Mapping short tandem repeats for liver gene expression traits helps prioritize potential causal variants for complex traits in pigs

Background Short tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds. Results We identified 5203 and 5868 significantly expression STRs (eSTRs, FDR < 1%) in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively. Conclusions This study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.

Aerobic Vaginitis Diagnosis Criteria Combining Gram Stain with Clinical Features: An Establishment and Prospective Validation Study

Wet-mount microscopy aerobic vaginitis (AV) diagnostic criteria need phase-contrast microscopy and keen microscopists, and the preservation of saline smears is less common in clinical practice. This research work developed new AV diagnostic criteria that combine Gram stain with clinical features. We enrolled 325 AV patients and 325 controls as a study population to develop new AV diagnostic criteria. Then, an independent group, which included 500 women, was used as a validation population. AV-related microscopic findings on Gram-stained and wet-mount smears from the same participants were compared. The accuracy of bacterial indicators from the two methods was verified by bacterial 16S rRNA V4 sequencing (n = 240). Logistic regression was used to analyse AV-related clinical features. The screened clinical features were combined with Gram-stain microscopic indicators to establish new AV diagnostic criteria. There were no significant differences in the leukocyte counts or the parabasal epitheliocytes (PBC) proportion between the Gram-stain and wet-mount methods (400×). Gram stain (1000×) satisfied the ability to identify bacteria as verified by 16S rRNA sequencing but failed to identify toxic leukocytes. The new criteria included: Lactobacillary grades (LBG) and background flora (Gram stain, 1000×), leukocytes count and PBC proportion (Gram stain, 400×), and clinical features (vaginal pH > 4.5, vagina hyperemia, and yellow discharge). These criteria satisfied the accuracy and reliability for AV diagnosis (Se = 86.79%, Sp = 95.97%, and Kendall’s W value = 0.899) in perspective validation. In summary, we proposed an alternative and valuable AV diagnostic criteria based on the Gram stain, which can make it possible to diagnose common vaginitis like AV, BV, VVC, and mixed infections on the same smear and can be available for artificial intelligence diagnosis in the future.

The prediction model of fatty liver was established by blood biochemical indexes

Background The main cause of chronic liver disease is fatty liver, which includes alcoholic fatty liver and non-alcoholic fatty liver disease. This study is aimed to establish the prediction model of fatty liver, and provide help for the prevention and treatment of fatty liver, especially NAFLD in the future. Methods Datasets from 2017 to March 2020 NHANES required for the analysis were downloaded from the NHANES web site and R 4.1.1. Software was used for data analysis. A total of 3762 subjects were enrolled in this study, which were divided into model construction group and model validation group in a 2:1 ratio. Results The study selected 6 indicators to build the prediction model, which are as follows: ALT, Platelet count, Creatinine, LDH, HS C-Reactive Protein, Glucose. Then the prediction model was constructed. The area under ROC curve of the model was 0.7471. In the validation population, the area under the ROC curve of the model was 0.7816. Conclusions F score has a good predictive effect on fatty liver, which can be used as an important means for the prevention and treatment of fatty liver, especially NAFLD in the future.

TAB-TICI Score: Successful Recanalization Score After Endovascular Thrombectomy in Acute Stroke

Background and Purpose: Successful reperfusion therapy is supposed to be comprehensive and validated beyond the grade of recanalization. This study aimed to develop a novel scoring system for defining the successful recanalization after endovascular thrombectomy.Methods: We analyzed the data of consecutive acute stroke patients who were eligible to undergo reperfusion therapy within 24 h of onset and who underwent mechanical thrombectomy using a nationwide multicenter stroke registry. A new score was produced using the predictors which were directly linked to the procedure to evaluate the performance of the thrombectomy procedure.Results: In total, 446 patients in the training population and 222 patients in the validation population were analyzed. From the potential components of the score, four items were selected: Emergency Room-to-puncture time (T), adjuvant devices used (A), procedural intracranial bleeding (B), and post-thrombectomy reperfusion status [Thrombolysis in Cerebral Infarction (TICI)]. Using these items, the TAB-TICI score was developed, which showed good performance in terms of discriminating early neurological aggravation [AUC 0.73, 95% confidence interval (CI) 0.67–0.78, P &lt; 0.01] and favorable outcomes (AUC 0.69, 95% CI 0.64–0.75, P &lt; 0.01) in the training population. The stability of the TAB-TICI score was confirmed by external validation and sensitivity analyses. The TAB-TICI score and its derived grade of successful recanalization were significantly associated with the volume of thrombectomy cases at each site and in each admission year.Conclusion: The TAB-TICI score is a valid and easy-to-use tool to more comprehensively define successful recanalization after endovascular thrombectomy in acute stroke patients with large vessel occlusion.

Genomic Prediction of Two Complex Orthopedic Traits Across Multiple Pure and Mixed Breed Dogs

