Cyanide is a poison widely used in cases of suicide or homicide. Although various methods to identify and quantify this substance are reported in the literature, they are mainly validated on biological fluids (e.g., blood and urine). In the present study, the Anion-Exchange Liquid Chromatography with Pulsed Amperometric Detection (IC-PAD) method was validated on blood and, for the first time, on gastric content, and organs (brain, lung, and liver). For each matrix, linearity, accuracy, precision, limit of detection (LOD), lower limit of quantification (LLOQ), matrix interferences, and carryover were assessed. The samples were extracted by steam distillation in acid environment for the following analysis by IC-PAD. Furthermore, cyanide values found in two real poisoning cases are reported. For each investigated matrix, the analytical method satisfied all acceptance criteria for validation: it showed a good precision and accuracy, selectivity, and sensitivity with no carryover and matrix interference. The extraction by steam distillation in acid environment REDUCED the interference of the matrices and ALLOWED to perform the analysis with good precision and accuracy. In case #1, analysis showed a blood cyanide concentration of 0.99 mu g/ml. In case #2, cyanide concentrations were 1.3 mu g/g in brain, 0.8 mu g/g in lung, 1.6 mu g/g in liver, and 1.2 mu g/g in gastric content. The cyanide concentrations found in the two reported cases have been suitable to cause death by poisoning.

Introduction: Charred bodies represents a significant challenge for forensic pathologists due to the destructive effects of fire on human remains. Although most fire-related deaths are accidental, cases of suicide and homicide are not uncommon. Case Report: We report a peculiar case of a severely burned body discovered inside a torched vehicle. Under judicial investigation, a full autopsy was performed, including macroscopic and microscopic examination of key anatomical structures: the inspection of oral cavity revealed no soot deposits; a foreign object (a chestnut) was found anterior to the epiglottis, though not lodged within the glottis; no thermal injuries or soot were observed in the upper or lower airways. Histological analysis excluded thermal damage at the alveolar–capillary interface. Alveolar spaces appeared both hyperinflated and ectatic, likely due to septal rupture, suggestive of acute pulmonary emphysema and multiorgan congestion. Carboxyhemoglobin levels were below 5%, indicating a low level which did not support intravital inhalation of combustion gases. Based on the comprehensive medico-legal findings, the cause of death was attributed to an asphyxial mechanism. It was further demonstrated that the burning of the body occurred post-mortem. DNA extraction from two dental specimens enabled positive identification of the victim. Subsequent investigations confirmed the case to be a femicide. The perpetrator, following a domestic altercation over jealousy, suffocated his young wife and attempted to simulate accidental choking by placing a chestnut in her mouth. He then staged a vehicular fire to mimic a fatal accident. Conclusions: The case underlines that a multidisciplinary forensic approach is essential, and must integrate different methodologies and the analysis of both circumstantial evidence and scene investigation.

Artificial intelligence (AI) is being investigated as a means to advance forensic histopathology from visual description to quantitative mapping, pattern recognition, and multimodal reconstruction of death. Recent developments in human postmortem analysis include validated whole-slide digitisation as foundational infrastructure and task-specific AI applications for diatom detection, glomerular quantification, coronary morphometry, myocardial classification, immunohistochemistry scoring, and virtual staining. AI-based postmortem interval (PMI) estimation remains experimental; the multi-organ study referenced in this commentary utilised an animal model, and human forensic validity has not yet been established. Foundation and vision-language models may ultimately extend these capabilities across organs and integrate microscopic findings with autopsy, toxicological, radiological, and circumstantial evidence. Postmortem tissue, however, is subject to autolysis, decomposition, agonal events, resuscitation, and non-standardised sampling, while the reference diagnosis is often a probabilistic whole-case opinion rather than an objective slide label. This work introduces FORENS-AI (FORensic Histopathology Evidence Network and Safeguards for AI), a conceptual architecture designed to convert computational outputs into traceable medicolegal evidence. FORENS-AI has not been empirically validated and is not presented as a standard, scoring system, clinical decision rule, or practice recommendation. The framework represents findings as an evidence graph, separates the Slide, Case, and Claim Gates, and proposes progressively stronger validation as outputs move from measurement to temporal, mechanistic, or causal inference. The primary aim is not to automate certification of death, but to identify safeguards to evaluate before AI-derived histological findings are permitted to inform expert opinion.