Introduction: This systematic review and meta-analysis present a comprehensive evaluation of paper-based microfluidic devices, focusing on their applications in immunoassays. These devices are emerging as innovative solutions to democratize access to diagnostic technologies, especially in resource-limited settings. Our review consolidates findings from diverse studies to outline advancements in paper-based microfluidic technology, including design intricacies and operational efficacy. Key advantages such as low cost, portability, and ease of use are highlighted.Materials and Methods: The review categorizes literature based on the design and operational nuances of these diagnostic tools, exploring various methodologies, fabrication techniques, detection methods, and applications, particularly in protein science. The meta-analysis extends to the diverse applications of these technologies, providing a framework for classifying and stratifying their uses in diagnostics.Results and discussion: Notable findings include a critical analysis of performance metrics, such as sensitivity and specificity. The review addresses challenges, including the need for further validation and optimization for broader clinical applications. A critical discussion on the validation processes, including cross-validation and rigorous control testing, is provided to ensure the robustness of microfluidic devices. This study offers novel insights into the computational strategies underpinning these technologies and serves as a comprehensive roadmap for future research, potentially broadening the impact across the protein science universe.

Diabetes Mellitus Type 1 (DM1) is an autoimmune disease characterized by the destruction of beta cells in the pancreas. Although amyloid formation has been well-studied in Diabetes Mellitus Type 2 (DM2), its role in DM1 remains unclear. Understanding how islet amyloid polypeptide (IAPP) contributes to beta cell dysfunction and death in DM1 could provide critical insights into disease mechanisms and pave the way for novel diagnostic and therapeutic strategies. A systematic review and epistemic meta-analysis was conducted using a modified PICO framework, focusing on studies related to DM1 and the IAPP aggregation process. Searches in PubMed, BIREME, and Web of Science yielded 37 relevant articles, which were analyzed and individually evaluated based on specific quality criteria. Studies that experimentally identified the formation of IAPP oligomers in DM1 were selected, along with relevant review articles. Experimental studies from human and animal models detected the presence of IAPP oligomers in DM1 patients, as well as in nonobese diabetic (NOD) and homozygous mice. Techniques like Western Blot (WB), Transmission Electron Microscopy (TEM) and Congo red staining detected various oligomers sizes, with smaller ones showing higher cytotoxicity. IAPP oligomers have been detected in the pancreatic islets of DM1 patients, contributing to beta cell damage and disease progression.

The limitations of conventional diabetes management are increasingly evident. As a result, both type 1 and 2 diabetes in pediatric populations have become major global health concerns. As new technologies emerge, particularly artificial intelligence (AI), they offer new opportunities to improve diagnostic accuracy, treatment outcomes, and patient self-management. A PRISMA-based systematic review was conducted using PubMed, Web of Science, and BIREME. The research covered studies published up to February 2025, where twenty-two studies met the inclusion criteria. These studies examined machine learning algorithms, continuous glucose monitoring (CGM), closed-loop insulin delivery systems, telemedicine platforms, and digital educational interventions. AI-driven interventions were consistently associated with reductions in HbA1c and extended time in range. Furthermore, they reported earlier detection of complications, personalized insulin dosing, and greater patient autonomy. Predictive models, including digital twins and self-learning neural networks, significantly improved diagnostic accuracy and early risk stratification. Digital health platforms enhanced treatment adherence. Nonetheless, the barriers included unequal access to technology and limited long-term clinical validation. Artificial intelligence is progressively reshaping pediatric diabetes care toward a predictive, preventive, personalized, and participatory paradigm. Broader implementation will require rigorous multiethnic validation and robust ethical frameworks to ensure equitable deployment.

