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 investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

[Objective] To explore the strategy of blood management in patients with massive transfusion in the frigid plateau region. [Methods] The treatment process of a patient with liver rupture in the frigid plateau region was analyzed, and the blood management strategy of the frigid plateau region was discussed in combination with the difficulties of blood transfusion and literature review. [Results] The preoperative complete blood count (CBC) test results of the patient were as follows: RBC 3.14×10¹²/L, Hb 10⁶ g/L, HCT 30.40%, PLT 115.00×10⁹/L; coagulation function: PT 18.9 s, FiB 1.31 g/L, DD > 6 μg/mL, FDP 25.86 μg/mL; ultrasound examination and imaging manifestations suggested liver contusion and laceration / intraparenchymal hematoma, splenic contusion and laceration, and massive blood accumulation in the abdominal cavity; it was estimated that the patient's blood loss was ≥ 2 000 mL, and massive blood transfusion was required during the operation; red blood cell components were timely transfused during the operation, and the blood component transfusion was guided according to the patient's CBC and coagulation function test results, providing strong support and guarantee for the successful treatment of the patient. The patient recovered well after the operation, and the CBC test results were as follows: RBC 4.32×10¹²/L, Hb 144 g/L, HCT 39.50%, PLT 329.00×10⁹/L; coagulation function: APTT 29.3 s, PT 12.1 s, FiB 2.728 g/L, DD>6 μg/mL, FDP 25.86 μg/mL. The patient was discharged after 20 days, and regular follow-up reexamination showed no abnormal results. [Conclusion] Individualized blood management strategy should comprehensively consider the patient’s clinical symptoms, the degree of hemoglobin decline, dynamic coagulation test results and existing treatment conditions. Efficient and reasonable patient blood management strategies can effectively improve the clinical outcomes of massive transfusion patients in the frigid plateau region.

Objective:To evaluate the efficacy and safety of once-daily tacrolimus extended-release capsules (OD-TAC) in pediatric liver transplant recipients after conversion from twice-daily tacrolimus (TD-TAC).Methods:A retrospective analysis was performed on pediatric liver transplant recipients at Tianjin First Center Hospital between January 2014 and December 2020 who were converted from TD-TAC to OD-TAC with a follow-up of at least 12 months. After conversion, all patients received OD-TAC monotherapy. The daily dose conversion ratio from TD-TAC to OD-TAC ranged from 2∶1 to 1∶2. Clinical data including demographics, tacrolimus dosage, trough concentrations, liver function, and adverse events were collected. Continuous variables with normal distribution were expressed as Mean±SD and compared using independent-samples t-test or ANOVA; non-normally distributed variables were expressed as median ( Q1, Q3) and compared using Mann-Whitney U or Kruskal-Wallis H tests. Categorical variables were expressed as frequency and percentage, and compared using χ 2 test or Fisher's exact test. P<0.05 was considered statistically significant. Results:A total of 290 children were enrolled, including 140 males (48.3%) and 150 females (51.7%). The median age at transplantation was 7.34 (6.03, 12.34) months, and the median time to conversion was 36 (29, 48) months post-transplant. Tacrolimus daily doses at 3, 6, and 12 months after conversion were slightly higher than before conversion, but without statistical significance (all P>0.05). Trough tacrolimus levels at 6 and 12 months after conversion were 2.34±1.02 μg/L and 2.23±1.07 μg/L, respectively, both lower than pre-conversion (2.77±1.43 μg/L), with statistical significance ( P=0.02 and P<0.01). Serum creatinine levels at 6 and 12 months post-conversion were 2.63±0.63 mmol/L and 2.76±0.68 mmol/L, respectively, both higher than before conversion (2.57±1.90 mmol/L, P<0.05). Triglyceride level at 12 months post-conversion was 0.87±0.25 mmol/L, significantly lower than pre-conversion (1.05±0.55 mmol/L, P<0.05). Two patients developed transient bilirubin elevation at 3 months, and another two developed transient triglyceride elevation at 6 months; all recovered without intervention. No new-onset diabetes was observed during follow-up. Thirteen patients experienced acute rejection. One patient (0.3%) died three years after conversion due to hepatic venous outflow obstruction, while all others survived. Conclusion:In pediatric liver transplant recipients, OD-TAC provides comparable efficacy and safety to TD-TAC.

