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.

Objective To analyze the situation of insecticide-treated nets (ITNs) ownership in malaria-endemic African countries from 2010 to 2023, so as to provide insights into China’s deeper participation in malaria control in Africa. Methods The study period from 2010 to 2023 was divided into three phases: the baseline phase (from 2010 to 2015), the middle phase (from 2016 to 2019), and the final phase (from 2020 to 2023), a total of 11 African countries with at least one Demographic and Health Survey (DHS) in each phase were included. Data pertaining to ITNs in 33 surveys of the above 11 African counties from 2010 to 2023 were captured from the DHS database, and the proportions of sources of ITNs and ITN ownership in each phase (number of ITNs ownership per person, overall ownership rate, and ownership rate per two residents) were calculated. The differences in numbers of ITNs per person between urban and rural areas and specified by socioeconomic status were analyzed. Results The proportions of ITNs from distribution campaigns were 60.24% to 94.01% and 50.46% to 85.04% in 11 African countries in the middle and final phases, respectively. The median numbers (interquartile range) of INTs ownership per person were 0.22 (0.50), 0.33 (0.50) and 0.33 (0.50) in the baseline, middle, and final phases, and the overall ownership rates [95% confidence interval (CI)] were 59.77% (59.50%, 60.05%), 70.32% (70.06%, 70.57%), and 69.21% (68.95%, 69.47%), while the ownership rates per two residents were 26.91% (26.66%, 27.16%), 38.07% (37.80%, 38.34%), and 36.56% (36.29%, 36.84%), respectively. The number of ITNs per person showed a significant increase followed by a significant decrease in 7 countries during all three phases (H = 102.518 to 2 327.440, all P < 0.05; Z = -48.886 to -4.653, all P < 0.016 7 after Bonferroni correction). In 33 surveys, there were 31 (Z = -26.719 to -2.472, P < 0.05) and 28 surveys (Z = -27.316 to -4.068, P < 0.001) with significant differences in numbers of ITNs ownership per person between households in urban and rural areas and with different socioeconomic status, including 20 surveys with a significantly higher number of ITNs ownership per person in households in rural areas than in urban areas, and 17 surveys with a significantly higher number of ITNs ownership per person among the poorest households than among the richest households. Conclusions There are substantial disparities in ITNs ownership in 11 African countries. Intensified co-operation on malaria prevention and control measures, such as ITNs, is recommended between China and African countries to build a global community of health for all.

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.

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.

To investigate the effects of Pithecellobium clypearia extract on the tissue structure,in-flammatory lesions as well as microbial diversity in the intestinal of yellow-feathered broilers.2401-day-old yellow-feathered broilers were randomly divided into four groups(groups A,B,C and D),groups A,B and C were supplemented with Pithecellobium clypearia extract in basal diets with concentrations of 0.5,1.0 and 2.0 g/kg,respectively.Group D served as the control group without adding Pithecellobium clypearia extract in diets,and the full trial period lasted for 70 d.Duodenum and jejunum samples were collected on the 20th,40th and 70th days of the test,the vil-lous/crypt ratio of duodenum and jejunum were calculated,and the mRNA expression level of in-flammatory cytokine as well as related pathways were detected in each group,respectively.In addition,the contents of cecum were collected at 70 th day of the experiment and the microbial di-versity in cecum were also analysed by 16S rDNA sequencing.The results showed that adding 0.5 and 1.0 g/kg of Pithecellobium clypearia extract in the diet could significantly increase the veloci-ty height/crypt depth ratio of duodenum and jejunum(P＜0.05)compared to control group,as well as the mRNA expression level of tight junction protein(CLDN1 and CLDN5)in jejunum,which further improved the structure of mucous of intestinal.Pithecellobium clypearia extract could significantly(P＜0.05)decrease the mRNA expression level of inflammatory cytokine inclu-ding IL-1β,IL-8 and TNF-α,as well as the related pathway genes such as TLR4,MyD88 and NF-κB in jejunum,thus reduced the inflammatory lesions in intestinal.Pithecellobium clypearia ex-tract also could significantly increase the abundance of beneficial microbial such as Parabacteroide and Prevotellaceae,while significantly decrease the abundance of pathogenic microbial such as Proteobacteria in cecum(P＜0.05)and improve the microbial diversity in intestinal.In summary,Pithecellobium clypearia extract could improve the structure of intestinal tissue and the gut barri-er function,as well as the microbial diversity in cecum,and also decrease the inflammatory lesions in jejunum,which is helpful to the intestinal health for yellow-feathered broilers.The present study provides scientific basis for the development of Pithecellobium clypearia as a safe feed additive in the future.

