Construction of SNP fingerprint and population genetic analysis of honeysuckle germplasm resources in China

Introduction The flower buds of Lonicera japonica Thunb. are widely used in Chinese medicine for their anti-inflammatory properties, and they have played an important role in the fight against SARS COVID-19 and other major epidemics. However, due to the lack of scientific and accurate variety identification methods and national unified standards, scattered and non-standardized management in flower bud production has led to mixed varieties that have caused significant difficulties in the cataloging and preservation of germplasm resources and the identification, promotion, and application of new L. japonica varieties. Methods In this study, we evaluated the population structure, genetic relationships, and genetic fingerprints of 39 germplasm resources of Lonicera in China using simplified genome sequencing technology. Results A total of 13,143,268 single nucleotide polymorphisms (SNPs) were identified. Thirty-nine samples of Lonicera were divided into four subgroups, and the population structure and genetic relationships among existing Lonicera germplasm resources were determined using principal component analysis, population structure analysis, and phylogenetic tree analysis. Through several stringent selection criteria, 15 additional streamlined, high-quality DNA fingerprints were filtered out of the validated 50 SNP loci and verified as being able to effectively identify the 39 Lonicera varieties. Discussion To our knowledge, this is the first comprehensive study measuring the diversity and population structure of a large collection of Lonicera varieties in China. These results have greatly broadened our understanding of the diversity, phylogeny, and population structure of Lonicera. The results may enhance the future analysis of genetic diversity, species identification, property rights disputes, and molecular breeding by providing a scientific basis and reference data for these efforts.

LDANet: Automatic lung parenchyma segmentation from CT images.

Automatic segmentation of the lung parenchyma from computed tomography (CT) images is helpful for the subsequent diagnosis and treatment of patients. In this paper, based on a deep learning algorithm, a lung dense attention network (LDANet) is proposed with two mechanisms: residual spatial attention (RSA) and gated channel attention (GCA). RSA is utilized to weight the spatial information of the lung parenchyma and suppress feature activation in irrelevant regions, while the weights of each channel are adaptively calibrated using GCA to implicitly predict potential key features. Then, a dual attention guidance module (DAGM) is designed to maximize the integration of the advantages of both mechanisms. In addition, LDANet introduces a lightweight dense block (LDB) that reuses feature information and a positioned transpose block (PTB) that realizes accurate positioning and gradually restores the image resolution until the predicted segmentation map is generated. Experiments are conducted on two public datasets, LIDC-IDRI and COVID-19 CT Segmentation, on which LDANet achieves Dice similarity coefficient values of 0.98430 and 0.98319, respectively, outperforming a state-of-the-art lung segmentation model. Additionally, the effectiveness of the main components of LDANet is demonstrated through ablation experiments.

Dumbbell-type triplex molecular switch-based logic molecular assays of SARS-CoV-2.

Accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great importance to control the COVID-19 pandemic. The gold standard assays for COVID-19 diagnostics are mainly based on separately detecting open reading frame 1ab (ORF1ab) and nucleoprotein (N) genes by RT-PCR. However, the current approaches often obtain false positive-misdiagnose caused by cross-contamination or undesired amplification. To address this issue, herein, we proposed a dumbbell-type triplex molecular switch (DTMS)-based, logic-gated strategy for high-fidelity SARS-CoV-2 RNA detection. The DTMS consists of a triple-helical stem region and two-loop regions for recognizing the ORF1ab and N genes of SARS-CoV-2. Only when the ORF1ab and N gene are concurrent, DTMS experiences a structural rearrangement, thus, bringing the two pyrenes into spacer proximity and leading to a new signal readout. This strategy allows detecting SARS-CoV-2 RNA with a detection limit of 1.3 nM, independent of nucleic acid amplification, holding great potential as an indicator probe for screening of COVID-19 and other population-wide epidemics.

