Myosteatosis is one of the common complications in patients with end-stage liver disease, which is significantly associated with poor outcomes after liver transplantation. Currently, diagnostic criteria of myosteatosis have not been established, and CT is the most commonly used for diagnosis. The pathogenesis of myosteatosis is multifactorial, and the pathophysiological mechanisms linking it to end-stage liver disease are not fully understood. An increasing number of scholars have recognized that the severity of myosteatosis is closely related to its clinical consequences, but there are no effective treatment options available. This article reviews the pathophysiological mechanisms and diagnostic methods of myosteatosis, and its impact on the prognosis of liver transplant recipients, and discusses current treatment strategies to provide references for the perioperative management of liver transplant recipients.

Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

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/Aims: Belching can occur either independently or concurrently with reflux symptoms. This study aims to explore the relationship between belching and reflux and explore the underlying mechanisms involved. Methods: Firstly, patients with isolated belching symptoms or concurrent belching and reflux symptoms were retrospectively enrolled. High-resolution esophageal impedance manometry (HRIM) and 24-hour pH impedance monitoring were used to assess belching types and temporal relationship between belching and reflux. Secondly, subjects with belching disorder were prospectively recruited. Postprandial HRIM monitoring was used to clarify the relationship and underlying mechanisms between belching and reflux. Results: In the initial phase, 139 subjects were enrolled. Regardless of patients' symptom spectrum (with or without gastroesophageal reflux symptoms), the proportion of preceding-reflux belching was significantly higher in those with objective evidence of gastroesophageal reflux disease, compared to those without (both P < 0.05), which indicated that belching could trigger reflux events and led to more severe reflux burden. To clarify the potential mechanisms involved, 44 belching subjects were prospectively enrolled in the second phase of this study. HRIM analysis demonstrated that maximum intragastric pressure, gastric–lower esophageal sphincter pressure gradient and gastric–esophageal pressure gradient were significantly higher in preceding-reflux belching than those in independent belching. Conclusions This study demonstrated that belching might trigger reflux by increasing intragastric pressure, elevating the gastric-sphincter pressure gradient, which enables gastric contents to pass through the esophagogastric junction.These findings have crucial clinical significance, suggesting that reducing the occurrence of belching might be a potential treatment for refractory reflux.

Background: The hypotension prediction index (HPI) is an algorithm designed to predict hypotension. Some studies have reported that HPI-guided hemodynamic management strategies decrease intraoperative hypotension and complications; however, the effect of HPI on reducing perioperative complications is controversial. This meta-analysis aimed to assess the efficacy of the HPI in reducing major complications and intraoperative hypotension. Methods: We conducted this meta-analysis according to the PRISMA statement and Cochrane Handbook guidelines. A comprehensive literature review was conducted to identify studies focusing on the efficacy of HPI-guided management in reducing intraoperative hypotension and postoperative complications. The PubMed, Embase, Scopus, and Web of Science databases were searched, and the resulting data were combined to calculate the pooled mean differences or risk ratios (RRs) with 95% CIs of both randomized controlled trials (RCTs) and retrospective studies, as appropriate. Heterogeneity and potential publication bias were also assessed. Results: Nineteen articles (12 RCTs and 7 retrospective studies) with 2570 recruited patients were included in this meta-analysis. The critical evaluation of the study quality revealed a low risk of bias in the included RCTs. Among the non-randomized trials, one was rated 7, two were rated 8, and the remaining four were rated 9 on the Newcastle-Ottawa Scale, indicating high quality and a low risk of bias. HPI-guided management significantly reduced intraoperative hypotension and associated major complications (RR = 0.79, 95% CI [0.69–0.90], I2 = 0, P < 0.001). Blood loss and length of hospital stay were comparable between the groups. Conclusions HPI-guided management significantly reduced intraoperative hypotension and major complications.

Hepatocellular carcinoma (HCC) imposes a major health and economic burden worldwide, with disproportionate effects in low- and middle-income countries (LMICs). Surveillance in high-risk populations, typically using semiannual ultrasound and alpha-fetoprotein testing, has been shown to be cost-effective by enabling earlier detection and improving survival. Yet, its overall value is reduced by poor adherence and the limited sensitivity of ultrasound, particularly in patients with metabolic-associated steatotic liver disease. Emerging approaches—including abbreviated magnetic resonance imaging, multi-biomarker models (e.g., gender, age, AFP, AFP-L3, and DCP), and liquid biopsy assays such as methylated DNA markers—demonstrate greater diagnostic accuracy and potential economic advantages compared with conventional methods. Integration of artificial intelligence into imaging may further enhance efficiency and reduce downstream costs. Moving toward precision surveillance, guided by individualized risk stratification that incorporates etiology, fibrosis stage, and molecular profiles, can optimize allocation of resources and maximize cost-effectiveness at the population level. Interventions to improve adherence, including mailed outreach and behavioral economic incentives, have shown both clinical benefit and cost savings, underscoring the role of implementation science. Because socioeconomic disparities influence both access and outcomes, economic models must explicitly address equity to achieve sustainable impact. Future research should prioritize prospective trials that evaluate not only clinical performance but also the real-world cost-effectiveness of novel technologies and stratified surveillance strategies. For LMICs, adapting proven models into affordable, context-appropriate programs is essential. By combining prevention, precision risk assessment, innovative technologies, and equitable implementation, HCC surveillance can deliver both clinical and economic value, reducing the global burden of disease.

