Objective: To investigate the prevalence and associated factors of depressive symptoms among junior and senior high school students in Beijing from 2019 to 2023, in order to provide a scientific basis for interventions targeting high risk groups. Methods: From 2019 to 2023, a stratified cluster random sampling method was used to select 88 927 junior and senior high school students from 16 districts in Beijing. The Center for Epidemiologic Studies Depression Scale(CES-D) was conducted to assess depressive symptoms. The Chi square test was used to compare the detection rates of depressive symptoms among different student groups, and the trend Chi square test was employed for trend analysis of detection rates across the years. Multivariate Logistic regression analysis was applied to examine the association between the detection of depressive symptoms and related factors among junior and senior high school students. Results: From 2019 to 2023, the prevalence rates of depressive symptoms among junior and senior high school students in Beijing were 20.45%, 18.19%, 16.64%, 17.89% and 18.17%, respectively, with an overall downward trend ( χ 2 trend =27.51, P <0.01). Multivariate Logistic regression analysis revealed that after adjusting for gender, monitoring year, educational stage,family structure,boarding status and has taken a medical leave of absence in the past year unhealthy dietary behaviors ( OR=1.80, 95%CI =1.73-1.87), physical inactivity ( OR=1.24, 95%CI =1.19-1.29), try smoking ( OR=1.46, 95%CI =1.35-1.58), try alcohol( OR=1.96, 95%CI =1.88-2.05), Internet addiction ( OR=3.88, 95%CI =3.57-4.22), and adverse ear related behavior ( OR=1.82, 95%CI =1.71-1.93) were all associated with an increased risk of depressive symptoms among junior and senior high school students (all P <0.05). Conclusions The prevalence depression symptoms among middle school students in Beijing showed a fluctuating downward trend from 2019 to 2023. Targeted interventions should be adopted to reduce the occurrence of depression symptoms among junior and senior high school students.

ObjectiveTo establish a mouse model of rheumatoid arthritis with interstitial lung disease （RA-ILD） in DBA/1 mice using Porphyromonas gingivalis （Pg） infection combined with collagen-induced arthritis （CIA）， and to comprehensively evaluate pathological characteristics in joints， lungs， and serum. MethodsForty DBA/1 mice were randomly divided into four groups， i.e.， Control， Pg infection （Pg）， CIA， and Pg infection combined with CIA （Pg+CIA）， with 10 mice in each group. Arthritis clinical symptoms were evaluated by recording arthritis incidence and clinical scores. Micro-CT scanning was used to assess knee joint pathology. Histopathological changes and collagen deposition in knee joints and lung tissues were analyzed using hematoxylin-eosin （HE） and Masson staining. Immunohistochemistry was performed to detect protein expression of α-smooth muscle actin （α-SMA）， typeⅠ collagen （ColⅠ）， and fibronectin （FN） in lung tissues. Real-time quantitative polymerase chain reaction（Real-time PCR）was used to measure mRNA expression levels of α-SMA， ColⅠ， FN， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and IL-1β in lung tissues. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of Pg， cyclic citrullinated peptide （CCP）， and immunoglobulin G （IgG）. ResultsJoint lesions： The CIA and Pg+CIA groups showed 100% arthritis incidence， with evident joint redness， swelling， and deformity. The number of affected limbs was 27 and 28， and clinical scores were 68 and 70， respectively. No obvious clinical symptoms were observed in the Pg group. Histopathological and imaging analyses showed severe joint lesions in the CIA and Pg+CIA groups， with significantly increased histopathological scores， bone mineral density， bone volume fraction， trabecular thickness， and trabecular number compared to the Control group （P<0.01）. No obvious joint pathology was observed in the Pg group. Lung lesions： The Pg+CIA group exhibited marked alveolar inflammation， interstitial inflammatory cell infiltration， and alveolar wall thickening， with pronounced blue staining of collagen fibers. Histopathological scores and collagen area ratios were significantly higher than those of the Control， Pg， and CIA groups （P<0.05）. Lung protein and mRNA expression levels of α-SMA， ColⅠ， and FN were markedly increased， and mRNA levels of IL-6， TNF-α， and IL-1β were significantly elevated compared to the Control group （P<0.05）. Serology： The Pg+CIA group showed significantly higher levels of CCP， Pg， and IgG compared with the Control， Pg， and CIA groups （P<0.05）. ConclusionDBA/1 mice subjected to Pg infection combined with CIA exhibited pronounced symptoms and pathological features of RA-ILD， along with elevated serum anti-CCP antibody levels. This model represents a novel RA-ILD mouse model， providing a valuable experimental tool for investigating RA-ILD pathogenesis and developing new therapeutics， and serves as a basis for establishing anti-cyclic citrullinated peptide antibody （ACPA）-positive RA-ILD animal models.

