Abstract To promote adolescents active participation in physical activity and improve sleep quality, the article analyzes the relationship of adolescent physical activity with subjective sleep satisfaction, sleep latency, sleep continuity, sleep efficiency, and sleep duration. It explores potential mechanisms underlying the link between physical activity and sleep quality, including physiological mechanisms (circadian rhythms, body temperature, neuroendocrine systems, and immune function), and psychological mechanisms (stress relief, improvement of negative emotions, and promotion of mental relaxation). Based on existing research, it is recommended that adolescents engage in moderate to vigorous physical activity daily to promote improved sleep quality.

ObjectiveTo investigate the inhibitory effect of Biejiajian Pills （BJJW） on the growth of liver cancer， as well as its potential mechanism in mediating the AMP-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR） pathway through mitochondrial energy metabolism. MethodsHuman hepatoma Huh7 cells were used to establish a nude mouse model of subcutaneous xenograft tumor. A total of 18 tumor-bearing nude mice were randomly divided into model group， BJJW group （2.2 g/kg）， and metformin group （250 mg/kg）， and the corresponding drug was given by gavage for 14 consecutive days. Tumor volume and weight were monitored during the experiment； HE staining was used to observe histopathological changes； the levels of reactive oxygen species （ROS） and adenosine triphosphate （ATP） in tumor tissue were measured； immunohistochemistry and Western blotting were used to measure the expression levels of proteins associated with the AMPK/mTOR pathway. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups， and the Tukey’s test was used for further comparison between two groups； the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between multiple groups， and the Dunn’s test was used for further comparison between two groups. ResultsCompared with the model group， the BJJW group had a tumor inhibition rate of 45.73%， with significant reductions in both tumor volume and weight （P<0.01）. Pathological examination showed that compared with the model group， the BJJW group had a significant reduction in the number of tumor cells and the presence of extensive necrosis. Mechanistic studies showed that compared with the model group， the BJJW group had a significant increase in ROS level （P<0.001） and a significant reduction in ATP level （P<0.001）， as well as significant increases in p-AMPK/AMPK ratio （0.81±0.20 vs 0.13±0.04， P<0.01） and p-ULK1/ULK1 ratio （0.69±0.17 vs 0.18±0.13， P<0.01） and a significant reduction in p-mTOR/mTOR ratio （1.34±0.16 vs 3.20±0.62， P<0.01）. ConclusionBJJW may inhibit the growth of liver cancer by inducing mitochondrial energy metabolism dysfunction， increasing the level of ROS， reducing the level of ATP， and activating the AMPK/mTOR signaling pathway.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

ObjectiveTo explore the clinical safety of Feilike Mixture （FLK） in the real world. MethodsThe safety of all children who received FLK from 29 institutions in 12 provinces between January 21，2021 and December 25，2021 was evaluated through prospective centralized surveillance and a nested case control study. ResultsA total of 3 035 juveniles were included. There were 29 research centers involved，which are distributed across 12 provinces，including one traditional Chinese medicine （TCM） hospital and 28 general hospitals. The average age among the juveniles was （4.77±3.56） years old，and the average weight was （21.81±12.97） kg. Among them，119 cases （3.92%） of juveniles had a history of allergies. Acute bronchitis was the main diagnosis for juveniles，with 1 656 cases （54.46%）. FLK was first used in 2 016 cases （66.43%），and 142 juvenile patients had special dosages，accounting for 4.68%. Among them，92 adverse drug reactions （ADRs） occurred，including 73 cases of gastrointestinal system disorders，10 cases of metabolic and nutritional disorders，eight cases of skin and subcutaneous tissue diseases，two cases of vascular and lymphatic disorders，and one case of systemic diseases and various reactions at the administration site. The manifestations of ADRs were mainly diarrhea，stool discoloration，and vomiting，and no serious ADRs occurred. The results of multi-factor analysis indicated that special dosages （the use of FLK）［odds ratio （OR） of 2.642， 95% confidence interval （CI） of 1.105-6.323］，combined administration： spleen aminopeptide （OR of 4.978， 95%CI of 1.200-20.655），and reason for combined administration： anti-infection （OR of 1.814， 95%CI of 1.071-3.075） were the risk factors for ADRs caused by FLK. Conclusion92 ADRs occurred among 3 035 juveniles using FLK. The incidence of ADRs caused by FLK was 3.03%，and the severity was mainly mild or moderate. Generally，the prognosis was favorable after symptomatic treatment such as drug withdrawal or dosage reduction，suggesting that FLK has good clinical safety.

Objective To investigate the value of artificial intelligence-assisted compressed sensing (ACS) technology for intravenous gadolinium contrast-enhanced magnetic resonance imaging of the inner ear using three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) sequence. Methods The patients received gadolinium contrast-enhanced magnetic resonance imaging using ACS and united compressed sensing (uCS) 3D-FLAIR at Zhongshan Hospital, Fudan University from January to November 2024 were prospectively enrolled. The repetition time was 16 000 ms, and acquisition time was 6 min 40 s and 10 min 24 s in ACS 3D-FLAIR and uCS 3D-FLAIR, respectively. The images on the two sequences were evaluated independently by two radiologists. The image quality of the two sequences was subjectively evaluated and compared. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between the two sequences. The grading consistencies using two sequences and between the two doctors were analyzed. Results There was no statistically difference in subjective score of image quality between the two sequences. SNR and CNR of the ACS 3D-FLAIR sequence were significantly higher than those of the uCS 3D-FLAIR sequence (P＜0.001). The kappa values of grades of cochlear and vestibular endolymphatic hydrops were 0.942 and 0.888 using two sequences (P＜0.001). The kappa values of grades of cochlear and vestibular endolymphatic hydrops using the ACS 3D-FLAIR sequence between the two doctors were 0.784 and 0.831, respectively (P＜0.001); the kappa values of grades of cochlear and vestibular endolymphatic hydrops using uCS 3D-FLAIR sequence between the two doctors were 0.725 and 0.756, respectively (P＜0.001). Conclusions ACS 3D-FLAIR could provide higher SNR and CNR than uCS 3D-FLAIR, and is more suitable for intravenous gadolinium contrast-enhanced magnetic resonance imaging of the inner ear; the endolymphatic hydrops grades using ACS 3D-FLAIR is similar to use uCS 3D-FLAIR.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.