Hasil untuk "Pathology"

Scott A Oakes, F. Papa

Jessica W. Wu, S. A. Hussaini, I. M. Bastille et al.

Tau protein can transfer between neurons transneuronally and trans-synaptically, which is thought to explain the progressive spread of tauopathy observed in the brain of patients with Alzheimer's disease. Here we show that physiological tau released from donor cells can transfer to recipient cells via the medium, suggesting that at least one mechanism by which tau can transfer is via the extracellular space. Neuronal activity has been shown to regulate tau secretion, but its effect on tau pathology is unknown. Using optogenetic and chemogenetic approaches, we found that increased neuronal activity stimulates the release of tau in vitro and enhances tau pathology in vivo. These data have implications for disease pathogenesis and therapeutic strategies for Alzheimer's disease and other tauopathies.

Ph. Gitta L. Coaker, Graduate Program Coordinator Theresa Garcia, Lead Faculty Advisor Johan Leveau et al.

C. Holling, G. Meffe

R. DeLellis

A. Wagner

R. Ludwig, K. Vanhoorelbeke, F. Leypoldt et al.

Autoantibodies are frequently observed in healthy individuals. In a minority of these individuals, they lead to manifestation of autoimmune diseases, such as rheumatoid arthritis or Graves’ disease. Overall, more than 2.5% of the population is affected by autoantibody-driven autoimmune disease. Pathways leading to autoantibody-induced pathology greatly differ among different diseases, and autoantibodies directed against the same antigen, depending on the targeted epitope, can have diverse effects. To foster knowledge in autoantibody-induced pathology and to encourage development of urgently needed novel therapeutic strategies, we here categorized autoantibodies according to their effects. According to our algorithm, autoantibodies can be classified into the following categories: (1) mimic receptor stimulation, (2) blocking of neural transmission, (3) induction of altered signaling, triggering uncontrolled (4) microthrombosis, (5) cell lysis, (6) neutrophil activation, and (7) induction of inflammation. These mechanisms in relation to disease, as well as principles of autoantibody generation and detection, are reviewed herein.

J. Lechner, Olivia E. O'Leary, Alan W. Stitt

M. Schoots, Sanne J. Gordijn, S. Scherjon et al.

The most important function of the placenta is the exchange of nutrients and oxygen between a mother and her fetus. To establish a healthy functioning placenta, placentation needs to occur with adequate remodelling of spiral arteries by extravillous trophoblasts. When this process is impaired, the resulting suboptimal and inadequate placenta function results in the manifestation of pregnancy complications. Impaired placenta function can cause preeclampsia and leads to fetal growth restriction due to hypoxia. Presence of hypoxia leads to oxidative stress due to an imbalance between reactive oxygen species and antioxidants, thereby causing damage to proteins, lipids and DNA. In the placenta, signs of morphological adaptation in response to hypoxia can be found. Different placental lesions like maternal or fetal vascular malperfusion or chronic villitis lead to a decreased exchange of oxygen between the mother and the fetus. Clinically, several biomarkers indicative for oxidative stress, e.g. malondialdehyde and reduced levels of free thiols are found. This review aims to give an overview of the causes and (potential) role of placental oxidative stress in the development of placental parenchymal pathology and its clinical consequences. Also, therapeutic options aiming at prevention or treatment of hypoxia of the placenta and fetus are described.

M. Zarella, Douglas Bowman, F. Aeffner et al.

CONTEXT.— Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. Its basic function is to digitize glass slides, but its impact on pathology workflows, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and intrainstitutional and interinstitutional collaboration exemplifies a significant innovative movement with far-reaching effects. Although the benefits of WSI to pathology practices, academic centers, and research institutions are many, the complexities of implementation remain an obstacle to widespread adoption. In the wake of the first regulatory clearance of WSI for primary diagnosis in the United States, some barriers to adoption have fallen. Nevertheless, implementation of WSI remains a difficult prospect for many institutions, especially those with stakeholders unfamiliar with the technologies necessary to implement a system or who cannot effectively communicate to executive leadership and sponsors the benefits of a technology that may lack clear and immediate reimbursement opportunity. OBJECTIVES.— To present an overview of WSI technology-present and future-and to demonstrate several immediate applications of WSI that support pathology practice, medical education, research, and collaboration. DATA SOURCES.— Peer-reviewed literature was reviewed by pathologists, scientists, and technologists who have practical knowledge of and experience with WSI. CONCLUSIONS.— Implementation of WSI is a multifaceted and inherently multidisciplinary endeavor requiring contributions from pathologists, technologists, and executive leadership. Improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology, can help prospective users identify the best path for success.

