Hasil untuk "Pathology"

Willemijn J Jansen, R. Ossenkoppele, D. Knol et al.

IMPORTANCE Cerebral amyloid-β aggregation is an early pathological event in Alzheimer disease (AD), starting decades before dementia onset. Estimates of the prevalence of amyloid pathology in persons without dementia are needed to understand the development of AD and to design prevention studies. OBJECTIVE To use individual participant data meta-analysis to estimate the prevalence of amyloid pathology as measured with biomarkers in participants with normal cognition, subjective cognitive impairment (SCI), or mild cognitive impairment (MCI). DATA SOURCES Relevant biomarker studies identified by searching studies published before April 2015 using the MEDLINE and Web of Science databases and through personal communication with investigators. STUDY SELECTION Studies were included if they provided individual participant data for participants without dementia and used an a priori defined cutoff for amyloid positivity. DATA EXTRACTION AND SYNTHESIS Individual records were provided for 2914 participants with normal cognition, 697 with SCI, and 3972 with MCI aged 18 to 100 years from 55 studies. MAIN OUTCOMES AND MEASURES Prevalence of amyloid pathology on positron emission tomography or in cerebrospinal fluid according to AD risk factors (age, apolipoprotein E [APOE] genotype, sex, and education) estimated by generalized estimating equations. RESULTS The prevalence of amyloid pathology increased from age 50 to 90 years from 10% (95% CI, 8%-13%) to 44% (95% CI, 37%-51%) among participants with normal cognition; from 12% (95% CI, 8%-18%) to 43% (95% CI, 32%-55%) among patients with SCI; and from 27% (95% CI, 23%-32%) to 71% (95% CI, 66%-76%) among patients with MCI. APOE-ε4 carriers had 2 to 3 times higher prevalence estimates than noncarriers. The age at which 15% of the participants with normal cognition were amyloid positive was approximately 40 years for APOE ε4ε4 carriers, 50 years for ε2ε4 carriers, 55 years for ε3ε4 carriers, 65 years for ε3ε3 carriers, and 95 years for ε2ε3 carriers. Amyloid positivity was more common in highly educated participants but not associated with sex or biomarker modality. CONCLUSIONS AND RELEVANCE Among persons without dementia, the prevalence of cerebral amyloid pathology as determined by positron emission tomography or cerebrospinal fluid findings was associated with age, APOE genotype, and presence of cognitive impairment. These findings suggest a 20- to 30-year interval between first development of amyloid positivity and onset of dementia.

A. Madabhushi, George Lee

J. Crary, J. Trojanowski, J. Schneider et al.

John T Jones, A. Haegeman, E. Danchin et al.

I. Voineagu, Xinchen Wang, P. Johnston et al.

Autism spectrum disorder (ASD) is a common, highly heritable neurodevelopmental condition characterized by marked genetic heterogeneity. Thus, a fundamental question is whether autism represents an aetiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain. Here, we demonstrate consistent differences in transcriptome organization between autistic and normal brain by gene co-expression network analysis. Remarkably, regional patterns of gene expression that typically distinguish frontal and temporal cortex are significantly attenuated in the ASD brain, suggesting abnormalities in cortical patterning. We further identify discrete modules of co-expressed genes associated with autism: a neuronal module enriched for known autism susceptibility genes, including the neuronal specific splicing factor A2BP1 (also known as FOX1), and a module enriched for immune genes and glial markers. Using high-throughput RNA sequencing we demonstrate dysregulated splicing of A2BP1-dependent alternative exons in the ASD brain. Moreover, using a published autism genome-wide association study (GWAS) data set, we show that the neuronal module is enriched for genetically associated variants, providing independent support for the causal involvement of these genes in autism. In contrast, the immune-glial module showed no enrichment for autism GWAS signals, indicating a non-genetic aetiology for this process. Collectively, our results provide strong evidence for convergent molecular abnormalities in ASD, and implicate transcriptional and splicing dysregulation as underlying mechanisms of neuronal dysfunction in this disorder.

