Chronic mucocutaneous candidiasis (CMC) is an infectious phenotype characterized by recurrent or persistent infections caused by Candida species that affect the skin, nails, oral, and genital mucosae for a duration exceeding six months. Current research suggests that CMC is an immunodeficiency disease with a complex pathogenesis. Patients with CMC have various defects in nonspecific and/or specific immunity against Candida infection, resulting in the inability of patients to defend themselves against Candida infection. CMC can be stratified into primary CMC and secondary CMC based on etiology. Primary CMC is often associated with genetic mutations leading to immunodeficiencies in T helper cell 17 and interleukin-17, whereas secondary CMC is frequently linked to factors such as human immunodeficiency virus infection, diabetes mellitus, and immunosuppressive therapy. Primary CMC typically manifests as Candida infections, with distinct genetic mutations often correlating to varied concomitant symptoms. Secondary CMC may present with not only superficial mucosal Candida infections and manifestations of the underlying primary disease but also with invasive fungal infections. Diagnosing CMC requires an integration of medical history and clinical presentation, supplemented by the outcomes of auxiliary diagnostic procedures, including microscopic examination of fungal smear, fungal culture, immunological testing, and genetic sequencing and analysis. Furthermore, confirming primary CMC requires exclusion of the aforementioned secondary factors. At present, antifungal drugs such as triazoles, echinocandins, and polyenes are the main treatment for CMC. Moreover, immunotherapy with biologics such as Janus kinase (JAK) inhibitors provides more options for the clinical treatment of patients with CMC. Gene therapy also has potential clinical application value. In this review, we discuss the etiologies, pathogenesis, clinical manifestations, diagnosis, and treatments of CMC, aiming to provide a reference for the clinical diagnosis and treatment of CMC.

Objective: To explore the impact of sleep quality, experience of childhood maltreatment, and their interaction on depressive symptoms among middle school students, so as to provide the reference for early intervention of depressive symptoms among middle school students. Methods: From September to December 2023, a questionnaire survey was conducted among 1 231 students from two secondary schools in Harbin, Heilongjiang Province by a convenient sampling method. The survey included general demographic information, Childhood Trauma Questionnaire Short Form, Pittsburgh Sleep Quality Index and Short Version of Center for Epidemiological Studies Depression Scale. The Chi square test was used to analyze the differences in depressive symptom, sleep quality and childhood maltreatment among students with different demographic characteristics. Correlation analysis was conducted using Logistic regression, and interaction analysis was performed by both additive and multiplicative interaction models. Results: The detection rate of depressive symptoms among middle school students was 22.7%, and the rate for high school students (35.2%) was significantly higher than that for middle school students (17.0%) ( χ 2=50.35, P <0.01). The detection rates of depressive symptoms among middle school students with a history of childhood maltreatment and poor sleep quality were 45.8% and 44.0%, respectively. Multivariate Logistic regression analysis showed that compared to students without a history of childhood maltreatment, students with a history of childhood maltreatment had a higher risk of depressive symptoms ( OR =4.49，95% CI =3.31~ 6.09 , P <0.01);students with poor sleep quality had a higher risk of depressive symptoms than students with good sleep quality ( OR = 5.99，95% CI =4.37~8.22, P <0.01).The interaction results showed that the presence of childhood maltreatment and poor sleep quality had an additive interaction on the occurrence of depression in middle school students. Compared with students without childhood maltreatment and having good sleep quality, students with childhood maltreatment and poor sleep quality had a 22.49 times higher risk of developing depression ( OR =22.49，95% CI =14.22~35.59, P <0.01). Conclusion Depressive symptoms among middle school students are associated with childhood maltreatment and poor sleep quality, and there is an additive interaction between childhood maltreatment and poor sleep quality on the impact of depressive symptoms.

ObjectivePatients with hepatic glycogen storage disease（GSD）have recurrent episodes of hypoglycemia. This study aimed to investigate and analyze blood glucose and biochemical indicators in pediatric patients with hepatic GSD， thus provide data support for hypoglycemia prevention and its clinical management. MethodsA cross-sectional field study was conducted among patients with hepatic GSD treated in the Department of Pediatrics of Guangdong Provincial People's Hospital on July 14， 2024. We collected the peripheral blood samples of the patients and their healthy family controls on site， then analyzed and compared their blood glucose and biochemical indicators. ResultsOf the 44 patients with hepatic GSD， there were 34 males and 10 females， including GSD Ib（n =14）， GSD Ia（n=15）， GSD Ⅲ（n=2）， GSD Ⅵ（n=7）and GSD Ⅸ（n=6）. The average age was 7.60（5.08-11.98）years. All patients were on uncooked cornstarch（UCCS）therapy. Of the patients， 77.3%（34/44）had hepatomegaly， 61.4%（27/44）had recurrent hypoglycemia， 61.4%（27/44）had blood glucose ≤ 3.9 mmol/L， 18.2%（8/44）had blood glucose ≤ 2.8 mmol/L， and none of the 8 cases was GSD Ib. The lowest blood glucose level was 1.19 mmol/L and no episodes of hypoglycemia occurred. Of the family control subjects， 65.9%（29/44）had blood glucose ≤ 3.9 mmol/L. There was no significant difference in hypoglycemia prevalence between hepatic GSD group and control group（P=0.658）. The hepatic GSD patients had hyperlactacemia， hyperuricemia and hypercholesterolemia prevalence rates of 65.9%， 45.5% and 9.1%， respectively， as compared with 18.2%， 43.2% and 15.9%， respectively， for the family control subjects. No significant difference was found in the prevalence rates of hyperuricemia and hypercholesterolemia between the two groups（P=0.830 and P=0.334， respectively）. ConclusionsAsymptomatic hypoglycemia is common in patients with hepatic GSD， especially in non-GSD-Ib patients. It is necessary to optimize the diet management of UCCS， conduct dynamic blood glucose monitoring and follow a light diet， so as to decrease hyperuricemia and hypercholesterolemia， avoid and reduce the serious adverse reactions and complications caused by severe hypoglycemia.

