BackgroundFamily functioning is one of the factors influencing non-suicidal self-injury （NSSI） behaviors in adolescents with depressive disorders. Previous studies have treated family functioning as a unitary construct， which may obscure the differential impacts of specific dimensions on NSSI behaviors. ObjectiveTo explore the relationships between various dimensions of family functioning and NSSI behaviors in adolescents with depressive disorders， aiming to provide precise targets for family-based interventions for adolescents with depressive disorders who exhibit NSSI behaviors. MethodsIn this cross-sectional study， 217 adolescent patients who were treated at the outpatient or inpatient department of The First Psychiatric Hospital of Harbin from January to July 2025 and met the diagnostic criteria for depressive disorders as stipulated in the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） were included as the research subjects. Assessments included a self-designed questionnaire， the Hamilton Depression Scale-17 item （HAMD-17）， and the Family Assessment Device （FAD）. Univariate Logistic regression analysis was employed to investigate the association between each dimension of family functioning and the NSSI behaviors， and multivariate Logistic regression was used to test the independent effect of each dimension of family functioning on the NSSI behaviors. ResultsA total of 204 cases （94.01%） of adolescent patients with depressive disorders completed the valid questionnaire survey. Among them， 134 cases （65.69%） exhibited NSSI behaviors （NSSI group）， and 70 cases （34.31%） did not exhibit NSSI behaviors （non-NSSI group）. Compared with the non-NSSI group， the NSSI group had a higher HAMD-17 score ［（20.97±7.50） vs. （17.79±6.95）， t=8.705， P=0.004］， a higher FAD total score ［（155.68±21.84） vs. （148.87±22.72）， t=4.348， P=0.038］， and a higher problem-solving dimension score ［（2.54±0.49） vs. （2.34±0.51）， t=7.399， P=0.007］. All the differences were statistically significant. The results of the Logistic regression analysis showed that the FAD total score （OR=1.014， 95% CI： 1.001–1.028， P=0.041） and the problem-solving dimension score （OR=2.241， 95% CI： 1.228–4.090， P=0.009） were both risk factors for NSSI behaviors. After adjusting for gender， age， residence， educational level， monthly family income， and whether being an only child， the correlation between the FAD total score and NSSI behaviors was not statistically significant （OR=1.010， 95% CI： 0.995–1.025， P=0.185）， while the correlation between the FAD problem-solving dimension score and NSSI behaviors remained statistically significant （OR=2.000， 95% CI： 1.028–3.889， P=0.041）. ConclusionImpaired problem-solving capacity within family functioning may constitute a risk factor for NSSI behaviors in adolescents with depressive disorders. ［Funded by Research Project of Heilongjiang Provincial Health Commission （number， 20240303090148， 20230303090154）］

BACKGROUND:β-Caryophyllene has a variety of pharmacological activities such as antioxidant,anti-inflammatory and anti-apoptotic,which may have a better therapeutic effect on osteoarthritis.OBJECTIVE:To investigate the effect and mechanism of β-caryophyllene on mouse osteoarthritis.METHODS:Forty C57BL/6J mice were randomly divided into sham group,model group,low-dose β-caryophyllene group and high-dose β-caryophyllene group,with 10 mice in each group.Hulth method was used to construct an osteoarthritis model in the latter three groups.Four weeks after modeling,70 and 140 mg/kg/d β-caryophyllene was intragastrically given in the low-and high-dose β-caryophyllene groups,respectively,and normal saline was given by gavage in the sham group and the model group,once a day,for 4 weeks.After administration,knee joint morphological changes were observed by hematoxylin-eosin staining,serum levels of inflammatory factors(tumor necrosis factor-α,interleukin-1β,interleukin-6,and interleukin-10)were detected by ELISA,and oxidative stress indexes(glutathione peroxidase,superoxide dismutase,and malondialdehyde)were detected by chemiluminescence.The expression levels of key proteins in the Sonic hedgehog(Shh)/glioma associated