The coronavirus disease 2019 (COVID-19) pandemic is the defining global health crisis of our time and the greatest challenge facing the world. Meteorological parameters are reportedly crucial factors ...affecting respiratory infectious disease epidemics; however, the effect of meteorological parameters on COVID-19 remains controversial. This study investigated the effects of temperature and relative humidity on daily new cases and daily new deaths of COVID-19, which has useful implications for policymakers and the public. Daily data on meteorological conditions, new cases and new deaths of COVID-19 were collected for 166 countries (excluding China) as of March 27, 2020. Log-linear generalized additive model was used to analyze the effects of temperature and relative humidity on daily new cases and daily new deaths of COVID-19, with potential confounders controlled for, including wind speed, median age of the national population, Global Health Security Index, Human Development Index and population density. Our findings revealed that temperature and relative humidity were both negatively related to daily new cases and deaths. A 1 °C increase in temperature was associated with a 3.08% (95% CI: 1.53%, 4.63%) reduction in daily new cases and a 1.19% (95% CI: 0.44%, 1.95%) reduction in daily new deaths, whereas a 1% increase in relative humidity was associated with a 0.85% (95% CI: 0.51%, 1.19%) reduction in daily new cases and a 0.51% (95% CI: 0.34%, 0.67%) reduction in daily new deaths. The results remained robust when different lag structures and the sensitivity analysis were used. These findings provide preliminary evidence that the COVID-19 pandemic may be partially suppressed with temperature and humidity increases. However, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19.
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•First study to explore the effects of temperature and humidity on the daily new cases and deaths of COVID-19 worldwide.•We used log-linear GAM to analyze the effects.•We considered the lag effects and the cumulative effects of weather conditions.•Temperature and relative humidity were both negatively related to the daily new cases and daily new deaths of COVID-19
The abundance of intramuscular fat (marbling) and tenderness are 2 key determining factors of beef quality,whereas muscle growth determines the meat production efficiency. Marbling accumulation is ...due to both hyperplasia and hypertrophy of intramuscular fat cells (adipocytes). On the other hand, intramuscular fibroblasts are major contributors for the formation of connective tissue and its cross-linking, which are responsible for background toughness of beef.Interestingly, muscle cells, adipocytes, and fibroblasts are derived from a common pool of mesenchymal progenitors during embryonic development. In the early embryos, a portion of progenitor cells in anlage commit to the myogenic lineage,whereas nonmyogenic cells become adipo-fibrogenic cells or other cells. These myogenic cells proliferate extensively and further develop into primary and secondary muscle fibers and satellite cells, whereas adipo-fibrogenic cells form the stromal-vascular fraction of muscle where intramuscular adipocytes and fibroblasts reside. Strengthening prenatal myogenesis and muscle development enhances lean growth, whereas promoting intramuscular adipocyte formation elevates marbling. Because the abundance of progenitor cells in animals declines as their development progresses, it is more effective to manipulate progenitor cell differentiation during early development. Maternal nutrition and other environmental factors affect progenitor cell commitment, proliferation, and differentiation, which programs muscle growth and marbling fat development of offspring, affecting the quantity and quality of meat production.
With the advancement of nanotechnology, several nanoparticles have been synthesized as antimicrobial agents by utilizing biologically derived materials. In most cases, the materials used for the ...synthesis of nanoparticles from natural sources are extracts. Natural extracts contain a wide range of bioactive components, making it difficult to pinpoint the exact component responsible for nanoparticle synthesis. Furthermore, the bioactive component present in the extract changes according to numerous environmental factors. As a result, the current work intended to synthesize gold (AuNPs) and zinc oxide (ZnONPs) nanoparticles using pure phloroglucinol (PG). The synthesized PG-AuNPs and PG-ZnONPs were characterized using a UV-Vis absorption spectrophotometer, FTIR, DLS, FE-TEM, zeta potential, EDS, and energy-dispersive X-ray diffraction. The characterized PG-AuNPs and PG-ZnONPs have been employed to combat the pathogenesis of
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is recognized as one of the most prevalent pathogens responsible for the common cause of nosocomial infection in humans. Antimicrobial resistance in
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has been linked to the development of recalcitrant phenotypic characteristics, such as biofilm, which has been identified as one of the major obstacles to antimicrobial therapy. Furthermore,
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generates various virulence factors that are a major cause of chronic infection. These PG-AuNPs and PG-ZnONPs significantly inhibit early stage biofilm and eradicate mature biofilm. Furthermore, these NPs reduce
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virulence factors such as pyoverdine, pyocyanin, protease, rhamnolipid, and hemolytic capabilities. In addition, these NPs significantly reduce
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swarming, swimming, and twitching motility. PG-AuNPs and PG-ZnONPs can be used as control agents for infections caused by the biofilm-forming human pathogenic bacterium
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By 31 December 2020, Coronavirus disease 2019 (COVID-19) had been prevalent worldwide for one year, and most countries had experienced a complete seasonal cycle. The role of the climate and ...environment are essential factors to consider in transmission.
