Background and Objective: Premature ventricular complex (PVC) burden exhibits one of three circadian types, classified as fast-type, slow-type, and independent-type PVC. It is unknown whether PVC circadian types have different heart rate variability (HRV) parameter values. Therefore, this study aimed to evaluate differences in HRV circadian rhythm among fast-, slow-, and independent-type PVC. Methods: This cross-sectional observational study consecutively recruited 65 idiopathic PVC subjects (23 fast-, 20 slow-, and 22 independent-type) as well as five control subjects. Each subject underwent a 24-hour Holter to examine PVC burden and HRV. HRV analysis included components that primarily reflect global, parasympathetic, and sympathetic activities. Repeated measures analysis of variance was used to compare differences in HRV circadian rhythm by PVC type. Results. The average PVC burden was 15.7%, 8.4%, and 13.6% in fast-, slow-, and independent-type idiopathic PVC subjects, respectively. Global, parasympathetic nervous system, and sympathetic nervous system HRV parameters were significantly lower in independenttype PVC versus fast- and slow-type PVC throughout the day and night. Furthermore, we unexpectedly found that tendency towards sympathetic activity dominance during nighttime was only in independent-type PVC. Conclusion The HRV parameters are reduced in patients with independent-type PVC compared to fast- and slowtype PVC. Future research is warranted to determine possible differences in the prognosis between the three PVC types.
Ventricular Premature Complexes ; Circadian Rhythm ; Autonomic Nervous System

BACKGROUND: It is crucial to understand the seasonal variation of Metabolic Syndrome (MetS) for the detection and management of MetS. Previous studies have demonstrated the seasonal variations in MetS prevalence and its markers, but their methods are not robust. To clarify the concrete seasonal variations in the MetS prevalence and its markers, we utilized a powerful method called Seasonal Trend Decomposition Procedure based on LOESS (STL) and a big dataset of health checkups. METHODS: A total of 1,819,214 records of health checkups (759,839 records for men and 1,059,375 records for women) between April 2012 and December 2017 were included in this study. We examined the seasonal variations in the MetS prevalence and its markers using 5 years and 9 months health checkup data and STL analysis. MetS markers consisted of waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG). RESULTS: We found that the MetS prevalence was high in winter and somewhat high in August. Among men, MetS prevalence was 2.64 ± 0.42 (mean ± SD) % higher in the highest month (January) than in the lowest month (June). Among women, MetS prevalence was 0.53 ± 0.24% higher in the highest month (January) than in the lowest month (June). Additionally, SBP, DBP, and HDL-C exhibited simple variations, being higher in winter and lower in summer, while WC, TG, and FPG displayed more complex variations. CONCLUSIONS This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.
Male ; Female ; Humans ; Metabolic Syndrome/epidemiology* ; Seasons ; Prevalence ; Climate ; Insulin Resistance ; Triglycerides

Circadian clock is an internal mechanism evolved to adapt to cyclic environmental changes, especially diurnal changes. Keeping the internal clock in synchronization with the external clock is essential for health. Mismatch of the clocks due to phase shift or disruption of molecular clocks may lead to circadian disorders, including abnormal sleep-wake cycles, as well as disrupted rhythms in hormone secretion, blood pressure, heart rate, body temperature, etc. Long-term circadian disorders are risk factors for various common critical diseases such as metabolic diseases, cardiovascular diseases, and tumor. To prevent or treat the circadian disorders, scientists have conducted extensive research on the function of circadian clocks and their roles in the development of diseases, and screened hundreds of thousands of compounds to find candidates to regulate circadian rhythms. In addition, melatonin, light therapy, exercise therapy, timing and composition of food also play a certain role in relieving associated symptoms. Here, we summarized the progress of both drug- and non-drug-based approaches to prevent and treat circadian clock disorders.
Circadian Rhythm ; Circadian Clocks ; Melatonin/physiology*

