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.

Pre-Emptive Use of Rituximab in Epstein-Barr Virus Reactivation: Incidence, Predictive Factors, Monitoring, and Outcomes.

Post-transplant lymphoproliferative disease (PTLD) is a fatal complication of hematopoietic cell transplantation (HCT) associated with the Epstein-Barr virus (EBV). Multiple factors such as transplant type, graft-versus-host disease (GVHD), human leukocyte antigens (HLA) mismatch, patient age, and T-lymphocyte-depleting treatments increase the risk of PTLD. EBV reactivation in hematopoietic cell transplant recipients is monitored through periodic quantitative polymerase chain reaction (Q-PCR) tests. However, substantial uncertainty persists regarding the clinically significant EBV levels for these patients. Guidelines recommend initiating EBV monitoring no later than four weeks post-HCT and conducting it weekly. Pre-emptive therapies, such as the reduction of immunosuppressive therapy and the administration of rituximab to treat EBV viral loads are also suggested. In this study, we investigated the occurrence of EBV-PTLD in 546 HCT recipients, focusing on the clinical manifestations and risk factors associated with the disease. We managed to identify 67,150 viral genomic copies/mL as the cutoff point for predicting PTLD, with 80% sensitivity and specificity. Among our cohort, only 1% of the patients presented PTLD. Anti-thymocyte globulin (ATG) and GVHD were independently associated with lower survival rates and higher treatment-related mortality. According to our findings, prophylactic measures including regular monitoring, pre-emptive therapy, and supportive treatment against infections can be effective in preventing EBV-related complications. This study also recommends conducting EBV monitoring at regular intervals, initiating pre-emptive therapy when viral load increases, and identifying factors that increase the risk of PTLD. Our study stresses the importance of frequent and careful follow-ups of post-transplant complications and early intervention in order to improve survival rates and reduce mortality.

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.