Recently at the Medical Historical Museum of McGill University, Dr. Rick Fraser discovered a microscope slide prepared in 1876 from the lung of a patient with pneumoconiosis. Photomicrographs show the presence of coal dust particles in alveolar cells. This case and several related ones had been reported in 1875 by William Osler, who also had demonstrated the cellular uptake of carbon particles in kittens injected with India ink. In 1869 a Philadelphia physician described the uptake of bacteria by leukocytes in saliva and urine. Both investigators postulated a protective role for this cellular phenomenon. Neither of these reports has been generally cited in histories of immunology. These two papers are summarized here along with a short review of other reports describing phagocytosis which predating Metchnikoff's entrance into the field.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.cellimm.2006.05.008 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.
J Pathol Inform
December 2024
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
View Article and Find Full Text PDFCanadian Multicentric Pan-TRK (CANTRK) Immunohistochemistry Harmonization Study.
Mod Pathol
January 2024
Department of Pathology and Laboratory Medicine, Royal University Hospital, Saskatchewan Health Authority, and College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. Electronic address:
Tumor-agnostic testing for NTRK1-3 gene rearrangements is required to identify patients who may benefit from TRK inhibitor therapies. The overarching objective of this study was to establish a high-quality pan-TRK immunohistochemistry (IHC) screening assay among 18 large regional pathology laboratories across Canada using pan-TRK monoclonal antibody clone EPR17341 in a ring study design. TRK-fusion positive and negative tumor samples were collected from participating sites, with fusion status confirmed by panel next-generation sequencing assays.
View Article and Find Full Text PDFWhole slide image representation in bone marrow cytology.
Comput Biol Med
November 2023
McMaster University, Hamilton, Canada; William Osler Health System, Brampton, Canada. Electronic address:
Article Synopsis
- The study focuses on using AI to create compact representations of whole slide images (WSIs) in bone marrow aspirate cytology, which is essential for hematology diagnosis.
- Traditional visual inspection of these specimens is complex and varies among experts, making the development of AI tools beneficial for clinical decision-making.
- The authors applied an AI system to classify and count cells, achieving better performance in image retrieval and diagnostic prediction compared to random guessing, showcasing the potential of deep learning in improving diagnostics in hematology.
Applied machine learning in hematopathology.
Int J Lab Hematol
June 2023
Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.
An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications.
View Article and Find Full Text PDFBiased data, biased AI: deep networks predict the acquisition site of TCGA images.
Diagn Pathol
May 2023
Nature Inspired Computational Intelligence (NICI), Ontario Tech University, Oshawa, ON, Canada.
Background: Deep learning models applied to healthcare applications including digital pathology have been increasing their scope and importance in recent years. Many of these models have been trained on The Cancer Genome Atlas (TCGA) atlas of digital images, or use it as a validation source. One crucial factor that seems to have been widely ignored is the internal bias that originates from the institutions that contributed WSIs to the TCGA dataset, and its effects on models trained on this dataset.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!