Brain aging is characterized by several molecular and cellular changes grouped as the hallmarks or pillars of aging, including organelle dysfunction, metabolic and nutrition‐sensor changes, stem cell attrition, and macromolecular damages. Separately and collectively, these features degrade the most critical neuronal function: transmission of information in the brain. It is widely accepted that aging is the leading risk factor contributing to the onset of the most prevalent pathological conditions that affect brain functions, such as Alzheimer’s, Parkinson’s, and Huntington’s disease. One of the limitations in understanding the molecular mechanisms involved in those diseases is the lack of an appropriate cellular model that recapitulates the “aged” context in human neurons. The advent of the cellular reprogramming of somatic cells, i.e., dermal fibroblasts, to obtain directly induced neurons (iNs) and induced pluripotent stem cell‐ (iPSC‐) derived neurons is technical sound advances that could open the avenues to understand better the contribution of aging toward neurodegeneration. In this review, we will summarize the commonalities and singularities of these two approaches for the study of brain aging, with an emphasis on the role of mitochondrial dysfunction and redox biology. We will address the evidence showing that iNs retain age‐related features in contrast to iPSC‐derived neurons that lose the aging signatures during the reprogramming to pluripotency, rendering iNs a powerful strategy to deepen our knowledge of the processes driving normal cellular function decline and neurodegeneration in a human adult model. We will finally discuss the potential utilization of these novel technologies to understand the differential contribution of genetic and epigenetic factors toward neuronal aging, to identify and develop new drugs and therapeutic strategies.

Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to detect liver cancer but has limitations in early-stage HCC detection. This study aimed to implement a machine-learning (ML) approach to identify the most relevant laboratory variables to predict AFP alteration using constrained multidimensional data from Chilean and Italian HT-1 cohorts. A longitudinal retrospective study analyzed 219 records from 35 HT-1 patients, including 8 with HCC and 5 diagnosed through newborn screening. The dataset contained biochemical and demographic variables that were analyzed using the eXtreme Gradient Boosting algorithm, which was trained to predict abnormal AFP levels (>5 ng/mL). Four key variables emerged as significant predictors: alanine transaminase (ALT), alkaline phosphatase, age at diagnosis, and current age. ALT emerged as the most promising indicator of AFP alteration, potentially preceding AFP level changes and improving HCC detection specificity at a cut-off value of 29 UI/L (AUROC = 0.73). Despite limited data from this rare disease, the ML approach successfully analyzed follow-up biomarkers, identifying ALT as an early predictor of AFP elevation and a potential biomarker for HCC progression.