Objective: To explore the influencing factors of anxiety symptoms and to construct a predictive model based on machine learning algorithms, so as to provide support for the prevention and management of anxiety symptoms among junior high school students. Methods: From April to May 2023, a stratified random cluster sampling method was adopted to select 8 176 junior high school students from Zhengzhou and Shangqiu citys. All participants completed the Adolescent Self rating Life Events Checklist, the 10item Connor-Davidson Resilience Scale, the School Connectedness Scale, the Parent-Child Cohesion Questionnaire, and the 7 item Generalized Anxiety Disorder Scale. Logistic regression analysis identified the associated factors of anxiety symptoms among junior high school students. Predictive models were constructed using Logistic regression, Random Forest, and eXtreme Gradient Boosting (XGBoost) algorithms, with SHapley Additive exPlanations analysis explaining the optimal model. Results: The detection rate of anxiety symptoms among junior high school students was 16.3%. Logistic regression analysis showed that junior high school students who were female ( OR =1.22), in the ninth grade ( OR =1.27), living in urban areas ( OR =1.37), having a father with a college education or above ( OR =1.26), having a mother with a senior high school education ( OR =1.26), and experiencing higher levels of negative life events ( OR =1.05) reported a higher risk of anxiety symptoms(all P <0.05). In contrast, those with moderate family economic status ( OR =0.71), moderate academic burden ( OR =0.59), low academic burden ( OR =0.54), moderate sleep quality ( OR =0.46), good sleep quality ( OR =0.26), excellent sleep quality ( OR =0.15), higher levels of psychological resilience ( OR =0.96), higher levels of school connectedness ( OR =0.96), and higher levels of parent-child cohesion ( OR =0.98) reported a lower risk of anxiety symptoms (all P <0.05). Three machine learning models demonstrated good predictive performance for anxiety symptoms among junior high school students (all AUC>0.8), with the XGBoost model achieving the best predictive performance. SHAP analysis revealed that negative life events, sleep quality, school connectedness, psychological resilience and parent-child cohesion were the top five relevant factors for predicting anxiety symptoms. Conclusions The detection rate of anxiety symptoms among junior high school students is relatively high. The XGBoost model is the optimal predictive model for anxiety symptoms in the population. Negative life events, sleep quality, school connectedness, psychological resilience, and parent-child cohesion are significant correlates of anxiety symptoms among junior high school students.

BackgroundAnxiety symptoms have become a public health issue affecting the physical and mental health of the elderly population. Mental health literacy is a predictor of anxiety symptoms in the elderly. Currently， there are limited studies on the pathogenic mechanism between the two. Exploring the relationship and mechanism between mental health literacy and anxiety symptoms among the elderly is of great significance for improving the mental health level of the elderly. ObjectiveTo investigate the impact of mental health literacy on anxiety symptoms in the elderly， and to analyze the role of insomnia in this process， in order to provide references for the formulation of prevention and intervention strategies for anxiety symptoms in the elderly. MethodsFrom August 2021 to December 2022， a total of 10 650 older adults aged 60 years old and above were selected from a city in Sichuan Province using a multistage stratified sampling method. Participants completed the self-compiled demographic questionnaire， the Insomnia Severity Index （ISI）， the Generalized Anxiety Disorder Scale-7 item （GAD-7）， and the National Mental Health Literacy Questionnaire （NMHLQ）. Spearman correlation analysis was adopted to examine the correlation between the scores of the scales. Model 4 in SPSS 27.0 plugin Process 4.1 was employed to test the pathway of insomnia between mental health literacy （and its various dimensions） and anxiety symptoms. ResultsAmong the participants， 9 609 cases （90.23%） completed the valid questionnaire survey， and 1 680 cases （17.48%） were found to have anxiety symptoms. The total score of the NMHLQ for the elderly， as well as the scores of the knowledge， awareness， and skills dimensions， were negatively correlated with the GAD-7 score and the ISI score （r_s=-0.506–-0.054， P<0.01）， and the ISI score was positively correlated with the GAD-7 score （r_s=0.666， P<0.01）. Insomnia served as the mediating pathway between the mental health literacy and anxiety symptoms， with an indirect effect value of -0.210 （95% CI： -0.227–-0.193）， accounting for 54.97% of the total effect. Insomnia was the mediating pathway between the mental health literacy knowledge and anxiety symptoms of the elderly， with an indirect effect value of -0.161 （95% CI： -0.178–-0.144）， accounting for 52.61% of the total effect. Insomnia played a mediating role in the relationship between the awareness of mental health literacy and anxiety symptoms， with an indirect effect value of -0.323 （95% CI： -0.342–-0.302）， accounting for 76.36% of the total effect. Insomnia was the mediating pathway between the mental health literacy skills and anxiety symptoms of the elderly， with an indirect effect value of -0.172 （95% CI： -0.187–-0.159）， accounting for 53.75% of the total effect. ConclusionThe dimensions of mental health literacy， knowledge， awareness and skills of the elderly not only directly affect anxiety symptoms， but also indirectly influence anxiety symptoms through the pathway of insomnia.［Funded by Medical Research Project Plan of Sichuan Province （number， S23049）］

