BackgroundPrevious studies have identified a close relationship among psychological neglect and abuse， negative affect， different stages of adolescence， and suicidal ideation. However， the mechanisms underlying the impact of psychological abuse and neglect on suicidal ideation among left-behind adolescents remain unclear， and this field of research is still in its relative infancy. ObjectiveTo explore the relationship between psychological neglect/abuse and suicidal ideation among left-behind adolescents， as well as the mediating role of negative affect and the moderating effect of different stages of adolescence， so as to provide insights for preventing and intervening suicidal ideation in this population. MethodsFrom November 2021 to May 2022， a cluster random sampling technique was utilized to select 2 309 left-behind adolescents in western China. Assessments were conducted using the Child Psychological Abuse and Neglect Scale （CPANS）， the Positive and Negative Suicide Ideation （PANSI） and the Positive and Negative Affect Schedule for Children （PANAS-C）. Spearman correlation coefficients were calculated across all samples， and Process 4.1 was employed to test the mediating role of negative affect and the moderating role of different stages of adolescence in the pathway linking psychological abuse/neglect to suicidal ideation. ResultsA total of 2 119 left-behind adolescents （mean age： 14.94±1.20 years） completed the study， with males comprising 51.34% （1 088/2 119） and females 48.66% （1 031/2 119）.Among left-behind adolescents， scores on CPANS psychological neglect subscale showed positive correlations with both psychological abuse subscale scores and PANAS-C negative affect subscale scores （r=0.446， 0.496， P<0.01）. Additionally， CPANS psychological neglect and psychological abuse subscale scores were also positively correlated with PANSI scores （r=0.487， 0.508， P<0.01）. Furthermore， PANAS-C negative affect subscale scores demonstrated a positive correlation with PANSI scores （r=0.499， P<0.01）. Negative affect partially mediated the relationship between psychological abuse/psychological neglect and suicidal ideation， with effect sizes of 0.166 （95% CI： 0.141~0.191） and 0.131 （95% CI： 0.112~0.152）. Different stages of adolescence moderated the latter part （negative emotion → suicidal ideation） of the indirect mediation path from psychological neglect to suicidal ideation through negative affect （β=-0.066， P<0.01）. ConclusionBoth psychological neglect and psychological abuse may influence suicidal ideation among left-behind adolescents via negative affect. Moreover， different stages of adolescence may moderate the indirect path from psychological neglect to suicide ideation through negative affect.

Bronchopulmonary dysplasia（BPD）is a chronic respiratory disease characterized by developmental disorders and abnormal repair of lung tissue in premature infants.Its clinical manifestations are characterized by persistent respiratory distress syndrome，which can cause progressive lung function damage and significantly affect the quality of life and long-term prognosis of the affected children.Vitamin D has been proven to have multiple lung protective effects in recent years.It not only participates in regulating normal lung development，but also effectively inhibits inflammatory reactions，promotes the biosynthesis of pulmonary surfactant，regulates cell apoptosis，and maintains lung tissue function and structural integrity.The above physiological functions are significantly correlated with the pathophysiology of BPD，providing a new theoretical perspective for further elucidating the pathogenesis of BPD.Based on existing evidence，vitamin D may provide new treatment and research directions for BPD.

Objective:To explore the key components and mechanism of Qufeng Ningfei Powder in treating asthma through qualitative analysis of its blood components, combined with network pharmacology and molecular docking techniques.Methods:The blood components of Qufeng Ningfei Powder were qualitatively analyzed using UPLC-QE-Orbitrap-MS technology. R language was employed to analyze chip data from the GEO database, obtaining a list of differentially expressed genes. SwissTargetPrediction was utilized to predict the targets of drug components. Asthma-related targets were searched through databases such as OMIM, GeneCards, and TTD. The intersection of drug and disease targets was identified using Venn online analysis tool, constructing a "drug-component-target-disease" network to screen potential core components. A protein-protein interaction network (PPI) of core targets was constructed using STRING platform and Cytoscape software. GO function enrichment and KEGG pathway analysis were conducted using DAVID database to validate potential mechanism. Molecular docking was performed to verify the interaction between key components and core targets.Results:A total of 64 components were identified, from which 53 active components were screened, corresponding to 609 targets. Further searching disease databases revealed 96 target genes related to asthma, with an intersection of 6 genes between drug and asthma differential genes. Core target gene IL6 and its corresponding core compound were determined through network topology analysis. Molecular docking confirmed the binding of the main active components of Qufeng Ningfei Powder with the core target protein IL6.Conclusion:The blood components of Qufeng Ningfei Powder may regulate IL-17 through IL6, counteract airway remodeling, oxidative stress, and airway hyperresponsiveness, and thus treat asthma.

