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Record W3118908644 · doi:10.3389/fcell.2020.620471

Intravital Imaging of Pulmonary Immune Response in Inflammation and Infection

2021· review· en· W3118908644 on OpenAlexaff
Nazli Alizadeh-Tabrizi, Stefan Hall, Christine Lehmann

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2021
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInflammationLungIntravital microscopyPneumoniaSepsisImmune systemMedicineCystic fibrosisPathologyFibrosisImmunologyRadiologyMicrocirculationInternal medicine

Abstract

fetched live from OpenAlex

Intravital microscopy (IVM) is a unique imaging method providing insights in cellular functions and interactions in real-time, without the need for tissue extraction from the body. IVM of the lungs has specific challenges such as restricted organ accessibility, respiratory movements, and limited penetration depth. Various surgical approaches and microscopic setups have been adapted in order to overcome these challenges. Among others, these include the development of suction stabilized lung windows and the use of more advanced optical techniques. Consequently, lung IVM has uncovered mechanisms of leukocyte recruitment and function in several models of pulmonary inflammation and infection. This review focuses on bacterial pneumonia, aspiration pneumonia, sepsis-induced acute lung Injury, and cystic fibrosis, as examples of lung inflammation and infection. In addition, critical details of intravital imaging techniques of the lungs are discussed.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.273
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 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

Citations17
Published2021
Admission routes1
Has abstractyes

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