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.

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.

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.

Objective To investigate the effect of knockdown PRTN3 on polarization phenotype and anti-tumor function of macrophages in breast cancer.Methods A mouse monocyte/macrophage cell line(RAW264.7)with knockdown of PRTN3 was constructed.The mRNA and protein levels of PRTN3 were detected by RT-qPCR and flow cytometry.Macrophages were co-cultured with tumor cells,and the polarization phenotypes of macrophages were detected by flow cytometry,and the mRNA levels of polarization and cytokines related genes were detected by RT-qPCR.The tumor phagocytosis and tumor killing ability of macrophages were determined by in vitro phagocytosis assay and tumor killing assay.The effect of macrophages with PRTN3 knockdown on tumor progression and metasta-sis was compared in an orthotopic transplantation model of mouse breast cancer.Results The mRNA and protein levels of PRTN3 in the RAW264.7 cell line with stable knockdown of PRTN3 were significantly decreased(P＜0.01).Knockdown of PRTN3 in RAW264.7 cells upregulated the expression of M1 phenotype-related genes(CD80,CD86,iNOS,IL-1β,IL-6 and TNF-α)(P＜0.05).After macrophages co-culture with tumor cells,macrophages with knockdown of PRTN3 upregulated the expression of M1 phenotype-related genes(CD80,CD86,iNOS,IL-1β,IL-6 and TNF-α)(P＜0.01).Knockdown of PRTN3 enhanced the phagocyto-sis(P＜0.05)and tumor killing ability(P＜0.05)of macrophages in vitro.Adoptive transfusion of PRTN3-knockdown macrophages into tumor-bearing mice inhibited the growth of in situ breast cancer and reduced lung metastasis(P＜0.05).Conclusions PRTN3 regulates phenotypic polarization of macrophages,and knockdown of PRTN3 enhances the anti-tumor function of macrophages.

Objective : To construct a humanized mouse model of the interleukin-9 receptor(IL-9R) coding DNA sequence(CDS) gene and to verify the genotype and IL-9R expression in mice. Methods : The CRISPR/Cas9 genome editing technology was used to replace the exon 2-7 fragment of the il-9r gene in mouse embryonic stem cells with the corresponding human IL-9R sequence. After verifying the completion of the gene fragment replacement, tetraploid embryos were constructed and microinjected back into the oviducts of surrogate mice. Through surrogacy by female mice, homozygous humanized mice were obtained. DNA was extracted from the homozygous humanized mice IL-9R CDS gene, and their genotypes were identified by agarose gel electrophoresis after PCR amplification. Western blot was used to detect the expression of IL-9R in the spleen and thymus of homozygous humanized mice with either wild-type(WT) or IL-9R gene humanization. Results : Gel electrophoresis after PCR amplification showed that mice with only a 1 805 bp band amplified using WT primers were wild-type, while mice with 2 553 bp and 2 340 bp bands amplified using 5KI and 3KI primers, respectively, were homozygous humanized mice with IL-9R CDS gene. Western blot results indicated that the tissues of homozygous humanized mice model with IL-9R CDS gene expressed IL-9R significantly. Conclusion The humanized mouse model with IL-9R CDS gene has been successfully constructed and characterized.

Objective: To construct microRNA(miR)-22 gene knockout(miR-22-/-) mice using CRISPR/Cas 9 technology, to breed miR-22-/- mice and to identify their genotypes. Methods : In this experiment, CRISPR/Cas 9 technology was used to construct miR-22-/- genetically engineered mice. After gene identification, the F0 generation miR-22-/- mice were mated with wild-type mice in the same litter to obtain F1 generation miR-22-/- mice. The miR-22 knockout efficiency was analyzed at the RNA level by real-time fluorescence quantitative polymerase chain reaction(qPCR). Western blot was used to detect the interaction between miR-22 and target genes. Results : miR-22-/- mice were successfully constructed using CRISPR/Cas 9 technology, gene identification was performed on the bred mice, and three stable genotypes of miR-22+/+,miR-22+/-, and miR-22-/- were identified. The real-time fluorescence quantitative PCR detection results confirmed that miR-22-/- mice showed almost no expression of miR-22 in the heart, liver, lung, kidney, spleen, and thymus tissues compared to wild-type mice in the same litter. Western blot analysis showed that the relative expression level of NLRP3 protein in miR-22-/- mouse tissues was lower than that in wild-type mice. Conclusion A miR-22-/- mouse model is successfully constructed, and stable genetic homozygous miR-22-/- mice is obtained. This indicates that miR-22 has an inhibitory effect on the downstream target gene NLRP3.

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.

The inference of tissue origin of body fluid stains is crucial for case investigation and court proceedings.However,traditional methods for identification of body fluid stains,such as morpho-logical,chemical,and immunoassay identifications have certain limitations,and there is an urgent need for more efficient methods for confirmatory experiments.In recent years,the rapid development of tran-scriptomics technology has provided new means for the identification of tissue origin of body fluid stains.Different types of RNA in the transcriptome have their own advantages.This paper elaborates in detail on the application of different types of RNA,such as mRNA,miRNA,circRNA,lncRNA,piRNA and microbial transcriptomics in body fluid identification,and summarizes their respective ad-vantages and limitations,in order to provide a reference for related research.