ObjectiveTo clarify the expression of TRIM28 in non-M3 acute myeloid leukemia （AML） and its correlation with clinical indicators and prognosis， and to further explore the effect of TRIM28 expression levels on the proliferation and apoptosis of AML cells using small interfering RNA. MethodsThe GSE34577 dataset was analyzed using R software to compare TRIM28 expression between healthy controls and non-M3 acute myeloid leukemia （AML） patients. Clinical samples from non-M3 AML patients were collected， with TRIM28 expression levels measured using real-time quantitative PCR （qPCR）. The analysis focused on correlations between TRIM28 expression and various clinical indicators， treatment efficacy， and patient prognosis. Furthermore， small interfering RNA （siRNA） technology was employed to downregulate TRIM28 expression in human primary AML cells （HL60 cell line）. The effects on cell proliferation and apoptosis were then assessed through CCK-8 assays and flow cytometry， respectively. ResultsThe results showed that TRIM28 was up-regulated in non-M3 AML of both online database GSE34577 and clinical samples （P<0.000 1）， TRIM28 expression of new diagnosis group and relapsed refractory group was higher than iron deficiency anemia group （P<0.01）， and there was no significance between different French-American-British classification systems subtype. TRIM28 expression was higher in non-M3 AML patients with a poor genetic prognosis stratified as moderate than in the good prognosis group， and TRIM28 expression was associated with NPM1 combined with the FLT3-ITD mutation， positively correlated with age， bone marrow blast， peripheral blood blast and white blood cell， negatively correlated with hemoglobin. In addition， interference TRIM28 greatly inhibited cell proliferation and promoted cell apoptosis. ConclusionThis study reveals that TRIM28 is highly expressed in non-M3 AML and associated with prognosis， and plays a key role in the proliferation and apoptosis of AML cells， suggesting that TRIM28 may serve as a novel therapeutic target for non-M3 AML.

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.

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 explore the association role of anxiety and depressive symptoms with Internet gaming disorder (IGD) and emotional-behavioral problems among junior high school students, so as to provide references for preventing the occurrence and progression of emotional-behavioral problems in middle school students. Methods: From September to October 2024, a random cluster sampling method was adopted to select 38 069 students from 28 junior high schools in 14 counties (cities and districts) and functional areas of Jining City, Shandong Province. Surveys were conducted by Nine item Internet Gaming Disorder Scale-Short Form, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Strengths and Difficulties Questionnaire. The correlation of emotional and behavioral issues, online game addiction, anxiety and depressive symptoms among junior high school students was analyzed by Speaman correlation analysis. Mediation analysis was used to explore the role of anxiety and depressive symptoms between IGD and emotional-behavioral problems among junior high school students. Results: The detection rates of emotional-behavioral problems, anxiety symptoms, depressive symptoms, and IGD among junior high school students were 19.24 %, 9.48%, 16.69%, and 19.68%, respectively. The scores of emotional-behavioral problems, anxiety symptoms, depressive symptoms and IGD were 10.00 (6.00, 15.00), 3.00 (0.00, 7.00), and 4.00 (1.00, 8.00), 13.00 (10.00, 19.00), respectively. There were positive correlations between each pair of emotional-behavioral problem, IGD, anxiety symptoms, and depressive symptoms scores ( r =0.51-0.81, all P <0.01). Anxiety and depressive symptoms played mediating roles between IGD and emotional-behavioral problems among junior high school students, with mediating effect values of 0.16 (95% CI =0.15-0.17) and 0.12 (95% CI =0.11-0.14), respectively (both P <0.01). Conclusions Anxiety and depressive symptoms mediate the relationship between IGD and emotional-behavioral problems among junior high school students. It is necessary to identify early signs of IGD among junior high school students, strengthen intervention for anxiety and depressive symptoms, to prevent the occurrence and development of emotional and behavioral issues.

