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Record W2281793066 · doi:10.1149/ma2016-01/39/1968

(Invited) Simple Microfluidic Sample Preparation for Point of Care Diagnostics

2016· article· en· W2281793066 on OpenAlexaff
Ravi Selvaganapathy, Jun Yang, Sondos Ayyash

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMicrofluidicsPoint of careMiniaturizationSample (material)Computer sciencePoint-of-care testingLab-on-a-chipExploitSample preparationNanotechnologyMaterials scienceMedicine

Abstract

fetched live from OpenAlex

Sample preparation is a crucial set of unit operations that is performed on the sample to make it suitable for detection or sensing. Miniaturization of the sample preparation steps and their integration with the sensor is important for developing truly point of care sensing systems or the so called “Lab-on-Chip” devices. An ideal point of care device should be able to take raw sample in and provide information out. Sample preparation in such a setting should be performed with minimal intervention by the user, in an automated way and possibly without the use of external reagents. Although many microfluidic Lab-on-Chip devices have been developed with integrated sample preparation capability, the focus of the innovation has been on the sensor itself. They have typically used the same sequence of unit operations as the laboratory tests that they are based on and do not necessarily exploit the advantages of miniaturization to optimize sample preparation. We posit here that these devices should be developed with a focus on easy and simple sample preparation with minimal to no user intervention in order to find widespread applicability Here, we present examples of developing devices with focus on redesigning sample preparation methods to enable ease of use. First we demonstrate a simple microfluidic device (shown in Figure 1a) that can be used to analyse the cell free DNA concentration, a biomarker that is a prognostic indicator of mortality, at the bedside of septic patients. The device can separate cell free DNA from whole blood, concentrate it and quantify the concentration in a small form factor and under 5 min using small amount (10 uL) of blood. The device exploits the advantages of miniaturization in combining several unit operations that are normally performed sequentially in the laboratory so that the assay can be done faster. In another example, we exploit the advantage of miniaturization to perform bacterial culture assays faster. Bacterial culture and staining is the oldest method of diagnosis of bacterial infection. However, they are slow and take anywhere between 1 day (E.Coli) to few weeks (Mycobacteria). Molecular assays based on nucleic acids or proteins as biomarkers have emerged over the past few decades as faster methods to identify a pathogen. However, these assays do not provide viability or drug effectiveness information that are useful for medical professionals to formulate their treatment. In order to make culture based detection faster, we have developed a novel platform that automatically segments a sample in thousands of nanovolume wells. The device design is optimized to automatically segment the sample using a simple squeegee and under 1 min (shown in Figure 1b) which is suitable for point of care applications in resource poor settings. Metabolic activity of the bacteria aliquoted into these nanowells can be measured as an indicator of their presence and viability. By segmenting the sample into small volumes we show that the analysis time can be made much smaller especially at low concentrations. We also demonstrate single cell detection. The compact form factor of these devices and the minimal need for external reagents make them ideally suited for point of care testing. Figure 1

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.117
Threshold uncertainty score0.392

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1170.104

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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
GenreMethods

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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Citations0
Published2016
Admission routes1
Has abstractyes

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