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.

Objective To analyze the factors affecting the prevalence of hyperuricemia(HUA)in an island troop.Methods A total of 1 113 soldiers stationed on an island from December 2021 to December 2022 were selected as research objects by cluster sampling.Their lifestyle and health information were collected.Physical examination and laboratory detection were conducted.Multivariate logistic regression was used to analyze the influencing factors of HUA.Results The prevalence rate of HUA was 21.02%(234/1 113).There were significant differences in the body mass index(BMI),waist-to-hip ratio,triglyceride,alanine aminotransferase,and creatinine between the soldiers with hyperuricemia and the soldiers with normal blood uric acid(P<0.05).Multivariate logistic regression analysis showed that BMI≥24(OR=1.49,95%CI:1.09-2.05),abnormal liver function(OR=2.26,95%CI:1.31-3.92),and dyslipidemia(OR=1.46,95%CI:1.01-2.12)were positively correlated with hyperuricemia;age>30 years old(OR=0.59,95%CI:0.37-0.93)and exercise time>1 h per week(OR=0.46,95%CI:0.22-0.97)were negatively correlated with HUA.Conclusion The prevalence rate of hyperuricemia is at a high level in an island troop.BMI≥24,age≤30 years old,exercise time≤1 h per week,abnormal liver function,and dyslipidemia are the risk factors for HUA.Prevention and control measures should be taken as early as possible for the soldiers with these risk factors.

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

Pregnancy complicated with acute myeloid leukemia is uncommon,requiring the collaborative management by specialists from departments of hematology,obstetrics,anesthesiology,and neonatology for both the parturient and the neonate.This article reports an anesthesic management process of a parturient woman with acute myeloid leukemia and reviews relevant literature published in recent years to systematically summarize the approach for anesthesia-related perinatal management of such patients.
Humans ; Female ; Pregnancy ; Leukemia, Myeloid, Acute/complications* ; Pregnancy Complications, Neoplastic ; Adult ; Anesthesia, Obstetrical/methods*

Objective To characterize the expression patterns of serum microRNA-497(miR-497)in patients with colorectal cancer(CRC)and to investigate its associations with clinicopathological characteristics,diagnostic performance,and long-term prognostic outcomes.Methods This study retrospectively analyzed data from 122 patients with CRC admitted to the hospital between March 2020 and March 2022(CRC group),and enrolled 100 healthy individuals undergoing routine physical examinations(healthy control group)for comparison.Serum samples were collected from all participants prior to any surgical intervention,and the expression levels of miR-497 in serum were quantified using real-time quantitative polymerase chain reaction(qRT-PCR).Simulta-neously,the levels of carcinoembryonic antigen(CEA)and carbohydrate antigen 199(CA199)were measured.To investigate the association between miR-497 expression and clinicopathological characteristics,we evaluated its diagnostic performance using receiver operating characteristic(ROC)curve analysis.Furthermore,Kaplan-Meier survival analysis and Cox proportional hazards regression models were employed to assess its impact on patient prognosis.Results Compared to healthy individuals,CRC patients exhibited significantly lower serum miR-497 expression levels(P<0.001).Notably,miR-497 expression was strongly correlated with TNM stage progression and lymph node metastasis(P<0.001),but showed no significant association with tumor location,patient sex,or age.Diagnostic evaluation using ROC curves demonstrated that miR-497 achieved an AUC of 0.845 for CRC detection,outperforming CEA(AUC=0.748)and CA19-9(AUC=0.702),with DeLong's test confirming the statistically significant differences(P<0.05).Kaplan-Meier survival analysis revealed a significantly higher 3-year DFS rate among patients with high miR-497 expression(84.06%)compared to those with low expression(64.58%),with median DFS not reached in the high-expression group versus 36 months in the low-expression group(P=0.015).Multivariate Cox regression analysis confirmed that reduced miR-497 expression(HR=1.923,95%CI:1.184～3.125),advanced TNM stage(HR=2.511,95%CI:1.421～4.437),and lymph node metastasis(HR=1.753,95%CI:1.151～2.664)were independently associated with poorer disease-free survival outcomes.Conclusions Serum miR-497 is downregulated in patients with CRC and is significantly associated with tumor progression and poor prognosis.It demonstrates high diagnostic accuracy and strong potential for prognostic evalua-tion,highlighting its promise as a biomarker for auxiliary diagnosis and outcome prediction in CRC.

