Objective To explore the factors affecting the quality of psychological assistance hotline connections in Shaanxi Province, and to provide a basis for optimizing services. Methods A total of 149 calls with suicidal tendencies were included from January to March 2022, and data were collected by 31 trained assessors through standardized questionnaires (general information, suicide risk, emotional intensity, and wiring characteristics). Results The results showed that 56.38% of the callers were female, with age groups concentrated between ≤ 18 years old (29.53%) and 19-34 years old (43.62%). The call duration was mainly between 31 and 45 minutes (50.34%). Operators conducted a suicide risk assessment on the callers, with 38.9% having a comprehensive assessment, 38.9% having an incomplete assessment, and 22.1% having no assessment. The main mental disorders of the callers were depression (48.32%), anxiety (15.44%), and bipolar disorder (14.77%), with 25.50% having comorbidities of ≥ 2 disorders. Emotional scores were as follows: depression (4.11 ± 0.76), sadness (3.97 ± 1.03), and despair (3.78 ± 1.05). There were significant differences in depression, anger, despair, and sadness among the callers with different levels of danger (t=4.79, 3.35, 15.79, 4.24, all P<0.05). Women had higher levels of fear than men (t=3.10, P<0.01). The longer the call duration, the higher the level of despair (t=5.66, P<0.01). Multiple regression analysis showed that incomplete suicide risk assessment by operators (B=-2.36), general procedures for operators' connections (B=5.44), and technical factors (B=2.01) significantly affected the quality of psychological assistance hotlines (all P<0.05). Conclusion Callers with suicidal tendencies generally have serious mental and psychological problems and prominent negative emotions. Strengthening the suicide risk assessment ability of operators and standardizing processes and service attitudes are key to improving the quality of psychological assistance hotlines.

Childhood obesity has become a global public health issue. Current research indicates that early life obesogen exposure has emerged as a significant risk factor for childhood obesity. While obesogens have been confirmed to influence the development and progression of childhood obesity through mechanisms such as endocrine disruption and epigenetic programming, controversies remain regarding the establishment of causal relationships, assessment of combined exposures, and validation of transgenerational effects in humans. In recent years, novel approaches including multi-omics technologies, exposome-based analysis, and multigenerational cohort studies have integrated dynamic biomarker monitoring with analyses of social-environmental interactions, offering new perspectives and methodologies for constructing a systematic "exposure-mechanism-outcome" research framework. This article reviews literature from PubMed and Web of Science up to August 2025 on the association between early life obesogen exposure and childhood obesity, summarizing evidence on the health effects of early life obesogen exposure, major exposure pathways and internal exposure assessment, interactions and amplifying effects of social and environmental factors, as well as the biological mechanisms underlying obesogen action. It further examines current research frontiers and challenges, aiming to provide a theoretical foundation for early prevention and precision intervention of childhood obesity.

Objectives: Obesity has emerged as a major public health concern in the United States, with prevalence increasing steadily over the past 4 decades. This mini-review summarizes recent trends and subgroup disparities using data from the National Health and Nutrition Examination Survey (NHANES) and related publications. Methods: Data from NHANES cycles conducted between 1988 and 2023 were reviewed to examine temporal trends in adult obesity prevalence, with particular attention to differences by age, sex, race, and interactions between sex and age as well as sex and race. Results: The prevalence of adult obesity increased from approximately 15% in the late 1970s to 42.4% in 2017–2018. For the first time in nearly 3 decades, consecutive declines were observed, with prevalence falling to 40.3% in 2021–2023. Disparities persist: middle-aged adults and non-Hispanic Black females continue to experience the highest prevalence of obesity, whereas non-Hispanic Asian populations consistently exhibit the lowest rates. Conclusions Recent declines may indicate progress in obesity control; however, targeted and equity-focused interventions remain essential to sustain improvements and reduce persistent disparities.

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.

Chikungunya fever(CHIKF)is a mosquito-borne infectious disease caused by Chikungunya virus(CHIKV)infection,which has become a significant public health threat of global concern in recent years.The acute phase of CHIKF is primarily characterized by sudden high fever,severe polyarthralgia,and rash.The disabling arthralgia may progress to chronic arthritis,with symptoms lasting for months or even years.This not only severely reduces patients'quality of life and impairs their labor capacity,but also imposes a heavy socioeconomic burden.In recent years,significant progress has been made in CHIKF-related research.This review elaborates on the latest research achievements in the fields of the disease's epidemiology,clinical manifestations,pathogenic mechanisms,immune response,diagnostic techniques,and prevention and treatment strategies,aiming to provide comprehensive and in-depth references for researchers and clinicians in relevant fields.

