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.

Objective To investigate the role of p300 in lipid metabolism disorders.Methods Bioinformatics analysis was performed to analyze the expression patterns of p300 in lipid metabolism disorder-related diseases and its correlation with SREBP-1c and downstream lipid metabolic enzymes.Immunofluorescence assay was used to detect the expression of p300 in the liver tissues of the patients with varying disease severity of non-alcoholic fatty liver disease(NAFLD).A mouse model of lipid metabolism disorder was established in male C57BL/6J mice by feeding high-fat diet(HFD)for 12 weeks.Western blotting was employed to assess p300 expression level in the liver tissues of HFD-fed mice.A cell model of lipid metabolism disorder was established in HepG2/AML-12 cells induced with free fatty acid(FFA).The effects of siRNA-mediated knockdown of p300 was observed to measure the levels of intracellular total cholesterol(TC)and triglyceride(TG),lipid deposition,and production of reactive oxygen species(ROS).Results Clinically,p300 was highly expressed in lipid metabolism disorders,and its level was positively correlated with NAFLD severity(P<0.05).Gene Set Enrichment Analysis(GSEA)revealed that p300 expression was significantly associated with fatty acid metabolism,cholesterol homeostasis,lipogenesis,PPAR signaling pathway,and peroxisome pathway.In vivo,p300 was significantly up-regulated in the livers of HFD-fed mice(P<0.01).In vitro,FFA stimulation markedly increased p300 expression in both HepG2 and AML-12 cells(P<0.01),whereas p300 knockdown significantly reduced intracellular TG and TC levels(P<0.01),attenuated lipid droplet accumulation,and reversed FFA-induced ROS elevation(P<0.01).Furthermore,p300 expression was positively correlated with the expression of SREBP-1c and its downstream key lipid synthesis enzymes.Conclusion p300 may promote hepatic lipid accumulation by acetylating and activating SREBP-1c and regulating downstream lipid metabolic enzymes,thereby affecting lipid synthesis and oxidative stress.These findings suggest that p300 may be a potential therapeutic target for lipid metabolism disorder-related diseases.

Children who ingest corrosive substances by mistake usually have a small amount of exploratory ingestion，so perforation caused by injury is rare，and emergency surgical treatment is rarely required in the acute stage.But it is very easy to cause esophageal stenosis in the long term.Esophageal stenosis can last for weeks to months，and the children's difficult swallowing can lead to chronic pain in the esophagus and long-term malnutrition.At present，the main clinical treatment is endoscopic esophageal dilation，which requires multiple dilation treatments.The treatment period of corrosive esophageal stenosis in children is long，if supplemented with effective drugs during treatment，it is expected to reduce the degree of esophageal stenosis，reduce the number of dilation，and shorten the treatment period.On the other hand，early administration of protective drugs can promote benign wound healing after esophageal injury.This article reviews the research progress on drug treatment for corrosive esophageal stenosis in children.

Non-coding RNAs(ncRNAs),encompassing microRNAs(miRNAs),long non-coding RNAs(lncRNAs),and circular RNAs(circRNAs),have emerged as critical regulators of gene expression and cellular function.In alcohol-associated liver disease(ALD),chronic alcohol consumption disrupts the expression and function of ncRNAs in the liver and circulation,contributing to the disease's pathogenesis and progression.Dysregulated ncRNAs influence key pathways involved in hepatocyte injury,lipid metabolism,inflam-mation,and hepatic stellate cell(HSC)activation,thereby exacerbating steatosis,inflammation,and fibrosis.Furthermore,extracellular vesicles play a pivotal role in mediating ncRNA-driven intercellular communication,amplifying liver damage and fibrosis.This review provides a comprehensive overview of the multifaceted roles of ncRNAs in ALD,with a focus on their mechanistic contributions to disease development and progression.Additionally,we discuss the potential of ncRNAs as diagnostic biomarkers and therapeutic targets,emphasizing their translational relevance in addressing the burden of ALD.

