Esophageal cancer （EC） is a highly prevalent malignant tumor in China. The phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway， as one of the key oncogenic pathways， can promote the cell cycle progression， proliferation， migration， and invasion， induce chemoresistance， and inhibit apoptosis and autophagy of EC cells. Traditional Chinese medicine （TCM）， with the advantages of targeting multiple points with multiple components to delay cancer progression， can target the PI3K/Akt signaling pathway for EC treatment. This article preliminarily discusses the molecular mechanism and role of the PI3K/Akt signaling pathway in EC and elaborates on the specific targets and efficacy of TCM in treating EC through intervention in the PI3K/Akt signaling pathway in the past five years. TCM materials and extracts inhibiting the PI3K/Akt signaling pathway in EC include Borneolum， spore powder of Ganoderma lucidum without spore coat， extract of Celastrus orbiculatus， root extract of Taraxacum， and Bruceae Fructus oil emulsion. TCM active ingredients exerting the effect include flavonoids， terpenoids， saponins， phenols， polysaccharides， alkaloids， and other compounds. TCM compound prescriptions with such effect include Qige San， Huqi San， Xuanfu Daizhetang， Tongyoutang and its decomposed prescriptions， Liujunzi Tang， and Xishenzhi Formula. In addition， TCM injections such as Compound Kushen Injection and Kang'ai injection also inhibit the PI3K/Akt signaling pathway in EC. This paper summarizes the role of the PI3K/Akt signaling pathway in EC and the TCM interventions， aiming to provide reference for the research and clinical application of new drugs for EC.

Background With China's socioeconomic development, significant lifestyle changes have occurred among occupational groups, leading to alterations in cardiovascular metabolic risk factors. However, few studies have examined the secular trends of these risk factors in China's working population. Objective To analyze the trends in cardiovascular metabolic risk factors among the occupational population in nine provinces of China from 2009 to 2018, and to explore the associations between different occupational types and these risk factors, along with their clustering patterns, thereby providing evidence for targeted interventions. Methods This study utilized data from the China Health and Nutrition Survey (CHNS) in 2009, 2015, and 2018. The dataset covered 13274 employed individuals aged 18-59 years. Only participants with complete blood test results and physical measurement data were included in the final analysis. The average annual percentage change (AAPC) in the positive rates of risk factors was calculated using Joinpoint regression to analyze temporal trends. Multinomial logistic regression and binary logistic regression models were employed to examine the cross-sectional associations between occupational types (categorized as white-collar workers, agricultural workers, blue-collar workers, service workers, and others) and nine cardiovascular metabolic risk factors and their clustering (defined as having ≥2 risk factors) in 2018. Results A total of 8451 participants were included. From 2009 to 2018, significant increasing trends (AAPC>0, P<0.05) were observed for overweight, obesity, central obesity, borderline elevated triglycerides (TG), borderline elevated to elevated total cholesterol (TC), borderline elevated low-density lipoprotein cholesterol (LDL-C), elevated blood pressure and hypertension, diabetes. The most rapid increases were found for obesity (AAPC=6.07%, 95%CI: 3.42%, 8.88%), hypertension (AAPC=4.10%, 95%CI: 0.62%, 7.77%), and central obesity (AAPC=3.27%, 95%CI: 1.24%, 5.36%). The clustering rate of risk factors increased from 62.3% in 2009 to 73.2% in 2018 (AAPC=1.87%, 95%CI: 1.02%, 2.73%). The cross-sectional analysis in 2018, after adjusting for confounders, showed that compared with white-collar workers, blue-collar workers had a significantly lower risk of overweight (OR=0.67, 95%CI: 0.48, 0.95), central obesity (OR=0.65, 95%CI: 0.45, 0.92), and borderline high TG (OR=0.54, 95%CI: 0.35, 0.82). In contrast, agricultural workers had a higher risk of hypertension (OR=1.76, 95%CI: 1.14, 2.72). Conclusion Between 2009 and 2015, the burden of most cardiometabolic risk factors increases significantly among the Chinese occupational population, with the rate of increase slowing by 2018. Risks vary across occupational types, among which blue- collar workers report lower target outcomes. Targeted interventions focusing on the rapidly increasing risk factors and high-risk occupational groups are recommended.

