Objective: To investigate the incidence and influencing factors of new onset suicidal behaviors among junior and senior high school students, so as to provide evidence for early warning and intervention. Methods: Using a cluster sampling method, baseline data were collected from September to November 2023 among 2 379 adolescents selected from 2 junior high schools, 2 general senior high schools and 2 vocational senior high schools in Wuhan, Hubei Province, with follow up carried out from September to November 2024. Data were collected using the School Adaptation Scale, the Essential Resilience Scale, the Personal Social Support Scale, the Adverse Childhood Experiences Questionnaire, the Adolescent Life Events Scale, the Pittsburgh Sleep Quality Index, the Center for Epidemiologic Studies Depression Scale, and the Columbia-Suicide Severity Rating Scale. Multivariate Logistic regression analysis was used to identify factors influencing new onset suicidal behavior among junior and senior high school students. Results: Among junior and senior high school students without suicide risk at baseline, 53.55% remained stable risk free during follow up, while 13.28% developed new suicidal behavior during follow up, of which suicidal ideation, suicide plan and suicide attempts accounted for 11.69%, 8.83% and 6.52%, respectively. Multivariate Logistic regression revealed that increased depressive symptoms at follow up elevated the risk of overall new onset suicidal behaviors ( OR =4.14), ideation ( OR = 4.59), plans ( OR =4.24), and attempts ( OR =4.09); poor sleep quality also increased the risk of overall new onset suicidal behaviors ( OR =2.23), plans ( OR =2.03), and attempts ( OR =2.65) (all P <0.05). Non boarding junior and senior high school students had lower risks of overall new onset suicidal behaviors and ideation ( OR=0.70 and 0.49, respectively); secondary school students with academic performance ranking in the top 20%-30% of their class had a lower risk of new onset ideation ( OR =0.27); and those whose place of origin was outside the city had a lower risk of new onset plans ( OR =0.49) (all P <0.05). Conclusions New onset suicidal behaviors among junior and senior high school students exhibit dynamic and stage specific differences. Depressive symptoms serve as a common core risk factor across all stages, whereas sleep quality, academic performance, boarding status, and place of origin exert differential influences at distinct stages.

Objective To explore the value of MRI radiomics based on the frontal and temporal lobes combined with multiple machine learning algorithms in the diagnosis of mild cognitive impairment(MCI).Methods Patients who underwent cranial MR examination were retrospectively selected.According to the inclusion and exclusion criteria,a total of 173 subjects were finally included and randomly divided into training set and test set in a ratio of 7∶3.After delineating the regions of interest(ROI)of the frontal and temporal lobes on T2-fluid attenuated inversion recovery(FLAIR)images,radiomics features were extracted based on the Pyradiomics data package.Features were screened through inter-and intraclass correlation coefficient(ICC),independent samples t-test,and the LightGBM algorithm.Diagnostic models were constructed using support vector machine(SVM),random forest(RF),decision tree(DT),K-nearest neighbor(KNN),gradient boosting decision tree(GBDT),and extreme gradient boosting(XGBoost)combined with 10-fold cross-validation respectively.The training set was further divided into 9 training data sets and 1 validation data set through 10-fold cross-validation,and the hyperparameters were optimized through iterative cycles.The diagnostic efficacy of the model was evaluated by receiver operating characteristic(ROC)curve and area under the curve(AUC),and the DeLong test was applied to compare the differences between different models.Results The AUC of the radiomics models constructed by SVM,DT,RF,KNN,GBDT,XGBoost in the training set were 0.951,0.992,0.998,0.957,1.000,and 1.000 respectively,in the validation set were 0.890,0.843,0.934,0.878,0.930,and 0.945 respectively,and in the test set were 0.902,0.711,0.899,0.849,0.889,and 0.882 respectively.Conclusion MRI radiomics based on the frontal and temporal lobes combined with multiple machine learning algorithms can diagnose MCI,and the model constructed based on SVM shows the highest diagnostic value.

To ensure the safety and efficacy of Chinese herbs,it is of great significance to conduct rapid quality detection of Chinese herbs at every link of their supply chain.Spectroscopic technology can reflect the overall chemical composition and structural characteristics of Chinese herbs,with the multi-component and multitarget characteristics of Chinese herbs.This review took the genus Paris as an example,and applications of spectroscopic technology with machine learning(ML)in supply chain of the genus Paris from seeds to medicinal materials were introduced.The specific contents included the confirmation of germplasm resources,identification of growth years,cultivar,geographical origin,and original pro-cessing and processing methods.The potential application of spectroscopic technology in genus Paris was pointed out,and the prospects of combining spectroscopic technology with blockchain were pro-posed.The summary and prospects presented in this paper will be beneficial to the quality control of the genus Paris in all links of its supply chain,so as to rationally use the genus Paris resources and ensure the safety and efficacy of medication.

