Background/Aims: Dysregulated cholesterol metabolism is a hallmark of hepatocellular carcinoma (HCC) that drives tumor initiation and progression. However, clinical targeting of cholesterol metabolism has yielded limited benefits due to stringent feedback in tumor cells. Identifying a central mediator capable of restoring cholesterol homeostasis within the cell’s intrinsically fine-tuned regulatory framework is urgently needed. Methods: We integrated a proteomic dataset from patients with cholesterol-dysregulated HCC into a global cholesterol metabolic regulatory network to identify potential therapeutic targets for disrupted cholesterol homeostasis. The prognostic significance of the candidate targets was further validated in an independent cohort through immunohistochemistry. Functional and mechanistic studies were conducted in vitro using HCC cell lines and in vivo using mouse models. The pharmacological efficacy of the candidate agent was evaluated in both subcutaneous and orthotopic HCC mouse models. Results: ER lipid raft-associated 1 (ERLIN1), a pivotal regulator of cholesterol metabolism reprogramming, was identified as an independent favorable prognostic indicator in HCC. ERLIN1 constrains HCC progression both in vitro and in vivo by stabilizing the INSIG1–SCAP–SREBP2 axis and maintaining the metabolic balance of intracellular cholesterol. Under hypoxia, impaired factor-inhibiting hypoxia-1-dependent hydroxylation of ASB11 at asparagine residues 90 and 92 enhances ASB11-mediated ERLIN1 degradation. Pharmacological targeting of this axis using zoledronic acid (ZoA) attenuated HCC progression by weakening the ASB11–ERLIN1 interaction and restoring cholesterol homeostasis. Conclusions ERLIN1 represents a druggable metabolic vulnerability in cholesterol-dysregulated HCC. Targeting the ASB11–ERLIN1 axis with the clinically approved ZoA reestablishes cholesterol homeostasis and offers a promising therapeutic strategy to overcome the current limitations of cholesterol-targeted HCC therapies.

Objective:To propose a functional method for locating the shoulder joint center of rotation aimed at rapid estimation of the upper limb reachable domain envelope, thereby informing ergonomic design and task optimization.Methods:In March 2024, shoulder kinematics during gait were recorded from ten adults using a three-dimensional motion-capture system. Assuming the existence of a point near the glenohumeral joint that maintains a fixed spatial relationship to the humerus and the acromion, we estimated both static and dynamic centers of rotation. Localization accuracy was quantified by the standard deviation of distance residuals to upper-arm markers. Upper-limb joint angles and anthropometric parameters were modeled via regression; combined with maximal joint ranges of motion, these were used to infer the reachable domain envelope.Results:The static center of rotation was located approximately twenty-two millimeters medial to the acromial landmark in the coronal plane and thirty-seven millimeters inferior to it. The standard deviation of the residuals for the distances from the dynamic shoulder joint center of rotation to upper-arm markers averaged 1.02 mm, which was 47.42% lower than that of the static center of rotation and 66.56% lower than that of the acromion. Moreover, the trajectory of this dynamic center showed a strong correlation with upper-limb joint angles ( R2>0.7) . Conclusion:The proposed method enables rapid and accurate estimation of the upper limb reachable domain envelope to support ergonomic design and may help reduce the risk of work-related musculoskeletal disorders.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective:To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods:Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian′s onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE).Results:SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics ( Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P<0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.997 7±0.000 7 and 0.997 1±0.001 6, PSNRs of 39.625 0±4.684 4 and 36.272 2±5.566 3, and RMSEs of 0.004 1±0.002 7 and 0.006 3±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.996 8±0.001 0 and 0.996 2±0.001 5, PSNRs of 38.548 7±3.637 4 and 36.007 3±4.437 8, and RMSEs of 0.004 3±0.002 2 and 0.006 1±0.0037, respectively. Conclusions:This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments.