Objective: To explore whether digital facial and tongue diagnostic technologies can support the assessment of coronary heart disease (CHD) patients for coronary artery stenosis severity, and examine potential associations between digital tongue diagnosis features and myocardial biomarkers. Methods: The TFDA-1 face and tongue diagnosis instrument and the TDAS analysis system were used to perform intelligent visual examination and analysis of the facial and tongue in CHD patients who attended the Department of Cardiology at Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine between October 2, 2023 and July 31, 2024. Variables were screened using principal component analysis (PCA) and multicollinearity analysis to construct four machine learning models, including random forest, LightGBM, decision tree, and naive Bayes, for the early prediction of coronary artery stenosis severity. Model performance metrics, including sensitivity, specificity, precision, F1 score, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated. Visual analyses were performed using the SHapley Additive exPlanations (SHAP) interpreter and decision curve analysis. For patients after percutaneous coronary intervention (PCI), a conceptual model linking cardiac biomarkers and tongue diagnosis was constructed using the partial least squares structural equation modeling (PLS-SEM), and its validity was assessed. Results: A total of 459 CHD patients were enrolled and assigned to a PCI group and a non-PCI group (which comprised two subgroups: mild stenosis or less group, moderate stenosis or greater group). For sublingual vein (SV) features, the PCI group had lower SV-a and SV-b than the other groups (P < 0.01 and P < 0.05, respectively). For tongue surface features, the PCI group had significantly higher tongue body (TB)-L, TB-a, and TB-b (P < 0.05, P < 0.01, and P < 0.001, respectively), as well as higher tongue coating (TC)-a and TC-b (P < 0.01 and P < 0.001, respectively). Age, SV-a, SV-b, creatine kinase-myocardial band (CK-MB), CK, TC-a, lip-L, and lip-b were incorporated in the machine learning models. The random forest model performed best, with an AUC of 0.924, an F1 score of 0.839, precision of 0.807, accuracy of 0.864, sensitivity of 0.873, and specificity of 0.839. Decision curve analysis indicated that both LightGBM and random forest had clinical utility. PLS-SEM confirmed the pathway relationships: myocardial biomarkers → TB and myocardial biomarkers → TC (coefficient = – 0.238, t = 2.239, P = 0.025, and coefficient = – 0.270, t = 2.522, P = 0.012, respectively). Conclusion This study developed a noninvasive early warning model for coronary artery stenosis in patients with CHD. It applied PLS-SEM to investigate the association between post-PCI cardiac biomarkers and tongue diagnosis, and validated the proposed association chain. These findings suggest that intelligent traditional Chinese medicine (TCM) visual diagnosis integrated with modern digital technology may support CHD risk assessment and comprehensive health management.

Objective: To evaluate the methodological quality and reporting quality of clinical practice guidelines or expert consensus on blood transfusion and blood use (BTBU) in China (hereinafter referred to as "guidelines/consensus"), providing reference for the development and updating of future BTBU guidelines/consensus. Methods: A systematic search was conducted in Chinese databases such as China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Data-base, relevant guideline release platforms, and professional association websites. BTBU-related guidelines/consensus published between January 2006 and December 2025 were included. According to the inclusion and exclusion criteria, after completing literature screening and data extraction, three evaluators independently assessed the quality of the included literature using the Scientificity, Transparency, and Applicability of Guidelines Rating Tool (STAR), the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ), and the International Practice Guideline Reporting Standards (RIGHT). Results: A total of 29 documents were included, including 5 guidelines, 22 consensus statements, and 2 protocols. The results of the STAR assessment showed that the total score was (21.7±8.8)%. The domain score ratios for registration, research protocol, funding, working group, conflict of interest, clinical question, evidence, consensus method, recommendation, accessibility and other domains were (2.2±12.1)%, 0, (36.7±25.1)%, (18.3±7.6)%, (19.2±15.3)%, (16.6±23.1)%, (15.1±6.3)%, (9.6±21.2)%, (54.3±18.4)%, (31.7±10.4)% and (10.3±31.0)%, respectively. For the AGREE Ⅱ assessment, the methodological quality of 26 articles was rated Grade B and 3 articles Grade C. The results of the RIGHT assessment indicated that the overall reporting rate of all guidelines/consensuses was (65.3±4.0)%. Conclusion: The methodological and reporting quality of current expert guidelines/consensus on BTBU in China still need improvement, and guidelines and consensus should be strictly formulated and standardized according to the standards of STAR, AGREE II and RIGHT.