COVID-19 vaccine and menstrual conditions in female: data analysis of the Vaccine Adverse Event Reporting System (VAERS).

BACKGROUND: In reports of adverse reactions following vaccination with the coronavirus disease 2019(COVID-19) vaccines, there have been fewer reports of concern for menstrual disorders in female. OBJECTIVE: Our study employed Vaccine Adverse Event Reporting System (VAERS) to investigate and analyze the relationship between COVID-19 Vaccines and menstrual disorders in female. METHODS: We collected reports of menstrual disorders in VAERS from July 2, 1990 to November 12, 2021, and performed a stratified analysis. The potential relationship between COVID-19 vaccine and reports of menstrual disorders was evaluated using the Reporting Odds Ratio (ROR) method. RESULTS: A total of 14,431 reports of menstrual disorders were included in the study, and 13,118 were associated with COVID-19 vaccine. The ROR was 7.83 (95% confidence interval [95%CI]: 7.39-8.28). The most commonly reported event was Menstruation irregular (4998 reports), and a higher percentage of female aged 30-49 years reported menstrual disorders (42.55%) after exposure to COVID-19 Vaccines. Both for all reports of menstrual disorders (ROR = 5.82; 95%CI: 4.93-6.95) and excluding reports of unknown age (ROR = 13.02; 95%CI: 10.89-15.56),suggest that female age may be associated with menstrual disorders after vaccination with the COVID-19 Vaccines. CONCLUSION: There is a potential safety signal when the COVID-19 vaccine is administered to young adult female (30-49 years old), resulting in menstrual disorders in. However, due to the well-known limitations of spontaneous reporting data, it is challenging to explicity classify menstrual disorders as an adverse event of the COVID-19 Vaccines, and reports of adverse reactions to COVID-19 Vaccines in this age group should continue to be tracked.

Cas12a-assisted RTF-EXPAR for accurate, rapid and simple detection of SARS-CoV-2 RNA.

Developing highly accurate and simple approaches to rapidly identify and isolate SARS-CoV-2 infected patients is important for the control of the COVID-19 pandemic. We, herein, reported the performance of a Cas12a-assisted RTF-EXPAR strategy for the identification of SARS-CoV-2 RNA. This assay combined the advantages of RTF-EXPAR with CRISPR-Cas12a can detect SARS-CoV-2 within 40 min, requiring only isothermal control. Particularly, the simultaneous use of EXPAR amplification and CRISPR improved the detection sensitivity, thereby realizing ultrasensitive SARS-CoV-2 RNA detection with a detection limit of 3.77 aM (∼2 copies/µL) in an end-point fluorescence read-out fashion, and at 4.81 aM (∼3 copies/µL) level via a smartphone-assisted analysis system (RGB analysis). Moreover, Cas12a increases the specificity by intrinsic sequence-specific template recognition. Overall, this method is fast, sensitive, and accurate, needing minimal equipment, which holds great promise to meet the requirements of point-of-care molecular detection of SARS-CoV-2.

COVID-19 vaccine and Menstrual conditions in female: data analysis of the Vaccine Adverse Event Reporting System (preprint)

Background: In reports of adverse reactions following vaccination with the coronavirus disease 2019(COVID-19) vaccines, there have been fewer reports of concern for menstrual disorders in female. Objective: Our study used Vaccine Adverse Event Reporting System(VAERS)to investigate and analyze the relationship between COVID-19 Vaccines and menstrual disorders in female. Methods: We collected reports of menstrual disorders in VAERS from July 2, 1990 to November 12, 2021, and performed a stratified analysis. The potential relationship between COVID-19 vaccine and reports of menstrual disorders was evaluated using the Reporting Odds Ratio (ROR) method. Results: A total of 14,431 reports of menstrual disorders were included in the study, and 13,118 were associated with COVID-19 vaccine. The ROR was 7.83 (95% confidence interval [95%CI]:7.39-8.28). The most commonly reported event was Menstruation irregular (4998 reports), and a higher percentage of female aged 30-49 years reported menstrual disorders (42.55%) after exposure to COVID-19 Vaccines. Both for all reports of menstrual disorders (ROR=5.82;95%CI:4.93-6.95) and excluding reports of unknown age (ROR=13.02;95%CI:10.89-15.56), suggest that female age may be associated with menstrual disorders after vaccination with the COVID-19 Vaccines. Conclusion: Our study suggests a potential safety signal among female who received the COVID-19 vaccine, which may cause menstrual disorders in young adult female (30-49 years old). However, due to the well-known limitations of spontaneous reporting data, it is challenging to directly define menstrual disorders as an adverse event of the COVID-19 Vaccines, and reports of adverse reactions to COVID-19 Vaccines in this age group should continue to be tracked.