OBJECTIVE: To assess the independent and combined effects of air pollutants, meteorological factors, and greenspace exposure on new tuberculosis (TB) cases. METHODS: TB case data from Shanghai (2013-2018) were obtained from the Shanghai Center for Disease Control and Prevention. Environmental data on air pollutants, meteorological variables, and greenspace exposure were obtained from the National Tibetan Plateau Data Center. We employed a distributed-lag nonlinear model to assess the effects of these environmental factors on TB cases. RESULTS: Increased TB risk was linked to PM _2.5, PM ₁₀, and rainfall, whereas NO ₂, SO ₂, and air pressure were associated with a reduced risk. Specifically, the strongest cumulative effects occurred at various lags: PM _2.5 ( RR = 1.166, 95% CI: 1.026-1.325) at 0-19 weeks; PM ₁₀ ( RR = 1.167, 95% CI: 1.028-1.324) at 0-18 weeks; NO ₂ ( RR = 0.968, 95% CI: 0.938-0.999) at 0-1 weeks; SO ₂ ( RR = 0.945, 95% CI: 0.894-0.999) at 0-2 weeks; air pressure ( RR = 0.604, 95% CI: 0.447-0.816) at 0-8 weeks; and rainfall ( RR = 1.404, 95% CI: 1.076-1.833) at 0-22 weeks. Green space exposure did not significantly impact TB cases. Additionally, low temperatures amplified the effect of PM _2.5 on TB. CONCLUSION Exposure to PM _2.5, PM ₁₀, and rainfall increased the risk of TB, highlighting the need to address air pollutants for the prevention of TB in Shanghai.
China/epidemiology* ; Humans ; Air Pollutants/analysis* ; Tuberculosis/epidemiology* ; Incidence ; Meteorological Concepts ; Particulate Matter/adverse effects* ; Environmental Exposure ; Male ; Female ; Adult ; Air Pollution ; Middle Aged

Objective:To evaluate the efficacy and safety of once-daily tacrolimus extended-release capsules (OD-TAC) in pediatric liver transplant recipients after conversion from twice-daily tacrolimus (TD-TAC).Methods:A retrospective analysis was performed on pediatric liver transplant recipients at Tianjin First Center Hospital between January 2014 and December 2020 who were converted from TD-TAC to OD-TAC with a follow-up of at least 12 months. After conversion, all patients received OD-TAC monotherapy. The daily dose conversion ratio from TD-TAC to OD-TAC ranged from 2∶1 to 1∶2. Clinical data including demographics, tacrolimus dosage, trough concentrations, liver function, and adverse events were collected. Continuous variables with normal distribution were expressed as Mean±SD and compared using independent-samples t-test or ANOVA; non-normally distributed variables were expressed as median ( Q1, Q3) and compared using Mann-Whitney U or Kruskal-Wallis H tests. Categorical variables were expressed as frequency and percentage, and compared using χ 2 test or Fisher's exact test. P<0.05 was considered statistically significant. Results:A total of 290 children were enrolled, including 140 males (48.3%) and 150 females (51.7%). The median age at transplantation was 7.34 (6.03, 12.34) months, and the median time to conversion was 36 (29, 48) months post-transplant. Tacrolimus daily doses at 3, 6, and 12 months after conversion were slightly higher than before conversion, but without statistical significance (all P>0.05). Trough tacrolimus levels at 6 and 12 months after conversion were 2.34±1.02 μg/L and 2.23±1.07 μg/L, respectively, both lower than pre-conversion (2.77±1.43 μg/L), with statistical significance ( P=0.02 and P<0.01). Serum creatinine levels at 6 and 12 months post-conversion were 2.63±0.63 mmol/L and 2.76±0.68 mmol/L, respectively, both higher than before conversion (2.57±1.90 mmol/L, P<0.05). Triglyceride level at 12 months post-conversion was 0.87±0.25 mmol/L, significantly lower than pre-conversion (1.05±0.55 mmol/L, P<0.05). Two patients developed transient bilirubin elevation at 3 months, and another two developed transient triglyceride elevation at 6 months; all recovered without intervention. No new-onset diabetes was observed during follow-up. Thirteen patients experienced acute rejection. One patient (0.3%) died three years after conversion due to hepatic venous outflow obstruction, while all others survived. Conclusion:In pediatric liver transplant recipients, OD-TAC provides comparable efficacy and safety to TD-TAC.

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.