oncogene homolog 1(Gli1)signaling pathway were detected by immunohistochemistry and western blot.RESULTS AND CONCLUSION:(1)Compared with the sham group,a large number of inflammatory cells infiltrated in the knee joint of mice in the model group,cartilage tissue was seriously damaged,serum levels of tumor necrosis factor-α,interleukin-1β,interleukin-6,interleukin-10 and malondialdehyde were significantly increased(P＜0.01),the activities of glutathione peroxidase and superoxide dismutase were significantly decreased(P＜0.01),and the relative expression levels of Shh and Gli1 in the knee joint were significantly increased(P＜0.01).(2)Compared with the model group,in the low-and high-dose β-caryophyllene groups,inflammatory cell infiltration in the mouse knee joint was decreased,cartilage tissue injury was alleviated,serum levels of tumor necrosis factor-α,interleukin-1 β,interleukin-6 and malondialdehyde were significantly decreased(P＜0.05),the activities of glutathione peroxidase and superoxide dismutase were significantly increased(P＜0.01),and the expression levels of Shh and Gli1 in the knee joint were significantly decreased(P＜0.01).The above-mentioned improvements were more significant in the high-dose β-caryophyllene group than the low-dose β-caryophyllene group.To conclude,β-caryophyllene can improve osteoarthritis,and its mechanism may be related to reducing inflammation and oxidative stress damage by regulating the Shh/Gli1 signaling pathway.

BACKGROUND:β-Caryophyllene has a variety of pharmacological activities such as antioxidant,anti-inflammatory and anti-apoptotic,which may have a better therapeutic effect on osteoarthritis.OBJECTIVE:To investigate the effect and mechanism of β-caryophyllene on mouse osteoarthritis.METHODS:Forty C57BL/6J mice were randomly divided into sham group,model group,low-dose β-caryophyllene group and high-dose β-caryophyllene group,with 10 mice in each group.Hulth method was used to construct an osteoarthritis model in the latter three groups.Four weeks after modeling,70 and 140 mg/kg/d β-caryophyllene was intragastrically given in the low-and high-dose β-caryophyllene groups,respectively,and normal saline was given by gavage in the sham group and the model group,once a day,for 4 weeks.After administration,knee joint morphological changes were observed by hematoxylin-eosin staining,serum levels of inflammatory factors(tumor necrosis factor-α,interleukin-1β,interleukin-6,and interleukin-10)were detected by ELISA,and oxidative stress indexes(glutathione peroxidase,superoxide dismutase,and malondialdehyde)were detected by chemiluminescence.The expression levels of key proteins in the Sonic hedgehog(Shh)/glioma associated oncogene homolog 1(Gli1)signaling pathway were detected by immunohistochemistry and western blot.RESULTS AND CONCLUSION:(1)Compared with the sham group,a large number of inflammatory cells infiltrated in the knee joint of mice in the model group,cartilage tissue was seriously damaged,serum levels of tumor necrosis factor-α,interleukin-1β,interleukin-6,interleukin-10 and malondialdehyde were significantly increased(P＜0.01),the activities of glutathione peroxidase and superoxide dismutase were significantly decreased(P＜0.01),and the relative expression levels of Shh and Gli1 in the knee joint were significantly increased(P＜0.01).(2)Compared with the model group,in the low-and high-dose β-caryophyllene groups,inflammatory cell infiltration in the mouse knee joint was decreased,cartilage tissue injury was alleviated,serum levels of tumor necrosis factor-α,interleukin-1 β,interleukin-6 and malondialdehyde were significantly decreased(P＜0.05),the activities of glutathione peroxidase and superoxide dismutase were significantly increased(P＜0.01),and the expression levels of Shh and Gli1 in the knee joint were significantly decreased(P＜0.01).The above-mentioned improvements were more significant in the high-dose β-caryophyllene group than the low-dose β-caryophyllene group.To conclude,β-caryophyllene can improve osteoarthritis,and its mechanism may be related to reducing inflammation and oxidative stress damage by regulating the Shh/Gli1 signaling pathway.

