20:I[192697,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/1270dead247da766.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/2d7aa753e81324a8.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/263f2cdcb9aa2341.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 21:T530,To study the detection limits of chromosomal microaberrations in non-invasive prenatal testing with aim for five target microdeletion syndromes, including DiGeorge, Prader-Willi/Angelman, 1p36, Cri-Du-Chat, and Wolf-Hirschhorn syndromes. We used known cases of pathogenic deletions from ISCA database to specifically define regions critical for the target syndromes. Our approach to detect microdeletions, from whole genome sequencing data, is based on sample normalization and read counting for individual bins. We performed both an in-silico study using artificially created data sets and a laboratory test on mixed DNA samples, with known microdeletions, to assess the sensitivity of prediction for varying fetal fractions, deletion lengths, and sequencing read counts. The in-silico study showed sensitivity of 79.3% for 10% fetal fraction with 20M read count, which further increased to 98.4% if we searched only for deletions longer than 3Mb. The test on laboratory-prepared mixed samples was in agreement with in-silico results, while we were able to correctly detect 24 out of 29 control samples. Our results suggest that it is possible to incorporate microaberration detection into basic NIPT as part of the offered screening/diagnostics procedure, however, accuracy and reliability depends on several specific factors.22:T4f5,Recent research studies are showing breast tissues as a place where various species of microorganisms can thrive and cannot be considered sterile, as previously thought. We analysed the microbial composition of primary tumour tissue and normal breast tissue and found differences between them and between multiple breast cancer phenotypes. We sequenced the transcriptome of breast tumours and normal tissues (from cancer-free women) of 23 individuals from Slovakia and used bioinformatics tools to uncover differences in the microbial composition of tissues. To analyse our RNA-seq data (rRNA depleted), we used and tested Kraken2 and Metaphlan3 tools. Kraken2 has shown higher reliability for our data. Additionally, we analysed 91 samples obtained from SRA database, originated in China and submitted by Sichuan University. In breast tissue, the most enriched group were Proteobacteria, then Firmicutes and Actinobacteria for both datasets, in Slovak samples also Bacteroides, while in Chinese samples Cyanobacteria were more frequent. We have observed changes in the microbiome between cancerous and healthy tissues and also different phenotypes of diseases, based on the presence of circulating tumour cells and few other markers.

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2	Engineering	2	2	90
3	Health Sciences	2	2	131
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5	Natural Sciences	1	1	11

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