Objective: To address the challenges of systematically identifying herb pairs in traditional Chinese medicine (TCM), we proposed HerbGL, a framework for predicting potential herb pairs that integrates network propagation and graph regularization. Methods: Based on the assumption that herbal actions induce subtle perturbations in biological systems, a framework named HerbGL was proposed. Random walk with restart (RWR) was first applied to the protein-protein interaction (PPI) network to reconstruct herb-specific perturbation effects and generate weighted subnetworks. Then, to quantify affinity between herb pairs, two network-proximity metrics, Closeness and PageRank, were computed from the weighted subnetworks to construct herb-pair affinity matrices. Finally, these matrices, together with known herb pairs (derived from co-occurrence analysis of TCM formulas with a threshold determined from the inflection point of the frequency distribution), were incorporated into a graph regularization model to predict potential herb pairs. Model performance was assessed through baseline comparison, ablation and robustness experiment under different ratios of positive and negative samples, using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), accuracy, and precision as evaluation metrics. Furthermore, the predicted herb pairs were validated through both literature evidence and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Results: The weighted subnetworks constructed by RWR provided a refined simulation of herb-specific perturbation effects, which formed the basis for subsequent affinity modeling and prediction. Analysis of herb pair co-occurrence frequencies revealed a marked change around 150, which was selected as the threshold to distinguish herb pairs from non-herb pairs. HerbGL exhibited superior predictive performance compared with baseline models (AUROC = 0.970 5, AUPRC = 0.955 5, accuracy = 0.726 6, precision = 0.970 6). Ablation results showed that removing the Closeness and PageRank metrics substantially degraded performance (AUROC = 0.819 1, AUPRC = 0.876 8), confirming their necessity. Robustness evaluation under an imbalanced positive-to-negative sample ratio of 1 : 5 yielded AUROC = 0.969 6 and AUPRC = 0.840 4, indicating stable predictive ability. Moreover, multiple case studies further validated the rationality of the predicted herb pairs, such as Fangfeng (Saposhnikoviae Radix) and Qingpi (Citri Reticulatae Pericarpium Viride) which are recorded in Liangpeng Huiji (《良朋汇集》, Collection of Excellent Recipes) Vol. 3: Fangfeng Shengma Tang (防风升麻汤). Additionally, pathway enrichment analysis of the Renshen (Ginseng Radix et Rhizoma) and Lianqiao (Forsythiae Fructus) pair further supported the biological plausibility of their compatibility. Conclusion HerbGL offers an effective and biologically informed framework for identifying herb pairs in TCM. Beyond improving herb pair prediction, the framework also provides data support for research on herb compounds and mechanisms, thereby supporting data-driven exploration of TCM compatibility.

Knee osteoarthritis is a common joint disease within osteoarthritis,characterized by pain,swelling,and limited functionality as the main clinical manifestations.In severe cases,it affects daily life and falls under the category of"impediment syndrome"or"bone impediment"in TCM.The author believes that the theory of"bones,tendons,and muscles"is closely related to this disease.Treatment should focus on simultaneously nourishing the liver,spleen and kidneys,considering tendons,bones and muscles,while also dispelling wind,cold and dampness.The clinical application of Sanbi Decoction has shown good efficacy,and this discussion aimed to provide ideas for the diagnosis and treatment of knee osteoarthritis.

Background Non-optimal temperatures pose significant threats to public health. Analyzing the association between temperature exposure and the number of emergency cases of acute alcohol intoxication can provide evidence for optimizing emergency resource allocation and response strategies. Objective To analyze the overall impact and lag effects of non-optimal temperatures on the number of 120 emergency calls for acute alcohol intoxication in Wuxi, and to assess the attributable risk, in order to provide empirical evidence for formulating climate-adaptive public health strategies. Methods Call records of acute alcohol intoxication from Wuxi's 120 emergency service, concurrent air pollutant data, and meteorological data (including daily mean temperature) were collected from January 1, 2014 to December 31, 2020. Distributed lag nonlinear modeling was used for time-series analysis, with cross-basis functions to capture the nonlinear relationship and lag effects between temperature and emergency volume. Confounding factors such as long-term trends, humidity, pollutants [ultimately including ozone (O₃) and fine particulate matter (PM_2.5)], day of the week, and holidays were controlled. The maximum lag period was set to 14 days. Single-day lag and cumulative lag effects of extreme temperatures were analyzed, followed by sensitivity analysis. Effects were quantified using relative risk (RR) and 95% confidence intervals (95%CI), and attributable fractions and numbers for different temperature ranges were calculated. Results A total of 10705 emergency cases of acute alcohol intoxication were included during the study period, with males accounting for 89.5% and individuals aged 18–50 years accounting for 83.4%. A non-linear "U-shaped" association was observed between daily mean temperature and the number of emergency calls, with an optimal temperature of 13.3 ℃. The effect of the extreme low temperature (1.88 ℃) was statistically significant from lag1 to lag3 after exposure (P<0.05), with the maximum effect at lag1 (RR=1.063, 95%CI: 1.002, 1.129). The extreme high temperature (32.3 ℃) showed an immediate effect, with maximum effect at lag0 (RR=1.374, 95%CI: 1.248, 1.512). The cumulative effect analysis showed that the risk associated with high temperature was the highest at lag0–3 (RR=2.000, 95%CI: 1.667, 2.399), while the risk associated with low temperature was the highest at lag0–14 (RR=1.462, 95%CI: 1.042, 2.051). The attribution analysis indicated that the mild heat (>24.7–30.8 ℃) and heat (>30.8–33.5 ℃) contributed the largest numbers of attributable cases, with 591 cases (95%CI: 56, 1032) and 190 cases (95%CI: 51, 309), respectively. The corresponding attributable fractions were 5.81% (95%CI: 0.80%, 9.94%) and 1.87% (95%CI: 0.49%, 2.96%), respectively. Conclusion Both extreme high and low temperatures significantly increase the number of 120 emergency calls for acute alcohol intoxication in Wuxi. High temperature exhibit an acute effect, while low temperature show a delayed effect. Mild heat have a significant impact on the emergency burden. It is recommended that emergency departments optimize resource allocation according to climatic characteristics and strengthen emergency responses during extreme weather to reduce alcohol-related health burden.

