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Record W2580798605

Investigating the Mechanisms of Antimicrobial Resistance

2016· article· en· W2580798605 on OpenAlexaff
Nicole Yokubynas, K.M. Iyer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHistorical Medical Research and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAntibiotic resistanceAntibioticsAntimicrobialBiologyIntensive care medicineDrug resistanceBiotechnologyMicrobiologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Since their discovery, antimicrobial agents have revolutionized the treatment of infectious disease. However, increased use of these agents contributed to the development of microbial resistance almost immediately, rendering these treatments less effective, or often useless. Despite considerable pressures on the scientific community, few new classes of antimicrobial agents have been discovered since the antibiotic era (1950-1970). Microbes’ astonishing ability to adapt to antibiotics has posed a serious threat to the modern health care system. In order to reduce the prevalence of resistance and develop new antimicrobial agents, it is crucial to understand the origins of antibiotics, the development of resistance through evolutionary mechanisms, and the biochemical mode of action of antibiotics along with their associated resistance pathways. This review investigates the various mechanisms of antibiotic resistance, from both an evolutionary and biochemical standpoint. Microbes are able to adapt and mutate at unparalleled rates through mechanisms such as horizontal gene transfer and high reproduction rates. Acquired resistance mechanisms include modifying enzymes, point mutations in the target site of antibiotics, and reduced uptake of antibiotics. This paper concludes by considering responses to the current crisis in microbial resistance, such as preventative measures and the development of new antibiotics.

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)0.001
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 score0.451
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.030
GPT teacher head0.300
Teacher spread0.270 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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