Background Although current evidence suggests a link between outdoor air pollution and depressive symptoms, the effect of solid fuel use (a significant indoor air pollutant) on depressive symptoms in China's rural middle-aged and elderly population remains poorly understood. Objective To explore the association between solid fuel use for cooking and depressive symptoms among middle-aged and elderly people in rural areas of China, and to provide a basis for the prevention and control of depressive symptoms among residents in rural areas. Methods Data were obtained from the 2020 China Family Panel Studies (CFPS), depressive symptoms were assessed using 8-item Center for Epidemiologic Studies Depression Scale (CES-D), and cooking fuel type was self-reported. Subsequently, two-level binary unconditional logistic regression models were fitted to assess the impact of solid fuel use for cooking on depressive symptoms. Results A total of 7991 participants aged 45 years and older [mean age (58.52±9.18) years] were included in the study, with a mean CES-D score of 6.07±4.48 and a mean positive depressive symptoms rate of 34% (40.54% in the solid fuel group and 30.81% in the clean fuel group, χ²=74.40, P<0.001). After controlling for potential confounding covariates, rural middle-aged and elderly residents in the solid fuel group had a higher risk of reporting depressive symptoms than those in the clean fuel group (OR=1.36, 95%CI: 1.20-1.54), an association that was unaffected by participant characteristics and was positive across age and gender groups. Conclusion There is a positive association between solid fuel use for cooking and depressive symptoms in middle-aged and elderly people in rural areas of China. It is recommended that rural areas promote the upgrading of stoves through the installation of ventilation equipment or the switch from solid cooking fuel to clean fuel. This approach is directed toward reducing exposure to household air pollution, alleviating the prevalence of depression among middle-aged and elderly residents in vast rural areas, and enhancing their mental health.

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.

Objective: To investigate the weight loss effect of a comprehensive intervention model combining caloric restriction (CR), physical activity (PA), behavioral therapy (BT), breathing exercise (BE), and functional movement corrective training (FMCT)-referred to as the "CPBBF" model in overweight and obese female college students, so as to provide a reference for scientific weight loss interventions for college students. Methods: From March to May 2022, 46 overweight and obese female college students from Chongqing Water Resources and Electric Engineering College were recruited and randomly divided into an experimental group (24 participants) and a control group (22 participants). The control group received CR (prohibiting ad libitum snacking), PA in the first week, high intensity interval training (HIIT) for 30 s, and moderate intensity continuous training (MICT) for 1-5 min alternate 4 sets, duration 15-20 min. From the second week, adjust to HIIT and MICT alternating 3 min each for 5 sets, totaling 30 min, 4 times/week, 70 min/time and BT (60-90 min/session, 3 times/week). The experimental group incorporated FMCT (10-15 min of focused training per session, integrated with PA and daily life) and BE (advocating a gradual transition to proper breathing methods in daily life and low intensity training, 5 sessions/day, 10 min each). Body oxygen level test (BOLT), Functional Movement Screen (FMS), sports exercise attitude, and body composition indicators were measured at baseline (T0), after 12 weeks of intervention (T1), and after one year of follow up (T2). The differences were analyzed between groups through generalized estimation equations, and mixed effect model analysis was employed to explore predictive relationships among variables. Results: The results of the generalized estimation equation showed that time main effects of BOLT values, FMS scores, and exercise attitude among female college students were statistically significant ( Wald χ 2=18.75, 14.89, 12.45, all P <0.01); further intragroup comparisons revealed that BOLT， functional motor screening (FMS) scores, and physical exercise attitudeof female college students in the experimental group increased compared to T0, while the control group only showed an increase at T1 (all P <0.05). The group main effects for the aforementioned three indicators were statistically significant ( Wald χ 2=6.33, 5.21, 4.88), and the time by group interactions of BOLT values and FMS scores were also statistically significant ( Wald χ 2=4.56, 3.97) (all P <0.05). The time main effects of body weight, body mass index (BMI), and body fat ratio(BFR) in female college students were statistically significant ( Wald χ 2=44.27, 13.90, 82.33); further intragroup comparisons revealed that the experimental group of female college students showed a decrease in body weight, BMI and BFR at T1 and T2 compared to T0, while the control group only showed a decrease in these indicators at T1 (all P <0.05). The group main effects of weight and BFR were statistically significant ( Wald χ 2= 4.11 , 6.46), and the time by group interaction of BFR was statistically significant ( Wald χ 2=8.73) (all P <0.05).The results of mixed effect model analysis showed that BOLT ( β =1.52) and FMS ( β =1.81) could both positively predict physical exercise attitude, and physical exercise attitude had statistically significant negative predictive effects on weight, BMI, and BFR ( β =-0.08, -0.03 , -0.03) (all P <0.01). Conclusion The "CPBBF" comprehensive intervention effectively maintains weight loss effects by modulating the energy compensation mechanism with strong robustness.