We explored the association between global meteorological conditions (including mean temperature, wind speed, relative humidity and diurnal temperature range) and new cases of COVID-19 in the whole past year. We assessed the relative risk of meteorological factors to the onset of COVID-19 by using generalized additive models (GAM) and further analyzed the hysteresis effects of meteorological factors using the Distributed Lag Nonlinear Model (DLNM).
Our findings revealed that the mean temperature, wind speed and relative humidity were negatively correlated with daily new cases of COVID-19, and the diurnal temperature range was positively correlated with daily new cases of COVID-19. These relationships were more apparent when the temperature and relative humidity were lower than their average value (21.07°Cand 66.83%). The wind speed and diurnal temperature range were higher than the average value(3.07 m/s and 9.53 °C). The maximum RR of mean temperature was 1.30 under −23°C at lag ten days, the minimum RR of wind speed was 0.29 under 12m/s at lag 24 days, the maximum RR of range of temperature was 2.21 under 28 °C at lag 24 days, the maximum RR of relative humidity was 1.35 under 4% at lag 0 days. After a subgroup analysis of the countries included in the study, the results were still robust.
As the Northern Hemisphere enters winter, the risk of global covid-19 remains high. Some countries have ushered in a new round of COVID-19 epidemic. Thus, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19 in winter.
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•We used time series analysis to study the relationship between meteorological factors and the transmission of COVID-19.•Global prevalence figures of COVID-19 and meteorological data for a whole year were analyzed.•We use a distributed lag linear model to study the lag effect of meteorological factors on the incidence of COVID-19.
•This study examines the association between Fintechs and SME efficiency.•We utilize firm-level data from OECD countries and apply GMM estimation method.•Our findings reveal that Fintechs improve SME ...efficiency.•We also show that culture moderates the link between Fintechs and SME efficiency.
Small and Medium Enterprises (SMEs) play a vital role in an economy; therefore, it is important to study the avenues that contribute towards their viability. As a result, we examine the impact of financial technologies (FinTechs) on SME efficiency. Using the Generalized Method of Moments methodology and 1,617 SME firms from 22 OECD countries during the period 2011–2018, we find that FinTechs are positively associated with SME efficiency. Interesting results emerge when we incorporate culture. Masculine societies positively moderate the link between FinTechs and SME efficiency. We also find that individualistic and long-term oriented cultures negatively affect the association between FinTechs and SME efficiency. Our findings have multiple implications. This study suggests the need for countries to introduce policies supporting FinTech startups in order to improve SME efficiency. Moreover, if the SME managers aim to achieve higher firm efficiency, then adopting FinTechs may act as a mechanism to attain this objective. Further, it may be important to consider both FinTechs and culture when evaluating cross-border investments.
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this ...study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.
Farm-animal species play crucial roles in satisfying demands for meat on a global scale, and they are genetically being developed to enhance the efficiency of meat production. In particular, one of ...the important breeders' aims is to increase skeletal muscle growth in farm animals. The enhancement of muscle development and growth is crucial to meet consumers' demands regarding meat quality. Fetal skeletal muscle development involves myogenesis (with myoblast proliferation, differentiation, and fusion), fibrogenesis, and adipogenesis. Typically, myogenesis is regulated by a convoluted network of intrinsic and extrinsic factors monitored by myogenic regulatory factor genes in two or three phases, as well as genes that code for kinases. Marker-assisted selection relies on candidate genes related positively or negatively to muscle development and can be a strong supplement to classical selection strategies in farm animals. This comprehensive review covers important (candidate) genes that regulate muscle development and growth in farm animals (cattle, sheep, chicken, and pig). The identification of these genes is an important step toward the goal of increasing meat yields and improves meat quality.
ObjectivesThe secondary impacts of the COVID-19 pandemic on adverse maternal and neonatal outcomes remain unclear. In this study, we aimed to evaluate the association between the COVID-19 pandemic ...and the risk for adverse pregnancy outcomes.DesignWe conduced retrospective analyses on two cohorts comprising 7699 pregnant women in Beijing, China, and compared pregnancy outcomes between the pre-COVID-2019 cohort (women who delivered from 20 May 2019 to 30 November 2019) and the COVID-2019 cohort (women who delivered from 20 January 2020 to 31 July 2020). The secondary impacts of the COVID-2019 pandemic on pregnancy outcomes were assessed by using multivariate log-binomial regression models, and we used interrupted time-series (ITS) regression analysis to further control the effects of time-trends.SettingOne tertiary-level centre in Beijing, ChinaParticipants7699 pregnant women.ResultsCompared with women in the pre-COVID-19 pandemic group, pregnant women during the COVID-2019 pandemic were more likely to be of advanced age, exhibit insufficient or excessive gestational weight gain and show a family history of chronic disease (all p<0.05). After controlling for other confounding factors, the risk of premature rupture of membranes and foetal distress was increased by 11% (95% CI, 1.04 to 1.18; p<0.01) and 14% (95% CI, 1.01 to 1.29; p<0.05), respectively, during the COVID-2019 pandemic. The association still remained in the ITS analysis after additionally controlling for time-trends (all p<0.01). We uncovered no other associations between the COVID-19 pandemic and other pregnancy outcomes (p>0.05).ConclusionsDuring the COVID-19 pandemic, more women manifested either insufficient or excessive gestational weight gain; and the risk of premature rupture of membranes and foetal distress was also higher during the pandemic.
