Neuropathological findings in Down syndrome, Alzheimer's disease and control patients with and without SARS-COV-2: preliminary findings.

The SARS-CoV-2 virus that led to COVID-19 is associated with significant and long-lasting neurologic symptoms in many patients, with an increased mortality risk for people with Alzheimer's disease (AD) and/or Down syndrome (DS). However, few studies have evaluated the neuropathological and inflammatory sequelae in postmortem brain tissue obtained from AD and people with DS with severe SARS-CoV-2 infections. We examined tau, beta-amyloid (Aß), inflammatory markers and SARS-CoV-2 nucleoprotein in DS, AD, and healthy non-demented controls with COVID-19 and compared with non-infected brain tissue from each disease group (total n = 24). A nested ANOVA was used to determine regional effects of the COVID-19 infection on arborization of astrocytes (Sholl analysis) and percent-stained area of Iba-1 and TMEM 119. SARS-CoV-2 antibodies labeled neurons and glial cells in the frontal cortex of all subjects with COVID-19, and in the hippocampus of two of the three DS COVID-19 cases. SARS-CoV-2-related alterations were observed in peri-vascular astrocytes and microglial cells in the gray matter of the frontal cortex, hippocampus, and para-hippocampal gyrus. Bright field microscopy revealed scattered intracellular and diffuse extracellular Aß deposits in the hippocampus of controls with confirmed SARS-CoV-2 infections. Overall, the present preliminary findings suggest that SARS-CoV-2 infections induce abnormal inflammatory responses in Down syndrome.

Down Syndrome Biobank Consortium: A perspective.

Individuals with Down syndrome (DS) have a partial or complete trisomy of chromosome 21, resulting in an increased risk for early-onset Alzheimer's disease (AD)-type dementia by early midlife. Despite ongoing clinical trials to treat late-onset AD, individuals with DS are often excluded. Furthermore, timely diagnosis or management is often not available. Of the genetic causes of AD, people with DS represent the largest cohort. Currently, there is a knowledge gap regarding the underlying neurobiological mechanisms of DS-related AD (DS-AD), partly due to limited access to well-characterized brain tissue and biomaterials for research. To address this challenge, we created an international consortium of brain banks focused on collecting and disseminating brain tissue from persons with DS throughout their lifespan, named the Down Syndrome Biobank Consortium (DSBC) consisting of 11 biobanking sites located in Europe, India, and the USA. This perspective describes the DSBC harmonized protocols and tissue dissemination goals.

Who's your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry.

PURPOSE: There are currently more than 480 primary immune deficiency (PID) diseases and about 7000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. In a proof-of-concept data mining study, we aimed to predict PID diagnoses using a supervised machine learning algorithm based on classification tree boosting. METHODS: Through a data query at the USIDNET registry we obtained a database of 2396 patients with common diagnoses of PID, including their clinical and laboratory features. We kept 286 features and all 12 diagnoses to include in the model. We used the XGBoost package with parallel tree boosting for the supervised classification model, and SHAP for variable importance interpretation, on Python v3.7. The patient database was split into training and testing subsets, and after boosting through gradient descent, the predictive model provides measures of diagnostic prediction accuracy and individual feature importance. After a baseline performance test, we used the Class Weighting Hyperparameter, or scale_pos_weight to correct for imbalanced classification. RESULTS: The twelve PID diagnoses were CVID (1098 patients), DiGeorge syndrome, Chronic granulomatous disease, Congenital agammaglobulinemia, PID not otherwise classified, Specific antibody deficiency, Complement deficiency, Hyper-IgM, Leukocyte adhesion deficiency, ectodermal dysplasia with immune deficiency, Severe combined immune deficiency, and Wiskott-Aldrich syndrome. For CVID, the model found an accuracy on the train sample of 0.80, with an area under the ROC curve (AUC) of 0.80, and a Gini coefficient of 0.60. In the test subset, accuracy was 0.76, AUC 0.75, and Gini 0.51. The positive feature value to predict CVID was highest for upper respiratory infections, asthma, autoimmunity and hypogammaglobulinemia. Features with the highest negative predictive value were high IgE, growth delay, abscess, lymphopenia, and congenital heart disease. For the rest of the diagnoses, accuracy stayed between 0.75 and 0.99, AUC 0.46-0.87, Gini 0.07-0.75, and LogLoss 0.09-8.55. DISCUSSION: Clinicians should remember to consider the negative predictive features together with the positives. We are calling this a proof-of-concept study to continue with our explorations. A good performance is encouraging, and feature importance might aid feature selection for future endeavors. In the meantime, we can learn from the rules derived by the model and build a user-friendly decision tree to generate differential diagnoses.

