cGAS suppresses ß-cell proliferation by a STING-independent but CEBPß-dependent mechanism.

AIMS/HYPOTHESIS: cGAS (cyclic GMP-AMP synthase) has been implicated in various cellular processes, but its role in ß-cell proliferation and diabetes is not fully understood. This study investigates the impact of cGAS on ß-cell proliferation, particularly in the context of diabetes. METHODS: Utilizing mouse models, including cGAS and STING (stimulator of interferon genes) knockout mice, we explored the role of cGAS in ß-cell function. This involved ß-cell-specific cGAS knockout (cGASßKO) mice, created by breeding cGAS floxed mice with transgenic mice expressing Cre recombinase under the insulin II promoter. We analyzed cGAS expression in diabetic mouse models, evaluated the effects of cGAS deficiency on glucose tolerance, and investigated the molecular mechanisms underlying these effects through RNA sequencing. RESULTS: cGAS expression is upregulated in the islets of diabetic mice and by high glucose treatment in MIN6 cells. Both global cGAS deficiency and ß-cell-specific cGAS knockout mice lead to improved glucose tolerance by promoting ß-cell mass. Interestingly, STING knockout did not affect pancreatic ß-cell mass, suggesting a STING-independent mechanism for cGAS's role in ß-cells. Further analyses revealed that cGAS- but not STING-deficiency leads to reduced expression of CEBPß, a known suppressor of ß-cell proliferation, concurrently with increased ß-cell proliferation. Moreover, overexpression of CEBPß reverses the upregulation of Cyclin D1 and D2 induced by cGAS deficiency, thereby regulating ß-cell proliferation. These results confirm that cGAS regulation of ß-cell proliferation via a CEBPß-dependent but STING-independent mechanism. CONCLUSIONS/INTERPRETATION: Our findings highlight the pivotal role of cGAS in promoting ß-cell proliferation and maintaining glucose homeostasis, potentially by regulating CEBPß expression in a STING-independent manner. This study uncovers the significance of cGAS in controlling ß-cell mass and identifies a potential therapeutic target for enhancing ß-cell proliferation in the treatment of diabetes.

Genome comparison of long-circulating field CnmeGV isolates from the same region.

Cnaphalocrocis medinalis granulovirus (CnmeGV), belonging to Betabaculovirus cnamedinalis, can infect the rice pest, the rice leaf roller. In 1979, a CnmeGV isolate, CnmeGV-EP, was collected from Enping County, China. In 2014, we collected another CnmeGV isolate, CnmeGV-EPDH3, at the same location and obtained the complete virus genome sequence using Illumina and ONT sequencing technologies. By combining these two virus isolates, we updated the genome annotation of CnmeGV and conducted an in-depth analysis of its genome features. CnmeGV genome contains abundant tandem repeat sequences, and the repeating units in the homologous regions (hrs) exhibit overlapping and nested patterns. The genetic variations within EPDH3 population show the high stability of CnmeGV genome, and tandem repeats are the only region of high genetic variation in CnmeGV genome replication. Some defective viral genomes formed by recombination were found within the population. Comparison analysis of the two virus isolates collected from Enping showed that the proteins encoded by the CnmeGV-specific genes were less conserved relative to the baculovirus core genes. At the genomic level, there are a large number of SNPs and InDels between the two virus isolates, especially in and around the bro genes and hrs. Additionally, we discovered that CnmeGV acquired a segment of non-ORF sequence from its host, which does not provide any new proteins but rather serves as redundant genetic material integrated into the viral genome. Furthermore, we observed that the host's transposon piggyBac has inserted into some virus genes. Together, dsDNA viruses could acquire non-coding genetic material from their hosts to expand the size of their genomes. These findings provide new insights into the evolution of dsDNA viruses.

The Application of Problem-Based Learning Combined With Case-Based Learning in EEG Teaching.

