(Invited) Simple Microfluidic Sample Preparation for Point of Care Diagnostics
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".