Objective: To explore the dynamic developmental trajectories of college students class belongingness during their college years and its longitudinal predictive effects on depressive symptoms, so as to provide targeted insights for precise campus psychological interventions. Methods: In October 2021 (T1), a total of 4 720 college students from a university in Shandong Province were selected by cluster sampling method and followed up for 3 years. Surveys were conducted annually (T2: October 2022, T3: October 2023, T4: October 2024). The Class Belongingness Scale and Patient Health Questionnaire-9 (PHQ-9) were used to assess students class belongingness and depressive symptoms. Latent growth mixture modeling was employed to identify trajectories of class belonging, and multinomial Logistic regression analysis was used to examine the predictive effects of these trajectory classes on depressive symptoms. Results: Mean scores of class belongingness across T1-T4 were (73.24±11.95, 74.76±12.25, 75.25±12.38, 77.64±11.63), and the scores of depressive symptoms were [1.00 (0, 5.00), 0 (0, 3.00), 0 (0, 2.00), 0 (0, 2.00)]. The developmental trajectories of class belongingness were categorized into three types: the high-starting ascending group ( 56.61 %), the low-starting descending group (11.91%), and the medium-starting stable group (31.48%). Multinomial Logistic regression analysis showed that, compared to the medium-starting stable group, the high-starting ascending group had a lower probability of developing mild depressive symptoms ( OR=0.27, 95%CI =0.15-0.47) and moderate or above depressive symptoms ( OR=0.29, 95% CI = 0.14-0.60) (both P <0.05). Conversely, the low-starting descending group had a higher probability of developing mild depressive symptoms ( OR=2.31, 95%CI =1.65-3.22) and moderate or above depressive symptoms ( OR=7.49, 95%CI = 3.82-14.69) (both P <0.05). Conclusion Declining trajectory of class belongingness is a risk factor for depressive symptoms, while sustained upward trend may mitigate such risks.

INTRODUCTION: The diagnosis of sarcopenia relies on key indicators such as handgrip strength, walking speed and muscle mass. Developing a composite index that integrates these measures could enhance clinical evaluation in older adults. This study aimed to standardise and combine these metrics to establish a z score for the sarcopenia composite index (ZoSCI) tailored for the ageing population. Additionally, we explore the risk factors associated with ZoSCI to provide insights into early prevention and intervention strategies. METHOD: This retrospective study analysed data between January 2017 and December 2021 from an elderly health programme in Taiwan, applying the Asian Working Group for Sarcopenia criteria to assess sarcopenia. ZoSCI was developed by standardising handgrip strength, walking speed and muscle mass into z scores and integrating them into a composite index. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values, and multiple regression analysis identified factors influencing ZoSCI. RESULTS: Among the 5047 participants, the prevalence of sarcopenia was 3.7%, lower than the reported global prevalence of 3.9-15.4%. ROC curve analysis established optimal cut-off points for distinguishing sarcopenia in ZoSCI: -1.85 (sensitivity 0.91, specificity 0.88) for males and -1.97 (sensitivity 0.93, specificity 0.88) for females. Factors associated with lower ZoSCI included advanced age, lower education levels, reduced exercise frequency, lower body mass index and creatinine levels. CONCLUSION This study introduces ZoSCI, a new compo-site quantitative indicator for identifying sarcopenia in older adults. The findings highlight specific risk factors that can inform early intervention. Future studies should validate ZoSCI globally, with international collaborations to ensure broader applicability.
Humans ; Sarcopenia/physiopathology* ; Male ; Aged ; Female ; Retrospective Studies ; Hand Strength ; Taiwan/epidemiology* ; ROC Curve ; Aged, 80 and over ; Risk Factors ; Walking Speed ; Geriatric Assessment/methods* ; Prevalence ; Muscle, Skeletal ; Middle Aged

Objective:To express,purify,prepare antibodies,and analyze the subcellular localization of Toxoplasma gondii thioredoxin 20(Trx20),and to provide the reference for the development of Toxoplasma gondii vaccine.Methods:Bioinformatics-related websites and software were used to perform bioinformatics analysis of the Trx20 protein;specific primers were designed to amplify the target fragment and construct the prokaryotic expression vector;the protein was expressed in vitro and purified;experimental animals were immunized to prepare antibodies;enzyme-linked immunosorbent assay(ELISA)method was used to detect the titer of the polyclonal antibodies;Western blotting method was used to verify the specificity and sensitivity of the antibodies and to determine the natural expression of the protein;immunofluorescence assay(IFA)was used to analyze the subcellular localization of the protein.Results:The bioinformatics analysis results showed that Trx20 protein was a relatively stable hydrophilic protein with a molecular formula of C2172H3412N548O616S20,containing 424 amino acids,a predicted relative molecular mass of 47 700,and a theoretical isoelectric point of 8.55;it was predicted that the protein had one signal peptide,no transmembrane region,contained one domain named"Thioredoxin like Superfamily",and had 35 phosphorylation sites,one N-glycosylation site,and 17 antigenic determinants;in the secondary structure,alpha-helices accounted for 41.51％of the total amino acids,and random coils accounted for 39.86％;the recombinant plasmids pET-28a-Trx20 and pGEX-4T-1-Trx20 were successfully constructed,and the soluble recombinant protein was expressed and purified;polyclonal antibodies were successfully prepared with a titer as high as 1:64 000,and they specifically recognized the endogenous Trx20 protein in Toxoplasma gondii;the subcellular localization results showed that Trx20 protein was widely distributed in the cytoplasm of the parasite.Conclusion:Toxoplasma gondii Trx20 protein is a secretory protein containing phosphorylation/glycosylation modification sites and a thioredoxin domain,and it is localized in the cytoplasm of the parasite.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

