Biology subjects

Daza, A.

Publications and source records attributed to Daza, A..

3 recordsLinked to original sources

bioRxiv2026

Bone Marrow Mesenchymal Stem Cells Therapy for Premature Ovarian Insufficiency: A Systematic Review and Meta-analysis of Preclinical Studies

BackgroundPremature ovarian insufficiency (POI) affects approximately 1% of women under 40 and is characterized by elevated levels of gonadotropins, reduced estradiol, impaired folliculogenesis, and infertility. Bone marrow-derived mesenchymal stem cell (BM-MSC)-based therapy has emerged as a promising regenerative strategy in preclinical POI models. This systematic review and meta-analysis evaluated BM-MSC-based interventions, including cell transplantation and secretome/extracellular vesicle administration, in animal models of POI. MethodsA systematic review and meta-analysis was conducted following PRISMA guidelines. PubMed, Web of Science, Scopus, ScienceDirect, and the Cochrane Library were searched from inception to February 19, 2025. Preclinical studies assessing BM-MSC-based interventions in animal models of POI were included. ResultsThirty-four studies comprising 1,357 animals were included. Compared with controls, BM-MSC-based therapy increased serum estradiol (standardized mean difference [SMD] 3.11; 95% confidence interval [CI] 2.38-3.84) and anti-Mullerian hormone (SMD 1.86; 95% CI 1.03-2.69), while reducing follicle-stimulating hormone (SMD -3.54; 95% CI -4.37 to -2.71) and luteinizing hormone (SMD -3.44; 95% CI -5.17 to -1.70). Follicular counts increased across developmental stages, with fewer atretic follicles. Reproductive outcomes improved, including normal estrous cycles (risk ratio [RR] 7.80; 95% CI 3.15-19.34), pregnancy occurrence (RR 3.72; 95% CI 2.14-6.44), and offspring number (SMD 1.57; 95% CI 1.04-2.09). ConclusionBM-MSC-based therapy consistently improved hormonal, follicular, and reproductive outcomes in preclinical POI models. More well-designed, standardized, and adequately controlled studies to confirm these findings are warranted. Systematic review registration: CRD42023449053

cell biology↗

bioRxiv2025

Protein language models accelerate the discovery of Plastic-Degrading Enzymes

Plastic pollution presents a critical environmental challenge, necessitating innovative and sustainable solutions. In this context, biodegradation using microorganisms and enzymes offers an environmentally friendly alternative. This work introduces an AI-driven frame-work that integrates machine learning (ML) and generative models to accelerate the discovery and design of plastic-degrading enzymes. By leveraging pre-trained protein language models and curated datasets, we developed seven ML-based binary classification models to identify enzymes targeting specific plastic substrates, achieving an average accuracy of 89%. The framework was applied to over 6,000 enzyme sequences from the RemeDB to classify enzymes targeting diverse plastics, including PET, PLA, and Nylon. Besides, generative learning strategies combined with trained classification models in this work were applied for de novo generation of PET-degrading enzymes. Structural bioinformatics validated potential candidates through in-silico analysis, highlighting differences in physicochemical properties between generated and experimentally validated enzymes. Moreover, generated sequences exhibited lower molecular weights and higher aliphatic indices, features that may enhance interactions with hydrophobic plastic substrates. These findings highlight the utility of AI-based approaches in enzyme discovery, providing a scalable and efficient tool for addressing plastic pollution. Future work will focus on experimental validation of promising candidates and further refinement of generative strategies to optimize enzymatic performance.

bioengineering↗

bioRxiv2024

Peptipedia v2.0: A peptide sequence database and user-friendly web platform. A major update

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and anti-ageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877 {+/-} 0.0530 and 0.873 {+/-}0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machinelearning tools to facilitate the application of machine-learning strategies to study peptide sequences. Peptipedia v2.0 is accessible under the Creative Commons CC BY-NC-ND 4.0 license at https://peptipedia.cl/.

bioinformatics↗