Sleep disorders are common in children and adolescents, especially in those with neurodevelopmental and mental disorders, but treatment strategies remain limited.The main therapeutic methods for sleep disorders in children and adolescents include cognitive behavioral therapy, surgical therapy, drug therapy, and physical therapy.Conventional cognitive behavioral therapy is expensive; plus, the number of qualified sleep physicians and therapists is limited, and patient′s compliance is low.Drug therapy is the most commonly used treatment for insomnia in children and adolescents, but it lacks evidence-based indications and safety and has many adverse reactions, and its long-term efficacy is unknown.Physical therapy that changes the activity of central and peripheral nerves through physical stimulation such as magnetism, electricity, sound and light can provide valuable alternative or complementary treatment for individuals with sleep disorders who can′t get conventional treatment, have poor tolerance or are ineffective.In this paper, 4 common clinical physical therapies (transcranial magnetic stimulation, transcranial direct current stimulation, light therapy, and hyperbaric oxygen therapy) and their application in treating sleep disorders in children and adolescents are reviewed, providing scientific basis and new ideas for the future clinical treatment of sleep disorders in children and adolescents.

Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.

Objective:To compare the importance of 11 common influencing factors for fall and fall-induced injury reported previously in the elderly.Methods:The data were collected from the follow-up of the China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2018. Binary logistic regression model and negative binomial regression model were used to test the significance of correlations between 11 factors and the incidence of fall and fall-induced injury during this period. The absolute value of the β^ was used to evaluate importance of 11 influencing factors. Results:This study included 9 279, 6 153, 4 142, 4 148, and 3 583 old persons. The cumulative incidence rates of fall in the 2 nd, 3 rd, 4 th, 5 th, and 7 th years were 19.4% (95% CI: 18.6%-20.2%), 22.1% (95% CI: 21.0%-23.1%), 31.9% (95% CI: 30.4%-33.3%), 35.1% (95% CI: 33.6%-36.5%), and 43.2% (95% CI: 41.6%-44.8%), respectively. The cumulative incidence rates of fall-induced injury were 8.4% (95% CI: 7.8%-8.9%), 9.4% (95% CI: 8.7%-10.1%), 15.1% (95% CI: 14.0%-16.2%), 16.2% (95% CI: 15.1%-17.3%), and 22.0% (95% CI: 20.6%-23.3%). The results of multivariate logistic regression and negative binomial regression analyses showed that in the 11 factors, only gender, history of fall, and depressive symptoms were identified as common risk factors for fall and fall-induced injury in the elderly in all the follow up visits (all P<0.05); the history of fall had the highest absolute value of β^ in all models, while gender ranked second except for the 5-year fall-induced injury model. Conclusions:Of the 11 influencing factors for fall and fall-induced injury reported by previous literature, only gender, history of falls, and depressive symptoms were identified as common risk factors for fall and fall-induced injury in the eldely in the 2 nd, 3 rd, 4 th, 5 th, and 7 th years follow-up visits. History of fall and gender were important influencing factors for fall and fall-induced injury in the elderly.

