The magnitude of the diagnostic benefit conferred by performing histopathological examinations after medico-legal/forensic autopsies remains debatable. We have tried to address this issue by reviewing a series of histopathology referrals concerning medico-legal autopsies in real-world routine practice. We present an audit of the consultations provided to forensics by clinical pathologists at our institute between 2015 and 2018. Over this period, 493 post-mortem examinations were performed by forensic pathologists. Of these cases, 52 (11%) were referred for histopathology. Gross assessment was requested in 22/52 (42%) cases. Histopathology examination was performed on single organs in 15/52 (29%) cases, primarily on the lung and heart, whereas parenchymatous multi-organ analysis was carried out in 14/52 (27%) cases. Bone-marrow sampling was studied in 4/52 (8%) cases. Immunohistochemistry was needed in 16/52 (31%) cases, special stains in 9/52 (21%) cases and molecular analysis in 4/52 (8%) cases. Focusing on technical processes, standard methodology on pre-analytical procedures was changed in 10/52 (19%) cases in order to answer specific diagnostic questions. We showed that although most of the time the diagnosis is clear by the end of dissection on the basis of the macroscopic findings, histopathology can provide, modify or confirm the cause of death in many medico-legal/forensic cases. Therefore, it is desirable that forensic pathologists and clinical pathologists establish robust working relationships in a cooperative environment. We conclude that it is important to implement guidelines based on real-world routine practice in order to identify cases where histopathology can provide useful contributions, which in our experience applied to 11% of forensic cases.
Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models.
Antibodies have been the workhorse for diagnostic immunohistochemistry to specifically interrogate the expression of certain protein to aid in histopathological diagnosis. This review introduces another dimension of histochemistry that employs aptamers as the core tool, the so-called aptahistochemistry. Aptamers are an emerging class of molecular recognition elements that could recapitulate the roles of antibodies. The many advantageous properties of aptamers suited for this diagnostic platform are scrutinized. An in-depth discussion on the technical aspects of aptahistochemistry is provided with close step-by-step comparison to the more familiarized immunohistochemical procedures, namely functionalization of the aptamer as a probe, antigen retrieval, optimization with emphasis on incubation parameters and visualization methods. This review offers rationales to overcome the anticipated challenges in transition from immunohistochemistry to aptahistochemistry, which is deemed feasible for an average diagnostic pathology laboratory.