Sichuan Province is a hilly schistosomiasis-endemic province in China with the highest endemicity and largest coverage. During the period of more than 70 years of integrated control, there are five distinct phases of schistosomiasis control in Sichuan Province, including baseline investigation, Oncomelania hupensis snail control, chemotherapy for both humans and livestock, management of source of Schistosoma japonicum infections, and sustained control and intensified surveillance and early warning, and a package of key interventions are implemented, including environmental modification, snail control with chemical treatment, large-scale chemotherapy for humans and livestock, management of source of S. japonicum infections, intensified integrated control, establishment of an early warning and surveillance system. As a result, schistosomiasis has been eliminated across all 63 disease-endemic counties (cities, districts) in Sichuan Province by June 2025. Government-led multi-sectoral collaborations, science and technology-supported precision control strategies, and a sustainable control model tailored to the ecological and socio-economic conditions in endemic regions are essential to achieving the target of schistosomiasis elimination in Sichuan Province.

This paper systematically analyzes schistosomiasis transmission risks during the elimination stage and reviews the evolution of the schistosomiasis surveillance system in China from a One Health perspective, with a special emphasis on schistosomiasis surveillance and response practices during the elimination stage from a One Health perspective. These practices include precise screening of human schistosomiasis under low-endemicity conditions, systematic monitoring of schistosomiasis in domestic and wild animals, ecological management of Oncomelania snails, and information-coordinated responses. Nevertheless, the current surveillance and response system for schistosomiasis suffers from challenges of insufficient cross-sectoral coordination and complex external environments, which constrain precise schistosomiasis surveillance and response in China. Therefore, it is urgently necessary to integrate coordination mechanisms, empower the surveillance system with technological innovation, improve capacity building, and actively promote diverse social participation in the One Health framework, so as to consolidate current schistosomiasis control achievements and achieve the target for schistosomiasis elimination across China, which may provide a replicable technical model for global zoonoses control.

ObjectiveTo address the key challenges in breast electrical impedance tomography (EIT) reconstruction including the severe ill‑posedness of the inverse problem, limited measurement information from the electrodes, low contrast of small lesions, as well as the common drawbacks of traditional reconstruction methods such as boundary blurring, low spatial resolution, and weak small-target detection capability, and to overcome the limitations of existing deep learning approaches namely low training efficiency, insufficient physical consistency, and substantial information loss during the low‑to‑high dimensional mapping process, this study proposes a multi‑scale attention and dynamic sparse mask fusion network (MADS‑Net). The proposed method aims to enhance high‑resolution reconstruction accuracy, small-lesion detectability, noise robustness, and computational efficiency in breast‑screening-oriented EIT, thereby providing a feasible solution for the application of EIT in breast functional imaging. MethodsThe proposed network adopts a strategy of “locate first, reconstruct later”. It realizes adaptive allocation of computational resources through a dynamic mask module, alleviates information loss in the low-to-high dimensional mapping process via a multi-scale attention mechanism, and embeds physical constraints to improve the physical rationality of reconstruction results. The performance of the proposed method was validated through systematic numerical simulations, agar phantom experiments, and porcine tissue experiments. Comparisons were made with mainstream methods including the Gauss-Newton algorithm, convolutional neural networks, U-Net, and Deep D-Bar. The image correlation coefficient (ICC), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR) were adopted as quantitative evaluation metrics. Furthermore, lesion‑level evaluation metrics were introduced to assess the capability of different methods in reconstructing abnormal target boundaries and small‑sized target regions. ResultsExperimental results demonstrate that MADS-Net outperforms all comparison methods across all test scenarios. In numerical simulations, it achieves higher accuracy in target morphology and spatial location recovery as well as better background suppression. In agar phantom experiments, its detection and imaging reliability for small targets is significantly superior to the comparison methods. In porcine tissue experiments, the model maintains good generalization performance and can adapt to complex biological tissue imaging scenarios. Quantitative results show that MADS-Net achieves an average ICC of 0.917 6±0.048 8, an RMSE as low as0.060 6±0.031 6, and a PSNR of (23.893 9±2.685 3) dB. It successfully detects tiny targets as small as 5 mm within a 100 mm diameter sensing field, reduces the total training duration by 23.5% compared with the baseline method, and maintains stable reconstruction performance even under strong noise interference. ConclusionThe results demonstrate that MADS-Net achieves favorable EIT reconstruction performance in numerical simulations, agar phantom experiments, and porcine tissue experiments, even under common practical imaging challenges including low signal-to-noise ratio and blurred tissue boundaries. This study provides a methodological reference for further research on EIT in breast-screening-related scenarios and other medical functional imaging tasks, and also offers a low-cost, radiation-free alternative for breast lesion screening that aligns with the clinical demand for non-invasive diagnostic tools. It should be noted that real clinical breast data have not yet been used for validation in this study, and the clinical applicability of the proposed method requires further investigation. Future work will prioritize collecting multi-center real clinical breast datasets, and further optimize the model’s generalization ability to adapt to individual differences in breast tissue composition to promote its clinical application.