Objective:To evaluate the diagnostic efficacy and practicality of the Jaundice color card (JCard) as a screening tool for neonatal jaundice.Methods:Following the standards for reporting of diagnostic accuracy studies (STARD) statement, a multicenter prospective study was conducted in 9 hospitals in China from October 2019 to September 2021. A total of 845 newborns who were admitted to the hospital or outpatient department for liver function testing due to their own diseases. The inclusion criteria were a gestational age of ≥35 weeks, a birth weight of ≥2 000 g, and an age of ≤28 days. The neonate′s parents used the JCard to measure jaundice at the neonate′s cheek. Within 2 hours of the JCard measurement, transcutaneous bilirubin (TcB) was measured with a JH20-1B device and total serum bilirubin (TSB) was detected. The Pearson′s correlation analysis, Bland-Altman plots and the receiver operating characteristic (ROC) curve were used for statistic analysis.Results:Out of the 854 newborns, 445 were male and 409 were female; 46 were born at 35-36 weeks of gestational age and 808 were born at ≥37 weeks of gestational age. Additionally, 432 cases were aged 0-3 days, 236 cases were aged 4-7 days, and 186 cases were aged 8-28 days. The TSB level was (227.4±89.6) μmol/L, with a range of 23.7-717.0 μmol/L. The JCard level was (221.4±77.0) μmol/L and the TcB level was (252.5±76.0) μmol/L. Both the JCard and TcB values showed good correlation ( r=0.77 and 0.80, respectively) and agreements (96.0% (820/854) and 95.2% (813/854) of samples fell within the 95% limits of agreement, respectively) with TSB. The JCard value of 12 had a sensitivity of 0.93 and specificity of 0.75 for identifying a TSB ≥205.2?μmol/L, and a sensitivity of 1.00 and specificity of 0.35 for identifying a TSB ≥342.0?μmol/L. The TcB value of 205.2?μmol/L had a sensitivity of 0.97 and specificity of 0.60 for identifying TSB levels of 205.2 μmol/L, and a sensitivity of 1.00 and specificity of 0.26 for identifying TSB levels of 342.0 μmol/L. The areas under the ROC curve (AUC) of JCard for identifying TSB levels of 153.9, 205.2, 256.5, and 342.0 μmol/L were 0.96, 0.92, 0.83, and 0.83, respectively. The AUC of TcB were 0.94, 0.91, 0.86, and 0.87, respectively. There were both no significant differences between the AUC of JCard and TcB in identifying TSB levels of 153.9 and 205.2 μmol/L (both P>0.05). However, the AUC of JCard were both lower than those of TcB in identifying TSB levels of 256.5 and 342.0 μmol/L (both P<0.05). Conclusions:JCard can be used to classify different levels of bilirubin, but its diagnostic efficacy decreases with increasing bilirubin levels. When TSB level are ≤205.2 μmol/L, its diagnostic efficacy is equivalent to that of the JH20-1B. To prevent the misdiagnosis of severe jaundice, it is recommended that parents use a low JCard score, such as 12, to identify severe hyperbilirubinemia (TSB ≥342.0 μmol/L).

Objective:To explore the effects of the core symptoms of attention deficit hyperactivity disorder (ADHD) and behavior problems on the sleep of children with ADHD and their correlation.Methods:In this cross-sectional study, a total of 289 children aged 6-12 years with ADHD who visited the Outpatient Department of the Center for Cognition and Sleep at the People′s Hospital of Guangxi Zhuang Autonomous Region from June 2021 to December 2022 were selected.The Swanson, Nolan and Pelham Rating Scale Ⅳ was used to assess ADHD core symptoms (attention deficit and hyperactivity-impulsivity), the Conners Parent Symptom Questionnaire was used to assess behavior problems (conduct, learning, psychosomatic symptoms, and anxiety), and the Sleep Disturbance Scale for Children (SDSC) was used to assess sleep.Multivariate Logistic regression, multivariate linear regression, and path analysis were performed for statistical analysis of the data.Results:Multivariate Logistic regression analysis showed that after controlling age, gender, verbal intelligence quotient and performance intelligence quotient, attention deficit ( P=0.008, OR=2.60, 95% CI: 1.28-5.28), psychosomatic symptoms ( P=0.027, OR=6.77, 95% CI: 1.24-36.93) and anxiety ( P=0.013, OR=3.96, 95% CI: 1.43-11.67) increased the risk of sleep disturbances in children with ADHD (SDSC score >39 points).Path analysis showed that attention deficit ( β=0.244, P=0.001), psychosomatic symptoms ( β=0.114, P=0.046) and anxiety ( β=0.216, P<0.001) directly affected the SDSC score.Hyperactivity-impulsivity indirectly affected the SDSC score through psychosomatic symptoms ( β=0.142, P=0.039), and attention deficit indirectly affected the SDSC score through anxiety ( β=0.321, P<0.001). Conclusions:The severer the core symptoms of ADHD and behavior problems are, the higher the degree of sleep disturbances in children with ADHD is.Psychosomatic symptoms and anxiety are directly associated with the sleep disturbances of ADHD children.Attention deficit has direct or anxiety-mediated indirect impact on the sleep of ADHD children.Hyperactivity-impulsivity indirectly affects the sleep of children with ADHD through psychosomatic symptoms.

