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Record W2947172274 · doi:10.1101/652990

Aggregated and hyperstable damage-associated molecular patterns are released during ER stress to modulate immune function

2019· preprint· en· W2947172274 on OpenAlexaff
Alexander Andersohn, M. Iveth Garcia, Ying Fan, Max C. Thompson, Askar M. Akimzhanov, Abdikarim Abdullahi, Marc G. Jeschke, Darren Boehning

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersUniversity of Texas Health Science Center at Houston
KeywordsUnfolded protein responseInflammationImmune systemCell biologyIn vivoChronic stressProteaseDampImmunologyMedicineChemistryBiologyInternal medicineEndoplasmic reticulumBiochemistry

Abstract

fetched live from OpenAlex

Abstract Chronic ER stress occurs when protein misfolding in the ER lumen remains unresolved despite activation of the unfolded protein response. We have shown that traumatic injury such as a severe burn leads to chronic ER stress in vivo leading to systemic inflammation which can last for more than a year. The mechanisms linking chronic ER stress to systemic inflammatory responses is not clear. Here we show that induction of chronic ER stress leads to the release of known and novel damage-associated molecular patterns (DAMPs). The secreted DAMPs are aggregated and markedly protease resistant. ER stress-derived DAMPs activate dendritic cells which are then capable of polarizing naïve T cells. Our findings indicate that induction of chronic ER stress may lead to the release of hyperstable DAMPs into the circulation resulting in persistent systemic inflammation and adverse outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

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