MétaCan
Menu
Back to cohort
Record W4401992046 · doi:10.3390/bios14090421

Optical Interferometric Device for Rapid and Specific Detection of Biological Cells

2024· article· en· W4401992046 on OpenAlexfundno aff
Sándor Valkai, Dániel Petrovszki, Zsombor Fáskerti, Margaréta Baumgärtner, Brigitta Biczók, Kira Dakos, Kevin Dósa, Berill B. Kirner, Anna E. Kocsis, Krisztina Nagy, István Andó, András Dér

Bibliographic record

VenueBiosensors · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsMaterials scienceOpticsDetection limitInterferometryInterference (communication)LaserOptical fiberOptoelectronicsComputer scienceChemistryChromatographyPhysics

Abstract

fetched live from OpenAlex

Here, we report a rapid and accurate optical method for detecting cells from liquid samples in a label-free manner. The working principle of the method is based on the interference of parts of a conical laser beam, coming from a single-mode optical fiber directly, and reflected from a flat glass surface. The glass is functionalized by antibodies against the cells to be detected from the liquid sample. Cells bound to that surface modify the reflected beam, and hence, change the resulting interference pattern, too. By registering and interpreting the variation in the image, the presence of cells from the sample can be detected. As for a demonstration, cell suspensions from a U937 cell line were used in glass chambers functionalized by antibodies (TMG6-5 (mIgG1)) to which the cells specifically bind. The limit of detection (LOD) of the method was also estimated. This proof-of-concept setup offers a cost-effective and easy-to-use way of rapid and specific detection of any type of cells (including pathogens) from suspensions (e.g., body fluids). The possible portability of the device predicts its applicability as a rapid test in clinical diagnostics.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.225
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBiosensorsSame topicMicrofluidic and Bio-sensing TechnologiesFrench-language works237,207