ObjectiveTo investigate the therapeutic effect of paeoniflorin on metabolic dysfunction-associated fatty liver disease （MASLD） combined with insulin resistance （IR）， and to elucidate its regulatory mechanisms on the Toll-like receptor 4 （TLR4）/nuclear transcription factor （NF）-κB-insulin receptor substrate 1 （IRS1） inflammation-insulin signaling axis. MethodsMASLD mouse model was established using a 60% high-fat diet. Mice were randomly assigned to a normal control group， a MASLD model group， and paeoniflorin groups （low dose： 25 mg·kg^-1·d^-1， medium dose： 50 mg·kg^-1·d^-1， high dose： 100 mg·kg^-1·d^-1）， receiving intragastric administration for consecutive 12 weeks. Concurrently， a palmitic acid/oleic acid （PA/OA）-induced lipid deposition model was established in HepG2 cells. Cell counting kit-8 （CCK-8） assay was performed to determine the optimal concentrations of PA/OA and paeoniflorin for intervention. Mice body weight， liver index， and serum levels of alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， triglycerides （TG）， and total cholesterol （TC） were measured. Fasting blood glucose （FBG） and fasting insulin （FINS） levels were measured， and the homeostatic model assessment of insulin resistance （HOMA-IR） was calculated， while an oral glucose tolerance test （OGTT） was performed to evaluate glucose metabolism. Hematoxylin and eosin （HE） staining was used to observe pathological changes and inflammatory cell infiltration in liver tissue， and oil red O staining was used to evaluate lipid droplet deposition in liver tissue and HepG2 cells. Potential targets were screened using network pharmacology and molecular docking. Western blot analysis was performed to detect the expression of HSP90AA1， TLR4， p-NF-κB p65， p-IRS1， and p-Akt proteins in liver tissue and HepG2 cells. ResultsCompared with the normal control group， mice in the MASLD model group exhibited significantly increased body weight， liver index， and serum levels of ALT， AST， TG， and TC， as well as markedly elevated FBG， FINS， and HOMA-IR， and impaired glucose tolerance （P<0.01）. HE staining revealed marked hepatic steatosis and inflammatory cell infiltration， while oil red O staining showed a significant increase in lipid droplet deposition. Compared with the MASLD model group， after intervention with various doses of paeoniflorin， the increase in body weight and liver index was reduced， serum ALT， AST， TG， and TC levels decreased， and FBG， FINS， and HOMA-IR were significantly lowered， with glucose tolerance markedly improved （P<0.05， P<0.01）， showing a certain dose-dependent trend. HE and oil red O staining results showed a marked reduction in hepatic steatosis and lipid deposition. In vitro experiments demonstrated that， compared with the control group， PA/OA treatment significantly induced increased lipid deposition in HepG2 cells. Compared with the model group， paeoniflorin intervention significantly reduced intracellular lipid droplets， and lipid deposition showed a dose-dependent decreasing trend. Mechanism studies indicated that， compared with the normal control group， the MASLD model group exhibited significantly elevated expression of TLR4， HSP90AA1， and p-NF-κB p65， while p-IRS1 （Ser307） expression was elevated and p-Akt （Ser473） expression was reduced （P<0.01）. Compared with the model group， the expression of the aforementioned proteins was significantly reversed in all treatment groups following intervention （P<0.05， P<0.01）. ConclusionPaeoniflorin significantly alleviates lipid deposition and insulin resistance in MASLD mice. Its mechanism of action may involve targeting HSP90AA1， TLR4， and NF-κB1 to regulate the TLR4/NF-κB-IRS1 inflammation-insulin signaling axis. This regulation suppresses inflammatory responses and restores insulin signaling， thereby ameliorating abnormalities in glucose and lipid metabolism.

