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.

Environmental pollution is a significant public health challenge worldwide, and investigating the causal relationship between environmental exposure and population health outcomes is a key objective of environmental epidemiology research. In recent years, the complexity of environmental exposures has increasingly come to the forefront, making it challenging for observational studies that dominate environmental epidemiology to accurately estimate causal effects. Causal inference methods are particularly advantageous in controlling for confounding factors, thus holding great potential in environmental epidemiology research. Researchers can use appropriate causal inference methods to simulate the process of randomization, providing strong support for revealing the causal relationship between environmental exposure and health outcomes. However, there is a lack of reviews on the application of causal inference methods in environmental epidemiology studies in China. Therefore, this study introduced the basic principles of common causal inference statistical methods in environmental epidemiology, summarized the applicable conditions, advantages and disadvantages of various methods, and provided R software implementation codes for these methods, aiming to offer guidance for optimizing research design and practicing causal inference statistical methods.

Objective: To explore the application method and clinical efficacy of endoscopic and computerized navigation technology in maxillofacial foreign body removal surgery, and to provide a reference for the clinical application of this technology. Methods: This study, which was approved by the Medical Ethics Committee of the hospital, retrospectively analyzed the data of five patients with maxillofacial foreign bodies who were admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2018 to December 2024. All patients underwent preoperative CT scanning. Intraoperatively, endoscopic and computer navigation techniques were used in combination or separately according to the location, size, and adjacency of the foreign body to important neurovascular vessels. The foreign body was precisely localized by endoscopic magnification and direct visualization, and the optimal surgical path was designed and verified under the real-time guidance of computerized navigation to accurately remove the foreign body. The type of foreign body, location, length and diameter, duration of surgery, length of incision, success rate of foreign body removal, postoperative complications, and follow-up were recorded and analyzed. Results: The foreign body was successfully removed in all five patients with a success rate of 100%. The intraoperative computerized navigation system was accurate in positioning, and the alignment stability was not significantly affected by mandibular movement; the endoscope provided good illumination and exposure of the operative field. All surgical incisions were small, and no serious complications, such as foreign body residue, important neurovascular injury, or infection, occurred after surgery. One month after the operation, the patients were followed up and recovered well. Conclusion The combination of endoscopy and computer navigation or separately assisted technology can provide a clear field and real-time positioning for maxillofacial foreign body removal, effectively avoiding important anatomical structures, thus realizing safe and complete foreign body removal with minimized trauma. This assistive technology significantly improves the accuracy and safety of the operation and has clinical promotion value.

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.

Fungal keratitis represents a significant cause of blindness, with current therapeutic approaches yielding limited success. The disease's onset and progression are primarily driven by fungal virulence factors and the host's immune response. The innate immune system is the first to respond, with neutrophils playing a pivotal role in the antifungal defense. Although neutrophils are critical for pathogen clearance, their excessive or abnormal activation can lead to tissue damage, exacerbating the disease. Thus, elucidating the mechanisms underlying neutrophil activity in fungal keratitis is crucial for refining treatment strategies. This article aims to systematically review the principal antimicrobial mechanisms employed by neutrophils, including phagocytosis, degranulation, and the formation of neutrophil extracellular traps(NETs). Furthermore, it explores the crosstalk between neutrophils and macrophages, alongside their collective impact and underlying mechanisms in the context of fungal keratitis. Exploration of the mechanisms of fungal keratitis facilitates precise intervention and enhances the efficacy of treatment.

Background: Diabetic macrovascular and microvascular complications often coexist and may share similar risk factors and pathological pathways. We aimed to investigate whether 10-year atherosclerotic cardiovascular disease (ASCVD) risk, which is commonly assessed in diabetes management, can predict incident diabetic nephropathy (DN) and retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods: This prospective cohort study enrolled 2,891 patients with clinically diagnosed T2DM who were free of ASCVD, nephropathy, or retinopathy at baseline in the Guangzhou (2017–2022) and Shaoguan (2019–2021) Diabetic Eye Study in southern China. The 10-year ASCVD risk was calculated by the Prediction for ASCVD Risk in China (China-PAR) equations. Multivariable- adjusted Cox proportional hazard models were developed to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). The area under the receiver operating characteristic curve (AUC) was used to evaluate predictive capability. Results: During follow-up, a total of 171 cases of DN and 532 cases of DR were documented. Each 1% increment in 10-year ASCVD risk was associated with increased risk of DN (pooled HR, 1.122; 95% CI, 1.094 to 1.150) but not DR (pooled HR, 0.996; 95% CI, 0.979 to 1.013). The model demonstrated acceptable performance in predicting new-onset DN (pooled AUC, 0.670; 95% CI, 0.628 to 0.715). These results were consistent across cohorts and subgroups, with the association appearing to be more pronounced in women. Conclusion Ten-year ASCVD risk predicts incident DN but not DR in our study population with T2DM. Regular monitoring of ASCVD risk in routine diabetes practice may add to the ability to enhance population-based prevention for both macrovascular and microvascular diseases, particularly among women.

