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Record W2601082718 · doi:10.2527/asasmw.2017.255

255 Using environmental swabbing to quantify the effectiveness of chemical disinfectant to reduce the quantity of porcine epidemic diarrhea virus RNA on feed manufacturing surfaces

2017· article· en· W2601082718 on OpenAlexaboutno aff
M. B. Muckey, R. A. Cochrane, Jason C Woodworth, Steven S Dritz, C. R. Stark, A. D. Yoder, Jianfa Bai, J. Zhang, P. C. Gauger, Rodger Main, Cassandra K Jones

Bibliographic record

VenueJournal of Animal Science · 2017
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsDisinfectantDiarrheaVirusVirologyPorcine epidemic diarrhea virusRNABiologyChemistryMedicineBiochemistry

Abstract

fetched live from OpenAlex

Porcine epidemic diarrhea virus (PEDV) is a possible hazard in feed mills that could impact swine health. If the virus enters a feed mill, it quickly becomes widely distributed and is difficult to decontaminate from surfaces. The objective of this study was to evaluate a variety of liquid and dry sanitation treatments that could be used to reduce the amount of PEDV found on feed manufacturing surfaces in feed mills. This experiment was replicated 3 times and was designed as a 5 × 10 factorial with main effects of 5 different feed manufacturing surfaces and 10 sanitizing treatments. Surfaces included stainless steel, plastic, rubber, woven polypropylene tote bag, and sealed concrete coupons (103 cm2). One milliliter (1 × 105 TCID50/mL) of stock PEDV was applied to each surface and allowed to completely dry for 60 min. Next, chemical treatments were applied for 15 min: 1) no sanitation treatment (control); 2) untreated rice hulls; 3) rice hulls treated with formaldehyde-based commercial product (Sal CURB; Kemin Industries Inc., Des Moines, IA), 4) liquid formaldehyde-based commercial product (Sal CURB); 5) dry commercial benzoic acid and eubiotic blend (VevoVitall and CRINA; DSM Nutritional Products Inc., Parsippany, NJ); 6) liquid ammonium chloride, isopropanol, and hydrogen peroxide–based commercial food-grade sanitizer (DrySan Duo; Ecolab, St. Paul, MN); 7) liquid hydrogen peroxide commercial product (INTERvention; Virox Technologies Inc. Ontario, Canada); 8) liquid quaternary ammonium glutaraldehyde commercial product (Synergize; Preserve International, Reno NV); 9) liquid sodium hypochlorite commercial sanitizer (Bleach; Clorox, Oakland, CA); and 10) liquid medium-chain fatty acid blend of caprylic, capronic, and capric acids. The quantity of PEDV RNA was determined using quantitative reverse transcription PCR (qRT-PCR). All main effects and interaction were highly significant (P ≤ 0.001). Concentrated liquid Sal CURB was the sanitizer most effective at removing PEDV RNA across surfaces followed by liquid bleach (42.9, 35.2, and 26.2 CT for Sal CURB, bleach, and untreated control, respectively). Rubber belting obtained from a bucket elevator retained the most PEDV RNA of any tested surface, whereas the polyethylene tote bag retained the least (28.0 and 31.4 CT for rubber and tote bag, respectively). Additional research is necessary to identify the role of sanitizer on PEDV infectivity and to develop dry sanitizers capable of removing PEDV mRNA on animal food manufacturing surfaces.

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 score0.002
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 head0.061
GPT teacher head0.384
Teacher spread0.323 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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