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.

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.

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).

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

ObjectivePhotoacoustic pump-probe imaging can effectively eliminate the interference of blood background signal in traditional photoacoustic imaging, and realize the imaging of weak phosphorescence molecules and their triplet lifetimes in deep tissues. However, background differential noise in photoacoustic pump-probe imaging often leads to large fitting results of phosphorescent molecule concentration and triplet lifetime. Therefore, this paper proposes a novel triplet lifetime fitting method for photoacoustic pump-probe imaging. By extracting the phase of the triplet differential signal and the background noise, the fitting bias caused by the background noise can be effectively corrected. MethodsThe advantages and feasibility of the proposed algorithm are verified by numerical simulation, phantom and in vivo experiments, respectively. ResultsIn the numerical simulation, under the condition of noise intensity being 10% of the signal amplitude, the new method can optimize the fitting deviation from 48.5% to about 5%, and has a higher exclusion coefficient (0.88>0.79), which greatly improves the fitting accuracy. The high specificity imaging ability of photoacoustic pump imaging for phosphorescent molecules has been demonstrated by phantom experiments. In vivo experiments have verified the feasibility of the new fitting method proposed in this paper for fitting phosphoometric lifetime to monitor oxygen partial pressure content during photodynamic therapy of tumors in nude mice. ConclusionThis work will play an important role in promoting the application of photoacoustic pump-probe imaging in biomedicine.

Objective: To establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.