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Record W4404348880 · doi:10.1097/mrm.0000000000000323

Approaches of therapeutic drug conjugates for bacterial infections

2022· article· en· W4404348880 on OpenAlexaff
Aiesheh Gholizadeh‐Hashjin, Farzaneh Lotfipour, Tooba Gholikhani

Bibliographic record

VenueReviews in Medical Microbiology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDrugConjugatePharmacologyMedicineChemistryMicrobiologyBiologyMathematics

Abstract

fetched live from OpenAlex

Drug conjugates are novel subjects in biology. Drug conjugates are a newfound major of particularly potent biopharmaceutical drugs, which have been evaluated as a diagnostic and therapeutic approach for bacterial infections. The resistance of antibiotics is a pivotal threat to public health totalities and considered strategies decrease resistance. The aim of the present review is to present an overview of the therapeutic studies including these fields. Special attention has been presented to antimicrobial drug conjugates in two decades. The authors introduce an overview of the studies explaining the research and development of current drug conjugates for bacterial diseases. The current project indicates the reason behind the production, biological functions and enhancement of the novel drug conjugates. Novel approaches and methodologies used for the research in this area have been described. The inventions described in this review have been brought from various databases such as Scopus, Nature, PubMed, Elsevier, Springer from 1999 to 2021.All the Conjugations of these drugs discussed in this review are indicated to exhibit enhanced efficacy, delivery, targeting capabilities and less deleterious effects. Versatile strategies were presented to obtain these aims.

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)0.001
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 score0.971
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.287
Teacher spread0.259 · 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
Published2022
Admission routes1
Has abstractyes

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