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 establish a rapid, accurate, and decentralized workflow for bacterial whole-genome sequencing (WGS) and risk profiling within the shelf-life of platelet concentrates, and to characterize the species, virulence, antimicrobial resistance (AMR), and immune evasion mechanisms of co-existing bacteria in qualified platelet products, thereby providing a scientific basis for transfusion safety assessment. Methods: Three units of platelet concentrates, which tested negative by routine bacterial screening, were collected from the Chengdu Blood Center between May and June 2025. Samples were enriched at 37℃under six aerobic and nine anaerobic conditions for 7 days. Using a culturomics strategy, aliquots were plated for isolation on days 1, 3, 5, and 7 to obtain cultivable isolates, with negative culture controls included to exclude contamination. High-molecular-weight genomic DNA was extracted via mechanical grinding, purified, and size-selected. Sequencing libraries were constructed and sequenced on the G-seq500 single-molecule nanopore sequencing platform. Genomes were assembled using Flye and polished with NextPolish, with quality evaluated by BUSCO and CheckM. Taxonomic identification was performed using GTDB-Tk. Functional annotation and database comparisons were conducted to analyze virulence factors, AMR genes, and genes related to immune evasion and environmental adaptation. Results: Viable bacteria were successfully isolated from all three qualified platelet units within their shelf life. The isolates were identified as Bacillus albus, Niallia taxi, and Staphylococcus warneri. Nanopore sequencing generated 92 227-109 813 reads (totaling 680-758 Mb) with an N50 of 7 625-8 584 bp and Q20/Q30 scores of 97%/93%, respectively. All three genomes were assembled into complete circular chromosomes with 1-3 plasmids, achieving >93% completeness. Functional analysis revealed that B. albus carried multiple hemolysins, metalloproteases, and multidrug resistance genes, indicating the highest potential pathogenicity and AMR risk. S. warneri exhibited a typical multidrug resistance profile and regulatory network characteristic of coagulase-negative staphylococci, suggesting intermediate virulence. N. taxi harbored few canonical virulence factors and lacked functional AMR determinants, presenting a "low-virulence, low-resistance" profile. Notably, all three strains were enriched in genes encoding antimicrobial peptide resistance systems (e.g., dltABCD, mprF, GraRS, BceAB) and antioxidant enzymes, suggesting a strong capacity to withstand immune stress in the blood environment. Conclusion: Viable bacteria can be recovered from qualified platelet concentrates that test negative by routine screening. Nanopore WGS enables rapid strain-level identification and comprehensive risk profiling of virulence, resistance, and immune adaptation traits. The functional repertoires of these "co-existing" isolates range from environmental adaptation to potential pathogenicity, representing an underappreciated risk for transfusion-transmitted infections in susceptible recipients.

ObjectiveTo explore the interaction and competitive binding of Homo sapiens long intergenic non-protein-coding RNA 1134 （LINC01134） to CCCTC-binding factor CTCF， affecting the transcription of cyclin-dependent kinase inhibitor （p21） and influencing the proliferation of A549 cells， in order to investigate the possible mechanism of Astragali Radix and Hedyotis diffusa （A-H） in inhibiting A549 proliferation by regulating this axis. MethodsRNA-binding protein immunoprecipitation （RIP） assays were conducted to examine the interaction between LINC01134 and CTCF， and chromatin immunoprecipitation （ChIP） assays were used to study the effect of LINC01134 overexpression on the interaction between CTCF and p21. Stable A549 cell lines （oe-NC and oe-LINC01134） were established using lentiviral transfection， and each group was treated with 10% A-H drug-containing serum. Real-time PCR and Western blot analyses were performed to detect the effects of A-H on the expression of LINC01134， CTCF， and p21 in A549 cells. Cell counting kit-8 （CCK-8） and colony formation assays were used to assess the effects of A-H on A549 cell proliferation via LINC01134. Flow cytometry was employed to evaluate the effects of A-H on the A549 cell cycle through LINC01134， and Western blot was used to detect changes in cell cycle proteins. ResultsCompared with the IgG group， the oe-CTCF group showed a significantly increased abundance of LINC01134 aggregates （P0.01）. Compared with the oe-Vector group， p21 abundance in CTCF complexes was significantly reduced in the oe-LINC01134 group （P0.01）. Compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed the expression of LINC01134 and p21 （P0.05）， but had no significant regulatory effect on CTCF. Compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed cell viability at 72 h （P0.05）， inhibited malignant proliferation （P0.05）， and reversed the proportions of cells in the G₀/G₁ and S phases （P0.01）. Furthermore， compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed the expression of Cyclin D₁， CDK4， Cyclin E， CDK2， phosphorylated retinoblastoma protein （p-Rb）， and E2F transcription factor 3 （E2F3） （P0.01）. ConclusionA-H regulates the LINC01134-CTCF-p21 axis to block the G₁/S phase transition of A549 cell cycle， accelerate cellular senescence， and inhibit malignant proliferation.