Cold ischemia-reperfusion injury (CIRI) is a major contributor to delayed graft function and long-term graft failure after kidney transplantation.Its pathogenesis involves multiple mechanisms, including energy metabolism disorders, oxidative stress and sterile inflammatory responses.Increasing evidence indicates that immune cells play a central regulatory role in the initiation and progression of CIRI.Damage-associated molecular patterns released during cold ischemia and reperfusion activate innate immunity, leading to the rapid recruitment of neutrophils, macrophages, dendritic cells and amplification of inflammatory cascades.Subsequently, adaptive immune cells, including T cells and B cells, are activated and further contribute to sustained inflammation, disruption of immune tolerance and graft injury.Through cytokine signaling, chemokine-mediated recruitment and antigen presentation, different immune cell subsets form a complex interactive network that ultimately determines the severity and outcome of CIRI.This review summarizes the roles and mechanisms of innate and adaptive immune cells in renal allograft CIRI, highlights their interconnections, and discusses emerging immune cell-targeted therapeutic strategies, aiming to provide a theoretical basis for improving clinical outcomes in kidney transplantation.

Objective:To investigate the application of time series models in forecasting pre-hospital emergency ambulance trips in Handan City and develop the LPro ensemble model for improved prediction accuracy to support emergency resource allocation.Methods:Pre-hospital emergency data from Handan Emergency Medical Command Center (2019-2023) were retrospectively analyzed. From 324 799 original records, 289 949 valid records were included after cleaning. The training set (2019-2022: 215 918 records) included 35 527 records in 2019, 52 015 in 2020, 61 836 in 2021, and 66 540 in 2022. The validation set (2023) contained 74 031 records. ARIMA, linear trend seasonal, exponential smoothing, and Prophet models were fitted to the training set. The LPro ensemble model was constructed using MAPE-based weighting (linear trend seasonal model: 0.38, Prophet: 0.62). Performance metrics included MAPE, RMSE, MAE, and R 2. Results:Data showed annual growth (compound annual growth rate 23.27%) and seasonal patterns (October peaks, February troughs). Ambulance dispatches increased annually with monthly cyclical patterns. For 2023 validation predictions: ARIMA (MAPE 8.76%, RMSE 619, MAE 491, R 2 0.4563), linear trend seasonal (MAPE 9.83%, RMSE 671, MAE 545, R 2 0.3608), Prophet (MAPE 8.43%, RMSE 562, MAE 503, R 2 0.5513), exponential smoothing (MAPE 8.08%, RMSE 643, MAE 410, R 2 0.4124). LPro model showed superior performance (MAPE 7.05%, RMSE 491, MAE 393, R 2 0.6570), with 16.37% lower MAPE, 12.63% lower RMSE, 21.87% lower MAE, and 19.17% higher R 2 versus Prophet. Conclusion:The LPro ensemble model substantially enhances prediction accuracy and reliability, offering scientific support for emergency resource optimization and dispatch scheduling in Handan City.

Resectable non-small cell lung cancer (NSCLC) is prone to recurrence and metastasis after simple surgery. Although patients can benefit from preoperative neoadjuvant chemotherapy and postoperative adjuvant chemotherapy, the 5-year survival rate is not significantly improved. In recent years, with the rise of immunotherapy, NSCLC immunotherapy has gradually received attention. Many explorations have been made on resectable NSCLC immunotherapy, and satisfactory results have been obtained. With the release of multiple phase 3 research results, a new chapter in resectable NSCLC immunotherapy has officially opened. However, there are still many problems in the immunotherapy of resectable NSCLC. This article reviews the current relevant research and provides reference for clinical application.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

Rheumatoid arthritis (RA) represents a persistent autoimmune condition distinguished by a multifaceted etiology that encompasses both genetic and environmental factors. Recent progress in understanding the mechanisms behind RA pathogenesis has delved into the critical role of epigenetic regulatory processes, including DNA methylation, histone modifications, and the regulation by microRNAs (miRNAs). These findings provide new insights into the intricate nature of RA and pave the way for innovative therapeutic strategies. This review consolidates the latest developments in the epigenetic regulation of RA, concentrating on how these mechanisms affect the dysregulated signaling pathways associated with the disease. We analyze the roles of specific proteins that function as 'writers', 'erasers', and 'readers' in epigenetic modifications, highlighting their potential as targets for therapeutic intervention. Additionally, in view of the significance of miRNAs in the pathogenesis of RA, we deliberate on their involvement in disease progression and explore miRNA-based treatment strategies. By integrating these diverse epigenetic dimensions, this review offers a comprehensive epigenetic perspective on RA pathogenesis and identifies promising avenues for future research and therapeutic interventions.

