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Record W2536312836 · doi:10.1002/adfm.201603519

Liposome‐Supported Peritoneal Dialysis for the Treatment of Hyperammonemia‐Associated Encephalopathy

2016· article· en· W2536312836 on OpenAlexaff
Valentina Agostoni, Soo Hyun Lee, Vincent Forster, Meriam Kabbaj, Cristina R. Bosoi, Mélanie Tremblay, Matthias Zadory, Christopher F. Rose, Jean‐Christophe Leroux

Bibliographic record

VenueAdvanced Functional Materials · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsHyperammonemiaEncephalopathyPeritoneal dialysisLiposomeCerebral edemaHepatic encephalopathyCirrhosisMedicineInternal medicineMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Hyperammonemia can lead to cerebral dysfunction, encephalopathy, coma, and death if not treated adequately. The poor prognosis associated with this condition reflects the unmet medical need for effective ammonia‐lowering treatments. Here, the translational potential of liposome‐supported peritoneal dialysis (LSPD), a recently‐developed detoxification strategy for the removal of small ionizable molecules like ammonia, is described. Dialysis fluids supplemented with micrometer‐sized, transmembrane pH‐gradient liposomes are prepared via an innovative, osmotic shock‐based method overcoming sterilization and long‐term stability issues. LSPD is able to sequester ammonia in healthy rats in relation to the injected dose, buffering capacity of the liposomal core, and membrane composition. In a rat model of cirrhosis, LSPD outperforms conventional peritoneal dialysis in lowering plasmatic ammonia levels and attenuating brain edema. LSPD does not trigger any hypersensitive reaction in pigs, a side effect commonly observed upon the injection of colloids in this animal model and in humans. These findings support the development of LSPD for the treatment of hyperammonemia‐induced encephalopathy.

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 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 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 score0.045
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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 teacher head, 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".

Quick stats

Citations30
Published2016
Admission routes1
Has abstractyes

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