Objective:To explore the effects of graft recipient weight ratio (GRWR) on the early prognosis (within 1 year after operation) of recipients of different ages after split liver transplantation (SLT) in children.Methods:From April 2015 to December 2022, the relevant clinical data were retrospectively reviewed for 188 children aged under 12 years undergoing initial SLT. Based upon operative age, they were assigned into groups of L (age≤18 months, 123 cases) and H (18 months< age≤12 years, 65 cases). Draw receiver operating characteristic (ROC) curves for predicting survival rates in H and L groups using GRWR and determine the cut-off value, and subgroup dassification was based the value. Compare the general condition, intraoperative condition, postoperative condition, and major complications of recipients. Follow-ups were conducted until 12 months post-SLT, death or retransplantation within 12months post-SLT. Kaplan-Meier survival rate analysis was utilized for comparing early postoperative survival rate of recipient/graft. The incidence of major early postoperative complications was examined by χ2 test or Fisher exact probability method. Results:The survival rate of recipients at Month 12 post-SLT was 92.6% (174/188), and graft survival rate was 91.0% (171/188). The survival rate of recipients in group L at Month 12 post-SLT was 94.3% (116/123), and graft survival rate was 92.7% (114/123). The GRWR value determined of 3.1 %. According to the level of GRWR, group L was divided into groups of L-L (GRWR≤3.1%, 36 cases) and L-H (GRWR>3.1%, 87 cases) while group H groups of H-L (GRWR≤3.1%, 55 cases) and H-H (GRWR>3.1%, 10 cases). The survival rates of recipients in groups L-L/L-H were 88.9% (32/36) and 96.6% (84/87) at Month 12 post-SLT. Inter-group difference was not statistically significant ( P=0.077). Graft survival rates were 83.3% (30/36) and 96.6% (84/87 ). Inter-group difference was statistically significant ( P=0.007). The intraoperative cold ischemia time were 479.0 (194.0, 593.0) min and 204.0 (122.0, 495.0) min in groups L-L/L-H. Inter-group difference was statistically significant ( P=0.002 ). The incidence of hepatic artery thrombosis were 13.9 % (5/36) and 2.3 % (2/87) in groups L-L/L-H. Inter-group difference was statistically significant ( P=0.036). The survival rate of recipients in group H at Month 12 post-SLT was 89.2% (58/65), and graft survival rate was 87.7% (57/65). No significant inter-group difference existed during surgery ( P>0.05 ). The survival rates of recipients in group H-L/H-H at Month 12 post-SLT were 92.7 % (51 /55) and 70.0 % (7/10 ). Inter-group difference was statistically significant ( P=0.019). Graft survival rates were 90.9% (50/55) and 70.0% (7/10). Inter-group difference was statistically significant ( P=0.036). No significant inter-group difference existed in the incidence of complications ( P>0.05) . Conclusion:During pediatric SLT, recipients of different ages have different requirements for GRWR. GRWR≤3.1 % implies poor early prognosis of recipients aged ≤18 months and GRWR>3.1% is associated with poor early prognosis of recipients aged between 18 months and 12 years.

Objective:To explore the risk factors and treatments of portal vein thrombosis (PVT) in children after pediatric living donor liver transplantation (pLDLT) .Method:From January 2014 to December 2021, the relevant clinical data were retrospectively reviewed for 975 LDLT children at Department of Pediatric Organ Transplantation of Tianjin First Central Hospital. Based upon the postoperative occurrence of PVT, they were assigned into two groups of PVT (19 cases) and non-PVT (956 cases). Univariate and multivariate analyses were performed for screening the risk factors of PVT post-LDLT and discussing the managements and prognoses of PVT.Result:Among them, overall incidence of PVT post-LDLT was 1.9% (19/975), and median time for an initial occurrence of PVT 8 (1-495) day. Single-factor analysis indicated that donor height ( P=0.014), operative duration ( P=0.002) and vascular interposition ( P=0.001) were correlated with the occurrence of postoperative PVT post-pLDLT. Multifactorial analysis revealed that operative duration ( P=0.008) and vascular interposition ( P<0.01) were independent risk factors for PVT post-pLDLT. For 19 cases of postoperative PVT, the measures included surgical thrombectomy (8 cases), urokinase thrombolysis plus warfarin anticoagulation (3 cases), interventional treatment (3 cases), warfarin anticoagulation (4 cases) and retransplantation (1 cases). After treatment, the outcomes were a disappearance of PVT (15 cases), symptomatic improvement (2 cases) and unrelated mortality (2 cases) . Conclusion:During pLDLT, intraoperative placement of blood vessels and operative duration are independent risk factors for the occurrence of PVT. Timely standardized treatment may achieve satisfactory therapeutic outcomes.