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.

[Objective] To explore the strategy of blood management in patients with massive transfusion in the frigid plateau region. [Methods] The treatment process of a patient with liver rupture in the frigid plateau region was analyzed, and the blood management strategy of the frigid plateau region was discussed in combination with the difficulties of blood transfusion and literature review. [Results] The preoperative complete blood count (CBC) test results of the patient were as follows: RBC 3.14×10¹²/L, Hb 10⁶ g/L, HCT 30.40%, PLT 115.00×10⁹/L; coagulation function: PT 18.9 s, FiB 1.31 g/L, DD > 6 μg/mL, FDP 25.86 μg/mL; ultrasound examination and imaging manifestations suggested liver contusion and laceration / intraparenchymal hematoma, splenic contusion and laceration, and massive blood accumulation in the abdominal cavity; it was estimated that the patient's blood loss was ≥ 2 000 mL, and massive blood transfusion was required during the operation; red blood cell components were timely transfused during the operation, and the blood component transfusion was guided according to the patient's CBC and coagulation function test results, providing strong support and guarantee for the successful treatment of the patient. The patient recovered well after the operation, and the CBC test results were as follows: RBC 4.32×10¹²/L, Hb 144 g/L, HCT 39.50%, PLT 329.00×10⁹/L; coagulation function: APTT 29.3 s, PT 12.1 s, FiB 2.728 g/L, DD>6 μg/mL, FDP 25.86 μg/mL. The patient was discharged after 20 days, and regular follow-up reexamination showed no abnormal results. [Conclusion] Individualized blood management strategy should comprehensively consider the patient’s clinical symptoms, the degree of hemoglobin decline, dynamic coagulation test results and existing treatment conditions. Efficient and reasonable patient blood management strategies can effectively improve the clinical outcomes of massive transfusion patients in the frigid plateau region.

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 analyze the changes in wastewater disinfection quality and influencing factors of medical institutions in Nanjing from 2020 to 2024, providing a reference for infection control departments in medical institutions regarding wastewater monitoring and management. Methods　A total of 28 medical institutions in Nanjing were selected as the survey subjects. Microbial and total residual chlorine tests were conducted on hospital wastewater samples from 2020 to 2024 to compare the changes in the qualified rate of wastewater disinfection over the past five years. A current status investigation was also carried out on wastewater disinfection management, wastewater discharge, wastewater treatment equipment, and wastewater online monitoring systems in these hospitals. Results　From 2020 to 2024, 140 samples of hospital wastewater were collected. Over the past five years, the disinfection quality of hospital wastewater showed a downward trend, with statistically significant differences (χ²trend=6.986, P<0.05). The qualified rate for microbial indicators was 82.14% (115/140), while the on-site qualified rate for the total residual chlorine test in 2024 was only 56.52%. Among the 28 surveyed medical institutions, 85.71% (24/28) outsourced disinfection work to third-party companies, while 14.29% (4/28) carried out disinfection by institutional staff, with no statistically significant difference (χ2=0.200, P>0.05) in the qualified rate of disinfection. Sodium hypochlorite was used to disinfect wastewater in 82.14%(23/28) of the institutions, while other disinfection methods included chlorine dioxide (7.14%, 2/28), potassium monopersulfate (7.14%, 2/28), and ozone (3.57%, 1/28). A statistically significant difference in disinfection qualification rates was observed between sodium hypochlorite and chlorine dioxide (χ2=6.802, P<0.05). Additionally, wastewater online monitoring systems had been installed in 25 institutions, but 16.00% (4/25) of them had yet to achieve full project monitoring coverage. Conclusion　From 2020 to 2024, the quality of wastewater disinfection in medical institutions in Nanjing has declined, highlighting an urgent need to enhance wastewater monitoring. This would help reduce the impact of pathogenic microorganisms and other pollutants from hospital wastewater on the living environment.

INTRODUCTION: Foreign body (FB) ingestion is a common paediatric emergency. While guidelines exist for urgent intervention, less is known of the natural progress of FBs passing through the gastrointestinal tract (GIT). We reviewed these FB transit times in an outpatient cohort. METHODS: A retrospective review was performed on all children (≤18 years) treated for radiopaque FB ingestion at two major tertiary paediatric centres from 2015 to 2016. Demographic data, FB types, outcomes and hospital visits (emergency department [ED] and outpatient) were recorded. All cases discharged from the ED with outpatient follow-up were included. We excluded those who were not given follow-up appointments and those admitted to inpatient wards. We categorised the outcomes into confirmed passage (ascertained via abdominal X-ray or reported direct stool visualisation by patients/caregivers) and assumed passage (if patients did not attend follow-up appointments). RESULTS: Of the 2,122 ED visits for FB ingestion, 350 patients who were given outpatient follow-up appointments were reviewed (median age 4.35 years [range: 0.5-14.7], 196 [56%] male). The largest proportion (16%) was aged 1-2 years. Coins were the most common ingested FB, followed by toys. High-risk FB (magnets or batteries) formed 9% of cases ( n =33). The 50 th centile for FB retention was 8, 4 and 7 days for coins, batteries and other radiopaque FBs, respectively; all confirmed passages occurred at 37, 7 and 23 days, respectively. Overall, 197 (68%) patients defaulted on their last given follow-up. CONCLUSION This study provides insight into the transit times of FB ingested by children, which helps medical professionals to decide on the optimal time for follow-up visits and provide appropriate counsel to caregivers.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Male ; Eating ; Emergency Service, Hospital ; Foreign Bodies/diagnostic imaging* ; Gastrointestinal Tract/diagnostic imaging* ; Gastrointestinal Transit ; Retrospective Studies ; Singapore ; Tertiary Care Centers

INTRODUCTION: Acute severe ulcerative colitis (ASUC) is a significant cause of disease morbidity. One-third of patients with ASUC are steroid refractory. Rescue therapy may not successfully induce remission, necessitating colectomy. We aimed to identify predictors of rescue therapy and colectomy in ASUC assessed within 24 h of admission for early risk stratification. METHODS: We conducted a retrospective cohort study of 58 admissions for ASUC among 47 patients from August 2002 to January 2022. Serum biomarkers assessed were measured on admission. Primary outcomes were the need for rescue therapy during the same admission and colectomy within 1 year of admission. RESULTS: Rescue therapy (all with infliximab) was given in 20 (34.5%) of the admissions. Colectomy was done within 1 year for nine (15.5%) of the admissions. An elevated C-reactive protein (CRP) of >30 mg/L (relative risk [RR] 1.63), a CRP-albumin ratio of >0.85 (RR 1.63), and a composite factor of both CRP > 30 mg/L and age ≥60 years (RR 2.37) were significantly associated with the need for rescue therapy. Hypoalbuminaemia ≤ 25 g/L (RR 4.35) and the use of biologics at presentation (RR 1.54) were significantly associated with colectomy within 1 year of admission, while a CRP of ≥ 80 mg/L was a significant protective factor (RR 0.70). CONCLUSION Patients with ASUC who have elevated CRP or CRP-albumin ratio on admission should be considered at risk for steroid-refractory disease. Those with hypoalbuminaemia on admission and using biologics at presentation are more likely to require colectomy in the first year after admission for ASUC.
Humans ; Colitis, Ulcerative/therapy* ; Colectomy ; Retrospective Studies ; Male ; Female ; Middle Aged ; Adult ; C-Reactive Protein/metabolism* ; Infliximab/therapeutic use* ; Biomarkers/blood* ; Acute Disease ; Aged ; Severity of Illness Index ; Treatment Outcome

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female