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Record W2116719441 · doi:10.1139/w05-114

Protease activities of <i>Acanthamoeba polyphaga</i> and <i>Acanthamoeba castellanii</i>

2006· article· en· W2116719441 on OpenAlexvenueno aff
José de Jesús Serrano-Luna, Isaac Cervantes-Sandoval, Jesús Calderón, Fernando Navarro‐García, Vı́ctor Tsutsumi, Mineko Shibayama

Bibliographic record

VenueCanadian Journal of Microbiology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsnot available
Fundersnot available
KeywordsAcanthamoebaProteasesProteaseBiologyMicrobiologyCysteine proteaseSerine proteaseProteolytic enzymesBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Acanthamoeba spp. are free-living amoebae that cause amoebic granulomatous encephalitis, skin lesions, and ocular amoebic keratitis in humans. Several authors have suggested that proteases could play a role in the pathogenesis of these diseases. In the present work, we performed a partial biochemical characterization of proteases in crude extracts of Acanthamoeba spp. and in conditioned medium using 7.5% SDS-PAGE copolymerized with 0.1% m/v gelatin as substrate. We distinguished a total of 17 bands with proteolytic activity distributed in two species of Acanthamoeba. The bands ranged from 30 to 188 kDa in A. castellanii and from 34 to 144 kDa in A. polyphaga. Additionally, we showed that the pattern of protease activity differed in the two species of Acanthamoeba when pH was altered. By using protease inhibitors, we found that the proteolytic activities belonged mostly to the serine protease family and secondly to cysteine proteases and that the proteolytic activities from A. castellanii were higher than those in A. polyphaga. Furthermore, aprotinin was found to inhibit crude extract protease activity on Madin-Darby canine kidney (MDCK) monolayers. These data suggest that protease patterns could be more complex than previously reported.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.981

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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 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

Citations48
Published2006
Admission routes1
Has abstractyes

Explore more

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