In the context of continuously deepening medical and health system reforms and comprehensively promoting the "Healthy China" strategy, Henan Provincial People's Hospital has established a regional collaborative and vertically integrated critical care service structure and network. This initiative aims to enhance information empowerment, strengthen regional collaboration, improve the insufficient primary medical services, and ensure timely and effective treatment for critically ill patients. By establishing a comprehensive dispatch service platform for regional collaborative critical care, building a "top-down" remote medical collaboration network, and forming a cross-regional specialty alliance for critical care, the hospital has improved the efficiency of medical services and enhanced regional capabilities for treating critically ill patients. Simultaneously, for critically serious patients and those with complex diseases at primary medical institutions, a one-stop consultation and referral service has been implemented. This service adopts a "three specialists" approach and a multidisciplinary consultation mechanism within the hospital, constructs a multi-dimensional critical care transfer mode integrating air, ground, and the internet, creates a regional collaborative rescue mode, and implements full-cycle treatment for critically serious patients. The comprehensive, flexible, and efficient service pathway for regional collaborative critical care established by this system ensures timely and safe treatment for critically ill patients, promotes the distribution of high-quality medical resources, and effectively addresses issues such as uneven distribution of high-quality medical resources and varying levels of critical care capabilities. It has facilitated the formation of a new tiered diagnosis and treatment order characterized by "first diagnosis at the primary level, two-way referral, separate treatment for acute and chronic diseases, and vertical integration". This approach has enhanced the diagnostic and comprehensive service capabilities of primary medical institutions. Currently, by strengthening information empowerment and sharing, creating a full-process critical care diagnosis and treatment model, providing medical assistance and cultivating primary-level critical care talent, and promoting appropriate technologies, the hospital has gradually overcome challenges such as barriers to information exchange and sharing between hospitals, overloaded critical care teams, high pressure on patient reception and transfer, and limited critical care capabilities at primary medical institutions. This article summarizes the construction and practical application of this regionally coordinated critical care system, aiming to provide a reference for the management of critical care treatment.
Humans ; China ; Critical Care/organization & administration* ; Delivery of Health Care/organization & administration*

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Gout significantly impacts both physical health and quality of life,while current pharmacological treatments face notable limitations.TCM non-pharmacological therapies have shown promising potential in the management of gout,offering diverse approaches with favorable efficacy.This article summarized the characteristics,clinical efficacy and mechanisms of different TCM non-pharmacological therapies for treating gout.Recent studies suggest that these therapies may be applied across all clinical stages of gout.During the acute phase,they can rapidly reduce joint inflammation and relieve pain.In the intercritical phase,they help prevent recurrence,decrease the frequency of attacks,and shorten episode duration.In the chronic tophaceous phase,they alleviate persistent symptoms,improve joint function,and support minimally invasive tophi removal.TCM non-pharmacological therapies have their own characteristics and good safety,and can be combined for clinical use,providing TCM treatment strategies for gout.

Knee osteoarthritis is a common joint disease within osteoarthritis,characterized by pain,swelling,and limited functionality as the main clinical manifestations.In severe cases,it affects daily life and falls under the category of"impediment syndrome"or"bone impediment"in TCM.The author believes that the theory of"bones,tendons,and muscles"is closely related to this disease.Treatment should focus on simultaneously nourishing the liver,spleen and kidneys,considering tendons,bones and muscles,while also dispelling wind,cold and dampness.The clinical application of Sanbi Decoction has shown good efficacy,and this discussion aimed to provide ideas for the diagnosis and treatment of knee osteoarthritis.

Gout significantly impacts both physical health and quality of life,while current pharmacological treatments face notable limitations.TCM non-pharmacological therapies have shown promising potential in the management of gout,offering diverse approaches with favorable efficacy.This article summarized the characteristics,clinical efficacy and mechanisms of different TCM non-pharmacological therapies for treating gout.Recent studies suggest that these therapies may be applied across all clinical stages of gout.During the acute phase,they can rapidly reduce joint inflammation and relieve pain.In the intercritical phase,they help prevent recurrence,decrease the frequency of attacks,and shorten episode duration.In the chronic tophaceous phase,they alleviate persistent symptoms,improve joint function,and support minimally invasive tophi removal.TCM non-pharmacological therapies have their own characteristics and good safety,and can be combined for clinical use,providing TCM treatment strategies for gout.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Lumbar disc herniation is mainly manifested as lower back pain,numbness,weakness,and radiating pain in the lower limbs,which seriously affects the patients'work and quality of life.In clinical practice,it has been found that this disease always belongs to the category of deficiency in healthy qi and excess in pathogenic factors,often accompanied by kidney deficiency and cold dampness.Kidney deficiency is the root cause,while cold dampness is the symptoms.The two factors interact with each other and cause back pain.The treatment is based on dispersing cold and dampness,tonifying the kidneys and strengthening the waist,and the classic ancient formula Shenzhuo Powder is safe and effective.