Objective:To investigate the relationship between screen time and content, and the mental health status of adolescents. The findings will inform the formulation of targeted intervention policies to enhance adolescent mental health.Methods:Between September and November 2023, 5 197 students from 64 junior high, senior high, and vocational schools across 13 districts in Wuhan were recruited, using the stratified whole-cluster random sampling to investigate their screen behavior and mental health status. Mental health status was measured using the Mental Health Inventory for Chinese Middle School Students (MMHI-60). A generalized additive model was used to explore the nonlinear association between screen time and mental health status. Additionally, a mixed-effects model was utilized to explore the dose-response associations between average daily total screen time, screen time for different content types, and adolescents' mental health status and the impact of the proportion of different screen contents on mental health outcomes.Results:The age of the participants was (14.40±1.48) years, with 56.07% being boys. The MMHI-60 score averaged 1.73±0.70. The M( Q1,Q3) for daily total screen time was 50.00 (0.00,128.57) minutes. The M( Q1,Q3) for screen time dedicated to gaming, studying, socializing, and watching videos were 0.00 (0.00, 20.00), 8.57 (1.64, 44.50), 4.28 (0.00, 30.00), and 0.00 (0.00, 25.71) minutes, respectively. A non-linear association was observed between average daily screen time and adolescent mental health problem score, 0-1 hour of daily screen time was beneficial for adolescent mental, compared to no screen time. However, screen time exceeding 1 hour was detrimental, with the negative impact increasing alongside screen time duration. When total daily screen time was held constant, the proportion of time spent on gaming ( β=0.14, 95% CI: 0.05-0.23, P=0.003) and video ( β=0.21, 95% CI: 0.09-0.28, P<0.001) was positively correlated with mental health problems, whereas the proportion of time spent on studying was negatively correlated with mental health problems ( β=-0.17, 95% CI: -0.24 - -0.11, P<0.001). Conclusions:Moderate screen time is advantageous for adolescent mental health. However, it is crucial to minimize the proportion of screen time dedicated to video and gaming activities to mitigate potential adverse effects.

To ensure the safety and efficacy of Chinese herbs, it is of great significance to conduct rapid quality detection of Chinese herbs at every link of their supply chain. Spectroscopic technology can reflect the overall chemical composition and structural characteristics of Chinese herbs, with the multi-component and multitarget characteristics of Chinese herbs. This review took the genus Paris as an example, and applications of spectroscopic technology with machine learning (ML) in supply chain of the genus Paris from seeds to medicinal materials were introduced. The specific contents included the confirmation of germplasm resources, identification of growth years, cultivar, geographical origin, and original processing and processing methods. The potential application of spectroscopic technology in genus Paris was pointed out, and the prospects of combining spectroscopic technology with blockchain were proposed. The summary and prospects presented in this paper will be beneficial to the quality control of the genus Paris in all links of its supply chain, so as to rationally use the genus Paris resources and ensure the safety and efficacy of medication.

Objective:To investigate the relationship between screen time and content, and the mental health status of adolescents. The findings will inform the formulation of targeted intervention policies to enhance adolescent mental health.Methods:Between September and November 2023, 5 197 students from 64 junior high, senior high, and vocational schools across 13 districts in Wuhan were recruited, using the stratified whole-cluster random sampling to investigate their screen behavior and mental health status. Mental health status was measured using the Mental Health Inventory for Chinese Middle School Students (MMHI-60). A generalized additive model was used to explore the nonlinear association between screen time and mental health status. Additionally, a mixed-effects model was utilized to explore the dose-response associations between average daily total screen time, screen time for different content types, and adolescents' mental health status and the impact of the proportion of different screen contents on mental health outcomes.Results:The age of the participants was (14.40±1.48) years, with 56.07% being boys. The MMHI-60 score averaged 1.73±0.70. The M( Q1,Q3) for daily total screen time was 50.00 (0.00,128.57) minutes. The M( Q1,Q3) for screen time dedicated to gaming, studying, socializing, and watching videos were 0.00 (0.00, 20.00), 8.57 (1.64, 44.50), 4.28 (0.00, 30.00), and 0.00 (0.00, 25.71) minutes, respectively. A non-linear association was observed between average daily screen time and adolescent mental health problem score, 0-1 hour of daily screen time was beneficial for adolescent mental, compared to no screen time. However, screen time exceeding 1 hour was detrimental, with the negative impact increasing alongside screen time duration. When total daily screen time was held constant, the proportion of time spent on gaming ( β=0.14, 95% CI: 0.05-0.23, P=0.003) and video ( β=0.21, 95% CI: 0.09-0.28, P<0.001) was positively correlated with mental health problems, whereas the proportion of time spent on studying was negatively correlated with mental health problems ( β=-0.17, 95% CI: -0.24 - -0.11, P<0.001). Conclusions:Moderate screen time is advantageous for adolescent mental health. However, it is crucial to minimize the proportion of screen time dedicated to video and gaming activities to mitigate potential adverse effects.

