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Gerald J. Berry

Researcher at Stanford University

Publications -  316
Citations -  25593

Gerald J. Berry is an academic researcher from Stanford University. The author has contributed to research in topics: Transplantation & Lung transplantation. The author has an hindex of 74, co-authored 298 publications receiving 22061 citations. Previous affiliations of Gerald J. Berry include Santa Clara Valley Medical Center & University of California, Los Angeles.

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Revision of the 1990 Working Formulation for the Standardization of Nomenclature in the Diagnosis of Heart Rejection

TL;DR: This article summarizes the revised consensus classification of lung allograft rejection and recommends the evaluation of antibody-mediated rejection, recognizing that this is a controversial entity in the lung, less well developed and understood than in other solid-organ grafts, and with no consensus reached on diagnostic features.
Journal Article

Revision of the 1990 working formulation for the classification of pulmonary allograft rejection : Lung Rejection Study Group

TL;DR: This article summarizes the updated classification for pulmonary allograft rejection, which is based on perivascular and interstitial mononuclear infiltrates and divided into bronchiolitis obliterans--active or inactive--and vascular atherosclerosis--accelerated arterial or venous sclerosis.
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Antigen-specific regulatory T cells develop via the ICOS–ICOS-ligand pathway and inhibit allergen-induced airway hyperreactivity

TL;DR: It is demonstrated that TR cells and the ICOS–ICOS-ligand signaling pathway are critically involved in respiratory tolerance and in downregulating pulmonary inflammation in asthma.
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A molecular cell atlas of the human lung from single-cell RNA sequencing.

TL;DR: Droplet- and plate-based single cell RNA sequencing applied to ~75,000 human cells across all lung tissue compartments and circulating blood, combined with a multi-pronged cell annotation approach, have allowed them to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung.
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Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.

TL;DR: It is suggested that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology.