Ulcerative colitis（UC） is a chronic non-specific inflammatory bowel disease characterized by inflammation and ulceration of the colonic mucosa and submucosa， and its complex pathogenesis involves immune abnormality， oxidative stress and other factors. The nuclear transcription factor E₂-related factor 2（Nrf2）， encoded by the Nfe212 gene， plays a central role in antioxidant responses. It not only activates various antioxidant response elements such as heme oxygenase-1（HO-1） and quinone oxidoreductase 1（NQO1）， but also enhances the activity of glutathione-S-transferase（GST） and superoxide dismutase 1（SOD1）， effectively eliminating reactive oxygen species（ROS） accumulated in the body， and mitigating oxidative stress-induced damage to intestinal mucosa. In addition， Nrf2 can reduce the release of inflammatory factors and infiltration of immune cells by regulating immune response， cell apoptosis and autophagy pathways， thereby alleviating intestinal inflammation and promoting the repair and regeneration of damaged mucosa. Based on this， this paper reviews the research progress of Chinese materia medica in the prevention and treatment of UC by modulating the Nrf2 signaling pathway. It deeply explores the physiological role of Nrf2， the molecular mechanism of activation， the protective effect in the pathological process of UC， and how active ingredients in Chinese materia medica regulate the Nrf2 signaling pathway through multiple pathways to exert their potential mechanisms. These studies have revealed in depth that Chinese materia medica can effectively combat oxidative stress by regulating the Nrf2 signaling pathway. It can also play a role in anti-inflammatory， promoting autophagy， inhibiting apoptosis， protecting the intestinal mucosal barrier， and promoting intestinal mucosal repair， providing new ideas and methods for the multi-faceted treatment of UC.

Objective: To explore the impact of sleep quality, experience of childhood maltreatment, and their interaction on depressive symptoms among middle school students, so as to provide the reference for early intervention of depressive symptoms among middle school students. Methods: From September to December 2023, a questionnaire survey was conducted among 1 231 students from two secondary schools in Harbin, Heilongjiang Province by a convenient sampling method. The survey included general demographic information, Childhood Trauma Questionnaire Short Form, Pittsburgh Sleep Quality Index and Short Version of Center for Epidemiological Studies Depression Scale. The Chi square test was used to analyze the differences in depressive symptom, sleep quality and childhood maltreatment among students with different demographic characteristics. Correlation analysis was conducted using Logistic regression, and interaction analysis was performed by both additive and multiplicative interaction models. Results: The detection rate of depressive symptoms among middle school students was 22.7%, and the rate for high school students (35.2%) was significantly higher than that for middle school students (17.0%) ( χ 2=50.35, P <0.01). The detection rates of depressive symptoms among middle school students with a history of childhood maltreatment and poor sleep quality were 45.8% and 44.0%, respectively. Multivariate Logistic regression analysis showed that compared to students without a history of childhood maltreatment, students with a history of childhood maltreatment had a higher risk of depressive symptoms ( OR =4.49，95% CI =3.31~ 6.09 , P <0.01);students with poor sleep quality had a higher risk of depressive symptoms than students with good sleep quality ( OR = 5.99，95% CI =4.37~8.22, P <0.01).The interaction results showed that the presence of childhood maltreatment and poor sleep quality had an additive interaction on the occurrence of depression in middle school students. Compared with students without childhood maltreatment and having good sleep quality, students with childhood maltreatment and poor sleep quality had a 22.49 times higher risk of developing depression ( OR =22.49，95% CI =14.22~35.59, P <0.01). Conclusion Depressive symptoms among middle school students are associated with childhood maltreatment and poor sleep quality, and there is an additive interaction between childhood maltreatment and poor sleep quality on the impact of depressive symptoms.

Objective To investigate the impact of anesthesia and surgery on hippocampal expression of Alzheimer’s disease (AD)-associated proteins in 5×FAD transgenic mice and explore potential sex differences. Methods 5×FAD mice were crossbred with C57BL/6J wild-type (WT) mice to generate offspring for genotypic confirmation. Four-month-old 5×FAD mice and littermate (LM) WT controls were allocated into 8 experimental groups (n＝8/group): female/male 5×FAD control group, female/male 5×FAD anesthesia/surgery group, female/male LM control group, and female/male LM anesthesia/surgery group. Anesthesia/surgery groups underwent laparotomy under 1.4% isoflurane anesthesia, while control groups received no intervention. Hippocampal tissues were collected 24 hours post-procedure for Western blotting analysis of β-catenin, glycogen synthase kinase 3 beta (GSK3β), and phosphorylated GSK3β (p-GSK3β) levels. Results Female 5×FAD mice demonstrated significant reductions in β-catenin levels and p-GSK3β expression compared to both sex-matched LM controls and male 5×FAD counterparts (P＜0.05). No significant differences in these proteins were observed in male 5×FAD mice following anesthesia/surgery. Conclusions These findings reveal sex-specific responses to perioperative stress in AD, suggesting that anesthesia and surgery may affect female AD patients through hippocampal β-catenin/GSK3β pathway modulation.

