This article provides a systematic interpretation and review of the Society of Critical Care Medicine 2024 Guidelines on Adult ICU Design. Developed based on the Grading of Recommendations Assessment, Development and Evaluation methodology, the guideline address five core themes: ICU layout, room design, infection control, infrastructure, and staff spaces, and put forward 17 evidence-based recommendations, including 5 good practice statements. This article briefly introduces the guideline development methods and evidence characteristics, and focuses on summarizing key recommendations, including high-visibility layouts, single-bed ward settings, natural lighting and noise reduction strategies, integrated infection prevention and control design, remote monitoring, and flexible capacity expansion capabilities. Furthermore, it delves into the applicability and localized implementation pathways within China's healthcare environment, analyzing limitations in terms of evidence quality, specialty applicability, and cost-effectiveness. Furthermore, by integrating the Chinese Guidelines for the Construction and Development of Critical Care Medicine (2025 Edition) and current domestic practices, the article proposes tiered and phased implementation recommendations. This article aims to provide domestic healthcare institutions with a scientific reference that balances international cutting-edge concepts with China's real-world conditions for the construction and renovation of ICUs, thereby promoting the development of intensive care environments centered on patient safety, healthcare worker well-being, and infection control.

OBJECTIVE To analyze the specific risks and long-term toxicities of four BCR-ABL1 tyrosine kinase inhibitors （TKIs）（imatinib， dasatinib， nilotinib， and bosutinib） in pediatric patients with hematological malignancies. METHODS Adverse drug event （ADE） reports submitted to the the United States FDA Adverse Event Reporting System （FAERS） from January 2012 to December 2024， with imatinib， dasatinib， nilotinib， and bosutinib as the primary suspect drugs， were collected. Data mining was performed using the reporting odds ratio method and proportional reporting ratio method. ADE terms were classified and summarized by system organ class （SOC） and preferred term （PT） according to the Medical Dictionary for Drug Regulatory Activities （MedDRA， version 26.0）. Meanwhile， the ADE reports were divided by age into the adult group （≥18 years） and the pediatric group （＜18 years） to compare the differences in ADE between the two groups. RESULTS A total of 1 512 pediatric ADE reports were included： 993 for imatinib， 391 for dasatinib， 112 for nilotinib， and 16 for bosutinib. Among the reported ADEs， the patients were mainly aged 12-＜18 years； the reports mainly originated from the United States， France， and Japan； and the primary indications were chronic myeloid leukemia and acute lymphoblastic leukemia. A total of 5 256 ADE signals were mined， among which 235 were positive signals， involving 1 103 PT across 27 SOC. The top five PT ranked by the number of positive signals were nausea， febrile neutropenia， abdominal pain， neutropenia， and anemia. The top two SOC were general disorders and administration site conditions， and gastrointestinal disorders. Compared with the adult group， the pediatric group had relatively higher proportions of events related to infections and infestations as well as blood and lymphatic system disorders. Pediatric long-term toxicity signals primarily included growth retardation， accompanied by signals related to endocrine system abnormalities and bone metabolism abnormalities. Specific signals included imatinib-associated septic shock， dasatinib-associated chylothorax， and nilotinib-associated electrocardiographic QT interval prolongation. CONCLUSIONS When pediatric patients use BCR-ABL1 TKIs， priority monitoring of infection risk and hematologic parameters is required， along with long-term follow-up of height， endocrine， and bone metabolism parameters. Targeted screening and management of drug-specific signals should be performed to ensure the long-term safety of pediatric medication.

ObjectiveTo explore the application of virtual reality biofeedback training combined with oral localization therapy in dysphagia after oral cancer surgery. MethodsFrom May, 2023 to July, 2024, 86 patients with dysphagia after oral cancer surgery in Zhejiang Provincial People's Hospital were randomly divided into control group (n = 43) and experimental group (n = 43). The control group received conventional swallowing function training, while the experimental group added virtual reality biofeedback training combined with oral positioning therapy, for four weeks. The Standardized Swallowing Function Assessment Scale (SSA), Functional Oral Intake Scale (FOIS) and M.D.Anderson Dysphagia Inventory (MDADI) were used for evaluation before intervention, and two weeks, four weeks and eight weeks after intervention. ResultsFor scores of SSA , the main effects of group (F = 150.190, P < 0.001, η²_p = 0.641) and time (F = 230.870, P < 0.001, η²_p = 0.733), as well as the interaction effect (F = 16.910, P < 0.001, η²_p = 0.168) were all significant. For scores of FOIS, the main effects of group (F = 59.601, P < 0.001, η²_p = 0.415) and time (F = 89.464, P < 0.001, η²_p = 0.516), as well as the interaction effect (F = 7.990, P < 0.001, η²_p = 0.087) were all significant. For scores of MDADI, the main effects of group (F = 33.133, P < 0.001, η²_p = 0.283) and time (F = 49.650, P < 0.001, η²_p = 0.371), as well as the interaction effect (F = 3.224, P = 0.023, η²_p = 0.037) were all significant. ConclusionVirtual reality biofeedback training combined with oral localization therapy could improve the swallowing function, oral feeding ability and overall quality of life of patients with dysphagia after oral cancer surgery.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

