58 cases of sexual violence bearing forensic interest: congruence between the victim's report and the data from laboratory analyses.

The assistance provided by specialised healthcare personnel to victims of a sexual violence cannot focus just on the clinical intervention appropriate for the lesions suffered by the patient, but must also take legal and forensic needs into account. Anamnestic data represents a crucial step towards the finding of forensic evidence. Our retrospective study aims to analyse the congruence between verbal reports from abused women and the laboratory data to the end of identifying ways for enhancing the gathering of anamnestic data. We considered 960 medical records related to sexual violence that reached the Rape Centre "Soccorso Violenza Sessuale" of Turin between 2003 and 2013. Having consulted the register of evidence, we selected the cases for which the local judicial authority had asked for expert advice on biological material. The selected cases have been gathered in two different categories depending on whether the victim could or could not recall the events. Then, we looked at the results of the cytological analysis performed to identify the presence of sperm cells, at the results of the body fluid identification, and at the results of the DNA quantitation. Our findings strongly suggest that forensic investigations should be carried out independently from the presence of memories of the traumatic events on the victim's part. Moreover, they suggest that forensic investigations should also be pursued in the presence of a negative cytologic examination.

An initial exploration of machine learning for establishing associations between genetic markers and THC levels in Cannabis sativa samples.

Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate statistical models and Machine Learning approaches to establish correlations between specific genotypes and tetrahydrocannabinol (Δ9-THC) content (%) in C. sativa samples. 132 cannabis leaves samples were obtained from legal growers in Piedmont, Italy, and illegal drug seizures in Turin. Samples were genetically profiled using a 13-loci STR multiplex and their Δ9-THC content was detected through quantitative GC-MS analysis. This study aims to assess the use of supervised classification modelling on genetic data to distinguish cannabis samples into legal and illegal categories, revealing distinct clusters characterized by unique allele profiles and THC content. t-distributed Stochastic Neighbor Embedding (t-SNE), Random Forest (RF) and Partial Least Squares Regression (PLS-R) were executed for the machine learning modelling. All the tested models resulted effective discriminating between legal samples and illegal. Although further validation is necessary, this study presents a novel forensic investigative approach, potentially aiding law enforcement in significant marijuana seizures or tracking illicit drug trafficking routes.