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.

Despite significant advances in the field of critical care medicine over the past three decades, veno-arterial extracorporeal membrane oxygenation (V-A ECMO) remains the primary temporary mechanical circulatory support modality for patients with acute severe circulatory failure. With the accumulation of clinical experience and the increasing maturity of operational techniques in V-A ECMO, its technical management—particularly hemodynamic management—has become a key factor influencing patient outcomes. To further improve patient survival, the Chinese Critical Care Ultrasound Study Group, in collaboration with the Hemodynamic Therapy of Critical Care Collaborative Group and the Critical Care Medicine Branch of the China International Exchange and Promotive Association for Medical and Health Care, organized experts in critical care medicine to develop the Consensus on Hemodynamic Management in Adult Veno-Arterial Extracorporeal Membrane Oxygenation (2026 Edition). Based on years of clinical experience and the latest evidence-based medical evidence, this consensus formulates 15 systematic recommendations covering the entire process of V-A ECMO, including initiation, management during support, and weaning, aiming to promote standardized application of hemodynamic management in V-A ECMO.

Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

Objective:To investigate factors influencing frequent episodes (≥ 4 episodes within 1 year) in children with moderate-to-severe atopic dermatitis (AD) in China.Methods:A national multicenter cross-sectional study was conducted. Patients under the age of 18 years diagnosed with moderate-to-severe AD were enrolled at dermatology clinics in 18 medical institutions across 12 provinces and municipalities in China between June 12 and August 8, 2023. At the time of the visit, their guardians completed a structured questionnaire covering demographic characteristics, clinical features of AD, personal and family history, factors associated with frequent episodes of moderate-to-severe AD, compliance with treatment, and disease awareness. Statistical analyses included t tests, one-way analysis of variance, rank-sum tests, and chi-square tests, with multiple-response analysis applied for multiple-choice questions. Results:A total of 965 valid questionnaires were collected, and 965 children with moderate-to-severe AD were included. Among them, there were 531 males and 434 females, 678 (70.3%) were aged 2 - < 12 years, 837 (86.7%) were from urban areas, the age at onset was 2.47 ± 3.03 years, and the median frequency of AD episodes in the past year was 4 times. These children were divided into 2 groups based on the median episode frequency: < 4-episode group (439 cases, 45.5%) and ≥ 4-episode group (526 cases, 54.5%). Compared with the < 4-episode group, children in the ≥ 4-episode group showed younger ages at onset (2.22 ± 2.98 years vs. 2.76 ± 3.06 years, P = 0.006) and higher proportions of patients with comorbid allergic diseases in both the children themselves (82.9% [436/526] vs. 69.7% [306/439], χ2 = 23.42, P < 0.001) and their relatives (66.0% [347/526] vs. 57.4% [252/439], χ2 = 7.46, P = 0.006). Children in the ≥ 4- episode group also had higher monthly usage of moisturizers (150 [30, 300] g vs. 60 [6, 200] g) and daily frequency of moisturizer use, greater disease awareness, but more severe fear of medication use (all P < 0.05). The region and the human development index level were both significantly associated with the episode frequency (both P < 0.001), with the highest proportion of children from South China in the ≥ 4- episode group (36.3%, 191/526). Children in the ≥ 4-episode group also had a longer duration of topical glucocorticoid use than those in the < 4-episode group ( Z = -2.21, P = 0.027). External triggers associated with AD episodes mainly included heat exposure (50.36%, 486/965), hot water bathing (40.73%, 393/965), seafood (23.52%, 227/965), and dust mites (33.37%, 322/965) . Conclusion:In children with moderate-to-severe AD in China, factors influencing frequent episodes may include residence in southern or economically developed regions, earlier age at onset, having a personal or family history of allergic diseases, and fear of medication use.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

