ReviewDiscover nano2026
Toward therapnostic integration as a new paradigm for precision management of interstitial cystitis driven by medical-engineering convergence.
Review in Discover nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) is a complex chronic inflammatory disease involving mucosal barrier defects, neurogenic inflammation, mast cell activation, and tissue remodeling. Current diagnosis lacks objective biomarkers, relying instead on symptom assessment and exclusion of confusable diseases. Cystoscopy is recommended for identifying Hunner lesions per ESSIC/AUA guidelines, and first-line intravesical GAG replacement therapies only achieve sustained response rates of 20-38% at 12 months. Medical-engineering integration and materials innovations propel IC management toward multi-scale regulation and intelligent repair. This article proposes intelligent materials and systems to advance IC from stepwise diagnosis to an integrated "sensing-feedback-execution" closed loop. At the diagnostic level, it integrates medical phenotyping with engineering multidimensional detection to construct a synergistic system of subjective symptoms, objective molecules, and physical function: attomolar long non-coding RNA(lncRNA) detection via CRISPR-graphene transistors, glycosaminoglycans (GAGs) quantification with
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Registered trials
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