Urothelial carcinoma metastases impacting the CNS: A 20-year retrospective series.

INTRODUCTION: Our group has a longstanding interest in metastases impacting the central nervous system (CNS), including spread from prostatic adenocarcinomas, thyroid carcinomas, and breast carcinomas, most of which metastasize to CNS sites at a later time after the primary tumor is well-known. However, one of the least frequent types of systemic malignancies to metastasize to brain or spine is urothelial carcinoma. Thus, few large studies from a single institution exist. Fewer still detail the interval between first diagnosis of primary tumor and CNS lesion, or whether a patient might have their first presentation of cancer in the brain or spine, thus prompting review of our 20-year experience. MATERIALS: Case identification via text word search of pathology databases from our adult and referral hospitals, 2002 to present. Demographic and clinical data were extracted from reports and the medical record. RESULTS: 15 cases, 11 male: 4 female, age range 37-82 years were identified. Nine had metastases to brain parenchyma, 5 to vertebral column impacting spinal cord, and 2 to skull, one of which had tumor extension into right parietal lobe. Strikingly, 5 of 15 patients had had their CNS-impacting metastasis as their first presentation of neoplastic disease. CONCLUSIONS: CNS metastasis of urothelial carcinoma is a rare occurrence; nevertheless, pathologists should include urothelial carcinoma in their differential diagnosis as a type of cancer that can first present with a CNS-impacting metastasis.

Cerebral Amyloid Angiopathy-related Inflammation Presenting With a Cystic Lesion in Young-onset Alzheimer Disease.

We describe a patient with cerebral amyloid angiopathy-related inflammation (CAA-ri) presenting as Alzheimer disease (AD) with a mass lesion with symptom onset at age 59. He was found to have a nonenhancing lesion in the right temporal lobe on magnetic resonance imaging without evidence of hemorrhage. He underwent a biopsy which showed amyloid beta in blood vessel walls and a perivascular inflammatory infiltrate consistent with CAA-ri. Neurofibrillary tangles were present and a flortaucipir positron emission tomography showed bilateral signal highest in the lateral temporal and parietal cortices. A lumbar puncture showed tau, p-tau, and amyloid beta levels consistent with AD without evidence of inflammation. He was homozygous for the APOE ε4 allele. He died at age 67. A focus of CAA-ri can be present in the context of AD with a mass lesion and without evidence of hemorrhage, significant ischemic changes, or overt inflammation on cerebrospinal fluid examination.

A repository of grade 1 and 2 meningioma MRIs in a public dataset for radiomics reproducibility tests.

