Hasil untuk "Pathology"

P. Baldelli, J. Meldolesi

Abstract REST [RE1-silencing transcription factor (also called neuron-restrictive silencer factor)] is known to repress thousands of possible target genes, many of which are neuron specific. To date, REST repression has been investigated mostly in stem cells and differentiating neurons. Current evidence demonstrates its importance in adult neurons as well. Low levels of REST, which are acquired during differentiation, govern the expression of specific neuronal phenotypes. REST-dependent genes encode important targets, including transcription factors, transmitter release proteins, voltage-dependent and receptor channels, and signaling proteins. Additional neuronal properties depend on miRNAs expressed reciprocally to REST and on specific splicing factors. In adult neurons, REST levels are not always low. Increases occur during aging in healthy humans. Moreover, extensive evidence demonstrates that prolonged stimulation with various agents induces REST increases, which are associated with the repression of neuron-specific genes with appropriate, intermediate REST binding affinity. Whether neuronal increases in REST are protective or detrimental remains a subject of debate. Examples of CA1 hippocampal neuron protection upon depolarization, and of neurodegeneration upon glutamate treatment and hypoxia have been reported. REST participation in psychiatric and neurological diseases has been shown, especially in Alzheimer’s disease and Huntington’s disease, as well as epilepsy. Distinct, complex roles of the repressor in these different diseases have emerged. In conclusion, REST is certainly very important in a large number of conditions. We suggest that the conflicting results reported for the role of REST in physiology, pathology, and disease depend on its complex, direct, and indirect actions on many gene targets and on the diverse approaches used during the investigations.

C. Ménard, M. Pfau, G. Hodes et al.

Studies suggest that heightened peripheral inflammation contributes to the pathogenesis of major depressive disorder. We investigated the effect of chronic social defeat stress, a mouse model of depression, on blood–brain barrier (BBB) permeability and infiltration of peripheral immune signals. We found reduced expression of the endothelial cell tight junction protein claudin-5 (Cldn5) and abnormal blood vessel morphology in nucleus accumbens (NAc) of stress-susceptible but not resilient mice. CLDN5 expression was also decreased in NAc of depressed patients. Cldn5 downregulation was sufficient to induce depression-like behaviors following subthreshold social stress whereas chronic antidepressant treatment rescued Cldn5 loss and promoted resilience. Reduced BBB integrity in NAc of stress-susceptible or mice injected with adeno-associated virus expressing shRNA against Cldn5 caused infiltration of the peripheral cytokine interleukin-6 (IL-6) into brain parenchyma and subsequent expression of depression-like behaviors. These findings suggest that chronic social stress alters BBB integrity through loss of tight junction protein Cldn5, promoting peripheral IL-6 passage across the BBB and depression.Chronic social defeat stress induces loss of protein claudin-5, leading to abnormalities in blood vessel morphology, increased blood brain barrier permeability, infiltration of immune signals and depression-like behaviors.

D. Saslow, D. Solomon, Herschel W. Lawson et al.

J. Mansfield, S. Genin, Shimpei Magori et al.

H. Lassmann

T. Harach, N. Marungruang, N. Duthilleul et al.

Alzheimer’s disease is the most common form of dementia in the western world, however there is no cure available for this devastating neurodegenerative disorder. Despite clinical and experimental evidence implicating the intestinal microbiota in a number of brain disorders, its impact on Alzheimer’s disease is not known. To this end we sequenced bacterial 16S rRNA from fecal samples of Aβ precursor protein (APP) transgenic mouse model and found a remarkable shift in the gut microbiota as compared to non-transgenic wild-type mice. Subsequently we generated germ-free APP transgenic mice and found a drastic reduction of cerebral Aβ amyloid pathology when compared to control mice with intestinal microbiota. Importantly, colonization of germ-free APP transgenic mice with microbiota from conventionally-raised APP transgenic mice increased cerebral Aβ pathology, while colonization with microbiota from wild-type mice was less effective in increasing cerebral Aβ levels. Our results indicate a microbial involvement in the development of Abeta amyloid pathology, and suggest that microbiota may contribute to the development of neurodegenerative diseases.

S. Hoda, Esther Cheng

Yun Liu, Krishna Gadepalli, Mohammad Norouzi et al.

Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task. At 8 false positives per image, we detect 92.4% of the tumors, relative to 82.7% by the previous best automated approach. For comparison, a human pathologist attempting exhaustive search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides. In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal. Our approach could considerably reduce false negative rates in metastasis detection.

C. Roufosse, N. Simmonds, M. C. Clahsen‐van Groningen et al.

Abstract The Banff Classification of Allograft Pathology is an international consensus classification for the reporting of biopsies from solid organ transplants. Since its initial conception in 1991 for renal transplants, it has undergone review every 2 years, with attendant updated publications. The rapid expansion of knowledge in the field has led to numerous revisions of the classification. The resultant dispersal of relevant content makes it difficult for novices and experienced pathologists to faithfully apply the classification in routine diagnostic work and in clinical trials. This review shall provide a complete and simple illustrated reference guide of the Banff Classification of Kidney Allograft Pathology based on all publications including the 2017 update. It is intended as a concise desktop reference for pathologists and clinicians, providing definitions, Banff Lesion Scores and Banff Diagnostic Categories. An online website reference guide hosted by the Banff Foundation for Allograft Pathology (www.banfffoundation.org) is being developed, which will be updated with future refinement of the Banff Classification from 2019 onward.

Alexandre Bejanin, D. Schonhaut, R. La Joie et al.

Dds Brad W. Neville, Dds Douglas D. Damm, Dds Carl M. Allen et al.

L. Barnes

F. Tavassoli, P. Devilee

M. Miettinen, J. Lasota

R. van der Kant, L. Goldstein, R. Ossenkoppele

Naomi Ramer, M. Cohen, Trina Sengupta et al.

Firmly established as the textbook of choice on the subject, Oral Pathology is unique in its comprehensive coverage at a level suitable for both undergraduate and postgraduate dental students and practitioners. Highly illustrated in full colour, the new edition continues to provide clear, concise and comprehensive coverage of the clinical and pathological features of a wide range of oral diseases. It discusses their causes and the underlying mechanisms, and describes how changes in the structure and function of oral tissues relate to clinical presentation.

R. Baergen, D. Heller

This study describes the pathology and clinical information on 20 placentas whose mother tested positive for the novel Coronovirus (2019-nCoV) cases. Ten of the 20 cases showed some evidence of fetal vascular malperfusion or fetal vascular thrombosis. The significance of these findings is unclear and needs further study.

Axel D. Meneses, Shunsuke Koga, J. O'leary et al.

Transactive response DNA binding protein of 43 kDa (TDP-43) is an intranuclear protein encoded by the TARDBP gene that is involved in RNA splicing, trafficking, stabilization, and thus, the regulation of gene expression. Cytoplasmic inclusion bodies containing phosphorylated and truncated forms of TDP-43 are hallmarks of amyotrophic lateral sclerosis (ALS) and a subset of frontotemporal lobar degeneration (FTLD). Additionally, TDP-43 inclusions have been found in up to 57% of Alzheimer’s disease (AD) cases, most often in a limbic distribution, with or without hippocampal sclerosis. In some cases, TDP-43 deposits are also found in neurons with neurofibrillary tangles. AD patients with TDP-43 pathology have increased severity of cognitive impairment compared to those without TDP-43 pathology. Furthermore, the most common genetic risk factor for AD, apolipoprotein E4 ( APOE4 ), is associated with increased frequency of TDP-43 pathology. These findings provide strong evidence that TDP-43 pathology is an integral part of multiple neurodegenerative conditions, including AD. Here, we review the biology and pathobiology of TDP-43 with a focus on its role in AD. We emphasize the need for studies on the mechanisms that lead to TDP-43 pathology, especially in the setting of age-related disorders such as AD.

Z. Ahmad, Shabina Rahim, Maha Zubair et al.

Background The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world. Main text Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted. Conclusion AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care.

M. Plass, Michael Kargl, T. Kiehl et al.

The current move towards digital pathology enables pathologists to use artificial intelligence (AI)‐based computer programmes for the advanced analysis of whole slide images. However, currently, the best‐performing AI algorithms for image analysis are deemed black boxes since it remains – even to their developers – often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black‐box machine‐learning systems more transparent. These XAI methods are a first step towards making black‐box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive ‘what‐if’‐questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human‐in‐the‐loop and bringing medical experts' experience and conceptual knowledge to AI processes.