OBJECTIVE: The aim of this study is to explore the application effectiveness and value of combining problem-based learning (PBL) and case-based learning (CBL) in clinical electroencephalography (EEG) education. METHODS: A total of 104 standardized training for residents and refresher physicians from the Neurology Department of the First Affiliated Hospital of Chongqing Medical University, Neurology Department of Chongqing Yubei Hospital, and Neurology Department of Banan Hospital of Chongqing Medical University were enrolled. According to randomization principles, 52 participants were assigned into the PBL-CBL combination group and 52 subjects were assigned into the control group. We used statistical methods to compare the differences between the 2 groups in basic theory, case analysis, practical assessment scores, and teaching satisfaction. RESULTS: In terms of basic theory, case analysis, practical assessment scores, and teaching satisfaction, there were significant differences between the 2 groups, and the PBL-CBL combination group was superior to the control group (P < .05). CONCLUSION: In clinical EEG education, the teaching model of combining PBL and CBL has certain application effects and value.

Continuous motion of particles attached to cavitation bubbles.

Microbubble-mediated therapeutic gene or drug delivery is a promising strategy for various cardiovascular diseases (CVDs), but the efficiency and precision need to be improved. Here, we propose a cavitation bubble-driven drug delivery strategy that can be applied to CVDs. A bubble-pulse-driving theory was proposed, and the formula of time-averaged thrust driven by bubble pulses was derived. The continuous motion of particles propelled by cavitation bubbles in the ultrasonic field is investigated experimentally by high-speed photography. The cavitation bubbles grow and collapse continuously, and generate periodic pulse thrust to drive the particles to move in the liquid. Particles attached to bubbles will move in various ways, such as ejection, collision, translation, rotation, attitude variation, and circular motion. The cavity attached to the particle is a relatively large cavitation bubble, which does not collapse to the particle surface, but to the axis of the bubble perpendicular to the particle surface. The cavitation bubble expands spherically and collapses asymmetrically, which makes the push on the particle generated by the bubble expansion greater than the pull on the particle generated by the bubble collapse. The time-averaged force of the cavitation bubble during its growth and collapse is the cavitation-bubble-driven force that propels the particle. Both the cavitation-bubble-driven force and the primary Bjerknes force act in the same position on the particle surface, but in different directions. In addition to the above two forces, particles are also affected by the mass force acting on the center of mass and the motion resistance acting on the surface, so the complex motion of particles can be explained.

SPINK4 modulates inhibition of glycolysis against colorectal cancer progression.

Dysregulation of glycolysis is frequently linked to aggressive tumor activity in colorectal cancer (CRC). Although serine peptidase inhibitor, Kazal type 4 (SPINK4) has been linked to CRC, its exact linkage to glycolytic processes and gene expression remains unclear. Differentially expressed genes (DEGs) were screened from two CRC-related datasets (GSE32323 and GSE141174), followed by expression and prognostic analysis of SPINK4. In vitro techniques such as flow cytometry, western blotting, transwell assay, and quantitative real-time polymerase chain reaction (qRT-PCR) were used to assess SPINK4 expression in CRC cells. Its effects on apoptosis, glycolysis, and the cell cycle were also investigated. Finally, the impact of SPINK4 overexpression on tumor development was assessed using a xenograft model, while histological and immunohistochemical analyses characterized SPINK4 expression patterns in CRC tissues. SPINK4 expression was downregulated in CRC, correlating with poor patient prognosis. In vitro assays confirmed that overexpression of SPINK4 reduced CRC cell proliferation, invasion, and migration, while its knockdown promoted these processes and caused G1 arrest. SPINK4 also regulated apoptosis by altering caspase activation and Bcl-2 expression. Besides, SPINK4 overexpression altered glycolytic activity, reduced 2-Deoxy-D-glucose (2-DG) absorption, and controlled critical glycolytic enzymes, resulting in alterations in metabolic pathways, whereas SPINK4 knockdown reversed this effect. SPINK4 overexpression significantly reduced tumor volume in vivo, indicating its inhibitory role in carcinogenesis. Moreover, high expression of SPINK4, hexokinase 2 (HK2), glucose transporter 1 (GLUT1), lactate dehydrogenase A (LDHA), and pyruvate kinase M2 (PKM2) was observed in CRC tissues. As a key inhibitor of glycolytic metabolism in CRC, SPINK4 promises metabolic intervention in CRC therapy due to its impact on tumor growth and cell proliferation.

