Human coronaviruses are RNA viruses that are sensitive to ultraviolet (UV) radiation. Sunlight contains UVA (320–400 nm), UVB (260–320 nm) and UVC (200–260 nm) action spectra. UVC can inactivate coronaviruses, including severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The incidence and mortality of coronavirus disease 2019 (COVID-19) are considered to be correlated with vitamin D levels. Vitamin D synthesis in human skin is closely related to exposure to UVB radiation. Therefore, the incidence and mortality of COVID-19 are also considered to be correlated with Vitamin D levels. In this study, Spearman and Kendall rank correlation analysis tests were used to analyze the correlation between the average percent positive of five human coronaviruses (SARS-CoV-2, CoVHKU1, CoVNL63, CoVOC43, and CoV229E) in the U.S. and the corresponding sunlight UV radiation dose The results indicated that the monthly average percent positive of four common coronaviruses was significantly negatively correlated with the sunlight UV radiation dose. The weekly percent positive of SARS-CoV-2 during April 17, 2020 to July 10, 2020 showed a significant negative correlation with the sunlight UV radiation dose in census regions 1 and 2 of the U.S. while no statistical significance in the other regions. Additionally, sunlight UV radiation also showed some negative effects with respect to the early SARS-CoV-2 transmission.

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Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) is a unique PPIase belonging to the parvulin family, and it isomerizes peptide bond between phospho-(Ser/Thr) and Pro. Pin1 has been linked to the pathogenesis of various human diseases; however, its exact biological functions remain unclear. The aim of the present study is to explore the expression pattern of Pin1 in patients with refractory epilepsy and in a chronic pilocarpine-induced epileptic mouse model. Using Western blot, immunofluorescence and immunoprecipitation analysis, we found that Pin1 protein was mainly distributed in neurons, demonstrated by colocalization with the dendritic marker, MAP2. However, the expression of Pin1 decreased remarkably in epileptic patients and experimental mice. Furthermore, the reciprocal coimmunoprecipitation analysis showed that Pin1 interacted with NR2A and NR2B-containing NMDA receptors not AMPA receptors in epileptic mouse models. Our results are the first to indicate that the expression of Pin1 in epileptic brain tissue could play important roles in epilepsy.

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Spike density (SD) is an agronomically important character in wheat. In addition, an optimized spike structure is a key basis for high yields. Identification of quantitative trait loci (QTL) for SD has provided a genetic basis for constructing ideal spike morphologies in wheat. In this study, two recombinant inbred line (RIL) populations (tetraploid RIL AM and hexaploid RIL 20828/SY95-71 (2SY)) previously genotyped using the wheat55K SNP array were used to identify SD QTL. A total of 18 QTL were detected, and three were major and one was stably expressed (QSd.sau-2SY-7A.2, QSd.sau-AM-5A.2, QSd.sau-AM-7B, and QSd.sau-2SY-2D). They can explain up to 23.14, 19.97, 12.00, and 9.44% of phenotypic variation, respectively. QTL × environment and epistatic interactions for SD were further analyzed. In addition, pyramiding analysis further revealed that there were additive effects between QSd.sau-2SY-2D and QSd.sau-2SY-7A.2 in 2SY, and QSd.sau-AM-5A.2 and QSd.sau-AM-7B in AM. Pearson’s correlation between SD and other agronomic traits, and effects of major or stable QTL on yield related traits indicated SD significantly impacted spike length (SL), spikelet number per spike (SNS) and kernel length (KL). Several genes related to spike development within the physical intervals of major or stable QTL were predicted and discussed. Collectively, our research identified QTL with potential applications for modern wheat breeding and broadening the genetic basis of SD.