ObjectiveBased on transcriptomics， to explore the mechanism of Hei Xiaoyaosan regulating the phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） signaling pathway to improve the learning and memory ability of insomnia rats with liver depression syndrome. MethodsSixty 8-week-old male SD rats were randomly divided into the blank group， model group， eszopiclone group （0.09 mg·kg^-1）， and low， medium， and high dose groups of Hei Xiaoyaosan （3.82， 7.65， 15.30 g·kg^-1）， with ten rats in each group. Except for the blank group， the other groups were induced insomnia rat model with liver depression by chronic restraint， tail clamping stimulation and intraperitoneal injection of p-chlorophenylalanine （PCPA）. Each treatment group received intragastric administration according to the specified dosage， once a day for 14 consecutive days. The pentobarbital sodium cooperative sleep test， open field test， and Morris water maze test were used to test the sleep quality， depressive-like behavior， and learning and memory abilities of rats. Additionally， enzyme-linked immunosorbent assay （ELISA） was used to detect the contents of 5-hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， brain-derived neurotrophic factor （BDNF）， glial cell line-derived neurotrophic factor （GDNF） and nitric oxide （NO） in hippocampus. Hematoxylin-eosin （HE） staining was performed to observe pathological changes of the hippocampal tissue， while terminal deoxynucleotidyl transferase deoxyuridine triphosphate （dUTP） nick end labeling （TUNEL） was used to evaluate apoptosis of hippocampal neurons. Transcriptomic sequencing technology was employed to identify differentially expressed genes in hippocampus between the model group and the blank group， as well as between the medium-dose group of Hei Xiaoyaosan and the model group. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis were performed on the intersecting genes. Subsequently， the enriched key genes and signaling pathways were analyzed and verified. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was utilized to assess the mRNA expression levels of phosphatase and tensin homolog （PTEN）， B-cell lymphoma-2 （Bcl-2）-like protein 11 （BCL2L11）， and mitogen-activated protein kinase 1 （MAPK1） in hippocampus， and Western blot was employed to evaluate the protein expressions of PI3K， phosphorylation （p）-PI3K， Akt， p-Akt， Bcl-2， Bcl-2-associated X protein （Bax）， and cleaved Caspase-3 in the same tissue. ResultsCompared with the blank group， the model group exhibited a reduction in body weight， an increase in sleep latency， and a decrease in sleep duration （P<0.01）. Additionally， rats showed obvious depression-like behavior， and their learning and memory abilities decreased. Furthermore， the contents of 5-HT， GABA， NO， BDNF and GDNF in hippocampus decreased （P<0.01）. Histological examination revealed a disorganized cell arrangement in the CA1 region of the hippocampus， characterized by irregular cell shapes， a reduced cell count， deeply stained and pyknotic nuclei， increased vacuolar degeneration， and an elevated apoptosis rate （P<0.01）. Compared with the model group， the body weight of the high and medium dose groups of Hei Xiaoyaosan increased， the sleep latency shortened and the sleep time prolonged （P<0.05， P<0.01）. Additionally， depression-like behavior and learning and memory abilities of rats were significantly improved， the levels of 5-HT， GABA， NO， BDNF and GDNF in the hippocampus increased （P<0.05， P<0.01）. These interventions also ameliorated pathological damage in the hippocampal CA1 area and reduced the apoptosis of hippocampal neurons （P<0.01）. Transcriptomic sequencing results indicated that Hei Xiaoyaosan might exert a therapeutic effect by regulating PI3K/Akt pathway through key mRNAs such as PTEN， BCL2L11， and MAPK1. The roles of these key mRNAs and proteins within PI3K/Akt pathway were further validated. In comparison to the blank group， the expression levels of PTEN， BCL2L11 and MAPK1 mRNA in the hippocampus of rats in the model group were increased （P<0.01）， while the protein expression levels of p-PI3K， p-Akt and Bcl-2 were decreased （P<0.01）， and the protein expression levels of PTEN， Bax and cleaved Caspase-3 were increased （P<0.01）. Compared with the model group， the high-dose and medium-dose groups of Hei Xiaoyaosan could down-regulate the expressions of PTEN， BCL2L11 and MAPK1 mRNAs （P<0.01）， up-regulate the expressions of p-PI3K， p-Akt and Bcl-2 proteins （P<0.01）， and down-regulate the protein expressions of PTEN， Bax and cleaved Caspase-3 （P<0.05， P<0.01）. ConclusionHei Xiaoyaosan may regulate PI3K/Akt signaling pathway by down-regulating expressions of key genes such as PTEN， BCL2L11 and MAPK1， and thus improve the learning and memory abilities of insomnia rats with liver depression syndrome.