Objective: To study the effect of diurnal temperature range on the number of elderly inpatients with ischemic stroke in Hunan Province. Method: Demographic and disease data, meteorological data, air quality data, population, economic and health resource data of elderly inpatients with ischemic stroke were collected in 122 districts/counties of Hunan Province from January to December 2019. The relationships between the diurnal temperature range and the number of elderly inpatients with ischemic stroke were analyzed by using the distributed lag non-linear model, including the cumulative lag effect of the diurnal temperature range in different seasons, extremely high diurnal temperature range and extremely low diurnal temperature range. Results: In 2019, 152 875 person-times were admitted to the hospital for ischemic stroke in the elderly in Hunan Province. There was a non-linear relationship between the diurnal temperature range and the number of elderly patients with ischemic stroke, with different lag periods. In spring and winter, with the decrease in diurnal temperature range, the risk of admission of elderly patients with ischemic stroke increased (P_trend<0.001, P_trend=0.002);in summer, with the increase in diurnal temperature range, the risk of admission of elderly patients with ischemic stroke increased (P_trend=0.024);in autumn, the change in the diurnal temperature range would not cause a change in admission risk (P_trend=0.089). Except that the lag effect of the extremely low diurnal temperature range in autumn was not obvious, the lag effect occurred in other seasons under extremely low and extremely high diurnal temperature ranges. Conclusion: The high diurnal temperature range in summer and the low diurnal temperature range in spring and winter will increase the risk of admission of elderly patients with ischemic stroke, and the risk of admission of elderly patients with ischemic stroke will lag under the extremely low and extremely high diurnal temperature ranges in the above three seasons.
Humans ; Aged ; Temperature ; Ischemic Stroke ; Inpatients ; Cold Temperature ; Hot Temperature ; Seasons ; China/epidemiology*

Objective: To analyze the spatial and temporal distribution characteristics of seasonal A(H3N2) influenza [influenza A(H3N2)] in China and to provide a reference for scientific prevention and control. Methods: The influenza A(H3N2) surveillance data in 2014-2019 was derived from China Influenza Surveillance Information System. A line chart described the epidemic trend analyzed and plotted. Spatial autocorrelation analysis was conducted using ArcGIS 10.7, and spatiotemporal scanning analysis was conducted using SaTScan 10.1. Results: A total of 2 603 209 influenza-like case sample specimens were detected from March 31, 2014, to March 31, 2019, and the influenza A(H3N2) positive rate was 5.96%(155 259/2 603 209). The positive rate of influenza A(H3N2) was statistically significant in the north and southern provinces in each surveillance year (all P<0.05). The high incidence seasons of influenza A (H3N2) were in winter in northern provinces and summer or winter in southern provinces. Influenza A (H3N2) clustered in 31 provinces in 2014-2015 and 2016-2017. High-high clusters were distributed in eight provinces, including Beijing, Tianjin, Hebei, Shandong, Shanxi, Henan, Shaanxi, and Ningxia Hui Autonomous Region in 2014-2015, and high-high clusters were distributed in five provinces including Shanxi, Shandong, Henan, Anhui, and Shanghai in 2016-2017. Spatiotemporal scanning analysis from 2014 to 2019 showed that Shandong and its surrounding twelve provinces clustered from November 2016 to February 2017 (RR=3.59, LLR=9 875.74, P<0.001). Conclusion: Influenza A (H3N2) has high incidence seasons with northern provinces in winter and southern provinces in summer or winter and obvious spatial and temporal clustering characteristics in China from 2014-2019.
Humans ; Influenza, Human/epidemiology* ; China/epidemiology* ; Influenza A Virus, H3N2 Subtype ; Seasons ; Cluster Analysis

The circadian clock is an evolutionary molecular product that is associated with better adaptation to changes in the external environment. Disruption of the circadian rhythm plays a critical role in tumorigenesis of many kinds of cancers, including prostate cancer (PCa). Integrating circadian rhythm into PCa research not only brings a closer understanding of the mechanisms of PCa but also provides new and effective options for the precise treatment of patients with PCa. This review begins with patterns of the circadian clock, highlights the role of the disruption of circadian rhythms in PCa at the epidemiological and molecular levels, and discusses possible new approaches to PCa therapy that target the circadian clock.
Humans ; Male ; Carcinogenesis ; Circadian Clocks/physiology* ; Circadian Rhythm/physiology* ; Prostatic Neoplasms/physiopathology*

