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Enregistrement W2778695364

Lab-on-a-Chip Coming of Age

2017· article· en· W2778695364 sur OpenAlexaboutno aff
Manny Frishberg

Notice bibliographique

RevueResearch-Technology Management · 2017
Typearticle
Langueen
DomaineEngineering
ThématiqueElectrowetting and Microfluidic Technologies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMicroscale chemistryLab-on-a-chipMicrofluidicsChipMicroelectromechanical systemsComputer scienceSample (material)NanotechnologyPhoneScientific instrumentEngineeringComputer hardwareTelecommunicationsMaterials sciencePhysics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,048
Score d'incertitude au seuil0,160

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,010
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,002
Communication savante0,0060,011
Science ouverte0,0040,005
Intégrité de la recherche0,0070,012
Charge utile insuffisante (le modèle a refusé de juger)0,0480,054

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,046
Tête enseignante GPT0,331
Écart entre enseignants0,286 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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