Objective: To investigate the association between sleep duration and depressive symptoms in children and adolescents, so as to provide scientific evidence for promoting mental health and preventing depression among relevant populations. Methods: A total of 2 192 children and adolescents aged 10-17 years with complete data from the 2018 China Family Panel Studies Database were included. Eight item Center for Epidemiologic Studies Depressive Scale(CES-D8) was used to assess participants depressive levels, and sleep duration was assessed via questionnaire. Multivariate Logistic regression model was used to analyze the association between different sleep duration categories and depressive symptom occurrence among children and adolescents. A restricted cubic spline(RCS) model analyzed the dose response relationship between sleep duration and the risk of depressive symptoms occurrence and segmented Logistic regression models to identify dose response effects. Results: Among the surveyed children and adolescents, 524(23.91%) exhibited depressive symptoms. Compared to those with sufficient sleep, children aged 10-12 years had a higher risk of depressive symptoms on average per day( OR =1.66, 95% CI =1.19-2.33) and during weekdays( OR =1.76, 95% CI =1.26-2.46), as well as in adolescents aged 13-17 years on a daily basis( OR =1.40，95% CI =1.06-1.85) and during weekdays( OR = 1.48，95% CI =1.12-1.95), and excessive sleep in adolescents on rest days was also significantly associated with higher risk of depressive symptoms( OR =1.65，95% CI =1.11-2.43)(all P <0.05). RCS analysis results indicate that children s sleep duration exhibits a linear negative correlation with the risk of depressive symptoms(all P nonlinear >0.05), while adolescents sleep duration showed a U shaped association with depressive symptoms(all P nonlinear <0.05) on a daily basis, during weekdays and weekends, with potential threshold effects at 10.00, 9.88, and 9.60 hours, respectively. Conclusions Sleep duration among children and adolescents is associated with depressive symptoms, with notable age related differeneces. It is recommended to develop targeted age specific interventions to reduce the risk of depressive symptoms in children and adolescents.

Objective: To compare the effects of seasonal autoregressive integrated moving average (SARIMA) , seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) and long short-term memory neural network (LSTM) models in predicting the incidence of hand, foot, and mouth disease (HFMD). Methods: Monthly incidence data of HFMD in Kunming City from 2010 to 2019 were collected. SARIMA, SARIMAX and LSTM models were established using the monthly incidence of HFMD from 2010 to 2018 to predict the monthly incidence of HFMD from January to December 2019. The prediction performance of the three models was compared using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The optimal prediction model was selected based on the principle of minimizing MSE, RMSE, MAE and MAPE. Results: The HFMD cases were reported every month in Kunming City from 2010 to 2019, with the incidence fluctuating between 188.27/105 and 363.15/105. The disease exhibited a biennial high-incidence bimodal distribution. Among the four evaluation indicators for the training and testing sets, the LSTM model had the smaller values: MSE was 63.182 and 102.745, RMSE was 7.949 and 10.136, MAE was 6.535 and 7.620, and MAPE was 46.726% and 31.138%. The LSTM model performed the better, followed by the SARIMA model, while the SARIMAX model had the relatively poorest performance. Conclusion The LSTM model outperforms the SARIMA and SARIMAX models in predicting the incidence of HFMD.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Objective To investigate the screening results and factors affecting abnormal detection rates among high-risk groups of esophageal cancer and to explore effective intervention measures. Methods We investigated and collected the information on gender, education level, age, marital status, symptoms of reflux esophagitis (heartburn, acid reflux, belching, hiccup, foreign body sensation in the pharynx, and difficulty swallowing), consumption of pickled vegetables, salt use, and esophageal cancer incidence of villagers in a natural village in Wenfeng District, Anyang City, Henan Province. Changes in reflux esophagitis symptoms in the high-incidence area of esophageal cancer before and after 16 years were observed, and the relationship of such changes with esophageal cancer was analyzed. Results In 2008, 711 cases were epidemiologically investigated, including 213 cases with one of the symptoms of reflux esophagitis such as heartburn, acid reflux, belching, hiccups, foreign body sensation in the pharynx, and difficulty swallowing (sorted in accordance with the abovementioned symptoms; if there are multiple symptoms, then the former is taken); 55 cases with at least two related symptoms, among which the most symptom was heartburn in 108 cases (15.1%); followed by acid reflux, belching, etc. In 2024, the same group of people was investigated, and eight people were missing because of relocation. A total of 703 people were successfully investigated, of which 189 cases (26.8%) had one of the symptoms of reflux esophagitis, such as heartburn (sorted in accordance with relevant symptoms, if there are multiple symptoms, then the former will be selected); 167 cases (23.7%) had at least two related symptoms, among which the most symptom was heartburn in 139 cases (19.7%); followed by acid reflux. Over 16 years, among the 703 people investigated, 45 cases had esophageal cancer and they had one or more of the abovementioned symptoms. No significant differences in the reflux esophagitis-like symptoms were found between 2008 and 2024 (P=0.26); no significant differences in the composition of reflux esophagitis-like symptoms were found between 2008 and 2024 (P=0.299). Conclusion The detection rate of reflux esophagitis-related symptoms in the population in a high-risk area of esophageal cancer has not decreased in the past 16 years, and the high-risk factors still exist. Esophageal cancer is closely related to reflux esophagitis, and clinical practice can provide early diagnosis and treatment for high-risk population.

