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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.025

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