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 effects and mechanisms of Buyang huanwu decoction （BYHWD） and its active fractions in ameliorating non-alcoholic fatty liver disease. METHODS BYHWD and its effective fractions obtained through ethanol precipitation， as well as 30% ethanol， 50% ethanol， and 75% ethanol fractions （namely， the CC effective fraction， 30YC effective fraction， 50YC effective fraction， and 75YC effective fraction）， were prepared. These preparations were administered to rats via intragastric administration to prepare corresponding drug-containing serum （blank serum and simvastatin-containing serum were prepared using the same protocol）. Human L02 hepatocytes were divided into control group， model group， simvastatin-containing serum group， BYHWD-containing serum group， CC-containing serum group， 30YC-containing serum group， 50YC-containing serum group， and 75YC-containing serum group. Except for the control group， other groups were given 0.2 mol/L oleic acid for 24 h to induce a lipid accumulation model， and then intervened with 20% drug-containing serum/blank serum for 24 h. The lipid deposition in cells was observed， and the proportion of lipid droplet area was calculated； the levels of triglycerides （TG） and indicators of oxidative stress [malondialdehyde （MDA）， superoxide dismutase （SOD）] as well as liver function [alanine amino- transferase （ALT）， aspartate amino-transferase （AST）] in cells were detected； protein and mRNA expressions of AMP-activated protein kinase （AMPK）/sterol regulatory element-binding protein-1 （SREBP-1）/glycerol-3-phosphate acyltransferase （GPAT） signaling pathway were also measured. RESULTS Compared with the control group， cells in the model group exhibited severe cellular steatosis， with a significantly increased proportion of lipid droplet area， as well as the elevated levels of TG， ALT， AST， and MDA in cells， along with significantly up-regulated mRNA and protein expression levels of SREBP-1 and GPAT （P＜0.05）. The level of SOD， mRNA expression of AMPK， as well as the protein phosphorylation level of AMPK were decreased significantly （P＜0.05）. Compared with the model group， cellular steatosis was alleviated in all drug-containing serum groups， and the levels of most of the aforementioned quantitative indicators were significantly reversed （P＜0.05）. CONCLUSIONS BYHWD and its active fractions can exert a therapeutic effect on improving non-alcoholic fatty liver disease by regulating the AMPK/SREBP-1/GPAT signaling pathway， inhibiting oxidative stress responses， and reducing lipid deposition.

Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

Objective To investigate the 30-day mortality risk factors in elderly patients with heart failure with reduced ejection fraction (HFrEF) after isolated coronary artery bypass grafting (CABG) and to construct a nomogram for predicting mortality risk. Methods A retrospective analysis of elderly (≥70 years) HFrEF patients undergoing isolated CABG at Tianjin Chest Hospital from 2010 to 2024 was performed. Simple random sampling in R software was used to divide the dataset into training and validation sets in a 7 : 3 ratio. The training set was further divided into survivors and non-survivors. Univariate logistic regression was performed to identify differences between groups, followed by multivariate logistic regression to select independent risk factors for death and to establish a death-risk nomogram, which underwent internal validation. The predictive value of the nomogram was assessed by plotting receiver operating characteristic (ROC) curves, calibration curves, and decision-curve analyses for both the training and validation sets. Results A total of 656 patients were included. The training set consisted of 458 patients (survivors 418, deaths 40); the validation set consisted of 198 patients (survivors 180, deaths 18). In the training set, univariate analysis showed significant differences between survivors and deaths for creatinine (Cr) level, brain natriuretic peptide (BNP), maximum Cr, intra-aortic balloon pump (IABP) use, assisted ventilation, reintubation, hyperlactatemia, low cardiac output syndrome, and renal failure (P<0.05). After multivariable logistic regression, five independent risk factors were identified: IABP use (OR=3.391, 95%CI 1.065-11.044, P=0.038), reintubation (OR=15.991, 95%CI 4.269-67.394, P<0.001), hyperlactatemia (OR=8.171, 95%CI 2.057-46.089, P=0.007), Cr (OR=4.330, 95%CI 0.997-6.022, P=0.024), and BNP (OR=1.603, 95%CI 1.000-2.000, P=0.010). Accordingly, a nomogram predicting mortality risk was constructed. The ROC and calibration analyses indicated good predictive value: area under the curve (AUC) in the training set was 0.898 (95%CI 0.831-0.966) and in the validation set was 0.912 (95%CI 0.805-1.000). Calibration and decision-curve analyses showed good agreement and clinical utility. Conclusion The nomogram incorporating IABP use, reintubation, hyperlactatemia, creatinine, and BNP provides good predictive value for 30-day mortality after CABG in elderly patients with HFrEF and demonstrates potential clinical utility.