Objective To characterize the expression patterns of serum microRNA-497(miR-497)in patients with colorectal cancer(CRC)and to investigate its associations with clinicopathological characteristics,diagnostic performance,and long-term prognostic outcomes.Methods This study retrospectively analyzed data from 122 patients with CRC admitted to the hospital between March 2020 and March 2022(CRC group),and enrolled 100 healthy individuals undergoing routine physical examinations(healthy control group)for comparison.Serum samples were collected from all participants prior to any surgical intervention,and the expression levels of miR-497 in serum were quantified using real-time quantitative polymerase chain reaction(qRT-PCR).Simulta-neously,the levels of carcinoembryonic antigen(CEA)and carbohydrate antigen 199(CA199)were measured.To investigate the association between miR-497 expression and clinicopathological characteristics,we evaluated its diagnostic performance using receiver operating characteristic(ROC)curve analysis.Furthermore,Kaplan-Meier survival analysis and Cox proportional hazards regression models were employed to assess its impact on patient prognosis.Results Compared to healthy individuals,CRC patients exhibited significantly lower serum miR-497 expression levels(P<0.001).Notably,miR-497 expression was strongly correlated with TNM stage progression and lymph node metastasis(P<0.001),but showed no significant association with tumor location,patient sex,or age.Diagnostic evaluation using ROC curves demonstrated that miR-497 achieved an AUC of 0.845 for CRC detection,outperforming CEA(AUC=0.748)and CA19-9(AUC=0.702),with DeLong's test confirming the statistically significant differences(P<0.05).Kaplan-Meier survival analysis revealed a significantly higher 3-year DFS rate among patients with high miR-497 expression(84.06%)compared to those with low expression(64.58%),with median DFS not reached in the high-expression group versus 36 months in the low-expression group(P=0.015).Multivariate Cox regression analysis confirmed that reduced miR-497 expression(HR=1.923,95%CI:1.184～3.125),advanced TNM stage(HR=2.511,95%CI:1.421～4.437),and lymph node metastasis(HR=1.753,95%CI:1.151～2.664)were independently associated with poorer disease-free survival outcomes.Conclusions Serum miR-497 is downregulated in patients with CRC and is significantly associated with tumor progression and poor prognosis.It demonstrates high diagnostic accuracy and strong potential for prognostic evalua-tion,highlighting its promise as a biomarker for auxiliary diagnosis and outcome prediction in CRC.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

A patient was admitted to hospital due to umbilical pain.Gram-staining presented short,positive coc-cobacillus in the purulent secretions from the umbilical cord of the patient.Meanwhile,matrix-assisted laser desorp-tion/ionization time-of-flight mass spectrometry(MALDI-TOF MS),16S rRNA detection,and in vitro antimicro-bial susceptibility testing were performed on the isolated strains.Corynebacterium tuberculostearicum was identified by Gram-staining,MALDI-TOF MS and 16S rRNA.Antimicrobial susceptibility testing results showed that the strain was sensitive to penicillin,ceftriaxone,cefepime,meropenem,vancomycin,daptomycin,linezolid,compound sulfamethoxazole,ampicillin,gentamicin and tetracycline.Smear of microorganisms is helpful for the identification of Corynebacterium tuberculostearicum morphology,and MALDI-TOF MS is necessary for rapid identification of strains.Antimicrobial susceptibility testing can also assist the empirical treatment for infection due to this strain.

Objective To evaluate the bioequivalence and safety of two pyrazinamide tablets in healthy Chinese subjects.Methods An open,randomized,single-dose,two-sequence,two-cycle,double-cross trial design was used.All 48 healthy subjects(24 in fasting and 24 in fed trial)were randomized to receive a single oral dose of a 0.5 g pyrazinamide tablet(test or reference)per cycle.The plasma concentration of the drug was determined by liquid chromatography coupled to tandem mass spectrometry method.The pharmacokinetic parameters were calculated by WinNonlin v8.2,and the bioequivalence was evaluated by SAS 9.4.Results In the fasting group,the Cmax of the test and reference preparation of pyrazinamide tablets were(13.28±2.82)and(12.88±4.49)μg·mL-1,the AUC0-t were(139.17±26.58)and(138.63±28.92)h·μg·mL-1,the AUC0-∞ were(148.96±33.65)and(148.71±36.97)h·μg·mL-1 respectively.In the fed group,the Cmax of the test and reference preparation of pyrazinamide tablets were(11.89±1.96)and(11.99±1.92)μg·mL-1,the AUC0-t were(138.22±37.21)and(141.68±25.80)h·μg·mL-1,the AUC0-∞ were(152.20±32.41)and(151.04±28.05)h·μg·mL-,respectively.The 90％confidence intervals of Cmax,AUC0-t and AUC0-∞ geometric mean ratios of the test and reference preparation were all within 80.00％to 125.00％.The incidence of adverse events was 16.70％for both the test and reference preparation in the fasting group and 8.30％for both the test and reference preparation in the fed group,all of which were mild in severity.Conclusion The test and reference preparation of pyrazinamide tablets were bioequivalent,safe and well tolerated in healthy Chinese subjects under fasting and fed conditions.