MétaCan
Menu
Back to cohort
Record W3039013102 · doi:10.1007/s10570-020-03314-3

Inkjet printing of paraffin on paper allowslow-cost point-of-care diagnostics for pathogenic fungi

2020· article· en· W3039013102 on OpenAlexfundno aff
Anusha Prabhu, M. S. Giri Nandagopal, Hardik Ramesh Singhal, Naresh Kumar Mani

Bibliographic record

VenueCellulose · 2020
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsnot available
FundersVision Group on Science and TechnologyUniversidad Complutense de MadridScience and Engineering Research BoardIndian Council of Medical ResearchMcGill University
KeywordsFilter paperMaterials scienceNanotechnologyInkwellSolventMicrofluidicsResazurinCinnamaldehydeChemical engineeringChromatographyComposite materialChemistryMicrobiologyOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Abstract We present a high resolution, ultra-frugal printing of paper microfluidic devices using in-house paraffin formulation on a simple filter paper. The patterns printed using an office inkjet printer formed a selective hydrophobic barrier of 4 ± 1 µm thickness with a hydrophilic channel width of 275 µm. These printed patterns effectively confine common aqueous solutions and solvents, which was verified by solvent compatibility studies. SEM analysis reveals that the solvent confinement is due to pore blockage in the filter paper. The fabricated paper-based device was validated for qualitative assessment of Candida albicans (pathogenic fungi) by using a combination of L-proline β-naphthylamide as the substrate and cinnamaldehyde as an indicator. Our studies reveal that the pathogenic fungi can be detected within 10 min with the limit of detection (LOD) of 0.86 × 10 6 cfu/mL. Owing to its simplicity, this facile method shows high potential and can be scaled up for developing robust paper-based devices for biomarker detection in resource-limited settings. Graphic abstract

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 score0.003
Threshold uncertainty score0.011

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)0.0030.001

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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

Citations55
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCelluloseSame topicBiosensors and Analytical DetectionFrench-language works237,207