Objective To systematically review the studies on predictive models for delayed graft function (DGF) after kidney transplantation. Methods Databases including China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and CINAHL were searched to collect studies on predictive models for DGF after kidney transplantation published from the establishment of each database to June 29, 2025. Two researchers screened the literatures according to the inclusion and exclusion criteria, evaluated the quality of the literatures using the prediction model risk of bias assessment tool (PROBAST), and conducted a meta-analysis of the common predictors of the models using R software. Results A total of 12 literatures were included, involving 14 predictive models with sample sizes ranging from 103 to 24 653 cases. Donor serum creatinine level, cold ischemia time, donor age and donor body mass index were the top four common predictors. All the predictive models were at high risk of bias and low in applicability. The results of meta-analysis showed that abnormal donor body mass index, advanced donor age, prolonged cold ischemia time and elevated donor serum creatinine level were all associated with an increased risk of DGF after transplantation (all P<0.01), but there was high heterogeneity among the studies. Fixed-effect model and random-effect model were used to re-pool the effect sizes separately. The results indicated that the fixed-effect model and random-effect model had good consistency in terms of donor body mass index, donor age and cold ischemia time, while there was a significant difference in the effect sizes of the two models for donor serum creatinine level. Conclusions The predictive models for DGF risk after kidney transplantation have good predictive performance, but the overall risk of bias is high. In the future, large-sample, multicenter and high-quality prospective clinical studies should be carried out to optimize the predictive models, so as to improve their predictive ability and clinical application value.

In recent years， repetitive transcranial magnetic stimulation （rTMS） has garnered significant attention as a therapeutic approach for sleep disorders in the elderly. However， the prevailing rTMS protocols are predominantly developed based on normative neurophysiological data derived from young adults and fail to incorporate individualized parameters tailored to the brain characteristics of the elderly. To address this gap， the consensus development group synthesized the latest evidence from 2010 to 2025 and established a standardized rTMS protocol specifically for elderly patients with sleep disorders. Adhering to the Appraisal of Guidelines for Research and Evaluation II （AGREE II） framework， systematically screened randomized controlled trials （RCTs） and systematic reviews regarding rTMS in the treatment of sleep disorders across various conditions. Meanwhile， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system was employed to rigorously grade the quality of evidence and the strength of recommendations. This consensus guideline delineates precise rTMS protocols for the management of sleep disorders in the elderly， highlights the adjustment of stimulation intensity according to scalp-cortex distance recommends either MRI‑guided neuronavigation or the Beam F3/F4 heuristic approach for accurate target localization， thereby providing precise rTMS intervention protocol for sleep disorders in the elderly， aiming to enhance clinical efficacy while ensuring treatment safety. ［Funded by National Key Research and Development Program （number， 2023YFC3603200）； General Program of Shenzhen Science and Technology Innovation Commission （number， JCYJ20240813112859008， JCYJ20240813112900002）； Youth Program of Shenzhen Kangning Hospital （number， KN2023A004）； www.guidelines-registry.cn number， PREPARE-2026CN530］

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.

This article reports a diagnostically and therapeutically challenging case of small intestinal marginal zone lymphoma. The patient presented with recurrent abdominal pain as the chief complaint, and imaging revealed multifocal small bowel wall thickening with high uptake, multisegmental luminal stenosis, and proximal dilation. Initial diagnostic workup, including gastroscopy, colonoscopy, and enteroscopy with biopsy, failed to establish a definitive diagnosis. Empirical anti-tuberculosis therapy was ineffective. A repeat enteroscopic biopsy performed over eight months after symptom onset eventually confirmed the diagnosis of mucosa-associated lymphoid tissue (MALT) extranodal marginal zone lymphoma. Despite three different chemotherapy regimens, the patient's intestinal obstruction symptoms persisted, with imaging still showing multifocal bowel wall thickening and hypermetabolic activity. A critical diagnostic dilemma arose regarding whether the PET/CT-positive lesions represented residual lymphoma or fibrotic scarring, whether further chemotherapy adjustments were warranted, and whether surgical resection was necessary. Multidisciplinary discussion concluded that imaging had limited discriminatory value in this scenario and that surgical intervention should be pursued if feasible. The patient successfully underwent partial small bowel resection, with postoperative pathology confirming no residual lymphoma but significant fibrotic changes. The patient has since resumed a normal diet, with body weight nearly restored to pre-illness levels. This case highlights that fibrotic transformation is a common sequela of treated marginal zone lymphoma and that PET/CT may misleadingly suggest residual disease, potentially leading to unnecessary chemotherapy. Timely surgical intervention is crucial in such scenarios.

