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 Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

BackgroundSevere mental disorders are characterized by high recurrence rate， high disability rate， high rates of harmful incidents， and low treatment-seeking rate， with affected patients demonstrating increased frequencies of dangerous behaviors. Ningxia Hui Autonomous Region has implemented community management for patients with severe mental disorders across the region since 2004， while the current status and regional characteristics of the managed patients remain unclear. ObjectiveTo analyze the current status of management and treatment of patients with severe mental disorders in Ningxia Hui Autonomous Region， and to explore their regional distribution characteristics， so as to provide references for optimizing regional prevention and control strategies. MethodsPatients with severe mental disorders diagnosed and registered in the Severe Mental Disorder Management Information Platform of Ningxia Hui Autonomous Region from August 1， 2011 to December 31， 2021 were selected. Patients' basic information， management indicators， and treatment metrics were extracted from the platform， followed by descriptive statistical analysis of the corresponding data. ResultsAs of December 31， 2021， the permanent resident population of Ningxia Hui Autonomous Region was 6 946 540， with 29 787 registered patients with severe mental disorders. The majority of the patients were female （50.25%）， aged 18-59 years （79.01%）， with educational level of junior high school or below （84.63%）， married （52.87%）， farmers （56.01%）， and diagnosed with schizophrenia （55.91%）， while ethnic minority patients accounted for a relatively high proportion （31.35%）. In 2021， the reported prevalence rate of severe mental disorders in Ningxia Hui Autonomous Region was 0.43%， with standardized management and regular medication adherence rates at 90.39% and 66.34%， respectively. The standardized management rate in 8 counties/districts （36.36%） was lower than the average level of Ningxia Hui Autonomous Region， while 10 counties/districts （45.45%） showed below-average medication adherence rates， of which 6 counties/districts（60.00%） were located in the south-central region. ConclusionPatients with severe mental disorders in Ningxia Hui Autonomous Region are predominantly young and middle-aged adults with low level of education， and those in the central-southern region demonstrate lower medication adherence. ［Funded by Key Research and Development Program Project of Ningxia Hui Autonomous Region （number， 2023BEG02029）］

Objective To observe the value of conventional ultrasound(US)and contrast-enhanced ultrasound(CEUS)for diagnosing partially cystic papillary thyroid carcinomas(PCPTC).Methods Thirty patients with PCPTC(32 nodules)and 50 patients with benign partially cystic thyroid nodules(PCTN)(52 nodules)were retrospectively enrolled.US and CEUS manifestations were compared between groups,and the efficacy of US and US combined with CEUS for diagnosing PCPTC were analyzed.Results Significant differences of margin,solid part echo,liquid part location,microcalcification and blood flow distribution of the nodules showed with US,as well as enhancement phase,enhancement intensity,enhancement uniformity,peripheral high-enhancement ring and regression mode of lesions shown on CEUS were found between groups(all P＜0.05).The area under the curve(AUC)of US combined with CEUS for diagnosing PCPTC was 0.942,significantly higher than that of US alone(0.750,P＜0.001).Conclusion US had certain value for diagnosing PCPTC.Combining with CEUS could effectively improve diagnostic efficacy of US for PCPTC.

Objective To observe the value of prenatal ultrasound evaluation on morphology of Sylvian fissure(SF)during the second and third trimesters for screening fetal abnormal cerebral cortical development.Methods Totally 876 fetuses in the second and third trimesters were retrospectively included.Prenatal ultrasound was performed to observe the morphology of fetal SF and whether there was complicated abnormalities of the development of cerebral cortical and other structures.Then prenatal ultrasound was regularly reexamined,and the pregnancy outcome,while the growth and development of newborns were followed up.Results Among 876 fetuses,normal SF morphology was observed in 861 fetuses(861/876,98.29％),while 11 fetuses(11/876,1.26％)had delayed SF development(normal SF morphology but inconsistent with gestational week)and 4 fetuses(4/876,0.46％)had abnormal SF morphology,all complicated with abnormal cerebral cortical development and/or intracranial and extracranial structural malformations,and reexamination of prenatal ultrasound showed that SF morphology was consistent with the gestational week in 4 fetuses,SF morphology still did not match the gestational week in 4 fetuses,and SF morphology was still abnormal in 2 fetuses.Among these 15 fetuses,6 were successfully born and grew well after followed up until 10-15 months,4 were induced labor,while the rest 4(3 with delayed SF development and 1 with abnormal SF morphology)complicated with other severe malformations and 1 with abnormal SF morphology were induced labor or lost to follow-up,hence not undergoing ultrasound re-examination.Conclusion Prenatal ultrasound evaluation on SF morphology during the second and third trimesters had important value for screening fetal abnormal cerebral cortical development.