BACKGROUND:The reasonable implantation range of femoral prosthesis in unicompartmental knee arthroplasty in patients with osteoporosis has not been investigated,and previous studies have often been based on unicompartmental knee arthroplasty models in normal bone,with fewer mechanical studies in models with non-normal bone.Complications after unicompartmental knee arthroplasty have been shown to be highly associated with osteoporosis. OBJECTIVE:To analyze the biomechanical effects of the coronal inclination of the Sled fixed platform femoral prosthesis on unicompartmental knee arthroplasty in patients with osteoporosis and to find the correlation between osteoporosis and mid-and long-term complications after unicompartmental knee arthroplasty. METHODS:Based on the digital imaging technology to obtain the data of the knee joint and prosthesis,a normal bone knee model is then created by using specialized software such as Mimics and Geomagic studio.Based on a validated normal bone knee model,an osteoporotic knee model was created by changing the material parameters.Totally 14 unicompartmental knee arthroplasty finite element models were created using Sled fixed platform femoral prosthesis:standard position(0°),varus and valgus angles:3°,6°,9° in the normal bone and osteoporosis groups.Stress changes on the surface of polyethylene liner,cancellous bone under tibial prosthesis,and cortical bone were calculated and analyzed in all unicompartmental knee arthroplasty models. RESULTS AND CONCLUSION:(1)In the osteoporotic models,the high stress values of the polyethylene liner surface and the cancellous bone under the tibial prosthesis increased with the increase of the tilt angle of the femoral prosthesis,and the high stress values of the cortical bone surface under the tibial prosthesis increased with the increase of the prosthesis valgus angles and decreased with the increase of the varus angles.(2)For the polyethylene liner surface as well as the subcortical bone surface of the tibial prosthesis,the high stress values of the models for each inclination angle in the osteoporosis group were greater than those of the corresponding models in the normal bone group.For the surface of the cancellous bone under the tibial prosthesis,the high stress values of the tilt angle models of the osteoporosis groups were smaller than those of the normal bone groups.(3)Osteoporosis may cause biomechanical abnormalities in the internal structures of the knee after unicondylar replacement,increasing the potential risk of postoperative aseptic loosening of the prosthesis and periprosthetic fractures.Varus and valgus of the femoral prosthesis in the coronal plane should be avoided as much as possible when performing medial unicompartmental knee arthroplasty with a Sled fixation platform in osteoporotic knees.

Objective To analyze the prescription patterns of Professor Huang Feng,a nationally renowned traditional Chinese medicine(TCM)practitioner,in treating osteomyelitis using data mining methods.Methods Prescription data from effective medical records of osteomyelitis treated by Professor Huang Feng between January 2018 and December 2022 were collected and screened.Microsoft Excel,SPSS Modeler 18.0,and SPSS Statistics 25 were used to analyze the frequency and the distribution of properties,flavors,and meridian tropism of prescribed medications,along with association rule analysis and cluster analysis of high-frequency drugs.Results A total of 137 prescriptions involving 86 Chinese medicinals were included.Eighteen high-frequency medicinals(frequency＞30 times)were identified,namely Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma,Achyranthis Bidentatae Radix,Bletillae Rhizoma,Rehmanniae Radix,Paeoniae Radix Alba,Dendrobii Caulis,Polygalae Radix,Lablab Semen Album,Corydalis Rhizoma,Angelicae Dahuricae Radix,Drynariae Rhizoma,Sanguisorbae Radix,Poria,and Mume Fructus.Most of the prescribed medicinals were neutral in nature,sweet,bitter,and pungent in flavor,and had the meridian tropism of liver,spleen,and lung meridians.Association rule analysis yielded 67 drug association rules,and the high-support combinations were the drug combinations of Astragali Radix respectively with Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma and Achyranthis Bidentatae Radix,reflecting the compatibility principles of supplementing and invigorating qi-blood,activating blood circulation to resolve stasis,and draining dampness to remove toxins.Cluster analysis revealed three core clusters:Cluster 1 consisted of Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Smilacis Glabrae Rhizoma,Angelicae Sinensis Radix,Bletillae Rhizoma,Paeoniae Radix Alba,Angelicae Dahuricae Radix,Mume Fructus,Polygalae Radix and Sanguisorbae Radix;Cluster 2 consisted of Rehmanniae Radix and Dendrobii Caulis;Cluster 3 consisted of Achyranthis Bidentatae Radix,Lablab Semen Album,Corydalis Rhizoma and Poria.Conclusion For the treatment of osteomyelitis,Professor Huang Feng follows the principle of combining supporting healthy qi with eliminating pathogens,focuses on clearing damp-heat and pathogenic toxins accompanied by activating blood circulation to resolve stasis,and lays stress on adaptation to local condition and activating spleen-stomach to reinforce vital qi.

