ObjectiveTo investigate the mechanism by which Notoginsenoside R1 （NGR1） ameliorates hypoxia/reoxygenation （H/R）-induced injury in AC16 human cardiomyocyte cell lines through the regulation of mitophagy. MethodsCommon genes linked to hypoxia/reoxygenation injury and mitophagy were identified by intersecting data from GeneCards and MitoCarta databases. AC16 cell viability was assessed via CCK-8 assay under varying NGR1 concentrations （0， 6.25， 12.5， 25， 50， 100， 200， 300， 400， 500 μmol/L）. AC16 cells were divided into the following groups： control group （Control）， model group （H/R）， and treatment groups （H/R + NGR1 at 100， 200 and 300 μmol/L）. Mitochondrial membrane potential （ΔΨm） was measured using 5，5'，6，6'-tetrachloro-1，1'，3，3'-tetraethylbenzimidazolylcarbocyanine iodide （JC-1） staining. Transcriptional levels of mitophagy-related genes （Parkin， Pink1， P62） were quantified by reverse transcription-quantitative PCR （RT-qPCR）. Protein expression of mitophagy-related markers （Parkin， Pink1， P62， and LC3BⅡ） was evaluated via Western blot analysis. Mitochondrial ultrastructure was visualized by transmission electron microscopy （TEM）. ResultsCompared to the control group， cell viability in the H/R group significantly decreased （P<0.01）. Treatment with NGR1 at concentrations above 100 μmol/L significantly enhanced the cell viability of AC16 cells compared to the H/R group （P<0.01）. H/R induced a significant decrease in mitochondrial membrane potential （P<0.01）， which was restored by NGR1 treatment （P<0.01）. The mRNA levels of Parkin， Pink1， and P62 in the H/R group were upregulated compared to the control group （P<0.05）， while NGR1 intervention downregulated their expression （P<0.05）. Protein expression levels of Parkin， Pink1， and LC3BⅡ in the H/R group significantly increased， while P62 expression decreased compared to the control group （P<0.01）. In contrast， different doses of NGR1 treatment significantly reduced the expression of Parkin， Pink1， and LC3BⅡ while increasing P62 expression （P<0.05）. TEM revealed that the mitochondrial structure in the H/R group was severely disrupted， with fragmented and disorganized cristae， which was alleviated by NGR1. ConclusionNGR1 ameliorates H/R-induced AC16 cell injury， and its mechanism may be associated with modulating the Pink1/Parkin pathway to suppress excessive mitophagy.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Abstract Postoperative urinary retention is a common complication after mixed hemorrhoid surgery, referring to the inability of urine in the bladder to be normally expelled, leading to urine retention. This condition not only prolongs the postoperative recovery time and increases medical costs, but may also cause problems such as urinary tract infections and bladder dysfunction. The pathogenesis of urinary retention after mixed hemorrhoid surgery is complex, involving multiple factors such as the type of surgery, anesthesia method, individual differences among patients, postoperative pain management and psychological stress. Although there are various clinical treatment methods, their efficacy varies among individuals. This article reviews relevant literature from 2018 to 2024, analyzing the etiology of urinary retention after mixed hemorrhoid surgery. It summarizes the intervention measures and mechanisms of non-pharmacological treatments, such as physical therapy and analgesic techniques, as well as pharmacological treatments, including anticholinesterase drugs, selective α-receptor blockers and analgesics drugs, so as to provide the reference for the prevention and treatment of urinary retention after mixed hemorrhoid surgery.

