The outbreak of COVID-19 has spread across the world and was characterized as a pandemic. To protect medical laboratory personnel from infection, most laboratories inactivate the clinical samples before testing. However, the effect of inactivation on the detection results remains unknown. Here, we used a digital PCR assay to determine the absolute SARS-CoV-2 RNA copy number in 63 nasopharyngeal samples and assess the effect of inactivation methods on viral RNA copy number. Viral inactivation was performed with three different methods: (1) incubation with TRIzol® LS Reagent for 10 min at room temperature, (2) heating in a waterbath at 56°C for 30 min, and (3) high-temperature treatment, including 121°C autoclaving for 20 min, 100°C boiling for 20 min, and 80°C heating for 20 min. Compared to the amount of RNA in the original sample, TRIzol treatment destroyed 47.54% of N gene and 39.85% of ORF 1ab. For samples treated at 56°C for 30 min, the copy number of N gene and ORF 1ab was reduced by 48.55% and 56.40%, respectively. Viral RNA copy number dropped by 50–66% after 80°C heating for 20 min. Nearly no viral RNA was detected after autoclaving at 121°C or boiling at 100°C for 20 min. These results indicated that inactivation reduced the quantity of detectable viral RNA and may cause false negative results especially in weakly positive cases. Thus, TRIzol is recommended for sample inactivation in comparison to heat inactivation as Trizol has the least effect on RNA copy number among the tested methods.
To explore the epidemiological, phylogeographic, and migration characteristics of human rabies in Shaanxi province, China from 2009 to 2015. The collected data were described and the sequenced glycoprotein (G) and nucleoprotein (N) genes were implemented to estimate the evolutionary rates and phylogeographic patterns using BEAST v.1.8.2. A total of 269 rabies cases were reported and 70.26% of the cases were male and 61.71% were between the ages of 19-59. The majority of the cases were farmers (83.27%). The estimated evolutionary rate of the N genes was 2.4 × 10 substitutions/site/year and the G genes was 3.4 × 10 . The time of the most recent common ancestor (TMRCA) was estimated around 1990. We detected viral migration paths from Sichuan, Guizhou, and Hunan to Hanzhong prefecture of Shaanxi and then spreaded to Xi'an and other prefectures. The main population affected by rabies virus was male adult farmers. The evolution rate of rabies viruses in Shaanxi was similar with the prior results reported by others and the ancestor virus should be circulating in neighboring province Sichuan around 1990 and then transmitted to Shaanxi. Promptly standard wound treatment and timely post-exposure prophylaxis should be compulsory for the dog-bitten victims.
Geese are one of the most economically important waterfowl. However, the low reproductive performance and egg quality of geese hinder the development of the goose industry. The identification and application of genetic markers may improve the accuracy of beneficial trait selection. To identify the genetic markers associated with goose reproductive performance and egg quality traits, we performed a genome-wide association study (GWAS) for body weight at birth (BBW), the number of eggs at 48 weeks of age (EN48), the number of eggs at 60 weeks of age (EN60) and egg yolk color (EYC). The GWAS acquired 2.896 Tb of raw sequencing data with an average depth of 12.44× and identified 9,279,339 SNPs. The results of GWAS showed that 26 SNPs were significantly associated with BBW, EN48, EN60, and EYC. Moreover, five of these SNPs significantly associated with EN48 and EN60 were in a haplotype block on chromosome 35 from 4,512,855 to 4,541,709 bp, oriented to TMEM161A and another five SNPs significantly correlated to EYC were constructed in haplotype block on chromosome 5 from 21,069,009 to 21,363,580, which annotated by TMEM161A, CALCR, TFPI2, and GLP1R. Those genes were enriched in epidermal growth factor-activated receptor activity, regulation of epidermal growth factor receptor signaling pathway. The SNPs, haplotype markers, and candidate genes identified in this study can be used to improve the accuracy of marker-assisted selection for the reproductive performance and egg quality traits of geese. In addition, the candidate genes significantly associated with these traits may provide a foundation for better understanding the mechanisms underlying reproduction and egg quality in geese.
Background The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. Methods 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. Results Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values < 0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914–0.943) and 0.827 (95% CI, 0.716–0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845–0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score. Conclusions Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.
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