Objective To explore the potential causal relationship between occupational pneumoconiosis (hereinafter referred to as "pneumoconiosis") and five mental disorders (depression, bipolar disorder, schizophrenia, insomnia and anxiety) using the two-sample Mendelian randomization (MR) method. Methods Single nucleotide polymorphisms (SNPs) loci associated with pneumoconiosis and five mental disorders were screened from Genome-Wide Association Studies. Inverse variance weighting (IVW), weighted median (WM) and MR-Egger regression methods were used to evaluate the significance of the causal relationship between pneumoconiosis and five mental disorders. Sensitivity analysis was used to evaluate the accuracy and reliability of the research results. Results After matching data of pneumoconiosis and the five mental disorders, 16 SNPs were ultimately included as instrumental variables in this study. The result of MR analysis revealed a positive causal relationship between pneumoconiosis and both depression [IVW: odds ratio (OR) and 95% confidence interval (CI) was 1.017 (1.000-1.035), P<0.05] and bipolar disorder [IVW: OR(95%CI)was 1.046(1.009-1.083), P<0.05; WM: OR (95%CI) was 1.055(1.007-1.105), P<0.05]. Result of sensitivity analysis indicated there was no heterogeneity and horizontal pleiotropy in the above results. There was no causal association observed between pneumoconiosis and schizophrenia, insomnia, or anxiety disorders (all P>0.05). Conclusion This study provides genetic evidence supporting a positive causal relationship between pneumoconiosis and both depression and bipolar disorder.

Acute Gelsemium poisoning is a systemic disease primarily affecting the central nervous system and respiratory symptoms caused by the ingestion of a substantial amount of Gelsemium within a short period. It manifests as sudden onset and rapid progression, primarily caused by accidental ingestion due to misidentification, and posing significant health risks. The compilation of the Technical Plan for Emergency Health Response to Acute Gelsemium Poisoning describes in detail the specialized practice and technical requirements in the process of handling acute Gelsemium poisoning, including accident investigation and management, laboratory testing and identification, in-hospital treatment, and health monitoring. The guidelines clarify key procedures and requirements such as personal protection, investigation elements, etiology determination, medical rescue, and health education. The key to acute Gelsemium poisoning investigation lies in promptly identifying the toxin through exposure history, clinical manifestations, and sample testing. Because there is no specific antidote for Gelsemium poisoning, immediate removal from exposure, rapid elimination of the toxin, and respiratory monitoring are critical on-site rescue measures. Visual identification of food or herbal materials, followed by laboratory testing to determine Gelsemium alkaloids in samples is a rapid effective screening method. These guidelines offer a scientific, objective, and practical framework to support effective emergency responses to acute Gelsemium poisoning incidences.