Patients with depression are more likely to have chronic gastrointestinal (GI) symptoms than the general population, but such symptoms are considered only somatic symptoms of depression and lack special attention. There is a chronic lack of appropriate diagnosis and effective treatment for patients with depression accompanied by GI symptoms, and studying the association between depression and GI disorders (GIDs) is extremely important for clinical management. There is growing evidence that depression is closely related to the microbiota present in the GI tract, and the microbiota-gut-brain axis (MGBA) is creating a new perspective on the association between depression and GIDs. Identifying and treating GIDs would provide a key opportunity to prevent episodes of depression and may also improve the outcome of refractory depression. Current studies on depression and the microbially related gut-brain axis (GBA) lack a focus on GI function. In this review, we combine preclinical and clinical evidence to summarize the roles of the microbially regulated GBA in emotions and GI function, and summarize potential therapeutic strategies to provide a reference for the study of the pathomechanism and treatment of depression in combination with GI symptoms.
Humans ; Gastrointestinal Microbiome/physiology* ; Depression/microbiology* ; Gastrointestinal Diseases/physiopathology* ; Brain ; Animals ; Brain-Gut Axis ; Gastrointestinal Tract/microbiology*

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is challenging to treat and lacks targeted therapeutic drugs in the clinic. Natural active ingredients provide promising opportunities for discovering and developing targeted therapies for TNBC. This study investigated the effects of daurisoline on TNBC and elucidated its potential mechanisms. Using network pharmacology, a correlation was identified between daurisoline, derived from Menispermum dauricum, and breast cancer, particularly involving the Notch signaling pathway. The effects of daurisoline on the proliferation, migration, and apoptosis of MDA-MB-231 and MDA-MB-468 cells were evaluated in vitro. Additionally, the impact of daurisoline on the growth of MDA-MB-231 xenograft tumors in nude mice was assessed through in vivo experiments. Expression levels of Notch signaling pathway-related proteins, including Notch-1, NICD, PSEN-1, Bax, and Bcl-2, were examined using molecular docking and Western blotting to explore the underlying mechanisms of daurisoline’s anti-breast cancer effects. It was revealed that daurisoline could effectively inhibit the proliferation and migration of MDA-MB-231 and MDA-MB-468 cells and promote apoptosis. Furthermore, it significantly reduced the growth of subcutaneous tumors in nude mice. Notably, daurisoline could reduce the hydrolytic activity of γ-secretase by binding to the catalytic core PSEN-1, thereby inhibiting activation of the γ-secretase/Notch axis and contributing to its anti-TNBC effects.This study supported the development of naturally targeted drugs for TNBC and provided insights into the research on dibenzylisoquinoline alkaloids, such as daurisoline.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is challenging to treat and lacks targeted therapeutic drugs in the clinic. Natural active ingredients provide promising opportunities for discovering and developing targeted therapies for TNBC. This study investigated the effects of daurisoline on TNBC and elucidated its potential mechanisms. Using network pharmacology, a correlation was identified between daurisoline, derived from Menispermum dauricum, and breast cancer, particularly involving the Notch signaling pathway. The effects of daurisoline on the proliferation, migration, and apoptosis of MDA-MB-231 and MDA-MB-468 cells were evaluated in vitro. Additionally, the impact of daurisoline on the growth of MDA-MB-231 xenograft tumors in nude mice was assessed through in vivo experiments. Expression levels of Notch signaling pathway-related proteins, including Notch-1, NICD, PSEN-1, Bax, and Bcl-2, were examined using molecular docking and Western blotting to explore the underlying mechanisms of daurisoline’s anti-breast cancer effects. It was revealed that daurisoline could effectively inhibit the proliferation and migration of MDA-MB-231 and MDA-MB-468 cells and promote apoptosis. Furthermore, it significantly reduced the growth of subcutaneous tumors in nude mice. Notably, daurisoline could reduce the hydrolytic activity of γ-secretase by binding to the catalytic core PSEN-1, thereby inhibiting activation of the γ-secretase/Notch axis and contributing to its anti-TNBC effects.This study supported the development of naturally targeted drugs for TNBC and provided insights into the research on dibenzylisoquinoline alkaloids, such as daurisoline.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.