Recently, male reproductive health has attracted extensive attention, with the adverse effects of circadian disruption on male fertility gradually gaining recognition. However, the mechanism by which circadian disruption leads to damage to male reproductive system remains unclear. In this review, we first summarized the dual regulatory roles of circadian clock genes on the male reproductive system: (1) circadian regulation of testosterone synthesis via the hypothalamic-pituitary-testicular (HPT) and hypothalamic-pituitary-adrenal (HPA) axes; (2) non-circadian regulation of spermatogenesis. Next, we further listed the possible mechanisms by which circadian disruption impairs male fertility, including interference with the oscillatory function of the reproductive system, i.e., synchronization of the HPT axis, crosstalk between the HPT axis and the HPA axis, as well as direct damage to germ cells by disturbing the non-oscillatory function of the reproductive system. Future research using spatiotemporal omics, epigenomic assays, and neural circuit mapping in studying the male reproductive system may provide new clues to systematically unravel the mechanisms by which circadian disruption affects male reproductive system through circadian clock genes.
Male ; Humans ; Animals ; Circadian Clocks/physiology* ; Hypothalamo-Hypophyseal System/physiology* ; Circadian Rhythm/genetics* ; Spermatogenesis/physiology* ; Pituitary-Adrenal System/physiology* ; Testis/physiology* ; Testosterone/biosynthesis* ; CLOCK Proteins ; Infertility, Male/physiopathology*

A 7-year-old girl was admitted to the hospital with rapidly progressive vision loss. Since 1 year of age, she had exhibited developmental delay accompanied by visual impairment and neutropenia. Combined with genetic testing and molecular pathogenicity analysis, she was diagnosed with Cohen syndrome (CS) caused by compound heterozygous variants in VPS13B (c.6940+1G>T and c.2911C>T). The c.6940+1G>T variant resulted in exon 38 skipping, leading to a frameshift and premature termination. Reverse transcription quantitative polymerase chain reaction revealed significantly reduced VPS13B gene expression (P<0.05). Bioinformatic analysis suggested that both variants likely produce truncated proteins. This case highlights that integrating clinical features with molecular pathogenicity assessment (DNA, RNA, and protein analysis) can improve early diagnostic accuracy for CS.
Humans ; Female ; Child ; Vesicular Transport Proteins/genetics* ; Developmental Disabilities/etiology* ; Muscle Hypotonia/etiology* ; Myopia/etiology* ; Heterozygote ; Intellectual Disability/etiology* ; Microcephaly/etiology* ; Obesity/genetics* ; Growth Disorders/etiology* ; Retinal Degeneration/genetics* ; Psychomotor Disorders/genetics* ; Fingers/abnormalities*

OBJECTIVE: To investigate possible associations between physical function assessment scales, such as Short Physical Performance Battery (SPPB) and Berg Balance Scale (BBS), with all-cause mortality in acute decompensated heart failure (ADHF) patients. METHODS: A total of 108 ADHF patients were analyzed from October 2020 to October 2022, and followed up to May 2023. The association between baseline clinical characteristics and all-cause mortality was analyzed by univariate Cox regression analysis, while for SPPB and BBS, univariate Cox regression analysis was followed by receiver operating characteristic curves, in which the area under the curve represented their predictive accuracy for all-cause mortality. Incremental predictive values for both physical function assessments were measured by calculating net reclassification index and integrated discrimination improvement scores. Optimal cut-off value for BBS was then identified using restricted cubic spline plots, and survival differences below and above that cut-off were compared using Kaplan-Meier survival curves and the log-rank test. The clinical utility of BBS was measured using decision curve analysis. RESULTS: For baseline characteristics, age, female, blood urea nitrogen, as well as statins, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, or angiotensin receptor-neprilysin inhibitors, were predictive for all-cause mortality for ADHF patients. With respect to SPPB and BBS, higher scores were associated with lower all-cause mortality rates for both assessments; similar area under the curves were measured for both (0.774 for SPPB and 0.776 for BBS). Furthermore, BBS ≤ 36.5 was associated with significantly higher mortality, which was still applicable even adjusting for confounding factors; BBS was also found to have great clinical utility under decision curve analysis. CONCLUSIONS BBS or SPPB could be used as tools to assess physical function in ageing ADHF patients, as well as prognosticate on all-cause mortality. Moreover, prioritizing the improvement of balance capabilities of ADHF patients in cardiac rehabilitation regimens could aid in lowering mortality risk.

