Objective To evaluate the short-term effects of comprehensive health management interventions for stroke high-risk population screening on the prevention and treatment of ischemic stroke, and to provide reference and basis for improving and exploring health management and prevention strategies for stroke high-risk population. Methods From 2018 to 2022, 13 community health service centers in Jiading District, Shanghai were selected in the present study. Based on information push platform, stroke risk assessment and health intervention follow-up were conducted for community residents through convenience sampling. The residents were divided into a full course intervention group (intervention group) and a routine intervention group (control group) according to different health intervention measures and forms. The incidence of ischemic stroke in the two groups of survey subjects was tracked within 36 months. Results A total of 52144 subjects were included in the study. The total number of patients in the full course intervention group was 14227, with an incidence density of 577.32/100 000 (556.49/100 000-598.12/100 000), which was lower than that of the conventional intervention group (37 917), with an incidence density of 1 485.47/100 000 (1 464.99/100 000-1 505.94/100 000) (χ²=2490.212, P<0.001). The relative risk of the full course intervention group was 0.39, and the relative risk of stroke risk factors in the full course intervention group from low to high was 0.33, 0.43, 0.45, and 0.49, respectively. The incidence density of males in the full course intervention group was 660.76 (627.46/100 000 - 694.05/100 000), with a relative risk of 0.43, and the incidence density of female patients was 509.71/100 000 (483.37/100 000 - 536.05/100 000), with a relative risk of 0.35. The overall incidence density of the population under 62 years old gourp, 62-75 years old group and over 75 years old group was 197.45/100 000 (173.09/100 000 -221.80/100 000), 608.36/100 000 (580.19/100 000-636.54/100 000), and 1 025.06/100 000 (958.51/100 000-1 091.61/100 000), with relative risks of 0.51, 0.44, and 0.38, respectively. Conclusion Comprehensive health management measures can effectively reduce the short-term risk of ischemic stroke, and should be further promoted and improved to enhance the effectiveness of stroke prevention and control.

Hydrogenases, as a class of highly efficient and reversible biological catalysts, can catalyze the reduction of protons to molecular hydrogen, thus demonstrating great potential in a wide range of fields such as renewable energy production and green chemistry. Despite their significant potential, the large-scale industrial application of hydrogenases has long been constrained by several inherent limitations, including high sensitivity to molecular oxygen, the challenges in the in vitro reconstitution and maturation of their catalytic centers, and the inefficiency and instability of the natural electron transfer pathways. To overcome these limitations and enhance the catalytic performance of hydrogenases, researchers have developed various strategies, among which enzyme molecular engineering, photo-driven modification, and enzyme immobilization techniques are the most common exploration directions. Particularly, enzyme immobilization technology is widely used to improve the reusability of hydrogenases, but traditional immobilization methods often come with disadvantages in practical applications, such as complex multi-step procedures and insufficient biocompatibility of the immobilization materials. In recent years, bioencapsulation technology has emerged as a promising alternative strategy to enhance the catalytic performance of hydrogenases. This method utilizes biologically derived encapsulation materials to construct physically confined and precisely defined chemical microenvironments around the enzyme molecules, offering simpler self-assembly processes and superior biocompatibility. With these biomimetic constructs, bioencapsulation technology not only provides better oxygen tolerance but also helps to create a local microenvironment conducive to sustained catalytic function. This article systematically reviews the latest research progress of two main bioencapsulation strategies for hydrogenases: one is the encapsulation technology based on protein-based nanocages; the other is the engineering strategy for whole-cell hydrogenase expression. In the nanocage-based systems, this article focuses on the structural and functional characteristics of virus-like capsids and carboxysome protein shells, which serve as efficient enzyme encapsulation scaffolds, not only providing a stable physical barrier to prevent oxygen diffusion but also enabling high-density enzyme loading, thereby promoting substrate channeling effects and electron transfer kinetics. This article also discusses whole-cell encapsulation systems, which achieve hydrogenase compartmentalization within engineered cellular structures or by using external natural polysaccharide-based encapsulation matrices to wrap whole-cell catalysts. Bioencapsulation strategies can bring multiple synergistic benefits: they can effectively protect hydrogenases from oxygen-mediated inactivation, significantly delay the decline of catalytic activity over time, and enhance the hydrogen production rate by increasing the local concentration of active enzyme molecules and optimizing the electron transfer efficiency from redox partners to the catalytic center.Despite the significant progress made, several technical challenges remain to be addressed. The main obstacles include limited enzyme loading and encapsulation efficiency, insufficient long-term stability of encapsulation materials under operating conditions, and the need to improve the matching of the photo-biological interface in systems integrating light-harvesting components with enzymatic catalysis. Future efforts can focus on the integration of multiple technological approaches, such as using computer-aided protein design to optimize encapsulation structures, developing engineered electron transfer pathways to enhance catalytic conversion efficiency, and designing composite multifunctional materials with both structural stability and functional adaptability. These directions collectively aim to achieve efficient, stable, and scalable hydrogen production applications of bioencapsulated hydrogenase systems.