The coil adverse events in the U.S.Food and Drug Administration Manufacturer and User Facility Device Experience(MAUDE)database from January 2021 to June 2024 were analyzed retrospectively.The risks of coils during the clinical application and their causes were explored with hospital survey and expert demonstration in Shandong Province.Some improving measures were put forward for the safe use of coils,including implementing the main responsibility of the registrant,enhancing the professional skills of the using institutions and strengthening the supervision of the supervisory authorities.[Chinese Medical Equipment Journal,2025,46(6):83-87]

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

Objective To explore the mediating role of mindful attention and awareness in depressive symptoms and insomnia severity among patients with comorbid depression and insomnia.Methods A cross-sectional study was conducted,enrolling 267 patients with comorbid depression and insomnia who were treated in the outpatient Department of Medical Psychology of Second Affiliated Hospital of Army Medical University,from March to May 2024.Basic demographic and clinical data were collected using a general information questionnaire.Depressive symptom severity was measured via the Patient Health Questionnaire-9(PHQ-9),insomnia severity via the Insomnia Severity Index(ISI),perceived stress via the Perceived Stress Scale-10(PSS-10),and mindful attention and awareness via the Mindful Attention Awareness Scale(MAAS).Pearson correlation analysis was used to examine the correlations between depressive severity,insomnia severity,perceived stress,and mindful attention and awareness.Mediation analysis was performed using Process 4.1.Results The PHQ-9 score was(13.80±5.98)and the ISI score was(17.10±5.56)in the 267 patients.Pearson correlation analysis showed that depressive severity and insomnia severity were positively correlated with perceived stress(r=0.531,0.351,P<0.001)and negatively correlated with mindful attention and awareness(r=-0.373,-0.350,P<0.001).Mediation analysis using Process 4.1 indicated that the combined mediating effect of mindful attention and awareness and insomnia between perceived stress level and depressive level was 0.157,with a 95%confidence interval(CI)of 0.102～0.217,and the total mediating effect was significant(P<0.001).Conclusion Perceived stress directly positively affects depression and indirectly exacerbates depression through insomnia as a mediator,and mindful attention and awareness can weaken the promoting effect of perceived stress on insomnia.

This study was a single-center retrospective study, to investigate the correlation between the relative level of miR-200a in exfoliated cells of peritoneal dialysis (PD) effluent and clinical indicators of peritoneal function in PD patients. Clinical data of PD patients who underwent treatment in the Department of Nephrology, the First Affiliated Hospital of Nanchang University, from January 2024 to December 2024 were collected, and the relative level of miR-200a in exfoliated cells of PD effluent was assessed. Patients were divided into three groups according to dialysis duration (Group A:≤1 year; Group B:>1 to <5 years; Group C:≥5 years), and differences in clinical data among the groups were compared. The correlation between the relative level of miR-200a and serum albumin, serum calcium, serum phosphorus, serum creatinine, hemoglobin, the neutrophil/high-density lipoprotein cholesterol ratio, the monocyte/high-density lipoprotein cholesterol ratio, the lymphocyte/high-density lipoprotein cholesterol ratio, and 24-hour urine volume in PD patients was analyzed. The results showed that there were no statistically significant differences among the three groups in gender, age, creatinine clearance rate, or Kt/V (all P>0.05). However, statistically significant differences were found among the three groups in peritoneal transport type ( χ2=13.518, P=0.001) and ultrafiltration volume ( H=6.905, P=0.032). Based on the 4-hour PD effluent creatinine/blood creatinine in the peritoneal equilibration test, patients were divided into the low transporter/low average transporter group (L group) and the high transporter/high average transporter group (H group). A statistically significant difference in the relative level of miR-200a was observed between the two groups [11.70 (1.09, 20.49) vs. 0.73 (0.46, 1.98), t=4.545, P<0.001]. The relative level of miR-200a was positively correlated with ultrafiltration volume and 24-hour urine output ( r=0.328, P=0.030; r=0.516, P<0.001), and negatively correlated with dialysis vintage ( r=-0.496, P<0.001). These results indicate that miR-200a level in PD effluent may, to some extent, reflect patients' peritoneal membrane function.