PURPOSE: Meningiomas are the most common primary brain tumors in adults with management varying widely based on World Health Organization (WHO) grade. However, there are limited datasets available for researchers to develop and validate radiomic models. The purpose of our manuscript is to report on the first dataset of meningiomas in The Cancer Imaging Archive (TCIA). ACQUISITION AND VALIDATION METHODS: The dataset consists of pre-operative MRIs from 96 patients with meningiomas who underwent resection from 2010-2019 and include axial T1post and T2-FLAIR sequences-55 grade 1 and 41 grade 2. Meningioma grade was confirmed based on the 2016 WHO Bluebook classification guideline by two neuropathologists and one neuropathology fellow. The hyperintense T1post tumor and hyperintense T2-FLAIR regions were manually contoured on both sequences and resampled to an isotropic resolution of 1 × 1 × 1 mm3 . The entire dataset was reviewed by a certified medical physicist. DATA FORMAT AND USAGE NOTES: The data was imported into TCIA for storage and can be accessed at https://doi.org/10.7937/0TKV-1A36. The total size of the dataset is 8.8GB, with 47 519 individual Digital Imaging and Communications in Medicine (DICOM) files consisting of 384 image series, and 192 structures. POTENTIAL APPLICATIONS: Grade 1 and 2 meningiomas have different treatment paradigms and are often treated based on radiologic diagnosis alone. Therefore, predicting grade prior to treatment is essential in clinical decision-making. This dataset will allow researchers to create models to auto-differentiate grade 1 and 2 meningiomas as well as evaluate for other pathologic features including mitotic index, brain invasion, and atypical features. Limitations of this study are the small sample size and inclusion of only two MRI sequences. However, there are no meningioma datasets on TCIA and limited datasets elsewhere although meningiomas are the most common intracranial tumor in adults.

Recipient Reaction and Composition of Autologous Sural Nerve Tissue Grafts into the Human Brain.

Parkinson's disease (PD) is a severe neurological disease for which there is no effective treatment or cure, and therefore it remains an unmet need in medicine. We present data from four participants who received autologous transplantation of small pieces of sural nerve tissue into either the basal forebrain containing the nucleus basalis of Meynert (NBM) or the midbrain substantia nigra (SN). The grafts did not exhibit significant cell death or severe host-tissue reaction up to 55 months post-grafting and contained peripheral cells. Dopaminergic neurites showed active growth in the graft area and into the graft in the SN graft, and cholinergic neurites were abundant near the graft in the NBM. These results provide a histological basis for changes in clinical features after autologous peripheral nerve tissue grafting into the NBM or SN in PD.

Locus coeruleus integrity is related to tau burden and memory loss in autosomal-dominant Alzheimer's disease.

Abnormally phosphorylated tau, an indicator of Alzheimer's disease, accumulates in the first decades of life in the locus coeruleus (LC), the brain's main noradrenaline supply. However, technical challenges in in-vivo assessments have impeded research into the role of the LC in Alzheimer's disease. We studied participants with or known to be at-risk for mutations in genes causing autosomal-dominant Alzheimer's disease (ADAD) with early onset, providing a unique window into the pathogenesis of Alzheimer's largely disentangled from age-related factors. Using high-resolution MRI and tau PET, we found lower rostral LC integrity in symptomatic participants. LC integrity was associated with individual differences in tau burden and memory decline. Post-mortem analyses in a separate set of carriers of the same mutation confirmed substantial neuronal loss in the LC. Our findings link LC degeneration to tau burden and memory in Alzheimer's, and highlight a role of the noradrenergic system in this neurodegenerative disease.

Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network.

The Grade of meningioma has significant implications for selecting treatment regimens ranging from observation to surgical resection with adjuvant radiation. For most patients, meningiomas are diagnosed radiologically, and Grade is not determined unless a surgical procedure is performed. The goal of this study is to train a novel auto-classification network to determine Grade I and II meningiomas using T1-contrast enhancing (T1-CE) and T2-Fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. Ninety-six consecutive treatment naïve patients with pre-operative T1-CE and T2-FLAIR MR images and subsequent pathologically diagnosed intracranial meningiomas were evaluated. Delineation of meningiomas was completed on both MR images. A novel asymmetric 3D convolutional neural network (CNN) architecture was constructed with two encoding paths based on T1-CE and T2-FLAIR. Each path used the same 3 × 3 × 3 kernel with different filters to weigh the spatial features of each sequence separately. Final model performance was assessed by tenfold cross-validation. Of the 96 patients, 55 (57%) were pathologically classified as Grade I and 41 (43%) as Grade II meningiomas. Optimization of our model led to a filter weighting of 18:2 between the T1-CE and T2-FLAIR MR image paths. 86 (90%) patients were classified correctly, and 10 (10%) were misclassified based on their pre-operative MRs with a model sensitivity of 0.85 and specificity of 0.93. Among the misclassified, 4 were Grade I, and 6 were Grade II. The model is robust to tumor locations and sizes. A novel asymmetric CNN with two differently weighted encoding paths was developed for successful automated meningioma grade classification. Our model outperforms CNN using a single path for single or multimodal MR-based classification.

