Forensic science, an interdisciplinary field encompassing the collection, examination, and presentation of evidence in legal proceedings, has recently embraced lipidomics as a valuable tool. Lipidomics, a subfield of metabolomics, specializes in the analysis of lipid structures and functions, offering insights into biological processes that can aid forensic investigations. While not a substitute for DNA analysis in personal identification, lipidomics complements this technique by focusing on small biological molecules, with distinct sample requirements. This review comprehensively explores the current applications of lipidomics in forensic science. The review commences with an introduction to the concept and historical background of lipidomics, subsequently delving into its utilization in diverse areas such as drug analysis, ethyl alcohol and substitute assessment, latent fingermark detection, fire debris analysis, and seafood authentication. By showcasing the various biological materials and methods employed, this review underscores the potential of lipidomics as a powerful adjunct in forensic investigations.
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http://dx.doi.org/10.1039/d4mo00124a | DOI Listing |
BMC Pediatr
December 2024
Pediatric Nursing Department, Faculty of Nursing, Benha University, Benha, Egypt.
Background: Aluminum phosphide is an excellent insecticide available as a chalky white or brown tablet. Aluminum phosphide is traded in the Egyptian market as tablets under the brand name celphos. To date, no specific antidotes for aluminum phosphide poisoning have been identified.
View Article and Find Full Text PDFSchizophrenia (Heidelb)
December 2024
Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Schizophrenia (SZ), schizoaffective disorder (SZA), bipolar disorder (BD), and psychotic depression (PD) are associated with premature death due to preventable general medical comorbidities (GMCs). The interaction between psychosis, risk factors, and GMCs is complex and should be elucidated. More research particularly among those with SZA or PD is warranted.
View Article and Find Full Text PDFTalanta
December 2024
Hyphenated Mass Spectrometry Laboratory, Faculty of Science, University of Technology Sydney, PO Box 123, Broadway, 2007 NSW, Australia; School of Life Sciences, Faculty of Science, University of Technology Sydney, PO Box 123, Broadway, 2007 NSW, Australia.
The importance of sample preparation selection if often overlooked particularly for untargeted multi-omics approaches that gained popularity in recent years. To minimize issues with sample heterogeneity and additional freeze-thaw cycles during sample splitting, multiple -omics datasets (e.g.
View Article and Find Full Text PDFJ Forensic Sci
December 2024
Laboratory for Human Craniofacial and Skeletal Identification (HuCS-ID Lab), School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia.
Linear regression (LR) models that use cranial dimensions to estimate facial soft tissue thicknesses (FSTTs) have been posited by Simpson and Henneberg to assist craniofacial identification. For these regression equations to work well, the independent (craniometrics) and dependent (FSTTs) variables must be tightly correlated; however, such relationships have not been routinely demonstrated for adult humans. To examine the strength of these relationships further, this study employed magnetic resonance (MR) imaging to unambiguously measure cranial dimensions and FSTTs for 38 adult cadavers.
View Article and Find Full Text PDFPLoS One
December 2024
Faculty of Economics and Management, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. At the same time, ML opens up new questions for experimental research because it can generate previously impossible observations.
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