Objective To investigate the fatigue and workload status among medical students,and to explore the latent profiles of fatigue and workload and their effects on sleep and emotion.Methods A cross-sectional study design with convenience sampling was employed to distribute a comprehensive survey via mixed online and offline modes,and medical college students were enrolled as the subjects for this investigation.The general demographic data,depression,anxiety and stress scale,Pittsburgh sleep quality index,Epworth sleepiness scale,insomnia severity index,National Aeronautics and Space Administration task load index(NASA-TLX)and fatigue scale-14(FS-14)were used to investigate the basic information of the medical students,their emotions(depression,anxiety and stress),sleep(sleep quality,sleepiness and insomnia),workload and fatigue status.Based on latent profile analysis,the types of workload-fatigue profiles and differences in sleep and emotion were analyzed.Results A total of 485 medical students were enrolled,with an average age of(22.07±2.42)years.The total score of the NASA-TLX was 64.44±12.50,and the total score of the FS-14 was 7.90±3.63.Latent profile analysis identified 3 distinct workload-fatigue profiles:low workload-medium fatigue group(12.8％),medium workload-low fatigue group(32.8％),and high workload-high fatigue group(54.4％).Among these,the medium workload-low fatigue group exhibited the highest performance level(all P＜0.05),while the low workload-medium fatigue group showed the lowest effort level and performance level(all P＜0.05).The high workload-high fatigue group showed the highest task-related demand and frustration level(all P＜0.05).Regarding sleep and emotional status,the medium workload-low fatigue group had significantly better outcomes compared to the high workload-high fatigue group and the low workload-medium fatigue group(all P＜0.05).Conclusion Medical students experience a heavy workload and subjective fatigue.It is essential to appropriately adjust their workload,prioritize sleep and emotional well-being,and alleviate fatigue levels,so as to sustain personal physical and mental health.

Objective To investigate the effect of β-arrestin1(ARRB1)on cell proliferation,migration and invasion in oral squamous cell carcinoma(OSCC).Methods Based on The Cancer Genome Atlas(TCGA)database,the expression profiles of ARRB1 in OSCC were analyzed,and then Gene Set Enrichment Analysis(GSEA)was used to suggest the possible signaling pathways involved,and to explore its potential impact on the prognosis of OSCC patients.Immuinohistochemistry(IHC)was performed to detect the expression of ARRB1 in OSCC tumor tissues and adjacent tissues,and the correlation between ARRB1 expression and clinicopathological features was statistically analyzed.The expression profiles of ARRB1 in SCC-15,CAL-27 and HOK cell lines were verified by qPCR and Western blotting.The ARRB1 overexpression plasmid model was constructed,and its effects on the proliferation,migration and invasion of OSCC cells were analyzed by clone formation,EdU,scratch and Transwell assays.Results TCGA showed that the expression level of ARRB1 was significantly lower in head and neck squamous cell carcinoma(HNSC)and OSCC tissues than the corresponding normal tissues(P<0.01).The expression of ARRB1 in OSCC tissues was correlated with tumor differentiation,lymph node metastasis and TNM stage(P<0.05).The OSCC patients with high expression of ARRB1 had a lower survival rate than those with low expression(P<0.01),which was consistent with the results of bioinformatics analysis.The expression level of ARRB1 in SCC-15 and CAL-27 cells was lower than that of HOK cells(P<0.01),and its overexpression significantly inhibited cell proliferation(P<0.05),migration(P<0.01)and invasion(P<0.01).Conclusion ARRB1 is lowly expressed in OSCC,its overexpression inhibits the proliferation,migration and invasion of OSCC cells,and it is related to prognosis improvement.

