Complex rearrangement in acute myeloid leukemia M2 with RUNX1/RUNX1T1 fusion involving chromosomes 8, 17 and 21.

BACKGROUND: The translocation t(8;21)(q22;q22) is one of the most frequent chromosomal abnormalities associated with acute myeloid leukemia (AML) sub type M2. About 3-5 % of cases with additional chromosomal abnormalities, including structural and numerical ones, are reported to include a complex translocation t(8;21;N). CASE PRESENTATION: Here we report a chromosome rearrangement observed in a 19 years-old female diagnosed with AML-M2. When subjected to (molecular) cytogenetic analyses a complex three-way translocation involving chromosomes 8, 17 and 21 was detected, forming not a t(8;21;17) as one would expect. Real time-polymerase chain reaction analysis using 6 AML specific markers showed the presence of RUNX1/RUNX1T1 fusion gene transcripts identical to those found in classical translocation t(8;21) coupled with presence of FLT3-ITD mutation identified by fragment analysis. CONCLUSIONS: The present case highlights importance of complex rearrangements rarely encountered in AML, suggesting that all involved regions harbor critical candidate genes regulating the pathogenesis of AML, leading to novel as well as well-known leukemia associated chromosomal aberrations.

Clinical Manifestations of Chromosomal Anomalies and Polymorphic Variations in Patients Suffering from Reproductive Failure.

BACKGROUND: Human reproduction is the most intricate event as ~ 20% of human pregnancies end in miscarriages for which chromosomal anomalies are a common factor. The chromosomal variations associated with reproductive failures include translocations, inversions, supernumerary marker chromosomes, heterochromatic polymorphisms, etc., Till date, the significance of heteromorphic variants in reproductive failures is unclear. AIM: The aim of this study is to investigate the role of chromosomal anomalies and polymorphic variations in reproductive failure. MATERIALS AND METHODS: Chromosomal analysis using GTG banding was performed on 638 couples (1276 individuals). RESULTS: In the present study, 138 of 1276 individuals showed chromosomal variations with respect to heterochromatic variants and Robertsonian translocations. The most common variants observed across the population studied were the pericentric inversion of the chromosome 9 [inv(9)(p11q13), 3.68%] followed by pstk + on the short arm of chromosome 15 (15pstk+, 1.95%) and Robertsonian translocation of chromosomes 13 and 14 [rob(13;14)(q10;q10), 1.25%]. The maximum percentage of heterochromatic variation was observed in females with recurrent pregnancy loss (Groups A, 4.78%) and males with wives having recurrent miscarriages (Group B, 3.68%) and the minimum was recorded in patients with in vitro fertilization (IVF) failures (Group C, 0.23%) and couples having a history of the malformed child (Group F, 0.23%). CONCLUSIONS: High level of chromosomal polymorphic variations in patients with reproductive failures warrants their in-depth analysis to nail down the causative factors. Hence, cytogenetic analysis coupled with genetic counseling becomes indispensable for patients suffering from infertility, reproductive failures and pregnancy losses before IVF treatment to rule out the carrier status.

Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool.

BACKGROUND: The recent pandemic of CoVID-19 has emerged as a threat to global health security. There are very few prognostic models on CoVID-19 using machine learning. OBJECTIVES: To predict mortality among confirmed CoVID-19 patients in South Korea using machine learning and deploy the best performing algorithm as an open-source online prediction tool for decision-making. MATERIALS AND METHODS: Mortality for confirmed CoVID-19 patients (n = 3,524) between January 20, 2020 and May 30, 2020 was predicted using five machine learning algorithms (logistic regression, support vector machine, K nearest neighbor, random forest and gradient boosting). The performance of the algorithms was compared, and the best performing algorithm was deployed as an online prediction tool. RESULTS: The logistic regression algorithm was the best performer in terms of discrimination (area under ROC curve = 0.830), calibration (Matthews Correlation Coefficient = 0.433; Brier Score = 0.036) and. The best performing algorithm (logistic regression) was deployed as the online CoVID-19 Community Mortality Risk Prediction tool named CoCoMoRP (https://ashis-das.shinyapps.io/CoCoMoRP/). CONCLUSIONS: We describe the development and deployment of an open-source machine learning tool to predict mortality risk among CoVID-19 confirmed patients using publicly available surveillance data. This tool can be utilized by potential stakeholders such as health providers and policymakers to triage patients at the community level in addition to other approaches.