Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0011849 (diabetes)
 277,896 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Oral insulin delivery has revolutionized diabetes treatment, but challenges including degradation in the gastrointestinal environment and low permeation across the intestinal epithelium remain. Herein, to overcome these barriers, we developed a novel biodegradable nanocomposite microsphere embedded with metal-organic framework (MOF) nanoparticles. An iron-based MOF nanoparticle (NP) (MIL-100) was first synthesized as a carrier with an insulin loading capacity of 35%. The insulin-loaded MIL-100 nanoparticles modified with sodium dodecyl sulfate (Ins@MIL100/SDS) promoted insulin permeation across Caco-2 monolayer models in vitro. To improve resistance to the gastric acid environment, Ins@MIL100/SDS nanoparticles were embedded into a biodegradable microsphere to construct the nanocomposite delivery system (Ins@MIL100/SDS@MS). The microspheres effectively protected the MOF NPs from rapid degradation under acidic conditions and could release insulin-loaded MOF NPs in the simulated intestinal fluid. After the oral administration of Ins@MIL100/SDS@MS into BALB/c nude mice, increased intestinal absorption of the insulin was detected compared to the oral administration of free insulin or Ins@MIL100/SDS. Furthermore, significantly enhanced plasma insulin levels were obtained for over 6 h after oral administration of Ins@MIL100/SDS@MS into diabetic rats, leading to a remarkably enhanced effect in lowering blood glucose level with a relative pharmacological availability of 7.8%. Thus, the MOF-nanoparticle-incorporated microsphere may provide a new strategy for effective oral protein delivery.
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PMID:A Nanocomposite Vehicle Based on Metal-Organic Framework Nanoparticle Incorporated Biodegradable Microspheres for Enhanced Oral Insulin Delivery. 3234 Apr 52

Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. The aim of this study is to propose a multiple instance learning boosting algorithm (MIL-Boost) for creating a predictive model capable of early prediction of worsening insulin resistance (low vs high T2D risk) in terms of TyG index. The MIL-Boost is applied to past electronic health record (EHR) patients' information stored by a single GP. The proposed MIL-Boost algorithm proved to be effective in dealing with this task, by performing better than the other state-of-the-art ML competitors (Recall from 0.70 and up to 0.83). The proposed MIL-based approach is able to extract hidden patterns from past EHR temporal data, even not directly exploiting triglycerides and glucose measurements. The major advantages of our method can be found in its ability to model the temporal evolution of longitudinal EHR data while dealing with small sample size and variability in the observations (e.g., a small variable number of prescriptions for non-hospitalized patients). The proposed algorithm may represent the main core of a clinical decision support system.
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PMID:Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach. 3250 28