OBJECTIVES: To investigate the relationship between the polygenic risks for various psychiatric disorders and clinical and neuropsychological characteristics in children with attention-deficit/hyperactivity disorder (ADHD). METHODS: Using a cross-sectional design, 285 children with ADHD and 107 healthy controls were assessed using the Child Behavior Checklist, the Behavior Rating Inventory of Executive Function for parents, the Wechsler Intelligence Scale for Children, Fourth Edition, and the Cambridge Neuropsychological Test Automated Battery. Blood samples were collected for genetic data. Polygenic risk scores (PRSs) for various psychiatric disorders were calculated using the PRSice-2 software. RESULTS: Compared with the healthy controls, the children with ADHD displayed significantly higher PRSs for ADHD, major depressive disorder, anxiety disorder, and obsessive-compulsive disorder (P<0.05). In terms of daily-life executive function, ADHD-related PRS was significantly correlated with the working memory factor; panic disorder-related PRS was significantly correlated with the initiation factor; bipolar disorder-related PRS was significantly correlated with the shift factor; schizophrenia-related PRS was significantly correlated with the inhibition, emotional control, initiation, working memory, planning, organization, and monitoring factors (P<0.05). The PRS related to anxiety disorders was negatively correlated with total IQ and processing speed index (P<0.05). The PRS related to obsessive-compulsive disorder was negatively correlated with the processing speed index and positively correlated with the stop-signal reaction time index of the stop-signal task (P<0.05). CONCLUSIONS PRSs for various psychiatric disorders are closely correlated with the behavioral and cognitive characteristics in children with ADHD, which provides more insights into the heterogeneity of ADHD.
Humans ; Attention Deficit Disorder with Hyperactivity/genetics* ; Child ; Male ; Female ; Cross-Sectional Studies ; Neuropsychological Tests ; Multifactorial Inheritance ; Adolescent ; Mental Disorders/etiology* ; Executive Function ; Genetic Risk Score

OBJECTIVE: To investigate the characteristics of central nervous system regulation in patients with refractory overactive bladder (rOAB) using resting-state functional magnetic resonance imaging (rs-fMRI), and to analyze the differences in brain function and connection between the patients and healthy population. METHODS: From May 1 to November 30, 2024, we performed rs-fMRI for 47 rOAB patients and another 47 matched healthy controls, documented relevant clinical data from all the participants and obtained their Overactive Bladder Symptom Scores (OABSS) and Overactive Bladder Questionnaire (OAB-Q) scores. Based on rs-fMRI, we compared the results of Independent Component Analysis (ICA), amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo) and degree centrality (DC) between the rOAB patients and healthy controls. RESULTS: The rOAB patients, in comparison with the healthy controls, showed dramatically higher daytime urination frequency (11.64 ± 3.85) vs (5.76 ± 0.91), nighttime urination frequency (3.72 ± 1.64) vs (0.31 ± 0.47), OABSS (8.22 ± 2.21) vs (0.64±0.78), OAB-Q1 score (20.85 ± 5.28) vs (6.78 ± 1.04), and OAB-Q2 score (45.04 ± 12.11) vs (14.51 ± 1.66) (all P<0.01). No statistically significant differences were observed in the results of ICA and ALFF between the right superior frontal and right middle frontal regions in the rOAB patients (P>0.05), but fALFF, ReHo and DC were significantly decreased in the patients compared with those in the healthy controls (P<0.01). CONCLUSION Compared with healthy population, the functions and connection of the frontal superior right and frontal middle right brain regions in rOAB patients are significantly down-regulated, which may serve as new therapeutic targets.
Humans ; Urinary Bladder, Overactive/physiopathology* ; Magnetic Resonance Imaging ; Brain/physiopathology* ; Female ; Male ; Adult ; Surveys and Questionnaires ; Case-Control Studies ; Middle Aged ; Rest ; Brain Mapping

To explore the regulatory mechanism of drought stress on the synthesis of chlorogenic acid and luteoloside in Lonicera japonica, we designed five drought gradients (soil water contents of 30%, 24%, 17%, 14%, and 10%) and screened and verified the differentially expressed genes (DEGs) by RNA sequencing (RNA-seq) and reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, we employed HPLC to systematically measure the content changes of chlorogenic acid and luteoloside. The results revealed that drought significantly reduced the accumulation of secondary metabolites, and severe drought led to more obvious reductions. Under extreme drought (soil water content of 10%), the content of chlorogenic acid and luteoloside decreased significantly to 25.73 mg/g and 11.33 mg/g (with the decrease rates of 37.85% and 9.58%, respectively). A total of 77 454 genes were identified via transcriptome analysis, among which the number of DEGs reached 1 128 under the extraordinary drought. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses revealed that the DEGs were mainly involved in flavonoid synthesis, secondary metabolite biosynthesis, plant hormone signal transduction and the plant-pathogen interaction pathways, and the expression of key genes regulating the synthesis of chlorogenic acid and luteoloside was significantly downregulated. RT-qPCR verified the accuracy of the RNA-seq data. This study revealed that drought stress reduced the content of chlorogenic acid and luteoloside, the main secondary metabolites, by inhibiting the expression of key genes in the secondary metabolism pathways. The findings provide candidate gene resources for molecular breeding of drought-tolerant Lonicera japonica.
Lonicera/physiology* ; Chlorogenic Acid/metabolism* ; Droughts ; Stress, Physiological ; Gene Expression Regulation, Plant ; Glucosides/metabolism* ; Luteolin

