Biology subjects

Pellegrini, P.

Publications and source records attributed to Pellegrini, P..

4 recordsLinked to original sources

bioRxiv2026

Multi-Lab Testing of Early Preclinical Discoveries Identifies Promising Treatments

A fundamental challenge in drug development is the frequent failure of early laboratory research to translate into clinical benefit. One promising solution is to confirm findings from exploratory single-laboratory studies across multiple laboratories before clinical testing. We investigated this approach following the conduct of preclinical multi-laboratory studies across different fields of medicine. For this, we evaluated effect sizes, experimental rigor, and a set of criteria to identify determinants of confirmation success. When tested under increased rigor, only a fraction of multi-laboratory studies confirmed the initial results. The underlying effect size reduction was associated with outcome-relevant experimental differences between exploratory and confirmatory stages. In summary, multi-laboratory studies proved highly informative and served as an effective filter for promising treatments.

scientific communication and education↗

bioRxiv2026

Systematic identification of DNA methylation biomarkers for tumor-type-specific detection

DNA methylation biomarkers for cancer diagnostics often underperform when tumor and background tissues share epigenetic programs, or when complex specimens with mixed cellular composition dilute tumor-derived signals and increase variability. To address these limitations, we developed a gene-centric, browser-based discovery platform that integrates genome-wide methylomes with matched transcriptomes and reference layers spanning pan-cancer tissues and leukocytes, enabling background-aware filtering beyond binary tumor-normal contrasts. Candidate loci are prioritized using combined thresholds on methylation effect size and intra-group variability to penalize stochastic and heterogeneous variation. In colorectal cancer, methylation-sensitive restriction enzyme quantitative PCR (MSRE-qPCR) validation in independent tissue cohorts confirmed multiple candidate loci with AUCs of 0.81-1.00. Using the same framework, MSRE-qPCR validation distinguished hepatocellular carcinoma from cirrhotic liver, and analysis of public tumor methylomes identified subtype-specific markers in lung adenocarcinoma and squamous-cell carcinoma. This resource bridges genome-scale epigenomic discovery with clinically accessible PCR-based methylation assays.

bioinformatics↗

bioRxiv2026

Set-up, validation, evaluation, and cost-benefit analysis of an AI-assisted assessment of responsible research practices in a sample of life science publications

The (semi-)automated screening of publications for diverse quality and transparency criteria is at the core of systematic literature assessment. Typically, the assessment process involves two initial reviewers and one additional reviewer for cases that require reconciliation. Here, we explore to what extent this process can be assisted by Large Language Models (LLMs). Specifically, whether LLMs are capable of assessing responsible research practices (RRPs) in scientific papers in a robust way. We employed proprietary LLMs to assess an initial set of 37 papers across ten RRPs. The same papers were also reviewed by three human reviewers. We iteratively redesigned prompts to increase model accuracy compared to human ratings which we treated as the gold standard. The resulting pipeline was validated on an additional set of 15 papers. We show that LLM accuracy is comparable to single human reviewer performance (90% for LLM vs 86% for a single human reviewer). However, performance strongly depended on the specific RRPs with accuracy ranging from 40% to 100%. LLMs exhibited an affirmative bias, making more errors when practices were not reported in the papers. Overall, we show how such an approach potentially replaces one human reviewer, enabling AI-assisted assessment of research papers. We discuss how dataset imbalances, validation procedures, and implementation time limit the broad applicability of such approaches. Through this, we develop initial guidance on the utility of proprietary LLMs in evidence synthesis.

scientific communication and education↗

bioRxiv2021

Lack of p62 impairs glycogen aggregation and exacerbates pathology in a mouse model of myoclonic epilepsy of Lafora

BackgroundLafora disease (LD) is a fatal childhood-onset dementia characterized by the extensive accumulation of glycogen aggregates--the so-called Lafora Bodies (LBs)--in several organs. The accumulation of LBs in the brain underlies the neurological phenotype of the disease. LBs are composed of abnormal glycogen and various associated proteins, including p62, an autophagy adaptor that participates in the aggregation and clearance of misfolded proteins. MethodsTo study the role of p62 in the formation of LBs and its participation in the pathology of LD, we generated a mouse model of the disease (malinKO) lacking p62. ResultsDeletion of p62 prevented LB accumulation in skeletal muscle and cardiac tissue. In the brain, the absence of p62 altered LB morphology and increased susceptibility to epilepsy. ConclusionsThese results demonstrate that p62 participates in the formation of LBs and suggest that the sequestration of abnormal glycogen into LBs is a protective mechanism through which to reduce the deleterious consequences of its accumulation in the brain.

neuroscience↗