ObjectiveTo systematically identify the main chemical components of Linggui Zhugan granules （LGZGG）， and to explore the pharmacodynamic substance basis for the treatment of metabolic dysfunction-associated steatohepatitis （MASH）. MethodsThe chemical components of LGZGG were systematically analyzed by ultra-performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry （UPLC-Q-TOF-MS/MS）. The protein-protein interaction （PPI） network and "compound prescription-disease-component-target" network were constructed by network target analysis to predict the potential pharmacodynamic substances and core targets of LGZGG in the treatment of MASH. The potential pharmacodynamic substances were enriched by macroporous adsorption resin， and the chemical composition of the LGZGG-50% ethanol elution fraction （LGZGG-50） was analyzed by UPLC-Q-TOF-MS/MS. The mouse model of MASH was established by feeding a high-fat， high-cholesterol， and high-fructose diet for 12 weeks. Mice were randomly allocated into normal， model， positive drug （MGL-3196， 1 mg·kg^-1）， LGZGG （crude drug， 34 g·kg^-1·d^-1）， LGZGG-50 （crude drug， 34 g·kg^-1·d^-1） groups. The levels of aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C） in the serum were measured， and the pathological changes in the liver tissue were observed by hematoxylin-eosin （HE） and Sirius red staining. The in vivo efficacy of LGZGG and LGZGG-50 in the treatment of MASH was evaluated on the basis of the findings. The expression levels of adenosine monophosphate-activated protein kinase （AMPK）， phosphorylated AMPK （p-AMPK）， silent information regulator 2-related enzyme 1 （SIRT1）， peroxisome proliferator-activated receptor α （PPARα）， and carnitine palmitoyltransferase 1A （CPT1A） in the liver tissue were determined by Western blot to verify the regulatory effects of core targets. ResultsA total of 83 chemical components were identified from LGZGG， including 20 flavonoids， 13 terpenoids， 17 organic acids， 7 amino acids， 11 glycosides， 3 nucleosides， 3 aromatics， 3 alkaloids， 2 phenylpropanoids， 2 sugars， 1 nucleic acid， and 1 steroid. A total of 134 common targets were obtained by network target analysis. The core targets included SIRT1， PPARα， nuclear factor-kappa B subunit 1 （NF-κB1）， and interleukin-6 （IL-6）. Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis revealed that the targets were mainly enriched in the AMPK， PPAR and other signaling pathways. The topological analysis of the "compound prescription-disease-component-target" network showed that the potential pharmacodynamic substances of LGZGG against MASH were flavonoids and terpenoids. The relative content of flavonoids and terpenes in LGZGG-50 were 74.00% and 62.41% in positive and negative ion modes， respectively， indicating that LGZGG-50 effectively enriched total flavonoids and total terpenoids. The results of in vivo efficacy showed that compared with the model group， LGZGG and LGZGG-50 reducef the body weight， liver weight， serum TG， GLU， and ALT levels of MASH mice （P<0.05，P<0.01）， and LGZGG additionally increased the HDL-C level （P<0.01）. Both groups alleviated the pathological damage in the liver tissue. The results of Western blot showed that compared with the model group， the protein levels of p-AMPK/AMPK， SIRT1， PPARα， and CPT1A were up-regulated in the LGZGG group （P<0.05，P<0.01）， and those of p-AMPK/AMPK and CPT1A were up-regulated in the LGZGG-50 group （P<0.01）. ConclusionLGZGG ameliorates MASH， with the main pharmacodynamic substances being flavonoids and terpenoids. The mechanism may be related to the regulation of AMPK/SIRT1/PPARα signaling pathway， improvement of lipid metabolism， and alleviation of pathological damage in the liver tissue.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