Objective To systematically evaluate the incidence and influencing factors of axillary web syndrome(AWS)in postoperative breast cancer patients,and to provide evidence for reducing the incidence of axillary web syndrome.Methods A computer search was performed in China National Knowledge Infrastructure(CNKI),VIP,Wanfang,SinoMed,PubMed,Medline,Scopus,The Cochrane Library,Web of Science,Embase,searched for articles on AWS influencing factors of breast cancer published from the establishment of the database to January 6th,2025.The articles were screened according to the inclusion and exclusion criteria.Revman5.4 and Stata17.0 were used for systematic review.Results Fifteen studies involving 3979 breast cancer patients and 1 156 patients with AWS were included.The results of the Meta-analysis showed that there was significant statistical heterogeneity among the included studies(I2=97.0%,P<0.0001).Using the random effects model,the incidence of AWS was 32.2%[95%CI(0.24,0.40),P<0.0001].The influencing factors for AWS after breast cancer surgery are age,body mass index(BMI),total mastectomy,lymph node metastasis,and neoadjuvant chemotherapy.NAC),axillary lymph node dissection(ALND),and the number of harvested axillary lymph nodes.Conclusion The incidence of AWS after breast cancer surgery was high.Clinicians should give early nursing to the influencing factors,reduce the incidence of AWS and improve patients'quality of life after surgery.

Objective To establish a model to predict the severity of patients with COVID-19 associated diarrhea by analyzing the differences of laboratory detection indicators in different grades of patients with COVID-19 associated diarrhea.Methods A total of 649 COVID-19 patients combined with diarrhea hospitalized in Wuhan Huoshenshan Hospital from February 2020 to April 2020 were retrospectively selected,and the patients with obvious causes of diarrhea had been excluded.They were further divided into the common group(n=282),severe group(n=314),and critical group(n=53),and the differences in clinical and laboratory indicators among the three groups were compared.The XGBoost model was established,and its diagnostic efficacy in predicting the severity of patients with COVID-19 associated diarrhea was evaluated by the ROC curve.Results There were statistically significant differences in blood routine test,liver function,electrolytes,fecal occult blood and other laboratory indicators among the three groups of COVID-19 associ-ated diarrhea(P＜0.05).The white blood cell count,absolute value and percentage of neutrophils,and levels of serum lactate dehydro-genase(LDH),α-hydroxybutyrate dehydrogenase(α-HBDH),γ-glutamyl transpeptidase(GGT),B-type natriuretic peptide,and blord glucose(Glu)in the critical group were significantly higher than those in the common group and severe group(P＜0.05),while the percentages of lymphocytes,monocytes,eosinophils,and basophils,and chloride concentration were significantly lower than those in the common group and severe group(P＜0.05).The results of the ROC curve showed that the prediction model constructed by eight indicators,including C-reactive protein(CRP),LDH,interleukin-6(IL-6),Glu,PT％activity,chloride(Cl-),D-dimer(DD),and procalcitonin(PCT),had significant predictive value for critical patients(AUCROC=0.939),but no obvious predictive value for the patients in the common group(AUCROC=0.630)and severe group(AUCROC=0.553).Conclusion The COVID-19 patients com-bined with diarrhea have a higher probability of developing severe or critical conditions compared with those without diarrhea.The indi-cators such as CRP,LDH,IL-6,Glu,PT％activity,Cl-,DD,and PCT have significant predictive value on whether the COVID-19 patients combined with diarrhea turn to critical illness.

Objective To establish a model to predict the severity of patients with COVID-19 associated diarrhea by analyzing the differences of laboratory detection indicators in different grades of patients with COVID-19 associated diarrhea.Methods A total of 649 COVID-19 patients combined with diarrhea hospitalized in Wuhan Huoshenshan Hospital from February 2020 to April 2020 were retrospectively selected,and the patients with obvious causes of diarrhea had been excluded.They were further divided into the common group(n=282),severe group(n=314),and critical group(n=53),and the differences in clinical and laboratory indicators among the three groups were compared.The XGBoost model was established,and its diagnostic efficacy in predicting the severity of patients with COVID-19 associated diarrhea was evaluated by the ROC curve.Results There were statistically significant differences in blood routine test,liver function,electrolytes,fecal occult blood and other laboratory indicators among the three groups of COVID-19 associ-ated diarrhea(P＜0.05).The white blood cell count,absolute value and percentage of neutrophils,and levels of serum lactate dehydro-genase(LDH),α-hydroxybutyrate dehydrogenase(α-HBDH),γ-glutamyl transpeptidase(GGT),B-type natriuretic peptide,and blord glucose(Glu)in the critical group were significantly higher than those in the common group and severe group(P＜0.05),while the percentages of lymphocytes,monocytes,eosinophils,and basophils,and chloride concentration were significantly lower than those in the common group and severe group(P＜0.05).The results of the ROC curve showed that the prediction model constructed by eight indicators,including C-reactive protein(CRP),LDH,interleukin-6(IL-6),Glu,PT％activity,chloride(Cl-),D-dimer(DD),and procalcitonin(PCT),had significant predictive value for critical patients(AUCROC=0.939),but no obvious predictive value for the patients in the common group(AUCROC=0.630)and severe group(AUCROC=0.553).Conclusion The COVID-19 patients com-bined with diarrhea have a higher probability of developing severe or critical conditions compared with those without diarrhea.The indi-cators such as CRP,LDH,IL-6,Glu,PT％activity,Cl-,DD,and PCT have significant predictive value on whether the COVID-19 patients combined with diarrhea turn to critical illness.

