Additively manufactured multiplexed electrochemical device (AMMED) for portable sample-to-answer detection
Bibliographic record
Abstract
Abstract Portable sample-to-answer devices with applications in point-of-care settings have emerged to obviate the necessity of centralized laboratories for biomarker analysis. In this work, a smartphone-operated and additively manufactured multiplexed electrochemical device (AMMED) is presented for the portable detection of biomarkers in blood and saliva. AMMED is comprised of a customized portable potentiostat with a multiplexing feature, a 3D-printed sample collection cartridge to handle three samples of saliva and blood at the same time, a smartphone application to remotely control the potentiostat, and a 3D-printed-based multiplexed microfluidic electrochemical biosensor (test chip). Here, by employing additive manufacturing techniques, a simple, cleanroom-free, and scalable approach was proposed for the fabrication of the test chip. Moreover, these techniques can bring about easy integration of AMMED components. Additionally, the test chip can be compatible with different affinity-based bioassays which can be implemented in a multiplexed manner for detection. The AMMED components were successfully characterized in terms of electrochemical and fluidic performance. Particularly, to demonstrate the biosensing capabilities of the device, the spike protein of the SARS-CoV-2 omicron variant and a well-established aptameric assay were selected as the representative biomarker and the bioassay, respectively. The proposed device accurately and selectively detected the target of interest in a rapid (5 min) and multiplex manner with a dynamic detection range of 1–10,000 pg. ml -1 in different media; and the clinical feasibility was assessed by several saliva patient samples. AMMED offers a versatile sample-to-answer platform that can be used for the detection of various biomarkers present in biofluids.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".