The chapter Zhouyu in Guoyu says "Qi of the heaven and the earth moves without losing its order." With lucidity ascending and turbidity descending， Qi moves in a normal state， and Yin and Yang consolidate the foundation of the body. The mutual interference between lucidity and turbidity leads to the disorder of Qi movement， thus causing diseases. It is a pathological state of disorder between ascending and descending， as well as between entering and exiting， gradually evolving into a state of turbidity affecting lucidity and transforming into pathogen， which can be used to interpret and analyze the core of disease pathogenesis. The theory of lucidity and turbidity is connected with the harmony of nutrient and defensive aspects， Qi circulation， and sweat pore associating with Qi movement， and it has common implications with immune responses and nutrient metabolism system， intestinal mucosal barrier function， and mitochondrial energy synthesis. Modern studies have shown that intestinal flora imbalance， bile acid receptor inactivation， macrophage polarization imbalance， epithelial-mesenchymal transition， ferroptosis and other related microscopic pathological mechanisms are involved in the development and progression of ulcerative colitis. By delving into the common meaning of the classic theory of mutual interference between lucidity and turbidity in traditional Chinese medicine and modern medical pathological mechanisms， this paper summarizes the correspondence between the micropathological mechanism and the theory of mutual interference between lucidity and turbidity in the regulation and mamagement of ulcerative colitis. The combined use of sweet and warm medicinal materials consolidates the middle Qi and activates Qi circulation， thus ascending lucidity and descending turbidity. The combined use of pungent medicinal materials for dispersing and bitter medicinal materials for descending simultaneously raises warm and clear Qi. Wind-extinguishing medicinal materials facilitate the ascending of Qi and the opening of sweat pores. Accordingly， turbidity descends and lucidity ascends. The prescriptions incorporating these medication principles are in agreement with the therapeutic approach of following the normal flow of lucidity and turbidity. This paper clarifies the scientific connotation and micropathologic mechanism of ulcerative colitis from the perspective of mutual interference between lucidity and turbidity， providing new theories and prescriptions for the clinical diagnosis， treatment， and prevention of ulcerative colitis.

Objective: To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases, addressing the key limitations of insufficient complementary feature extraction and inadequate cross-modal feature fusion in existing automated diagnostic methods. Methods: The fundus multi-label classification dataset with 12 disease categories (FMLC-12) dataset was constructed by integrating complementary samples from Ocular Disease Intelligent Recognition (ODIR) and Retinal Fundus Multi-Disease Image Dataset (RFMiD), yielding 6 936 fundus images across 12 retinal pathology categories, and the framework was validated on both FMLC-12 and ODIR. Inspired by the holistic multi-regional assessment principle of the Five Wheels theory in traditional Chinese medicine (TCM) ophthalmology, the dual-branch multi-label network (DBMNet) was developed as a novel framework integrating complementary visual feature extraction with pathological correlation modeling. The architecture employed a TransNeXt backbone within a dual-branch design: one branch processed red-green-blue (RGB) images to capture color-dependent features, such as vascular patterns and lesion morphology, while the other processed grayscale-converted images to enhance subtle textural details and contrast variations. A feature interaction module (FIM) effectively integrated the multi-scale features from both branches. Comprehensive ablation studies were conducted to evaluate the contributions of the dual-branch architecture and the FIM. The performance of DBMNet was compared against four state-of-the-art methods, including EfficientNet Ensemble, transfer learning-based convolutional neural network (CNN), BFENet, and EyeDeep-Net, using mean average precision (mAP), F1-score, and Cohen's kappa coefficient. Results: The dual-branch architecture improved mAP by 15.44 percentage points over the single-branch TransNeXt baseline, increasing from 34.41% to 44.24%, and the addition of FIM further boosted mAP to 49.85%. On FMLC-12, DBMNet achieved an mAP of 49.85%, a Cohen’s kappa coefficient of 62.14%, and an F1-score of 70.21%. Compared with BFENet (mAP: 45.42%, kappa: 46.64%, F1-score: 71.34%), DBMNet outperformed it by 4.43 percentage points in mAP and 15.50 percentage points in kappa, while BFENet achieved a marginally higher F1-score. On ODIR, DBMNet achieved an F1-score of 85.50%, comparable to state-of-the-art methods. Conclusion DBMNet effectively integrates RGB and grayscale visual modalities through a dual-branch architecture, significantly improving multi-label fundus disease classification. The framework not only addresses the issue of insufficient feature fusion in existing methods but also demonstrates outstanding performance in balancing detection across both common and rare diseases, providing a promising and clinically applicable pathway for standardized, intelligent fundus disease classification.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

OBJECTIVE: Hepatocellular carcinoma (HCC) is sensitive to ferroptosis, a new form of programmed cell death that occurs in most tumor types. However, the mechanism through which ferroptosis modulates HCC remains unclear. This study aimed to investigate the oncogenic role and prognostic value of FANCD2 and provide novel insights into the prognostic assessment and prediction of immunotherapy. METHODS: Using clinicopathological parameters and bioinformatic techniques, we comprehensively examined the expression of FANCD2 macroscopically and microcosmically. We conducted univariate and multivariate Cox regression analyses to identify the prognostic value of FANCD2 in HCC and elucidated the detailed molecular mechanisms underlying the involvement of FANCD2 in oncogenesis by promoting iron-related death. RESULTS: FANCD2 was significantly upregulated in digestive system cancers with abundant immune infiltration. As an independent risk factor for HCC, a high FANCD2 expression level was associated with poor clinical outcomes and response to immune checkpoint blockade. Gene set enrichment analysis revealed that FANCD2 was mainly involved in the cell cycle and CYP450 metabolism. CONCLUSION To the best of our knowledge, this is the first study to comprehensively elucidate the oncogenic role of FANCD2. FANCD2 has a tumor-promoting aspect in the digestive system and acts as an independent risk factor in HCC; hence, it has recognized value for predicting tumor aggressiveness and prognosis and may be a potential biomarker for poor responsiveness to immunotherapy.
Humans ; Carcinoma, Hepatocellular/diagnosis* ; Liver Neoplasms/diagnosis* ; Immunotherapy ; Fanconi Anemia Complementation Group D2 Protein/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/metabolism*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

