The last imprint: Differential methylation assay for forensic identification of brain tissue and death determination

This study presents a sensitive and specific methylation-based assay for brain tissue identification. The approach enables reliable detection in degraded or composite materials and supports future integration of epigenetic biomarkers into forensic workflows for organ-source attribution.

Authors

Institutions

Publication Details

Journal
Journal of Biological Methods
Published
2025-11-14
DOI
https://doi.org/10.14440/jbm.0231
Primary Topic
Epigenetics and DNA Methylation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The last imprint: Differential methylation assay for forensic identification of brain tissue and death determination

Anat Zvi, Gal Bilinsky, Ofir Israeli, Adi Beth-Din et al.
Journal of Biological Methods
Epigenetics and DNA Methylation
article

The last imprint: Differential methylation assay for forensic identification of brain tissue and death determination

Anat Zvi, Gal Bilinsky, Ofir Israeli, Adi Beth-Din, Dan Grinstein
article en

Abstract

This study presents a sensitive and specific methylation-based assay for brain tissue identification. The approach enables reliable detection in degraded or composite materials and supports future integration of epigenetic biomarkers into forensic workflows for organ-source attribution.

Journal of Biological MethodsVol. 12(4)
Israel Institute for Biological Research (IL), Ministry of Defense (IL)
Openalex Percentile: Top 69%
Epigenetics and DNA Methylation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.