Objective: To investigate the status and associated factors of sports supplement usage among grade 9 students, so as to provide a scientific basis for targeted supervision and health education regarding sports supplement usage among junior high school students. Methods: From June to September 2025, a stratified cluster random sampling method was used to select 2 261 grade 9 students from 10 provinces (autonomous regions, municipalities) in China. A questionnaire survey was conducted on their sports supplement usage and related factors. The Chi square test was used to compare the usage rates of sports supplements among different groups of students, and binary Logistic regression analysis was employed to investigate the related factors of sports supplement usage among grade 9 students. Results: Totally 59.7% of the grade 9 students used sports supplements. The usage rate (62.5%) was higher among boys than girls (56.3%), higher among students from rural areas and towns/counties (66.5%, 66.2%) than those from urban districts (52.9%), higher among boarding students (65.2%) than non resident students (54.3%), higher among students whose parents occupations were businessmen and workers (fathers: 65.0%, 63.7%; mothers: 63.6%, 61.1%) than those whose parents were farmers and civil servants (fathers: 57.5%, 54.1%; mothers: 58.8%, 55.7%), higher among students with a monthly family income of 5 000- 10 000 yuan (66.3%) than those in other income groups, and higher among students in the high score zone for the entrance physical examination to senior high school (67.7%) than those in the medium and low score zones ( 56.3% , 56.5%) ( χ 2=8.99, 42.21, 27.98, 20.55, 8.20, 22.74, 24.70, respectively, all P <0.05). Binary Logistic regression analysis showed that boys ( OR =1.26), those from rural areas ( OR =1.59), boarding students ( OR =1.36), those with a monthly family income of 5 000- 10 000 yuan ( OR =1.41), and those in the high score zone for entrance physical examination to senior high school ( OR =1.34) were more likely to use sports supplements during the entrance physical examination to senior high school; the probability of sports supplement usage was lower among students whose fathers were civil servants ( OR =0.74) (all P <0.05). Conclusions The usage of sports supplements is relatively common among grade 9 students. Intervention measures should be targeted at specific populations to reduce the risk of misuse.

OBJECTIVE: To summarize the clinical and genetic characteristics of children with Silver-Russell syndrome (SRS) and improve the recognition of this disease. METHODS: A retrospective analysis was conducted on the clinical manifestations and genetic testing results of 29 children with SRS diagnosed at the Children's Hospital Affiliated to Zhengzhou University between March 2016 and June 2025. RESULTS: The 29 children had included 18 boys and 11 girls, with the age ranging from 2 months to 16 years. Their primary clinical manifestations included postnatal growth retardation (100%), small for gestational age (SGA) (100%), characteristic facial features (90%), limb asymmetry (83%), feeding difficulties (76%), ulnar deviation of the fifth finger (69%), body mass index (BMI) of < -2 SD (62%), and abnormal bone age (55%), including 15 cases with delayed bone age for an average of 1.5 years and 1 case with advanced bone age for 2.5 years. Additional manifestations included abnormal sexual development in 11 cases (38%), dental malocclusion in 11 cases (38%), allergic diseases in 10 cases (34%), cardiac diseases in 9 cases (31%), skeletal abnormalities in 7 cases (24%), renal hypoplasia in 5 cases (17%), and abnormal cranial MRI findings in 5 cases (17%). Twenty children were treated with recombinant human growth hormone (rhGH) at a dose of 0.1 ~ 0.15 U/(kg.d). Among them, 7 cases achieved annual height increase of ≥ 10 cm, 11 cases achieved annual height increase of ≥ 5 ~ 9 cm, and 2 cases achieved annual height increase < 5 cm. Twenty three children exhibited hypomethylation of imprinted genes in the chromosome region of 11p15, 4 presented maternal uniparental disomy of chromosome 7 [UPD(7)mat], and 2 had harbored nonsense variants of the HMGA2 gene. CONCLUSION SRS patients may present with diverse clinical manifestations including postnatal growth retardation, SGA, characteristic facial features, limb asymmetry, feeding difficulties, and ulnar deviation of the fifth finger. Most patients may exhibit abnormal methylation in the 11p15 region. rhGH therapy can improve the height of these patients.
Humans ; Silver-Russell Syndrome/diagnosis* ; Male ; Female ; Child ; Child, Preschool ; Infant ; Adolescent ; Retrospective Studies

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.

Objective To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.