Paper microchip with a graphene-modified silver nano-composite electrode for electrical sensing of microbial pathogens
Bibliographic record
Abstract
Rapid and sensitive point-of-care diagnostics are of paramount importance for early detection of infectious diseases and timely initiation of treatment. Here, we present cellulose paper and flexible plastic chips with printed graphene-modified silver electrodes as universal point-of-care diagnostic tools for the rapid and sensitive detection of microbial pathogens or nucleic acids through utilizing electrical sensing modality and loop-mediated isothermal amplification (LAMP). We evaluated the ability of the developed paper-based assay to detect (i) viruses on cellulose-based paper microchips without implementing amplification in samples with viral loads between 106 and 108 copies per ml, and (ii) amplified HIV-1 nucleic acids in samples with viral loads between 10 fg μl−1 and 108 fg μl−1. The target HIV-1 nucleic acid was amplified using the RT-LAMP technique and detected through the electrical sensing of LAMP amplicons for a broad range of RNA concentrations between 10 fg μl−1 and 108 fg μl−1 after 40 min of amplification time. Our assay may be used for antiretroviral therapy monitoring where it meets the sensitivity requirement of the World Health Organization guidelines. Such a paper microchip assay without the amplification step may also be considered as a simple and inexpensive approach for acute HIV detection where maximum viral replication occurs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".