Objective By using intravoxel incoherent motion(IVIM)double exponential model liver multi-b value diffusion-weighted imaging(DWI)scanning technique to analyze the lesion's IVIM parameters before and after interventional treatment of hepatocellular carcinoma(HCC)with conventional transcatheter arterial chemoembolization(TACE)combined with CalliSpheres drug-loaded microspheres(DEB-TACE),based on which to quantitatively evaluate the efficacy of interventional therapy for HCC.Methods A total of 40 HCC patients,who were admitted to the Department of Interventional Therapy of Anqing Municipal Hospital of China from June 2022 to November 2023 to receive DEB-T ACE,were enrolled in this study.Routine MR examination,DWI and IVIM-DWI scan were performed before and at 1,3 and 6 months after treatment,and a total of 40 interest lesions were selected.The ADC value,perfusion fraction(f),pure diffusion coefficient(D),and perfusion-related diffusion coefficient(D*)of each lesion were analyzed.The receiver operating characteristic(ROC)curve was drawn to analyze the prognosis assessment value of IVIM-DWI parameters.Results After DEB-TACE treatment,the ADC value and D value were increased,and the f value was decreased when compared with their preoperative values,the differences were statistically significant(all P＜0.01).The ADC value and D value in the patients of effective group were remarkably higher than those in the patients of ineffective group,the differences were statistically significant(both P＜0.01);the f value in the patients of effective group was slightly lower than that in the patients of ineffective group,and the difference was statistically significant(P＜0.05).The areas under ROC curve of ADC value,D value and f value for evaluating efficacy were 0.762,0.877,and 0.708 respectively.The area under the curve for the joint assessment of the three parameters was 0.928,with the highest efficacy.Conclusion IVIM-DWI can quantitatively determine the microperfusion and activity of HCC lesions before and after interventional DEB-TACE treatment,and it can also evaluate the curative efficacy of interventional therapy for HCC as well as the outcome of HCC lesions.

Objective:To propose a functional method for locating the shoulder joint center of rotation aimed at rapid estimation of the upper limb reachable domain envelope, thereby informing ergonomic design and task optimization.Methods:In March 2024, shoulder kinematics during gait were recorded from ten adults using a three-dimensional motion-capture system. Assuming the existence of a point near the glenohumeral joint that maintains a fixed spatial relationship to the humerus and the acromion, we estimated both static and dynamic centers of rotation. Localization accuracy was quantified by the standard deviation of distance residuals to upper-arm markers. Upper-limb joint angles and anthropometric parameters were modeled via regression; combined with maximal joint ranges of motion, these were used to infer the reachable domain envelope.Results:The static center of rotation was located approximately twenty-two millimeters medial to the acromial landmark in the coronal plane and thirty-seven millimeters inferior to it. The standard deviation of the residuals for the distances from the dynamic shoulder joint center of rotation to upper-arm markers averaged 1.02 mm, which was 47.42% lower than that of the static center of rotation and 66.56% lower than that of the acromion. Moreover, the trajectory of this dynamic center showed a strong correlation with upper-limb joint angles ( R2>0.7) . Conclusion:The proposed method enables rapid and accurate estimation of the upper limb reachable domain envelope to support ergonomic design and may help reduce the risk of work-related musculoskeletal disorders.

Objective:To investigate the impact of the number of examined lymph nodes (ELN) on survival outcomes in gastric cancer patients with postoperative pathological stage pN3b.Methods:This retrospective cohort study included 279 pN3b gastric cancer patients who underwent D2 gastrectomy at Nanfang Hospital, Southern Medical University (September 2008 to April 2023), with 35 patients receiving combination chemotherapy and anti-PD-1 therapy (immunotherapy group) and 244 receiving adjuvant chemotherapy alone (nonimmunotherapy group). Additionally, 422 patients with pN3b from the SEER database (2005 to 2020) were collected as an external validation cohort to determine the optimal cutoff value for the number of lymph nodes examined in the nonimmunotherapy group. The primary endpoints were overall survival (OS) and recurrence-free survival (RFS) in the nonimmunotherapy group of the Nanfang Hospital cohort, stratified by whether the number of examined lymph nodes was above or below the ELN optimal cutoff value. These findings were subsequently validated in the SEER cohort.Results:The optimal ELN cutoff value (34 nodes) was determined using X-tile software and by constructing an ELN-HR fitting model with inflection point identification. In the nonimmunotherapy group, patients with ELN >34 exhibited significantly prolonged survival compared to ELN ≤34 (median OS: 25.0 (95%CI:20.5-29.5) to 17.0 (95%CI:12.7-21.3) months, P=0.004; median RFS: 19.0 (95%CI:15.6-22.4) to 13.0 (95%CI:9.5-16.5) months, P=0.048). Multivariate Cox analysis also showed ELN >34 to be an independent protective factor for both OS (HR=0.576, 95%CI: 0.397-0.836) and RFS (HR=0.701, 95%CI: 0.492-0.998). In the SEER cohort, ELN >34 was associated with a 5-month OS extension (19 to 14 months, P=0.065), with multivariate analysis supporting its independent prognostic significance (HR=0.729, 95%CI: 0.580-0.915, P=0.006). Notably, in the immunotherapy group, patients with ELN >34 ( n=30) achieved a median OS of 41 months, but the median OS had not been reached in the ELN ≤34 group ( n=5) (1 death at 48 months). Conclusion:Higher ELN (>34) correlates with improved survival in nonimmunotherapy-treated pN3b gastric cancer patients. However, in pN3b gastric cancer patients treated with immunotherapy, the optimal ELN threshold requires further exploration to determine.

