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.

The construction of a training system for visiting physicians in the department of rare diseases in China is an important measure to improve the overall diagnosis and treatment capacity for rare diseases and address the critical challenge of insufficient knowledge and skills among clinicians in practice. This article systematically describes the visiting physician training system established by the Department of Rare Diseases at Peking Union Medical College Hospital. It summarizes the training objectives and positioning, design logic, and learning modules of the system, aiming to provide a reference for the construction of the specialized talent team for rare diseases in China.

To investigate the clinical efficacy of neoadjuvant imatinib in the treatment of rectal gastrointestinal stromal tumor (GIST).

Tuberous sclerosis complex (TSC) is an autosomal dominant genetic disorder primarily caused by pathogenic variants in the TSC1 or TSC2 genes. It is characterized by multisystem hamartomatous lesions and neuropsychiatric manifestations. Here we report a young male patient with TSC involving multiple organs, caused by a heterozygous frameshift mutation in TSC2 (c.2071delC, p.Arg691Alafs^*7). The patient initially presented with classic facial angiofibromas and hypomelanotic macules, and subsequently developed psychiatric and behavioral disturbances with auditory hallucinations. Neuroimaging revealed an intracranial mass lesion, which was confirmed as a subependymal giant cell astrocytoma on postoperative histopathology following surgery performed at an outside institution. After admission, the patient underwent a comprehensive diagnostic workup, and multidisciplinary treatment evaluation revealed extensive multisystem involve-ment, including the nervous system, skin, kidneys, lungs, heart, eyes, pancreas, liver and bones. Combined with relevant literature, this article discusses the molecular mechanisms, clinical phenotypes, and comprehensive management of TSC, in order to provide a reference for the standardized and individualized management of this disease.

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.

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.