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Record W4416287387 · doi:10.1371/journal.pone.0336988

Evaluation of a commercial pressure cooker for the preparation of agar media for a diagnostic microbiology laboratory

2025· article· en· W4416287387 on OpenAlexaff
Joseph E. Rubin, F. Huby, Roshan P. Madalagama, Shyamali de Alwis, Melinda Wyshynski, Rasika Jinadasa

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCookerAgarAutoclaveSterilization (economics)Agar plateHemolysisGeobacillus stearothermophilusClinical microbiology

Abstract

fetched live from OpenAlex

The ability to prepare sterilized media is a critical capability of any microbiology lab. Diagnostic labs in low-resource settings, which lack autoclave facilities, are therefore severely limited in their ability to perform basic assays such as bacterial culture or biochemical tests. This investigation aimed to validate the use of a commercially available pressure cooker as an autoclave substitute to produce agar plates. First, a Geobacillus stearothermophilus biological indicator was used to confirm adequate sterilization. Next, the colony morphology of several important bacterial species were compared on MacConkey and 5% sheep's blood agar plates prepared using the pressure cooker with those made in an autoclave. Finally, disc diffusion susceptibility testing was performed to determine whether the sterilization method impacts the inhibitory zone diameters. Overall, the morphology of colonies was similar on media prepared in both ways; key phenotypic characteristics (lactose fermentation, colour, shape, hemolysis and smell) were the same. Kirby-Bauer disc diffusion test results were nearly identical. These findings indicate that a commercially available pressure cooker may be suitable to prepare media in low-resource laboratories.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.010
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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)0.0000.000

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 head0.056
GPT teacher head0.316
Teacher spread0.260 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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