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Record W4380536834 · doi:10.3389/fddev.2023.1209534

Design of respirable sprayed microparticles of encapsulated bacteriophages

2023· review· en· W4380536834 on OpenAlex
Alberto Baldelli, Mingtao Liang

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

VenueFrontiers in Drug Delivery · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAntibioticsGuidelineAntibiotic resistanceBacteriaMicrobiologyNanotechnologyMedicineBiologyMaterials science

Abstract

fetched live from OpenAlex

Antibiotic resistance is exponentially increasing, and the number of deaths caused by bacterial infections is expected to surge. When dealing with the respiratory system, inefficient antibiotics heighten the chance of death from bacterial infection. However, the alternatives to antibiotics are limited. Bacteriophages are a valid option since they can target a specific type of bacterium. Bacteriophages are highly specific and can avoid any side effects when delivered. However, their poor stability makes their use inefficient. Encapsulation is commonly used to protect any bioactive compound for different types of delivery. In the case of respiratory delivery, particle engineering is used to generate stable dry powders to target the nasal or lung areas. This review article provides a guideline for engineering a process of nasal dry powders of encapsulated bacteriophages.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.275
Teacher spread0.240 · 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