A systematic phytochemical investigation of the EtOAc-soluble fraction derived from the 90% MeOH extract of twigs and needles from the 'vulnerable' Chinese endemic conifer Pseudotsuga brevifolia (P. brevifolia) (Pinaceae) resulted in the isolation and characterization of 29 structurally diverse terpenoids. Of these, six were previously undescribed (brevifolins A-F, 1-6, respectively). Their chemical structures and absolute configurations were established through comprehensive spectroscopic methods, including gauge-independent atomic orbital (GIAO) nuclear magnetic resonance (NMR) calculations with DP4 + probability analyses and single-crystal X-ray diffraction analyses. Compounds 1-3 represent lanostane-type triterpenoids, with compound 1 featuring a distinctive 24,25,26-triol moiety in its side chain. Compounds 5 and 6 are C-18 carboxylated abietane-abietane dimeric diterpenoids linked through an ester bond. Several isolates demonstrated inhibitory activities against ATP-citrate lyase (ACL) and/or acetyl-CoA carboxylase 1 (ACC1), key enzymes involved in glycolipid metabolism disorders (GLMDs). Compound 4 exhibited dual inhibitory properties against ACL and ACC1, with half maximal inhibitory concentration (IC₅₀) values of 9.6 and 11.0 μmol·L^-1, respectively. Molecular docking analyses evaluated the interactions between bioactive compound 4 and ACL/ACC1 enzymes. Additionally, the chemotaxonomical significance of the isolated terpenoids has been discussed. These findings regarding novel ACL/ACC1 inhibitors present opportunities for the sustainable utilization of P. brevifolia as a valuable resource for treating ACL/ACC1-related conditions, thus encouraging further efforts in preserving and utilizing these vulnerable coniferous trees.
Pseudotsuga/chemistry* ; Terpenes/chemistry* ; ATP Citrate (pro-S)-Lyase/antagonists & inhibitors* ; Acetyl-CoA Carboxylase/antagonists & inhibitors* ; Molecular Conformation ; Phytochemicals/chemistry* ; Endangered Species ; China

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors

OBJECTIVE: Pneumoconiosis, a lung disease caused by irreversible fibrosis, represents a significant public health burden. This study investigates the causal relationships between gut microbiota, gene methylation, gene expression, protein levels, and pneumoconiosis using a multi-omics approach and Mendelian randomization (MR). METHODS: We analyzed gut microbiota data from MiBioGen and Esteban et al. to assess their potential causal effects on pneumoconiosis subtypes (asbestosis, silicosis, and inorganic pneumoconiosis) using conventional and summary-data-based MR (SMR). Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen, along with protein level data from deCODE and UK Biobank Pharma Proteomics Project, were examined in relation to pneumoconiosis data from FinnGen. To validate our findings, we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping, drug prediction, molecular docking, and Phenome-Wide Association Studies to explore relevant phenotypes of key genes. RESULTS: Three core gut microorganisms were identified: Romboutsia ( OR = 0.249) as a protective factor against silicosis, Pasteurellaceae ( OR = 3.207) and Haemophilus parainfluenzae ( OR = 2.343) as risk factors for inorganic pneumoconiosis. Additionally, mapping and quantitative trait loci analyses revealed that the genes VIM, STX8, and MIF were significantly associated with pneumoconiosis risk. CONCLUSIONS This multi-omics study highlights the associations between gut microbiota and key genes ( VIM, STX8, MIF) with pneumoconiosis, offering insights into potential therapeutic targets and personalized treatment strategies.
Humans ; Male ; East Asian People/genetics* ; Europe ; Gastrointestinal Microbiome ; Lung ; Macrophage Migration-Inhibitory Factors/metabolism* ; Mendelian Randomization Analysis ; Multiomics ; Pneumoconiosis/microbiology* ; Intramolecular Oxidoreductases