AIM To study the chemical constituents from dichloromethane fraction of Dalbergia odorifera T.Chen heartwood.METHODS Separation and purification were performed using silica gel,Sephadex LH-20,thin-layer chromatography,and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Twenty-four compounds were isolated and identified as 7,2′-dihydroxy-4′-methoxy-isoflavanol(1),vanillin(2),2,2′-oxybis-(1,4-di-tert-butylbenzene)(3),7-hydroxy-6-methoxyflavone(4),sativan(5),5-hydroxy-4′,7-dimethoxyisoflavone(6),2-hydroxy-4,4′-dimethoxychalcone(7),7,2′,3′,4′-tetramethoxydihydroisoflavone(8),2,4,2′-trihydroxy-4′-methoxybenzil(9),ethyl-3-hydroxy-3-phenyl-2-propenoate(10),6,7-dimethoxy-2,3-dihydr-ochromen-4-one(11),sophorophenolone(12),apocynin(13),ethyl-2,4-dihydroxybenzoate(14),ethylparaben(15),methyl-2,4-dihydroxybenzoate(16),5,7-dihydroxy-6-methoxyflavanone(17),7-hydroxyflavanone(18),mimosifoliol(19),7-hydroxy-4′-methoxyisoflavane(20),virolane(21),5-hydroxy-7-methoxychromone(22),3-hydroxyl-5-methoxy-stilbene(23),2′,4′-dihydroxydihydrochalcone(24).CONCLUSION Compound 8 is new natural product,2-6,15,17-18 are isolated from this plant for the first time,7,9-14,16,20-24 are first isolated from genus Dalbergia.

Objective:To explore the feasibility of shortening the time of long exercise test (LET) from 120 to 60 minutes by analyzing the positive rate within 60 minutes among periodic paralysis (PP) patients who were positive in 120-minute test.Methods:The data of patients undergoing 120-minute LET from January 2015 to October 2021 in Beijing Tiantan Hospital, Capital Medical University were retrospectively analyzed, with 30%, 33%, and 40% as diagnostic cut-off values, respectively. PP patients with positive results within 120 minutes after exercise were enrolled in the study. The positive rate within 30 minutes and 60 minutes after exercise was calculated. The change rates of compound muscle action potential (CMAP) amplitude and the sensitivity and specificity of LET at 30 minutes, 60 minutes, and 120 minutes after exercise were analyzed. The change rate of CMAP amplitude in PP patients who did not show positive results within 60 minutes was further calculated.Results:A total of 254 patients were examined, including 114 PP patients. With 30%, 33%, and 40% as diagnostic cut-off values, the results showed that there were 88, 88, and 82 positive PP patients, respectively. Under each diagnostic cut-off values, the age of positive PP patients was (32±10) years, with a male proportion of 98% (86/88), 98% (86/88), and 99% (81/82), respectively; the positive rate of PP patients within 30 minutes after exercise was 60% (53/88), 58% (51/88), and 41% (34/82), respectively; the positive rate of PP patients within 60 minutes after exercise was 91% (80/88), 86% (76/88), and 83% (68/82), respectively. At the cut-off values of 30%, 33% and 40%, the change rate of CMAP amplitude at 30 minutes [-36% (-49%, -23%), -36% (-49%, -23%), -37% (-51%, -24%)], 60 minutes [-51% (-66%, -40%), -51% (-66%, -40%), -53% (-66%, -42%)] and 120 minutes [-57% (-67%, -45%), -57% (-67%, -45%), -58% (-67%, -46%)] after exercise showed statistically significant difference among 3 time points ( H=57.764, 57.764, 59.616, respectively, all P<0.001); the further comparison between time points showed that there was statistically significant difference in the change rate of CMAP amplitude between 60 minutes ( Z=5.419, 5.419, 5.531, respectively, all P<0.001), 120 minutes ( Z=7.325, 7.325, 7.431, respectively, all P<0.001) and 30 minutes after exercise, but there was no statistically significant difference in the change rate of CMAP amplitude between 120 minutes and 60 minutes after exercise ( Z=1.906, 1.906, 1.899, respectively, all P>0.05); the sensitivity of LET for the diagnosis of PP at 60 minutes after exercise was 70.2% (80/114), 66.7% (76/114) and 59.6% (68/114), and the specificity of LET for the diagnosis of PP was 77.9% (109/140), 84.3% (118/140) and 91.4%(128/140), respectively. When 30%, 33% and 40% were used as the diagnostic cut-off values, and the change rate of CMAP amplitude at 60 minutes after exercise fell below these cut-off values but showed a decline of ≥20%, ≥22% and ≥24%, respectively, the detection time should be extended to 120 minutes. Conclusions:Whether using 30%, 33%, or 40% as diagnostic cut-off values, it is feasible to shorten the LET time from 120 minutes to 60 minutes. The 60-minute LET has good sensitivity and specificity for the diagnosis of PP. It is recommended to extend the detection time to 120 minutes for patients with a ≥20%, ≥22%, or ≥24% decline in CMAP amplitude at 60 minutes after exercise while falling short of corresponding diagnostic cut-off values when 30%, 33%, and 40% are used as diagnostic cut-off values. This method can not only improve the examination efficiency of LET, but also minimize the missed diagnosis as much as possible.