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Record W2337485219 · doi:10.1021/acs.langmuir.6b00908

Surface Characterization of Hematin Anhydride: A Comparison between Two Different Synthesis Methods

2016· article· en· W2337485219 on OpenAlexafffund
E. Danae Guerra, D. Scott Bohle, Marta Cerruti

Bibliographic record

VenueLangmuir · 2016
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaCanada Research ChairsSecretaría de Educación Pública
KeywordsHemozoinSuccinic anhydrideAdsorptionChemistryHemeHemoglobinReactivity (psychology)Characterization (materials science)Combinatorial chemistrySurface modificationOrganic chemistryMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

During the intraerythrocytic stage of malaria, the parasite digests hemoglobin and aggregates the released heme as an insoluble crystalline material called hemozoin. This detoxification step is an excellent drug target for developing new antimalarials, which can bind to hemozoin surface to inhibit further growth. Although the bulk crystalline properties of hemozoin are well-known, the surface properties remain poorly defined. Here, we use a combination of spectroscopic and adsorption techniques to study the surface of synthetic hemozoin, hematin anhydride, produced by two different methods. We show that the two synthetic methods produce crystals with major differences, such as the amount of water adsorbed on the surface and surface carboxylate groups. These results imply that the methodology to produce hematin anhydride affects its surface reactivity; this information needs to be considered whenever hematin anhydride is used as a model to study host immune response or to design new antimalarials.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.389
Teacher spread0.338 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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