W. Travis, E. Brambilla, H. Müller-hermelink

L. Thompson

E. Jaffe

C. Basso, B. Aguilera, J. Banner et al.

Although sudden cardiac death (SCD) is one of the most important modes of death in Western countries, pathologists and public health physicians have not given this problem the attention it deserves. New methods of preventing potentially fatal arrhythmias have been developed and the accurate diagnosis of the causes of SCD is now of particular importance. Pathologists are responsible for determining the precise cause and mechanism of sudden death but there is still considerable variation in the way in which they approach this increasingly complex task. The Association for European Cardiovascular Pathology has developed these guidelines, which represent the minimum standard that is required in the routine autopsy practice for the adequate investigation of SCD. The present version is an update of our original article, published 10 years ago. This is necessary because of our increased understanding of the genetics of cardiovascular diseases, the availability of new diagnostic methods, and the experience we have gained from the routine use of the original guidelines. The updated guidelines include a detailed protocol for the examination of the heart and recommendations for the selection of histological blocks and appropriate material for toxicology, microbiology, biochemistry, and molecular investigation. Our recommendations apply to university medical centers, regionals hospitals, and all healthcare professionals practicing pathology and forensic medicine. We believe that their adoption throughout Europe will improve the standards of autopsy practice, allow meaningful comparisons between different communities and regions, and permit the identification of emerging patterns of diseases causing SCD. Finally, we recommend the development of regional multidisciplinary networks of cardiologists, geneticists, and pathologists. Their role will be to facilitate the identification of index cases with a genetic basis, to screen appropriate family members, and ensure that appropriate preventive strategies are implemented.

H. Tizhoosh, L. Pantanowitz

In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.

J. Goldblum, L. Lamps, J. McKenney et al.

N. Tickotsky, Tal Sagiv, J. Prilusky et al.

Esther Abels, L. Pantanowitz, F. Aeffner et al.

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

I. Stancu, Niels Cremers, Hannah Vanrusselt et al.

Brains of Alzheimer’s disease patients are characterized by the presence of amyloid plaques and neurofibrillary tangles, both invariably associated with neuroinflammation. A crucial role for NLRP3–ASC inflammasome [NACHT, LRR and PYD domains-containing protein 3 (NLRP3)–Apoptosis-associated speck-like protein containing a CARD (ASC)] in amyloid-beta (Aβ)-induced microgliosis and Aβ pathology has been unequivocally identified. Aβ aggregates activate NLRP3–ASC inflammasome (Halle et al. in Nat Immunol 9:857–865, 2008) and conversely NLRP3–ASC inflammasome activation exacerbates amyloid pathology in vivo (Heneka et al. in Nature 493:674–678, 2013), including by prion-like ASC-speck cross-seeding (Venegas et al. in Nature 552:355–361, 2017). However, the link between inflammasome activation, as crucial sensor of innate immunity, and Tau remains unexplored. Here, we analyzed whether Tau aggregates acting as prion-like Tau seeds can activate NLRP3–ASC inflammasome. We demonstrate that Tau seeds activate NLRP3–ASC-dependent inflammasome in primary microglia, following microglial uptake and lysosomal sorting of Tau seeds. Next, we analyzed the role of inflammasome activation in prion-like or templated seeding of Tau pathology and found significant inhibition of exogenously seeded Tau pathology by ASC deficiency in Tau transgenic mice. We furthermore demonstrate that chronic intracerebral administration of the NLRP3 inhibitor, MCC950, inhibits exogenously seeded Tau pathology. Finally, ASC deficiency also decreased non-exogenously seeded Tau pathology in Tau transgenic mice. Overall our findings demonstrate that Tau-seeding competent, aggregated Tau activates the ASC inflammasome through the NLRP3–ASC axis, and we demonstrate an exacerbating role of the NLRP3–ASC axis on exogenously and non-exogenously seeded Tau pathology in Tau mice in vivo. The NLRP3–ASC inflammasome, which is an important sensor of innate immunity and intensively explored for its role in health and disease, hence presents as an interesting therapeutic approach to target three crucial pathogenetic processes in AD, including prion-like seeding of Tau pathology, Aβ pathology and neuroinflammation.

Shidan Wang, Donghan M. Yang, Ruichen Rong et al.

With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and accurately remain significant challenges. Recently, deep learning algorithms have shown great promise in pathology image analysis, such as in tumor region identification, metastasis detection and patient prognosis. Many machine learning algorithms, including convolutional neural networks, have been proposed to automatically segment pathology images. Among these algorithms, segmentation deep learning algorithms such as fully-convolutional networks stand out for their accuracy, computational efficiency, and generalizability. Thus, deep learning-based pathology image segmentation has become an important tool in WSI analysis. In this review, we describe the pathology image segmentation process using deep learning algorithms in detail. The goals are to provide quick guidance for implementing deep learning into pathology image analysis, and to provide some potential ways of further improving segmentation performance. Although there have been previous reviews on using machine learning methods in digital pathology image analysis, to our knowledge, this is the first in-depth review of the applications of deep learning algorithms for segmentation in WSI analysis.