Objective: To examine the mediating effect of frailty on social isolation and cognitive function among the elderly. Methods: Demographic information, smoking, alcohol consumption and cognitive function of the elderly at ages of 60 years and older were collected from the China Health and Retirement Longitudinal Study 2020. Social isolation and frailty were evaluated using social isolation index and frailty index, respectively. The mediating effect of frailty on social isolation and cognitive function was analyzed using the Process program, and the significance of the mediating role was tested using the Bootstrap test. Results: A total of 2 822 individuals were enrolled, including 1 483 males (52.55%) and 1 339 females (47.45%). There were 2 497 (88.48%) and 325 (11.52%) individuals at ages of 60-<75 years and ≥75 years, respectively. The median cognitive function score was 14 (interquartile range, 16) points. There were 432 cases with social isolation (15.31%), with a median social isolation index of 10 (interquartile range, 5) points. The median frailty index was 0.11 (interquartile range, 0.15). There were 1 111 individuals without frailty, accounting for 39.37%; 1 214 individuals with pre-frailty, accounting for 43.02%; and 497 individuals with frailty, accounting for 17.61%. Mediating effect analysis showed that social isolation affected cognitive function directly and negatively with the effect value of -0.773 (95%CI: -0.899 to -0.647), and also affected cognitive function by frailty indirectly and negatively with the effect value of -0.147 (95%CI: -0.188 to -0.110), with the mediating effect contributed 15.98% of the total effect. Conclusion Frailty can directly or indirectly affect cognitive function among elderly through social isolation.

ObjectiveTo construct and validate a clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure （CHF），aiming to assist clinical diagnosis and provide tools and methods for individualized treatment of CHF. MethodsThe clinical data of patients with chronic heart failure treated at Dongzhimen Hospital of Beijing University of Chinese Medicine from January 2022 to January 2024 were retrospectively collected. The patients were randomly divided into a training group and a validation group with a ratio of 7∶3. First， the least absolute shrinkage and selection operator （LASSO） regression analysis was used to preliminarily screen the predictive factors affecting the diagnosis of Qi deficiency and blood stasis syndrome in CHF. Subsequently， the Logistic regression method was applied to conduct a more in-depth and detailed analysis of these factors. Variables with P<0.05 in the results of the multi-factor Logistic regression were carefully selected and included. Based on the regression coefficients obtained from this analysis， a model was constructed， and a nomogram was accurately drawn. Using R software，the receiver operating characteristic （ROC） curve，calibration curve，and decision curve analysis （DCA） were precisely drawn. These analyses were used to comprehensively evaluate the model from three crucial aspects： discrimination，calibration，and clinical applicability. Additionally， the accuracy，specificity，sensitivity，positive predictive value，and negative predictive value of the model were meticulously calculated to conduct a more all-round and comprehensive assessment. ResultsIn total， 168 cases were successfully obtained in the training group， and 71 cases were included in the validation group. After a thorough comparison， it was found that there were no statistically significant differences in the baseline data between the two groups. After being rigorously screened by the LASSO-multivariate logistic regression method， dark red tongue，smoking history，cardiac troponin I，and N-terminal pro-B-type natriuretic peptide （NT-ProBNP） were identified as the influencing factors for diagnosing patients with the Qi deficiency and blood stasis syndrome in CHF. The constructed model demonstrated an area under the curve （AUC） of 0.812 in the training group and 0.719 in the validation group. The calibration curve showed that the predicted curve of the model was close to the actual observed curve. DCA indicated that the model could provide substantial clinical benefits for patients at the decision thresholds ranging from 0.2 to 0.9. ConclusionThe clinical prediction model for Qi deficiency and blood stasis syndrome in chronic heart failure constructed in this study shows good performance. It has certain application value in clinical practice， which may contribute to the improvement of the diagnosis and treatment of CHF patients with this syndrome.