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Record W4353032308 · doi:10.4049/jimmunol.2200631

Systems Immunology: Origins

2023· article· en· W4353032308 on OpenAlexaff
Mark M. Davis

Bibliographic record

VenueThe Journal of Immunology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious DiseasesOpen Philanthropy ProjectDivision of Intramural Research, National Institute of Allergy and Infectious DiseasesHoward Hughes Medical Institute
KeywordsIconCitationDownloadComputer scienceWorld Wide WebInformation retrieval

Abstract

fetched live from OpenAlex

Fifteen years ago, human immunology was stagnating compared with the rapid pace of work with inbred mice.There was a desperate need for new strategies and methods that would allow us to leverage the advantages of immunology research in humans: the genetic and environmental diversity, the thousands of infectious diseases, and responses to clinically deployed treatments and vaccines.Development of the much-needed approaches led to a new field, systems immunology, with its emphasis on gathering as much information as possible from human blood samples, focusing on the cells and cytokines of the immune system, and organizing studies of vaccine responses in different human cohorts, including twins, the elderly, and children in high versus low pathogen environments.These types of studies have grown exponentially over the years, with great advances in technology and analysis, and have become an important way to understand the vast differences in human responses and what they can tell us about our own immune systems.What follows is a personal account of the role that I and my colleagues played in the early days of systems immunology.For a more extensive treatment of the current state of the field, I recommend some recent reviews (1, 2).For my part, almost two decades ago, I became alarmed that human immunology seemed to be almost at a standstill while murine work was racing ahead.This struck me as unsustainable because if all we do is improve the health of mice, even if the science is wonderful, we will lose public support and become another obscure academic curiosity.Although our work on imaging T cells and understanding the intricacies of cell-cell interactions was going well, I thought this problem was so compelling that I decided to shift my laboratory's focus to human immunology and to help develop more effective technologies and strategies.Both were clearly needed better technologies, because most of what we do in mouse immunology is impossible or very limited in humans, forcing immunologists to use a relatively small set of tools.Moreover, we needed distinct strategies for human immunology, especially because, as far as I could tell, the main strategy in mice was to create or find a model of a disease with the hope of uncovering the key to the human equivalent.But this wasn't working in most cases: lots of interesting data to be sure, but typically falling short of something "translatable" or failing in clinical trials.Some years before we made the switch, my then colleague at Stanford, Alan Krensky, told me, "Mark, we've cured cancer and autoimmunity in mice many times."This suggested to me that we were not facing just a medical problem; there was important immunology we knew very little about.But what should a new strategy look like?I thought it needed to be independent of mouse immunology, not because I think the immune systems of these two species are very different, but because if we wanted to understand why these models of disease or new HHS Public Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.459
Threshold uncertainty score0.771

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0100.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.4590.201

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.246
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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