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.

A centrifugation-free,single-reaction colorimetric method for detection of glucose,utilizing a dual nanozyme cascade system based on gold nanoparticles(AuNPs)and iron-doped carbon dots(FeCDs),was developed in this work.The AuNPs exhibited glucose oxidase-like activity to catalyze glucose oxidation for generation of H2O2,while the FeCDs demonstrated peroxidase-like activity to subsequently catalyze the H2O2-mediated oxidation of 3,3',5,5'-tetramethylbenzidine(TMB).To prevent interference from the blue signal generated by self-aggregation of AuNPs in subsequent quantitative detection,the reaction system was terminated with HCl,converting oxTMB into a stable yellow product.Based on changes in the absorbance at 450 nm of this yellow solution,a quantitative relationship was established between glucose concentration and absorbance at 450 nm(A450).Experimental results demonstrated that this sensor achieved a linear detection range of 44 μmol/L to 11.11 mmol/L(R2=0.993)with a detection limit of 30.68 μmol/L and spiked recoveries of 97.9%-104.7%.By integrating smartphone-based color recognition capabilities,a rapid visual detection platform was established for quantification of glucose through RGB analysis.The validation experimental results using commercial glucose injection samples further confirmed the practical application potential of this methodology.

Under the background of forensic medicine becoming a first-level discipline,the opportuni-ties and challenges of discipline development coexist.Starting from the actual situation and characteris-tics of local medical colleges and universities,this paper discusses the problems and solutions for the development of forensic medicine discipline from the perspective of building a regional high-level medical university.Combined with the experiences of carrying out forensic medicine education in our college,this paper supplies our thoughts and practices on improving the discipline system,enhancing the ability to serve society,perfecting the talent cultivation model and promoting forensic culture,to provide reference and inspiration for the development of forensic medicine in other universities,jointly promote the advancement of forensic medicine in China to a new stage,and contribute the wisdom and strength of forensic medical experts to the construction of a law-based China,a safe China and a healthy China.

OBJECTIVE To explore a new model of intelligent medication education for pharmaceutical outpatient clinics by constructing dynamic HTML web pages through the DeepSeek-V3-0324 large-scale model. METHODS Clinical pharmacists integrated key clinical information such as patients’ basic information， medication history and medication precautions in real time， and generated a standardized medication education list through the DeepSeek-V3-0324 large-scale model and manual review. RESULTS The DeepSeek-V3-0324 large-scale model was applied in the pharmaceutical outpatient clinics to generate a personalized medication education list， which could effectively solve the disunity of pharmacy guidance caused by the lack of standardization of medication education and the difference of individualized experience of pharmacists in the traditional pharmaceutical outpatient clinics in the face of complex cases， and medication errors caused by forgetting or misremembering information among certain special patient populations after receiving medication education. CONCLUSIONS The transformation and application of artificial intelligence technology in pharmaceutical outpatient clinics is an innovation of pharmaceutical outpatient service means， which can provide patients with immediate and personalized medication education and improve the quality of pharmaceutical care. However， it is also necessary to face the lag of database update and the lack of risk management， as well as the lack of diversification of medication education lists.

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. It has a high prevalence and poor prognosis. The application of antiarrhythmic drugs and even surgery cannot completely treat the disease, and there are many sequelae. AF can be classified into the category of "palpitation" in Chinese medicine according to its symptoms. Acupuncture has a significant effect on AF. The authors find that an important mechanism of acupuncture in AF treatment is to regulate the cardiac vagus nerve. Therefore, this article intends to review the distribution and function of vagus nerve in the heart, the application and the regulatroy effect for the treatment of AF.
Atrial Fibrillation/physiopathology* ; Humans ; Acupuncture Therapy ; Vagus Nerve/physiology* ; Animals

Early-life stress (ES) leads to cognitive dysfunction in female adolescents, but the underlying neural mechanisms remain elusive. Recent evidence suggests that the cell adhesion molecules NECTIN1 and NECTIN3 play a role in cognition and ES-related cognitive deficits in male rodents. In this study, we aimed to investigate whether and how nectins contribute to ES-induced cognitive dysfunction in female adolescents. Applying the well-established limited bedding and nesting material paradigm, we found that ES impairs recognition memory, suppresses prefrontal NECTIN1 and hippocampal NECTIN3 expression, and upregulates corticotropin-releasing hormone (Crh) and its receptor 1 (Crhr1) mRNA levels in the hippocampus of adolescent female mice. Genetic experiments revealed that the reduction of dorsal CA1 (dCA1) NECTIN3 mediates ES-induced object recognition memory deficits, as knocking down dCA1 NECTIN3 impaired animals' performance in the novel object recognition task, while overexpression of dCA1 NECTIN3 successfully reversed the ES-induced deficits. Notably, prefrontal NECTIN1 knockdown did not result in significant cognitive impairments. Furthermore, acute systemic administration of antalarmin, a CRHR1 antagonist, upregulated hippocampal NECTIN3 levels and rescued object and spatial memory deficits in stressed mice. Our findings underscore the critical role of dCA1 NECTIN3 in mediating ES-induced object recognition memory deficits in adolescent female mice, highlighting it as a potential therapeutic target for stress-related psychiatric disorders in women.
Animals ; Female ; Mice ; CA1 Region, Hippocampal/metabolism* ; Cell Adhesion Molecules/metabolism* ; CRF Receptor, Type 1/metabolism* ; Memory Disorders/etiology* ; Mice, Inbred C57BL ; Nectins/genetics* ; Receptors, Corticotropin-Releasing Hormone/antagonists & inhibitors* ; Recognition, Psychology/physiology* ; Stress, Psychological/complications*

Mutations in the cyclin-dependent kinase-like 5 gene (CDKL5) cause a severe neurodevelopmental disorder, yet the impact of truncating mutations remains unclear. Here, we introduce the Cdkl5^492stop mouse model, mimicking C-terminal truncating mutations in patients. 492stop/Y mice exhibit altered dendritic spine morphology and spontaneous seizure-like behaviors, alongside other behavioral deficits. After creating cell lines with various Cdkl5 truncating mutations, we found that these mutations are regulated by the nonsense-mediated RNA decay pathway. Most truncating mutations result in CDKL5 protein loss, leading to multiple disease phenotypes, and offering new insights into the pathogenesis of CDKL5 disorder.
Animals ; Disease Models, Animal ; Mice ; Protein Serine-Threonine Kinases/deficiency* ; Mutation/genetics* ; Epileptic Syndromes/genetics* ; Humans ; Dendritic Spines/pathology* ; Spasms, Infantile/genetics* ; Male ; Seizures/genetics* ; Mice, Inbred C57BL

OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*