Hydrogenases, as a class of highly efficient and reversible biological catalysts, can catalyze the reduction of protons to molecular hydrogen, thus demonstrating great potential in a wide range of fields such as renewable energy production and green chemistry. Despite their significant potential, the large-scale industrial application of hydrogenases has long been constrained by several inherent limitations, including high sensitivity to molecular oxygen, the challenges in the in vitro reconstitution and maturation of their catalytic centers, and the inefficiency and instability of the natural electron transfer pathways. To overcome these limitations and enhance the catalytic performance of hydrogenases, researchers have developed various strategies, among which enzyme molecular engineering, photo-driven modification, and enzyme immobilization techniques are the most common exploration directions. Particularly, enzyme immobilization technology is widely used to improve the reusability of hydrogenases, but traditional immobilization methods often come with disadvantages in practical applications, such as complex multi-step procedures and insufficient biocompatibility of the immobilization materials. In recent years, bioencapsulation technology has emerged as a promising alternative strategy to enhance the catalytic performance of hydrogenases. This method utilizes biologically derived encapsulation materials to construct physically confined and precisely defined chemical microenvironments around the enzyme molecules, offering simpler self-assembly processes and superior biocompatibility. With these biomimetic constructs, bioencapsulation technology not only provides better oxygen tolerance but also helps to create a local microenvironment conducive to sustained catalytic function. This article systematically reviews the latest research progress of two main bioencapsulation strategies for hydrogenases: one is the encapsulation technology based on protein-based nanocages; the other is the engineering strategy for whole-cell hydrogenase expression. In the nanocage-based systems, this article focuses on the structural and functional characteristics of virus-like capsids and carboxysome protein shells, which serve as efficient enzyme encapsulation scaffolds, not only providing a stable physical barrier to prevent oxygen diffusion but also enabling high-density enzyme loading, thereby promoting substrate channeling effects and electron transfer kinetics. This article also discusses whole-cell encapsulation systems, which achieve hydrogenase compartmentalization within engineered cellular structures or by using external natural polysaccharide-based encapsulation matrices to wrap whole-cell catalysts. Bioencapsulation strategies can bring multiple synergistic benefits: they can effectively protect hydrogenases from oxygen-mediated inactivation, significantly delay the decline of catalytic activity over time, and enhance the hydrogen production rate by increasing the local concentration of active enzyme molecules and optimizing the electron transfer efficiency from redox partners to the catalytic center.Despite the significant progress made, several technical challenges remain to be addressed. The main obstacles include limited enzyme loading and encapsulation efficiency, insufficient long-term stability of encapsulation materials under operating conditions, and the need to improve the matching of the photo-biological interface in systems integrating light-harvesting components with enzymatic catalysis. Future efforts can focus on the integration of multiple technological approaches, such as using computer-aided protein design to optimize encapsulation structures, developing engineered electron transfer pathways to enhance catalytic conversion efficiency, and designing composite multifunctional materials with both structural stability and functional adaptability. These directions collectively aim to achieve efficient, stable, and scalable hydrogen production applications of bioencapsulated hydrogenase systems.