ObjectiveTo explore the interaction and competitive binding of Homo sapiens long intergenic non-protein-coding RNA 1134 （LINC01134） to CCCTC-binding factor CTCF， affecting the transcription of cyclin-dependent kinase inhibitor （p21） and influencing the proliferation of A549 cells， in order to investigate the possible mechanism of Astragali Radix and Hedyotis diffusa （A-H） in inhibiting A549 proliferation by regulating this axis. MethodsRNA-binding protein immunoprecipitation （RIP） assays were conducted to examine the interaction between LINC01134 and CTCF， and chromatin immunoprecipitation （ChIP） assays were used to study the effect of LINC01134 overexpression on the interaction between CTCF and p21. Stable A549 cell lines （oe-NC and oe-LINC01134） were established using lentiviral transfection， and each group was treated with 10% A-H drug-containing serum. Real-time PCR and Western blot analyses were performed to detect the effects of A-H on the expression of LINC01134， CTCF， and p21 in A549 cells. Cell counting kit-8 （CCK-8） and colony formation assays were used to assess the effects of A-H on A549 cell proliferation via LINC01134. Flow cytometry was employed to evaluate the effects of A-H on the A549 cell cycle through LINC01134， and Western blot was used to detect changes in cell cycle proteins. ResultsCompared with the IgG group， the oe-CTCF group showed a significantly increased abundance of LINC01134 aggregates （P0.01）. Compared with the oe-Vector group， p21 abundance in CTCF complexes was significantly reduced in the oe-LINC01134 group （P0.01）. Compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed the expression of LINC01134 and p21 （P0.05）， but had no significant regulatory effect on CTCF. Compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed cell viability at 72 h （P0.05）， inhibited malignant proliferation （P0.05）， and reversed the proportions of cells in the G₀/G₁ and S phases （P0.01）. Furthermore， compared with the 10% blank + oe-LINC01134 group， the 10% A-H + oe-LINC01134 group reversed the expression of Cyclin D₁， CDK4， Cyclin E， CDK2， phosphorylated retinoblastoma protein （p-Rb）， and E2F transcription factor 3 （E2F3） （P0.01）. ConclusionA-H regulates the LINC01134-CTCF-p21 axis to block the G₁/S phase transition of A549 cell cycle， accelerate cellular senescence， and inhibit malignant proliferation.

ObjectiveTo investigate the effect of Sanet Vision Integrator (SVI)-based visuomotor integration training on the severity of neglect symptoms, upper limb motor function and activities of daily living (ADL) in patients with post-stroke unilateral spatial neglect (USN). MethodsFrom July, 2023 to September, 2025, 41 patients with post-stroke USN in Beijing Bo'ai Hospital were enrolled and randomly assigned to control group (n = 20) and experimental group (n = 21). Both groups received conventional rehabilitation and visual scanning training, while the experimental group received supplementary SVI-based visuomotor integration training, including saccadic, rotation and hand-eye coordination training, for eight weeks. They were assessed with Chinese Behavioral Inattention Test-Hong Kong version (CBIT-HK), Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and modified Barthel Index (MBI) before and after treatment. ResultsAfter treatment, all the scores significantly improved in both groups (|t| > 4.004, P < 0.001), and the total scores of CBIT-HK and the core visual search sub-items (star cancellation, line crossing and letter cancellation), FMA-UE and MBI were better in the experimental group than in the control group (t > 2.096, P < 0.05). ConclusionSVI-based visuomotor training can effectively alleviate the USN symptoms, broaden the span of visual attention, facilitate the functional recovery of the affected upper extremity, and improve ADL in patients after stroke.

To explore the clinical value of a multimodal predictive model based on multiparametric magnetic resonance imaging(MRI) radiomics combined with deep learning(DL) features for the preoperative noninvasive assessment of mismatch repair-deficient(MMRd) status in endometrial cancer(EC).