Canine hip dysplasia (CHD) and rupture of the cranial cruciate ligament (RCCL) are two complex inherited orthopedic traits of dogs. These two traits may occur concurrently in the same dog. Genomic prediction of these two diseases would benefit veterinary medicine, the dog’s owner, and dog breeders because of their high prevalence, and because both traits result in painful debilitating osteoarthritis in affected joints. In this study, 842 unique dogs from 6 breeds with hip and stifle phenotypes were genotyped on a customized Illumina high density 183 k single nucleotide polymorphism (SNP) array and also analyzed using an imputed dataset of 20,487,155 SNPs. To implement genomic prediction, two different statistical methods were employed: Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian method called BayesC. The cross-validation results showed that the two methods gave similar prediction accuracy (r = 0.3–0.4) for CHD (measured as Norberg angle) and RCCL in the multi-breed population. For CHD, the average correlation of the AUC was 0.71 (BayesC) and 0.70 (GBLUP), which is a medium level of prediction accuracy and consistent with Pearson correlation results. For RCCL, the correlation of the AUC was slightly higher. The prediction accuracy of GBLUP from the imputed genotype data was similar to the accuracy from DNA array data. We demonstrated that the genomic prediction of CHD and RCCL with DNA array genotype data is feasible in a multiple breed population if there is a genetic connection, such as breed, between the reference population and the validation population. Albeit these traits have heritability of about one-third, higher accuracy is needed to implement in a natural population and predicting a complex phenotype will require much larger number of dogs within a breed and across breeds. It is possible that with higher accuracy, genomic prediction of these orthopedic traits could be implemented in a clinical setting for early diagnosis and treatment, and the selection of dogs for breeding. These results need continuous improvement in model prediction through ongoing genotyping and data sharing. When genomic prediction indicates that a dog is susceptible to one of these orthopedic traits, it should be accompanied by clinical and radiographic screening at an acceptable age with appropriate follow-up.

Critical indexes of compositional nutrient diagnosis (CND) and its validation in wheat fields

Plant nutrient composition of can be used as an evaluation criterion for optimum plant growth. The objectives of present study were to (a) derive critical compositional nutrient (CND) norms for survived wheat fields and sufficiency ranges as CND nutrient index for validation samples, (b) provide a squared CND threshold nutrient imbalance index (CND r2) and compare with DRIS nutrient imbalance indices, (c) determine balanced nutrients concentration with CND indices. The yield cutoff value was 4,232 kg.ha-1. The CND indexes results indicate that Zn is the most deficient nutrient in wheat, followed by Cu, Fe, Mn and B, whereas N is the most excessive nutrient, followed by K, Ca, Mg and P. In the validation trials, the yield cutoff value were reported 5.023 kg.ha-1. The calculated CND r2 in the validation population was lower than that of the survey wheat fields, indicating a more balanced concentration of nutrients due to the application of fertilizer treatments. Significant principal component (PC) loadings were obtained after the varimax rotation. The first three PCs in high- and low-yielding subgroups and whole data set indicated 52.8, 54.6 and 48.8 % total variance, respectively. This study revealed that the decline in the wheat yield was due to the nutrient imbalance associated with multi nutrient deficiency (Zn, Cu, Fe, Mn and B) and multi nutrient excess (N, K, Ca, Mg and P).

Genomic Prediction across Structured Hybrid Populations and Environments in Maize

Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.

Can Inhibin B Reflect Ovarian Reserve of Healthy Reproductive Age Women Effectively?

ObjectiveThe reference range and potential value of inhibin B are still unclear and controversial. This study aimed to define the variation trend of inhibin B in healthy women with age and explore its value in the reflection of ovarian reserve.MethodsA total of 2524 healthy reproductive age women from eight medical institutes nationwide were recruited. The variation tendency of inhibin B with age was primarily established in the first group of 948 women and validated in another 605. We evaluated the relationship between inhibin B and classic ovarian reserve and function markers. The potency of inhibin B in predicting AFC &lt;5-7 was also estimated and compared with FSH.ResultsThe nomogram showed that serum levels of inhibin B rapidly decreased after the age of 40. Inhibin B was positively correlated with AMH (R = 0.57, P &lt; 0.001), AFC (R = 0.34, P &lt; 0.001) and testosterone (R = 0.10, P = 0.002), and negatively correlated with FSH (R = -0.41, P &lt; 0.001) and LH (R = -0.20, P &lt; 0.001) and FSH/LH (R=-0.18, P &lt; 0.001), while no correlation was found with PRL. Unexpectedly, Inhibin B (AUC = 0.74, P &lt; 0.001 for the establishment population; AUC = 0.78, P &lt; 0.001 for the validation population) had a slightly higher value than FSH (AUC = 0.71, P &lt; 0.001 for the establishment population; AUC = 0.72, P &lt; 0.001 for the validation population) in diagnosing AFC &lt;5-7.ConclusionsFor healthy reproductive age women, the decline of inhibin B can reflect decreased ovarian reserve effectively, having a good consistency with AMH and AFC. More importantly, inhibin B had an advantage in predicting AFC &lt;5-7 compared with FSH, which suggested the potential of inhibin B in predicting ovarian response. These results will be helpful to the clinical application of inhibin B in the evaluation of female ovarian reserve and the assessment of their reproductive capacity. Trial registration: http://clinicaltrials.gov; NCT02294500.

Incubation Period of COVID-19 From 11545 Patients in Observation Study

Background The incubation period is a key index of epidemiology in understanding of the spread of infectious diseases and the decision-making of the disease control. However, the incubation period of the emerging COVID-19 is still unclear. Methods Between January 19, 2020 and September 21, 2020, we collected information on 11545 patients in Mainland China outside Hubei. The 218 patients with precise data was validation population. The incubation period was fitted with lognormal model by the coarseDataTools package in R. Results In 11545 patients, the mean incubation period of COVID-19 was 7.1 days (95% Confidence interval [CI], 7.0–7.2). About 5.4% of patients had precise incubation period less than 3 days, 10.2% longer than 14 days, and 2.1% longer than 21 days. There was no statistically significant difference in incubation period between male and female (P = 0.603). It was similar in the 218 patients. The mean accurate incubation period was 6.8 days (6.2–7.4). Of which, 14.7% (32/218) of patients had incubation period less than 3 days, 12.4% (27/218) longer than 14 days, and 0.9% (2/218) longer than 21 days. Conclusions For COVID-19, the mean incubation period is 7.1 days and 10.2% of patients developed disease 14 days after infection, which challenges the current 14-day quarantine strategy.