Many studies have confirmed the important roles of nutritional status and micronutrients in the COVID-19 pandemic. Magnesium is a vital essential trace element that is involved in oxidative stress, inflammation, and many other immunological functions and has been shown to be associated with the outcome of COVID-19 infection. Here, we conducted a nationwide retrospective cohort study in the United States involving 1150 counties, 287,326,503 individuals, and 5,401,483 COVID-19 confirmed cases as of 30 September 2020 to reveal the infection risk of the populations distributed in low-magnesium areas in the early transmission of COVID-19. Our results indicate that the average county-level COVID-19 cumulative incidence in low-magnesium areas was significantly higher than in the control areas. Additionally, a significant negative nonlinear association was found between environmental magnesium concentration and the county-level COVID-19 cumulative incidence. Furthermore, the populations distributed in low environmental magnesium areas faced a higher COVID-19 infection risk (RR: 1.066; CI: 1.063–1.068), among which females (RR: 1.07; CI: 1.067–1.073), the 0–17 years subgroup (RR: 1.125; CI: 1.117–1.134), the 65+ years subgroup (RR: 1.093; CI: 1.087–1.098), black people (RR: 1.975; CI: 1.963–1.986), populations outside metro areas, and counties with a smaller population experienced higher risk of infection by COVID-19 than other subgroups. Considering that the magnesium intake of about half the population of the United States is below the daily required dose, our study will contribute to the creation of long-term public health strategies to help protect against COVID-19.

India has suffered from the second wave of COVID-19 pandemic since March 2021. This wave of the outbreak has been more serious than the first wave pandemic in 2020, which suggests that some new transmission characteristics may exist. COVID-19 is transmitted through droplets, aerosols, and contact with infected surfaces. Air pollutants are also considered to be associated with COVID-19 transmission. However, the roles of indoor transmission in the COVID-19 pandemic and the effects of these factors in indoor environments are still poorly understood. Our study focused on reveal the role of indoor transmission in the second wave of COVID-19 pandemic in India. Our results indicated that human mobility in the home environment had the highest relative influence on COVID-19 daily growth rate in the country. The COVID-19 daily growth rate was significantly positively correlated with the residential percent rate in most state-level areas in India. A significant positive nonlinear relationship was found when the residential percent ratio ranged from 100 to 120%. Further, epidemic dynamics modelling indicated that a higher proportion of indoor transmission in the home environment was able to intensify the severity of the second wave of COVID-19 pandemic in India. Our findings suggested that more attention should be paid to the indoor transmission in home environment. The public health strategies to reduce indoor transmission such as ventilation and centralized isolation will be beneficial to the prevention and control of COVID-19.

PurposeThe purpose is to accurately identify women at high risk of developing cervical cancer so as to optimize cervical screening strategies and make better use of medical resources. However, the predictive models currently in use require clinical physiological and biochemical indicators, resulting in a smaller scope of application. Stacking-integrated machine learning (SIML) is an advanced machine learning technique that combined multiple learning algorithms to improve predictive performance. This study aimed to develop a stacking-integrated model that can be used to identify women at high risk of developing cervical cancer based on their demographic, behavioral, and historical clinical factors.MethodsThe data of 858 women screened for cervical cancer at a Venezuelan Hospital were used to develop the SIML algorithm. The screening data were randomly split into training data (80%) that were used to develop the algorithm and testing data (20%) that were used to validate the accuracy of the algorithms. The random forest (RF) model and univariate logistic regression were used to identify predictive features for developing cervical cancer. Twelve well-known ML algorithms were selected, and their performances in predicting cervical cancer were compared. A correlation coefficient matrix was used to cluster the models based on their performance. The SIML was then developed using the best-performing techniques. The sensitivity, specificity, and area under the curve (AUC) of all models were calculated.ResultsThe RF model identified 18 features predictive of developing cervical cancer. The use of hormonal contraceptives was considered as the most important risk factor, followed by the number of pregnancies, years of smoking, and the number of sexual partners. The SIML algorithm had the best overall performance when compared with other methods and reached an AUC, sensitivity, and specificity of 0.877, 81.8%, and 81.9%, respectively.ConclusionThis study shows that SIML can be used to accurately identify women at high risk of developing cervical cancer. This model could be used to personalize the screening program by optimizing the screening interval and care plan in high- and low-risk patients based on their demographics, behavioral patterns, and clinical data.