From 2015 to 2019, the annual average incidence rate of scarlet fever was 7.80/100 000 in Yantai City, which showed an increasing trend since 2017 (χ²_trend=233.59, P<0.001). The peak period of this disease was from April to July and November to January of the next year. The ratio of male to female was 1.49∶1, with a higher prevalence among cases aged 3 to 9 years (2 357/2 552, 92.36%). Children in kindergartens, primary and middle school students, and scattered children were the high risk population, with the incidence rate of 159.86/100 000, 25.57/100 000 and 26.77/100 000, respectively. The global spatial auto-correlation analysis showed that the global Moran's I index of the reported incidence rate of scarlet fever in Yantai from 2015 to 2019 was 0.28, 0.29, 0.44, 0.48, and 0.22, respectively (all P values<0.05), suggesting that the incidence rate of scarlet fever in Yantai from 2015 to 2019 was spatial clustering. The local spatial auto-correlation analysis showed that the "high-high" clustering areas were mainly located in Laizhou City, Zhifu District, Haiyang City, Fushan District and Kaifa District, while the "low-high" clustering areas were mainly located in Haiyang City and Fushan District.
Child ; Humans ; Male ; Female ; Scarlet Fever/epidemiology* ; Spatial Analysis ; Cities/epidemiology* ; Seasons ; Risk Factors ; Incidence ; Cluster Analysis ; China/epidemiology*

Objective: To explore the feasibility of moving epidemic method (MEM) in the assessment of seasonal influenza (influenza) activity intensity from the perspective of urban agglomeration, assess influenza activity intensity in the Beijing-Tianjin-Hebei region from 2019 to 2021 and evaluate the reliability of surveillance data and the effectiveness of the MEM model application. Methods: The weekly reported incidence rate (IR) of influenza and the percentage of influenza-like illness (ILI%) from 2011-2021 in Beijing-Tianjin-Hebei region were collected to establish MEM models respectively. The model fitting effect and the reliability of the two data were evaluated for the purpose of establishing an optimal model to assess the influenza activity intensity in Beijing-Tianjin-Hebei region from 2019-2021. A cross-validation procedure was used to evaluate the performance of the models by calculating the Youden's index, sensitivity and specificity. Results: The MEM model fitted with weekly ILI% had a higher Youden's index compared with the model fitted with weekly IR at both Beijing-Tianjin-Hebei region level and provincial level. The MEM model based on ILI% showed that the epidemic threshold in Beijing-Tianjin-Hebei region during 2019-2020 was 4.42%, the post-epidemic threshold was 4.66%, with medium, high and very high intensity thresholds as 5.38%, 7.22% and 7.84%, respectively. The influenza season during 2019-2020 had 10 weeks (week 50 of 2019 to week 7 of 2020). The influenza season started in week 50 of 2019, and the intensity fluctuated above and below medium epidemic level for six consecutive weeks. The high intensity was observed in week 4 of 2020, the threshold of very high intensity was excessed in week 5, and the intensity gradually declined and became lower than the threshold at the end of the influenza season in week 8. The epidemic threshold was 4.29% and the post-epidemic threshold was 4.35% during 2020-2021. Influenza activity level never excessed the epidemic threshold throughout the year, and no epidemic period emerged. Conclusions: The MEM model could be applied in the assessment of influenza activity intensity in Beijing-Tianjin-Hebei region, and the use of ILI% to assess influenza activity intensity in this region was more reliable than IR data. Influenza activity intensity in Beijing-Tianjin-Hebei region was higher during 2019-2020 but significantly lower in 2020-2021.
Humans ; Beijing/epidemiology* ; Influenza, Human/epidemiology* ; Seasons ; Reproducibility of Results ; Epidemics ; China/epidemiology*

Circadian Clocks/genetics* ; Reactive Oxygen Species ; Methionine ; Gastrointestinal Microbiome ; Gastrointestinal Tract ; Racemethionine ; Gene Expression

Circadian Clocks ; ARNTL Transcription Factors