New insights into neuropathology and pathogenesis of autoimmune glial fibrillary acidic protein meningoencephalomyelitis.

Anti-glial fibrillary acidic protein (GFAP) meningoencephalomyelitis (autoimmune GFAP astrocytopathy) is a new autoimmune central nervous system (CNS) disease diagnosable by the presence of anti-GFAP autoantibodies in the cerebrospinal fluid and presents as meningoencephalomyelitis in the majority of patients. Only few neuropathological reports are available and little is known about the pathogenic mechanisms. We performed a histopathological study of two autopsies and nine CNS biopsies of patients with anti-GFAP autoantibodies and found predominantly a lymphocytic and in one autopsy case a granulomatous inflammatory phenotype. Inflammatory infiltrates were composed of B and T cells, including tissue-resident memory T cells. Although obvious astrocytic damage was absent in the GFAP-staining, we found cytotoxic T cell-mediated reactions reflected by the presence of CD8+/perforin+/granzyme A/B+ cells, polarized towards astrocytes. MHC-class-I was upregulated in reactive astrocytes of all biopsies and two autopsies but not in healthy controls. Importantly, we observed a prominent immunoreactivity of astrocytes with the complement factor C4d. Finally, we provided insight into an early phase of GFAP autoimmunity in an autopsy of a pug dog encephalitis that was characterized by marked meningoencephalitis with selective astrocytic damage with loss of GFAP and AQP4 in the lesions.Our histopathological findings indicate that a cytotoxic T cell-mediated immune reaction is present in GFAP autoimmunity. Complement C4d deposition on astrocytes could either represent the cause or consequence of astrocytic reactivity. Selective astrocytic damage is prominent in the early phase of GFAP autoimmunity in a canine autopsy case, but mild or absent in subacute and chronic stages in human disease, probably due to the high regeneration potential of astrocytes. The lymphocytic and granulomatous phenotypes might reflect different stages of lesion development or patient-specific modifications of the immune response. Future studies will be necessary to investigate possible implications of pathological subtypes for clinical disease course and therapeutic strategies.

Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.

The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists' NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists' estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.