Objective: To explore the impact of adverse childhood experiences (ACEs) on health risk behaviors (HRBs) among college students and the mediating role of psychological symptoms, so as to provide a basis for developing intervention strategies. Methods: From March to April 2023, a convenience cluster sample of 1 801 students from 12 universities in Nanning, Liuzhou, Guilin, Wuzhou of Guangxi completed an online survey. A self designed questionnaire, Adverse Childhood Experiences-International Questionnaire (ACE-IQ) and Symptom Checklist-90 (SCL-90) were used for evaluation tools. Binary Logistic regression, structural equation modeling (SEM) and Bootstrap methods were used to analyze the associations and mediating effects. Results: Overall, 71.2% of college students experienced at least one type of ACE, with emotional neglect (40.3%) and emotional abuse ( 25.2 %) having the highest detection rates. The top three HRBs were unhealthy diet (77.8%), physical inactivity (54.1%), and smoking/alcohol use (18.5%). Logistic regression showed that poor family functioning, abuse, and extra familial violence were each associated with an increased risk of smoking/alcohol use ( OR =1.14, 1.11, 1.18) and deliberate self harm ( OR =1.26, 1.19,1.30) (all P <0.05). Experience of abuse increased the risk of high risk sexual behavior and family dysfunction increaded the risk of physical inactivity, respectively ( OR = 1.07 , 1.04, both P <0.05). Mediation analysis revealed that anxiety ( β =0.20) and depression ( β = 0.09 ) partially mediated the pathway from poor family functioning to deliberate self harm; paranoia ( β =0.02) partially mediated the pathway from abuse to high risk sexual behavior; and obsessive-compulsive symptoms ( β =0.26) and depression ( β =0.10) partially mediated the pathway from extra familial violence to deliberate self harm (all P <0.05). Conclusion Psychological symptoms play a mediating role in the association between ACEs and HRBs, and mental health interventions may reduce the risk of HRBs among college students.

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.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Objective To characterize the expression patterns of serum microRNA-497(miR-497)in patients with colorectal cancer(CRC)and to investigate its associations with clinicopathological characteristics,diagnostic performance,and long-term prognostic outcomes.Methods This study retrospectively analyzed data from 122 patients with CRC admitted to the hospital between March 2020 and March 2022(CRC group),and enrolled 100 healthy individuals undergoing routine physical examinations(healthy control group)for comparison.Serum samples were collected from all participants prior to any surgical intervention,and the expression levels of miR-497 in serum were quantified using real-time quantitative polymerase chain reaction(qRT-PCR).Simulta-neously,the levels of carcinoembryonic antigen(CEA)and carbohydrate antigen 199(CA199)were measured.To investigate the association between miR-497 expression and clinicopathological characteristics,we evaluated its diagnostic performance using receiver operating characteristic(ROC)curve analysis.Furthermore,Kaplan-Meier survival analysis and Cox proportional hazards regression models were employed to assess its impact on patient prognosis.Results Compared to healthy individuals,CRC patients exhibited significantly lower serum miR-497 expression levels(P<0.001).Notably,miR-497 expression was strongly correlated with TNM stage progression and lymph node metastasis(P<0.001),but showed no significant association with tumor location,patient sex,or age.Diagnostic evaluation using ROC curves demonstrated that miR-497 achieved an AUC of 0.845 for CRC detection,outperforming CEA(AUC=0.748)and CA19-9(AUC=0.702),with DeLong's test confirming the statistically significant differences(P<0.05).Kaplan-Meier survival analysis revealed a significantly higher 3-year DFS rate among patients with high miR-497 expression(84.06%)compared to those with low expression(64.58%),with median DFS not reached in the high-expression group versus 36 months in the low-expression group(P=0.015).Multivariate Cox regression analysis confirmed that reduced miR-497 expression(HR=1.923,95%CI:1.184～3.125),advanced TNM stage(HR=2.511,95%CI:1.421～4.437),and lymph node metastasis(HR=1.753,95%CI:1.151～2.664)were independently associated with poorer disease-free survival outcomes.Conclusions Serum miR-497 is downregulated in patients with CRC and is significantly associated with tumor progression and poor prognosis.It demonstrates high diagnostic accuracy and strong potential for prognostic evalua-tion,highlighting its promise as a biomarker for auxiliary diagnosis and outcome prediction in CRC.