Objective: To characterize longitudinal trajectories of testicular development in boys and breast development in girls, so as to provide reference data for understanding patterns of pubertal sexual maturation. Methods: Based on the Shanghai Pudong New Area Cohort Study on Growth, Development and Health in Children and Adolescents, a baseline survey was conducted in 2020 using a mult stage cluster random sampling method. A total of 2 184 children who completed all follow ups during the primary school period from 13 elementary schools in Pudong New Area,Shanghai,with annual follow ups during 2021-2025. Testicular volume and Tanner stage of breast development were assessed by professional physicians using standardized visual inspection and palpation. The age distribution of testicular volume and breast development was fitted by using cumulative link mixed models and Turnbull s nonparametric maximum likelihood estimation method. Results: Median ages for testicular volumes of 2, 3, 4 and 5 mL in boys were 7.07, 9.24, 10.29, and 11.57 years old, respectively. Median ages for Tanner breast stages Ⅱ, Ⅲ, Ⅳ, and Ⅴ in girls were 8.55 , 10.17, 11.18, and 13.78 years old, respectively. Based on overweight and obesity, stratified analysis showed that earlier pubertal onset among overweight/obesity children, and the key milestones for pubertal initiation were testicular volume reaching 4 mL in boys and breast Tanner II in girls for 10.29, 10.83; 8.18, 9.00 years. Conclusion Overweight and obesity are associated with earlier pubertal initiation,but there are certain gender and developmental stage specific patterns.

Diabetic nephropathy （DKD）， as a common microvascular complication of diabetes mellitus （DM）， is a major cause of end-stage renal disease （ESRD）. Its clinical manifestations include increased urinary protein excretion， thickening of the glomerular basement membrane， and renal tubulointerstitial fibrosis. The pathogenesis of DKD is complex and involves multiple factors， including disordered glucose metabolism， hemodynamic alterations， and oxidative stress. Although modern medical approaches can alleviate certain symptoms， they still have limitations such as insufficient therapeutic targeting and prominent adverse effects. The transforming growth factor-β/Smad （TGF-β/Smad） signaling pathway is not only a tissue fibrosis pathway that has attracted considerable attention in recent years， but also regulates multiple protein molecules， including the glomerular podocyte slit diaphragm protein Podocin， interleukin-1β （IL-1β）， and superoxide dismutase （SOD）， thereby participating in various pathological processes and ultimately mediating renal injury. Flavonoid compounds， owing to their sustained pharmacological effects， broad spectrum of action， and high safety profile， have become ideal candidates for targeted therapy research in DKD. Existing studies have shown that these compounds can exert inhibitory effects on renal fibrosis， alleviate inflammatory responses， protect podocytes， and reduce oxidative stress by regulating the interactions between the TGF-β/Smad signaling pathway and the aforementioned protein molecules， thereby maintaining renal structure and function， reducing proteinuria， and significantly improving DKD lesions. This review briefly outlines the composition and functions of the TGF-β/Smad signaling pathway， elucidates the mechanisms by which this pathway regulates DKD， and focuses on summarizing major studies from the past decade on flavonoid-based interventions in DKD through targeted inhibition of the TGF-β/Smad signaling pathway. Furthermore， it discusses the considerable therapeutic potential of flavonoids in the treatment of this disease， aiming to provide a scientific basis for future clinical prevention and treatment of DKD and to promote the development of targeted drugs.

Gouty arthritis （GA） is caused by monosodium urate（MSU） deposition due to purine metabolism disorders. In traditional Chinese medicine （TCM）， it falls under the category of "dampness-heat Bi syndrome"， with core pathogenesis involving dampness-heat accumulation and dysfunction of the spleen and kidney. The dampness-heat syndrome is the most common and the primary syndrome type during acute attacks. In Western medicine， GA is associated with purine metabolism imbalance and inflammation triggered by MSU crystals， involving pathways such as NOD-like receptor protein 3 （NLRP3） inflammasome activation and Toll-like receptor 2/4 （TLR2/4） signaling. Clinically， colchicine and similar drugs are commonly used to treat GA， although long-term use carries potential side effects. Ermiao Formula analogs originate from ancient prescriptions， including Ermiao， Sanmiao， and Simiao compound formulas. All contain Atractylodis Rhizoma and Phellodendri Chinensis Cortex. Ermiaowan follow a 1∶1 formulation ratio. Sanmiaowan add Cyathulae Radix. Simiaowan further incorporate Coicis Semen. These formulas are rich in active ingredients， including alkaloids， terpenoids， flavonoids， and sterols， and treat GA through multi-component， multi-pathway， and multi-target mechanisms. Ermiaosan primarily exerts anti-inflammatory effects by inhibiting pathways such as TLR4/nuclear factor kappa-B （NF-κB） or regulating immune responses to reduce the release of inflammatory mediators， while also suppressing xanthine dehydrogenase （XDH） and xanthine oxidase （XO） activity to decrease uric acid production. Sanmiaowan enhance uric acid-lowering and anti-inflammatory effects through the guiding herb Cyathulae Radix， while also protecting cartilage from damage. Simiaowan utilizes Coicis Semen to regulate intestinal flora， alleviate dampness-heat symptoms， and exert multi-pathway anti-inflammatory and uric acid-lowering effects. The active ingredients contribute differently to uric acid metabolism regulation， anti-inflammation， antioxidant activity， and bone repair， resulting in varying therapeutic effects due to differences in formula composition. In summary， formulas derived from Ermiaosan demonstrate significant efficacy in treating dampness-heat type GA. This review summarizes their research progress and mechanisms， providing a reference for clinical application， new drug development， and further studies.

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.