In this study, we constructed a GLP-2R-HEK293 cell line and established a method for the determination of the in vitro biological activity of teduglutide based on HTRF, after optimizing experimental conditions and methodological verification. We also carried out relative potency detection of teduglutide pharmaceutical products using this method. The result showed that there was a quantitative-efficient relationship between the teduglutide activity and cAMP contents in GLP-2R-HEK293 cells, which conformed to four-parameter model. Method verification results of five concentrations of teduglutide (64%, 80%, 100%, 125% and 156%) met the requirements of the General Rules of Chinese Pharmacopoeia, 2020 edition, Volume IV (9401). We then analyzed the relative potency of three batches of teduglutide drug substances and three batches of drug products. The linearity, regression and parallelism of the obtained curves all fit the system suitability requirements. The relative potency of six batches of teduglutide was from 83% to 105%. In summary, the biological activity detection method established in this study was accurate, precise, simple and time-saving, which can be used for quality control of teduglutide pharmaceutical products.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

ObjectiveTo investigate the contamination status, transmission risk and drug resistance of Klebsiella pneumoniae (KP) on the object surfaces in the surrounding environment of hospitalized patients infected with carbapenem-resistant Klebsiella pneumoniae (CRKP) , so as to provide a scientific guidance for the prevention and control of healthcare-associated infection. MethodsSamples from the surfaces of objects in the surrounding environment of CRKP infected patients living in the intensive care unit (ICU) and hand specimens from healthcare workers were collected for KP isolation and identification, as well as drug susceptible test in a medical institution located in Minhang District, Shanghai from 2021 to 2023. Additionally, both univariate and multivariate logistic regression analyses were used to identify the influencing factors associated with KP contamination in the hospital environment. ResultsA total of 546 surface samples were collected from the surrounding environment objects of 15 patients infected with CRKP, with a KP detection rate of 6.59% (36/546).The KP detection rate in the ICU of general ward (10.22%) was higher than that in the ICU of emergency department (2.94%) (χ²=12.142, P<0.001). Moreover, the KP detection rate on the surfaces of patient-contacted items (15.66%) was higher than that on shared-use items (6.25%), cleaning items (10.00%), and medical supplies (3.30%) (χ²=17.943, P<0.001). Besides, the detection rate of KP in items sent out of hospital for disinfection (15.38%) was higher than that in those self-disinfected (4.20%) (χ²=19.996, P<0.001).The highest detection rate of KP was observed in high-temperature washing (15.13%, 18/119) (χ²=21.219, P<0.001), while the lowest detection rate was observed in antibacterial hand sanitizer with trichlorohydroxydiphenyl ether sanitizing factor (0, 0/60) ( χ²=21.219, P<0.001).The detection rate of KP in samples taken more than 24 hours after the last disinfection (23.08%) was higher than that in those taken at 4 to24 hours (12.90%) and less than 4 hours (4.22%) (χ²=23.398,P<0.001).ICU of general ward (OR=4.045, 95%CI: 2.206‒7.416), patient-contacted items (OR=3.113, 95%CI: 1.191‒8.141), and self-disinfection ( OR=0.241, 95%CI:0.144‒0.402) were influencing factors for KP contamination in environmental surface. From 2021 to 2023, the drug resistance rates of hospital environmental KP isolates showed an upward trend (P<0.001) to antibiotics such as ceftazidime and gentamicin. Furthermore, high drug resistance rates of KP (>90%) were observed to ciprofloxacin, levofloxacin, cefotaxime, ceftriaxone, and cefepime. ConclusionCRKP can be transmitted outward through the surfaces of objects in the patients’ surroundings, and the drug resistance situation is severe. In clinical settings, it is necessary to implement isolation measures for CRKP infection patients, to increase the frequency of disinfection for objects in their surroundings, to strengthen hand hygiene practices, and to use antibiotics appropriately.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.