Removal of phosphine from bloodstream using hemoperfusion device consisting of metal-promoted carbon nanotubes
Bibliographic record
Abstract
Aluminum phosphide (AlP) self-poisoning leads to severe toxicity with a high mortality rate in developing coun- tries. No effective methods to treat se- vere AlP poisoning have been identified to date. It is surmised that toxic levels are blood concentrations above 1.067 mg%. In some instances of toxic exposure, char- coal hemoperfusion is an effective way to remove the poisonous substances from the circulation. However, it seems that ad- sorption of phosphine gas (PH 3 ), the toxic ingredient of AlP, by activated charcoal is not adequate and, therefore, it is unlikely that charcoal hemoperfusion would be ef- fective in treating AlP poisoning. Carbon nanotubes (CNTs) are appropriate for supporting metal nanoparticles. Cobalt (Co) and cerium (Ce) nanoparticles sup- ported on CNTs (CoCe-CNTs) have cat- alytic properties in the phosphine decom- position reaction. We hypothesize that the substitution of charcoal with CoCe alloy supported on CNTs in hemoperfusion car- tridges can be used to remove PH 3 from the plasma compartment to cure ALP poi- soning. We believe it is possible for this novel extracorporeal technique to become an efficient method for PH 3 removal, en- hancing the patient's chance of survival.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".