Transcript- and protein-level analyses of the response of human eosinophils to glucocorticoids

Gadkari, Manasi; Makiya, Michelle; Legrand, Fanny; Stokes, Kindra; Brown, Thomas; Howe, Katherine N; Khoury, Paneez; Hu, Zonghui; Klion, Amy D.; Franco, Luis M.

doi:10.1038/sdata.2018.275

Cited by 8 publications

(4 citation statements)

References 21 publications

(23 reference statements)

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“…Cells were grown in 384-well plates (CellCarrier Ultra 384; PerkinElmer, Waltham, MA), fixed in 4% paraformaldheyde (PFA) and stained with 4 0 , 6 0 -diamidino-2-phenylindole (DAPI). Primary human eosinophils were obtained from healthy donors enrolled under NIH protocol NCT000090662 and purified as previously described (17). The protocol was approved by the Institutional Review Board of the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (NIH).…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Pipeline for Nucleus Segmentation

et al. 2020

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Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off‐the‐shelf deep learning models pretrained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning‐based biological image segmentation using small annotated image datasets. Published [2020]. This article is a U.S. Government work and is in the public domain in the USA

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Section: Methodsmentioning

confidence: 99%

A Deep Learning Pipeline for Nucleus Segmentation

et al. 2020

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“…Briefly, GC dampen neutrophil response to inflammatory stimuli ( 379 ), increase susceptibility to viral as well as opportunistic infections inducing lymphopenia (mainly of the CD4 + subset) by altering CD4/CD8 ( 380 ) and Th1/Th2 ratio subpopulations ( 381 ), suppress cytotoxic activity in natural killer cells ( 382 ), reduce eosinophil count ( 383 ), and reduce mast-cells interfering with IgE-mediated mast-cell degranulation and calcium release ( 384 ). Interestingly, some of these effects seem to be dose dependent and different in exogenous vs endogenous GC excess ( 375 ).…”

Section: The Adverse Effects Of Prescribed Gcsmentioning

confidence: 99%

Treating the Side Effects of Exogenous Glucocorticoids; Can We Separate the Good From the Bad?

et al. 2023

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It is estimated that 2% to 3% of the population are currently prescribed systemic or topical glucocorticoid treatment. The potent anti-inflammatory action of glucocorticoids to deliver therapeutic benefit is not in doubt. However, the side effects associated with their use, including central weight gain, hypertension, insulin resistance, type 2 diabetes (T2D), and osteoporosis, often collectively termed iatrogenic Cushing's syndrome, are associated with a significant health and economic burden. The precise cellular mechanisms underpinning the differential action of glucocorticoids to drive the desirable and undesirable effects are still not completely understood. Faced with the unmet clinical need to limit glucocorticoid-induced adverse effects alongside ensuring the preservation of anti-inflammatory actions, several strategies have been pursued. The coprescription of existing licensed drugs to treat incident adverse effects can be effective, but data examining the prevention of adverse effects are limited. Novel selective glucocorticoid receptor agonists and selective glucocorticoid receptor modulators have been designed that aim to specifically and selectively activate anti-inflammatory responses based upon their interaction with the glucocorticoid receptor. Several of these compounds are currently in clinical trials to evaluate their efficacy. More recently, strategies exploiting tissue-specific glucocorticoid metabolism through the isoforms of 11β-hydroxysteroid dehydrogenase has shown early potential, although data from clinical trials are limited. The aim of any treatment is to maximize benefit while minimizing risk, and within this review we define the adverse effect profile associated with glucocorticoid use and evaluate current and developing strategies that aim to limit side effects but preserve desirable therapeutic efficacy.

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“…Cells were grown in 384-well plates (CellCarrier Ultra 384, PerkinElmer, Waltham, MA), fixed in 4% PFA and stained with DAPI. Primary human eosinophils were obtained from healthy donors enrolled under NIH protocol NCT000090662 and purified as previously described (17). Eosinophils were plated in Poly-D-Lysine coated 384-well plates (CellCarrier Ultra 384, PerkinElmer, Waltham, MA), then fixed in 4% PFA and stained with DAPI.…”

Section: Cellsmentioning

confidence: 99%

A Deep Learning Pipeline for Nucleus Segmentation

Zaki

Gudla

Lee

et al. 2020

Preprint

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Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to improve and understand the training parameters that drive the performance of deep learning models trained on small, custom annotated image datasets, we have designed a computational pipeline to systematically test different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the training image dataset composition, size and pre-processing, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, state-of-the-art deep learning models pre-trained on large image datasets. Our work provides computational tools and a practical framework for the improvement of deep learning-based biological image segmentation using small annotated image datasets.

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Transcript- and protein-level analyses of the response of human eosinophils to glucocorticoids

Cited by 8 publications

References 21 publications

A Deep Learning Pipeline for Nucleus Segmentation

A Deep Learning Pipeline for Nucleus Segmentation

Treating the Side Effects of Exogenous Glucocorticoids; Can We Separate the Good From the Bad?

A Deep Learning Pipeline for Nucleus Segmentation

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