Hand osteoarthritis(HOA)is a disease of hand joint disorders,mainly manifested by hand interphalangeal joint and thumb carpal metacarpal joint pain,swelling,morning stiffness,limited movement,and even deformity,belonging to the category of TCM"bone arthralgia".The authors believe that HOA is more common with Taiyang Shaoyang disease,suitable for simultaneous treatment for Taiyang and Shaoyang,to operate the cardinal,regulate qi,blood,nutritive and defensive levels,dispel wind and cold,remove dampness and arthralgia,using modified Chaihu Guizhi Decoction,with confirmed efficacy.

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.

OBJECTIVE: To explore the effects of veno-venous extra corporeal carbon dioxide removal (V-V ECCO₂R) on local mechanical power and gas distribution in the lungs of patients with mild to moderate acute respiratory distress syndrome (ARDS) receiving non-invasive ventilation. METHODS: Retrospective research methods were conducted. Sixty patients with mild to moderate ARDS complicated with renal insufficiency who were transferred to the respiratory intensive care unit (RICU) through the 96195 platform critical care transport green channel from January 2018 to January 2020 at the collaborative hospitals of Henan Provincial People's Hospital were enrolled. According to different treatment methods, they were divided into a conventional treatment group and an ECCO₂R group, with 30 patients in each group. Both groups received standard treatments including primary disease treatment, airway management, and non-invasive ventilation. The conventional treatment group received bedside continuous renal replacement therapy (CRRT), and the ECCO₂R group received V-V ECCO₂R treatment. General information of patient such as gender, age, cause of disease, and acute physiology and chronic health evaluation II (APACHE II) were recorded; arterial blood gas analysis was performed before treatment and at 12 hours and 24 hours during treatment, recording arterial partial pressure of oxygen (PaO₂), arterial partial pressure of carbon dioxide (PaCO₂), and oxygenation index (PaO₂/FiO₂). Respiratory mechanics parameters [tidal volume, respiratory rate, maximal inspiratory pressure (MIP), and maximal expiratory pressure (MEP)] were recorded, and the rapid shallow breathing index (RSBI) was calculated; electrical impedance tomography (EIT) was used to measure regional of interest (ROI) values in different lung areas at 12 hours and 24 hours of treatment, and the pulmonary mechanical energy was calculated. RESULTS: The arterial blood gas analysis indicators, respiratory mechanics parameters, and pulmonary mechanical energy of patients in the conventional treatment group and ECCO₂R group improved significantly after 24 hours of treatment compared to 12 hours of treatment (all P < 0.05). The levels of PaCO₂, RSBI, total mechanical power, and non-dependent zone mechanical power in the ECCO₂R group were significantly lower than those in the conventional treatment group at both 12 hours and 24 hours during the treatment [PaCO₂ (mmHg, 1 mmHg ≈ 0.133 kPa): 44.03±2.96 vs. 49.96±2.50 at 12 hours, 41.65±3.21 vs. 48.53±2.33 at 24 hours; RSBI (times×min^-1×L^-1): 88.67±4.05 vs. 92.35±4.03 at 12 hours, 77.66±4.64 vs. 90.98±4.21 at 24 hours; total mechanical power (mJ): 10.40±1.15 vs. 12.93±1.68 at 12 hours, 11.13±1.18 vs. 14.05±1.69 at 24 hours; non-dependent zone mechanical power (mJ): 7.15±0.84 vs. 7.98±0.75 at 12 hours, 7.77±0.93 vs. 9.13±1.10 at 24 hours], and MEP and MIP in the ECCO₂R group were significantly higher than those in the conventional treatment group at both 12 hours and 24 hours during the treatment [MEP (cmH₂O, 1 cmH₂O ≈ 0.098 kPa): 89.88±5.04 vs. 86.09±5.57 at 12 hours, 96.57±2.59 vs. 88.66±2.98 at 24 hours; MIP (cmH₂O): 47.64±2.82 vs. 41.93±2.44 at 12 hours, 60.11±6.53 vs. 43.63±2.80 at 24 hours], the differences were statistically significant (all P < 0.05). CONCLUSIONS V-V ECCO₂R combined with non-invasive ventilation can effectively reduce the regional tidal volume, mechanical power, and respiratory rate in the non-gravitational dependent zones of patients with mild to moderate ARDS, and improve respiratory distress and oxygenation status.
Humans ; Respiratory Distress Syndrome/physiopathology* ; Retrospective Studies ; Carbon Dioxide ; Blood Gas Analysis ; Lung/physiopathology* ; Intensive Care Units ; Male ; Female ; Noninvasive Ventilation/methods* ; Continuous Renal Replacement Therapy/methods* ; APACHE ; Middle Aged

