Blood transfusions save millions of lives worldwide each year, yet formation of antibodies against non-self antigens remains a significant problem, particularly in frequently transfused patients. We designed and tested the Universal Blood Donor Typing (UBDT_PC1) array for automated high-throughput simultaneous typing of human erythroid, platelet, leukocyte, and neutrophil antigens (HEA, HPA, HLA, and HNA, respectively) to support selection of blood products matched beyond ABO/Rh. Typing samples from 6946 donors of European, African, Admixed American, South Asian, and East Asian ancestry at two different laboratories showed a genotype reproducibility of ≥99% for 17 244 variants, translating to 99.98%, 99.90%, and 99.93% concordance across 338 372 HEA, 53 270 HPA, and 107 094 HLA genotypes, respectively. Compared to previous clinical typing data, concordance was 99.9% and 99.6% for 245 874 HEA and 3726 HPA comparisons, respectively. HLA types were 99.1% concordant with clinical typing across 8130 comparisons, with imputation accuracy higher in Europeans versus non-Europeans. Seven variant RHD alleles, a GYPB deletion underlying the U− phenotype, and 14 high-frequency antigen negative types were also detected. Beyond blood typing, hereditary hemochromatosis-associated HFE variants were identified in 276 donors. We found that the UBDT_PC1 array can reliably type a wide range of blood cell antigens across diverse ancestries. Reproducibility and accuracy were retained when transfusion-relevant targets from the UBDT_PC1 array were incorporated into the UKBB_v2.2 genome-wide typing array. The results represent the potential for significant advancement towards improved patient care by reducing harm in transfused patients through extended matching.

Type 2 Diabetes (T2D) is a complex disease that arises from interaction between genetic and environmental factors. The activation of transposable elements (TEs) and the production of circular extrachromosomal DNA (eccDNA) may represent genetic mechanisms involved in cellular aging, metabolic alterations, and T2D development through distinct pathways. Although the origin and characteristics of TEs and eccDNA differ substantially, eccDNA can in some cases be derived from TEs. This review summarizes the current understanding of these mechanisms and examines the reported associations between T2D and either TEs or eccDNAs. These findings highlight the significant involvement of these molecules in disease pathogenesis, particularly in relation to aging, and underscore their great potential as biomarkers and targets for T2D prevention.

Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset (Di@bet.es study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request. Graphic

The enterobacteria that produce β-lactamases are the main focus of infections in the healthcare environment. This is due to the difficulty they present in terms of treatment, their ease of transmission, and the impact they represent at the economic and personal level. The bacteria of greatest clinical relevance are those with resistance to third and fourth generation cephalosporins, extended spectrum β-lactamase and AmpC. Currently, carbapenemics are one of the few antimicrobials effective against multi-drug resistant organisms. However, the emergence of carbapenem-resistant enterobacteria has increased health concerns. These microorganisms include K. pneumoniae, a pan-resistant bacteria with high morbidity and mortality rates in public health facilities. In this work we have carried out a review on the antimicrobial resistance genes found in its genome, as well as the resistance mechanisms involved. Finally, we will focus on the main outbreaks causing nosocomial infections during the last years.