Bibliographic record
Abstract
For more than a decade, researchers are trying to find out practical commercial tool for particle/cell detection and characterization with portable, low cost, specific and sensitive characteristics. The advance of Micro Electro Mechanical Systems (MEMS) and microfluidic technologies opened a major challenge for a large number of researchers, industrial health and bio companies to invest their time and budget into the avenue of point of care health instruments or devices helping the early detection of cancer cells within the human blood via circulating malignant cells. \nActual existing commercial flow cytometer that detects and identifies the type and size of cells are costly, time consuming and need the assistance of highly qualified operators. Moreover, in certain research activities, micro cytometers are investigated and assessed with different detection techniques such as optical, impedance spectroscopy, electromagnetic spectroscopy and many other techniques. \nThe aim of this research is to investigate an innovative mechanism that enables to characterize, identify and differentiate among various living cells including malignant tumor cells through the use of the electromagnetic energy detection technique. Cells are spatially centered in a microfluidic channel through dielectrophoresis technique then detected and characterized by measuring and interpreting the RF signal transmission of the cells passing one by one through the interrogation region in the microchannel. \n\tThe outcome of this research might help the clinical end user to gain certain important information about the condition of the patient, establish personalized treatment or track the effect of a treatment. Detection and counting of tumor cells may help identification of early stages of illness and help patient with early care that may significantly cut the overall cost of cancer management.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".