Efficacy and Safety of Cannabidiol and Tetrahydrocannabivarin on Glycemic and Lipid Parameters in Patients With Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled, Parallel Group Pilot Study

OBJECTIVE Cannabidiol (CBD) and D9-tetrahydrocannabivarin (THCV) are nonpsychoactive phytocannabinoids affecting lipid and glucose metabolism in animal models. This study set out to examine the effects of these compounds in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS In this randomized, double-blind, placebo-controlled study, 62 subjects with noninsulin-treated type 2 diabetes were randomized to five treatment arms: CBD (100 mg twice daily), THCV (5 mg twice daily), 1:1 ratio of CBD and THCV (5 mg/5 mg, twice daily), 20:1 ratio of CBD and THCV (100 mg/5 mg, twice daily), or matched placebo for 13 weeks. The primary end point was a change in HDL-cholesterol concentrations from baseline. Secondary/tertiary end points included changes in glycemic control, lipid profile, insulin sensitivity, body weight, liver triglyceride content, adipose tissue distribution, appetite, markers of inflammation, markers of vascular function, gut hormones, circulating endocannabinoids, and adipokine concentrations. Safety and tolerability end points were also evaluated. RESULTS Compared with placebo, THCV significantly decreased fasting plasma glucose (estimated treatment difference [ETD] = 21.2 mmol/L; P < 0.05) and improved pancreatic b-cell function (HOMA2 b-cell function [ETD = 244.51 points; P < 0.01]), adiponectin (ETD = 25.9 3 106 pg/mL; P < 0.01), and apolipoprotein A (ETD = 26.02 mmol/L; P < 0.05), although plasma HDL was unaffected. Compared with baseline (but not placebo), CBD decreased resistin (2898 pg/ml; P < 0.05) and increased glucose-dependent insulinotropic peptide (21.9 pg/ml; P < 0.05). None of the combination treatments had a significant impact on end points. CBD and THCV were well tolerated. CONCLUSIONS THCV could represent a newtherapeutic agent in glycemic control in subjects with type 2 diabetes.

Where and how to find data on safety: what do systematic reviews of complementary therapies tell us?

Background: Successfully identifying relevant data for systematic reviews with a focus on safety may require retrieving information from a wider range of sources than for ‘effectiveness’ systematic reviews. Searching for safety data continues to prove a major challenge. Objectives: To examine search methods used in systematic reviews of safety and to investigate indexing. Methods: Systematic reviews focusing on safety of complementary therapies and related interventions were retrieved from comprehensive searches of major databases. Data was extracted on search strategies, sources used and indexing in major databases. Safety related search terms were compared against index terms available on major databases. Data extraction by one researcher using a pre-prepared template was checked for accuracy by a second researcher. Results: Screening of 2563 records resulted in 88 systematic reviews being identified. Information sources used varied with the type of intervention being addressed. Comparison of search terms with available index terms revealed additional potentially relevant terms that could be used in constructing search strategies. Seventy-nine reviews were indexed on PubMed, 84 on EMBASE, 21 on CINAHL, 15 on AMED, 6 on PsycINFO, 2 on BNI and HMIC. The mean number of generic safety-related indexing terms on PubMed records was 2.6. For EMBASE the mean number was 4.8 with at least 61 unique terms being employed. Most frequently used indexing terms and subheadings were adverse effects, side effects, drug interactions and herb-drug interactions. Use of terms specifically referring to safety varied across databases. Conclusions: Investigation of search methods revealed the range of information sources used, a list of which may prove a valuable resource for those planning to conduct systematic reviews of safety. The findings also indicated that there is potential to improve safety-related search strategies. Finally, an insight is provided into indexing of and most effective terms for finding safety studies on major databases.

Identification of meat spoilage by FTIR spectroscopy and neural networks

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage