Biology subjects

Gomez, T.

Publications and source records attributed to Gomez, T..

3 recordsLinked to original sources

bioRxiv2026

Highly replicable multisite patterns of adolescent white matter maturation

The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S.-based neuroimaging initiative of adolescent brain maturation. Diffusion MRI (dMRI) provides unique insights into white matter organization, yet applying advanced processing pipelines and managing technical variability across scanning environments remains challenging at scale. To address these issues, we present ABCD-BIDS Community Collection (ABCC) release 3.1.0, including a curated resource of more than 24,000 fully processed ABCD dMRI datasets. ABCC provides fully processed images, nuanced image quality metrics, advanced microstructural measures, and person-specific bundle tractography. Evaluating these rich data revealed that measures of diffusion restriction and non-Gaussianity--in particular the intracellular volume fraction from NODDI and return-to-origin probability from MAP-MRI--were highly sensitive to neurodevelopment and robust to variation in image quality. Additionally, harmonization of microstructural features markedly improved the cross-vendor generalizability of developmental effects. Together, ABCC accelerates reproducible, rigorous research on adolescent white matter development.

neuroscience↗

bioRxiv2025

Anti-tumor effects of a novel cell penetrating peptide-based therapeutic approach to target Lactate Dehydrogenase C (LDHC) in triple negative breast cancer.

BackgroundLactate Dehydrogenase C (LDHC) is a promising candidate for therapeutic targeting thanks to its highly tumor-specific expression, immunogenicity, and pro-tumorigenic functions. Aberrant LDHC expression is associated with poor clinical outcomes in multiple cancers, including breast cancer. However, no specific LDHC inhibitors are currently available, highlighting the need for novel strategies to selectively target LDHC in tumor cells. This study explores the anti-tumor potential of cell-penetrating peptides (CPPs) to target LDHC in triple negative breast cancer (TNBC). MethodsFour CPPs were evaluated for their ability to deliver LDHC siRNA to tumor cells, including the positively charged 10R peptide (10R) and three bifunctional peptides containing the integrin v{beta}3 recognition motif Arg-Gly-Asp (RGD): 10R-RGD, cyclicRGD-10R (cRGD-10R), and internalizing RGD-10R (iRGD-10R). We characterized the physicochemical properties of all CPP:siRNA complexes, and determined their serum stability, cytotoxicity, cellular uptake, and LDHC silencing efficiency in vitro. The anti-tumor effects and cytotoxicity of cRGD-10R:siRNA and iRGD-10R:siRNA complexes were further assessed in a TNBC xenograft zebrafish model. ResultsAll four CPPs formed stable nanocomplexes with favorable safety profiles. The 10R-RGD and cRGD-10R peptides demonstrated the most efficient LDHC knockdown, reduced the clonogenic ability of TNBC cells and enhanced their treatment response to the chemotherapeutic drug olaparib in vitro. Treatment of TNBC xenograft zebrafish with 10R-RGD:siRNA and cRGD-10R:siRNA complexes significantly reduced tumor burden without inducing major toxicity. Conclusion Our findings demonstrate that CPP-based siRNA delivery provides a novel and safe approach to target LDHC, either as a monotherapy or in combination with common anti-cancer drugs, to enhance treatment outcomes.

cancer biology↗

bioRxiv2024

Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using MRIQC

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the "rating widget" - a utility that enables rapid annotation and minimizes bookkeeping errors. Integrating proper quality control checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged. RELATED LINKSO_ST_ABSKey reference(s) using this protocolC_ST_ABSEsteban, O. et al. (2017), PLoS ONE 12(9): e0184661. [10.1371/journal.pone.0184661] Esteban, O. et al. (2019), Sci Data 6, 30. [10.1038/s41597-019-0035-4] Esteban, O. et al. (2020), Nat Prot 15, 2186-2202. [10.1038/s41596-020-0327-3] Provins, C. et al. (2023), Front. Neuroinform. 1, 2813-1193. [10.3389/fnimg.2022.1073734] Bissett P. et al. (2024) Sci Data 11: 809. [10.1038/s41597-024-03636-y] Key data used in this protocolAmsterdam Open MRI Collection: Population Imaging of Psychology1 (AOMIC-PIOP1; ds002785 [https://openneuro.org/datasets/ds002785]).

neuroscience↗