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Record W4412640665 · doi:10.1111/jcmm.70478

Niosomal Hydrogel Loaded With Bromelain: A Promising Solution for Reducing Skin Collagen in Scleroderma Patients

2025· article· en· W4412640665 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

VenueJournal of Cellular and Molecular Medicine · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPineapple and bromelain studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNiosomeScleroderma (fungus)MedicineFibrosisDermatologyBiomedical engineeringMaterials sciencePathologyChemistryVesicle

Abstract

fetched live from OpenAlex

Skin fibrosis in scleroderma is a chronic and debilitating condition that affects the quality of life of patients. In this study, we fabricated a niosomal hydrogel containing bromelain to reduce skin collagen and improve skin softness in scleroderma patients. The niosomes were prepared using the thin film hydration method and exhibited a drug loading efficiency of 52.2%. They demonstrated high monodispersity, with mean sizes lower than 200 nm. The results showed a significant reduction in mean dermal thickness from 1195 μm before treatment to 750 μm after the 2-month treatment period. This reduction in dermal thickness and improved skin softness can be attributed to the decomposition of accumulated skin collagen in individuals with scleroderma. Additionally, the treatment was found to be effective in reducing skin collagen levels compared to conventional treatment options that merely impede collagen synthesis. The current study underscores the potential of bromelain niosomal hydrogel as a promising strategy for managing skin fibrosis in scleroderma.

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 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 score0.043
Threshold uncertainty score0.446

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 head0.006
GPT teacher head0.235
Teacher spread0.228 · 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