Objective:To explore the relationship among sleep phenotypes, attention deficit and hyperactivity impulsivity (ADHD) symptoms and cognitive information processing in children with ADHD.Methods:A total of 244 children with ADHD aged 6-12 were selected from December 2021 to December 2022.Swanson, Nolan and Pelham rating scale Ⅳ(SNAP-Ⅳ) was used to evaluate the core symptoms of ADHD.Sleep disturbance scale for children (SDSC) was used to evaluate six sleep phenotypes((disorders in initiating and maintaining sleep (DIMS), sleep breathing disorders(SDB), sleep-wake transition disorders(SWTD), disorders of arousal(DA), disorders of excessive somnolence (DOES), and Nocturnal hyperhidrosis(SHY)). Das-Naglieri cognitive function assessment system (DN-CAS) was used to evaluate the cognitive information processing (planning, simultaneous processing, attention and successive processing). Descriptive statistical analysis, Spearman correlation analysis, and mediation analysis were conducted by SPSSAU 23.0 and Zstats software, respectively.Results:Correlation analysis showed that DIMS, SDB, SWTD, DA and DOES were significantly and positively correlated with attention deficit ( r=0.190-0.349, all P<0.01).DIMS(2.14(1.71, 2.57)), SWTD(1.67(1.33, 2.00)) and SHY(2.00(1.50, 3.00)) were significantly positively correlated with hyperactive impulsivity (1.44(1.00, 2.00))( r=0.193, 0.242, 0.133, P<0.05). Attention deficit(1.78(1.44, 2.33)) was significantly and negatively correlated with successive processing(105.00(96.00, 112.00)) ( r=-0.127, P<0.05). The results of multiple linear regression analysis showed that after controlling for sex, age, verbal IQ and operational IQ, DIMS ( β=0.152, P<0.05) and SWTD ( β=0.178, P<0.05) had significant positive predictive effects on hyperactive impulsivity symptoms. DOES ( β=0.259, P<0.01) had significant positive predictive effects on attention deficit symptoms. Attention deficit ( β=-0.183, P<0.05) had a significant negative predictive effect on successive processing. Mediation effect analysis showed that attention deficit played a complete mediating role between DOES and successive processing(effect=-0.179, Bootstrap 95% CI=-0.196--0.110). Conclusion:Different sleep phenotypes are associated with ADHD core symptoms and different dimensions of cognitive information processing processes. DOES indirectly affects successive processing capability by attention deficit symptoms.

The incidence of breast cancer is increasing year by year,and its pathogenesis is highly complex.The dysregulation of gut microbiota function is closely related to the occurrence and development of breast cancer.The estrogen levels through enterohepatic circulation is regulated by β-glucuronidase produced by the gut microbiota,thereby influencing the occurrence and development of hormone receptor-positive breast cancer and leading to tamoxifen resistance.The metabolites from the gut microbiota,such as short-chain fatty acids(SCFAs)and lithocholic acid(LCA),can participate in regulating the tumor cell cycles and cell proliferation.The colonization of gut microbiota maintains the integrity of the intestinal barrier and regulates the anti-tumor immunity mediated by T lymphocytes.Maintaining gut microbiota homeostasis can enhance the efficacy of tumor chemotherapy and immunotherapy and reduce the adverse reactions in anti-tumor treatments.The targeted action of engineered probiotics in immunotherapy can improve the precision of drug treatment.The effect of gut microbiota on radiotherapy is not yet clear,but regulating gut microbiota can aid in the treatment of radiation enteritis.This review discusses the correlation and effect of gut microbiota on breast cancer and analyzes its role in the treatment of breast cancer.