Chemokines and lymphocyte homing in Sjögren's syndrome.

Sjögren's syndrome (SS) is a chronic systemic autoimmune disease that typically presents with lymphocyte, dendritic cell, and macrophage infiltration of exocrine gland ducts and the formation of ectopic germinal centers. The interactions of lymphocyte homing receptors and addressins and chemokines and their receptors, such as α4ß7/MAdCAM-1, LFA-1/ICAM-1, CXCL13/CXCR5, CCL25/CCR9, CX3CL1/CX3CR1, play important roles in the migration of inflammatory cells to the focal glands and the promotion of ectopic germinal center formation in SS. A variety of molecules have been shown to be involved in lymphocyte homing, including tumor necrosis factor-α, interferon (IFN)-α, IFN-ß, and B cell activating factor. This process mainly involves the Janus kinase-signal transducer and activator of transcription signaling pathway, lymphotoxin-ß receptor pathway, and nuclear factor-κB signaling pathway. These findings have led to the development of antibodies to cell adhesion molecules, antagonists of chemokines and their receptors, compounds interfering with chemokine receptor signaling, and gene therapies targeting chemokines and their receptors, providing new targets for the treatment of SS in humans. The aim of this study was to explore the relationship between lymphocyte homing and the pathogenesis of SS, and to provide a review of recent studies addressing lymphocyte homing in targeted therapy for SS.

Visualizing Electrochemical CO2 Conversion via the Emerging Scanning Electrochemical Microscope: Fundamentals, Applications and Perspectives.

With the rapid development and maturity of electrochemical CO2 conversion involving cathodic CO2 reduction reaction (CO2RR) and anodic oxygen evolution reaction (OER), conventional ex situ characterizations gradually fall behind in detecting real-time products distribution, tracking intermediates, and monitoring structural evolution, etc. Nevertheless, advanced in situ techniques, with intriguing merits like good reproducibility, facile operability, high sensitivity, and short response time, can realize in situ detection and recording of dynamic data, and observe materials structural evolution in real time. As an emerging visual technique, scanning electrochemical microscope (SECM) presents local electrochemical signals on various materials surface through capturing micro-current caused by reactants oxidation and reduction. Importantly, SECM holds particular potentials in visualizing reactive intermediates at active sites and obtaining instantaneous morphology evolution images to reveal the intrinsic reactivity of active sites. Therefore, this review focuses on SECM fundamentals and its specific applications toward CO2RR and OER, mainly including electrochemical behavior observation on local regions of various materials, target products and onset potentials identification in real-time, reaction pathways clarification, reaction kinetics exploration under steady-state conditions, electroactive materials screening and multi-techniques coupling for a joint utilization. This review undoubtedly provides a leading guidance to extend various SECM applications to other energy-related fields.

A Rapid Self-Assembling Peptide Hydrogel for Delivery of TFF3 to Promote Gastric Mucosal Injury Repair.

Self-assembled peptide-based nanobiomaterials exhibit promising prospects for drug delivery applications owing to their commendable biocompatibility and biodegradability, facile tissue uptake and utilization, and minimal or negligible unexpected toxicity. TFF3 is an active peptide autonomously secreted by gastric mucosal cells, possessing multiple biological functions. It acts on the surface of the gastric mucosa, facilitating the repair process of gastric mucosal damage. However, when used as a drug, TFF3 faces significant challenges, including short retention time in the gastric mucosal cavity and deactivation due to degradation by stomach acid. In response to this challenge, we developed a self-assembled short peptide hydrogel, Rqdl10, designed as a delivery vehicle for TFF3. Our investigation encompasses an assessment of its properties, biocompatibility, controlled release of TFF3, and the mechanism underlying the promotion of gastric mucosal injury repair. Congo red/aniline blue staining revealed that Rqdl10 promptly self-assembled in PBS, forming hydrogels. Circular dichroism spectra indicated the presence of a stable ß-sheet secondary structure in the Rqdl10 hydrogel. Cryo-scanning electron microscopy and atomic force microscopy observations demonstrated that the Rqdl10 formed vesicle-like structures in the PBS, which were interconnected to construct a three-dimensional nanostructure. Moreover, the Rqdl10 hydrogel exhibited outstanding biocompatibility and could sustainably and slowly release TFF3. The utilization of the Rqdl10 hydrogel as a carrier for TFF3 substantially augmented its proliferative and migratory capabilities, while concurrently bolstering its anti-inflammatory and anti-apoptotic attributes following gastric mucosal injury. Our findings underscore the immense potential of the self-assembled peptide hydrogel Rqdl10 for biomedical applications, promising significant contributions to healthcare science.

