Two Separate Cases: Complex Chromosomal Abnormality Involving Three Chromosomes and Small Supernumerary Marker Chromosome in Patients with Impaired Reproductive Function

A case report of Pallister-Killian syndrome with an unusual mosaic supernumerary marker chromosome 12 with interstitial 12p13.1-p12.1 duplication

Pallister-Killian syndrome (PKS) is a rare inherited disease with multiple congenital anomalies, profound intellectual disability, and the presence in the karyotype of sSMC - i(12)(p10). The frequency of PKS may be underestimated due to problems with cytogenetic diagnosis caused by tissue-specific mosaicism and usually a low percentage of peripheral blood cells containing sSMC. Such tissue-specific mosaicism also complicates a detailed analysis of the sSMC, which, along with the assessment of mosaicism in different tissues, is an important part of cytogenetic diagnosis in PKS. Unfortunately, a full-fledged diagnosis in PKS is either practically impossible or complicated. On the one hand, this is due to problems with the biopsy of various tissues (skin biopsy with fibroblast culture is most often used in practice); on the other - a low percentage of dividing peripheral blood cells containing sSMC, which often significantly complicates the analysis of its composition and organization. In the present study, a detailed analysis of sSMC was carried out in a patient with a characteristic clinical picture of PKS. A relatively high percentage of peripheral blood cells with sSMC (50%) made it possible to perform a detailed molecular cytogenetic analysis of de novo sSMC using chromosomal in situ suppression hybridization (CISS-hybridization), multicolor FISH (mFISH), multicolor chromosome banding (MCB), array CGH (aCGH), and quantitative real-time PCR (qPCR), and short tandem repeat (STR) - analysis. As a result, it was found that the sSMC is not a typical PKS derivative of chromosome 12. In contrast to the classical i(12)(p10) for PKS, the patient's cells contained an acrocentric chromosome consisting of 12p material. Clusters of telomeric repeats were found at the both ends of the sSMC. Furthemore, the results of aCGH and qPCR indicate the presence of interstitial 8.9 Mb duplication at 12p13.1-p12.1 within the sSMC, which leads to different representations of DNA from different segments of 12p within cells containing sSMC. The obtained data raise the question of the instability of the sSMC and, as a consequence, the possible presence of additional rearrangements, which, in traditional cytogenetic analysis of patients with PKS, are usually described as i(12)(p10).

Artificial intelligence for prenatal chromosome analysis

This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.

Chromosome-Centric View of Genome Organization and Evolution

Genetic material in all cellular organisms is packed into chromosomes, which represent essential units of inheritance, recombination, and evolution [...].