S. Mukhopadhyay, M. Feldman, Esther Abels et al.

Most prior studies of primary diagnosis in surgical pathology using whole slide imaging (WSI) versus microscopy have focused on specific organ systems or included relatively few cases. The objective of this study was to demonstrate that WSI is noninferior to microscopy for primary diagnosis in surgical pathology. A blinded randomized noninferiority study was conducted across the entire range of surgical pathology cases (biopsies and resections, including hematoxylin and eosin, immunohistochemistry, and special stains) from 4 institutions using the original sign-out diagnosis (baseline diagnosis) as the reference standard. Cases were scanned, converted to WSI and randomized. Sixteen pathologists interpreted cases by microscopy or WSI, followed by a wash-out period of ≥4 weeks, after which cases were read by the same observers using the other modality. Major discordances were identified by an adjudication panel, and the differences between major discordance rates for both microscopy (against the reference standard) and WSI (against the reference standard) were calculated. A total of 1992 cases were included, resulting in 15,925 reads. The major discordance rate with the reference standard diagnosis was 4.9% for WSI and 4.6% for microscopy. The difference between major discordance rates for microscopy and WSI was 0.4% (95% confidence interval, −0.30% to 1.01%). The difference in major discordance rates for WSI and microscopy was highest in endocrine pathology (1.8%), neoplastic kidney pathology (1.5%), urinary bladder pathology (1.3%), and gynecologic pathology (1.2%). Detailed analysis of these cases revealed no instances where interpretation by WSI was consistently inaccurate compared with microscopy for multiple observers. We conclude that WSI is noninferior to microscopy for primary diagnosis in surgical pathology, including biopsies and resections stained with hematoxylin and eosin, immunohistochemistry and special stains. This conclusion is valid across a wide variety of organ systems and specimen types.

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

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Ahmed Serag, A. Ion-Margineanu, H. Qureshi et al.

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

Loïc Chadoutaud, Alice Blondel, Hana Feki et al.

Computational pathology relies on effective representation learning to support cancer research and precision medicine. Although self-supervised learning has driven major progress at the patch and whole-slide image levels, representation learning at the single-cell level remains comparatively underexplored, despite its importance for characterizing cell types and cellular phenotypes. We introduce LEMON (Learning Embeddings from Morphology Of Nuclei), a self-supervised foundation model for scalable single-cell image representation learning. Trained on millions of cell images from diverse tissues and cancer types, LEMON learns robust and versatile morphological representations that support large-scale single-cell analyses in pathology. We evaluate LEMON on five benchmark datasets across a range of prediction tasks and show that it provides strong performance, highlighting its potential as a new paradigm for cell-level computational pathology. Model weights are available at https://huggingface.co/aliceblondel/LEMON.

R. Sengoku

The number of people with dementia worldwide is predicted to increase to 131.5 million by 2050. When studying dementia, understanding the basis of the neuropathological background is very important. Taking Alzheimer's disease (AD) neuropathology as an example, we know that the accumulation of abnormal structures such as senile plaques and neurofibrillary tangles is a hallmark. Macroscopic atrophy affects the entorhinal area and hippocampus, amygdala, and associative regions of the neocortex. Braak advocates the spread of tau deposits from the entorhinal to associative regions of the neocortex as the disease progresses. If the AD has only tau pathology, the degree and distribution of tau deposition may be associated with clinical symptoms. However, AD is also accompanied by amyloid‐β deposition and even atrophy. Although it is possible to make a neuropathological diagnosis of AD from the spread of amyloid and tau depositions, neuropathological abnormal protein accumulation cannot explain all clinical symptoms of AD. There is an ambiguity between clinical symptoms and neuropathological findings. It is important to understand neuropathological findings while understanding that this ambiguity exists. So, for the reader's help, first we briefly explain the changes in the brain with age, and then describe AD as a typical disease of dementia; finally we will describe the diseases that mimic AD for neurologists who are not experts in neuropathology.