Regulating the Topology of Covalent Organic Frameworks for Boosting Overall H2O2 Photogeneration.

Photocatalytic oxygen reduction reactions and water oxidation reactions are extremely promising green approaches for massive H2O2 production. Nonetheless, constructing effective photocatalysts for H2O2 generation is critical and still challenging. Since the network topology has significant impacts on the electronic properties of two dimensional (2D) polymers, herein, for the first time, we regulated the H2O2 photosynthetic activity of 2D covalent organic frameworks (COFs) by topology. Through designing the linking sites of the monomers, we synthesized a pair of novel COFs with similar chemical components on the backbones but distinct topologies. Without sacrificial agents, TBD-COF with cpt topology exhibited superior H2O2 photoproduction performance (6085 and 5448 µmol g-1 h-1 in O2 and air) than TBC-COF with hcb topology through the O2-O2⋅--H2O2, O2-O2⋅--O2 1-H2O2, and H2O-H2O2 three paths. Further experimental and theoretical investigations confirmed that during the H2O2 photosynthetic process, the charge carrier separation efficiency, O2⋅- generation and conversion, and the energy barrier of the rate determination steps in the three channels, related to the formation of *OOH, *O2 1, and *OH, can be well tuned by the topology of COFs. The current study enlightens the fabrication of high-performance photocatalysts for H2O2 production by topological structure modulation.

An epilepsy detection method based on multi-dimensional feature extraction and dual-branch hypergraph convolutional network.

Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies. Therefore, it is crucial to effectively and deeply explore the intrinsic features of epilepsy signals to reveal the rules of epilepsy occurrence and achieve accurate detection. Existing methods have faced the following issues: 1) single approach for feature extraction, resulting in insufficient classification information due to the lack of rich dimensions in captured features; 2) inability to deeply analyze the essential commonality of epilepsy signal after feature extraction, making the model susceptible to data distribution and noise interference. Thus, we proposed a high-precision and robust model for epileptic seizure detection, which, for the first time, applies hypergraph convolution to the field of epilepsy detection. Through a hypergraph network structure constructed based on relationships between channels in electroencephalogram (EEG) signals, the model explores higher-order characteristics of epilepsy EEG data. Specifically, we use the Conv-LSTM module and Power spectral density (PSD), a two-branch parallel method, to extract channel features from space-time and frequency domains to solve the problem of insufficient feature extraction, and can adequately describe the data structure and distribution from multiple perspectives through double-branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space in an attempt to reveal the essential commonality of epileptic signal feature extraction. Finally, using the ensemble learning concept, we accomplished epilepsy detection on the dual-branch hypergraph convolution. The model underwent leave-one-out cross-validation on the TUH dataset, achieving an average accuracy of 96.9%, F1 score of 97.3%, Pre of 98.2% and Re of 96.7%. In addition, the model was generalized performance tested on CHB-MIT scalp EEG dataset with leave-one-out cross-validation, and the average ACC, F1 score, Pre and Re were 94.4%, 95.1%, 95.8%, and 93.9% respectively. Experimental results indicate that the model outperforms related literature, providing valuable reference for the clinical application of epilepsy detection.

Novel Ag3PO4/ZnWO4-modified graphite felt electrode for photoelectrocatalytic removal of harmful algae: Performance and mechanism.