Objective
We aimed to investigate the association between visit‐to‐visit heart rate variability (VVHRV) and all‐cause mortality in patients diagnosed with atrial fibrillation (AF). Previous studies ...have shown a positive correlation between VVHRV and several adverse outcomes. However, the relationship between VVHRV and the prognosis of AF remains uncertain.
Methods
In our study, we aimed to examine the relationship between VVHRV and mortality rates among 3983 participants with AF, who were part of the AFFIRM study (Atrial Fibrillation Follow‐Up Investigation of Rhythm Management). We used the standard deviation of heart rate (HRSD) to measure VVHRV and divided the patients into four groups based on quartiles of HRSD (1st, <5.69; 2nd, 5.69–8.00; 3rd, 8.01–11.01; and 4th, ≥11.02). Our primary endpoint was all‐cause death, and we estimated the hazard ratios for mortality using the Cox proportional hazard regressions.
Results
Our analysis included 3983 participants from the AFFIRM study and followed for an average of 3.5 years. During this period, 621 participants died from all causes. In multiple‐adjustment models, we found that the lowest and highest quartiles of HRSD independently predicted an increased risk of all‐cause mortality compared to the other two quartiles, presenting a U‐shaped relationship (1st vs 2nd, hazard ratio = 2.28, 95% CI = 1.63–3.20, p < .01; 1st vs. 3rd, hazard ratio = 2.23, 95% CI = 1.60–3.11, p < .01; 4th vs. 2nd, hazard ratio = 1.82, 95% CI = 1.26–2.61, p < .01; and 4th vs. 3rd, hazard ratio = 1.78, 95% CI = 1.25–2.52, p < .01).
Conclusion
In patients with AF, we found that both lower VVHRV and higher VVHRV increased the risk of all‐cause mortality, indicating a U‐shaped curve relationship.
3983 participants from the AFFIRM study were followed for an average of 3.5 years, and 621 participants died from all causes. The lowest and highest quartiles of HRSD independently predicted an increased risk of all‐cause mortality compared to the other two, presenting a U‐shaped relationship.
The associations between trajectories of different health conditions and cognitive impairment among older adults were unknown. Our cohort study aimed to investigate the impact of various ...trajectories, including sleep disturbances, depressive symptoms, functional limitations, and multimorbidity, on the subsequent risk of cognitive impairment.
We conducted a prospective cohort study by using eight waves of national data from the Health and Retirement Study (HRS 2002-2018), involving 4319 adults aged 60 years or older in the USA. Sleep disturbances and depressive symptoms were measured using the Jenkins Sleep Scale and the Centers for Epidemiologic Research Depression (CES-D) scale, respectively. Functional limitations were assessed using activities of daily living (ADLs) and instrumental activities of daily living (IADLs), respectively. Multimorbidity status was assessed by self-reporting physician-diagnosed diseases. We identified 8-year trajectories at four examinations from 2002 to 2010 using latent class trajectory modeling. We screened participants for cognitive impairment using the 27-point HRS cognitive scale from 2010 to 2018 across four subsequent waves. We calculated hazard ratios (HR) using Cox proportional hazard models.
During 25,914 person-years, 1230 participants developed cognitive impairment. In the fully adjusted model 3, the trajectories of sleep disturbances and ADLs limitations were not associated with the risk of cognitive impairment. Compared to the low trajectory, we found that the increasing trajectory of depressive symptoms (HR = 1.39; 95% CI = 1.17-1.65), the increasing trajectory of IADLs limitations (HR = 1.88; 95% CI = 1.43-2.46), and the high trajectory of multimorbidity status (HR = 1.48; 95% CI = 1.16-1.88) all posed an elevated risk of cognitive impairment. The increasing trajectory of IADLs limitations was associated with a higher risk of cognitive impairment among older adults living in urban areas (HR = 2.30; 95% CI = 1.65-3.21) and those who smoked (HR = 2.77; 95% CI = 1.91-4.02) (all P for interaction < 0.05).
The results suggest that tracking trajectories of depressive symptoms, instrumental functioning limitations, and multimorbidity status may be a potential and feasible screening method for identifying older adults at risk of cognitive impairment.
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