Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models.

Environmental Epigenomics is a developing field to study the epigenetic effect on human health from exposure to environmental factors. Endocrine disrupting chemicals have been detected primarily in pharmaceutical drugs, personal care products, food additives, and food containers. Exposure to endocrine-disrupting chemicals (EDCs) has been associated with a high incidence and prevalence of many endocrine-related disorders in humans. Nevertheless, further evidence is needed to establish a correlation between exposure to EDC and human disorders. Conventional detection of EDCs is based on chemical structure and concentration sample analysis. However, substantial evidence has emerged, suggesting that cell exposure to EDCs leads to epigenetic changes, independently of its chemical structure with non-monotonic low-dose responses. Consequently, a paradigm shift in toxicology assessment of EDCs is proposed based on a comprehensive review of analytical techniques used to evaluate the epigenetic effects. Fundamental insights reported elsewhere are compared in order to establish DNA methylation analysis as a viable method for assessing endocrine disruptors beyond the conventional study approach of chemical structure and concentration analysis.

This work presents a catalysis-based electrochemical biosensor to evaluate the peroxidase-like activity of methemoglobin (Hb-PLA) after exposure to cigarette smoke (CS) at different time intervals. The system consists of a microelectrode array coupled with a PDMS chamber containing a methemoglobin solution (biorecognition element). Hydrogen peroxide (H₂O₂) acts as the substrate, while 3,3′,5,5′-tetramethylbenzidine (TMB) functions as the chromogenic substrate for the Hb-PLA through its oxidation reaction. A spectrophotometric technique is used as a reference method to assess the catalytic activity of methemoglobin. Positive control samples exhibited higher absorbance, indicating strong catalytic activity, whereas CS-exposed samples showed a marked reduction, which was confirmed by the negative control. Cyclic voltammetry revealed significant alterations in the oxidation and reduction peaks of the CS-exposed samples. Therefore, chronoamperometry was employed to quantify the charge transfer as the electrochemical response associated with Hb-PLA, yielding a sensitivity of 0.86 ± 0.06 (%Hb-PLA/mC) and a limit of detection (LOD) of 0.23 (mC). The results demonstrate that cigarette smoke impairs the Hb-PLA in a time-dependent manner, with longer exposure reducing the activity by up to 25%. The proposed biosensor provides a rapid, sensitive, and straightforward strategy for detecting functional alterations in solutions of methemoglobin induced by environmental pollutants such as cigarette smoke.

Throughout the entire coronavirus disease 19 (COVID-19) pandemic, there were disruptions in the supply chain of test materials around the world, primarily in poor- and middle-income countries. The use of 3D prints is an alternative to address swab supply shortages. In this study, the feasibility of the clinical use of 3D-printed swabs for oropharyngeal and nasopharyngeal sampling for the detection of SARS-CoV-2 infection was evaluated. For that purpose, paired samples with the 3D printed and the control swabs were taken from 42 adult patients and 10 pediatric patients, and the results obtained in the detection of SARS-CoV-2 by reverse transcription and quantitative polymerase chain reaction (RT-qPCR) were compared. Additionally, in those cases where the result was positive for SARS-CoV-2, the viral load was calculated by means of a mathematical algorithm proposed by us. For both adults and children, satisfactory results were obtained in the detection of SARS-CoV-2 by RT-qPCR; no significant differences were found in the quantification cycle values between the 3D-printed swab samples and the control samples. Furthermore, we corroborated that the 3D-printed swabs caused less discomfort and pain at the time of sampling. In conclusion, this study shows the feasibility of routinely using 3D-printed swabs for both adults and children. In this way, it is possible to maintain local and cheaper consumption along with fewer distribution difficulties.