Aim To study whether intravenous injec-tion of miR-129-3p mimetic(agomir)can resist bone loss caused by hind limb disuse,and to provide new i-deas for preventing bone loss in microgravity environ-ment.Methods Forty-eight C57BL/6J male mice were randomly divided into the control group(CON),tail suspension model group(TS),tail suspension+miR-129-3p agomir administration group(miRNA)and tail suspension+miR-129-3p negative sequence agomir control group(NC).The miRNA group was given 4 mg·kg-1 miR-129-3p agomir by intravenous injection into the medial canthus twice a week.The NC agomir group were consistent with those in the miR-129-3p agomir group,and the CON and TS groups were given only equal volumes of normal saline.After four weeks,all mice were sacrificed and samples were collected.Micro-CT scan of femur,three-point femur bending test,serum bone metabolism index detection,oxidative stress index detection and osteogenesis-related protein expression analysis in bone tissue were per-formed.Results After four weeks,the number of tra-becular bone in the TS group was significantly re-duced,and Tb.BMD,Tb.Th,Tb.N,Tb.BS/TV and Tb.BV/TV were significantly lower than those in the CON group(P＜0.01).While Tb.Sp TS group was significantly higher than the CON group(P＜0.05),the maximum load and flexural strength of the femur significantly decreased(P＜0.01),the content of ser-um bone formation index PINP was significantly lower than that of the CON group(P＜0.01),and the con-tent of bone resorption index CTX-I was significantly higher than that of the CON group(P＜0.01),the content of serum oxidative damage indexes 8-iso-PGF2α and 8-OHdG significantly increased(P＜0.01),and the expression of osteogenesis-related pro-teins in bone tissue markedly decreased(P＜0.01).However,the increase or decrease of all indexes in miRNA group was significantly lower than that in TS group.Conclusions miR-129-3p mimetic can signifi-cantly reduce bone loss caused by hind limb disuse.This experiment provides a new idea and method for preventing bone loss in microgravity environment.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

Osteosarcoma(OS) is the most common primary bone malignancy with a high propensity for local infiltration and metastasis.c-Jun N-terminal kinases(JNK)is an extremely im-portant member of the mitogen-activated protein kinase(MAPK)family,and the JNK signaling pathway has been shown to be in-volved in regulating OS development.In this paper,we review the research progress on the JNK signaling pathway regulating biological behaviors such as proliferation,migration,invasion,an-giogenesis,autophagy,apoptosis and pyroptosis of OS,as well as the oxidative stress and non-coding RNA regulation of OS through the JNK signaling pathway,to further explore the intrin-sic regulatory mechanisms of the JNK signaling pathway,so as to provide a new way of thinking in searching for the treatment of OS.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective To investigate anxiety-and depression-like behaviors in Cynomolgus monkeys with type 2 diabetes mellitus(T2DM),and to explore their correlations with biochemical parameters.Methods Cynomolgus monkeys were divided into a T2DM group(fasting plasma glucose(FPG)≥ 5.6 mmol/L)and control group(FPG＜4.2 mmol/L)(n=3 per group).Age,sex,body mass,body mass index(BMI),FPG,blood lipids,atherosclerosis index(AI),and tumor necrosis factor-α(TNF-α)levels were measured in both groups.Anxiety-like behaviors were assessed using the human intrude test(HIT)and depression-like behaviors were evaluated using the apathy feeding test(AFT).Relationships between behavioral parameters and biochemical/inflammatory markers were evaluated using Pearson's correlation analysis.Results(1)FPG,AI,and TNF-αlevels were significantly elevated while high-density lipoprotein cholesterol levels were reduced in the T2DM group compared with the control group(P＜0.05).(2)Regarding the HIT result,the durations of anxious(stare phase)and back-of-cage(stare phase)behaviors were higher in the T2DM group than in the control group(P＜0.05),while the durations of locomotor behaviors(baseline phase,profile phase,back phase)were significantly shorter(P＜0.05).(3)The AFT revealed that food retrieval latency was significantly delayed in the T2DM group(P＜0.05).Pearson's analysis identified a positive correlation between back-of-cage(stare phase)duration and AI(r=0.828,P=0.042),while locomotion during the back phase showed a negative corr-elation with AI(r=-0.842,P=0.035).Conclusions Cynomolgus monkeys with T2DM display distinct anxiety-and depression-like behavioral phenotypes,with a significant association between the AI and anxiety-related behaviors.These findings provide novel insights into the pathophysiological mechanisms linking T2DM with neuropsychiatric comorbidities.