Objective To explore the role of clinical pharmacists involved in the case of a patient with acute myeloid leukemia whose QTc interval prolongation was induced by gilteritinib,and to provide reference for drug treatment and monitoring of those patients.Methods The abnormal electrocardiogram(ECG)of a patient with acute myeloid leukemia was found in time by clinical pharmacists,who participated in clinical diagnosis and treatment by analyzing the patient's underlying diseases,diagnosis and treatment process,therapeutic drugs and their potential interactions.Results Clinical pharmacists suspected that the prolonged QTc interval was likely to be an adverse reaction caused by gilteritinib,and recommended immediate discontinuation of the drug and re-examination of the electrocardiogram.The physician took the suggestion to stop the suspected drug therapy with gilteritinib promptly,and ECG was rechecked 3 d later,and the QTc value returned to the normal range.Conclusion Clinical pharmacists participating in clinical diagnosis and treatment could provide better pharmaceutical care for patients.

Objective:To develop a rapid, simple and cost-effective quantitative TaqMan RT-PCR (RT-qPCR) that could be used as an alternative to sequencing for the detection of Omicron variants and to evaluate its performance.Methods:Primers and TaqMan probes targeting the conserved domains of SARS-CoV-2 ORF1ab and the high-frequency mutation sites in the S gene of Omicron variants were designed. Then a RT-qPCR for the detection of Omicron variants was established. The consistency of the method was verified using samples identified by whole-genome sequencing. The specificity and sensitivity of the method were also evaluated.Results:The established RT-qPCR could distinguish Omicron variants from early epidemic A strains and Alpha and Delta variants of SARS-CoV-2, and the results were consistent with those of whole-genome sequencing with a coincidence rate of 100% (28/28). There was no cross-reactivity with other six respiratory viruses or coxsackievirus group A16. For RNA standards, this method showed good linearity in the range of 10 9-10 3 copies/μl with a correlation coefficient ( R2) greater than 0.99 and detection sensitivity of 10 3 copies/μl. Conclusions:The RT-qPCR designed in this study for Omicron variant detection had good sensitivity and specificity and could be easily performed in laboratories, which would greatly facilitate the monitoring of Omicron variants.

Objective:To evaluate the cognitive information processes in children with autism spectrum disorder(ASD)based on the Das-Naglieri Cognitive Assessment System(DN: CAS)and to explore the characteristics of these cognitive processes.Methods:Thirty-two children with ASD and thirty-two healthy controls matched 1∶1 by gender and age were enrolled in this study.The cognition of all individuals was evaluated by the DN: CAS.SPSS 26.0 software was used for statistical analysis and Mann-Whitney U test was conducted to analyze the data. Results:Compared with the healthy control group, children with ASD had significantly lower scores in planning subtest of DN: CAS(23.50(19.00, 25.00) vs 30.50(27.00, 35.75), Z=-5.014, P<0.01), attention subtest of DN: CAS(25.50(20.25, 27.75) vs 33.00(28.00, 36.00), Z=-4.317, P<0.01), and the DN: CAS total score(115.00(106.00, 132.25) vs 139.00(127.00, 143.00), Z=-3.849, P<0.01). And there were significant differences in matching numbers(9.00(6.25, 10.00) vs 12.00(10.00, 13.75), Z=-4.317, P<0.01), planned codes(7.00(5.25, 8.00) vs 9.00(7.00, 11.00), Z=-3.404, P<0.01), planned connections(7.00(4.25, 9.75) vs 10.00(9.00, 12.00), Z=-4.383, P<0.01)in planning and expressive attention(8.00(6.25, 10.75) vs 11.50(8.25, 13.75), Z=-3.251, P<0.01), number detection(8.00(6.25, 10.00) vs 10.00(9.00, 12.00), Z=-3.628, P<0.01), receptive attention(8.00(6.00, 9.00) vs 10.00(9.00, 13.00), Z=-3.351, P<0.01)in attention between ASD children and healthy controls. Conclusion:ASD children have the planning and attention deficits.