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Record W2966792358 · doi:10.11159/icbes19.129

New Approaches in the Manufacture of Biomaterials for Betalactam Allergic Diagnose

2019· article· en· W2966792358 on OpenAlexvenueno aff
A. Sánchez, Cristobalina Mayorga, Daniel Collado, Marı́a José Torres, Ezequiel Pérez‐Inestrosa

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2019
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsnot available
FundersEuropean Regional Development FundUniversidad de Málaga
KeywordsComputer scienceBiochemical engineeringEngineering

Abstract

fetched live from OpenAlex

Betalactams are the most widely utilized drugs against infections but are the primary cause of allergic reactions to antibiotic drugs. REF1 An accurate diagnosis of these allergic reactions to betalactams is crucial to avoid the use of unnecessary alternative antibiotics that may reduce efficacy, lead to prolonged treatments, have a higher toxicity or induce bacterial resistance. The most consensual approach to diagnose betalactam allergy are in vivo tests. However, they are not risky free, require experienced personnel and are both time-consuming and expensive for health-care systems, being so in vitro test more appropriate or complementary to the in vivo tests. In vitro tests are not still widely used on account of their low sensitivity. Current efforts are in progress to improve these assays, thus allowing for better diagnosis of allergic responses within patients. REF 2 We report progress in the preparation of new functional materials for in vitro allergic diagnosis testing. In particular, the application of new approaches employing orthogonally functionalised fluorescent dyes based upon 4-amino-1,8 naphthalimide joined with the multivalence of polyamide dendrimers. REF 3 The in vitro diagnosis capabilities of these functional materials was verified by testing on patient sera samples, with results demonstrating their potential for application within the healthcare industry. Acknowledgments: The present study has been supported by MINECO CTQ2016-75870P; by Andalusian Regional Ministry Health (grants: PI-0250-2016); by the European Regional Development Fund (ERDF) and “Plan Propio Universidad de Málaga” (UMA-Andalucía-TECH).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.209

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 head0.017
GPT teacher head0.228
Teacher spread0.212 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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