Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm.

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

Genetics and Epigenetics in Acquired Hemophilia A: From Bench to Bedside.

Acquired hemophilia A (AHA) is a bleeding disorder characterized by the immunological inhibition of factor VIII (FVIII) of the hemostatic pathway leading to hemorrhagic events. Different domains of FVIII are the target of autoantibodies (mainly immunoglobulin (Ig) G) leading to the deficiency of FVIII. Several factors have been associated with the activation of the auto-immunity towards FVIII. Emerging evidence implicates CD4+ T cell activation in mediating this autoimmune response, with their involvement like that observed in congenital hemophilia A. Several genes such as HLA II DRB*16, DQB1*0502, and CTLA-4 + 49 are responsible for the pathogenesis of AHA. Epigenetic modifications and mainly long-coding RNAS (lncRNAs) are potentially contributing to the pathogenesis of AHA. The treatment approach of AHA includes the management of acute bleeding events and the administration of immunosuppressive medications. This review aimed to summarize the published data on the genetics and epigenetics of AHA. The severity and the mortality of this disease are creating an emerging need for further research in the field of the genetics and epigenetics of acquired hemorrhagic disorder.

The seasonal distribution of immune thrombotic thrombocytopenic purpura is influenced by geography: Epidemiologic findings from a multi-center analysis of 719 disease episodes.

Prior studies have suggested that immune thrombotic thrombocytopenic purpura (iTTP) may display seasonal variation; however, methodologic limitations and sample sizes have diminished the ability to perform a rigorous assessment. This 5-year retrospective study assessed the epidemiology of iTTP and determined whether it displays a seasonal pattern. Patients with both initial and relapsed iTTP (defined as a disintegrin and metalloprotease with thrombospondin type motifs 13 activity <10%) from 24 tertiary centers in Australia, Canada, France, Greece, Italy, Spain, and the US were included. Seasons were defined as: Northern Hemisphere-winter (December-February); spring (March-May); summer (June-August); autumn (September-November) and Southern Hemisphere-winter (June-August); spring (September-November); summer (December-February); autumn (March-May). Additional outcomes included the mean temperature in months with and without an iTTP episode at each site. A total of 583 patients experienced 719 iTTP episodes. The observed proportion of iTTP episodes during the winter was significantly greater than expected if equally distributed across seasons (28.5%, 205/719, 25.3%-31.9%; p = .03). Distance from the equator and mean temperature deviation both positively correlated with the proportion of iTTP episodes during winter. Acute iTTP episodes were associated with the winter season and colder temperatures, with a second peak during summer. Occurrence during winter was most pronounced at sites further from the equator and/or with greater annual temperature deviations. Understanding the etiologies underlying seasonal patterns of disease may assist in discovery and development of future preventative therapies and inform models for resource utilization.

A Retrospective Observational Study of Quality of Life in a Northern Greece Population of People with Haemophilia.

Haemophilia presents a significant challenge to the quality of life of affected individuals. Evaluating the health-related quality of life (HRQoL) of people with haemophilia (PwH) provides a valuable mean of assessing their perception of overall care outcomes, while also identifying influential factors across various age and condition severity demographics. This observational retrospective study determined the HRQoL of 100 adult PwH in Northern Greece through comprehensive analysis and interpretation of their HRQoL levels, particularly in domains concerning their physical, emotional, and mental well-being, obtained through the Haem-A-QoL index questionnaire. Disease severity and young age were significantly associated with the administration of prophylactic treatment (84.2% of patients with severe haemophilia and 65.2% of patients aged 18-30). The mean Haem-A-QoL score was 40.11 ± 17.38, with the lowest HRQoL observed in the 46-60 age group (46.16), and the highest in the ≥61 age groups (35.16). Notably, the 'Sports/Leisure' and 'Physical Health' domains exhibited the highest scores, in contrast to 'Family Planning' and 'Relationships/Sexuality'. Individuals with mild haemophilia recorded the lowest mean score (39.38), while those with a severe condition exhibited the highest (41.23). Age, disease severity, and physical activity emerged as primary determinants significantly affecting HRQoL outcomes.

Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm.

BACKGROUND: The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy. METHODS AND RESULTS: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%). CONCLUSIONS: Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual's risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.