Objective: To investigate the mechanism by which serum amyloid P component (SAP) alleviates periodontitis in mice, providing an experimental basis to establish SAP as a novel therapeutic agent for periodontitis. Methods: Ethical approval was obtained from the Institutional Animal Ethics Committee. Periodontitis models were established in wild-type (WT) mice and SAP-transgenic (SAP-Tg) mice, divided into four groups: WT control (WT group), WT periodontitis (WT+P group), SAP-Tg control (Tg group), and SAP-Tg periodontitis (Tg+P group). On day 7, the mice were euthanized, and periodontal tissues, teeth, and alveolar bone were collected. SAP protein expression was detected by enzyme-linked immunosorbent assay (ELISA). Micro-CT and HE staining were used to measure alveolar bone resorption (distance from the cementoenamel junction to the alveolar bone crest). Tartrate-resistant acid phosphatase (TRAP) staining was performed to assess osteoclast number, and immunohistochemistry (IHC) was employed to evaluate macrophage infiltration. The expression levels of inflammatory cytokines including interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) were measured by qRT-PCR. Oral microorganism composition was analyzed using 16S ribosomal RNA (16S rRNA) gene sequencing. Additionally, macrophages from WT and SAP-Tg mice were isolated to establish an in vitro inflammation model, divided into WT+LPS and Tg+LPS groups. The expression of macrophage polarization-related genes including inducible nitric oxide synthase (iNOS), CD86, CD163, and CD206) were assessed by qRT-PCR. After the induction of osteoclast differentiation, TRAP staining was performed. Results: ELISA results demonstrated that periodontal tissues from Tg+P group mice exhibited higher levels of SAP expression compared to the WT+P group. Micro-CT and HE staining analyses revealed that the Tg+P group showed reduced alveolar bone resorption, indicated by a shorter distance between the cementoenamel junction and alveolar bone crest, compared to the WT+P group. Furthermore, TRAP staining results indicated a decrease in osteoclast numbers in the Tg+P group compared to the WT+P group. IHC and qRT-PCR results indicated reduced macrophage infiltration and decreased expression of IL-1β, IL-6, and TNF-α in the Tg+P group. Oral microorganism sequencing showed no significant difference in periodontitis-associated pathogenic bacteria between WT+P and Tg+P groups. In vitro experiments demonstrated that compared to the WT+LPS group, the Tg+LPS group exhibited downregulated M1 macrophage markers (iNOS and CD86) and upregulated M2 macrophage markers (CD163 and CD206). TRAP staining confirmed fewer osteoclasts in the Tg+LPS group. Conclusion SAP overexpression effectively alleviates periodontitis severity in mice by inhibiting M1 macrophage polarization, reducing pro-inflammatory cytokine expression, and suppressing osteoclast differentiation, thereby attenuating alveolar bone resorption.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

Objective: To explore the cross sectional and longitudinal associations between 24 hour movement behaviors and physical fitness in preschool children, and to adopt the method of equal time substitution analysis to evaluate the impact of time redistribution of different activity behaviors on physical fitness scores,so as to provide a scientific basis for promoting the health of preschool children. Methods: A total of 193 preschool children aged 3-6 years were selected from three Shanghai districts (Jing an, Baoshan, Jiading) from October to December 2023 by the stratified cluster random sampling method. The 24 hour movement behaviors were monitored via 7 day accelerometry, and physical fitness was measured according to the National Physical Fitness Measurement Standards (Revised 2023, preschool section). From October to December 2024, the follow up of physical fitness among preschool children used the same testing method. The comparison between groups was conducted by t-test. Compositional regression analyses evaluated the relationship of 24 hour movement behaviors and physical fitness among preschool children. Results: At baseline, moderate to vigorous physical activity (MVPA) time was significantly higher in boys [(84.10±25.78)min/d] than in girls [( 70.44± 25.98)min/d]; the composite physical fitness score was significantly higher in boys (71.65±8.69) than in girls (68.84±9.89), and the differences were statistically significant ( t =3.65, 2.10, both P <0.01). After adjusting for gender, age and body mass index, the results of component multiple linear regression analysis showed that MVPA time proportion was positively correlated with the composite physical fitness score at baseline among preschool children ( β =6.61), but was negatively correlated with two legged continuous hopping time at 1 year ( β =-1.12) (both P <0.05). Light physical activity (LPA) time proportion was negatively correlated with walking on the balance beam time at 1 year ( β =-4.44), and sedentary behavior (SB) time proportion was negatively correlated with the composite score of physical fitness at baseline ( β =-6.55) (both P <0.05). Isotemporal substitution analysis revealed that replacing 10 minutes of sleep (SP), SB, and LPA with MVPA increased the baseline physical fitness composite score by 0.750, 0.689 and 0.575 units, respectively; at 1 year follow up, the composite score increased by 1.440, 1.419 and 1.430 units, respectively (all P <0.05). Conversely, replacing MVPA with 10 minutes of SP, SB, and LPA,resulted in decreases in baseline physical fitness composite scores of 0.836, 0.777 and 0.669 units, and reductions of 1.613, 1.592 and 1.598 units at 1 year follow up (all P < 0.05 ). Conclusions Preschool children s 24 hour movement behaviors, especially MVPA, are closely related to physical health. Implementing appropriate strategies to increase physical activity and reduce sedentary time may improve the physical fitness of preschoolers.