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Record W2045892365 · doi:10.1097/shk.0b013e3181c0f068

SEPSIS, LEUKOCYTES, AND NITRIC OXIDE (NO)

2010· review· en· W2045892365 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueShock · 2010
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsSepsisSeptic shockImmune systemNitric oxideImmunologyMedicineInflammationShock (circulatory)BiologyCell biologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Sepsis is exceedingly burdensome for hospital intensive care unit caregivers, and its incidence, as well as sepsis-related deaths, is increasing steadily. Sepsis is characterized by a robust increase in NO production throughout the organism that is driven by iNOS. Moreover, NO is an important factor in the development of septic shock and is synthesized by NOS, an enzyme expressed by a variety of cells, including vascular endothelium, macrophages, and neutrophils. However, the effects of NO on leukocyte functions, and the underlying mechanisms, are relatively unknown. Thus, the present review focuses on the effects of NO and its derivatives on cells of the immune system. Experimental evidences discussed herein show that NO induces posttranslational modifications of key proteins in targeted processes with the potential of deterring cellular physiology. Consequently, the manipulation of NO distribution in septic patients, used in conjunction with conventional treatments aimed at restoring normal immune functions, may represent a valuable therapeutic strategy.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.007

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 head0.019
GPT teacher head0.282
Teacher spread0.263 · 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