ObjectiveTo investigate the present situation of input, output, outcome and impact of all registered community-based rehabilitation stations in Inner Mongolia in China, and analyze how the input predict the output, outcome and impact. MethodsFrom March 1st to April 30th, 2025, a questionnaire survey was conducted on all registered community-based rehabilitation stations in Inner Mongolia, covering four dimensions: input, output, outcome and impact. A total of 1 365 questionnaires were distributed. The input included four items: laws and policies, human resources, equipment and facilities, and rehabilitation information management. The output included two items: technical paths and benefits/effectiveness. The outcome included three items: coverage rates, rehabilitation interventions and functional results. The impact included two items: health and sustainability. Each item contained several questions, all of which were described in a positive way. Each question was scored from one to five. A lower score indicated that the situation of the community-based rehabilitation station was more in line with the content described in the question. Regression analysis was performed using the total score of each item of input dimension as independent variables, and the total scores of the output, outcome and impact dimensions as dependent variables. ResultsA total of 1 262 valid questionnaires were collected. The mean values of input, output, outcome and impact of community-based rehabilitation stations were 1.827 to 1.904, with coefficient of variation of 45.892% to 49.239%. The regression analysis showed that, rehabilitation information management, human resources, and laws and policies significantly predicted the output dimension (R² = 0.910, P < 0.001). Meanwhile, all four items in the input dimension predicted both the outcome (R² = 0.850, P < 0.001) and impact dimensions (R² = 0.833, P < 0.001). ConclusionInput, output, outcome and impact of the community-based rehabilitation stations in Inner Mongolia were generally in line with the content of the questions, although some imbalances were observed. Additionally, the input of community-based rehabilitation stations could significantly predict their output, outcome and impact.

Objective To investigate the causal relationship between metabolic syndrome and breast cancer by using a bidirectional two-sample Mendelian randomization (MR) approach. Methods Genome-wide association study (GWAS) summary statistics for metabolic syndrome and breast cancer were acquired from the Integrative Epidemiology Unit GWAS database and the GWAS Catalog, with populations encompassing the United States and East Asia. A bidirectional causal design was employed: a forward analysis with metabolic syndrome as the exposure and breast cancer as the outcome, followed by a reverse analysis wherein their roles were interchanged. The inverse-variance weighting (IVW) method was primarily used for effect estimation, supplemented by MR-Egger regression, the weighted median method, the simple mode method, and the weighted mode method. Instrument variable strength was screened using the F-statistic (F>10). Robustness of the results was assessed through heterogeneity tests, horizontal pleiotropy tests, forest plots, and leave-one-out sensitivity analyses. Results The IVW analysis indicated no significant causal relationship between metabolic syndrome and breast cancer (OR=1.00, 95%CI: 0.97-1.03), P>0.05). Sensitivity analyses yielded consistent results, suggesting the good robustness of the study findings. Conclusion This study found no evidence to support a causal relationship, either positive or negative, between metabolic syndrome and breast cancer.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Hypertension ranks among the most prevalent chronic non-communicable diseases worldwide, with its prevalence rising steadily and exerting substantial impacts on the demographic composition of voluntary blood donors and the safety of blood supply. The eligibility of hypertensive individuals for safe blood donation and the relevant screening and management strategies have long remained controversial in transfusion medicine. This article systematically reviews the epidemiological characteristics of hypertension, donation-related physiological alterations and underlying risk mechanisms. By synthesizing evidence from international multicenter studies and regulatory practices across major nations, it conducts a comprehensive analysis of blood donation safety and donor admission criteria for hypertensive patients. Available evidence demonstrates that for hypertensives with well-stabilized blood pressure and no severe cardiac, cerebral or renal complications or target organ damage, the overall incidence of adverse reactions following blood donation is comparable to that in healthy donors, and no definitive correlation has been identified between blood pressure levels and vasovagal responses. Current mainstream international management frameworks are gradually shifting from screening reliant on a single blood pressure cutoff value toward an integrated strategy combining stratified risk classification and individualized dynamic assessment, with core focus on long-term blood pressure control, consistency of medication regimens and complication status. Meanwhile, emerging technologies including artificial intelligence, ambulatory blood pressure monitoring and wearable devices have opened innovative avenues for blood donation risk evaluation and full-cycle donor management. In light of existing clinical evidence, future efforts should further refine the multi-dimensional risk stratification system, facilitate the implementation of intelligent screening and continuous monitoring technologies, and scale up health education and follow-up management for hypertensive populations. Such measures aim to appropriately expand the pool of eligible blood donors while safeguarding donation safety, ultimately achieving coordinated improvement in blood safety and blood supply capacity.

