Objective: To identify the latent class characteristics of health literacy and related factors among junior high school students, so as to provide evidence for developing precise and systematic health literacy promotion strategies. Methods: In November 2024, a two stage random cluster sampling method was used to conduct a questionnaire survey among 8 933 junior high school students in Zhongshan. Health literacy was assessed across six dimensions: health behavior and lifestyle, disease prevention and control, mental health, growth development and puberty health, safety emergency and risk avoidance, and medical knowledge and appropriate healthcare utilization. Latent profile analysis was used to identify distinct health literacy classes, and multinomial Logistic regression was applied to analyze the related factors. Results: Three latent classes of health literacy among junior high school students were identified: the well balanced type(71.7%，6 406), the medical knowledge deficit type(22.3%，1 992), and the overall low literacy type(6.0%，537). Logistic regression analysis showed that girls had lower risks of belonging to the medical knowledge deficit type( OR =0.53, 95% CI =0.48-0.59) and the overall low literacy type( OR =0.27,95% CI =0.22-0.33) compared with boys(both P <0.05). Students in rural schools had the highest risks of belonging to these two profiles above [ OR (95% CI ) =1.89 (1.61-2.21), 3.18 (2.50-4.06),both P <0.05]. Junior high school students having ≥2 siblings were positively associated with belonging to these two profiles, with risks 1.60 (95% CI = 1.35-1.89) and 2.25 times (95% CI =1.66-3.05) higher than those of only children (both P <0.05). Junior high school students with parental education of bachelor s degree or above were associated with lower risk of belonging to the medical knowledge deficit type (father: OR =0.63, 95% CI =0.47-0.84; mother: OR =0.68, 95% CI = 0.52 -0.90,both P <0.05). Junior high school students with receiving health education courses ≥3 times per month were associated with lower risks of belonging to both the medical knowledge deficit type and overall low literacy type ( OR =0.51, 95% CI =0.43- 0.60 ; OR =0.33, 95% CI =0.25-0.42, both P <0.05). Conclusions Three latent classes of health literacy exist among junior high school students in Zhongshan. Targeted interventions should be implemented based on profile characteristics, with an emphasis on strengthening medical knowledge education and providing comprehensive support for vulnerable groups.

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.

Oral and maxillofacial tumor surgery is characterized by complex anatomical structures, extensive surgical trauma, and high demands for postoperative functional reconstruction. Perioperative complications and functional impairments significantly affect patients’ recovery process, quality of life, and long-term prognosis. Enhanced recovery after surgery (ERAS), grounded in evidence-based medicine, optimizes perioperative management through multidisciplinary collaboration and demonstrates substantial application potential in oral and maxillofacial surgery. Multiple prospective studies have confirmed that standardized airway management, goal-directed fluid and temperature management, and specialized ward-based care can shorten hospital stays, facilitate early enteral nutrition and ambulation, and reduce intensive care unit admission rates and postoperative complications. However, existing ERAS studies mainly focus on traditional clinical outcomes, with insufficient attention paid to functional recovery specific to patients with oral and maxillofacial tumors after surgery, including speech, swallowing, mastication, facial expression, and psychosocial function. Based on the structure-process-outcome quality evaluation model, this review summarizes the implementation pathways and evaluation framework of ERAS in oral and maxillofacial tumor surgery. Furthermore, integrating current international evidence and a large cohort study from our team evaluating a delayed extubation strategy in patients undergoing free flap reconstruction, we demonstrate that perioperative management aligned with ERAS principles can significantly shorten hospital stays, reduce postoperative complications, and decrease medical costs while maintaining safety. Future efforts should focus on specialized pathways for oral and maxillofacial surgery, strengthening long-term functional and quality-of-life follow-up, and exploring digital and precision rehabilitation tools to promote the transition of ERAS toward a comprehensive recovery model emphasizing functional restoration and social reintegration.

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.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

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.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Background Air pollution remains a critical public health issue, with persistent exposure to air pollutants continuing to pose significant health risks. Currently, research investigating the association between air pollution and myocardial infarction mortality in Shenzhen remains inadequate. Objective To quantitatively assess the association between air pollutants and myocardial infarction mortality in residents. Methods Based on the mortality surveillance system of Shenzhen Center for Disease Control and Prevention, we conducted a time-stratified case-crossover study of 10089 permanent residents who died from myocardial infarction in Shenzhen between 2013 and 2022. Using residential address information, we obtained individual-level exposure data for air pollutants from the China High Air Pollutants dataset and meteorological factors from the China Meteorological Administration Land Data Assimilation System. A time-stratified case-crossover study design was employed to construct a conditional logistic regression model to assess the association between short-term exposure to air pollutants and myocardial infarction mortality. The exposure-response relationship was visualized, and a comprehensive health risk assessment was performed. Results This study included a total of 10 089 cases of myocardial infarction deaths in Shenzhen. The median [interquartile range (IQR)] concentrations of fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) in Shenzhen from 2013 to 2022 were 24.59 (20.19) μg·m⁻³, 42.85 (28.42) μg·m⁻³, 8.53 (3.39) μg·m⁻³, 29.47 (13.56) μg·m⁻³, 0.77 (0.27) mg·m⁻³, and 86.53 (51.39) μg·m⁻³, respectively. The moving average concentrations of PM_2.5 and PM₁₀ over the current day and the previous 2 days (lag02) showed the highest risk of myocardial infarction mortality, with an odds ratio (OR) and 95% confidence interval (95%CI) of 1.004 (1.001, 1.007) and 1.004 (1.002, 1.006), respectively. At lag05, SO₂ demonstrated the strongest association with the risk of myocardial infarction mortality, with an OR (95%CI) of 1.042 (1.019, 1.065). The exposure-response relationships of PM_2.5, PM₁₀, and SO₂ with myocardial infarction mortality were approximately linear, whereas NO₂ exhibited a nonlinear relationship. At lag05, the highest risk of myocardial infarction mortality associated with NO₂ exposure was observed when the NO₂ concentration was ≤21.92 μg·m⁻³, with an OR (95% CI) of 1.024 (1.003, 1.046). The health risk assessment indicated that, using the WHO Air Quality Guidelines as the reference, the local PM_2.5 and NO₂ exposure led to an excess mortality of 398 and 298 cases, respectively. The sensitivity analysis revealed that after adjusting for O₃ and restricting the study period to 2013—2019, the effect estimates for PM_2.5, PM₁₀, NO₂, and SO₂ on myocardial infarction mortality slightly increased. Conclusion Short-term exposure to air pollutants, including PM_2.5, PM₁₀, NO₂, and SO₂, could increase the risk of myocardial infarction mortality. This study provides important scientific evidence for environmental health risk assessment and environmental management in Shenzhen.