Objective:To provide references for effective intervention of hyperuricemia (HUA) by investigating the prevalence of HUA in military pilots and analyzing its relevant influencing factors.Methods:The physical examination data of 2 700 military pilots who received physical examinations from January 2021 to October 2023 were retrospectively analyzed. The above pilots were divided into HUA group and non-HUA group according to whether they had HUA. The differences in age, height, weight, body mass index, flying hours, blood pressure, blood uric acid, fasting blood glucose, total bilirubin, albumin, blood urea nitrogen, blood creatinine, alanine aminotransferase, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol levels, as well as the prevalence of hypertension, hyperlipidemia, and fatty liver were compared between 2 groups. The pilots were grouped by age (≤29 years, 30-39 years, 40-49 years,≥50 years) and by the aircraft types (fighter, bombers, transporter, helicopter). The criteria for determining HUA, hypertension, hyperlipidemia, and fatty liver were based on the relevant guidelines. The influencing factors for the prevalence of HUA were analyzed through binary Logistic regression. Single factor binary Logistic regression was used to analyze the influencing factors of the prevalence of HUA in pilots, and P<0.05 index was included in multi-factor binary Logistic regression to further analyze the influencing factors of the occurrence of HUA. Results:There were 821 cases (30.41%) in HUA group and 1 879 cases (69.59%) in non-HUA group. The weight ( t=7.75, P<0.001), body mass index ( t=8.23, P<0.001), systolic blood pressure ( t=3.20, P=0.001), diastolic blood pressure ( t=3.06, P=0.002), serum uric acid ( t=62.83, P<0.001), blood urea nitrogen ( t=3.41, P=0.001), serum creatinine ( t=8.88, P<0.001), alanine aminotransferase ( t=5.56, P<0.001), total cholesterol ( t=3.27, P=0.001), triglyceride ( Z=9.54, P<0.001), as well as the prevalence of hypertension ( χ2=7.48, P=0.006), hyperlipidemia ( χ2=42.74, P<0.001), and fatty liver ( χ2=22.51, P<0.001) in HUA group were higher than those in non-HUA group, while high-density lipoprotein cholesterol was lower than that in the non-HUA group, and the difference was significant ( t=5.91, P<0.001). There was no significant difference in the prevalence of HUA among pilots of different age groups and aircraft types (both P>0.05). Overweight ( OR=1.392, 95% CI: 1.159-1.672), obesity ( OR=1.891, 95% CI: 1.197-2.987), and elevated level of triglyceride ( OR=1.246, 95% CI:1.128-1.377), blood urea nitrogen ( OR=1.079, 95% CI:1.000-1.163), and serum creatinine ( OR=1.036, 95% CI:1.028-1.044) were risk factors for HUA, and elevated high density lipoprotein cholesterol ( OR=0.567, 95% CI: 0.389-0.825) was a protective factor. Conclusions:The prevalence of HUA in military pilots is higher than that in the general population. Overweight, obesity, and elevated level of triglyceride, blood urea nitrogen, and serum creatinine are closely related to the prevalence of HUA, and comprehensive intervention measures should be taken for key targets.