A novel Ag3PO4/ZnWO4-modified graphite felt electrode (AZW@GF) was prepared by drop coating method and applied to photoelectrocatalytic removal of harmful algae. Results showed that approximately 99.21% of chlorophyll a and 91.57% of Microcystin-LR (MCLR) were degraded by the AZW@GF-Pt photoelectrocatalytic system under the optimal operating conditions with a rate constant of 0.02617 min-1 and 0.01416 min-1, respectively. The calculated synergistic coefficient of photoelectrocatalytic algal removal and MC-LR degradation by the AZW@GF-Pt system was both larger than 1.9. In addition, the experiments of quenching experiments and electron spin resonance (ESR) revealed that the photoelectrocatalytic reaction mainly generated •OH and •O2- for algal removal and MC-LR degradation. Furthermore, the potential pathway for photoelectrocatalytic degradation of MC-LR was proposed. Finally, the photoelectrocatalytic cycle algae removal experiments were carried out on AZW@GF electrode, which was found to maintain the algae removal efficiency at about 91% after three cycles of use, indicating that the photoelectrocatalysis of AZW@GF electrode is an effective emergency algae removal technology.

Identification of metabolism-related key genes as potential biomarkers for pathogenesis of immune thrombocytopenia.

Immune thrombocytopenia (ITP), an acquired autoimmune disease, is characterized by immune-mediated platelet destruction. A biomarker is a biological entity that contributes to disease pathogenesis and reflects disease activity. Metabolic alterations are reported to be associated with the occurrence of various diseases. As metabolic biomarkers for ITP have not been identified. This study aimed to identify metabolism-related differentially expressed genes as potential biomarkers for pathogenesis of ITP using bioinformatic analyses.The microarray expression data of the peripheral blood mononuclear cells were downloaded from the Gene Expression Omnibus database (GSE112278 download link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112278 ). Key module genes were intersected with metabolism-related genes to obtain the metabolism-related key candidate genes. The hub genes were screened based on the degree function in the coytoscape sofware. The key ITP-related genes were subjected to functional enrichment analysis. Immune infiltration analysis was performed using a single-sample gene set enrichment analysis algorithm to evaluate the differential infiltration levels of immune cell types between ITP patient and control. Molecular subtypes were identified based on the expression of hub genes. The expression of hub genes in the ITP patients was validated using quantitative real-time polymerase chain reaction analysis. This study identified five hub genes (ADH4, CYP7A1, CYP1A2, CYP8B1, and NR1H4), which were be associated with the pathogenesis of ITP, and two molecular subtypes of ITP. Among these hub genes, CYP7A1 and CYP8B1 involved in cholesterol metabolism,were further verified in clinical samples.

Machine learning and optical coherence tomography-derived radiomics analysis to predict persistent diabetic macular edema in patients undergoing anti-VEGF intravitreal therapy.

BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME. METHODS: A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated. RESULTS: The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p < 0.001). OCT-omics scores were also positively correlated with the rate of decline in central subfield thickness after treatment (Pearson's R = 0.44, p < 0.001). CONCLUSION: The developed OCT-omics model accurately assesses anti-VEGF treatment response in DME patients. The model's robust performance and clinical implications highlight its utility as a non-invasive tool for personalized treatment prediction and retinal pathology assessment.

The value of lung function assessment and Testin expression detection in clinicopathological features and prognosis of NSCLC patients.

OBJECTIVE: The aim of this study is to investigate the clinical value and potential prognostic significance of lung function assessment and Testin expression in non-small cell lung cancer (NSCLC) patients. METHODS: The NSCLC patients were classified into three groups according to lung function: group of normal lung function, group of PRISm (preserved ratio impaired spirometry) (FEV1, forced expiratory volume during the first second < 80% predicted and FEV1/FVC (forced vital capacity) ≥ 70%) and group of COPD (chronic obstructive pulmonary disease) (FEV1/FVC < 70%). The pre-operational clinicopathological characteristics of these patients were recorded and the markers of systemic inflammatory response, including neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and eosinophils (EOS), were compared between three groups. The expression of Testin in NSCLC samples was detected by IHC and we further explored the correlation between Testin expression and clinicopathological characteristics and prognosis of NSCLC patients. Finally, Cox regression analysis was conducted to study the prognostic factors of NSCLC patients. RESULTS: Of the 158 NSCLC patients, percentages of normal lung function, PRISm and COPD were 41.4%, 22.8% and 36.1%, respectively. Patients with tumor in the left lung were more likely to have pulmonary dysfunction (PRISm and COPD) than the right lung. The markers of systemic inflammatory response showed differences to various degree in the three groups and NSCLC patients with PRISm or COPD presented more unfavorable prognosis than patients with normal function. The expression of Testin correlated with lymph node metastasis, TNM stage and tumor invasion of NSCLC patients. Moreover, patients with low Testin expression exhibited poorer disease-free survival and overall survival than those with high Testin expression. In Cox regression analysis, we found that PRISm, COPD and Testin expression served as prognostic factors in NSCLC patients. CONCLUSIONS: The presence of COPD or PRISm influenced systemic inflammatory response and prognosis of NSCLC patients. Testin expression correlated with clinicopathological features and could be potentially used as a prognostic marker in NSCLC.