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.

Multidrug-resistant (MDR) bacterial infections have emerged as a serious challenge of global public health crisis. The overuse and misuse of conventional antibiotics have dramatically accelerated the emergence, evolution and worldwide spread of drug-resistant bacterial strains, necessitating urgent exploration of novel antibacterial strategies. Bacteriophages serve as natural bacterial predators offering distinct advantages including high host specificity, autonomous self-replication capabilities and cost-effective large-scale production. However, wild-type phages present significant clinical limitations due to their narrow host ranges, susceptibility to rapid immune clearance and poor penetration of bacterial biofilms, which severely restrict their therapeutic applications. The convergence of synthetic biology, nanotechnology and advanced gene editing technologies has accelerated the development of engineered bacteriophage platforms, providing programmable, scalable and clinically translatable pathways to overcome these inherent biological constraints. Here, we systematically delineate four fundamental strategies for engineered bacteriophage development. Chemical modification utilizes reactive functional groups such as amino, carboxyl and thiol moieties on capsid proteins through esterification, amidation or click chemistry reactions to achieve precise drug conjugation and surface functionalization. In vivo editing encompasses ultraviolet or chemical mutagenesis for random mutation induction, homologous recombination for targeted genetic alterations, recombineering methodologies including electroporation-mediated bacteriophage recombination engineering, and CRISPR-Cas systems for precise genome editing to enable exact genetic reconstruction and host range reprogramming. In vitro synthesis leverages genome engineering platforms where intact phage genomes are transferred into yeast or host bacteria to facilitate highly efficient homologous recombination, enabling large DNA fragment assembly and cross-gene host range expansion without bacterial toxicity constraints. Directed evolution combines artificial selection through mutation library screening with rational design approaches involving chimeric receptor binding protein construction or site-specific mutagenesis, effectively balancing the discovery of unknown adaptive pathways with targeted host specificity modification. Moreover, we comprehensively discuss therapeutic applications across diverse clinical scenarios. Engineered bacteriophage effectively disrupt bacterial biofilms through sophisticated functionalized delivery platforms including nanozyme-conjugated phages, phage-liposome nanoconjugates and bio-responsive hydrogels, demonstrating significantly enhanced bactericidal efficiency compared to unmodified free phages. These bioengineered vectors attenuate bacterial virulence and resensitize pathogens to antibiotics by delivering CRISPR-Cas systems or base editors to disrupt critical virulence factors such as pili, capsule synthesis machineries and quorum sensing systems, or by inactivating antibiotic resistance determinants including beta-lactamase genes. As an intelligent nanomedicine delivery platform, engineered bacteriophage enable precise pathogen elimination an through photocatalytic reactive oxygen species generation, immunomodulatory interventions, or controlled release of antibacterial drugs. Furthermore, oral administration of engineered bacteriophage facilitates microbiota modulation, which selectively eliminate intestinal pathogens while preserve beneficial commensal microbiota, thereby restoring microbial community balance and preventing complications associated with dysbiosis. Finally, we critically analyze persistent challenges including host strain matching complexity, evolution of bacterial resistance mechanisms, pharmacokinetic optimization requirements, optimal administration route selection, large-scale production quality control standards and clinical dosing determination protocols. Through multidisciplinary integration of synthetic biology, infectious disease medicine and immunology, future translational medicine studies of bacteriophage should establish comprehensive technical platforms encompassing rapid phage screening, intelligent rational design, rigorous in vivo evaluation and standardized clinical validation processes, ultimately advancing engineered bacteriophage from laboratory innovations to clinically approved therapeutics for effectively combating MDR bacterial infections.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