The gut-brain axis is a bidirectional communication pathway connecting the central nervous system and gastrointestinal tract,playing a key role in the occurrence and development of diseases related to this axis.The vermiform appendix,as a part of the gut that is connected to the cecum,has a unique anatomical location,a rich microbiome,and abundant immune cells.Appendicitis and appendectomy have been found to be associated with the development of diseases related to the gut-brain axis.This review first introduces the anatomy and functions of the vermiform appendix and then expounds the associations of appendicitis and appendectomy with diseases related to the gut-brain axis.Furthermore,this review summarizes and prospects the mechanisms of the vermiform appendix in affecting the occurrence and development of diseases related to the gut-brain axis.
Humans ; Appendix/anatomy & histology* ; Brain ; Appendicitis ; Appendectomy/adverse effects* ; Gastrointestinal Microbiome ; Brain-Gut Axis

Objective To explore the effects of home-based exercise rehabilitation on cardiac structure, valvular function, and exercise capacity in patients with severe aortic stenosis (AS) after transcatheter aortic valve replacement (TAVR). Methods 49 patients with severe AS who underwent TAVR at Zhongshan Hospital, Fudan University, from January 2024 to February 2025 were enrolled. They were divided into an exercise group (n＝25) or a non-exercise group (n＝24) based on participating or not in home-based rehabilitation after TAVR. The exercise group received 12 weeks of home-based exercise training (aerobic exercise plus resistance training every week); the non-exercise group received routine care. Transthoracic echocardiography (TTE) was used to assess cardiac structural parameters before discharge (T0) and after 12 weeks of exercise (T1). Functional outcomes including the 6-minute walk test (6MWT), Duke Activity Status Index (DASI), and Short Physical Performance Battery (SPPB) were compared between the two groups. A linear mixed-effects model was used to analyze the effect of home-based rehabilitation on echocardiographic parameters. Patients were stratified by baseline 6MWT (＜240 m as low-function subgroup, ≥240 m as high-function subgroup) to compare exercise-related outcomes between subgroups. Results At T1, the exercise group had a longer 6MWT distance than the non-exercise group (P＝0.012). The linear mixed-effects model showed that after 12 weeks of exercise, the left ventricular end-diastolic diameter (LVEDD) decreased in the exercise group but slightly increased in the non-exercise group, with a significant difference in changes over time between the two groups (P_interaction＝0.030). The exercise group also showed greater improvement in effective orifice area index (P_interaction＝0.028) and effective orifice area (P_interaction＝0.042) than the non-exercise group. Subgroup analysis revealed that in the low-function subgroup, the exercise group showed greater improvement in the 6MWT (P_interaction＝0.035) and the effective orifice area index (P_interaction＝0.046) compared to the non-exercise group; in the high-function subgroup, the exercise group showed greater improvement only in LVEDD compared to the non-exercise group (P_interaction＝0.046). Conclusions Home-based exercise rehabilitation improves exercise capacity, optimizes left ventricular remodeling, and enhances valvular function in patients with severe AS after TAVR, with greater benefits observed in patients with lower baseline 6MWT.

[摘 要] 目的：回顾性分析树突状细胞-细胞因子诱导的杀伤细胞（DC-CIK）不同抗原负载后在治疗恶性黑色素瘤（MM）中的临床疗效与安全性。方法：采集2012年10月至2024年12月期间东部战区总医院秦淮医疗区收治的42例晚期MM患者的外周血单个核细胞，经实验室体外诱导培养成DC和CIK。根据患者HLA-A2的表达将患者分为多肽组和细胞组，多肽组负载混合多肽，细胞组负载肿瘤细胞A375裂解物。DC与CIK培养成熟后再回输给患者。比较两组患者的客观临床反应及生存期，检测治疗前后两组患者外周血淋巴细胞亚群，观察患者回输后的不良反应。结果：42例MM患者中，0例达CR，0例PR，31例SD，11例PD；其中，多肽组18例SD，6例PD，细胞组13例SD，5例PD。多肽组疾病控制率为75.0%，细胞组为72.2%；42例患者中死亡12例，其中细胞组4例，多肽组8例。1年OS率多肽组为76.6%，细胞组为66.7%；2年OS率多肽组为43.8%，细胞组为66.7%；3年OS率多肽组为43.8%，细胞组为33.3%，多肽组3年OS率略高于细胞组，但两组之间无统计学差异（P = 0.445）。两组MM患者治疗前后淋巴细胞亚群无显著差异（均P > 0.05），两组患者均未出现严重不良反应。结论：细胞负载DC-CIK与混合多肽负载DC-CIK治疗MM患者是安全的，能使患者临床获益，但两组的近期疗效和长期生存有差异以及免疫反应均无显著性差异。