H. Rashidi, N. Tran, E. Betts et al.

Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (supervised, unsupervised, and reinforcement learning), introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms (linear regression, logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, random forest, convolutional neural networks).

Elisheva D. Shanes, Leena B. Mithal, Sebastian Otero et al.

Objectives: To describe histopathologic findings in the placentas of women with COVID-19 during pregnancy. Methods: Pregnant women with COVID-19 delivering between March 18, 2020 and May 5, 2020 were identified. Placentas were examined and compared to historical controls and women with placental evaluation for a history of melanoma. Results: 16 placentas from patients with SARS-CoV-2 were examined (15 with live birth in the 3rd trimester 1 delivered in the 2nd trimester after intrauterine fetal demise). Compared to controls, third trimester placentas were significantly more likely to show at least one feature of maternal vascular malperfusion (MVM), including abnormal or injured maternal vessels, as well as delayed villous maturation, chorangiosis, and intervillous thrombi. Rates of acute and chronic inflammation were not increased. The placenta from the patient with intrauterine fetal demise showed villous edema and a retroplacental hematoma. Conclusions: Relative to controls, COVID-19 placentas show increased prevalence of features of maternal vascular malperfusion (MVM), a pattern of placental injury reflecting abnormalities in oxygenation within the intervillous space associated with adverse perinatal outcomes. Only 1 COVID-19 patient was hypertensive despite the association of MVM with hypertensive disorders and preeclampsia. These changes may reflect a systemic inflammatory or hypercoagulable state influencing placental physiology.

Y. Butt, Maxwell L. Smith, H. Tazelaar et al.

Pathology of Vaping-Associated Lung Injury This letter describes findings in 17 patients with a history of vaping who had lung biopsies after presenting with symptoms and bilateral pulmonary opacit...

L. Barisoni, K. Lafata, S. Hewitt et al.

The emergence of digital pathology — an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis — is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases. Developments in digital pathology and computational image analysis have the potential to identify new disease mechanisms, improve disease classification and prognostication, and ultimately aid the identification of targeted therapies. In this Review, the authors provide an outline of the digital ecosystem in nephropathology and describe potential applications and challenges associated with the emerging armamentarium of technologies for image analysis. The introduction of digital pathology in clinical research, trials and practice has catalysed the development of novel machine-learning models for tissue interrogation with the potential to improve our ability to discover disease mechanisms, identify comprehensive, patient-specific phenotypes, classify kidney patients into clinically relevant categories, predict disease outcome and, ultimately, identify more targeted therapies. The development of computational image analysis tools for tissue interrogation has brought pathology to the forefront in this process of re-defining kidney diseases. The new nephropathology ecosystem offers several advantages over conventional pathology but also brings some challenges that need to be addressed collectively by all the stake holders, including pathologists, nephrologists, computer scientists, regulatory agencies and patient’s representatives; overcoming these challenges is a pre-requisite for these new machine-learning and computational pathology models to be fully deployed for patient care. The development of novel powerful computational tools for image analysis and data integration in kidney diseases has exposed the need to revise the curriculum for medical professionals to prepare the next generation to fully operate within the new digital pathology ecosystem. Ultimately, our ability to treat kidney diseases (actionable intelligence) will be largely based on the application of artificial (augmenting) intelligence tools and the establishment of synergistic human–machine protocols that integrate digital pathology data with clinical and molecular data for personalized nephrology.

Ana Carolina Pismel Lobo, Gabriela Cristina Santin, Elen de Souza Tolentino

Acute herpetic gingivostomatitis (AHGS) is the oral manifestation of HVS-1 primary infection. Despite being a self-limiting infection, AHGS can progress to severe complications. Dentists should be prepared to correctly diagnose and treat the disease. Therefore, the purpose of this study is to assess knowledge, attitude, and practice (KAP) of dentists regarding acute herpetic gingivostomatitis (AHGS) among children. A cross-sectional and descriptive study was carried out through a KAP Survey of 416 Brazilian dentists. Descriptive analyzes with absolute and relative frequencies were performed and possible associations between socio-demographic variables with the KAP questions were investigated using Chi-square and Fisher's exact tests (significance level 5%). Results revealed high knowledge scores among 68% of the dentists. The worst knowledge scores were found for AHGS complications. High scores were only associated with degree of education (p<0.005). For the treatment of AHGS, the responses were variable and signaled possible overtreatment in practice. Therapeutic possibilities beyond acyclovir are still lacking. This study highlights the importance of providing continuous education and integrating the practice of oral pathology into the practice of dentistry. It can help to increase knowledge, avoid overtreatment, and stimulate decision-making by the dentist in cases of complications.