Objective: To examine the association between academic stress and anxiety-depression comorbidity among college students and the potential moderating effect of physical activity, so as to provide a theoretical basis for the development of evidence based mental health promotion frameworks in higher education institutions. Methods: From October 2022 to June 2024, a total of 7 980 students from 21 universities across five provinces/municipalities in China, including Shanghai, Jiangsu, Zhejiang, Shandong, and Anhui, using a combination of convenience sampling and stratified random cluster sampling. Physical activity, academic stress, anxiety, and depression were assessed by using the 24 hour Movement Behaviors Questionnaire, the Chinese College Students Mental Health Screening Scale, the Generalized Anxiety Disorder-7, and the Center for Epidemiologic Studies Depression Scale, respectively. Binary Logistic regression was used to examine the associations between academic stress and anxiety, depression, and their comorbidity. The PROCESS macro was employed to test the moderating effect of physical activity. Results: The prevalence of anxiety-depression comorbidity among college students was relatively high (11.53%), with significant differences observed across grades and body mass index (BMI) categories ( χ 2=70.30, 24.63, both P <0.01). Binary Logistic regression revealed that, compared with low academic stress, moderate academic stress among male students was significantly associated with greater odds of anxiety ( OR =2.78), depression ( OR =2.75), and anxiety-depression comorbidity ( OR =2.89) (all P <0.01). High academic stress was associated with elevated odds of anxiety ( OR =6.66), depression ( OR =12.81), and anxiety-depression comorbidity ( OR =13.65) (all P <0.01). Among female students, moderate academic stress was associated with anxiety ( OR =2.58), depression ( OR =1.52), and anxiety-depression comorbidity ( OR =1.70) (all P <0.01). High academic stress increased the risk of anxiety ( OR =6.77), depression ( OR =6.81), and anxiety-depression comorbidity ( OR =7.48) (all P <0.05). The moderation analysis indicated that the interaction between total physical activity duration and academic stress had a significant netative moderating effect on anxiety-depression comorbidity among college students ( β =-0.03, P <0.01). Conclusions Academic stress increases the risk of anxiety, depression, and anxiety-depression comorbidity among college students. Physical activity may attenuate the adverse effects of academic stress on anxiety-depression comorbidity.

[Objective] To establish a new scoring system to predict the perioperative outcomes (operation time, intraoperative blood loss, and trifecta achievement) in patients undergoing robot-assisted partial nephrectomy (RAPN) by integrating the RENAL and Mayo adhesive probability (MAP) scores. [Methods] Clinical data of 178 patients with renal cell carcinoma who underwent RAPN performed by the same surgeon in our hospital during Jan.2015 and Jan.2022 were retrospectively analyzed.The RENAL and MAP scores of all patients were calculated.Linear regression and logistic regression were used to evaluate the associations between the components of the RENAL and MAP scores (a total of 6 variables) and perioperative outcomes.The factors with significant associations were then included into logistic regression analysis to identify independent predictors for constructing an assessment system for perioperative outcomes, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) to predict its efficacy. [Results] Multivariate linear regression analysis showed that tumor size (β=6.14, 95%CI: 1.93—10.34, P=0.004), exophytic rate (β=10.60, 95%CI: 3.44—17.76, P=0.004), and perinephric fat thickness (β=16.48, 95%CI: 8.52—24.45, P<0.001) were significantly associated with operation time.Tumor size (β=10.55 95%CI: 5.60—15.49, P<0.001) was associated with both intraoperative blood loss and trifecta achievement (OR=1.73, 95%CI: 1.26—2.36, P=0.001). Multivariate logistic regression analysis of these 3 factors identified tumor size (OR=9.07, 95% CI: 1.18—69.45, P=0.03) and perinephric fat thickness (OR=2.28, 95%CI: 1.86—6.04, P=0.01) as independent predictors of perioperative outcomes.Based on these findings, the tumor size and perinephric fat thickness (RP) scoring was constructed, which demonstrated better predictive ability than RENAL score or MAP score alone (RP vs.RENAL vs.MAP: 0.766 vs.0.548 vs.0.684). [Conclusion] The RP score includes fewer variables than the RENAL and MAP scores but outperforms them.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.