法医学研究ジャーナル

Teeth remains are often the only means of positive identification in an unidentified body after being subjected to high temperature injury. The routine use of dental implants made them as an active contributing element to the identification of unidentified cadavers. Their resistance to prolonged high temperature might make them a substantial contributor to the identification of high temperature burned bodies. The aim of the study was to observe the effects of high temperature on three dental implants of distinct elemental composition: C1-MIS®, made of Ti grade 23; BL- Straumann®, made of Ti grade 4 and of a Titanium-Zirconium alloy; Roxolid-Straumann®, by detecting the changes of their microstructural and elemental composition. Scanning Electron Microscopy and Electron Dispersive Spectroscopy were used to characterize the surface structure and elemental composition of the implants before and after implants being subjected to a high temperature protocol of 1375 °C for 30 minutes. Macroscopic and microscopic changes in the samples after exposure to the high temperature. Dental implants demonstrated specific macroscopic changes and microstructural deteriorations, after exposure to high temperature. After exposure to high temperature, dental implants demonstrated specific macroscopic changes and microstructural deteriorations. Although several changes occurred in the elemental content of the materials, the original elemental composition was preserved. The ability to discriminate between dental implants by elemental analyses can have a determinant impact on the identification process of burned bodies.

Anomaly detection in digital forensics data is critical for identifying suspicious activities and potential security breaches. This mini-review investigates the efficacy of a novel Support Vector Neural Network (SVNN) for anomaly detection in digital forensics datasets. By examining recent literature, this article elucidates the principles of SVNN, its advantages over traditional methods, and its application in detecting anomalous behavior in various forensic scenarios. Furthermore, it discusses challenges, opportunities, and future directions for enhancing anomaly detection using SVNN in digital forensics investigations.

Insulin overdose is a critical concern in forensic investigations due to its potential lethality and widespread availability. This mini-review explores the biochemical and toxicological effects of insulin overdose in rats, offering insights from a forensic perspective. By examining relevant literature, this article elucidates the mechanisms of insulin toxicity, including hypoglycemia, neurologic impairment, and cardiovascular complications. Additionally, it discusses forensic considerations such as postmortem findings, analytical techniques for insulin detection, and interpretation challenges in forensic pathology. Understanding the intricacies of insulin overdose in animal models provides valuable insights for forensic scientists and medical examiners in cases involving suspected insulin-related fatalities.