MétaCan
Menu
← Back to cohort
Record W4386853096 · doi:10.1149/ma2023-01532635mtgabs

Integration of Photoelectrochemical Signal Transduction with RNA-Cleaving Dnazymes for Culture-Free Detection of Bacteria

2023· article· en· W4386853096 on OpenAlexaff
Sadman Sakib, Zijie Zhang, Enas Osman, Farhaan Kanji, Fatemeh Bahkshandeh, Yingfu Li, Igor Zhitomirsky, Leyla Soleymani

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNanotechnologyDeoxyribozymeTransduction (biophysics)CleaveMaterials scienceBiosensorNanorodSignal transductionDetection limitChemistryBiologyBiophysicsCell biologyBiochemistryDNA

Abstract

fetched live from OpenAlex

There is a growing interest in developing ultrasensitive biological sensors that can be used for rapid testing at the point-of-need.1 A major hurdle in developing such sensors for detecting pathogens such as bacteria is that they require target enrichment or amplification to deliver the required limit-of-detection.2 Among signal transduction strategies, photoelectrochemical (PEC) signal readout, built on the use of light for enhancing electrochemical reactions, is emerging as an ultrasensitive signal transduction mechanism.3 However, the existing PEC platforms fail to deliver single step testing due to the existence of multiple manual steps, including the addition of biological materials labelled with inorganic photoactive nanoparticles, for signal transduction.3 RNA-cleaving DNAzymes (RCDs), a class of synthetic nucleic acids, have been selected for precisely identifying specific bacterial species without the need for sample processing.4 RCDs are molecular switches that cleave a segment of themselves in response to a particular bacterial target, combining biological recognition with signal transduction.4 We developed photoactive RCDs by tagging them with TiO2 nanomaterials for combining these molecular switches with PEC signal readout. We designed these molecular switches to make and then break semiconductive heterostructures in response to bacterial targets. These photoactive RCDs were the foundational basis for the design novel and highly sensitive PEC bacterial sensor. We developed two photoactive materials for use in the PEC bacterial assay: TiO2 nanorod clusters (rutile) that form high surface area photoelectrodes and sub-nanometer sized TiO2-nanoparticles (anatase) that link to RCDs to create photoactive reporter probes. Combining TiO2-assemblies and TiO2-nanoparticles gives rise to a semiconductor heterostructure that massively improves the photoexcitation efficiency of the combined material system and improves photocurrent generation. Our PEC bacterial sensor makes use of this phenomenon for bacterial detection by utilizing photoactive RCDs to modulate photocurrent by breaking and then rebuilding the TiO2 heterostructures, as a signaling mechanism. The assay consists of a release electrode – modified with photoactive RCDs and a capture electrode – modified with single-stranded DNA probes. Upon target interaction, RCDs release photoactive reporters which are captured by the probes, decreasing the release electrode signal while raising the capture electrode signal. The resulting biosensor can detect E. coli bacterial contamination with high specificity and has achieved a very low limit of detection of 21 CFU/mL in buffer and 18 CFU/mL in lake water samples. These results have set a new record for amplification-free detection of bacteria, that does not rely on target enrichment, reagent addition, or sample processing. This presents a new tool for rapid and in-field water testing. References Nat. Microbiol., 1, 16089 (2016). L. Castillo-Henríquez et al., Sensors, 20, 6926 (2020). A. Victorious, S. Saha, R. Pandey, T. F. Didar, and L. Soleymani, Front. Chem., 7, 617 (2019). I. Cozma, E. M. McConnell, J. D. Brennan, and Y. Li, Biosens. Bioelectron., 177, 112972 (2021).

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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicAdvanced biosensing and bioanalysis techniques→French-language works237,207→