ObjectiveTo characterize the seasonal patterns of influenza in Kunming City, Yunnan Province before, during, and after the COVID-19 pandemic, and provide scientific evidence for optimizing influenza prevention and control strategies. MethodsInfluenza-like illness (ILI) and etiological surveillance data for influenza from the 14^th week of 2010 to the 13^th week of 2024 in Kunming City of Yunnan Province were collected. Harmonic regression models were constructed to analyze the epidemic characteristics and seasonal patterns of influenza before (2010/2011‒2019/2020 influenza seasons), during (2020/2021‒2022/2023 influenza seasons), and after (2023/2024 influenza season) the COVID-19 pandemic. ResultsBefore the COVID-19 pandemic, influenza in Kunming City mainly exhibited an annual cyclic pattern without a significant semi-annual periodicity, peaking from December to February of the next year, with an epidemic duration of 20‒30 weeks. During the pandemic, influenza seasonality shifted, with an increase in semi-annual periodicity and an approximate one month delay in annual peaks. However, after the pandemic, the annual amplitude of influenza increased compared with that before the pandemic, and the epidemic duration extended by about one month. Although the annual peak largely reverted to the pre-pandemic levels, the annual peaks for different influenza subtypes/lineages had not fully recovered. ConclusionInfluenza seasonality in Kunming City underwent substantial alterations following the COVID-19 pandemic and has not yet fully reverted to pre-pandemic levels. Continuous surveillance on different subtypes/lineages of influenza viruses remains essential, and prevention and control strategies should be adjusted and optimized in a timely manner based on current epidemic trends.

This case report presents a 16-year-old male patient with deficiency of adenosine deaminase 2(DADA2). The patient had a history of Raynaud′s phenomenon with digital ulcers since childhood. As the disease progressed, the patient developed retinal vasculitis, intracranial hemorrhage, skin necrosis, severe malnutrition, refractory hypertension, and gastrointestinal bleeding. Genetic testing revealed compound heterozygous mutations in the ADA2 gene (paternal c.1072G > A pathogenic mutation + maternal c.1065C > A variant of unknown clinical significance). ADA2 enzyme activity was significantly reduced (0.1 U/L). A multidisciplinary treatment team developed an individualized plan to address key issues, including nutritional support, blood pressure control, rehabilitation, pain management, and balancing anticoagulation/bleeding risks. After systematic intervention, the patient′s condition showed partial improvement. This case reveals clinical challenges such as anticoagulation decisions and nutritional support of DADA2. It also emphasizes the importance of early molecular diagnosis, tumor necrosis factor-α inhibitor targeted therapy, and multidisciplinary collaboration in management, underlining the core value of precision medicine in rare disease management.

Diabetic cataract, a prevalent ocular complication of diabetes mellitus, arises from a complex interplay of pathological mechanisms, with oxidative stress and glycation stress playing central roles. Autophagy, a critical cellular self-protection mechanism, sustains intracellular homeostasis by selectively degrading damaged organelles and misfolded proteins, thereby counteracting the detrimental effects of oxidative and glycation stress under hyperglycemic conditions. Emerging evidence indicates a synergistic interaction between glycation stress and oxidative stress, which may exacerbate autophagic dysfunction and accelerate the onset and progression of diabetic cataract. However, the precise molecular mechanisms underlying this relationship remain incompletely understood. This review systematically examines the regulatory role of autophagy inthe pathogenesis of diabetic cataract, with a particular focus on how autophagic impairment influences disease progression under the combined effects of glycation and oxidative stress. By elucidating these mechanisms, the paper aims to provide novel insights into molecular diagnostic approaches and targeted therapeutic strategies for diabetic cataract.