Investigation of Fluorescence Coherence Tomography for Optofluidic Applications
Bibliographic record
Abstract
Conventional Flow Cytometers (FC) and Fluorescence-Activated-Cell-Sorters (FACS) are mechanically complex, bulky, and require specialized human operators, large sample volumes, and sample preparation procedures for proper diagnosis of diseases such as leukemia and malaria. For this reason, there has been an increasing demand for miniaturization, reduction of cost, and portability of such devices. Lab-on-a-chip devices, which integrate microfluidics with other technologies, have been emerging as a potential solution to miniaturization of FC/FACS technology. One serious limitation of lab-on-a-chip devices is their inability to extract shape or morphological information which is very useful for cell differentiation and characterization. To meet this challenge, optical imaging techniques and microfluidics are combined to form a subset field in ‘optofluidics’. This thesis will help explore this field which describes systems that combine optics and microfluidics. In this thesis, as proof of principle, the integration of a novel optical imaging technique called Fluorescence Coherence Tomography (FCT) with microfluidics is presented. The FCT was used to measure the position of flowing fluorescent particles in the cross section of the microfluidic channel (perpendicular to the direction of flow). This type of measurement was motivated by recent reports in the literature demonstrating that a cell’s position in a microchannel is highly sensitive to its size and stiffness, which in turn are important biomarkers for cell classification. By combining FCT with microfluidics, the long term goal is to provide researchers and scientists with new possibilities for biological investigations in optofluidic applications. The preliminary results acquired through this work are important for future development of applications in the miniaturization of molecule specific flow cytometry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".