Objective:To provide references for effective intervention of hyperuricemia (HUA) by investigating the prevalence of HUA in military pilots and analyzing its relevant influencing factors.Methods:The physical examination data of 2 700 military pilots who received physical examinations from January 2021 to October 2023 were retrospectively analyzed. The above pilots were divided into HUA group and non-HUA group according to whether they had HUA. The differences in age, height, weight, body mass index, flying hours, blood pressure, blood uric acid, fasting blood glucose, total bilirubin, albumin, blood urea nitrogen, blood creatinine, alanine aminotransferase, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol levels, as well as the prevalence of hypertension, hyperlipidemia, and fatty liver were compared between 2 groups. The pilots were grouped by age (≤29 years, 30-39 years, 40-49 years,≥50 years) and by the aircraft types (fighter, bombers, transporter, helicopter). The criteria for determining HUA, hypertension, hyperlipidemia, and fatty liver were based on the relevant guidelines. The influencing factors for the prevalence of HUA were analyzed through binary Logistic regression. Single factor binary Logistic regression was used to analyze the influencing factors of the prevalence of HUA in pilots, and P<0.05 index was included in multi-factor binary Logistic regression to further analyze the influencing factors of the occurrence of HUA. Results:There were 821 cases (30.41%) in HUA group and 1 879 cases (69.59%) in non-HUA group. The weight ( t=7.75, P<0.001), body mass index ( t=8.23, P<0.001), systolic blood pressure ( t=3.20, P=0.001), diastolic blood pressure ( t=3.06, P=0.002), serum uric acid ( t=62.83, P<0.001), blood urea nitrogen ( t=3.41, P=0.001), serum creatinine ( t=8.88, P<0.001), alanine aminotransferase ( t=5.56, P<0.001), total cholesterol ( t=3.27, P=0.001), triglyceride ( Z=9.54, P<0.001), as well as the prevalence of hypertension ( χ2=7.48, P=0.006), hyperlipidemia ( χ2=42.74, P<0.001), and fatty liver ( χ2=22.51, P<0.001) in HUA group were higher than those in non-HUA group, while high-density lipoprotein cholesterol was lower than that in the non-HUA group, and the difference was significant ( t=5.91, P<0.001). There was no significant difference in the prevalence of HUA among pilots of different age groups and aircraft types (both P>0.05). Overweight ( OR=1.392, 95% CI: 1.159-1.672), obesity ( OR=1.891, 95% CI: 1.197-2.987), and elevated level of triglyceride ( OR=1.246, 95% CI:1.128-1.377), blood urea nitrogen ( OR=1.079, 95% CI:1.000-1.163), and serum creatinine ( OR=1.036, 95% CI:1.028-1.044) were risk factors for HUA, and elevated high density lipoprotein cholesterol ( OR=0.567, 95% CI: 0.389-0.825) was a protective factor. Conclusions:The prevalence of HUA in military pilots is higher than that in the general population. Overweight, obesity, and elevated level of triglyceride, blood urea nitrogen, and serum creatinine are closely related to the prevalence of HUA, and comprehensive intervention measures should be taken for key targets.