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.

Background and purpose:SEC14L1P1,a pseudogene of the SEC14 family,is closely associated with the development of various tumors,but its role in oral squamous cell carcinoma(OSCC)has not been clarified.This study aimed to gain insights into the expression characteristics and subcellular localization of SEC14L1P1 in OSCC cells,as well as its effects on OSCC cell proliferation and migration.Methods:The expression of SEC14L1P1 in head and neck squamous cell carcinoma(HNSCC)tissues was analyzed by the ENCORI database;The expression of SEC14L1P1 and its relationship with patient prognosis in HNSCC was further analyzed using the GDC and UCSC Xena databases.The expression of SEC14L1P1 in OSCC cell lines was detected by real-time fluorescence quantitative polymerase chain reaction(RTFQ-PCR);RNA nucleoplasmic separation assay was performed to determine the localization of SEC14L1P1 in OSCC cells.SEC14L1P1 knockdown(SS-SEC14L1P1)group and knockdown control(SS-NC)group were established for CAL-27 cells,and SEC14L1P1 overexpression(SEC14L1P1)group and overexpression control(Vector)group were established for HN30 cells.The effects of SEC14L1P1 expression on the proliferation and migration abilities of cells in each group were assessed by cell counting kit-8(CCK-8)and transwell migration assays.RTFQ-PCR and Western blot experiments were used to detect the effects of altered SEC14L1P1 expression on the expression levels of epithelial-mesenchymal transition(EMT)-related genes.To investigate the effects of SEC14L1P1 on the proliferation of OSCC cells in vivo using a subcutaneous xenograft tumor model in nude mice,12 four-week-old BALB/c nude mice were randomly divided into two groups:the antisense oligonucleotide(ASO)-NC group and the ASO-SEC14L1P1 group,with 6 mice in each group.All mice were individually labeled.Further mechanistic studies were performed by analyzing molecules interacting with SEC14L1P1 through the RNAInter database,and the ENCORI database was queried for expression correlation between SEC14L1P1 and DHX9.The effect of altered SEC14L1P1 expression on the phosphoinositide 3-kinase(PI3K)/protein kinase B(AKT)pathway was detected by Western blot assay.Results:Database analysis showed that the expression of SEC14L1P1 was higher in HNSCC tissues than in normal tissues,and was strongly associated with poor patient prognosis.The RTFQ-PCR results showed that SEC14L1P1 was highly expressed in all six OSCC cell lines;RNA nucleoplasmic separation showed that SEC14L1P1 was mainly localized in the nucleus in CAL-27 and HN30 cells.Compared with SS-NC,the relative expression of SEC14L1P1 in the SS-SEC14L1P1 group was significantly lower and significantly inhibited cell proliferation and migration,while the relative expression of SEC14L1P1 in the SEC14L1P1 group was significantly higher compared with the Vector group,which also significantly increased cell proliferation and migration.The down-regulation of SEC14L1P1 was accompanied by increased mRNA and protein levels of E-cadherin,and decreased mRNA and protein levels of N-cadherin and vimentin,with the opposite result after SEC14L1P1 overexpression.In vivo experiments showed that the xenograft tumor weight and volume of the ASO-SEC14L1P1 group were significantly reduced.Further mechanistic studies revealed a positive correlation between SEC14L1P1 and DHX9 expressions,and DHX9 has been shown to activate the PI3K/AKT signaling pathway.Knockdown of SEC14L1P1 resulted in decreased protein expressions of phosphorylated-PI3K(p-PI3K)and phosphorylated-AKT(p-AKT),and overexpression of SEC14L1P1 increased protein expressions of p-PI3K and p-AKT.Conclusion:SEC14L1P1 showed high expression levels in OSCC cells and tissues and promoted the proliferation and migration of OSCC cells,a phenomenon that may be related to the regulation of the PI3K/AKT signaling pathway by SEC14L1P1,which in turn promotes EMT.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective To investigate the independent risk factors affecting the prognosis of chronic active antibody-mediated rejection (caAMR) after kidney transplantation. Methods A retrospective analysis was conducted on 61 patients who underwent renal biopsy and were diagnosed with caAMR. The patients were divided into caAMR group (n=41) and caAMR+TCMR group (n=20) based on the presence or absence of concurrent acute T cell-mediated rejection (TCMR). The patients were followed up for 3 years. The value of 24-hour urinary protein and estimated glomerular filtration rate (eGFR) at the time of biopsy in predicting graft loss was assessed using receiver operating characteristic (ROC) curves. The independent risk factors affecting caAMR prognosis were analyzed using the LASSO-Cox regression model. The correlation between grouping, outcomes, and Banff scores was compared using Spearman rank correlation matrix analysis. Kaplan-Meier analysis was used to evaluate the renal allograft survival rates of each subgroup. Results The 3-year renal allograft survival rates for the caAMR group and the caAMR+TCMR group were 83% and 79%, respectively. The area under the ROC curve (AUC) for predicting 3-year renal allograft loss was 0.83 ［95% confidence interval (CI) 0.70-0.97］ for eGFR and 0.78 (95% CI 0.61-0.96) for 24-hour urinary protein at the time of biopsy. LASSO-Cox regression analysis and Kaplan-Meier analysis showed that eGFR≤25.23 mL/(min·1.73 m²) and the presence of donor-specific antibody (DSA) against human leukocyte antigen (HLA) class I might be independent risk factors affecting renal allograft prognosis, with hazard ratios of 7.67 (95% CI 2.18-27.02) and 5.13 (95% CI 1.33-19.80), respectively. A strong correlation was found between the Banff chronic lesion indicators of renal interstitial fibrosis and tubular atrophy (P<0.05). Conclusions The presence of HLA class I DSA and eGFR≤25.23 mL/(min·1.73 m²) at the time of biopsy may be independent risk factors affecting the prognosis of caAMR.