The randomized controlled trials (RCTs) regarding acupuncture direction published from January 1st, 2013, to November 7th, 2023 were searched in China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, and VIP Chinese Journal Database. As a result, 21 RCTs were included. The problems identified included conceptual misunderstandings regarding acupuncture direction, incomplete selection strategies, confounding research factors, and inaccuracies in reporting. Based on the findings, four strategic approaches for enhancing therapeutic efficacy through acupuncture direction were summarized: aligning needle direction with the meridian pathway, directing the needle toward the lesion site, orienting the needle toward adjacent acupoints, and targeting special anatomical structures. Two additional strategies were proposed for optimizing the procedure: simplifying acupuncture operations and directing the needle toward safe anatomical sites. Recommendations were made to improve the rationality of research factor settings and the completeness of acupuncture operation reporting. Furthermore, three methods for reporting acupuncture direction were discussed: reporting the tip-pointed position, reporting the insertion angle and orientation, and reporting azimuth and polar angles, aiming to promote greater standardization and completeness in acupuncture practice and reporting.
Acupuncture Therapy/standards* ; Humans ; Acupuncture Points ; Randomized Controlled Trials as Topic ; Meridians

Objective To design a combat rescue specialized physical training simulator to solve the problems of the existing combat rescue physical traing in multifunctionality and simulation vividness.Methods The simulator was divided into three types for stretcher handling,land combat rescue and marine rescue based on the application scenerio and functional positioning,and into three grades of basic level,intensive level and ultra intensive level based on the loaded mass and additional weight object.The main components of the simulator included a manikin,a bionic joint and addtional weight objects.The manikin was made up of outer skin,inner liner and skeleton;the bionic joint was made of stainless steel with surface electrophoresis treatment,and was composed of high-strength medal bearing shafts with multiple disc springs and damping mechanisms;the additional weight objects involued in high-intensity cast iron or lead blocks,which were pre-embedded,mounted or srtapped into the simulator.The simulator was verified with body shape and mass detection,drop test,waterproof test and drag test.Results It's proved the simulator gained advantages in vividness for body shape and mass,bionic joint structure and adaptability to training environments and could be used for graded physical training in typical combat rescue scenerios.Conclusion The simulator developed solves the problems of the combat rescue specialized physical training equipment,and facilitates the enhancement of physical training of combat rescue personnel.[Chinese Medical Equipment Journal,2025,46(1):33-37]

AIM:To observe the effect of continuous light on the structure and differential metabolites of gut microbiota in mice.METHODS:The mice were randomly divided into normal light(light/dark,LD)group and 24-hour continuous light(light/light,LL)group.The body weight,fasting blood glucose,serum free fatty acids,serum triacylglycerol and serum total cholesterol levels of each group of mice were measured after 10 weeks.Fresh feces were collected,and 16S rRNA sequencing technology was used to study the effect of continuous light on the diversity,structure,and species composition of gut microbiota in mice.Additionally,liquid chromatography-mass spectrometry(LC-MS)analysis was per-formed to observe the effect of continuous light on the metabolites in mice.RESULTS:Compared with the LD group,the body weight,fasting blood glucose and lipid levels of the LL group were increased(P＜0.05).At the phylum level,the proportion of Firmicutes in the LL group increased,while the proportion of Bacteroidetes decreased.At the class level,the abundance of norank_f_Muribaculaceae and Prevotellaceae_UCG-001 in the LL group decreased significantly,while the abundance of Lactobacillus,Turicibacter and Odoribacter increased significantly.Non-targeted metabolomics analysis iden-tified 65 and 73 differential metabolites under positive and negative modes,involving six major metabolic pathways,in-cluding ABC transporters,purine metabolism,pyrimidine metabolism,secondary bile acid biosynthesis,protein digestion and absorption,and choline metabolism in cancer.CONCLUSION:The structure and metabolites of gut microbiota in mice exposed to continuous light are relatively specific,and inosine may be a key biomarker and potential therapeutic tar-get for biological clock disorders.