Objective To investigate the factors influencing global longitudinal strain(GLS)measured by cardiac magnetic resonance(CMR)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods Clinical data of 315 hospitalized patients diagnosed with acute STEMI who underwent percutaneous coronary intervention(PCI)at the First Medical Center of Chinese PLA General Hospital from June 2016 to September 2021 were retrospectively collected.After analyzing CMR images of all patients,GLS and other strain parameters were obtained,and then the patients were divided into two groups according to the median GLS.In order to balance gender and age differences,1:1 propensity score matching was performed,and 206 patients were eventually included:GLS>-11.3%group(indicating severe GLS impairment,n=103)and GLS≤-11.3%group(n=103).Baseline characteristics,laboratory indicators,coronary angiographic parameters,electrocardiogram(ECG)features,and CMR parameters were compared between the two groups.Variables showing significant differences were analyzed for their correlation with GLS.Multivariate logistic regression and multiple stepwise linear regression analyses were performed to identify factors associated with GLS impairment.Results Compared with GLS≤-11.3%group,GLS>-11.3%group had significantly higher peak levels of creatine kinase-MB(CK-MB)and troponin T(TnT)(P<0.001).A higher proportion of patients in GLS>-11.3%group had the left anterior descending artery(LAD)as the culprit vessel,while a lower proportion had the right coronary artery(RCA)as the culprit vessel(P<0.001).Additionally,GLS>-11.3%group had longer QRS duration(P<0.001)and a higher incidence of pathological Q waves(P=0.001).Regarding CMR parameters,GLS>-11.3%group exhibited larger global circumferential strain(GCS),infarct size(IS),and left ventricular end-systolic volume(LVESV),as well as lower global radial strain(GRS)and left ventricular ejection fraction(LVEF)(P<0.001).Multivariate logistic regression indicated that peak TnT(OR=1.092,P=0.001),LAD culprit vessel(OR=3.744,P<0.001),and QRS duration(OR=1.026,P<0.001)were significantly associated with severely impaired GLS.Multiple stepwise linear regression analysis showed that the logarithmic value of peak TnT,LAD as the culprit vessel,and the square root of QRS duration were linearly correlated with GLS values(adjusted R2=0.301,P<0.001),and these independent variables explained 30.1%of the variation in GLS.Conclusion Elevated peak TnT,prolonged QRS duration,and LAD as the culprit vessel are significantly associated with severe GLS impairment in STEMI patients,indicating more severe myocardial infarction and worse left ventricular function.

Objective To compare the efficacy of stenting in the treatment of idiopathic intracranial hypertension(IIH)complicated by different types of venous sinus stenosis(VSS).Methods The clinical data of 48 patients with IIH complicated by VSS,who received stenting therapy at the First Affiliated Hospital of Zhengzhou University of China from January 2019 to September 2023,were retrospectively analyzed.According to the type of VSS,the patients were divided into intrinsic stenosis group(n=20)and the extrinsic stenosis group(n=28).The improvement of symptoms,Frisén grade of papilledema,lumbar puncture opening pressure(LPOP),trans-stenosis pressure gradient(△P)of VSS,and surgery-related complications were compared between the two groups.Results The mean age of the patients in the intrinsic stenosis group was greater than that of the patients in the extrinsic stenosis group(41.60 years vs.35.25 years,P=0.049).The length of the narrowed segment in the extrinsic stenosis group was 22.5 mm,which was significantly longer than 19.0 mm in the intrinsic stenosis group(P=0.007).The postoperative Frisén grade of papilledema in the extrinsic stenosis group was obviously lower than that in the intrinsic stenosis group(P=0.037).No statistically significant differences in the other clinical data existed between the two groups(all P＞0.05).After stenting,all of the median △P,mean LPOP,and median Frisén grade of papilledema were decreased significantly when compared with their preoperative values(all P＜0.001),and the postoperative 3-day median Frisén grade of papilledema in the extrinsic stenosis group was much lower(P=0.037).The patients were followed up for one year,the clinical symptoms of the patients in both groups were improved to varying degrees.At the time of discharge,the proportion of patients having no symptoms of papilledema in the extrinsic stenosis group was 57.1％,which was higher than 22.2％in the intrinsic stenosis group(P=0.049),and no statistically significant differences in the improvements of other symptoms existed between the two groups(all P＞0.05).There was no significant difference in the incidence of complications between the two groups(P=0.563).Conclusion Venous sinus stenting can effectively treat patients with IIH complicated by different types of VSS.