Objective To systematically evaluate the incidence and influencing factors of axillary web syndrome(AWS)in postoperative breast cancer patients,and to provide evidence for reducing the incidence of axillary web syndrome.Methods A computer search was performed in China National Knowledge Infrastructure(CNKI),VIP,Wanfang,SinoMed,PubMed,Medline,Scopus,The Cochrane Library,Web of Science,Embase,searched for articles on AWS influencing factors of breast cancer published from the establishment of the database to January 6th,2025.The articles were screened according to the inclusion and exclusion criteria.Revman5.4 and Stata17.0 were used for systematic review.Results Fifteen studies involving 3979 breast cancer patients and 1 156 patients with AWS were included.The results of the Meta-analysis showed that there was significant statistical heterogeneity among the included studies(I2=97.0%,P<0.0001).Using the random effects model,the incidence of AWS was 32.2%[95%CI(0.24,0.40),P<0.0001].The influencing factors for AWS after breast cancer surgery are age,body mass index(BMI),total mastectomy,lymph node metastasis,and neoadjuvant chemotherapy.NAC),axillary lymph node dissection(ALND),and the number of harvested axillary lymph nodes.Conclusion The incidence of AWS after breast cancer surgery was high.Clinicians should give early nursing to the influencing factors,reduce the incidence of AWS and improve patients'quality of life after surgery.

Objective To develop multimodal joint cognitive representations for the research of visual cognitive activities of the brain,enhance the classification performance of visual information cognitive representations,predict brain electro-encephalogram(EEG)responses from visual image features,and decode visual images from EEG signals.Methods A architecture combining a multimodal variational autoencoder network with the Mixture of Product Experts(MoPoE)approach and with a style generation adversarial network based on adaptive discriminator augmentation(Style-GAN2-ADA)was used for facilitating the learning of cognitive representations and the encoding and decoding of EEG signals.This framework not only catered to classification tasks but also enabled cross-modal generation of images and EEG data.Results The present study integrated features from different modalities,enhancing the classification accuracy of cognitive representations of visual information.By aligning the feature spaces of diverse modalities into a cohesive latent space,cross-modal generation tasks were made possible.The cross-modal generation results of EEG and images,derived from this unified latent space,outperformed the one-way mapping methods that involved transition from one modality to another employed in previous research.Conclusion This study effectively integrates and aligns information from various modalities,enabling the classification performance of joint cognitive representations beyond any single modality.Moreover,the study demonstrates superior outcomes in cross-modal generation tasks compared to modality-specific unidirectional mappings,which is expected to offer a new line of thought for the effective unified encoding and decoding modeling of visual cognitive information in the brain.

Objective To investigate the influencing factors for tendency towards depression in college students having music listening habits with music APP,and develop a prediction model and further optimize it.Methods A total of 1 157 college students were subjected with convenient sampling and surveyed with questionaires between April and May 2023.Univariate analysis and logistic regression analysis were employed to identify the influencing factors.Then a prediction model was constructed based on these factors.SMOTEENN over-sampling algorithm was utilized to enhance the dataset and construct the prediction model.Results Logistic regression analysis revealed that female(OR=1.730,95％CI:1.257～2.396),senior grade(OR=2.649,95％CI:1.198～7.506),postgraduate grade(OR=2.041,95％CI:1.231～3.885),major in Science(OR=1.573,95％CI:1.052～2.350),listening for a duration of 0.5～2 h(OR=1.661,95％CI:1.011～2.695),music style of melancholy(OR=2.668,95％CI:1.701～4.226)and of nostalgia(OR=1.751,95％CI:1.086～2.837),and frequency of comments on 0～5％of songs(OR=2.938,95％CI:1.018～8.417)were independent risk factors for depressive tendency.Time since listening to music for 1～3 years(OR=0.547,95％CI:0.347～0.872),listening to music from 14:00 to 18:00(OR=0.375,95％CI:0.167～0.845)and 18:00 to 21:00(OR=0.313,95％CI:0.148～0.671),and preference for Chinese style songs(OR=0.711,95％CI:0.541～0.941)were independent protective factors.The logistic early warning model based on SMOTEENN algorithm demonstrated optimal predictive performance with an AUC value of 0.923.Conclusion Our constructed logistic regression model has identified 9 independent influencing factors associated with depression tendency among college students.The early warning model based on SMOTEENN algorithm can predict the depression tendency more accurately for college students.