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.

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

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

INTRODUCTION: The primary objective of this guideline is to establish evidence-based recommendations for the clinical use of parenteral nutrition (PN) in adult patients within the acute hospital setting in Singapore. METHOD: An expert workgroup, consisting of healthcare practitioners actively involved in clinical nutrition support across all public health institutions, systematically evaluated existing evidence and addressed clinical questions relating to PN therapy. RESULTS: This clinical practice guideline developed 30 recommendations for PN therapy, which cover these key aspects related to PN use: indications, patient assess-ment, titration and formulation of PN bags, access routes and devices, and monitoring and management of PN-related complications. CONCLUSION This guideline provides recommendations to ensure appropriate and safe clinical practice of PN therapy in adult patients within the acute hospital setting.
Humans ; Singapore ; Parenteral Nutrition/adverse effects* ; Adult