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.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

The characteristics of plateau field conditions and the application of the medical equipment and devices were summarized.The influences of plateau field conditions on the use and maintenance of the medical equipment and devices were pointed out,and some countermeasures were put forward accordingly.The causes for the problems were analyzed,and some suggestions were proposed from the aspects of top-level design,standard and personnel training.References were provided for the use and maintenance of medical equipment and devices during stationed field training and support for military operations other than war in plateau field areas.[Chinese Medical Equipment Journal,2025,46(3):86-89]

OBJECTIVE: To investigate the effectiveness of Xuanshen Yishen Decoction (XYD) in the treatment of hypertension. METHODS: Hospital electronic medical records from 2019-2023 were utilized to emulate a randomized pragmatic clinical trial. Hypertensive participants were eligible if they were aged ⩾40 years with baseline systolic blood pressure (BP) ⩾140 mm Hg. Patients treated with XYD plus antihypertensive regimen were assigned to the treatment group, whereas those who followed only antihypertensive regimen were assigned to the control group. The primary outcome assessed was the attainment rate of intensive BP control at discharge, with the secondary outcome focusing on the 6-month all-cause readmission rate. RESULTS: The study included 3,302 patients, comprising 2,943 individuals in the control group and 359 in the treatment group. Compared with the control group, a higher proportion in the treatment group achieved the target BP for intensive BP control [8.09% vs. 17.5%; odds ratio (OR)=2.29, 95% confidence interval (CI)=1.68 to 3.13; P<0.001], particularly in individuals with high homocysteine levels (OR=3.13; 95% CI=1.72 to 5.71; P<0.001; P for interaction=0.041). Furthermore, the 6-month all-cause readmission rate in the treatment group was lower than in the control group (hazard ratio=0.58; 95% CI=0.36 to 0.91; P=0.019), and the robustness of the results was confirmed by sensitivity analyse. CONCLUSIONS XYD could be a complementary therapy for intensive BP control. Our study offers real-world evidence and guides the choice of complementary and alternative therapies. (Registration No. ChiCTR2400086589).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Antihypertensive Agents/pharmacology* ; Blood Pressure/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Hypertension/physiopathology* ; Patient Readmission ; Treatment Outcome

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre and early symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with postsymptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over 9 years. The most common adverse events (AEs) within 2 months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with postsymptomatic juvenile MLD.
Humans ; Leukodystrophy, Metachromatic/genetics* ; Pilot Projects ; Genetic Therapy/methods* ; Hematopoietic Stem Cell Transplantation ; Male ; Follow-Up Studies ; Female ; Lentivirus/genetics* ; Child ; Child, Preschool ; Hematopoietic Stem Cells/metabolism* ; Cerebroside-Sulfatase/metabolism* ; Adolescent

Rapid and ultrasensitive detection of pathogen-associated biomarkers is vital for the early diagnosis and therapy of bacterial infections. Herein, we developed a close-packed and ordered Au@AgPt array coupled with a cascade triggering strategy for surface-enhanced Raman scattering (SERS) and colorimetric identification of the Staphylococcus aureus biomarker micrococcal nuclease (MNase) in serum samples. The trimetallic Au@AgPt nanozymes can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) molecules to SERS-enhanced oxidized TMB (oxTMB), accompanied by the color change from colorless to blue. In the presence of S. aureus, the secreted MNase preferentially cut the nucleobase AT-rich regions of DNA sequences on magnetic beads (MBs) to release alkaline phosphatase (ALP), which subsequently mediated the oxTMB reduction for inducing the colorimetric/SERS signal fade away. Using this "on-to-off" triggering strategy, the target S. aureus can be recorded in a wide linear range with a limit of detection of 38 CFU/mL in the colorimetric mode and 6 CFU/mL in the SERS mode. Meanwhile, the MNase-mediated strategy characterized by high specificity and sensitivity successfully discriminated between patients with sepsis (n = 7) and healthy participants (n = 3), as well as monitored the prognostic progression of the disease (n = 2). Overall, benefiting from highly active and dense "hot spot" substrate, MNase-mediated cascade response strategy, and colorimetric/SERS dual-signal output, this methodology will offer a promising avenue for the early diagnosis of S. aureus infection.

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

Objective:To adapt the Mindful Breastfeeding Scale (MINDF-BFS) into Chinese and assess its reliability and validity among breastfeeding mothers in China.Methods:Following the Beaton cross-cultural adaptation guideline, the original scale was translated, back-translated, discussed by experts, pre-tested, culturally adapted, and revised to develop the Chinese version of the MINDF-BFS. A convenience sampling method was used to select 305 postpartum women from Guangzhou Women and Children's Medical Center, Guangzhou Medical University, who visited between March and June 2024, as the study participants. The reliability and validity of the Chinese version of MINDF-BFS were evaluated.Results:The Chinese version of MINDF-BFS consisted of nine items, with the item-level content validity index ranged from 0.900 to 1.000, and the average scale-level content validity index was 0.990. Exploratory factor analysis extracted one common factor, with a variance contribution of 73.290%. Confirmatory factor analysis showed a good model fit. The Cronbach's α coefficient for the scale was 0.923, the split-half reliability coefficient was 0.915, and the test-retest reliability coefficient was 0.926.Conclusions:The Chinese version of MINDF-BFS has good psychometric properties and is suitable for assessing the mindful breastfeeding levels of Chinese postpartum women.