Objective To develop a three-compartment kinetic fatigue model for the isometric muscle endurance of the hip,knee,and ankle joints at 50％IPT(isometric peak torque),so as to provide a theoretical basis for simulation-based assessments and load evaluations in biomechanics and sports science.Methods The IPT of the hip,knee,and ankle joints was measured in 40 male university students.Isometric endurance tests were then performed on all three joints at 50％IPT until exhaustion.Electromyography data and endurance time(ET)of major lower limb muscles were collected concurrently.The differences between ETs predicted by models based on previously recommended F and R parameters and actual ETs were analyzed.Subsequently,experimental ETs were used in a grid search to optimize Fand R parameters,allowing for the development of an accurate three-compartment kinetic model.Results The ET of the hip and ankle joints was significantly longer than that of the knee joint(P＜0.001).Models using previously recommended Fand R parameters overestimated ET,with significantly higher predicted values than experimentally measured ET(P＜0.001),as well as elevated root mean squared error(RMSE)and mean relatvie error(MRE)values.The grid search successfully identified Fand R parameters for the three-compartment model in isometric endurance tests of lower limb joints,with no statistical difference between model-predicted ET and experimental ET(P＞0.05).Conclusions The developed model in this study can serve as an indirect measurement tool for evaluating load in similar activities.

Objective:To assess the prognostic impact of the thoracic aorta and aortic valve calcification volume (TAC, AVC) score based on CT measurementsin patients undergoing transcatheter aortic valve implantation (TAVI).Methods:We retrospectively analyzed 102 patients who underwent TAVI for severe aortic stenosis from March 2018 to April 2022 at Fuwai Central China Cardiovascular Hospital. The patients were stratified into low TAC and AVC group (TAC low, AVC low) and high TAC and AVC group (TAC high, AVC high) based on median TAC and AVC. The independent risk factors affecting the prognosis of TAVI patients were analyzed using univariate and multivariate Cox proportional risk regression, and the independent risk factors affecting the prognosis of TAVI were analyzed by survival curve. Results:A total of 102 patients were included with a median follow-up of 695 (602, 923) days, during which 9 (8.8%) all-cause deaths and 33 (32.4%) composite end-point events occurred. Univariate Cox risk regression analysis found that TAC was a risk factor for all-cause mortality events in TAVI patients ( P=0.039), TAC and AVC were risk factors for composite endpoint events in TAVI patients ( P=0.047, 0.035).TAC was an independent predictor of all-cause mortality after TAVI in multivariate analysis ( HR=8.971, 95% CI 1.121-71.790, P=0.039), and TAC and AVC were independent risk factors for composite endpoint events after TAVI ( HR=2.243, 95% CI 1.099-4.578, P=0.026; HR=2.346, 95% CI 1.146-4.804, P=0.020). Kaplan-Meier survival curves showed that high TAC and AVC scores increased the risk of end-point events ( P<0.05). Conclusion:CT-quantified TAC and AVC volume scores are independent prognostic markers in TAVI patients, with greater calcification burden portending poorer clinical outcomes.