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Complexities in the Rat Iron Loading Model for Pre-Clinical Testing of Iron Chelators.

2004· article· en· W2547050165 on OpenAlexaff
Jasmina Novaković, Angelo Tesoro, Jake J. Thiessen, Fernando Tricta, John T. Connelly, Michael Spino

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsMicropharma (Canada)University of Toronto
Fundersnot available
KeywordsDeferiproneChelation therapyDeferoxamineChelationToxicityChemistryMedicinePharmacologyPathologyThalassemiaPhysiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Transfusion-dependent iron overload, such as occurs in beta-thalassaemia (Cooley’s Anaemia), leads to lethal cardiac toxicity in the second decade of life if not treated by iron chelation, but even with subcutaneous desferrioxamine (DFO) cardiac disease remains a problem, although delayed by 1–2 decades. As we design novel iron chelators, we are testing them in various animal models of iron overload. While assessing outcomes we have observed relatively sparse reports of systematic studies on organs, tissues, cellular, or subcellular iron distribution. Therefore we initiated a series of studies to characterize iron distribution using various approaches. Multiple intraperitoneal injections of iron dextran, 200 mg/kg/week X 4 − 16 weeks, followed by an equilibration period of minimum 1–2 weeks was studied as a means of increasing total body iron load in hundreds of rats under various conditions. Sacrifice varied from 6 weeks to 1 year post iron loading and the concentration of iron in liver, heart, and other tissues, organs, cells and subcellular organelles was examined. Quantitatively, in untreated rats (no chelators), the liver/heart iron ratio was about 10:1, consistent with the accumulation observed in post-mortem studies in humans prior to extensive use of iron chelation. Much less-well described has been the distribution of iron in lymphatic tissues. Our studies revealed that lymph nodes become visibly enlarged. In addition, randomly distributed brown spots appeared in the omentum. Such changes persisted up to one year after iron loading, regardless whether they were treated daily with chelators (DFO or deferiprone) in standard doses for four months. Even after a single intraperitoneal iron-dextran injection of 200 mg/kg, changes were visible. Histopathological analysis (hematoxilin-eosin for general histology and Perl’s Prussian Blue for iron) showed extensive iron accumulation in the omentum, and in the cortical and subcortical regions of the enlarged lymph nodes. Electron microscopy revealed cellular (macrophages) and subcellular (mitochondria) iron localization in the lymph nodes. When iron was administered as iron sucrose (single ip dose), iron accumulation was more extensive in the omentum and in the peritoneal fat in comparison to iron dextran, but the enlargement of the lymph nodes was not observed. Quantitative iron measurement (via validated HPLC method) in the liver and heart after a single iron dextran (N=30, up to 29th day) and iron sucrose dose (N=6 up to 50th day) was in agreement with the histological observations. Iron accumulation in the omentum and lymph nodes after four months of chelation treatment and one year after iron loading indicated the resistance of these unusual iron “pools” to chelation therapy. These studies confirm that different iron formulations may result in different patterns of iron distribution and they also raise questions about the suitability of rats as an animal model for transfusional iron overload in humans.

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)0.000
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.056
GPT teacher head0.322
Teacher spread0.266 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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