Objective To investigate the features of resting-state electroencephalography(EEG)in stroke patients with limited upper limb movement,and assess its potential utility in evaluating upper limb motor function.Methods From March to August,2024,a total of 71 stroke patients with limited upper limb movement were enrolled as stroke group at the Rehabilitation Hospital Affiliated to Fujian University of Traditional Chinese Medicine;while 63 healthy participants matched for age and sex were recruited as control group.They were tested with 19-chan-nel resting-state EEG(rsEEG),calculating of the pairwise derived brain symmetry index(pdBSI)and Delta/Al-pha ratio(DAR).The motor function was assessed with Fugl-Meyer Assessment-Upper Extremities(FMA-UE),the upper limb recovery was evaluated with Brunnstrom Stages and the activities of daily living was assessed with modified Barthel index(MBI)in stroke patients,Results Compared with the control group,the pdBSI of Global lead,Frontal region,Central region and Posterior region across Delta,Alpha and Beta1 frequency bands were significantly higher in the stroke group than in the control group(|Z|>2.289,P<0.05);as well as the pdBSI of Global lead,Central region and Posterior region across The-ta bands(|Z|>3.016,P<0.01),the pdBSI of Global lead,Frontal region,Central region and Posterior region across Beta2 bands(|Z|>3.222,P<0.01),DAR of Global lead,Frontal region,Central region and Posterior re-gion(|Z|>6.565,P<0.001).In the stroke group,the pdBSI of Global lead(r=-0.280,P=0.018)and Central region(r=-0.304,P=0.010)across the Delta band were significantly negatively correlated with FMA-UE,as well as the pdBSI of Global leads(r=-0.289,P=0.014),Central region(r=-0.244,P=0.040)and Posterior region(r=-0.356,P=0.002)across the Beta1 band,and the DAR of Global lead(r=-0.431,P<0.001),Fron-tal region(r=-0.429,P<0.001),Central region(r=-0.491,P<0.001)and Posterior region(r=-0.482,P<0.001).Conclusion Asymmetry in spectral power between hemispheres in the Delta(0.5 to 4 Hz)and Beta(13 to 20 Hz)bands is found in stroke patients,especially in the central region,which correlates with upper limb function limitations.pdBSI Delta,pdBSI Beta1 and DAR are potential neuroelectrophysiological markers for assessing upper limb motor function in stroke patients.

This research investigated the contamination level,distribution of drug-resistant strains,and molecular epidemiologi-cal characteristics of Campylobacter,and further explored transmission pathways and prevention strategies.Cecum,chicken carcass,chicken product,and environmental samples,as well as swabs from workers'hands,were collected from a slaughterhouse in a large broiler group in the Jiaodong area between August 2023 and July 2024.Quantitative contamination assessment of Campylobacter in chicken carcasses and chicken products was performed.After microbial mass spectrometry identification,the representative strains of different links were selected for drug resistance testing and whole genome sequencing(WGS).On the basis of the sequencing results,the resistance genes,virulence genes,multilocus sequence typing(MLST),and phylogenetic characteristics of representative strains were analyzed.Homology comparisons were performed between isolates and strains from patients with diarrhea in the NCBI database.A total of 297 Campylobacter strains were isolated from 806 samples,and the overall detection rate was 36.85%.The detection rate of Campylobacter was highest in the evisceration process(47.33%),followed by the cutting process(35.64%).Overall,the Campylo-bacter detection rate first increased,then decreased,and subsequently increased.Drug sensitivity testing revealed that 90 isolates were resistant to nalidixic acid and ciprofloxacin,and 94.97%of isolates were resistant to tetracycline.WGS showed that both Campylo-bacter jejuni(C.jejuni)and Campylobacter coli(C.coli)carried many drug resistance and virulence genes.ST-14176 of C.jejuni was isolated for the first time herein.The predominant ST-8261 strain of C.jejuni and ST-860,ST-829,and ST-1586 strains of C.coli are known to cause human diarrhea.LOS expression genes associated with Guillain-Barré syndrome(GBS)were detected in both C.jejuni isolates from the slaughter chain and patients with GBS.Some strains exhibited close genetic relatedness to human-derived Campylo-bacter strains from the NCBI database.The detection rate of Campylobacter in the slaughterhouse first increased,then decreased,and subsequently increased,and the quantitative contamination level of each link was similar to the detection rate.Quantitative analysis of chicken carcasses/products revealed that the average bacterial load was highest in eviscerated carcasses(102.80 cfu/g),and the high-est amount of Campylobacter in chicken products reached 451.80 cfu/g.Abundant drug resistance genes and virulence genes were iden-tified,and the drug resistance genes were highly correlated with the drug resistance rate.Therefore,surveillance intensity and control measures for Campylobacter in slaughter processes should be strengthened.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.