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.

The SARS-CoV-2 coronavirus is responsible for the COVID-19 pandemic resulting in a global health emergency. Given its rapid spread and high number of infected individuals, a diagnostic tool for a rapid, simple, and cost-effective detection was essential. In this work, we developed a COVID-19 diagnostic test, that incorporates a human internal control, based on the Reverse Transcription Loop-Mediated Isothermal Amplification (RT-LAMP). When working with synthetic SARS-CoV-2 RNA, the optimized RT-LAMP assay has a sensitivity of 10 viral copies and can be detected by fluorescence in less than 15 min or by the naked eye in 25 min using colorimetric RT-LAMP. To avoid the RNA extraction step, a pre-treatment of the sample was optimized. Subsequently, a validation was performed on 268 trypsin treated samples (including nasopharyngeal, buccal, and nasal exudates) and amplified with colorimetric RT-LAMP to evaluate its sensitivity and specificity in comparison with RT-qPCR of extracted samples. The validation results showed a sensitivity and specificity of 100% for samples with Ct ≤ 30. The rapid, simple, and inexpensive RT-LAMP SARS-CoV-2 extraction-free procedure developed may be an alternative test that could be applied for the detection of SARS-CoV-2 or adapted to detect other viruses present in saliva or nasopharyngeal samples with higher sensitivity and specificity of the antibody test.

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

Numerous genes involved in different metabolic diseases have been identified, and this number is increasing