Hepatic fibrosis refers to excessive accumulation and abnormal proliferation of fibrous connective tissue in the liver triggered by multiple pathogenic factors， and it may progress to liver cirrhosis， portal hypertension， and liver cancer. The pathological mechanisms of hepatic fibrosis involve hepatocyte injury， inflammatory cell infiltration with the release of inflammatory mediators， hepatic stellate cell activation， and extracellular matrix deposition. Recent studies have focused on mitochondrial dysfunction in disease progression， including the molecular pathways for hepatic fibrosis driven by metabolic disorders， energy deficiency， oxidative stress， mitochondrial dynamic imbalance， and autophagic dysfunction， all of which can induce liver injury. This article reviews the latest advances in hepatic fibrosis， in order to provide new therapeutic strategies for clinical management.

Despite breakthroughs in immunotherapy for solid tumors, significant variations in treatment efficacy persist. Up to 80% of cancer patients suffer from malnutrition, which leads to: lymphoid atrophy and reduced T-cell reserves; deficiency of substrates required for T-cell activation and expansion; concurrent inflammation hindering T-cell infiltration into tumors; and cachexia accelerating PD-1 antibody clearance. Clinical studies confirm that severe malnutrition significantly impairs immune responses and increases the risk of treatment toxicity. Therefore, implementing standardized nutritional therapy is crucial for optimizing the reserve, activation, expansion, and infiltration capacity of immune cells, thereby providing a sound immune system foundation for immunotherapy. Immunonutrition therapy, by enhancing immunonutrients such as arginine, omega-3 polyunsaturated fatty acids, and nucleotides, reduces the secretion of pro-inflammatory mediators and promotes T-cell activation and proliferation. This enhances anti-tumor immune responses, prolongs survival, and advances cancer treatment towards multimodal combination and precision approaches.

Environmental pollution is a significant public health challenge worldwide, and investigating the causal relationship between environmental exposure and population health outcomes is a key objective of environmental epidemiology research. In recent years, the complexity of environmental exposures has increasingly come to the forefront, making it challenging for observational studies that dominate environmental epidemiology to accurately estimate causal effects. Causal inference methods are particularly advantageous in controlling for confounding factors, thus holding great potential in environmental epidemiology research. Researchers can use appropriate causal inference methods to simulate the process of randomization, providing strong support for revealing the causal relationship between environmental exposure and health outcomes. However, there is a lack of reviews on the application of causal inference methods in environmental epidemiology studies in China. Therefore, this study introduced the basic principles of common causal inference statistical methods in environmental epidemiology, summarized the applicable conditions, advantages and disadvantages of various methods, and provided R software implementation codes for these methods, aiming to offer guidance for optimizing research design and practicing causal inference statistical methods.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

Objective To explore the application of plasma 7 microRNA (miR7) testing in the differential diagnosis of very early-stage hepatocellular carcinoma (HCC). Methods This study is a multicenter real-world study. Patients with single hepatic lesion (maximum diameter≤2 cm) who underwent plasma miR7 testing at Zhongshan Hospital, Fudan University, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Anhui Provincial Hospital, and Peking University People’s Hospital between January 2019 and December 2024 were retrospectively enrolled. Patients were divided into very early-stage HCC group and non-HCC group, and the clinical pathological characteristics of the two groups were compared. The value of plasma miR7 levels, alpha-fetoprotein (AFP), and des-gamma-carboxy prothrombin (DCP) in the differential diagnosis of very early-stage HCC was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). In patients with both negative AFP and DCP (AFP＜20 ng/mL, DCP＜40 mAU/mL), the diagnostic value of plasma miR7 for very early-stage HCC was analyzed. Results A total of 64 528 patients from 4 hospitals underwent miR7 testing, and 1 682 were finally included, of which 1 073 were diagnosed with very early-stage HCC and 609 were diagnosed with non-HCC. The positive rate of miR7 in HCC patients was significantly higher than that in non-HCC patients (67.9% vs 24.3%, P＜0.001). ROC curves showed that the AUCs for miR7, AFP, and DCP in distinguishing HCC patients from the non-HCC individuals were 0.718, 0.682, and 0.642, respectively. The sensitivities were 67.85%, 43.71%, and 44.45%, and the specificities were 75.70%, 92.78%, and 83.91%, respectively. The pairwise comparison of AUCs showed that the diagnostic efficacy of plasma miR7 detection was significantly better than that of AFP or DCP (P＜0.05). Although its specificity was slightly lower than AFP and DCP, the sensitivity was significantly higher. Among patients negative for both AFP and DCP, miR7 maintained an AUC of 0.728 for diagnosing very early-stage HCC, with 67.82% sensitivity and 77.73% specificity. Conclusions Plasma miR7 testing is a potential molecular marker with high sensitivity and specificity for the differential diagnosis of small hepatic nodules. In patients with very early-stage HCC lacking effective molecular markers (negative for both AFP and DCP), miR7 can serve as a novel and effective molecular marker to assist diagnosis.

