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Record W2512849891 · doi:10.1002/9781118943182.ch14

Respirable Bacteriophage Aerosols for the Prevention and Treatment of Tuberculosis

2016· other· en· W2512849891 on OpenAlexaff
Graham F. Hatfull, Reinhard Vehring

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBacteriophageMycobacterium smegmatisMycobacterium tuberculosisBiologyMicrobiologyTuberculosisVirologyMycobacteriumPhage therapyBacteriaMedicineGeneticsEscherichia coli

Abstract

fetched live from OpenAlex

This chapter discusses the general question of if and how respirable bacteriophages could be used therapeutically for tuberculosis infections. The ongoing accumulation of clinically prevalent antibiotic-resistant strains of Mycobacterium tuberculosis and the difficulties encountered in treating patients with multidrug- and extensively drug-resistant strains, alternative therapies warrant consideration. Bacteriophages are viruses that infect bacterial hosts.Their host-range differ for each phage isolate, but typically narrow, sometimes constrained to one or a few strains within a bacterial species. It is relatively unusual for phages to infect hosts within more than one genus. This typically occurs only when the genera are very closely related. The chapter discusses the mycobacteriophages, which are bacteriophages that infect hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. It reviews the basic aspects of bacteriophages. The chapter considers the availability and features of mycobacteriophages with therapeutic potential. It also considers ways in which these phages might be manufactured as an inhaled pharmaceutical aerosol.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2016
Admission routes1
Has abstractyes

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