Shared and distinct mechanisms of iron acquisition by bacterial and fungal pathogens of humans
Bibliographic record
Abstract
Iron is the most abundant transition metal in the human body and its bioavailability is stringently controlled. In particular, iron is tightly bound to host proteins such as transferrin to maintain homeostasis, to limit potential damage caused by iron toxicity under physiological conditions and to restrict access by pathogens. Therefore, iron acquisition during infection of a human host is a challenge that must be surmounted by every successful pathogenic microorganism. Iron is essential for bacterial and fungal physiological processes such as DNA replication, transcription, metabolism, and energy generation via respiration. Hence, pathogenic bacteria and fungi have developed sophisticated strategies to gain access to iron from host sources. Indeed, siderophore production and transport, iron acquisition from heme and host iron-containing proteins such as hemoglobin and transferrin, and reduction of ferric to ferrous iron with subsequent transport are all strategies found in bacterial and fungal pathogens of humans. This review focuses on a comparison of these strategies between bacterial and fungal pathogens in the context of virulence and the iron limitation that occurs in the human body as a mechanism of innate nutritional defense.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".