro2a cell line with stable transfection of plasmid with silent expression of SNHG15 was constructed. Neuro2a cell lines transfected with miR-153-3p mimic (miR-153-3p-mimics) and miR-153-3p inhibitor (miR-153-3p-inhibition) were constructed. Expression of SNHG15, mi R-200a, FOXO3 and ATG7 in mouse brain tissue and N2a cells was identified by qRT-PCR. Western blot (WB) analysis of mouse brain tissue and Neuro2a cells revealed the presence of the proteins ATG5, Cle-caspase-3, Bax, Bcl-2, LC3 II/I and P62 (WB). The representation and distribution of LC3B were observed by immunofluorescence. The death of cells was measured using a technique called flow cytometry (FACS). SNHG15 was highly expressed in cerebral ischaemia-reperfusion injury model. Down-regulation of SNHG15 lead to lower apoptosis rate and decreased autophagy. Dual luciferase assay and co-immunoprecipitation (CoIP) found lncRNA SNHG15/miR-153-3p/ATG5. Compared to cells transfected with NC suppression, cells transfected with miR-153-3p-inhibition had substantially greater overexpression of LC 3 II/I, ATG5, cle-Caspase-3, and Bax, as determined by a recovery experiment, the apoptosis rate was elevated, yet both P62 and Bcl-2 were significantly lower and LC3+ puncta per cells were significantly increased. Co-transfection of miR-153-3p-inhibition and sh-SNHG15 could reverse these results. LncRNA SNHG15 regulated autophagy and prevented cerebral ischaemia-reperfusion injury through mediating the miR-153-3p/ATG5 axis.

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. A Random Forest (RF) classifier model was built to distinguish the BMI categories based on the gut bacterial composition. Results: Fifty-six taxa consisting mainly of Firmicutes were identified that having significant associations with BMI; 2 OTUs belonging to Ruminococcaceae and 1 belonging to Lachnospiraceae had relatively strong positive correlations with body fate rate, waistline and most of serum biochemical properties. Based on the 56 BMI-associated OTUs, the RF model showed a robust classification accuracy (AUC 0.96) for predicting the obese phenotype. Gender-specific differences in the gut microbiome composition was obtained, and a lower relative abundance of Odoribacter genus was particularly found in obese boys. Functional analysis revealed a deficiency in bacterial gene contents related to peroxisome and PPAR signaling pathway in the obese subjects for both genders. Conclusions: This study reveals unique features of gut microbiome in terms of microbial composition and metabolic functions in obese adolescents, and provides a baseline for reference and comparison studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00236-9.

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. Weighted gene co-expression network analysis was performed using R programming. Significant module-trait correlations were identified according to module membership and genetic significance. Metascape was used for the functional enrichment of differentially expressed genes between groups, and the hub genes were identified via protein-protein interaction network analysis. The hub genes were further validated by analyzing Gene Expression Omnibus (GSE48060 and GSE141512) datasets. A total of 9,633 messenger ribonucleic acids were detected in three modules, among which the blue module was highly correlated with the Gensini score. The hub genes were significantly enriched in the myeloid leukocyte activation pathway, suggesting its important role in the progression of atherosclerosis. Among these genes, the Mediterranean fever gene (MEFV) may play a key role in the progression of atherosclerosis and CAD severity.

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. Obtaining high-quality vibration data using DAS requires a robust coupling between the fiber optic cable and the ground layer. The study utilized the DAS system to detect vibration signals generated by vehicles operating on the campus road of Beijing Jiaotong University. Three distinct deployment methods were employed: the uncoupled fiber on the road, the underground communication fiber optic cable ducts, and the cement-bonded fixed fiber optic cable on the road shoulder, and compared for their outcomes. Vehicle vibration signals under the three deployment methods were analyzed using an improved wavelet threshold algorithm, which was verified to be effective. The results indicate that for practical applications, the most effective deployment method is the cement-bonded fixed fiber optic cable on the road shoulder, followed by the uncoupled fiber on the road, and the underground communication fiber optic cable ducts are the least effective. This has important implications for the future development of DAS as a tool for various fields.

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. In this paradigm, with the guidance of the source and target molecule pairs of the desired properties, a heuristic optimization solution can be derived: given an input molecule, we first predict which atom can be viewed as the optimization center, and then the nearby regions are optimized around this center. We then propose an effective and efficient learning framework, Teacher and Student polish, to capture the dependencies in the optimization steps. A teacher component automatically identifies and annotates the optimization centers and the preservation, removal, and addition of some parts of the molecules; a student component learns these knowledges and applies them to a new molecule. The proposed paradigm can offer an intuitive interpretation for the molecular optimization result. Experiments with multiple optimization tasks are conducted on several benchmark datasets. The proposed approach achieves a significant advantage over the six state-of-the-art baseline methods. Also, extensive studies are conducted to validate the effectiveness, explainability, and time savings of the novel optimization paradigm.

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. We first suggest an attributed graph node presentation algorithm. It can effectively integrate protein gene ontology attributes and network topology, and adaptively aggregates low- or high-order graph structural information according to the node distribution by considering graph node smoothness. Based on the obtained node representations, core cliques and expansion algorithms are applied to find functional modules in PPI networks. Comprehensive performance evaluations and case studies indicate that the framework significantly outperforms state-of-the-art methods. We also presented potential functional modules based on their confidence.

