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Record W2212961605 · doi:10.1089/ther.2015.0020

Therapeutic Hypothermia Does Not Mitigate Iron-Induced Injury in Rat

2015· article· en· W2212961605 on OpenAlexafffund
Shannon Wowk, Yonglie Ma, Frederick Colbourne

Bibliographic record

VenueTherapeutic Hypothermia and Temperature Management · 2015
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsMedicineNeuroprotectionHypothermiaToxicityIntracerebral hemorrhageStroke (engine)AnesthesiaIschemiaBleedPharmacologyBrain edemaSalineStriatumEdemaSubarachnoid hemorrhageSurgeryInternal medicineDopamine

Abstract

fetched live from OpenAlex

Intracerebral hemorrhage (ICH) is often a devastating stroke, and there are no clinically proven neuroprotective treatments. Considerable research points to iron toxicity as a leading contributor to secondary damage after ICH. Iron, released from degraded erythrocytes, catalyzes free radical production, thereby causing cell death in the ensuing days and weeks. Therapeutic hypothermia (TH) is a potential neuroprotective strategy for ICH, but results from animal studies are inconsistent and generally weaker than that found in ischemia models. Thus, we examined whether TH (∼33°C for 24-72 hours) would specifically mitigate iron toxicity, which we modeled by infusing 3.8 μg of FeCl2 in 30 μL of sterile saline into the striatum of rats. Rats were subjected to whole-body cooling beginning 1 hour after FeCl2 infusion. Use of TH reduced (p = 0.025) the small bleed caused by FeCl2 infusion (∼6 μL). However, TH did not lessen FeCl2-induced edema at 24 and 72 hours postinfusion, nor were behavioral impairments (e.g., walking) or brain injury (at 7 and 28 days) attenuated by TH. These results suggest that TH does not directly protect against iron toxicity, which indicates that this is not a means by which TH improves the outlook after ICH.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.285
Teacher spread0.257 · 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.

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

Citations3
Published2015
Admission routes2
Has abstractyes

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