Objective To explore the value of MRI radiomics based on the frontal and temporal lobes combined with multiple machine learning algorithms in the diagnosis of mild cognitive impairment(MCI).Methods Patients who underwent cranial MR examination were retrospectively selected.According to the inclusion and exclusion criteria,a total of 173 subjects were finally included and randomly divided into training set and test set in a ratio of 7∶3.After delineating the regions of interest(ROI)of the frontal and temporal lobes on T2-fluid attenuated inversion recovery(FLAIR)images,radiomics features were extracted based on the Pyradiomics data package.Features were screened through inter-and intraclass correlation coefficient(ICC),independent samples t-test,and the LightGBM algorithm.Diagnostic models were constructed using support vector machine(SVM),random forest(RF),decision tree(DT),K-nearest neighbor(KNN),gradient boosting decision tree(GBDT),and extreme gradient boosting(XGBoost)combined with 10-fold cross-validation respectively.The training set was further divided into 9 training data sets and 1 validation data set through 10-fold cross-validation,and the hyperparameters were optimized through iterative cycles.The diagnostic efficacy of the model was evaluated by receiver operating characteristic(ROC)curve and area under the curve(AUC),and the DeLong test was applied to compare the differences between different models.Results The AUC of the radiomics models constructed by SVM,DT,RF,KNN,GBDT,XGBoost in the training set were 0.951,0.992,0.998,0.957,1.000,and 1.000 respectively,in the validation set were 0.890,0.843,0.934,0.878,0.930,and 0.945 respectively,and in the test set were 0.902,0.711,0.899,0.849,0.889,and 0.882 respectively.Conclusion MRI radiomics based on the frontal and temporal lobes combined with multiple machine learning algorithms can diagnose MCI,and the model constructed based on SVM shows the highest diagnostic value.

Objective:To assess the efficacy of different fusion strategies involving the convolutional block attention module (CBAM) and Unet for automatic pancreas segmentation in enhanced CT images of patients with acute pancreatitis.Methods:A retrospective analysis was conducted on 1 158 patients with acute pancreatitis admitted to the Affiliated Hospital of North Sichuan Medical College between January 1st, 2016 and July 30th, 2021. Among them, 141 patients with first-episode acute pancreatitis were randomly categorized into mild, moderate, and severe cases. The test set comprised 5 mild and 15 severe cases, while the remaining 126 cases were used for training. Within the training set, 20% of the data was randomly allocated as the validation set. Different fusion paths of the CBAM and Unet networks were trained, utilizing the Dice similarity coefficient, Hausdorff distance (HD), and pixel accuracy (PA) as evaluation metrics. The model demonstrating the best performance on the validation set was selected and evaluated on the test set. Additionally, the Unet model was combined with the attention gate attention mechanism (AttentionUnet) in the skip connection, and the ResBlock replaced the original convolution module (ResUnet) in the Unet network. Moreover, the skip connection branch module of feature extraction was integrated with CBAM (ResUnet_CBAM) for comparison.Results:Unet_CBAM achieved better results on the test set with a Dice value of 80.06%, a HD value of 3.765 9 and a PA value of 0.992 3, all surpassing other fusion strategies. The segmentation accuracy of the pancreatic region in CT images of acute pancreatitis patients was notably enhanced compared to Unet and its related variant networks.Conclusions:The Unet network integrated into CBAM after skip connection can better perform pancreatic segmentation on enhanced CT images of patients with acute pancreatitis and can effectively improve the efficiency of relevant personnel in pancreatic segmentation on enhanced CT images of patients with acute pancreatitis.

Objective To investigate the clinical characteristics of testicular torsion,and to provide the strategies for its early diagnosis and timely therapy.Methods The clinical data in the patients with testicular torsion diagnosed by surgical exploration from March 2011 to December 2016 were reviewed.The clinical characteristics were compared between the testis-preserving group and testis-resection group.The latest advances in the diagnosis and treatment of testicular torsion were summarized.Results A total of 64 cases of testicular torsion were included in this study.There were 27 cases in the testicle-preserving group with the age range of(8-40) years old,the average age was(19.00±5.28) years old,the onset time range was(1-24) h,the average time was (8.78 ± 6.73) h;the intraoperative sper matic cord twist range was (180-540) ° and average (290.00 ±103.92) °;6 cases were misdiagnosis with the misdiagnosis rate of 22.22 ％.There were 37cases in the testicle-resection group with the age range of(10-34),the average age was(19.00 ± 7.45) years old;the onset time range was (12-168) h,the average time was(66.92 ± 47.01) h,the intraoperative spermatic cord twist range was(180-720)° and average(457.00± 168.88)°;22 cases were misdiagnosed with the misdiagnosis rate of 59.46 ％.The onset time,spermatic cord twist degree and misdiagnosis rate had statistically significant difference between the two groups(P＜0.05).Conclusion Testicular torsion is an important urinary emergency condition which can not be ignored.Timely and effective treatment is the key to save the testicles.

Phased array transducers are very attractive because the beam generated by the arrays can be electronically focused and steered. The present work characterizes far-field 2D properties of phased array system by functions that are deduced from rectangle source, rectangle line array and phased array based on point source. Results are presented for the distribution of ultrasound intensity on plane xoz and on x-axis by simulation using numerical calculation. It is shown that the shape of response of rectangle line array is modulated by the single array element. It is also demonstrated that the delay time of phased array is the key to steer the beam, sacrificing the value of main lobe and increasing the number of side lobes.
Computer Simulation ; Models, Theoretical ; Transducers ; Ultrasonics ; instrumentation ; methods