Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology.

AIMS: Resource-strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings. METHODS: We used simple information theory calculations on a brain cancer simulation model and real-world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto-adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH-mutant tumors. RESULTS: Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53, and ATRX status have significant information for the diagnosis of IDH-mutant tumors. The predictive models may facilitate the reduction of false-positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing. CONCLUSIONS: We posit that this approach provides an improvement on the cIMPACT-NOW workflow recommendations for IDH-mutant tumors and a framework for future resource and testing allocation.

Astrocyte regional heterogeneity revealed through machine learning-based glial neuroanatomical assays.

Evaluation of reactive astrogliosis by neuroanatomical assays represents a common experimental outcome for neuroanatomists. The literature demonstrates several conflicting results as to the accuracy of such measures. We posited that the diverging results within the neuroanatomy literature were due to suboptimal analytical workflows in addition to astrocyte regional heterogeneity. We therefore generated an automated segmentation workflow to extract features of glial fibrillary acidic protein (GFAP) and aldehyde dehydrogenase family 1, member L1 (ALDH1L1) labeled astrocytes with and without neuroinflammation. We achieved this by capturing multiplexed immunofluorescent confocal images of mouse brains treated with either vehicle or lipopolysaccharide (LPS) followed by implementation of our workflows. Using classical image analysis techniques focused on pixel intensity only, we were unable to identify differences between vehicle-treated and LPS-treated animals. However, when utilizing machine learning-based algorithms, we were able to (1) accurately predict which objects were derived from GFAP or ALDH1L1-stained images indicating that GFAP and ALDH1L1 highlight distinct morphological aspects of astrocytes, (2) we could predict which neuroanatomical region the segmented GFAP or ALDH1L1 object had been derived from, indicating that morphological features of astrocytes change as a function of neuroanatomical location. (3) We discovered a statistically significant, albeit not highly accurate, prediction of which objects had come from LPS versus vehicle-treated animals, indicating that although features exist capable of distinguishing LPS-treated versus vehicle-treated GFAP and ALDH1L1-segmented objects, that significant overlap between morphologies exists. We further determined that for most classification scenarios, nonlinear models were required for improved treatment class designations. We propose that unbiased automated image analysis techniques coupled with well-validated machine learning tools represent highly useful models capable of providing insights into neuroanatomical assays.

Neuropathologists play a key role in establishing the extent of COVID-19 in human patients.

SARS-CoV2 infection causes COVID-19, and represents the most emergent health care crisis of our generation. Ample evidence in the scientific literature suggests that SARS-CoV, MERS-CoV, and endemic human coronaviruses infect brain cells. We delineate a rationale for encouraging evaluation of the brain, and in particular the brainstem, in COVID-19 so that potential neuropathological mechanisms can be delineated.

Decreased Activity and Genetic Polymorphisms of CYP2C19 in Behçet's Disease.

Behçet's disease (BD) is a systemic autoimmune disorder. Cytochrome P450 enzymes (CYPs) are responsible for various drug metabolism reactions as well as those of endogenous substances which may be associated with autoimmune disease susceptibility. Recently, we reported that in patients with BD, CYP2C9 seems to be down-regulated due to inflammation. In the same Turkish patients with BD, we investigated whether also CYP2C19 activity is decreased. Lansoprazole (30 mg) was given as a probe drug to evaluate CYP2C19 activity in 59 patients with BD and 27 healthy control volunteers. An HPLC method was used to determine plasma lansoprazole and its metabolite, 5-hydroxy lansoprazole, concentrations. The genotyping for CYP2C19 *2, *3 and *17 polymorphisms was made using PCR-RFLP. The median lansoprazole/5-hydroxy lansoprazole metabolic ratio (MR) in patients with BD was 2.6-fold higher as compared to the healthy control group (p = 0.001, 22.6 (1.3-26) and 8.8 (0.5-140) as median and range, respectively). The CYP2C19*17*17 genotype frequency was found to be significantly less in the BD group as compared to the healthy controls (1.7% versus 14.8% in controls, p = 0.01). Additionally, colchicine treatment did not affect the CYP2C19 enzyme activity in six patients (p = 0.43). In conclusion, the patients with BD had lower CYP2C19 enzyme activity and lower frequency of the CYP2C19*17 allele as compared to those of the healthy controls. Further studies are warranted on the mechanisms underlying this relation. This study should also be applied to other autoimmune diseases similarly characterized by local or systemic inflammation.

Relation of the Allelic Variants of Multidrug Resistance Gene to Agranulocytosis Associated With Clozapine.

Clozapine use is associated with leukopenia and more rarely agranulocytosis, which may be lethal. The drug and its metabolites are proposed to interact with the multidrug resistance transporter (ABCB1/MDR1) gene product, P-glycoprotein (P-gp). Among various P-glycoprotein genetic polymorphisms, nucleotide changes in exons 26 (C3435T), 21 (G2677T), and 12 (C1236T) have been implicated for changes in pharmacokinetics and pharmacodynamics of many substrate drugs. In this study, we aimed to investigate the association between these specific ABCB1 polymorphisms and clozapine-associated agranulocytosis (CAA). Ten patients with a history of CAA and 91 control patients without a history of CAA, despite 10 years of continuous clozapine use, were included. Patient recruitment and blood sample collection were conducted at the Hacettepe University Faculty of Medicine, Department of Psychiatry, in collaboration with the members of the Schizophrenia and Other Psychotic Disorders Section of the Psychiatric Association of Turkey, working in various psychiatry clinics. After DNA extraction from peripheral blood lymphocytes, genotyping was performed using polymerase chain reaction and endonuclease digestion. Patients with CAA had shorter duration of clozapine use but did not show any significant difference in other clinical, sociodemographic characteristics and in genotypic or allelic distributions of ABCB1 variants and haplotypes compared with control patients. Among the 10 patients with CAA, none carried the ABCB1 all-variant haplotype (TT-TT-TT), whereas the frequency of this haplotype was approximately 12% among the controls. Larger sample size studies and thorough genetic analyses may reveal both genetic risk and protective factors for this serious adverse event.