Bibliographic record
Abstract
Star Trek famously spawned some real-world inventions--most notably, the flip phone, based on Captain Kirk's communicator. Now another piece of Star Trek tech is a step closer to reality: Dr. McCoy's medical tricorder. The actual device, known as a lab-on-a-chip (LOC), looks and acts less like McCoy's machine than like a cross between a credit card and a computer chip, but the dream of taking a sample, say a single drop of blood, and performing thousands of biochemical operations on it to get a precise diagnosis from a compact machine is the same. LOCs use millions of micrometerscale channels to move a sample liquid through their components, which include integrated microscale pumps, electrodes, valves, sensors, and micro-electromechanical systems (MEMS) that allow the tiny machine to perform a number of analytic functions. Together, these microminiaturized components can handle liquid samples as small as a few trillionths of a liter, sorting cells and monitoring chemical reactions on a tiny scale. Most LOC developments to date have come in the area of human diagnostics and DNA analysis, but researchers have also applied the technology to crystallography, studying chemical reactions on the micro and nano scales, and chemical synthesis. The microfluidic technology that is at the base of the LOC has its genesis in space; it's a by-product of NASA's and DARPA's investments in micro-lithography in the 1960s--for making what became computer chips--and in the development of MEMS in the 1990s. In fact, the first LOCs were built on silicon, using the same wet- and dry-etching techniques as microprocessors. These early models provided effective proof-of-concept, but a number of problems kept them in the laboratory. Silicon-based LOCs were not transparent, and silicon's electrical conductivity meant they could not be used for operations requiring high voltage. Moreover, the difficulties of designing and producing silicon-based LOCs rendered them impractical for most commercial applications. Glass solved some of the problems, but it was not until scientists learned to use molds to mass-produce LOCs in malleable plastics that research on LOC applications took off. In the past few years, researchers have been exploring how LOC technology could be used to detect microorganisms that cause malaria, tuberculosis, diarrhea, whooping cough, and Dengue fever, or the toxins produced by E. coli, Salmonella, and Shigella. Handheld devices using LOCs are being used to identify various strains of HIV, allowing treatment plans to be tailored for individual patients. In 2014, a team from University of Alberta used a disposable plastic chip containing a desiccated hydrogel that can be stored at room temperature combined with a small, portable machine to identify specific species of Plasmodium, the parasite that causes malaria, from small blood samples. The chip exceeded the sensitivity of microscopy, the current standard for diagnosis in the field, by a factor of 10 to 50. Washington State University researchers have brought LOC technology yet a step closer to McCoy's multipurpose tricorder. The team has developed a low-cost, smartphone-based device that can analyze eight samples at once to catch a cancer biomarker with up to 99 percent accuracy. Their eight-channel, iPhone 5-based spectrometer uses a common test called ELISA (enzyme-linked immunosorbent assay) to detect a known biomarker for lung, prostate, liver, breast, and epithelial cancers. Other developments have looked toward making LOCs more useable in a wide variety of environments. Researchers at the Stanford University School of Medicine Genome Center have created a paper LOC that can be made using an inkjet printer and commonly available nanoparticle inks. On the paper LOC, microfluidic devices can be made either by stacking layers of paper and double-sided adhesive tape, patterned to guide the fluid within and between layers of paper or, by using origami to fold channels into the paper without tape. …
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.048 | 0.054 |
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".