AIM To investigate effects of petroleum ether fraction from Derris eriocarpa How on glucose and lipid metabolism in a mouse model of metabolic syndrome(MS).METHODS KM mice were fed a high-fat diet and administered streptozotocin intraperitoneally to establish MS models.The MS mice were then randomly assigned to the model group,the metformin hydrochloride group,the lovastatin group,the ursolic acid group,and the high-,medium-and low-dose D.eriocarpa petroleum ether fraction groups,with 10 mice in each group.Ten additional mice maitained on a normal diet served as the normal control group.After 4 weeks of intragastric administration,glucose and lipid metabolism indicators were measured.Hepatic pathological changes were assessed using HE staining and oil red O staining.Liver tissue mRNA expressions of ATF3,PEPCK,FXR,CYP7A1,HNF4ɑ,CYP8B1 and SRB1 were quantified by RT-qPCR.Hepatic protein expressions of ATF3,HNF4ɑ,PEPCK,FXR and CYP7A1 was analyzed by Western blot in MS mice.RESULTS Compared to the model group,the high-dose D.eriocarpa petroleum ether fraction group exhibited significant glucose tolerance improvement(reduced OGTT-AUC,P＜0.01);favorable serum lipid modulation in terms of increased HDL-C levels(P＜0.01)and decreased TG,TC,LDL-C(P＜0.01);reduced renal biomarkers(BUN,SCR)and hepatotoxic indicators of TBA,AST and ALT activities(P＜0.01);alleviated hepatic lipid accumulation and histopathological damage;downregulated mRNA and protein expressions of ATF3,HNF4ɑ and PEPCK,as well as CYP8B1 mRNA expression(P＜0.01);and upregulated mRNA and protein expressions of FXR and CYP7A1,along with SRB1 mRNA expression(P＜0.01).CONCLUSION D.eriocarpa petroleum ether fraction ameliorates glucose and lipid metabolism dysregulation in MS mice by modulating the ATF3/HNF4ɑ/CYP7A1 signaling pathway,consequently eliciting hypoglycemic,hypolipidemic,hepatoprotective and nephroprotective effects.

Under the guidance of China′s national innovation-driven development strategy, advancing the deep integration of industry-academia-research in the healthcare sector has become a crucial pathway for enhancing medical technology levels and promoting high-quality development of hospitals. In 2021, Peking University Third Hospital established a hospital-enterprise joint research and development platform centered on hospital leadership, grounded in collaborative innovation theory. By translating the theoretical principles of " goal alignment, resource complementarity, risk sharing, and benefit sharing" into a multidimensional institutional framework, dynamic consensus mechanisms, and specialized support systems, the hospital developed a multi-tiered implementation pathway of " strategic guidance-platform implementation-research and development deployment". This approach enabled precise matching of resources and needs, efficiently advancing the innovation, transformation, and application of medical technologies. By the end of 2024, the platform had established stable partnerships with 22 enterprises, cumulatively undertaken 123 clinical research projects, as well as achieved a series of breakthroughs in fields such as cell therapy and intelligent imaging. This platform could solve problems such as divergent goals and institutional deficiencies in traditional medical enterprise cooperation, which could help to form a good innovation ecosystem and provide reference and inspiration for the construction of China′s medical technology innovation system.

AIM To improve the quality standard for Dahuang Sodium Bicarbonate Tablets.METHODS TLC was adopted in the qualitative identification of rhein,emodin,chrysophanol,aloe-emodin,physcion and rhapontin,after which UPLC fingerprints were established,content determination of aloe emodin-8-O-β-D-glucoside,rhein-8-O-β-D-glucoside,emodin-8-O-β-D-glucoside,chrysophanol-8-O-β-D-glucoside,physcion-8-O-β-D-glucoside,aloe-emodin,rhein,emodin,chrysophanol,physcion were performed.RESULTS The clear TLC plots demonstrated good specificity.There were 20 common peaks in the fingerprints for 8 batches of samples with the similarities of more than 0.535.Ten constituents showed good linear relationships within their own ranges(r≥0.997 0),whose average recoveries were 98.7％-104.1％with the RSDs of 0.59％-2.13％,whose content ranges were 0.274 1-2.005 9,0.547 8-5.192 2,1.209 2-3.936 3,1.039 3-3.330 2,0.307 0-1.248 1,0.118 2-0.674 7,0.115 1-0.970 3,0.224 5-1.789 3,0.881 4-4.882 6,0.801 4-5.099 1 mg/g,respectively.CONCLUSION This simple and stable method can effectively control the quality of Rhei Radix et Rhizoma in Dahuang Sodium Bicarbonate Tablets.