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.

Methods: Fifty patients who underwent UBE-TLIF and 50 patients who underwent W-TLIF, each with >2 years of follow-up, were retrospectively analyzed. Outcomes included operative parameters, time to postoperative mobilization, paravertebral muscle atrophy and fat infiltration rates, clinical scores (Visual Analog Scale [VAS], Oswestry Disability Index [ODI], Japanese Orthopaedic Association [JOA]), modified Macnab criteria, fusion rates, and complications. Results: Compared with W-TLIF, the UBE-TLIF group had significantly less intraoperative blood loss, shorter operative times, and lower postoperative drainage volumes (p <0.05). The UBE-TLIF group showed faster postoperative recovery and shorter hospital stays. At 6 months, 1 year, and 2 years, W-TLIF patients had higher multifidus and erector spinae atrophy, and greater paravertebral muscle fat infiltration (p <0.05). The UBE-TLIF group also had lower VAS and ODI scores at 1 year and 2 years (p <0.05) and fewer surgical complications (6% vs. 10%). Fusion rates (94% vs. 92%) and modified Macnab outcomes (88% vs. 86%) were comparable (p >0.05). Conclusions UBE-TLIF is associated with reduced intraoperative trauma, quicker recovery, and fewer complications. In the long-term, it better preserves paravertebral muscle integrity and provides superior pain and functional outcomes.

Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

OBJECTIVE: This study aimed to reexplore minimum iodine excretion and to build a dietary iodine recommendation for Chinese adults using the obligatory iodine loss hypothesis. METHODS: Data from 171 Chinese adults (19-21 years old) were collected and analyzed based on three balance studies in Shenzhen, Yinchuan, and Changzhi. The single exponential equation was accordingly used to simulate the trajectory of 24 h urinary iodine excretion as the low iodine experimental diets offered (iodine intake: 11-26 μg/day) and to further deduce the dietary reference intakes (DRIs) for iodine, including estimated average requirement (EAR) and recommended nutrient intake (RNI). RESULTS: The minimum iodine excretion was estimated as 57, 58, and 51 μg/day in three balance studies, respectively. Moreover, it was further suggested as 57, 58, and 51 μg/day for iodine EAR, and 80, 81, and 71 μg/day for iodine RNI or expressed as 1.42, 1.41, and 1.20 μg/(day·kg) of body weight. CONCLUSION The iodine DRIs for Chinese adults were established based on the obligatory iodine loss hypothesis, which provides scientific support for the amendment of nutrient requirements.
Humans ; Iodine/administration & dosage* ; Male ; Female ; China ; Young Adult ; Diet ; Adult ; Nutritional Requirements ; East Asian People

This paper summarizes Professor LIU Fengbin's clinical experience in treating autoimmune liver disease （AILD） using the method of nourishing yin and removing stasis based on stage differentiation. He believes that the pathogenesis of AILD generally involves both deficiency in essence and excess in manifestation， with essence deficiency often presenting as liver and kidney yin deficiency， which may progress to spleen deficiency and yang deficiency over time. The excess manifestation commonly includes qi stagnation， blood stasis， damp-heat， and phlegm toxicity. Clinically， he advocates for the treatment principle of nourishing yin and removing stasis. On the foundation of nourishing liver and kidney yin， different pathological factors causing stasis are eliminated according to their nature. Treatment is also tailored to different stages of AILD. In the early and asymptomatic stages， liver qi stagnation and spleen deficiency are prominent， warranting a therapeutic approach of soothing the liver， regulating qi and strengthening the spleen. The modified Chaishao Qizhi Decoction （柴芍气滞汤） is used. During the symptomatic stage， pathogenic factors become more pronounced， often accompanied by a significant deficiency of vital qi， with damp-heat， water retention， and phlegm toxicity as key pathological features. The treatment should focus on strengthening the spleen and dispelling dampness， using modified Sijunzi Decoction （四君子汤） combined with Yinchen Wuling Powder （茵陈五苓散）. In the liver function decompensation stage， vital qi is severely deficient while pathogenic factors persist， with damp-heat， phlegm toxicity， and blood stasis obstructing the liver collaterals. Treatment should focus on nourishing blood， softening the liver， strengthening the spleen， and resolving stasis， using the modified Ruangan Yangxue Decoction （软肝养血汤）. Throughout the treatment process， emphasis is placed on tonifying the liver and kidneys while protecting yin fluids.