S. Ogino, J. Nowak, Tsuyoshi Hamada et al.

Evidence indicates that diet, nutrition, lifestyle, the environment, the microbiome, and other exogenous factors have pathogenic roles and also influence the genome, epigenome, transcriptome, proteome, and metabolome of tumor and nonneoplastic cells, including immune cells. With the need for big-data research, pathology must transform to integrate data science fields, including epidemiology, biostatistics, and bioinformatics. The research framework of molecular pathological epidemiology (MPE) demonstrates the strengths of such an interdisciplinary integration, having been used to study breast, lung, prostate, and colorectal cancers. The MPE research paradigm not only can provide novel insights into interactions among environment, tumor, and host but also opens new research frontiers. New developments-such as computational digital pathology, systems biology, artificial intelligence, and in vivo pathology technologies-will further transform pathology and MPE. Although it is necessary to address the rarity of transdisciplinary education and training programs, MPE provides an exemplary model of integrative scientific approaches and contributes to advancements in precision medicine, therapy, and prevention.

Xiangde Luo, Jinxi Xiang, Yuanfeng Ji et al.

Computational pathology holds substantial promise for improving diagnosis and guiding treatment decisions. Recent pathology foundation models enable the extraction of rich patch-level representations from large-scale whole-slide images (WSIs), but current approaches for aggregating these features into slide-level predictions remain constrained by design limitations that hinder generalizability and reliability. Here, we developed nnMIL, a simple yet broadly applicable multiple-instance learning framework that connects patch-level foundation models to robust slide-level clinical inference. nnMIL introduces random sampling at both the patch and feature levels, enabling large-batch optimization, task-aware sampling strategies, and efficient and scalable training across datasets and model architectures. A lightweight aggregator performs sliding-window inference to generate ensemble slide-level predictions and supports principled uncertainty estimation. Across 40,000 WSIs encompassing 35 clinical tasks and four pathology foundation models, nnMIL consistently outperformed existing MIL methods for disease diagnosis, histologic subtyping, molecular biomarker detection, and pan- cancer prognosis prediction. It further demonstrated strong cross-model generalization, reliable uncertainty quantification, and robust survival stratification in multiple external cohorts. In conclusion, nnMIL offers a practical and generalizable solution for translating pathology foundation models into clinically meaningful predictions, advancing the development and deployment of reliable AI systems in real-world settings.

Hamid R. Tizhoosh

Despite their successes in vision and language, foundation models have stumbled in pathology, revealing low accuracy, instability, and heavy computational demands. These shortcomings stem not from tuning problems but from deeper conceptual mismatches: dense embeddings cannot represent the combinatorial richness of tissue, and current architectures inherit flaws in self-supervision, patch design, and noise-fragile pretraining. Biological complexity and limited domain innovation further widen the gap. The evidence is clear-pathology requires models explicitly designed for biological images rather than adaptations of large-scale natural-image methods whose assumptions do not hold for tissue.

Noor Shaker, Mohamed AbouZleikha, Nuha Shaker

Digital pathology has emerged as a transformative approach to tissue analysis, offering unprecedented opportunities for objective, quantitative assessment of histopathological features. However, the complexity of implementing artificial intelligence (AI) solutions in pathology workflows has limited widespread adoption. Here we present ORCA (Optimized Research and Clinical Analytics), a comprehensive no-code AI platform specifically designed for digital pathology applications. ORCA addresses critical barriers to AI adoption by providing an intuitive interface that enables pathologists and researchers to train, deploy, and validate custom AI models without programming expertise. The platform integrates advanced deep learning architectures with clinical workflow management, supporting applications from tissue classification and cell segmentation to spatial distribution scoring and novel biomarker discovery. We demonstrate ORCA's capabilities through validation studies across multiple cancer types, showing significant improvements in analytical speed, reproducibility, and clinical correlation compared to traditional manual assessment methods. Our results indicate that ORCA successfully democratizes access to state-of-the-art AI tools in pathology, potentially accelerating biomarker discovery and enhancing precision medicine initiatives.

Jakub R. Kaczmarzyk, Rishul Sharma, Peter K. Koo et al.

Foundation models for computational pathology have shown great promise for specimen-level tasks and are increasingly accessible to researchers. However, specimen-level models built on these foundation models remain largely unavailable, hindering their broader utility and impact. To address this gap, we developed SpinPath, a toolkit designed to democratize specimen-level deep learning by providing a zoo of pretrained specimen-level models, a Python-based inference engine, and a JavaScript-based inference platform. We demonstrate the utility of SpinPath in metastasis detection tasks across nine foundation models. SpinPath may foster reproducibility, simplify experimentation, and accelerate the adoption of specimen-level deep learning in computational pathology research.