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Record W4285397861 · doi:10.1149/ma2022-01532190mtgabs

Development of a Low-Cost Lateral Flow Assay for Rapid Detection of Vibrio Cholerae

2022· article· en· W4285397861 on OpenAlexaff
Jonathan Mayry, Rebecca Mac, Michelle Huynh, Sushanta K. Mitra

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVibrio choleraeCholeraMicrobiologyDetection limitAptamerChromogenicVirologyBiologyBacteriaChemistryChromatographyMolecular biology

Abstract

fetched live from OpenAlex

Cholera is an acute, often fatal diarrheal disease that primarily affects areas with poor sanitation and unclean water. Cholera is caused by toxigenic strains of Vibrio cholerae ( V. cholerae ), which cause the body to rapidly dehydrate upon colonizing the intestine. The WHO estimates that 2.9 million people contract cholera each year, resulting in approximately 95000 deaths [1]. Current methods for detection of V. cholerae are either inferential (detection of fecal coliforms or monitoring weather patterns) or diagnosed after an infection (stool tests) [2]. Even methods that have been developed for direct detection of V. cholerae fall short of detecting environmental concentrations of the bacterium (10-10 4 CFU/mL) [3]; a lateral flow assay (LFA) using gold nanoparticle-labelled antibodies for the simultaneous detection of O1 and O139 strains of V. cholerae achieved a limit of detection of 10 8 CFU/mL for O1 and 10 7 CFU/mL for O139 [4]. The current work aims to develop an LFA for the detection of V. cholerae at environmental conditions in drinking water under limited resource settings. By incorporating novel aptamers as antigen detecting agents, carbon nanoparticles as colorimetric indicators, and N-acetylglucosamine (GlcNAc)–a known chemoattractant for V. cholerae –in the sample pad, the limit of detection will be improved without the need for lengthy preprocessing steps or costly reagents. The antigen detection agent employed in the LFA is the aptamer V.ch27 which was identified by Mojarad and Gargaria [5]. This aptamer was shown to have a favourable secondary structure at 21°C and high binding affinity to the surface of V. cholerae bacteria. Aptamers are a synthetic alternative to conventionally used antibodies for this application. They exhibit specificities comparable to those of monoclonal antibodies, are cheaper to synthesize, and can be easily immobilized to surfaces via biotin-avidin complexing. Amorphous carbon nanoparticles (ACNPs) were chosen as the colorimetric indicator for the assay due to their strong black color and low cost [6]. For instance, carbon nanoparticles have shown a 100-fold decrease in the limit of detection for dengue fever antigen when compared to gold nanoparticles [7]. The ACNPs were synthesized by collecting the soot from burning candles followed by size selection by centrifugation, as previously described Zhang et al [8]. The chemotaxis of V. cholerae to GlcNAc is well documented in literature [9]. The sample pad of the LFA contains a GlcNAc solution which diffuses into the water sample. V. cholerae swim up the GlcNAc concentration gradient and accumulate in high concentrations at the sample pad. This in turn results in a high concentration of analyte entering the assay, and consequently, a greater signal intensity. Previous studies have shown that chemotaxis can be exploited to collect nearly all of the bacteria in a 100 mL water sample [10,11]. The fabricated LFA device will provide a rapid, accessible, and low-cost platform for V. cholerae detection in the water sources available to rural and low-income communities, without the need for technical training or well-established laboratory infrastructure. Successful implementation of the device will result in the overall reduction of global deaths due to cholera, as well as provide the framework for future preventative initiatives aimed towards cholera outbreak prediction and monitoring. References [1] “Cholera: The Forgotten Pandemic,” World Health Organization , 22-Oct-2018. [Online]. Available: https://www.who.int/cholera/the-forgotten-pandemic/en/ . [Accessed: 14-Nov-2020]. [2] https://link-springer-com.proxy.lib.uwaterloo.ca/chapter/10.1007%2F978-3-319-38884-7_15 [3] S. Schauer, et al., Applied and Environmental Microbiology , vol. 78, no. 20, pp. 7369–7375, Oct. 2012. [4] C. Y. Yu et al., Journal of Microbiological Methods , vol. 86, no. 3, pp. 277–282, Sep. 2011. [5] A. E. Mojarad and S. L. Mousavi Gargaria, Iranian Journal of Microbiology , vol. 12, no. 4, pp. 263–272, Aug. 2020. [6] E. M. Linares et al.,, J. Immunol. Methods , vol. 375, no. 1-2, pp. 264-270, Jan. 2012. [7] J. D. Bishop et al. Royal Soc. Chem. , vol. 19, pp. 2486-2499., Apr. 2019. [8] X. Zhang et al., J. Agric. Food. Chem , vol. 65, no. 35, pp. 8063-8071, Aug. 2017. [9] G. Reddi et al., Plos One , vol. 13, no. 7, Jul. 2018. [10] X. Ling and S. Roseman, PNAS , vol. 101, no. 2, pp. 627-631, Jan. 2004 [11] S. Dasgupta, N. S. K. Gunda, and S. K. Mitra, Environmental Science: Water Research & Technology , vol. 2, pp. 931-941, 2016.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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".

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Citations3
Published2022
Admission routes1
Has abstractyes

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