👤 Sorin Hostiuc

In forensic pathology, accurately estimating the time since injury is essential. Current histological and imaging approaches commonly miss subtle temporal changes, especially in deaths occurring within hours of injury. This review discusses the timing of neuroinflammation after traumatic brain injury and emphasizes possible markers for estimating the time of injury in forensic cases. Promising markers include microglial activation (allograft inflammatory factor 1 and transmembrane protein 119, detectable within 10 min to 2 h), β-amyloid precursor protein accumulation (20-35 min), high-mobility group box 1 translocation (2-6 h), cytokine fluctuations (IL-1β and TNF-α peak between 4 and 24 h, IL-6 shows delayed, extended elevation), sequential leukocyte infiltration (neutrophils from 2 to 48 h, lymphocytes after 3-5 days), blood-brain barrier breakdown markers such as fibrinogen and IgG leakage, loss of tight junction proteins (2-3 h), matrix metalloproteinase-9 activity (peaking at 24-48 h), and reactive astrocytosis with increased glial fibrillary acidic protein levels (from 12 to 24 h onward). The association between injury severity and inflammation is influenced by factors such as age, genetics (e.g., APOE ε4), coexisting conditions, and preexisting inflammation, which reduce the reliability of individual markers. A multiparametric approach may offer the best prospects to improve the accuracy of post-traumatic and post-mortem interval assessment in medicolegal cases. Show less

Acute myocardial infarction occurs when blood supply to a particular coronary artery is cut off, causing ischemia or hypoxia and subsequent heart muscle destruction in the vascularized area. With a mortality rate of 17% per year, myocardial infarction (MI) is still one of the top causes of death globally. Numerous studies have been done to identify the genetic risk factors for myocardial infarction, as a positive family history of heart disease is one of the most potent cardiovascular risk factors. The goal of this review is to compile all the information currently accessible in the literature on the genes associated with AMI. We performed a big data analysis of genes associated with acute myocardial infarction, using the following keywords: "myocardial infarction", "genes", "involvement", "association", and "risk". The analysis was done using PubMed, Scopus, and Web of Science. Data from the title, abstract, and keywords were exported as text files and imported into an Excel spreadsheet. Its analysis was carried out using the VOSviewer v. 1.6.18 software. Our analysis found 28 genes which are mostly likely associated with an increased risk for AMI, including: PAI-1, CX37, IL18, and others. Also, a correlation was made between the results obtained in the big data analysis and the results of the review. The most important genes increasing the risk for AMI are lymphotoxin-a gene (LTA), LGALS2, LDLR, and APOA5. A deeper understanding of the underlying functional genomic circuits may present new opportunities for research in the future. Show less