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.

The genome tagging project (GTP) plays a pivotal role in addressing a critical gap in the understanding of protein functions. Within this framework, we successfully generated a human influenza hemagglutinin-tagged sperm-specific protein 411 (HA-tagged Ssp411) mouse model. This model is instrumental in probing the expression and function of Ssp411. Our research revealed that Ssp411 is expressed in the round spermatids, elongating spermatids, elongated spermatids, and epididymal spermatozoa. The comprehensive examination of the distribution of Ssp411 in these germ cells offers new perspectives on its involvement in spermiogenesis. Nevertheless, rigorous further inquiry is imperative to elucidate the precise mechanistic underpinnings of these functions. Ssp411 is not detectable in metaphase II (MII) oocytes, zygotes, or 2-cell stage embryos, highlighting its intricate role in early embryonic development. These findings not only advance our understanding of the role of Ssp411 in reproductive physiology but also significantly contribute to the overarching goals of the GTP, fostering groundbreaking advancements in the fields of spermiogenesis and reproductive biology.
Animals ; Female ; Humans ; Male ; Mice ; Spermatids/metabolism* ; Spermatogenesis/physiology* ; Spermatozoa/metabolism* ; Thioredoxins/genetics*

OBJECTIVE: To investigate the clinical characteristics and prognosis of peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). METHODS: Clinical data of 10 patients with PTCL-NOS in Gansu Provincial Hospital from May 2016 to June 2023 were collected. The treatment outcomes were evaluated, and the factors affecting prognosis were analyzed. RESULTS: The median age of onset for the 10 patients was 60.7 (47-75) years, with 7 males and 3 females. Nine cases received chemotherapy, while one case died suddenly after diagnosis, and the median course of chemotherapy was 6.9 (1-13) courses. Assessing the efficacy, 3 patients achieved complete remission (CR) while 7 patients showed progression. Age, sex, lactate dehydrogenase (LDH) level, Ki-67 and the presence of hemophagocytic lymphohistocytosis (HLH) were not statistically correlated with CR rate ( P >0.05). Patients with IPI score 3-5, and Ann Arbor stage III-IV had statistically lower CR rates (both P <0.05). Age, B symptoms, LDH level ,hemoglobin, Ki-67 index and PLR value were not statistically correlated with overall survival (OS) time ( P >0.05). Male, platelet <150×10⁹/L, IPI score 3-5, Ann Arbor stage III-IV, presence of HLH, NLR≥4.05, and LMR <2.81 were statistically correlated with shorter OS (all P <0.05). Among the 10 patients, 3 cases have survived and are still in CR status, while 7 cases have died, with a median survival time of 7.5 (1-85) months. CONCLUSIONS Patients with IPI score 3-5 and Ann Arbor stage III-IV have low CR rate and poor prognosis. The OS of patients who are male, with platelet <150×10⁹/L, IPI score 3-5, Ann Arbor stage III-IV, complication of HLH, NLR≥4.05, and LMR <2.81 is short, and prognosis is poor.
Humans ; Lymphoma, T-Cell, Peripheral/diagnosis* ; Male ; Prognosis ; Middle Aged ; Female ; Aged

