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Record W1824190268 · doi:10.5539/jfr.v4n5p51

Transcriptomic Response of Salmonella Typhimurium Heat Shock Gene Expression Under Thermal Stress at 48 °C

2015· article· en· W1824190268 on OpenAlexvenueno aff
Sujata A. Sirsat, Christopher A. Baker, Si Hong Park, Arunachalam Muthaiyan, Scot E. Dowd, Steven C. Ricke

Bibliographic record

VenueJournal of Food Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsnot available
Fundersnot available
KeywordsSalmonella entericaSalmonellaHeat shock proteinTranscriptomeVirulenceHeat shockMicrobiologyGeneQuorum sensingBiologyGene expressionBacteriaGenetics

Abstract

fetched live from OpenAlex

Salmonella enterica has been associated with a variety of food products, and thermal treatments are commonly used to reduce or eliminate pathogens from these foods. While the physiological response of Salmonella to a wide range of lethal and sublethal heating temperatures has been examined, only limited information is available at the transcriptional level. The objective of this study was to investigate the transcriptional profile of Salmonella Typhimurium when subjected to thermal shock at 48 °C (10 min). Transcriptomic analysis was performed using partial microarrays (1152 genes) consisting of quorum sensing, virulence, membrane, and stress related genes. Apparent Salmonella viability based on the optical density decreased in response to thermal exposure. Transcriptomic analysis revealed induction of several heat shock and stress related genes due to thermal exposure at 48 °C. This research reveals that there is an impact of exposure to a sublethal temperature (48°C) on the subsequent transcriptional responses of S. Typhimurium.

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 score0.003

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 head0.182
GPT teacher head0.394
Teacher spread0.212 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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