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Background Exposure to air pollutants increases the risk of diseases in multiple systems, including respiratory and cardiovascular systems, yet its association with neurological diseases remains unclear. Objective To quantitatively evaluate the association between short-term exposure to air pollutants and outpatient and emergency visits for neurological diseases, identify potential susceptible populations, and quantify associated disease burden. Methods Daily 24-hour average concentrations of fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO), daily maximum 8-hour average concentration of ozone (O₃), daily meteorological data (24-hour average temperature, 24-hour average relative humidity), and data on daily outpatient and emergency department visits for neurological diseases from two hospitals in Conghua District, Guangzhou, China, were collected from 2015 to 2022. A time-stratified case-crossover design was adopted, and a conditional Poisson regression model was constructed to analyze the association between air pollution exposure and neurological disease visits. Two-pollutant models and sensitivity analysis were used to validate model stability. Stratified analyses by season (cold season: from November to March; warm season: from April to October), sex (male, female), and age (≤45 years, 46–60 years, ≥61 years) were performed to identify vulnerable group. Additionally, the number and proportion of neurological disease visits attributable to short-term air pollutant exposure were calculated. Results A total of 72 673 outpatient and emergency department visits for neurological diseases were included during the study period. Most of the patients were middle-aged and elderly individuals (69.89% were over 45 years old) and females (60.25%). The results of single-pollutant models showed that for each interquartile range (IQR) increase in exposure to PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃, the risk of outpatient and emergency department visits for neurological diseases increased by 7.54% (95%CI: 4.69%, 10.46%), 6.66% (95%CI: 3.92%, 9.46%), 16.72% (95%CI: 10.58%, 23.19%), 8.12% (95%CI: 4.82%, 11.53%), 5.60% (95%CI: 2.34%, 8.97%), and 6.11% (95%CI: 2.91%, 9.40%), respectively. The results of the two-pollutant model showed that the association between PM_2.5 and SO₂ exposure and outpatient and emergency department visits for neurological diseases were relatively stable. The stratified analyses showed that the effect of SO₂ was stronger in the cold season. It was estimated that 8.32% (95%CI: 5.55%, 10.96%) and 6.65% (95%CI: 4.27%, 8.96%) of the outpatient and emergency department visits were attributable to short-term exposure to SO₂ and PM_2.5, respectively. Conclusion Exposure to PM_2.5 and SO₂ is associated with increased risks of outpatient and emergency visits for neurological diseases. SO₂ shows stronger effects during the cold season, and exposure to air pollution contributes to up to 8.32% of neurological disease visits.

Background: Diabetic macrovascular and microvascular complications often coexist and may share similar risk factors and pathological pathways. We aimed to investigate whether 10-year atherosclerotic cardiovascular disease (ASCVD) risk, which is commonly assessed in diabetes management, can predict incident diabetic nephropathy (DN) and retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods: This prospective cohort study enrolled 2,891 patients with clinically diagnosed T2DM who were free of ASCVD, nephropathy, or retinopathy at baseline in the Guangzhou (2017–2022) and Shaoguan (2019–2021) Diabetic Eye Study in southern China. The 10-year ASCVD risk was calculated by the Prediction for ASCVD Risk in China (China-PAR) equations. Multivariable- adjusted Cox proportional hazard models were developed to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). The area under the receiver operating characteristic curve (AUC) was used to evaluate predictive capability. Results: During follow-up, a total of 171 cases of DN and 532 cases of DR were documented. Each 1% increment in 10-year ASCVD risk was associated with increased risk of DN (pooled HR, 1.122; 95% CI, 1.094 to 1.150) but not DR (pooled HR, 0.996; 95% CI, 0.979 to 1.013). The model demonstrated acceptable performance in predicting new-onset DN (pooled AUC, 0.670; 95% CI, 0.628 to 0.715). These results were consistent across cohorts and subgroups, with the association appearing to be more pronounced in women. Conclusion Ten-year ASCVD risk predicts incident DN but not DR in our study population with T2DM. Regular monitoring of ASCVD risk in routine diabetes practice may add to the ability to enhance population-based prevention for both macrovascular and microvascular diseases, particularly among women.