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Record W3174898664 · doi:10.3389/fimmu.2021.621440

Immune Dysregulation and the Increased Risk of Complications and Mortality Following Respiratory Tract Infections in Adults With Down Syndrome

2021· review· en· W3174898664 on OpenAlexaff
Tomer Illouz, Arya Biragyn, M. Florencia Iulita, Lisi Flores Aguilar, Mara Dierssen, Ilario De Toma, Stylianos E. Antonarakis, Eugene Yu, Yann Hérault, Marie‐Claude Potier, Alexandra Botté, Randall J. Roper, Benjamin Sredni, Jacqueline London, William C. Mobley, André Strydom, Eitan Okun

Bibliographic record

VenueFrontiers in Immunology · 2021
Typereview
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsMcGill University
FundersNational Institute on AgingMedical Research CouncilClore Israel Foundation
KeywordsMedicineRespiratory tractRespiratory tract infectionsImmunologyDiseaseImmune systemImmune dysregulationRespiratory systemPopulationPneumoniaDown syndromeDementiaBronchiolitisVirusInternal medicine

Abstract

fetched live from OpenAlex

The risk of severe outcomes following respiratory tract infections is significantly increased in individuals over 60 years, especially in those with chronic medical conditions, i.e., hypertension, diabetes, cardiovascular disease, dementia, chronic respiratory disease, and cancer. Down Syndrome (DS), the most prevalent intellectual disability, is caused by trisomy-21 in ~1:750 live births worldwide. Over the past few decades, a substantial body of evidence has accumulated, pointing at the occurrence of alterations, impairments, and subsequently dysfunction of the various components of the immune system in individuals with DS. This associates with increased vulnerability to respiratory tract infections in this population, such as the influenza virus, respiratory syncytial virus, SARS-CoV-2 (COVID-19), and bacterial pneumonias. To emphasize this link, here we comprehensively review the immunobiology of DS and its contribution to higher susceptibility to severe illness and mortality from respiratory tract infections.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.407
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.327
Teacher spread0.298 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations56
Published2021
Admission routes1
Has abstractyes

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