Background: Type 2 diabetes (T2D) is one of the leading causes of mortality and is a public health challenge worldwide. Metformin is the first-choice treatment for T2D; its pharmacokinetics (PK) is facilitated by members of the solute carrier (SLC) superfamily of transporters, it is not metabolized, and it is excreted by the kidney. Although interindividual variability in metformin pharmacokinetics is documented in the Mexican population, its pharmacogenomics is still underexplored. We aimed to identify variants in metformin SLC transporter genes associated with metformin PK and response in Mexican patients. Methods: Using exome data from 2217 Mexican adults, we identified 86 biallelic SNVs in the eight known genes encoding SLC transporters, with a minor allele frequency ≥ 1%, which were analyzed in an inadequate glycemic control (IGC) association study in T2D metformin treated patients. Metformin PK was evaluated in a pediatric cohort and the effect of associated SNVs was correlated. Results: Functional annotation classified two SNVs as pathogenic. The association study revealed two blocks associated with IGC. These haplotypes comprise rs622591, rs4646272, rs4646273, and rs4646276 in SLC22A1; and rs1810126 and rs668871 in SLC22A3. PK profiles revealed that homozygotes of the SLC22A1 haplotype reached lower plasma metformin concentrations 2 h post administration than the other groups. Conclusions: Our findings highlight the potential of pharmacogenomics studies to enhance precision medicine, which may involve dosage adjustments or the exploration of alternative therapeutic options. These hold significant implications for public health, particularly in populations with a high susceptibility to develop metabolic diseases, such as Latin Americans.

Introduction: Protein Disulfide Isomerases (PDIs) and bacterial Dsb proteins are key members of the thioredoxin-fold superfamily, essential for oxidative protein folding in eukaryotic and prokaryotic systems, respectively. Despite their differences in cellular context, these proteins share a conserved thioredoxin domain architecture that enables catalysis of disulfide bond formation, isomerization, and reduction. This systematic review integrates biochemical, structural, and bioinformatic data to identify conserved features within the PDI and Dsb families that underline their catalytic functions. Methods: Using a PRISMA-based methodology, we screened and analyzed 96 relevant articles and conducted a comparative structural analysis of 11 representative PDI proteins, most of which lack experimentally resolved structures. We leveraged AlphaFold models alongside crystal structures of canonical PDI (PDIA1), DsbC, and DsbG. Results: We reveal conserved tertiary folds, catalytic motifs, and domain arrangements across species. These findings highlight the evolutionary conservation and structural versatility of thioredoxin-fold enzymes and underscore their biomedical relevance in diseases linked to protein misfolding, such as neurodegeneration, cancer, and infection. Discussion: The results offer a foundation for future experimental studies and therapeutic exploration targeting redox-regulating thioredoxin-fold proteins.

Misfolded proteins, including islet amyloid polypeptide (hIAPP), serum amyloid A (SAA), and amyloid-betas (Aβs) such as Aβ1-40 and Aβ1-42 oligomers, collectively referred to in our work as amyloid oligomers, have been implicated in the development of both metabolic and cardiovascular diseases. However, their potential role as early biomarkers of myocardial damage remains insufficiently explored. We conducted a systematic review following PRISMA guidelines and epistemic meta-analysis to investigate the association between misfolded protein oligomers and early myocardial injury. All English- and Spanish-language articles with titles, abstracts, or keywords relevant to the research topic and indexed in at least one of the following databases—PubMed, BIREME, or Web of Science—were included. A comprehensive search across major databases identified 30 eligible studies. Thirty studies met inclusion criteria, in humans and animals, and in vitro. Amyloid oligomers were consistently elevated in patients with acute myocardial infarction, type 2 diabetes, or coronary artery disease compared with controls. In specific individual cohorts, a higher SAA concentration correlated with major adverse cardiovascular events, where it acted as an independent predictor of mortality in reperfused AMI (RR 5.8; 95% CI: 1.3–27.7) and cardiac rupture (OR 8.8; 95% CI: 1.7–25.6). Our findings support the hypothesis that protein misfolding contributes to early myocardial injury and highlight its potential as a novel source of cardiometabolic biomarkers.