Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration - Nikhil N. Chaudhari; Owen M. Vega; Phoebe Imms; Jaron M. Kawamura; Nahian F. Chowdhury; Tamara Jafar; Andrei Irimia; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2026)
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can find aging-related structural alterations above and beyond cortical thickness and atrophy. Using a novel approach to DNN interpretability, we mapped brain aging progression in 25,539 cognitively normal UK Biobank adults aged 45–83 years. Cortical aging is found to involve anatomic features becoming prominent during the 50s within frontolateral, mesolimbic, cuneal, and occipitotemporal regions. From these foci, aging-related features propagate to adjacent areas at rates peaking in the 60s. Cortical thinning and atrophy do not trend closely with neurodegeneration, but important DNN-identifiable anatomic features have spatiotemporal dynamics that match those of amyloid or tau. Our results challenge the assumption that MRI cannot map anatomic features trending with neurodegeneration. Interpretable DNNs can empower MRI to quantify anatomic aging as a process of spatial feature expansion from focal regions into nearby structures in the sequence of neurodegenerative pathology. Traditional morphometrics explain only ∼1% of variance in DNN-identifiable features, which clarifies why the former are insensitive to anatomic changes involving neurodegeneration. Our results conceptualize brain aging in the context of spatial and temporal parallels between anatomic senescence and neuropathology. These findings may help to map cognitively normal adults’ neurodegenerative anatomy even without PET measurements. Graphical Abstract

Sex and APOE genotype specific brain regional vulnerability to Alzheimer's Disease - Qi Zeng; Minghui Wang; Erming Wang; Xianxiao Zhou; Peng Xu; Vahram Haroutunian; Dongming Cai; Bin Zhang; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2026)
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), and regional vulnerability was assessed with a composite vulnerability score (CVS) derived from associations between diffusion features and AD severity across clinical traits in each of the four sex-APOE groups (female or male, with or without APOE4). Brain parcellation with the Eve atlas revealed regions consistently affected across sex-APOE groups (e.g., parahippocampal and superior temporal gyri) and regions specific to individual groups (e.g., the cingulum in females with APOE4 and the middle frontal gyrus in males without APOE4). Gene co-expression network analysis of the matched blood expression data identified gene subnetworks linked to group-specific regional vulnerability, including a muscle tissue morphogenesis module regulated by NEURL1B and HIST1H2BN associated with middle frontal gyrus vulnerability. These findings demonstrate that sex and APOE genotype jointly shape region-specific WM vulnerability and its molecular signatures in AD. Understanding these interactions provides novel mechanistic insights and may inform precision approaches to drug development, biomarker discovery, and clinical trial design for AD.

Physicochemical characterization of isolated nanocrystals in starch and isolated rice starch: A new perspective - Porfirio E. Martinez-Munoz; Elsa Gutierrez-Cortez; Margarita I. Hernandez-Urbiola; Adriana Lopez-Leon; Oscar Y. Barron-Garcia; Mario E. Rodriguez-Garcia (Food Chemistry, 2025)

Validation of pharmacokinetic model for quizartinib quantified by UPLC-MS/MS in patients with FLT3-ITD negative newly diagnosed acute myeloid leukemia - Antonio Solana-Altabella; Carles Iniesta-Navalón; Maria Chovi-Trull; Rebeca Rodriguez-Veiga; Marina Lopez-Nogueroles; David Martínez-Cuadrón; Mayte Gil-Candel; Laura Torres-Miñana; Evelyn Acuña-Cruz; Isabel Cano-Ferri; Blanca Boluda; Irene Navarro-Vicente; et al. (European Journal of Clinical Pharmacology, 2025)
Purpose Quizartinib pharmacokinetics in FLT3-ITD negative acute myeloid leukemia (AML) remain largely unexplored. This study aims to validate a population pharmacokinetics model (popPK) for quizartinib in plasma samples of FLT3-ITD negative AML patients. To do so, an ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) method has been developed and validated for the quantification of quizartinib. Methods Plasma samples were collected from FLT3-ITD negative newly diagnosed AML patients undergoing quizartinib therapy at induction in the QUIWI phase II clinical trial [NCT04107727, PETHEMA group] between March 2020 and February 2022. The UPLC-MS/MS method was developed and validated. A previously described popPK model was validated using external validation techniques and implemented using the software NONMEM v7.5. Results The developed UPLC-MS/MS method demonstrated high accuracy and precision with a linear range of 6 to 200 ng/mL, with relative standard deviation between 3–11 and accuracy of 88–97% from nominal values. The external validation of the quizartinib popPK model showed minimal bias at the population level (MdPE: -9.86%; ME: 0.50 ng/mL, p = 0.964), but moderate imprecision (MdAPE: 32.28%) and suboptimal accuracy (F20: 24.5%; F30: 43.4%). Individual predictions improved performance, with negligible bias (MdPE: -0.50%), acceptable precision (MdAPE: 11.27%), and F20 (64.2%) and F30 (77.4%) exceeding predefined thresholds. Visual predictive checks confirmed adequate prediction of median concentrations, though some deviations occurred at extremes. Conclusion This study presents a replicable UPLC-MS/MS method for the determination of quizartinib in plasma. The validated popPK model can be used to optimize dosing strategies in future clinical studies.

The importance of tropical tree-ring chronologies for global change research - Peter Groenendijk; Flurin Babst; Valerie Trouet; Ze-Xin Fan; Daniela Granato-Souza; Giuliano Maselli Locosselli; Mulugeta Mokria; Shankar Panthi; Nathsuda Pumijumnong; Abrham Abiyu; Rodolfo Acuña-Soto; Eduardo Adenesky-Filho; et al. (Quaternary Science Reviews, 2025)

Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

CA19-9 capability as predictor of pancreatic cancer resectability in a Spanish cohort - Marta Herreros-Villanueva; Lourdes Ruiz-Rebollo; Mario Montes; Mario Rodriguez-Lopez; María Francisco; Joaquín Cubiella; Eduardo Iyo; Emilio Garabitos; Emma Martínez Moneo; Maider Martos; Enrique de Madaria; Ibon Martínez-Arránz; et al. (Molecular Biology Reports, 2020)

Development of Chitosan and Alginate Nanocapsules to Increase the Solubility, Permeability and Stability of Curcumin - Daniel Hernandez-Patlan; Bruno Solis-Cruz; Mario Alberto Cano-Vega; Eric Beyssac; Ghislain Garrait; Xochitl Hernandez-Velasco; Raquel Lopez-Arellano; Guillermo Tellez; Gustavo R. Rivera-Rodriguez (Journal of Pharmaceutical Innovation, 2019)

Polymorphism in the Matrix Metalloproteinase-2 Gene Promoter is Associated with Cervical Neoplasm Risk in Mexican Women - Luz M. Baltazar-Rodriguez; Aristoteles Anaya-Ventura; Maricela Andrade-Soto; Elisa A. Monrroy-Guizar; Jose R. Bautista-Lam; Gregorio Jonguitud-Olguin; Friedman R. Cepeda-Lopez; Veronica A. Centeno-Aguilar; Nelida A. Gonzalez-Hernandez; Alejandro D. Soriano-Hernández; Mario Ramirez-Flores; Angel Lugo-Trampe; et al. (Biochemical Genetics, 2008)