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Phrenic Nerve Stimulation Decreases Ventilation-Associated Kidney Injury in an ARDS Preclinical Model

2025· article· en· W4410276467 on OpenAlexaff
Thiago Bassi, Elizabeth Rohrs, K. Fernández, Marlena Ornowska, J. Witmann, Michelle Nicholas, S.C. Reynolds

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsFraser HealthSimon Fraser UniversityRoyal Columbian HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineARDSStimulationVentilation (architecture)AnesthesiaPhrenic nerveMechanical ventilationLungRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION: Mechanical ventilation (MV) is associated with a greater risk of acute kidney injury (VIKI). Neutrophil gelatinase-associated lipocalin (NAGL) is one of the most studied markers of kidney injury. Lung-protective ventilation with low tidal volumes and high positive end-expiratory pressure have been shown to mitigate kidney injury during MV. However, it is challenging to identify the optimal ventilatory settings to mitigate VIKI. Phrenic nerve stimulation resulted in lung-protective ventilation with better ventilation distribution compared to standard MV. This study investigated whether applying phrenic nerve stimulation to pigs with induced acute respiratory distress syndrome (ARDS) would result in VIKI mitigation. METHODS: Eighteen deeply sedated, human-size pigs were ventilated using volume-control: 8 ml/kg, PEEP 5 cmH2O, with rate and FiO2 set to achieve normal arterial blood gases. Injured-lung series: ARDS was induced using oleic acid via the pulmonary artery until PaO2/FiO2 was lower than 200 mmHg. Pigs were then ventilated for 12 hours post-injury. The study had three groups, two groups receiving temporary transvascular diaphragm neurostimulation (TTDN) synchronized to inspiration, one group on every breath (MV+TTDN100%) and the second on every second breath (MV+TTDN50%), targeting a reduction in ventilator pressure-time product of 15-20% and a third group on volume-control MV only (Control group). At the study end, kidney biopsies were assessed for IL-1a, IL-1ra, IL-1b, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-18, TNFa, NAGL, and VEGFa. Cytokine concentrations were measured in homogenized tissue using porcine-specific ELISA discovery assays via an external, blinded laboratory and normalized by weight. Statistical analysis used a non-parametric ranked test (Kruskal Wallis test). P-values <0.05 were considered statistically significant. RESULTS: Animals that received respectively TTDN 100% and 50% showed statistically significant lower kidney concentrations normalized by weight in percentage of NAGL than control, 18 vs 17 vs 29, p=0.012) and TNFa (26 vs 10 vs 52, p<0.0001) (Figure). The TTDN 100% group also showed greater IL-10 tissue concentrations normalized by weight in percentage compared to the other groups, 42 vs 9 vs 12, p=0.03. There was no statistically significant difference for the other tissue cytokines concentrations analyzed. CONCLUSIONS: In deeply sedated MV moderate ARDS pigs, diaphragm neurostimulation reduced tissue concentration of a biomarker for kidney injury, NAGL, and TNFa kidney concentration along with increased IL-10 tissue concentration, an anti-inflammatory cytokine, compared to the control group. These results are promising and grant further investigation in the clinical scenario for kidney protection due to diaphragm neurostimulation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.390
Teacher spread0.360 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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