Chinese medicine therapy for removing gallstones is one of the methods for the treatment of cholelithiasis.In the view of Professor Liu Fengbin,attacking of external pathogens,improper diet and emotional disorders contribute to the main causes of cholelithiasis,and the pathogenesis of cholelithiasis is due to qi stagnation of both liver and gallbladder,and internal obstruction of damp-heat.The occurrence of cholelithiasis is closely related to deficiency of spleen and stomach,and is correlated with the pathological factors of turbid phlegm and blood stasis.For the Chinese medicine treatment of cholelithiasis,Professor Liu follows the principle of"treatment in accordance with three categories of etiological factors"(i.e,seasons,environment and body constitution).He advocates the integration of traditional Chinese medicine and western medicine,and is good at utilizing Lingnan herbs and distinctive herbs that can dissolve stones and remove stones.The treatment for cholelithiasis is mainly through the therapies of soothing liver and alleviating depression,clearing heat and removing dampness,and normalizing gallbladder function to remove stones,and is also supplemented by the therapies of invigorating spleen and replenishing qi,regulating qi to resolve phlegm,and activiting qi movement and blood circulation.Modified Da Chaihu Decoction plus Sijin Decoction is often used as a basic formula for treating cholelithiasis,which is mainly composed of Desmodii Styracifolii Herba,Galli Gigerii Endothelium Corneum,Bupleuri Radix,Curcumae Radix,Scutellariae Radix,Aucklandiae Radix,Aurantii Fructus Immaturus stir-fried with bran,Paeoniae Radix Rubra,Linderae Radix,and Rhei Radix et Rhizoma.

Objective:To investigate the self-perceived cognitive decline status in the community population,and to explore the association between different dietary habits and self-perceived cognitive decline.Methods:A cross-sectional study was carried out in 11 879 community residents in the three regions of Weifang,Jining,and Zoucheng in Shandong Province.The Ascertain Dementia-8 and dietary habits information questionnaire were used to assess self-perceived cognitive decline and dietary habits,and their association were analyzed using single factor and multivariate logistic regression.Results:The detection rate of self-perceived cognitive decline was 21.4％.Lo-gistic regression showed that smoking in the past was positively associated with self-perceived cognitive decline(OR=1.40,95％CI:1.14-1.73).However,intake of fruits(often,OR=0.70,95％CI:0.52-0.94;everyday,OR=0.60,95％CI:0.44-0.81),nuts(daily,OR=0.62,95％CI:0.44-0.88),mushrooms(often,OR=0.74,95％CI:0.57-0.92)and high tryptophan foods(sometimes,OR=0.79,95％CI:0.68-0.91;everyday,OR=0.54,95％CI:0.34-0.87)were negatively associated with self-perceived cognitive decline.Conclusion:Smoking history might be a risk factor for self-perceived cognitive decline,and high frequency intake of fruits,nuts,mush-rooms,and high tryptophan foods might protective factors for it.

Objective:To analyze the trends and research hotspots in English publications from China′s anesthesiology discipline in international journals from 2000 to 2023 using the bibliometric analysis.Methods:Based on the results retrieved from the Science Citation Index Expanded database covering English publications from China′s anesthesiology discipline in international journals from January 2000 to December 2023, CiteSpace 6.3.R1 and VOSviewer 1.6.20.0 were employed to analyze the annual publication counts, institutions, authors, co-cited references, and keywords, etc. with knowledge graph generated.Results:A total of 53, 039 articles published in English from January 2000 to December 2023 were included. The annual number of articles published in English showed an overall increasing trend year by year. Basic research articles exhibited a phase of slow growth during 2000-2007, entered a period of accelerated growth from 2008, and demonstrated a slightly decreased trend by 2023. The number of clinical research articles increased rapidly since 2008, was equivalent to the basic research articles by 2020, surpassed basic research articles by 2023 and became the mainstream research direction of anesthesiology discipline. Among clinical research publications, case series studies predominated from 2000 to 2022, and cohort studies exceeded case series for the first time in 2023, ranking second only to the randomized controlled trials. Research hotspots in anesthesiology discipline predominantly focused on the relationship of neuropsychiatry to anesthesia, pain management, cardiovascular surgery and organ injury, COVID-19 pandemic and patient safety. In terms of journals, Medicine was the journal with the largest number of publications, with 1, 345 publications in 2023 and an impact factor of 1.3. American journals dominated the top 15 highly productive journals, and Anesthesiology had the highest h-index (60) and g-index (98) with an impact factor of 9.1. In terms of domestic high-yield institutions, Shanghai Jiao Tong University topped the list, publishing about 1, 200 papers between 2000 and 2023, followed by Sichuan University (about 850) and Capital Medical University (about 720). Conclusions:China′s anesthesiology research has demonstrated a meteoric rise in both the number of papers published and cited frequency, reflecting ascending scientific academic levels and innovation capacity in the the international arena. While acknowledging this quantitative expansion, recognition is warranted that enhancing research quality and international impact remains imperative to narrow the gap with advanced nations and gradually achieve advancement.

Cardiovascular disease has high disability and mortality rates,posing a serious threat to human health.Carotid atherosclerosis is closely related to cardiovascular events.Being non-invasive,high resolution(HR)-MRI has high resolution and unique advantages for evaluating characteristics of carotid plaques.The research progresses in correlations of HR-MRI characteristics of carotid atherosclerotic plaque and cardiovascular events were reviewed in this article.