ObjectiveTo investigate the association of the traditional Chinese medicine （TCM） syndrome types of primary liver cancer （PLC） with clinical features and multimodal quantitative radiological features on computed tomography （CT） and magnetic resonance imaging （MRI）， and to provide a reference for the objectification of TCM syndrome differentiation and precise diagnosis and treatment. MethodsA retrospective analysis was performed for the clinical data of 312 patients who were diagnosed with PLC in The First Affiliated Hospital of Hunan University of Chinese Medicine from March 2020 to June 2025， and according to the TCM syndrome type， they were divided into stagnation of liver Qi group with 40 patients， stagnation of liver Qi and spleen deficiency group with 109 patients， Qi stagnation and blood stasis group with 62 patients， dampness-heat toxin amassment group with 81 patients， and liver-kidney Yin deficiency group with 20 patients. Clinical features and multimodal quantitative radiological features were compared between the patients with different TCM syndrome types. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups； the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between multiple groups， and the Dunn’s multiple test was used for further comparison between two groups； the chi-square test was used for comparison of categorical data between groups， and the Bonferroni method was used for further comparison between two groups. ResultsThere were significant differences between the patients with different TCM syndrome types in China liver cancer staging （CNLC）， Child-Pugh class， alanine aminotransferase， aspartate aminotransferase， albumin， direct bilirubin， total bilirubin， prothrombin time， neutrophil， and albumin-bilirubin score （all P<0.05）. In the stagnation of liver Qi group， the patients with Child-Pugh class A accounted for 75.00%； among the patients with CNLC stage I PLC， the patients with stagnation of liver Qi accounted for 60.00%， and those with Qi stagnation and blood stasis syndrome accounted for 59.68%， while among the patients with CNLC stage IV PLC， the distribution proportion of dampness-heat toxin amassment （27.16%） and liver-kidney Yin deficiency （30.00%） was significantly higher than that of stagnation of liver Qi （2.50%） （all P<0.05）. Radiological examination showed that there were significant differences between the patients with different TCM syndrome types in the number of tumors， ascites， venous tumor thrombus， maximum tumor diameter， intrahepatic metastasis， and lymph node metastasis in the hepatic hilar and retroperitoneal regions （all P<0.05）. Compared with the patients with stagnation of liver Qi， the patients with liver depression and spleen deficiency or liver-kidney Yin deficiency were more likely to develop intrahepatic metastasis； the patients with liver depression and spleen deficiency， dampness-heat toxin amassment， or liver-kidney Yin deficiency were more likely to develop lymph node metastasis in the hepatic hilar and retroperitoneal regions； the patients with liver-kidney Yin deficiency were more likely to experience multiple tumors； the patients with liver depression and spleen deficiency or dampness-heat toxin amassment were more likely to develop ascites （all P<0.05）. Compared with the patients with Qi stagnation and blood stasis syndrome， the patients with liver depression and spleen deficiency had a significantly longer maximum tumor diameter and a significantly higher proportion of patients with venous tumor thrombus （both P<0.05）. Furthermore， among the 184 patients with MRI diffusion-weighted imaging sequences， the patients with dampness-heat toxin amassment or Qi stagnation and blood stasis syndrome had significantly higher ADC values and relative ADC values than those with stagnation of liver Qi （all P<0.05）. ConclusionThere are significant differences in CT/MRI radiological features and clinical features between PLC patients with different TCM syndrome types， among whom the patients with liver depression and spleen deficiency， dampness-heat toxin amassment， and liver-kidney Yin deficiency tend to exhibit progressive radiological features， and those with dampness-heat toxin amassment or Qi stagnation and blood stasis syndrome tend to have higher ADC values. These findings provide an objective basis for TCM syndrome differentiation in PLC.