OBJECTIVE: This study aimed to identify high-risk areas for type 2 diabetes mellitus (T2DM) mortality to provide relevant evidence for interventions in emerging economies. METHODS: Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality. The relationships between economic factors, air pollutants, and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model. RESULTS: A coastal district in East Guangdong, China, had the highest risk (Relative Risk [RR] = 4.58, P < 0.01), followed by the 10 coastal districts/counties in West Guangdong, China (RR = 2.88, P < 0.01). The coastal county in the Pearl River Delta, China (RR = 2.24, P < 0.01), had the third-highest risk. The remaining risk areas were two coastal counties in East Guangdong, 16 districts/counties in the Pearl River Delta, and two counties in North Guangdong, China. Mortality due to T2DM was associated with gross domestic product per capita (GDP per capita). In pilot assessments, T2DM mortality was significantly associated with carbon monoxide. CONCLUSION High mortality from T2DM occurred in the coastal areas of East and West Guangdong, especially where the economy was progressing towards the upper middle-income level.
Diabetes Mellitus, Type 2/epidemiology* ; China/epidemiology* ; Humans ; Risk Factors ; Spatio-Temporal Analysis ; Air Pollutants/analysis* ; Socioeconomic Factors ; Bayes Theorem ; Female ; Male ; Middle Aged

Atherosclerosis caused by disorders of lipid metabolism is the main pathological basis of atherosclerotic cardiovascular disease.Statins are the cornerstone of lipid-modulating therapy for this type of disease,but in practice there are still some patients with suboptimal lipid management.Proprotein convertase subtilisin/kexin type 9(PCSK9)inhibitors have been gradually applied as a new class of lipid-modulating drugs for the treatment in patients with this type of disease,and recent studies have shown that in addition to regulating lipid metabolism,PCSK9 inhibitors also have potential anti-inflammatory and anti-platelet activation effects.This article sorts out the multiple pharmacological mechanisms of action of PCSK9 inhibitors and the current status of clinical research of PCSK9 inhibitors.Besides,it discusses the factors that may affect the efficacy of PCSK9 inhibitors,in order to provide a reference for the safe and rational medication of PCSK9 inhibitors.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To observe the value of transesophageal echocardiography(TEE)for guiding left atrial appendage closure(LAAC)with LAmbre occluder.Methods Data of 40 non-valvular atrial fibrillation(NVAF)patients who underwent LA AC with LAmbre occluder were retrospectively analyzed.CT angiography(CTA)before treatment,TEE and digital subtraction angiography(DSA)findings during LAAC were comparatively observed,and the correlations of the anchor area diameter and left atrial appendage opening diameter measured with the above three as well as occluder size were analyzed,and TEE and DSA for evaluating peri-device leak(PDL)were compared.Results LAAC were successfully performed with LAmbre occlude in all 40 cases.The diameter of the fixed umbrella was positively correlated with anchor area diameter measured with CTA,TEE and DSA(r=0.79,0.82,0.91,all P＜0.01),of occlusion umbrella was positively correlated with left atrial appendage opening diameter measured with CTA,TEE and DSA(r=0.56,0.89,0.86,all P＜0.01).Immediately after the release of occluder in LAAC,PDL occurred in 16 cases and were detected with both TEE and DSA,while in the rest 24 cases no PDL was found with neither TEE nor DSA.Conclusion TEE had comparable value to DSA for guiding LAAC using LAmbre occluder.

Objective To construct an index system of clinicians'ability to cope with outburst respiratory infec-tious diseases,and provide a framework for developing corresponding training programs.Methods Based on litera-ture analysis and theoretical research,the first draft of competency index system was constructed,2 rounds of con-sultation to 23 experts were conducted using Delphi method,and the index weights were calculated by analytic hie-rarchy process.Results The effective recovery rate of correspondence questionnaire in 2 rounds of expert consulta-tion was 100％,and the expert authority coefficients of the first and second rounds were 0.81 and 0.84,respective-ly.Kendall's coefficient of concordant was 0.110-0.350,with statistical significance(all P＜0.01).After two rounds of expert consultation,an index system of clinicians'ability to cope with outburst respiratory infectious di-seases has been formed,including 3 first-level indexes,17 second-level indexes and 49 third-level indexes.Conclusion This index system not only can be used to evaluate clinicians'ability to cope with outburst respiratory infectious di-seases,but also can be used as content framework for the training program of clinicians'ability to cope with respira-tory infectious diseases.

Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8％(62 219/261 893),and gram-negative bacteria 76.2％(199 674/261 893).The most common species were E.coli(46.7％),E.faecium(10.4％),K.pneumoniae(9.8％),E.faecalis(8.7％),P.mirabilis(3.5％),P.aeruginosa(3.4％),SS.agalactiae(2.6％),and E.cloacae(2.1％).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2％,52.8％and 37.0％,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7％,18.5％,16.4％,and 40.3％,respectively.Lower than 10％of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90％of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was＜2％.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.