Genetic transformation is a fundamental tool in molecular biology research of medicinal plants. Tailoring transgenic technologies to each distinct medicinal plant would necessitate a substantial investment of time and effort. Here, we present a simple hairy root transformation method that does not require sterile conditions, utilizing Agrobacterium rhizogenes strain K599 and the visible RUBY reporter system. Transgenic hairy roots were obtained for six tested medicinal plant species, roots or rhizomes of which have recognized medicinal value, spanning four botanical families and six genera (Platycodon grandiflorus, Atractylodes macrocephala, Scutellaria baicalensis, Codonopsis pilosula, Astragalus membranaceus, and Glycyrrhiza uralensis). Furthermore, two previously identified Glycyrrhiza uralensis UGTs that convert liquiritigenin into liquiritin in heterologous systems were studied in planta using the method. Our results indicate that overexpression of GuUGT1 but not GuUGT10 and Cas9-mediated knockout of GuUGT1 profoundly influenced the accumulation of liquiritin and isoliquiritin in licorice roots. Therefore, the method described here represents a simple, rapid and widely applicable hairy root transformation method that enables fast gene functional study in medicinal plants.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective:To discern the association between autism-like behaviors and resilience within the ado-lescent demographic.Methods:A total of 7 063 middle school students were selected to assess ASD-like behaviors and resilience in adolescents using the Autism Spectrum Screening Questionnaire(ASSQ)as well as the Resilience Scale for Chinese Adolescent(RSCA).Subgroups bounded by P5 and P95 of the total ASSQ score,a comparative analysis of the resilience scores between these groups was executed.A correlation evaluation and linear regression a-nalysis was carried out between ASSQ and RSCA scores from all participants.Results:The RSCA scores within the high ASSQ scoring group,were inferior to those in the low scoring group.ASSQ scores were negatively correlated with RSCA scores for the full sample(Ps＜0.01);Social interaction scores on the ASSQ were negatively correlated with the five-factor RSCA scores(β=-0.23,-0.27,-0.11,-0.23,-0.37,Ps＜0.05).Conclusion:There was a negative association between autism spectrum disorder-like behaviors and resilience in adolescents,with more severe social interaction symptoms being associated with poorer resilience.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective:To discern the association between autism-like behaviors and resilience within the ado-lescent demographic.Methods:A total of 7 063 middle school students were selected to assess ASD-like behaviors and resilience in adolescents using the Autism Spectrum Screening Questionnaire(ASSQ)as well as the Resilience Scale for Chinese Adolescent(RSCA).Subgroups bounded by P5 and P95 of the total ASSQ score,a comparative analysis of the resilience scores between these groups was executed.A correlation evaluation and linear regression a-nalysis was carried out between ASSQ and RSCA scores from all participants.Results:The RSCA scores within the high ASSQ scoring group,were inferior to those in the low scoring group.ASSQ scores were negatively correlated with RSCA scores for the full sample(Ps＜0.01);Social interaction scores on the ASSQ were negatively correlated with the five-factor RSCA scores(β=-0.23,-0.27,-0.11,-0.23,-0.37,Ps＜0.05).Conclusion:There was a negative association between autism spectrum disorder-like behaviors and resilience in adolescents,with more severe social interaction symptoms being associated with poorer resilience.

Aim To explore the mechanism of action of Guizhi Jia Gegen decoction(GGD)in treating pneu-monia-enteritis induced by H1N1 influenza virus infec-tion in a mouse model,using network pharmacology and molecular docking techniques,followed by in vivo verification.Methods A pneumonia-enteritis mouse model was established,and the intervention effects of GGD on the model mice were evaluated using indica-tors such as body weight,rectal temperature,lung in-dex,colon length,H1N1 M gene expression,relative mRNA expression levels of inflammatory cytokines,and pathological sections of the lung and intestine.The targets of the blood-absorbed components of GGD were identified using the Swiss Target Prediction platform,and the disease targets were retrieved from the Gene-Cards platform.The intersecting targets were analyzed through PPI network analysis using the STRING data-base to identify core targets.GO analysis and KEGG pathway enrichment analysis were performed using the Metascape database.RT-qPCR was employed to vali-date the core targets and pathways.Molecular docking was conducted using AutoDock Tools software to verify the interactions between blood-absorbed components and key targets.Results GGD demonstrated signifi-cant therapeutic effects on the pneumonia-enteritis mouse model.The results of network pharmacology in-dicated that the therapeutic effects of GGD were strong-ly associated with targets such as TNF,ALB,PTGS2,MMP9,EGFR,ESR1,SRC,HSP90AA1,PPARG and MMP2.RT-qPCR results indicated that GGD could intervene in pneumonia-enteritis by regulating the targets TNF,ALB,EGFR and the related targets of the NF-κB pathway.Molecular docking results re-vealed that blood-absorbed components such as puerar-in and liquiritin could stably bind to TNF,ALB and EGFR.Conclusion Components such as puerarin and liquiritin in GGD may exert therapeutic effects on pneumonia-enteritis induced by H1N1 influenza virus infection by acting on targets such as TNF,ALB and EGFR.