Transfusion and Anemia Expertise Initiative-Control/Avoidance of Bleeding developed a strategy for platelet and plasma infusion management in critically ill children based on systematic reviews and consensus meetings of international multidisciplinary experts. One good practice statement and six expert consensus statements were proposed for plasma and platelet transfusions in critically ill children following severe trauma, traumatic brain injury, and/or intracranial hemorrhage. This article introduces the specific methods and basis for the formation of recommendations in this part of the guide.

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 Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

Objective To investigate the cross-sectional associations between different fatty liver classifications and cardiac structure and function in people undergo health examination. Methods A total of 6 545 adults who underwent health examinations at the Health Management Center of Zhongshan Hospital, Fudan University between January 1 and December 31, 2017, were retrospectively included. Demographic characteristics and laboratory data were collected. The hepatic steatosis was graded by ultrasonography. And patients with fatty liver were further stratified according to alanine aminotransferase (ALT) or aspartate aminotransferase (AST) levels, as well as the degree of liver fibrosis. Cardiac morphology and function were assessed by transthoracic echocardiography, and left ventricular geometric patterns were classified accordingly. Multivariate logistic regression analysis was performed to evaluate the associations between fatty liver classifications and cardiac abnormalities. Results There were 2 795 patients (42.7%) with fatty liver, of whom 832 (29.8%) had significant cardiac structural alterations and 1 500 (53.7%) had diastolic dysfunction. Severe fatty liver was risk factor for concentric remodeling and increased relative wall thickness (RWT), with odds ratios (ORs) of 1.27 (P＝0.012) and 1.27 (P＝0.009), respectively. Fatty liver accompanied by elevated ALT or AST was risk factor for concentric remodeling (OR＝1.45,1.56; P＝0.001, 0.001), increased RWT (OR＝1.48,1.57; P＜0.001, ＜0.001), and diastolic dysfunction (OR＝1.27, 1.32; P＝0.035, 0.040), respectively. Fatty liver with liver fibrosis was risk factor for concentric remodeling (OR＝1.83, P＝0.046) and diastolic dysfunction (OR＝2.64, P＝0.034). Conclusions Advanced fatty liver, including severe hepatic steatosis, accompanied by elevated liver enzymes or liver fibrosis, could increase risks of cardiac remodeling and diastolic dysfunction, while systolic function is preserved. For patients with fatty liver, it is recommended to undergo regular ultrasonography examination.

Objective: To investigate the association of special family structure and physical activity with psychological sub health among secondary vocational school students, so as to provide reference to inform mental health promotion in the population. Methods: From September to December 2024, a convenience sample was drawn from 16 schools including 5 141 secondary vocational school students across 8 provinces (municipalities) in China (Fujian, Chongqing, Guangdong, Guangxi, Hubei, Jiangxi, Shanghai, Zhejiang). A self developed questionnaire, Multidimensional Sub health Questionnaire of Adolescents and International Physical Activity Questionnaire Short Form were used to investigate and evaluate basic information, family structure, psychological sub health status and physical activity. Descriptive statistics and Logistic regression analysis were performed to explore relationships of special family structures and physical activity with psychological sub health in secondary vocational school students. Results: The detection rate of psychological sub health among secondary vocational school students was 11.0%. The reporting rates for secondary vocational school students who lost their father or lost their mother, whose parents divorced, whose parents remarried, and those from special family structures were 2.8%, 0.8%, 11.8%, 3.9%, and 14.7%, respectively. Moreover, the detection rates of psychological sub health and its sub dimensions significantly differed among secondary vocational school students grouped by family type (divorced parental families, remarried parental families and special family structures)( χ 2=5.90-22.67, all P <0.05). Binary Logistic regression indicated that maternal bereavement was associated with a higher risk of conduct problems [ AOR ( 95% CI )= 2.90(1.15-7.33)], and parental divorce was associated with a higher risk of psychological sub health ( AOR= 2.71 , 95%CI =1.04- 7.06 ) among secondary vocational school students (both P <0.05). Interaction analysis showed that both students with a special family structure and insufficient physical activity [ AOR (95% CI )=1.99(1.44-2.75)] and those with a non-special family structure and insufficient physical activity [ AOR (95% CI )=1.81(1.48-2.22)] were associated with increased risks of psychological sub health among secondary vocational school students (both P <0.01). Conclusion Special family structure and physical activity are associated with psychological sub health among secondary vocational school students; early identification and assessment based interventions for those with a special family structure should be strengthened, while school based physical activity should be implemented to increase physical activity levels,thereby reducing the risk of psychological sub health.

