Novel microfluidic approaches to circulating tumor cell separation and sorting of blood cells: A review
Bibliographic record
Abstract
The separation of circulating tumor cells (CTCs) that originate from tumor or cancer tissue plays an important role in cancer diagnostics, progression analyses, and treatment proficiency. Cancer metastasis occurs when CTCs spread throughout the body and invade healthy tissues, which leads to new tumors in that area. Although a dramatic rate of death begins from CTCs spreading around the body, valuable measures can be taken to control their development. A first step is separating these harmful cells from the bloodstream and then investigating their features to introduce complementary treatments that can affect the cancerous cells without damaging healthy cells. Numerous techniques have been developed for continuous and fast separation of CTCs. Over the last two decades, the reduction in reagent demand, sample volume, analysis time, and patient safety are just a few of the motivations that encourage researchers to study microfluidic instruments for CTC separation from other blood cells. Among them, inertial microfluidic devices are promising due to their simple structure and setup. However, one shortcoming of this technique is the need for pumps to drive fluid flow, a low ability to control cell movement, and the possibility of clogging the channel. One technique that may potentially overcome these shortcomings is the so-called rotational micro-fluidic platform. However, this technique alone is still not sufficient. In this paper, a detailed analysis of each technique that emphasizes both strengths and shortcomings is presented. Subsequently, a new approach that combines microfluidics with magnetic nanoparticles and is based on the antibody binding principle is proposed. The feasibility of implementing this combined technique will also be discussed.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".