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

ObjectiveTo explore the influencing factors of occupational hand-arm vibration disease (OHAVD) caused by handheld workpiece polishing. Methods A total of 222 OHAVD patients (case group), 275 hand-transmitted vibration-exposed workers (exposed group) and 243 healthy workers without hand-transmitted vibration exposure (control group) in a sports equipment manufacturing enterprise were selected as the study subjects using the convenience sampling method. Worksite survey of occupational health was conducted on these three groups, and the human vibration measurement equipment was used to measure the vibration exposure level of handheld vibration among the study subjects. The 8-hour energy equivalent frequency-weighted vibrating acceleration ［A(8)］ and cumulative vibration exposure level (CVEL) were calculated. Results The prevalence of coldness, numbness, tingling fingers, and vibration-induced white finger was higher in the exposed group and the case group compared with the control group (all P<0.05). The prevalence of the above-mentioned hand symptoms was higher in the case group compared with the exposed group (all P<0.05). The A(8) and CVEL levels of the study subjects in the case group were higher than those in the exposed group (all P<0.05). Binary logistic analysis result showed that age and CVEL were both influencing factors of OHAVD (all P<0.05). According to the restricted cubic spline models, CVEL of the study subjects in the exposed group had a positive nonlinear dose-response relationship with the risk of OHAVD (overall trend P<0.01, nonlinear P<0.01), indicating an increasing risk of OHAVD with increasing CVEL. Conclusion Hand-transmitted vibration exposure is a risk factor for OHAVD. Early intervention should be carried out for hand-transmitted vibration-exposed individuals to reduce vibration-exposed levels and control vibration exposure time.

Objective To investigate the attributable risk(AR)of Acinetobacter baumannii(AB)infection in criti-cally ill patients.Methods A multicenter retrospective cohort study was conducted among adult patients in inten-sive care unit(ICU).Patients with AB isolated from sterile body fluid and confirmed with AB infection in each cen-ter were selected as the infected group.According to the matching criteria that patients should be from the same pe-riod,in the same ICU,as well as with similar APACHE Ⅱ score(±5 points)and primary diagnosis,patients who did not infect with AB were selected as the non-infected group in a 1:2 ratio.The AR was calculated.Results The in-hospital mortality of patients with AB infection in sterile body fluid was 33.3％,and that of non-infected group was 23.1％,with no statistically significant difference between the two groups(P=0.069).The AR was 10.2％(95％CI:-2.3％-22.8％).There is no statistically significant difference in mortality between non-infected pa-tients and infected patients from whose blood,cerebrospinal fluid and other specimen sources AB were isolated(P＞0.05).After infected with AB,critically ill patients with the major diagnosis of pulmonary infection had the high-est AR.There was no statistically significant difference in mortality between patients in the infected and non-infec-ted groups(P＞0.05),or between other diagnostic classifications.Conclusion The prognosis of AB infection in critically ill patients is highly overestimated,but active healthcare-associated infection control for AB in the ICU should still be carried out.

Objective To explore the relationship and internal path between activities of daily living(ADL),sleep quality and mental health of community elderly people in Shanghai.Methods A questionnaire survey was conducted among community residents aged 60 years and older seeing doctors in community health care center of five streets in Shanghai during Sept to Dec,2021 using convenience sampling.Activities of Daily Living(ADL),Pittsburgh Sleep Quality Index(PSQI)and 10-item Kessler Psychological Distress Scale(K10)were adopted in the survey.Single factor analysis,correlation analysis and multiple linear regression were used to analyze the data.The effect relationship between the variables was tested using Bootstrap's mediated effects test.Results A total of 1 864 participants were included in the study.The average score was 15.53±4.47 for ADL,5.60±3.71 for PSQI and 15.50±6.28 for K10.The rate of ADL impairment,poor sleep quality,poor and very poor mental health of the elderly were 23.6%,27.3%,11.9%and 4.9%,respectively.ADL and sleep quality were all positively correlated with mental health(r=0.321,P＜0.001;r=0.466,P＜0.001);ADL was positively correlated with sleep quality(r=0.294,P＜0.001).Multiple linear results of factors influencing mental health showed that ADL(β= 0.457,95%CI:0.341-0.573),sleep quality(β =0.667,95%CI:0.598-0.737)and mental health were positively correlated(P＜0.001).Sleep quality partially mediated the relationship between ADL and mental health(95%CI:0.078-0.124)with an effect size of 33.0%.Conclusion Sleep quality is a mediator between ADL and mental health among community elderly people.Improving ADL and sleep quality may improve mental health in the population.