Objective To compare the clinical efficacy of medication and stenting in treating patients with idiopathic intracranial hypertension complicated by venous sinus stenosis.Methods The clinical data of 74 patients with idiopathic intracranial hypertension complicated by venous sinus stenosis,who were admitted to the First Affiliated Hospital of Zhengzhou University of China from January 2020 to June 2023,were retrospectively analyzed.The patients were divided into medication group(n=35,receiving drug therapy)and stenting group(n=39,receiving stent implantation therapy).Before and after treatment,lumbar puncture and fundus examinations were performed,and the postoperative improvements in intracranial pressure and papillary oedema were evaluated.The changes in the median papillary oedema Frisén grade and the average opening pressure of lumbar puncture were compared between the two groups during hospitalization period.The improvement degrees of the clinical symptoms determined at discharge,as well as at the 6 months and 12 months after discharge were compared between the two groups.The incidence of complications during the follow-up period in the two groups was recorded.Results The time interval from onset to treatment in the stenting group was longer than that in the medication group(2 months vs.one month,P=0.021),and the differences in the other baseline data between the two groups were not statistically significant(all P＞0.05).After treatment,different degrees of improvement were obtained in both groups(all P＞0.05).At the time of discharge,the degree of median papillary oedema in the stenting group was Frisén grade I,which was lower than Frisén grade Ⅱ in the medication group(P=0.011);the average opening pressure of lumbar puncture in the stenting group was 205.26 mm H2O,which was lower than 248.14 mm H2O in the medication group(P=0.002).The proportions of patients having no symptom or showing symptom improvement in the stenting group and in the medication group at the time of discharge were 74.4％and 45.7％respectively(P=0.017),which at the time of 6 months after discharge were 84.6％and 48.6％respectively(P=0.001)and at the time of 12 months after discharge were 87.2％and 57.1％respectively(P=0.004).No statistically significant difference in the incidence of complications existed between the two groups(10.3％and 8.6％respectively,P=1.000).Conclusion For the treatment of patients with idiopathic intracranial hypertension complicated by venous sinus stenosis,stent implantation therapy is superior to medication therapy in quickly and effectively relieving papillary oedema,decreasing lumbar puncture opening pressure,and improving their corresponding symptoms and signs,with satisfactory patient's prognosis and clinical safety.

Objective: Hundreds of countries have implemented lockdown policies to slow the spread of coronavirus disease-2019 (COVID-19), but the impact of these measures on maternal mental health is not well understood. Methods: This study integrated a stress-process model to examine the pathways from lockdown-related stressors to prenatal psychological outcomes, with COVID-19 coping strategies (COP) and self-efficacy in managing negative affect (NEG) as mediators and lockdown duration, hours on pandemic-related information, and number of pregnancies as moderators. Pregnant women in Shanghai completed the Regulatory Emotional Self-Efficacy Scale, COVID-19 Coping Scale, Depression, Anxiety, and Stress Scale-21. Structural equation modeling (SEM) was used to test and modify the hypothetical model, and moderated mediation and slope analyses were undertaken. Results: In the final SEM demonstrating satisfactory fit, three stressors—decreased household income, insufficient daily supplies, and acquired infections—showed positive direct relationships with NEG and COP. Acquired infections, NEG, and COP were identified as direct predictors of mental health outcomes. The relationship between these three stressors and mental health was mediated by NEG and COP. Additionally, the number of pregnancies moderated the mediating effect of COP; this effect was more pronounced among first-time pregnant women than those with multiple pregnancies. Conclusion This study provides insights into how lockdown measures impact psychological outcomes in pregnant women quarantined at home. Interventions aimed at increasing coping strategies may be more effective for primiparous women during future public health emergencies.

Objective:This study aimed to evaluate the value of CT-based radiomics machine learning models in predicting spread through air spaces(STAS)in lung adenocarcinoma(LUAD)and to determine the optimal peritumoral analysis region.Methods:Data from 378 pa-tients who underwent non-small cell lung cancer surgery at Zhejiang Cancer Hospital between January 2013 to January 2017 were retro-spectively analyzed.Logistic regression,random forest,and XGBoost models were constructed using regions extending 0,3,6,9,and 12 mm outward from the tumor margin.Results:The XGBoost model using the 6 mm peritumoral region performed best on the test set,with an AUC-ROC of 0.855(95%CI:0.756-0.950),followed by the XGBoost model using the 9 mm region.Decision curve analysis(DCA)indicated that the XGBoost models for the 6 mm and 9 mm regions had higher net clinical benefits.Feature analysis revealed that some wavelet trans-form features significantly contributed to STAS prediction.Conclusions:This preliminary study suggests that CT-based radiomics machine learning models have predictive value for STAS.The XGBoost model based on the 6 mm peritumoral region demonstrated the best perform-ance,and holds promise in assisting preoperative assessment.