Insights into non-informative results from non-invasive prenatal screening through gestational age, maternal BMI, and age analyses.

The discovery of cell-free fetal DNA fragments in the maternal plasma initiated a novel testing method in prenatal care, called non-invasive prenatal screening (NIPS). One of the limitations of NIPS is the necessity for a sufficient proportion of fetal fragments in the analyzed circulating DNA mixture (fetal fraction), otherwise, the sample is uninterpretable. We present the effect of gestational age, maternal body mass index (BMI), and maternal age on the fetal fraction (FF) of the sample. We retrospectively analyzed data from 5543 pregnant women with a single male fetus who underwent NIPS from which 189 samples received a repeat testing due to an insufficient FF. We showed the relationship between the failure rate of the samples after the repeated analysis, the FF, and the gestational age at the first sampling. Next, we found that different maternal BMI categories affect the FF and thus the chance of an informative redraw. A better understanding of the factors affecting the FF will reduce the number of non-informative calls from repeated analyzes. In this study, we provide helpful information to clinicians on how to approach non-informative analyses.

Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations.

Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving clinicians of tedious searching in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV records collected from the ClinVar database. Alternatively, the emerging machine learning-based tools, such as the recently published ISV (Interpretation of Structural Variants), showed promising ways of even fully automated predictions using broader characterization of affected genomic elements. Such tools utilize features additional to ACMG criteria, thus providing supporting evidence and the potential to improve CNV classification. Since both approaches contribute to evaluation of CNVs clinical impact, we propose a combined solution in the form of a decision support tool based on automated ACMG guidelines (MarCNV) supplemented by a machine learning-based pathogenicity prediction (ISV) for the classification of CNVs. We provide evidence that such a combined approach is able to reduce the number of uncertain classifications and reveal potentially incorrect classifications using automated guidelines. CNV interpretation using MarCNV, ISV, and combined approach is available for non-commercial use at https://predict.genovisio.com/ .

Systematic Genomic Surveillance of SARS-CoV-2 Virus on Illumina Sequencing Platforms in the Slovak Republic-One Year Experience.

To explore a genomic pool of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the pandemic, the Ministry of Health of the Slovak Republic formed a genomics surveillance workgroup, and the Public Health Authority of the Slovak Republic launched a systematic national epidemiological surveillance using whole-genome sequencing (WGS). Six out of seven genomic centers implementing Illumina sequencing technology were involved in the national SARS-CoV-2 virus sequencing program. Here we analyze a total of 33,024 SARS-CoV-2 isolates collected from the Slovak population from 1 March 2021, to 31 March 2022, that were sequenced and analyzed in a consistent manner. Overall, 28,005 out of 30,793 successfully sequenced samples met the criteria to be deposited in the global GISAID database. During this period, we identified four variants of concern (VOC)-Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2) and Omicron (B.1.1.529). In detail, we observed 165 lineages in our dataset, with dominating Alpha, Delta and Omicron in three major consecutive incidence waves. This study aims to describe the results of a routine but high-level SARS-CoV-2 genomic surveillance program. Our study of SARS-CoV-2 genomes in collaboration with the Public Health Authority of the Slovak Republic also helped to inform the public about the epidemiological situation during the pandemic.

The occurrence and dynamics of polychlorinated hydrocarbons in roe deer (Capreolus capreolus) in South-western Slovakia.

The goal of this study was to determined polychlorinated biphenyls (PCBs) and organochlorine pesticides in the depot fat of roe deer (Capreolus capreolus) coming from south-western Slovakia. The mutual correlations of the organic pollutants were analyzed. The study included dichlorodiphenyltrichloroethane (DDT), hexachlorobenzen (HCB), alpha-hexachlorocyclohexane and beta-hexachlorocyclohexane (α + ß-HCH), gamma-hexachlorocyclohexane (γ-HCH), and polychlorinated biphenyls (PCB-delor). The gas chromatograph with an electron capture detector ECD was used for analysis. The accumulations of organic pollutant in depot fat of roe deer were in following order: DDT > PCB-delor > α + ß-HCH > HCB > γ-HCH. Among all pollutants, DDT was accumulated significantly in the highest level in the samples. The significantly higher content of DDT, HCB, α + ß-HCH, and γ-HCH was detected in the adult animals when compared to the juveniles. Some strong positive correlations among pollutants, between HCB and DDT, α + ß-HCH and HCB, α + ß-HCH and HCB, between γ-HCH and other pollutants, and between PCB-delor and γ-HCH were found. Game animals are a part of human food chain and monitoring of the environment pollution by PCBs and other organic pollutants are worthy to study.

Seasonal, age and sex fluctuations in aflatoxin B1 content in the liver and kidney of brown hares (Lepus europaeus Pall).

The goal of this study was to monitor the accumulation of aflatoxin B1 in the liver and kidney of brown hares (Lepus europaeus Pall) in the region of south-western Slovakia. A total of 65 samples were involved for analysis by RIA method. Brown hares were divided into the groups according to age, sex and season (month). The sex was determined visually after shooting, and the age was assigned from dried eye lens. The average concentration of AFB1 in the liver of hares was 0.54 ± 0.053 µg/kg, and lower values were measured in the kidney (0.41 ± 0.038 µg/kg). The significantly (P < 0.05) higher values were found in winter months when compared to summer months. According to the age, juvenile animals showed significant higher accumulation of B1 in both organs than adults (P < 0.05). Wild animals can serve as a good model of real environmental contamination. Thus, monitoring of risk factors such as mycotoxins in the environment is important with regard to public health, as game animals constitute an important part of food chain for humans.