Is liver involvement overestimated in COVID-19 patients? A meta-analysis.

Background: Considering transaminase more than the upper limit of normal value as liver injury might overestimate the prevalence of liver involvement in COVID-19 patients. No meta-analysis has explored the impact of varied definitions of liver injury on the reported prevalence of liver injury. Moreover, few studies reported the extent of hypertransaminasemia stratified by COVID-19 disease severity. Methods: A literature search was conducted using PubMed and Embase. The pooled prevalence of liver injury and hypertransaminasemia was estimated. Results: In total, 60 studies were included. The overall prevalence of liver injury was 25%. Compared to subgroups with the non-strict definition of liver injury (33%) and subgroups without giving detailed definition (26%), the subgroup with a strict definition had a much lower prevalence of liver injury (9%). The overall prevalence of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) elevation was 19% and 22%. The prevalence of elevated ALT and AST were significantly higher in severe COVID-19 cases compare to non-severe cases (31% vs 16% and 44% vs 11%). In critically ill and fatal cases, no difference was found in the prevalence of elevated ALT (24% vs 30%) or AST (54% vs 49%). Sensitivity analyses indicated that the adjusted prevalence of ALT elevation, AST elevation, and liver injury decreased to 14%, 7%, and 12%. Conclusion: The overall prevalence of liver injury and hypertransaminasemia in COVID-19 patients might be overestimated. Only a small fraction of COVID-19 patients have clinically significant liver injury. The prevalence of hypertransaminasemia was significantly higher in severe COVID-19 cases compare to non-severe cases. Hence, in severe COVID-19 patients, more attention should be paid to liver function tests.

Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States.

BACKGROUND: The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 600,000 lives worldwide, causing tremendous public health, social, and economic damages. Although the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis. METHODS: We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010-2016) county-level exposures to NO2, PM2.5, and O3 and county-level COVID-19 case-fatality and mortality rates in the United States. We used both single- and multi-pollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level health care capacity, phase of epidemic, population mobility, population density, sociodemographics, socioeconomic status, race and ethnicity, behavioral risk factors, and meteorology. RESULTS: From January 22, 2020, to July 17, 2020, 3,659,828 COVID-19 cases and 138,552 deaths were reported in 3,076 US counties, with an overall observed case-fatality rate of 3.8%. County-level average NO2 concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models. When adjusted for co-pollutants, per interquartile-range (IQR) increase in NO2 (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 11.3% (95% CI 4.9%-18.2%) and 16.2% (95% CI 8.7%-24.0%), respectively. We did not observe significant associations between COVID-19 case-fatality rate and long-term exposure to PM2.5 or O3, although per IQR increase in PM2.5 (2.6 µg/m3) was marginally associated, with a 14.9% (95% CI 0.0%-31.9%) increase in COVID-19 mortality rate when adjusted for co-pollutants. DISCUSSION: Long-term exposure to NO2, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM2.5 and O3 exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO2 levels. Continuation of current efforts to lower traffic emissions and ambient air pollution may be an important component of reducing population-level risk of COVID-19 case fatality and mortality.