OBJECTIVES: To investigate the protective effects of dexmedetomidine (DEX) against heat stress (HS)-induced oncosis in human skeletal muscle cells (HSKMCs) and its underlying mechanisms. METHODS: A HSKMC model of HS-induced oncosis were established by 43 ℃ water bath for 4 h, and the effects of treatments with 30 μmol/L DEX, ML385 (a Nrf2 inhibitor) +DEX, si-Nrf2+HS, and si-Nrf2+DEX prior to modeling on cell viability was assessed using CCK-8 assay. Oncosis characteristics were evaluated using transmission electron microscopy and Annexin V-FITC/PI flow cytometry. The oxidative stress markers (GSH, GSH-Px, MDA, SOD and ROS), mitochondrial membrane potential, energy metabolism, and inflammatory cytokines (TNF-α, IL-6 and IL-1β) in the cells were quantified using standard kits, and the expressions of porimin, caspase-3 and Nrf2 pathway proteins were analyzed using Western blotting and qRT-PCR. RESULTS: HS induced typical oncotic features in HSKMCs including organelle swelling and cytoplasmic vacuolization. DEX pretreatment significantly attenuated these changes, reduced Annexin V⁺/PI⁺ cell ratio and cellular porimin expression, and lowered the levels of ROS and MDA while restoring GSH and SOD levels. DEX pretreatment also significantly increased the mitochondrial membrane potential and ATP level, upregulated the expressions of Nrf2, p-Nrf2, HO-1 and NQO1, and suppressed the expressions of TNF-α, IL-6 and IL-1β. The protective effects of DEX were obviously attenuated by interventions with ML385 or si-Nrf2. CONCLUSIONS DEX mitigates HS-induced HSKMC oncosis by activating the Nrf2/HO-1 pathway to relieve oxidative stress, mitochondrial dysfunction, and inflammatory responses.
Humans ; Dexmedetomidine/pharmacology* ; NF-E2-Related Factor 2/metabolism* ; Oxidative Stress/drug effects* ; Heat-Shock Response/drug effects* ; Signal Transduction/drug effects* ; Membrane Potential, Mitochondrial ; Muscle, Skeletal/cytology* ; Heme Oxygenase-1/metabolism* ; Apoptosis/drug effects*

Objective:To investigate the prenatal echocardiographic features of fetuses diagnosed with Ebstein anomaly (EA), identify prognostic factors affect the fetal and neonatal mortality, and evaluate the clinical value of the Simpson Andrews Sharland prognostic score (SAS prognostic score).Methods:A retrospective cohort study was conducted on 37 fetuses diagnosed with EA via prenatal and postnatal echocardiography at Guangdong Provincial People′s Hospital from June 2012 to June 2024. The echocardiographic features of EA patients were summarized. According to the patients′ survival statuses during the fetal and neonatal periods, they were divided into survival group and death group for a comparative analysis of key echocardiographic parameters, as well as SAS prognostic score. Also, receiver operator characteristic (ROC) curve was employed to assess the predictive abilities of various indicators. Finally, based on the medium-and long-term prognostic outcomes of EA cases, the predictive value of the SAS system was evaluated. The t test, Mann-Whitney U test, and Fisher exact test were used for group comparison. Results:Regarding the 37 EA cases, the gestational age at the initial diagnosis was (29±4) weeks. All of EA fetuses exhibited echocardiographic characterized by tricuspid regurgitation (TR) originating below the native tricuspid annulus with the severity varied, accompanied by manifestations such as right atrial enlargement. Of all cases, 5 cases (14%) died prenatally, and 32 cases (86%) were born alive. Postnatally, 4 cases died preoperatively, 1 case died postoperatively, and 27 cases survived. Compared with the survival group, the death group had a significantly higher average SAS prognostic score (6.9±1.1 vs. 2.0±1.5, t=9.17, P<0.001), right atrium (RA) to left atrium (LA) transverse diameter ratio (2.0±0.5 vs. 1.3±0.2, t=4.87, P=0.001) and TR area to RA area ratio (0.8±0.2 vs. 0.4±0.2, t=5.27, P<0.001). According to the ROC analysis, the optimal predictive value indicators are RA to LA transverse diameter ratio (AUC=0.93, 95% CI 0.81-1.00) and the TR area-to-RA area ratio (AUC=0.93, 95% CI 0.85-1.00); the optimal cut-point values were 1.5 and 0.5, respectively. Of 32 born alive cases, 21 cases (66%) didn′t undergo surgery, 2 cases (6%) underwent bidirectional Glenn surgery, and one case (3%) underwent tricuspid valvuloplasty. All 17 cases with SAS score≤2 survived, while all 9 cases with SAS score≥6 died. Among the 11 cases with a score from 3 to 5, 8 cases achieved a biventricular outcome. Conclusions:The typical echocardiographic feature of EA fetuses is that the originating point of TR is below the native tricuspid annulus and the severity can vary. The SAS score is essential for tiered prognosis. When the SAS is 3-5, dynamic monitoring for TR and RA enlargement should be employed to help guide prenatal intervention and reduce fetal and neonatal mortality.

Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.