Potassium affects the association between dietary intake of vitamin C and NAFLD among adults in the United States.

INTRODUCTION: Although the association between nonalcoholic fatty liver disease (NAFLD) and vitamin C has been well studied, the effects of dietary potassium intake on this relationship are still unclear. Thus, this study aimed to determine the effects of dietary potassium intake on the association between vitamin C and NAFLD. METHODS: We performed a cross-sectional learn about with 9443 contributors the usage of 2007-2018 NHANES data. Multiple logistic regression evaluation has been utilized to check out the affiliation of dietary vitamin C intake with NAFLD and advanced hepatic fibrosis (AHF). Subsequently, we plotted a smoothed match curve to visualize the association. Especially, the analysis of AHF was conducted among the NAFLD population. In addition, stratified evaluation used to be developed primarily based on demographic variables to verify the steadiness of the results. Effect amendment by way of dietary potassium intake used to be assessed via interplay checks between vitamin C and NAFLD in the multivariable linear regression. RESULTS: In this cross-sectional study, we found that vitamin C was negatively related to NAFLD and AHF. The relationship between vitamin C and NAFLD was different in the low, middle and high potassium intake groups. Furthermore, potassium intake significantly modified the negative relationship between vitamin C and NAFLD in most of the models. CONCLUSION: Our research showed that potassium and vitamin C have an interactive effect in reducing NAFLD, which may have great importance for clinical medication.

Two novel angiotensin-converting enzyme (ACE) and dipeptidyl peptidase IV (DPP-IV) inhibiting peptides from tilapia (Oreochromis mossambicus) skin and their molecular docking mechanism.

In the study, papain was used to hydrolyze tilapia (Oreochromis mossambicus) skin to obtain a tilapia skin hydrolysate (TSH) with dual angiotensin-converting enzyme (ACE) and dipeptidyl peptidase IV (DPP-IV) inhibitory activities. The resulting TSH was sequentially fractionated by ultrafiltration, size exclusion separation chromatography, and reverse-phase high-performance liquid chromatography. Its inhibitory effects on ACE and DPP-IV were determined by commercial reagent kits. Two peptides purified from TSH were identified as Gly-Pro-Leu-Gly-Ala-Leu (GPLGAL) and Lys-Pro-Ala-Gly-Asn (KPAGN) by the ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Inhibitory concentration (IC50) of GPLGAL on ACE and DPP-IV were 117.20 ± 1.69 and 187.10 ± 2.75 µM, respectively. IC50 of KPAGN on ACE and DPP-IV were 137.40 ± 2.33 and 259.20 ± 2.85 µM, respectively. The molecular simulation demonstrated that the binding affinities of GPLGAL to ACE and DPP-IV proteins were -8.5 and -7.4 kcal/mol, respectively, whereas those of KPAGN to ACE and DPP-IV proteins were -7.9 and -6.7 kcal/mol, respectively. GPLGAL interacted with 21 amino acid residues of the ACE active site, whereas KPAGN engaged with 19 amino acid residues. Additionally, GPLGAL interacted with 10 amino acid residues of the DPP-IV active site, whereas KPAGN engaged with 13 amino acid residues. The two peptides predominantly occupied the active sites of ACE (His513, Tyr523, and Ala354) and DPP-IV (Tyr662 and Arg125) through hydrogen bonding. This leads to the deactivation of ACE and DPP-IV. PRACTICAL APPLICATION: Accelerate tilapia skin development and high-value utilization; provide foundation for preparing the peptides with dual ACE and DPP-IV inhibiting activity.