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. In addition, novel functional materials including zero-dimensional nanomaterials, one-dimensional inorganic nanomaterials, two-dimensional layered materials, organic materials, and organic-inorganic composite materials with excellent properties are emerging to continuously improve the performance of devices. Here, the recent research progress of stretchable photodetectors in terms of both various design methods and functional materials is outlined. The optical performance and stretchable properties are also comprehensively reviewed. Finally, a summary and the challenges associated with the application of stretchable photodetectors are presented.

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. Materials and methods: This was a 3-year repeated cross-sectional study which conducted in outpatient setting of 52 hospitals in Shenzhen, China, using 2019 Beers criteria. The prevalence of potentially inappropriate medications, polypharmacy and comorbidities in older adults with diabetes in an outpatient setting was expressed as percentages. Logistic models were used to investigate the association between potentially inappropriate medication exposure and age, sex, polypharmacy and comorbidities. Results: Among the 28,484 older adults with diabetes in 2015, 31,757 in 2016 and 24,675 in 2017, the prevalence of potentially inappropriate medications was 43.2%, 44.88% and 42.40%, respectively. The top five potentially inappropriate medications were diuretics (20.56%), benzodiazepines (13.85%), androgens (13.18%), non-steroidal anti-inflammatory drugs (12.94%) and sulfonylureas (6.23%). After adjustment for age and polypharmacy, the probability of potentially inappropriate medication exposure was associated with chronic gastrointestinal diseases, followed by osteoarthritis and rheumatoid arthritis, chronic pulmonary disease, chronic kidney disease, tumor, dementia, chronic liver disease, hypertension, cardiovascular disease, cerebrovascular disease and hyperlipemia. Conclusion: Potentially inappropriate medications were common in older patients with diabetes in an outpatient visitation setting. Higher probability of potentially inappropriate medication exposure was associated with the comorbidity chronic gastrointestinal diseases as well as osteoarthritis and rheumatoid arthritis. To ensure that iatrogenic risks remain minimal for older adults with diabetes, the clinical comorbidities should be considered.

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. Smoothness is an indicator for assessing the degree of similarity of feature representations among nearby nodes in a graph. Oversmoothing in GCNs, caused by unnecessarily high orders of graph convolution, produces indistinguishable representations of nodes, such that the nodes in a graph tend to be grouped into fewer clusters, and pose a challenge due to the resulting performance drop. In this study, we propose a smoothness sensor for attributed graph clustering based on adaptive smoothness-transition graph convolutions, which senses the smoothness of a graph and adaptively terminates the current convolution once the smoothness is saturated to prevent oversmoothing. Furthermore, as an alternative to graph-level smoothness, a novel fine-grained nodewise-level assessment of smoothness is proposed, in which smoothness is computed in accordance with the neighborhood conditions of a given node at a certain order of graph convolution. In addition, a self-supervision criterion is designed considering both the tightness within clusters and the separation between clusters to guide the entire neural network training process. The experiments show that the proposed methods significantly outperform 13 other state-of-the-art baselines in terms of different metrics across five benchmark datasets. In addition, an extensive study reveals the reasons for their effectiveness and efficiency.

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. OBJECTIVE: We aimed to establish a high-precision stroke risk prediction model for hypertensive patients based on historical electronic medical record data and machine learning algorithms. METHODS: Based on the Shenzhen Health Information Big Data Platform, a total of 57,671 patients were screened from 250,788 registered patients with hypertension, of whom 9421 had stroke onset during the 3-year follow-up. In addition to baseline characteristics and historical symptoms, we constructed some trend characteristics from multitemporal medical records. Stratified sampling according to gender ratio and age stratification was implemented to balance the positive and negative cases, and the final 19,953 samples were randomly divided into a training set and test set according to a ratio of 7:3. We used 4 machine learning algorithms for modeling, and the risk prediction performance was compared with the traditional risk scales. We also analyzed the nonlinear effect of continuous characteristics on stroke onset. RESULTS: The tree-based integration algorithm extreme gradient boosting achieved the optimal performance with an area under the receiver operating characteristic curve of 0.9220, surpassing the other 3 traditional machine learning algorithms. Compared with 2 traditional risk scales, the Framingham stroke risk profiles and the Chinese Multiprovincial Cohort Study, our proposed model achieved better performance on the independent validation set, and the area under the receiver operating characteristic value increased by 0.17. Further nonlinear effect analysis revealed the importance of multitemporal trend characteristics in stroke risk prediction, which will benefit the standardized management of hypertensive patients. CONCLUSIONS: A high-precision 3-year stroke risk prediction model for hypertensive patients was established, and the model's performance was verified by comparing it with the traditional risk scales. Multitemporal trend characteristics played an important role in stroke onset, and thus the model could be deployed to electronic health record systems to assist in more pervasive, preemptive stroke risk screening, enabling higher efficiency of early disease prevention and intervention.

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. We present a detection system using Φ-OTDR technology and analyze the character of the acoustic signal of aircraft. Instead of runway monitoring in the airport or noise detection in the air, this study focuses on the detection of seismic vibration signal excited by the sound of aircraft. The Chebyshev filter is adopted to eliminate the impact of background noise and random noise from the original vibration signal; the short-time Fourier transform is used for time-frequency analysis. The experimental results showed that the seismic vibration signal excited by the aircraft sound is mainly low-frequency, which is under 5 Hz. Time delay of aircraft vibration signal in different locations of the optic fiber is recorded by the sensing system. The Doppler effect is also revealed by the time-domain analysis: the frequency increases when the aircraft is approaching and decreases when the aircraft moves away.