ObjectiveStroke poses a heavy burden due to its high mortality and morbidity rates. Accurate and real-time detection of lesions is pivotal for prompt clinical intervention and favorable prognosis. Electrical impedance tomography (EIT) and microwave tomography (MWT) have emerged as compelling alternatives for stroke screening, owing to their non-ionizing, non-invasive and portable nature. EIT provides information on tissue conductivity, and MWT offers high sensitivity to changes in dielectric properties. However, single-modality imaging is inherently limited, EIT suffers from low sensitivity to deep-seated tissues and severe ill-posedness of inverse problems, whereas MWT is challenged by strong nonlinearity in inverse scattering and susceptibility to modeling errors. Consequently, the clinical utility of standalone EIT or MWT for stroke diagnosis remains constrained by poor spatial resolution and imaging artifacts. To improve the accuracy and robustness of stroke imaging, a dual-modality fusion conditional denoising diffusion probabilistic model (DM-DDPM) was proposed for high-precision dual-modality image reconstruction. MethodsA dual-encoder network with a symmetric architecture and independently trained parameters was constructed to extract heterogeneous features separately from EIT boundary voltage measurements and MWT scattered field signals. Attentional feature fusion (AFF) is employed to integrate complementary information from the two modalities adaptively, generating robust fused priors that suppress redundant noise while preserving key physical characteristics. Subsequently, the fused priors are embedded into a Transformer-based diffusion model via a cross attention mechanism to guide the reverse denoising process. This approach effectively reduces artifacts and enhances the stability of conductivity distribution reconstruction. Time step embedding is introduced to enable the network to perceive the diffusion stage and further improve the accuracy of noise prediction. ResultsSimulated experiments demonstrated that DM-DDPM significantly outperforms single-modality and multi-modality networks under various noise levels. A head model simulation dataset was constructed based on COMSOL Multiphysics, and tests were carried out under 50 dB, 40 dB and 30 dB signal-to-noise ratio levels. At 30 dB, the average relative error (RE) was below 0.20, while the structural similarity index measure (SSIM) and correlation coefficient (CC) remained above 0.90 and 0.89, respectively. Compared with single-modality and multi-modality networks, artifacts were significantly reduced, lesion edges were clearer, and localization was more accurate. The model maintains high reconstruction quality and strong robustness for single, double, and triple lesions simultaneously. Furthermore, physical experiments were conducted using a 16-electrode EIT system and a 16-antenna MWT system with asynchronous data acquisition. These experiments confirmed the feasibility of the method in real-world scenarios and demonstrated that it can robustly reconstruct simulated lesions despite environmental interference and measurement noise, validating its reliability for practical clinical applications. ConclusionThe proposed method effectively combines complementary dual-modality information with a conditional diffusion model. Low accuracy and poor noise resistance in single-modality imaging were effectively addressed, while the noise amplification issue caused by direct multimodal data fusion was avoided. The proposed algorithm exhibits strong anti-noise interference ability and high imaging stability in both simulation and physical experiments. Precise localization of stroke lesions with different quantities was achieved, providing a high-precision, and practical technical support for clinical stroke detection.

Treatment resistance, recurrence, and metastasis of breast cancer remain critical issues affecting long-term patient survival. Chronic psychological stress may act as a host biological driver of breast cancer treatment resistance rather than a mere comorbidity. Through the sustained activation of the sympathetic nervous system and the hypothalamic-pituitary-adrenal axis, also referred to as the "brain-tumor axis", stress signaling reshapes inflammatory, immune, and metabolic networks within the tumor microenvironment and thereby influences chemotherapy, endocrine therapy, anti-HER2 therapy, immunotherapy, and radiotherapy. Current evidence indicates that the key pathways with translational potential are mainly the β-adrenergic axis and the glucocorticoid receptor (GR) pathway. The former is more involved in amplifying inflammatory responses, promoting immunosuppression and metastasis, whereas the latter is closely associated with anti-apoptosis, maintenance of tumor stemness, and reprogramming of estrogen receptor function. This review focuses on the discrepancies in evidence regarding these pathways between population-based studies and preclinical models, and discusses clinically operable non-pharmacologic interventions such as cognitive behavioral stress management, nurse navigation and treatment adherence support, and exercise interventions during the neoadjuvant phase. Although high-level evidence for direct survival benefit remains limited, these interventions have shown favorable effects on cortisol levels, depressive symptoms, quality of life, adherence, relative dose intensity, and some biology-related intermediate endpoints, making them valuable complementary approaches in comprehensive breast cancer management.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Intraoperative cell salvage (IOCS) has been widely applied as an important blood conservation measure in surgical operations. However, there is currently a lack of clinical practice guidelines for the implementation of IOCS in patients with malignant tumors. This report aims to provide clinicians with recommendations on the use of IOCS in patients with malignant tumors based on the review and assessment of the existed evidence. Data were derived from databases such as PubMed, Embase, the Cochrane Library and Wanfang. The guideline development team formulated recommendations based on the quality of evidence, balance of benefits and harms, patient preferences, and health economic assessments. This study constructed seven major clinical questions. The main conclusions of this guideline are as follows: 1) Compared with no perioperative allogeneic blood transfusion (NPABT), perioperative allogeneic blood transfusion (PABT) leads to a more unfavorable prognosis in cancer patients (Recommended); 2) Compared with the transfusion of allogeneic blood or no transfusion, IOCS does not lead to a more unfavorable prognosis in cancer patients (Recommended); 3) The implementation of IOCS in cancer patients is economically feasible (Recommended); 4) Leukocyte depletion filters (LDF) should be used when implementing IOCS in cancer patients (Strongly Recommended); 5) Irradiation treatment of autologous blood to be reinfused can be used when implementing IOCS in cancer patients (Recommended); 6) A careful assessment of the condition of cancer patients (meeting indications and excluding contraindications) should be conducted before implementing IOCS (Strongly Recommended); 7) Informed consent from cancer patients should be obtained when implementing IOCS, with a thorough pre-assessment of the patient's condition and the likelihood of blood loss, adherence to standardized internally audited management procedures, meeting corresponding conditions, and obtaining corresponding qualifications (Recommended). In brief, current evidence indicates that IOCS can be implemented for some malignant tumor patients who need allogeneic blood transfusion after physician full evaluation, and LDF or irradiation should be used during the implementation process.

Alopecia areata （AA） is a common hair loss disorder， and the core pathogenesis is the internal gene-ration of turbid toxin caused by qi movement disorder in the liver， spleen， and kidney. Turbid toxin serves as both a pivotal etiological trigger and a pathological driver of disease exacerbation. Clinically， AA can be classified into four principal patterns， including liver constraint with spleen deficiency， internal accumulation of damp-heat， liver-kidney depletion， and qi-blood depletion. Therapeutic strategies prioritize clearing and resolving turbid toxin while regulating the qi movement of the liver， spleen， and kidney. Accordingly， different formulas were applied. Self-formulated Shugan Jianpi Huazhuo Formula （疏肝健脾化浊方） is suggested to drain dampness， resolve turbidity， and unblock qi movement. Self-formulated Sanjiao Fenxiao Jiedu Formula （三焦分消解毒方） can be used to clear heat， drain dampness and resolve toxin. Self-formulated Zishen Yanggan Toudu Decoction （滋肾养肝透毒汤） can clear and vent latent toxins， while Self-formulated Guiqi Shengfa Didu Formula （归芪生发涤毒方） is employed to tonify qi and blood， purge toxins， and regenerate vitality. By differentiating and treating AA based on the functional patterns of the liver， spleen， and kidney， this approach expands the application scope of the turbid toxin theory and provides valuable insights for treatment of AA.

Objective To evaluate the clinical efficacy of artificial intelligence(AI)-assisted imaging diagnostic trials using multi-reader multi-case(MRMC)design,so as to provide a scientific basis for clinical evaluation of imaging diagnostic trials.Methods The MRMC design,widely used in imaging diagnostic trials,was adopted in this study.The Obuchowski-Rockette(OR)method of MRMC design was detailed,including model construction and test methods.A case study was conducted,collecting imaging data of 200 subjects from 3 hospitals,with 133 cases of rib fractures and 68 cases of non-rib fractures.Three radiologists reviewed all CT images of the subjects.The area under curve(AUC)value,sensitivity and specificity in detecting rib fractures between 2 reading modalities(radiologists with AI assistance vs radiologists reading independently)were compared.Results The AI-assisted reading group had an AUC value of 0.958,while the radiologist-independent reading group had an AUC value of 0.902,showing a significant difference(P＜0.001).The overall sensitivity and specificity of the AI-assisted reading group were 0.970 and 0.946,respectively;while the sensitivity and specificity of the radiologist-independent reading group were 0.838 and 0.966,respectively.The difference of sensitivity between groups was 0.131(95％confidence interval[CI]0.091-0.171),and the difference of specificity was-0.020(95％CI-0.059-0.020),indicating a significant difference in sensitivity but not in specificity between AI-assisted and radiologist-independent reading groups.Both groups had positive likelihood ratios(+LR)greater than 10 and negative likelihood ratios(-LR)less than 0.2,with positive predictive values approaching 1,suggesting that the diagnostic accuracy of the AI-assisted imaging diagnostic trials was high.Conclusion The AI-assisted reading method demonstrates a significant advantage in enhancing diagnostic efficiency,not only improving the diagnostic accuracy and detection rate of rib fractures,but also improving the work efficiency of radiologists and optimizing hospital services.