Reducing body myopathy associated with the LIM2 p.(His123Arg) FHL1 variant.

Reducing body myopathy (RBM) is a rare muscle disorder, with marked presence of characteristic intracytoplasmic aggregates in affected muscle fibers. RBM is associated with FHL1 gene mutations. Clinical presentations of RBM have ranged from early fatal to adult onset progressive muscle weakness. We present herein the clinical, electrodiagnostic, and muscle biopsy findings of a 17-year-old female with progressive muscle weakness and contracture. Muscle biopsy showed atrophic fibers that contained menadione nitroblue tetrazolium (NBT) positive reducing bodies. Genetic testing revealed a variant of uncertain significance in the FHL1 gene at a position known to be pathogenic when substituted by other amino acids (p.His123Arg). This variant was later reclassified as pathogenic.

Pulmonary capillary hemangiomatosis: a lesson learned.

Pulmonary capillary hemangiomatosis (PCH) is a rare and controversial entity that is known to be a cause of pulmonary hypertension and is microscopically characterized by proliferation of dilated capillary-sized channels along and in the alveolar walls. Clinically, it is mostly seen in adults. Clinical features are characterized by nonspecific findings such as shortness of breath, cough, chest pain, and fatigue. It can be clinically indistinguishable from pre-capillary pulmonary arterial hypertension disorders such as primary pulmonary arterial hypertension (PAH) or chronic thromboembolic pulmonary hypertension. However, the diagnostic distinction, which usually requires a multidisciplinary approach, is crucial in order to avoid inappropriate treatment with vasodilator medications usually used for PAH treatment. Prognosis of PCH remains poor with lung transplant being the only definitive treatment. We report an autopsy case of pulmonary capillary hemangiomatosis unmasked at autopsy that was treated with a prostacyclin analog, usually contraindicated in such patients. We emphasize that this entity should always be on the differential diagnosis in a patient with pulmonary hypertension and requires great vigilance on the part of the clinician, radiologist and pathologist to make the diagnosis and guide appropriate management.

Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides.

During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.

Pulmonary capillary hemangiomatosis: a lesson learned

Pulmonary capillary hemangiomatosis (PCH) is a rare and controversial entity that is known to be a cause of pulmonary hypertension and is microscopically characterized by proliferation of dilated capillary-sized channels along and in the alveolar walls. Clinically, it is mostly seen in adults. Clinical features are characterized by nonspecific findings such as shortness of breath, cough, chest pain, and fatigue. It can be clinically indistinguishable from pre-capillary pulmonary arterial hypertension disorders such as primary pulmonary arterial hypertension (PAH) or chronic thromboembolic pulmonary hypertension. However, the diagnostic distinction, which usually requires a multidisciplinary approach, is crucial in order to avoid inappropriate treatment with vasodilator medications usually used for PAH treatment. Prognosis of PCH remains poor with lung transplant being the only definitive treatment. We report an autopsy case of pulmonary capillary hemangiomatosis unmasked at autopsy that was treated with a prostacyclin analog, usually contraindicated in such patients. We emphasize that this entity should always be on the differential diagnosis in a patient with pulmonary hypertension and requires great vigilance on the part of the clinician, radiologist and pathologist to make the diagnosis and guide appropriate management.