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.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.

Objective:To understand the prevalence of psychoactive substance use and related factors in men who have unprotected sex with men in Zhejiang Province.Methods:Men who have unprotected sex with men were recruited by snowball sampling through men who have sex with men (MSM) social organization in Zhejiang to conduct a cross-sectional investigation from January 2022 to December 2023. The sample size was estimated to be 1 169 individuals. The information about their socio-demographic information, sexual behaviors, psychoactive substance use, and HIV testing were collected through on-site questionnaire survey. Logistic regression analysis model was used to analyze related factors about psychoactive substance use in this population.Results:A total of 1 914 men who have unprotected sex with men were included in the study, the average age of the study subjects was (31.43±10.46) years, and most of them were aged ≤35 years (70.8%, 1 356/1 914). Those with education level of high school or below accounted for 50.1% (960/1 914) and those who were not married accounted for 63.5% (1 216/1 914). Psychoactive substance use in the past 3 months was reported in 19.7% (377/1 914) of the subjects. Multivariate logistic regression analysis showed that the factors associated with psychoactive substance use in the past 3 months included age 26-35 years (a OR=1.65, 95% CI: 1.24-2.19), ≥2 sexual partners in the past 3 months (a OR=2.33, 95% CI: 1.82-2.97), STD diagnosed in the past 3 months (a OR=2.62, 95% CI: 1.45-4.74), and awareness of the HIV infection status of sexual partners (a OR=2.83, 95% CI: 2.12-3.78). Conclusions:The prevalence of psychoactive substance use in men who have unprotected sex with men was high in Zhejiang. It is necessary to strengthen the intervention in this population.

Objective To compare the clinical efficacy between liposuction combined with circumareolar small incision and three-port endoscopic surgery for the treatment of Simon Ⅱ gynecomastia (GYN). Methods Comparative case data of 120 patients with GYN were retrospectively analyzed, 61 patients in the open group underwent circumareolar small incision mastectomy after liposuction, and 59 patients in the endoscopic group underwent three-port endoscopic mastectomy after liposuction. The two groups were compared in terms of surgery-related indexes, occurrence of postoperative complications and patient satisfaction. Results The unilateral operation time of the open group was shorter than that of the endoscopic group, the unilateral gland resection weight in the open group was more than that in the endoscopic group, the hospitalization cost of the open group was less than that of the endoscopic group (all P<0.01). There was no significant difference in unilateral liposuction volume, drainage volume on the first postoperative day, and time to drain removal between the two groups (P>0.05). The incidence of complications in the open group and the endoscopic group were 8.2% and 13.6% respectively, and there was no significant difference between the two groups (P>0.05). The difference in the overall satisfaction scores between the two groups was not statistically significant (P>0.05). Conclusions Liposuction combined with circumareolar small incision or three-port endoscopic surgery both has good cosmetic effects in the treatment of Simon Ⅱ GYN. The operation with circumareolar small incision is simple, has a shorter operation time, costs less, and does not require special equipment, which is suitable for promotion and application in medical institutions.

Molecularly imprinted polymers(MIPs)of colchicine(COL)were prepared by precipitation polymerization with COL as template,methacrylic acid(MAA)as monomer,ethylene glycol dimethacrylate(EGDMA)as crosslinking agent,azobisisobutyronitrile(AIBN)as initiator and chloroform as porogen.The basic structure of the prepared MIPs was characterized by scanning electron microscopy(SEM),transmission electron microscopy(TEM)and Fourier transform infrared(FT-IR)spectrometry,and the adsorption properties of MIPs were evaluated by kinetic adsorption,isothermal adsorption and selective adsorption experiments.The results indicated that the prepared MIPs had uniform particles size,with a maximum adsorption capacity of 62.61 mg/g for COL and an imprinting factor of 3.74,and was suitable for sample pretreatment in determination of COL blood concentration,enabling the enrichment and separation of trace or even ultra-trace target components,thereby improving analytical capabilities.