The surgical management of empyema (excluding those caused by mycobacterium tuberculosis and non-tuberculous mycobacteria) is rapidly evolving towards minimally invasive, precise, and stepwise approaches. The traditional three-stage classification (exudative, fibrinopurulent, and organizing) has limitations in guiding dynamic clinical decision-making. For the first time, this consensus explicitly identifies two critical junctures in the pathological progression of empyema: "early transformation" (stage Ⅰ to Ⅱ) and "late transformation" (stage Ⅱ to Ⅲ), and thereby constructs a corresponding "identification-early warning-intervention" stepwise therapeutic framework. The consensus emphasizes that proactive debridement via video-assisted thoracoscopic surgery should be performed during the early transformation phase to halt disease progression. Conversely, during the late transformation phase, therapeutic goals should be rationally adjusted to prioritize adequate drainage, avoiding futile pleural decortication. Moreover, the consensus underscores the pivotal role of precise perioperative etiological diagnosis (e.g. metagenomic nest-generation sequencing) and standardized anti-infective therapy. Integrating practical experiences from multiple thoracic surgery centers in China and relevant evidence-based literature, this consensus formulates recommendations on the precise definitions of staging, surgical indications for each phase, key technical points, perioperative management, and training systems. It aims to promote the standardized and individualized surgical management of empyema, ultimately optimizing patient prognosis.

Tracheoesophageal fistula (TEF) is a disease characterized by an abnormal connection between the trachea and esophagus. Benign TEF often results from tracheal intubation injury, typically presenting as coughing exacerbated by swallowing, and can be potentially life-threatening. However, no unified surgical standard currently exists for TEF. The choice of surgical method mainly depends on the location and size of the fistula, as well as the condition of the surrounding tracheal tissue. For small-to-moderate fistulas, tracheal segmental resection with reconstruction is a classic approach. For large, complex, and refractory TEFs, musculocutaneous flap repair is considered an ideal option. This article reviews the application of several common musculocutaneous flaps—including the sternocleidomastoid, pectoralis major, and latissimus dorsi—in the repair of benign TEFs, with the aim of providing a clinical reference.

AIM:To observe the defocus state and myopia control in myopic children wearing single-vision, defocus, and orthokeratology lenses using multispectral refraction topography(MRT).METHODS: A total of 279 myopic patients aged 8-14 years old, with a spherical equivalent(SE)from -7.00 to -0.50 D, treated at the Chengdu Aier Eye Hospital from June 2022 to December 2023. Patients who volunteered for the study were assigned to three groups. A total of 94 cases were provided with single-vision spectacle lenses(SVL group), 90 cases received individualized ocular refraction customization(IORC group), and 95 cases received orthokeratology lenses(OK group). Simultaneously, the three groups were further categorized into low(-3.00 to -0.50 D), moderate(-6.00 to -3.25 D), and high myopia(-7.00 to -6.25 D)groups according to different SE. MRT was used to measure and compare the defocus changes of the retina in supperior, inferior, nasal, and temporal quadrants(RDV-S, RDV-I, RDV-N, RDV-T), and three angles of field of view, including 0-15°, 15°-30°, and 30°-45°(RDV-15, RDV-30, RDV-45)in the three groups(the data divide for the connected regions is grouped to the latter group). A one-way analysis of variance was used for intergroup comparisons. Univariate and multivariate linear regression analyses were used to analyze the factors related to changes in the axial length(AL)at 1 a after intervention.RESULTS:There were significant differences in 1-year SE and AL growth among patients in the SVL, IORC, and OK groups before and after intervention(P<0.001). The 1-year SE and the difference of AL growth in patients with low myopia was significantly different among SVL, IORC, and OK groups(P<0.001); however, there was no significant difference between the IORC and OK groups(P>0.05); there were significant differences in the SE and AL growth changes between the OK group and the IORC and SVL groups in moderate myopia(P<0.001); and there were significant differences between the OK group and the IORC and SVL groups in SE and AL growth of high myopia group after wearing lenses for 1 a(P<0.001), while there were no significant differences between the IORC and SVL groups(P>0.05). In addition, there were significant differences in the relative peripheral refractive errors(RPRE)of 4 quadrants and 3 eccentric regions among the three groups of patients in different degrees of myopia groups(P<0.001). Pair-wise comparison of the growth difference of eccentric D-RDV-15 in low myopia group after wearing lenses for 1 a showed significant differences between the SVL, IORC, and OK groups(P<0.001), but no significant differences between the IORC and OK groups(P>0.05). The angle of field of view D-RDV-30 in moderate myopia subgroups was statistically different between the SVL group and the IORC and OK groups after wearing lenses for 1 a(P<0.001), while the IORC and OK groups showed no significant differences(P>0.05); the angle of field of view D-RDV-15 in high myopia subgroups was statistically different between the OK group and the IORC and SVL groups after wearing lenses for 1 a(P<0.001), but there was no significant difference between the IORC and SVL groups(P>0.05). Univariate and multivariate linear regression model analysis showed that the changes in D-TRVD, D-RDV-45, D-RDV-N, and D-RDV-I correlated with the increase in the difference in 1 a AL.CONCLUSION: MRT can be used to guide the clinical control of myopia. Myopia development is related to the peripheral retinal defocus state, and the difference of defocus quantity in the inferior nasal side at 30°-45° eccentricity may be a factor regulating the rapid progression of myopia.

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.