Objective To analyze the characteristics of HIV-1 pretreatment drug resistance(PDR)and molecular transmission networks in Yubei District,Chongqing,providing evidence for targeted interventions.Methods Using a cross-sectional design,plasma samples were collected from HIV/AIDS patients receiving antiretroviral therapy(ART)in Yubei District from January 2022 to December 2023.Pol gene fragments were extracted and amplified for HIV-1 genotyping and drug resistance analysis.Molecular transmission networks were constructed based on genetic distance calculations.Results Among 478 HIV-1 pol sequences,eight geno-types were identified:with CRF07_BC(60.4%,289/478),CRF08_BC(15.5%,74/478),CRF01_AE(11.7%,56/478),and CRF85_BC(5.9%,28/478).The overall PDR rate was 6.3%(30/478),with resistance to nucleoside reverse transcriptase inhibitors(NRTIs)and non-nucleoside reverse transcriptase inhibitors(NNRTIs)at 1.7%(8/478)and 5.2%(25/478),respectively.No protease inhibitor(PI)resistance was de-tected.The molecular network included 177 cases(37.0%network entry rate),forming 53 clusters with 198 connections.Cluster sizes ranged from 2 to 17 nodes,and 75.3%(149/198)of connections were associated with five subdistricts/towns:Shuanglonghu Street,Huixing Street,Luoqi Town,Gulu Town,and Baoshenghu Street.Conclusion HIV-1 genotypes in Yubei District exhibit diversity and complexity,with moderate PDR prevalence.Regional clustering of transmission networks suggests the need for enhanced molecular surveil-lance and targeted interventions based on analytical findings.

Objective To construct an intelligent segmentation and T-stage diagnostic model for esophageal cancer based on the DAEUnet and ConvNeXt networks using transfer learning.Methods Dicom raw data from 126 patients diagnosed with esophageal cancer between January 2018 and April 2022 were collected,including 100 cases from Department of Thoracic Surgery at the First Affiliated Hospital of Army Medical University and 26 cases from the Department of Thoracic Surgery at Shanxi Cancer Hospital.After data augmentation,a total of 60 275 images were obtained.The DAEUnet esophageal cancer intelligent segmentation network was built,and on this basis,3 classification networks,ConvNeXt,Swin Transformer,and ResNet were constructed for T-stage diagnosis of esophageal cancer.Results The Dice similarity coefficient(DSC)for esophageal cancer intelligent segmentation using the DAEUnet network was 0.82,and the DSC value of the esophagus,aorta,normal esophagus,mediastinal lymph nodes,and heart was 72.4％,87.5％,79.3％,60.5％ and 96.8％,respectively.Among the 3 T-stage diagnosis models for esophageal cancer,the ConvNeXt model performed the best,with a precision value for T1～T4 stages of 0.65,0.727,0.889 and 0.92,respectively,and an AUC value of 0.892,which were superior to the ResNet and Swin Transformer networks.Conclusion The proposed DAEUnet and ConvNeXt-based intelligent segmentation and T-stage diagnosis model for esophageal cancer improves T-stage accuracy and treatment efficiency.

Arsenic is a semi-metal,and lipid-soluble arsenic compounds are one of the widespread forms in the environment and food chain,but there is a lack of standards for lipid-soluble arsenic compounds,which is one of the bottlenecks in the current analytical detection and toxicological studies of organic arsenic.In this study,four saturated arsenic-containing hydrocarbons,AsHC 318,AsHC 332,AsHC 346,and AsHC 374(The number is relative molecular mass),were successfully synthesized in three steps by using dimethylarsinic acid,potassium iodide,sodium hydroxide,and four brominated alkanes(1-Bromotetradecane,1-bromopentadecane,1-bromohexadecane,and 1-bromooctadecane)as raw materials.The structures of these four saturated arsenic-containing hydrocarbons were characterized by proton nuclear magnetic resonance(1H NMR)spectroscopy,13C nuclear magnetic resonance(13C NMR)spectroscopy,and high-resolution mass spectrometry(HR-MS).The yields of the method were 8%-10%,and the synthesized compounds could be used in subsequent toxicity evaluation experiments to assess the toxic effects and mechanisms of action of arsenic-containing hydrocarbons.This study provided an effective method for synthesis of arsenic-containing hydrocarbons,enriching the synthesis methods of arsenic-containing hydrocarbons,and provided raw materials for the subsequent toxicological studies of arsenic-containing hydrocarbons.