ObjectiveTo investigate the therapeutic effect of paeoniflorin on metabolic dysfunction-associated fatty liver disease （MASLD） combined with insulin resistance （IR）， and to elucidate its regulatory mechanisms on the Toll-like receptor 4 （TLR4）/nuclear transcription factor （NF）-κB-insulin receptor substrate 1 （IRS1） inflammation-insulin signaling axis. MethodsMASLD mouse model was established using a 60% high-fat diet. Mice were randomly assigned to a normal control group， a MASLD model group， and paeoniflorin groups （low dose： 25 mg·kg^-1·d^-1， medium dose： 50 mg·kg^-1·d^-1， high dose： 100 mg·kg^-1·d^-1）， receiving intragastric administration for consecutive 12 weeks. Concurrently， a palmitic acid/oleic acid （PA/OA）-induced lipid deposition model was established in HepG2 cells. Cell counting kit-8 （CCK-8） assay was performed to determine the optimal concentrations of PA/OA and paeoniflorin for intervention. Mice body weight， liver index， and serum levels of alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， triglycerides （TG）， and total cholesterol （TC） were measured. Fasting blood glucose （FBG） and fasting insulin （FINS） levels were measured， and the homeostatic model assessment of insulin resistance （HOMA-IR） was calculated， while an oral glucose tolerance test （OGTT） was performed to evaluate glucose metabolism. Hematoxylin and eosin （HE） staining was used to observe pathological changes and inflammatory cell infiltration in liver tissue， and oil red O staining was used to evaluate lipid droplet deposition in liver tissue and HepG2 cells. Potential targets were screened using network pharmacology and molecular docking. Western blot analysis was performed to detect the expression of HSP90AA1， TLR4， p-NF-κB p65， p-IRS1， and p-Akt proteins in liver tissue and HepG2 cells. ResultsCompared with the normal control group， mice in the MASLD model group exhibited significantly increased body weight， liver index， and serum levels of ALT， AST， TG， and TC， as well as markedly elevated FBG， FINS， and HOMA-IR， and impaired glucose tolerance （P<0.01）. HE staining revealed marked hepatic steatosis and inflammatory cell infiltration， while oil red O staining showed a significant increase in lipid droplet deposition. Compared with the MASLD model group， after intervention with various doses of paeoniflorin， the increase in body weight and liver index was reduced， serum ALT， AST， TG， and TC levels decreased， and FBG， FINS， and HOMA-IR were significantly lowered， with glucose tolerance markedly improved （P<0.05， P<0.01）， showing a certain dose-dependent trend. HE and oil red O staining results showed a marked reduction in hepatic steatosis and lipid deposition. In vitro experiments demonstrated that， compared with the control group， PA/OA treatment significantly induced increased lipid deposition in HepG2 cells. Compared with the model group， paeoniflorin intervention significantly reduced intracellular lipid droplets， and lipid deposition showed a dose-dependent decreasing trend. Mechanism studies indicated that， compared with the normal control group， the MASLD model group exhibited significantly elevated expression of TLR4， HSP90AA1， and p-NF-κB p65， while p-IRS1 （Ser307） expression was elevated and p-Akt （Ser473） expression was reduced （P<0.01）. Compared with the model group， the expression of the aforementioned proteins was significantly reversed in all treatment groups following intervention （P<0.05， P<0.01）. ConclusionPaeoniflorin significantly alleviates lipid deposition and insulin resistance in MASLD mice. Its mechanism of action may involve targeting HSP90AA1， TLR4， and NF-κB1 to regulate the TLR4/NF-κB-IRS1 inflammation-insulin signaling axis. This regulation suppresses inflammatory responses and restores insulin signaling， thereby ameliorating abnormalities in glucose and lipid metabolism.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.