Objective To analyze the medical resource consumption of healthcare-associated infection(HAI)in patients in different groups of disease diagnosis-related grouping(DRG)based on the DRG payment model,provide reference for optimizing prevention and control of HAI as well as resource management.Methods Medical records and DRG-related indicators of discharged patients from a municipal hospital in Sichuan Province from January 1 to December 31,2024 were analyzed retrospectively.Medical resource consumption of patients in HAI group and non-HAI group was compared.Differences in average length of hospital stay and average expense per hospitalization be-tween two groups of patients were analyzed using stratified analysis.Results In 2024,HAI incidence of discharged patients in DRG management in this hospital was 1.57％.There were statistically significant differences in age,gender,admission and discharge ways between the HAI group and the non-HAI group(all P＜0.05).The main HAI sites were lower respiratory tract,surgical site,urinary tract,and blood.The time consumption index(1.63 vs 0.85),average length of hospital stay(21.00 vs 5.00 days),expense consumption index(1.53 vs 0.92),ave-rage expense per hospitalization(44 700 vs 7 300),and multiple expense in HAI group were all higher than those in non-HAI group(all P＜0.05).The consumption of medical resources for bloodstream infection was relatively higher.Patients with HAI were mostly concentrated in the groups related to acute leukemia with major complications or co-morbidities(MCC),intracranial or craniotomy surgery with MCC,tracheotomy with mechanical ventilation for 96 hours,as well as gastric,esophageal,and duodenal surgery.The average length of hospital stay and average ex-pense per hospitalization of patients in HAI group were both higher than those in the non-HAI group,differences were statistically significant(both P＜0.05).Conclusion HAI significantly increase the consumption of medical resources.Based on DRG analysis,key disease groups for infection prevention and control can be further identified,and the consumption of medical resources can be more accurately and precisely evaluated,thereby optimizing the allocation of medical resources and improving hospital operational efficiency.

Objective To compare the accuracy differences of three-dimensional reconstruction of zygomatic-maxillary complex using cone beam computed tomography(CBCT)and multi-slice spiral computed tomography(MSCT)in the parameters related to zygomatic implantation.Methods Five adult skull specimens(10 zygomatic-maxillary complexes in total)were selected.According to the clinical application characteristics of zygomatic implantation,the parameter location for each skull specimen was conducted,and then CBCT and MSCT were used for scanning and three-dimensional reconstruction.Four parameters were measured on both skull specimens and reconstructed models:the thickness of the maxillary zygomatic process at the intersection of the zygomatic implant path and zygomaticomaxil-lary suture(LB-B1),the thickness of the zygomatic bone at the intersection of the zygomatic implant path and the midpoint of the zygomatic bone surface(LC-C1),the width of the alveolar ridge between the second premolar and the first molar(LAR),and the buccolingual width of the first molar(LM).The specimen group was measured by electronic vernier caliper,and the CBCT group and MSCT group were measured based on CBCT and MSCT.The absolute and relative errors of these four parameters were calculated,and the accuracy of the CBCT and MSCT three-dimensional reconstruction models were analyzed.Results All parameters demonstrated excellent measurement reliability and repeatability,with intra-class correlation coefficient(ICC)≥0.90.The measurement results of each parameter in the CBCT group and the MSCT group showed statistically significant differences compared with those in the specimen group(P<0.05).The measurement result of LM in the CBCT group was smaller than that in the MSCT group,and the difference was statistically significant(P<0.05).However,the measurement results of LB-B1,LC-C1 and LAR in the CBCT group showed no statistically significant difference compared with those in the MSCT group(P>0.05).The mean absolute error and relative error of the LM measurement results to the specimen group in the CBCT group were both smaller than those in the MSCT group,and the differences were statistically significant(P<0.05).There was no statistically significant difference in the mean absolute error or relative error of the LB-B1,LC-C1 and LAR measurement results to the specimen group between the CBCT group and the MSCT group(P>0.05).Conclusion CBCT and MSCT demonstrat comparable accuracy in three-dimensional reconstruction of the zygomatic-maxillary complex.However,CBCT exhibits superior accuracy for fine structures such as dental tissues,which is recommended as the primary choice for imaging data acquisition in zygomatic implantation.