Objective: To establish a machine learning-based precision blood donor recruitment model at the county level and assess its generalizability and applicability. Methods: A retrospective study was conducted using blood donation and SMS recruitment data from the Taicang Branch of the Suzhou Blood Center between 2019 and 2024. Multiple machine learning algorithms were employed, including extreme gradient boosting, support vector machine, k-nearest neighbor, logistic regression, decision tree, random forest, and multilayer perceptron. These were combined with techniques such as synthetic minority oversampling, undersampling, and cost-sensitive learning (using MFE and MSFE loss functions). Model parameters were optimized through grid search to identify the best-performing model. Results: In a prospective comparative study against conventional methods, the machine learning models increased the recruitment success rate among high-willingness donors by an average of 129.15%, and the recruitment efficiency per SMS improved by 125.02% compared with the traditional method. Under full-scale SMS sending, the recruitment rate per SMS increased by 42.61%, and SMS sending efficiency improved by 31.77%, significantly enhancing recruitment performance. Conclusion: This study represents the first application of a machine learning-based precision donor recruitment model at the county-level in China. The precise recruitment framework not only improves recruitment efficiency and reduces recruitment costs but also demonstrates strong scalability and generalizability. It provides a scientific and feasible intelligent pathway to ensure the safety and sustainability of the blood supply.

The rapid and non-destructive detection of constituent content of fresh tea leaves shows an important reference value for quality identification of tea.Visible near infrared(Vis-NIR)spectroscopy has been used for qualitative and quantitative analysis of chemical components in plant samples with the advantages such as simple,rapid and non-destructive detection.In this study,residual attention convolutional neural network(RACNN)was used to predict the internal constituent content of fresh tea leaves.Firstly,the reflectance spectral data of the samples in the Vis-NIR band range and the constituent contents of gallic acid(GA),gallocatechin(GC),epigallocatechin(EGC),and epigallocatechin gallate(ECG)in fresh tea leaves were collected.Based on the preprocessing of the spectral data,the contents of the four components were predicted using a partial least squares regression(PLSR)model,and the optimal preprocessing was determined.Subsequently,the characteristic bands were extracted using the random forest(RF)algorithm.Finally,the performances of PLSR,convolutional neural network(CNN)and RACNN models were compared.The results showed that for GA,the RACNN model worked best with a validation set coefficient of determination(R2)of 0.946 and a root mean square error of the prediction set(RMSEP)of 1.173;for GC,the RACNN model works best with a validation set R2 of 0.928 and RMSEP of 6.081;for EGC,the RACNN model works best with a validation set R2 of 0.891 and a RMSEP of 15.197;for ECG,the RACNN model worked best with a validation set R2 of 0.878 and a RMSEP of 7.837.The RACNN model established by Vis-NIR spectroscopy combined with chemometrics could realize the accurate detection of the contents of components in fresh tea.

Foodborne illnesses caused by Salmonella pose significant threats to worldwide public health safety.In this study,a rapid method for screening viable Salmonella in oyster sauce and milk was developed by utilizing an electronic microbial growth analyzer(EMGA).Target food samples were diluted 10-fold with RVS broth and loaded into test tubes.Test tubes were positioned in the EMGA to determine the bacterial growth curves and the time required to reach the maximum growth rate(Tmgr).Using Salmonella typhimurium(S.typhimurium)asan model species,there was linear relationship between the logarithmic value of viable bacterial concentration(lgC)and Tmgr over the range of 5×101-5×106 CFU/mL,with a detection limit of 10 CFU/mL.For oyster sauce,the regression equation was Tmgr(min)=-80.775lg[C/(CFU/mL)]+754.96(R2=0.9907),and the recovery rates of S.typhimurium ranged from 95.2%to 119.8%,with relative standard deviations(RSD)ranging from 3.5%to 16.3%.For milk,the regression equation was Tmgr(min)=-71.922 lg[C/(CFU/mL)]+618.65(R2=0.9985),with recovery rates ranging from 98.4%to 110.6%and RSD ranging from 6.4%to 12.8%.The EMGA method required only one portable instrument,and involving only three manual steps,i.e.,dilution,transfer,and insertion.When S.typhimurium contamination reached 106 CFU/mL,the total time consumption,from the unwrapping of samples to the readout of bacterial concentration,was no more than 7 h.When applied to detection of actual oyster sauce and milk samples,the new method demonstrated strong consistency with plate counting results in positive detection rates.This method was superior to the plate counting method,which was generally considered as a gold standard,in terms of accuracy,precision,simplicity and efficiency,representing a promising alternative for the on-site screening and quantification of viable Salmonella in oyster sauce and milk products.