Objectives:This study aims to investigate the association between Chinese visceral adiposity index(CVAI)and the risk of cardiovascular disease(CVD),and explore the sex differences.Methods:Participants were screened from the three sub-cohorts of Prediction for Atherosclerotic Cardiovascular Disease Risk in China(China-PAR)project,baseline information on body measure and biochemistry examinations were collected from 1998,2000-2001,and 2007-2008,separately.Participants were followed up to 2015.Cohort-stratified Cox proportional risk models were used to analyze the relationship between CVAI,both in continuous(per standard deviation increase)and categorical(quartiles,with Q1 as reference)scales,and CVD risk in the total population,men and women.The multiplicative interaction between sex and CVAI on CVD risk were calculated.Restricted cubic spline regression was employed to investigate the dose-response relationship.Results:A total of 98 464 participants without CVD at baseline were included.During the 723 508 person-years of follow-up,3 605 CVD events were recorded.After multivariate adjustment,the HRs(95％CIs)of CVD were 1.25(1.20-1.29),1.09(1.04-1.15),and 1.54(1.46-1.64)for per standard deviation increment in CVAI in the general population,men and women,respectively.Besides,compared with Q1 group,the HRs(95％CIs)in Q4 group were 1.87(1.67-2.10),1.33(1.14-1.54)and 3.84(3.09-4.78),correspondingly,and the effect of CVAI on the risk of CVD was significantly higher in women than in men(Pinteraction<0.05).Additionally,there was a positive dose-response relationship between CVAI and the risk of CVD.Conclusions:Elevated CVAI is an independent risk factor for CVD,especially in women.

Objective:To study the clinical efficacy and impact on quality of life of the first administration of different doses of 131iodine ( 131I) therapy in patients with differentiated thyroid cancer (DTC) after surgical resection. Methods:A total of 97 DTC patients who underwent elective surgical resection at Hongqi Hospital Affiliated to Mudanjiang Medical University from June 2022 to June 2024 were selected and divided into a high-dose group ( n = 49) and a low-dose group ( n = 48) based on their first postoperative treatment with different doses of 131I. All patients underwent DTC endoscopic thyroidectomy. The high-dose group received > 150 - 200 mCi 131I treatment after surgery, while the low-dose group received 100 - 150 mCi 131I treatment after surgery. Both groups of DTC patients were followed up for 12 months. A comparison was conducted between the two groups regarding the success rate of clearing residual thyroid tissue (referred to as thyroid clearance), disease-free survival rate, recurrence rate, incidence of adverse reactions, and changes in quality of life before and after treatment. Results:There was no statistically significant difference in the success rate of thyroid clearance between the two groups of DTC patients (χ 2 = 1.43, P = 0.232). The disease-free survival rate of DTC patients in the low-dose group was higher than that in the high-dose group (χ 2 = 4.86, P = 0.027), and the recurrence rate was lower than that in the high-dose group (χ 2 = 4.86, P = 0.027). The incidence of adverse reactions in DTC patients in the low-dose group was lower than that in the high-dose group (χ 2 = 4.59, P = 0.032). The scores of the short form 36 health survey (SF-36) of both groups of DTC patients after treatment were higher than those before treatment ( P < 0.05), and the SF-36 scores of DTC patients in the low-dose group were higher than those in the high-dose group after treatment ( P < 0.001). The scores of the thyroid cancer specific quality of life (THYCA-QoL) of both groups of DTC patients after treatment were lower than those before treatment ( P < 0.05), and the THYCA-QoL scores of DTC patients in the low-dose group were lower than those in the high-dose group after treatment ( t = 4.48, P < 0.001). Conclusions:The initial treatment effect of different doses of 131I after DTC surgical resection is comparable, but the low-dose 131I can improve the disease-free survival rate, reduce adverse reactions, and improve the quality of life of patients, which is worthy of clinical practice.