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.

Aim To investigate the effect of icariin(ICA)on chronic ulcerative colitis(UC)in mice.Methods Male SPF-grade C57BL/6J mice were ran-domly divided into the control group,model group,low-dose ICA group,and high-dose ICA group.Except for the control group,the rest of the mice were established as chronic UC models.Expressions of tight junction proteins,inflammatory factors,fibrosis markers,macro-phage markers,and MAPK signaling pathways in the colonic epithelium of each group were determined by Western blot,qPCR,immunohistochemistry;the degree of intestinal damage and fibrosis was detected by HE and Masson staining;the co-localization of different macrophage markers with related factors was examined by immunofluorescence.Results Treatment of ICA significantly improved the general condition of chronic UC mice,decreased the infiltration of M1-type macro-phages and the secretion of pro-inflammatory factors,inhibited the MAPK signaling pathway,reduced the co-localization expression of M1-type macrophages with the MAPK signaling pathway,and downregulated the infiltration of M2-type macrophages and the secretion of pro-fibrotic factors in the colon.Conclusion ICA mitigates the inflammatory damage and fibrosis of colon in chronic UC mice.

Objective To investigate the influencing factors of the course of perioperative prophylactic antibiotics in patients undergoing total hip arthroplasty,and explore management strategies for enhancing perioperative prophylactic medication administration.Methods The clinical data for patients undergoing total hip arthroplasty at Affiliated Hospital of Chengdu University from January,2020 to September,2024 were retrospectively collected.Patients were divided into a 24 h group and a 48-72 h group based on the duration of prophylactic antibacterial therapy.The general characteristics,surgical-related indicators,preoperative and postoperative laboratory test results,and surgical outcome measures between the two groups of patients were compared.Multivariate Logistic regression analysis was performed to identify influencing factors associated with prolonged duration of prophylactic antibacterial therapy.Results A total of 126 patients who underwent total hip arthroplasty were enrolled,including 74 cases in the 24 h group and 52 cases in the 48-72 h group.Univariate analysis results showed that there were statistically significant differences in the following indicators between the two groups:surgical cause,surgical duration,intraoperative blood loss,drainage duration of plasma drainage tubes,preoperative white blood cell count,and preoperative neutrophil count(P<0.05).The results of multivariate Logistic regression analysis showed that the reason for surgery and the duration of plasma drain tube drainage were the influencing factors of antimicrobial treatment course for total hip arthroplasty(P<0.05).The results of receiver operating characteristic curve analysis showed that the prediction model(constructed based on the drainage time of plasma drainage tube)for prophylactic antimicrobial treatment course to 48-72 h was 0.721.When the drainage time of plasma drainage tubes was≥40.56 h,the risk of requiring prophylactic antimicrobial therapy for an extended course of 48-72 h increased significantly.Conclusion The patient's surgical reason and the duration of plasma drain drainage may be related to the prophylactic antimicrobial course of more than 24 hours.Clinical pharmacists may utilize this parameter as a patient-specific characteristic,with the support of information systems,the hierarchical patient management can be implemented,thereby enhancing the effectiveness of medication surveillance and progressively elevating the 24 h discontinuation rate of perioperative prophylactic antibiotics.

Objective:To verify the applicability of the flight potential evaluation system in the psychological selection of pilots by testing the reliability and validity of the system.Methods:Between September and October 2021, 82 subjects, including 32 pilots and 50 volunteers, were recruited from the Aviation Force and the Air Force Medical Center to complete the flight potential evaluation system test. The pilots and volunteers were divided into the high score group (the top 27% in terms of scores) and low score group (the 27% from the bottom) according to the total score of the evaluation. In order to ensure the consistency of evaluation conditions, some subjects were selected to complete a second evaluation test one week later for reliability analysis. Six flight experts completed the Content Evaluation Form of the Flight Potential Evaluation System while the officer completed the Flight Performance Evaluation Questionnaire as the performance data of the pilots. The pass rates of the pilots and volunteers and discriminability of the flight potential evaluation system were analyzed to test the stability and effectiveness of the system.Results:①The flight potential evaluation system demonstrated a pass rate of 0.75 and a discriminability of 0.30 in pilots, compared with 0.30 and 0.51 in volunteers. The total scores of pilots and volunteers were (7.00±1.16) points and (3.38±2.15) points, respectively, with a statistically significant difference ( t=9.87, P<0.001). Significant differences were found in test scores between high-score group and low-score group for both pilots ( t=10.01, P<0.001) and volunteers ( Z=-4.65, P<0.001). ②Thirty-seven subjects (including 32 pilots and 5 volunteers, all from the Aviation Force) were tested twice, and the Pearson product-moment correlation coefficient for the paired results of 2 identical tests was r=0.750 ( P<0.001). ③As for the content of the evaluation system test, the experts′ degree of agreement and unanimity rate were 1. The evaluation score by the officer was positively correlated with the test score ( r=0.389, P=0.041). Conclusions:The flight potential evaluation system has a strong ability to distinguish flight-related abilities, suggesting that the system can be applied to the psychological selection of pilots and provide data for subsequent tests during the recruitment of candidates.

Aim To investigate the role of myotubula-rin related protein 7(MTMR7)in the pathogenesis of pulmonary hypertension manifested by pulmonary vas-cular intimal thickening,right ventricular hypertrophy,progressive right heart failure and dysfunction.Meth-ods A total of 40 healthy male C57BL/6J mice and Mtmr7-transgenic(Mtmr7-Tg)mice were divided into the control group,Mtmr7-Tg group,monocrotaline(MCT)group and MCT+Mtmr7-Tg group.Pulmonary artery acceleration time(PAT)and pulmonary artery ejection time(PET)of the pulmonary artery were measured by ultrasound.When the free wall of the right ventricle was separated,the right heart hypertro-phy index(RVHI)was calculated.Pulmonary artery remodeling was observed by immunostaining.Mouse pulmonary artery smooth muscle cells(PASMCs)were cultured in hypoxic environment to induce the prolifer-ation and migration.Results MTMR7 was expressed in pulmonary vessels.Compared to the wild-type mice,Mtmr7-Tg mice showed increased PAT/PET ratio(P＜0.05),reduced RVHI(P＜0.01)after MCT stimu-lus.PASMCs were transfected with adenovirus encond-ing Mtmr7 gene,which inhibited proliferation and mi-gration of PASMCs.After restoring the activity of ERK1/2 by chemerin-9,the proliferation and migra-tion ability of PASMCs was elevated.Conclusions MTMR7 can counteract the growth and mobility of mouse PASMCs induced by hypoxia,thereby comba-ting pulmonary arterial hypertension via reducing ERK1/2 phosphorylation.

Core competitiveness of medical colleges and universities refers to their unique advantages and abilities in teaching,research and serving society compared to other colleges.As an important part of core competitiveness of medical colleges,teachers'teaching ability directly affects students'learning effect and quality training,and also relates to overall promotion process of core com-petitiveness of medical colleges.From perspective of core competitiveness of medical colleges,this paper analyzes and combs develop-ment of teachers'teaching ability from theoretical definition of core competitiveness of medical colleges,finds bottleneck of improving teachers'teaching ability,and puts forward suggestions for improving teachers'teaching ability in medical colleges,so as to improve teachers'teaching ability in medical colleges.

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.

Background:Salvia miltiorrhiza Bunge,commonly known as"Danshen"in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geo-graphical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information represent-ing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72％by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

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.