Aspects of public health development in China's western region.

The public health level in a country is closely related to national development and quality of life. In order to appraise the level of health services in the western region of China, panel data of 124 prefecture-level units covering the period 2011 to 2021 was used together with a health evaluation index system based on four dimensions: quality of life, environmental situation, the level of health services and longevity. To assess this, we used entropy weights, standard deviation and coefficient of variation together with the geographical detector model that measures the stratified spatial heterogeneity. The results show that although public health services have improved overall, the various dimensions are still not balanced as longevity did not match up everywhere. While the developmental level of the various health dimensions presents a pattern of a relatively smooth increasing gradient in the west-central- east direction, the situation with respect to the north-centralsouth is more uneven with both ups and downs. However, a trend of continuous enhancement of all health dimensions was found with a significant positive correlation of spatial clustering, with hotspots and 'sub-hotspots' contracting from north to south, while coldspots and 'sub-coldspots' expanded from west to east. This can be seen as the result of multiple factors, with the level of urbanization and economic level as the dominant factors and government guidance, agglomeration capacity and industrial structure being auxiliary.

Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors.

Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective, which are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge to the research and development of machine learning-based kinase inhibitors' active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we present a novel Meta-learning-based inductive logistic matrix completion method for the Prediction of Kinase Inhibitors (MetaILMC). MetaILMC adopts a meta-learning framework to learn a well-generalized model from tasks with sufficient samples, which can fast adapt to new tasks with limited samples. As MetaILMC allows the effective transfer of the prior knowledge learned from kinases with sufficient samples to kinases with a small number of samples, the proposed model can produce accurate predictions for kinases with limited data. Experimental results show that MetaILMC has excellent performance for prediction tasks of kinases with few-shot samples and is significantly superior to the state-of-the-art multi-task learning in terms of AUC, AUPR, etc., various performance metrics. Case studies also provided for two drugs to predict Kinase Inhibitory scores, further validating the proposed method's effectiveness and feasibility. SCIENTIFIC CONTRIBUTION: Considering the potential correlation between activity prediction tasks for different kinases, we propose a novel meta learning algorithm MetaILMC, which learns a prior of strong generalization capacity during meta-training from the tasks with sufficient training samples, such that it can be easily and quickly adapted to the new tasks of the kinase with scarce data during meta-testing. Thus, MetaILMC can effectively alleviate the data scarcity problem in the prediction of kinase inhibitors.

Contributing factors and interventions for fear of falling in stroke survivors: a systematic review.

OBJECTIVES: The purpose of this study was to provide a comprehensive overview of the prevalence, measurement tools, influencing factors, and interventions for fear of falling (FOF) in stroke survivors. METHODS: A PRISMA-guided systematic literature review was conducted. PubMed, EMBASE, Cochrane, and Web of Science were systematically searched. The search time was up to February 2023. All observational and experimental studies investigating FOF in stroke patients were included. The assessment tool of the Joanna Briggs Institute was used to assess the quality of the included studies and the risk of bias assessment. (PROSPERO: CRD42023412522). RESULT: A total of 25 observational studies and 10 experimental studies were included. The overall quality of the included studies was "low" to "good." The most common tool used to measure the FOF was the Falls Efficacy Scale-International (FES-I). The prevalence of FOF was 42%- 93.8%. Stroke survivors with physical impairments have the highest prevalence of FOF. The main risk factors for the development of FOF in stroke survivors were female gender, use of assistive devices, balance, limb dysfunction, and functional mobility. The combination of cognitive behavioral and exercise interventions is the most effective strategy. CONCLUSIONS: This review suggests that the prevalence of FOF in stroke survivors is high and that understanding the factors associated with FOF in stroke patients can help develop multifactorial prevention strategies to reduce FOF and improve quality of life. In addition, a uniform FOF measurement tool should be used to better assess the effectiveness of interventions for stroke survivors. ETHICS APPROVAL: PROSPERO registration (CRD42023412522).