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Iron Acquisition Strategies of Bacterial Pathogens

2016· review· en· W2320508761 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

VenueMicrobiology Spectrum · 2016
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsBiologyPathogenVirulenceIron homeostasisSiderophoreHost (biology)Human healthPathogenic bacteriaMicronutrientMicrobiologyBacteriaHuman pathogenEcologyMedicineGeneticsGeneEnvironmental health

Abstract

fetched live from OpenAlex

Iron is an essential micronutrient for both microbes and humans alike. For well over half a century we have known that this element, in particular, plays a pivotal role in health and disease and, most especially, in shaping host-pathogen interactions. Intracellular iron concentrations serve as a critical signal in regulating the expression not only of high-affinity iron acquisition systems in bacteria, but also of toxins and other noted virulence factors produced by some major human pathogens. While we now are aware of many strategies that the host has devised to sequester iron from invading microbes, there are as many if not more sophisticated mechanisms by which successful pathogens overcome nutritional immunity imposed by the host. This review discusses some of the essential components of iron sequestration and scavenging mechanisms of the host, as well as representative Gram-negative and Gram-positive pathogens, and highlights recent advances in the field. Last, we address how the iron acquisition strategies of pathogenic bacteria may be exploited for the development of novel prophylactics or antimicrobials.

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 categoriesInsufficient 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 score0.986
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.286
Teacher spread0.272 · 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