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Record W2909378168 · doi:10.1016/j.addr.2019.01.003

Bacteriophage interactions with mammalian tissue: Therapeutic applications

2019· review· en· W2909378168 on OpenAlexaff
Haein Huh, Shirley Wong, Jesse St. Jean, Roderick Slavcev

Bibliographic record

VenueAdvanced Drug Delivery Reviews · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBacteriophageBiologyPhage therapyHuman viromeHuman healthBacterial virusComputational biologyCell biologyGenomeGeneticsGeneMedicineEscherichia coli

Abstract

fetched live from OpenAlex

The human body is a large reservoir for bacterial viruses known as bacteriophages (phages), which participate in dynamic interactions with their bacterial and human hosts that ultimately affect human health. The current growing interest in human resident phages is paralleled by new uses of phages, including the design of engineered phages for therapeutic applications. Despite the increasing number of clinical trials being conducted, the understanding of the interaction of phages and mammalian cells and tissues is still largely unknown. The presence of phages in compartments within the body previously considered purely sterile, suggests that phages possess a unique capability of bypassing anatomical and physiological barriers characterized by varying degrees of selectivity and permeability. This review will discuss the direct evidence of the accumulation of bacteriophages in various tissues, focusing on the unique capability of phages to traverse relatively impermeable barriers in mammals and its relevance to its current applications in therapy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.033
GPT teacher head0.323
Teacher spread0.289 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations176
Published2019
Admission routes1
Has abstractyes

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