What is she best known for?
The fields of study she is best known for:
- Internal medicine
- Immune system
- Virus
Sheila M. Keating spends much of her time researching Immunology, Virology, T cell, Vaccination and Plasmodium falciparum.
Her Immunology research incorporates themes from Vaccinia and Hematopoietic stem cell transplantation.
The concepts of her Hematopoietic stem cell transplantation study are interwoven with issues in Lymph node and Blood plasma.
In her study, Circumsporozoite protein is strongly linked to Immunogenicity, which falls under the umbrella field of Virology.
Her Virus research is multidisciplinary, relying on both Innate immune system and Immune system.
As a part of the same scientific family, she mostly works in the field of Stem cell, focusing on Peripheral and, on occasion, Viral load.
Her most cited work include:
- Recombinant modified vaccinia virus Ankara expressing antigen 85A boosts BCG-primed and naturally acquired antimycobacterial immunity in humans. (528 citations)
- Antiretroviral-Free HIV-1 Remission and Viral Rebound After Allogeneic Stem Cell Transplantation: Report of 2 Cases (303 citations)
- Long-Term Reduction in Peripheral Blood HIV Type 1 Reservoirs Following Reduced-Intensity Conditioning Allogeneic Stem Cell Transplantation (237 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Immunology, Virology, Internal medicine, Viral load and Immune system.
Inflammation, Vaccination, Cytokine, T cell and Chemokine are subfields of Immunology in which her conducts study.
Her study of Virus is a part of Virology.
Her biological study spans a wide range of topics, including Gastroenterology, Endocrinology, Oncology and Cardiology.
Her work deals with themes such as Stem cell, Hiv incidence and Transplantation, which intersect with Viral load.
Her research in Antibody intersects with topics in HIV Antigens and Antigen.
She most often published in these fields:
- Immunology (68.09%)
- Virology (49.47%)
- Internal medicine (23.94%)
What were the highlights of her more recent work (between 2018-2021)?
- Internal medicine (23.94%)
- Immune system (20.74%)
- Immunology (68.09%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Internal medicine, Immune system, Immunology, T cell and Avidity.
Her studies deal with areas such as Antibody, Critically ill, Physiology and Transplantation as well as Immune system.
Her Antibody study integrates concerns from other disciplines, such as Clinical trial, Cohort and Virology.
Her Virology research incorporates elements of Clinical microbiology and Antiretroviral therapy.
Her Immunology research integrates issues from Effects of sleep deprivation on cognitive performance and Serum albumin.
Sheila M. Keating interconnects Incidence and Viral load in the investigation of issues within Avidity.
Between 2018 and 2021, her most popular works were:
- Intradonor reproducibility and changes in hemolytic variables during red blood cell storage: results of recall phase of the REDS-III RBC-Omics study (25 citations)
- Piloting and implementation of quality assessment and quality control procedures in RBC-Omics: a large multi-center study of red blood cell hemolysis during storage. (14 citations)
- Assessing intra-lab precision and inter-lab repeatability of outgrowth assays of HIV-1 latent reservoir size. (11 citations)
In her most recent research, the most cited papers focused on:
- Internal medicine
- Immune system
- Virus
Gold standard, Statistics, Repeatability, Biomedical engineering and Confidence interval are her primary areas of study.
Her work carried out in the field of Statistics brings together such families of science as Incidence, Viral load and Antigen.
Her Antigen study typically links adjacent topics like Reproducibility.
Her Reproducibility research includes themes of Concordance, Red blood cell and Avidity.
Her Repeatability study combines topics in areas such as Reliability, Percentage point and Accuracy and precision.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