Objective To analyze the nonlinear relationship between hypothermic machine perfusion (HMP) parameters and delayed graft function (DGF) and optimize the construction of a predictive model for DGF. Methods The data of 923 recipients who underwent kidney transplantation from deceased donors were retrospectively analyzed. According to the occurrence of DGF, the recipients were divided into DGF group (n=823) and non-DGF group (n=100). Donor data, HMP parameters and recipient data were analyzed for both groups. The nonlinear relationship between HMP parameters and the occurrence of DGF was explored based on restricted cubic splines (RCS). Over-sampling, under-sampling and balanced sampling were used to address the imbalance in the proportion of DGF to construct logistic regression predictive models. The area under the curve (AUC) of each model was compared in the validation set, and a nomogram model was constructed. Results Donor BMI, cold ischemia time of the donor kidney, and HMP parameters (initial and final pressures, resistance, and perfusion time) were significantly different between the DGF and non-DGF groups (all P<0.05). The RCS analysis revealed a threshold-like nonlinear relationship between HMP parameters and the risk of DGF. Among the models constructed using different sampling methods, the balanced sampling model had the highest AUC. Using this model, a nomogram was constructed to stratify recipients based on risk scores. Recipients in the high-risk group had higher serum creatinine levels at 1, 6, and 12 months after kidney transplantation compared to those in the low-risk group (all P<0.05). Conclusions There is a nonlinear relationship between HMP parameters and the risk of DGF, and the threshold is helpful for organ quality assessment and monitoring of graft function after transplantation. The predictive model for DGF constructed on the base of balanced sampling algorithms helps perioperative decision-making and postoperative graft function monitoring of kidney transplantation.

It is proposed that cold qi-induced accumulation encapsulates the core pathogenesis of the inflammation-cancer transformation in inflammatory bowel disease （IBD）. Cold pathogens may serve as the initiating factor. When first invading the intestines， cold pathogens obstruct the flow of qi； over time， the lingering cold impairs the middle jiao （焦）， eventually leading to the accumulation of cold-phlegm and blood stasis. Based on the progressive nature of this transformation， the process can be divided into three stages， active stage， remission stage， and carcinogenic stage. In the active stage， the main pathogenesis involves stagnation of cold qi and accumulation of damp-heat in the intestines； in the remission stage， cold qi impairs the spleen， disrupting its transport and transformation functions； and in the carcinogenic stage， the mechanisms include cold-induced accumulation， phlegm accumulation from cold， and stagnation of cold and blood stasis. Accordingly， the treatment strategies are proposed.In the active stage， regulating qi， relieving stagnation， and harmonizing cold and heat； in the remission stage， warming yang， dispersing cold， tonifying qi， and strengthening the spleen； and in the carcinogenic stage， promoting qi circulation， dispersing cold， resolving phlegm， activating yang， and eliminating stasis to remove accumulation. These approaches aim to interrupt the transformation of IBD into colorectal cancer.

OBJECTIVES: To investigate the impact of high expression of transmembrane and coiled helix structural domain 1 (TMCO1) on prognosis of gastric cancer and the possible mechanisms. METHODS: TMCO1 expression in gastric cancer and its effect on gastric cancer progression and prognosis were analyzed using publicly available databases and clinical data of patients undergoing radical surgery in our hospital, and its possible biological functions were explored using KEGG and GO analyses. In gastric cancer HGC-27 cells, the effects of lentivirus-mediated TMCO1 overexpression and TMCO1 silencing on cell apoptosis, proliferation, invasion and migration were examined. RESULTS: TMCO1 expression was significantly elevated in gastric cancer tissues (P<0.05), and its high expression was positively correlated with cancer progression (P<0.001) and a lowered postoperative 5-year survival rate of the patients (P<0.05). Bioinformatic analyses suggested that TMCO1 may affect gastric cancer cell apoptosis via Wnt signaling. In HGC-27 cells, TMCO1 overexpression significantly promoted tumor cell proliferation, inhibited cell apoptosis, and enhanced cell migration and invasion, whereas TMCO1 silencing produced the opposite effects. Western blotting showed that β-catenin levels were significantly upregulated in TMCO1-overexpressing cells and downregulated in cells with TMCO1 silencing. CONCLUSIONS TMCO1 is overexpressed in gastric cancer tissues, and its high expression promotes gastric cancer progression and affects long-term prognosis of the patients possibly by activating the Wnt/ β-catenin signaling pathway to inhibit apoptosis of gastric cancer cells.
Humans ; Stomach Neoplasms/metabolism* ; Apoptosis ; Prognosis ; Cell Line, Tumor ; Cell Proliferation ; Cell Movement ; Wnt Signaling Pathway ; beta Catenin/metabolism* ; Gene Expression Regulation, Neoplastic