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Record W2762640183 · doi:10.1139/cjc-2017-0407

Common bed bugs can biosynthesize pheromone components from amino acid precursors in human blood

2017· article· en· W2762640183 on OpenAlexafffundvenue
Regine Gries, Huimin Zhai, Andrew R. Lewis, Robert Britton, Gerhard Gries

Bibliographic record

VenueCanadian Journal of Chemistry · 2017
Typearticle
Languageen
FieldMedicine
TopicDermatological diseases and infestations
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaMichael Smith Health Research BC
KeywordsHistidineChemistryHistamineCimex lectulariusMethionineBed bugPheromoneAmino acidBiochemistryDimethyl trisulfideChromatographyStereochemistryOrganic chemistryDimethyl disulfideBiologyToxicologyPharmacologyZoologyBotany

Abstract

fetched live from OpenAlex

We have recently shown that the aggregation pheromone of the common bed bug, Cimex lectularius, comprises a six-component blend of dimethyl disulfide (DMDS), dimethyl trisulfide (DMTS), (E)-2-hexenal, (E)-2-octenal, 2-hexanone, and histamine. Here, we tested the hypothesis that bed bugs biosynthesize some pheromone components from amino acid precursors in human blood, namely DMDS and DMTS from L-methionine and histamine from histidine. We tested this hypothesis by (i) allowing bed bugs to feed on and metabolize sheep blood enriched with 13 C-labelled histidine or 2 H-labelled methionine, (ii) extracting bed bug feces (a source of the aggregation pheromone), and (iii) analyzing feces extracts by GC-MS, HPLC-MS and NMR spectroscopy. The analyses revealed that bed bugs converted 2 H-methionine to 2 H-DMDS and 2 H-DMTS, and 13 C-histidine to 13 C-histamine. There is not enough histidine in human blood to account for the amount of histamine that bed bugs produce and excrete with their feces, and only a small proportion of the available 13 C-histidine was converted to 13 C-histamine in our study. Therefore, it is likely that bed bugs biosynthesize histamine, and possibly also DMDS and DMTS, primarily de novo.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2017
Admission routes3
Has abstractyes

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