Objective: To explore the efficacy of focal radiofrequency ablation (RFA) in the treatment of low-to-intermediate risk localized prostate cancer and its impact on postoperative urinary control and sexual function recovery，in order to explore the feasibility of minimally invasive methods for the treatment of localized prostate cancer. Methods: Clinical data of 28 patients with low-to-intermediate risk localized prostate cancer who underwent RFA in Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School during Jun.2017 and Feb.2021 were retrospectively analyzed.The 5-year failure-free survival (FFS) rate，surgery related complications，postoperative urinary control and sexual function were collected.The differences between the survival curves of patients in the low-risk and intermediate-risk subgroups were assessed with log-rank test and Breslow test. Results: All surgeries were successfully completed under local anesthesia.During the median follow-up of 43 (40-49) months，the 5-year FFS rate predicted by Kaplan-Meier method was 78.57%; 25 patients (89.29%) did not experience surgery-related complications; 27 patients (96.43%) were able to control urination; 1 patient developed new-onset sexual dysfunction.There was no significant difference in the survival curves between patients in the low-risk and intermediate-risk groups (P>0.05). Conclusion: RFA for patients with low-to-intermediate risk localized prostate cancer has good clinical efficacy，little impact on urinary control and sexual function recovery，and few postoperative complications，which can be used as one of the treatment options for these patients.

OBJECTIVE To optimize the prescription pre-review system in our hospital and evaluate its application effects. METHODS Aiming at the problems of imperfect rule base and high false positive rate in the early operation of the system， optimization measures were taken， including improving the content of the rule base， adjusting the interception level and prompt mode， refining the working model of prescription review pharmacists， and strengthening clinical communication. A retrospective cohort study was conducted， with prescription data from June to December 2023 （before optimization） as the control group and June to December 2024 （after optimization） as the observation group. Through inter group comparative analysis， the actual effect of optimizing the prescription pre-approval system was evaluated. RESULTS The prescription qualified rate increased from （82.51± 4.04）% before optimization to （90.98±1.55）% after optimization； the false positive rate decreased from （20.87±1.64）% before optimization to （7.41±2.04）% after optimization. The monthly range of prescription qualified rate narrowed from 10.24% to 4.11%， and the coefficient of variation decreased from 4.92% to 1.73%. The monthly range of false positive rate slightly increased from 4.40% to 5.34%， the coefficient of variation rose from 8.32% to 26.18%. CONCLUSIONS Through multi-dimensional optimizations of the prescription pre-review system in our hospital， its prescription review efficiency has been significantly enhanced， the quality of prescriptions has steadily improved， and the accuracy of reviews has notably improved.

This study explored the prescription pattern of traditional Chinese medicine(TCM) in the treatment of hypertensive left ventricular hypertrophy(LVH), so as to provide a relevant theoretical basis for the clinical diagnosis and treatment of hypertensive LVH. The study systematically searched the databases of CNKI, Wanfang, VIP, and SinoMed to screen out the qualified literature on TCM treatment of hypertensive LVH and used Microsoft Excel 2021 to establish the relevant prescription database. It also counted the frequency, property, flavor, and meridian affiliation of TCM in the prescriptions and classified their efficacy. The study used Lantern 5.0 and Rstudio software to analyze the hidden structural models and association rules of the high-frequency TCM with a frequency of >3.50% and adopted Origin 2024 software to visualize the data, so as to explore the prescription pattern of TCM in treating hypertensive LVH. The results showed that a total of 128 TCM prescriptions were included, involving 163 TCM with a total frequency of 1 242. The high-frequency TCM included Salviae Miltiorrhizae Radix et Rhizoma, Uncariae Ramulus Cum Uncis, Gastrodiae Rhizoma, Poria, and Chuanxiong Rhizoma, with the main efficacy from blood-activating and stasis-resolving herbs, tonic herbs, and liver-calming and wind-extinguishing herbs. The latent structure analysis(LSA) identified 10 latent variables, 20 latent classes, 7 comprehensive clustering models, and 23 core prescriptions. It was speculated that the common syndromes of hypertensive LVH included blood stasis obstructing the collaterals, ascending hyperactivity of liver Yang, Yin deficiency with Yang hyperactivity, and intermingled phlegm and blood stasis. The association rule analysis yielded 33 strong association rules, with the highest comprehensive association rule being Gastrodiae Rhizoma→Uncariae Ramulus Cum Uncis. Hypertensive LVH is characterized by asthenia in origin and asthenia in superficiality, with Yin deficiency and Qi deficiency as the origin and blood stasis and phlegm as the superficiality. Clinical treatment focuses on activating blood circulation, resolving stasis, tonifying Qi, and nourishing Yin, combined with syndrome-specific therapies such as calming wind and stopping convulsions, clearing heat, eliminating dampness and resolving phlegm, and promoting diuresis and reducing swelling.
Drugs, Chinese Herbal/therapeutic use* ; Data Mining ; Humans ; Hypertension/complications* ; Hypertrophy, Left Ventricular/physiopathology* ; Medicine, Chinese Traditional ; Drug Prescriptions