Development of a thermosensitive hydrogel based on Polaxamer 407 and gellan gum with inclusion complexes (Sulfobutylated-ß-cyclodextrin-Farnesol) as a local drug delivery system.

This work proposes the development of a thermosensitive local drug release system based on Polaxamer 407, also known as Pluronic® F-127 (PF-127), Gellan Gum (GG) and the inclusion complex Sulfobutylated-ß-cyclodextrin (CD) with Farnesol (FOH). Rheological properties of the hydrogels and their degradation were studied. According to the rheological results, a solution of 20% w/v of PF-127 forms a strong gel with a gelling temperature of about 25 °C (storage modulus of 15,000 Pa). The addition of the GG increased the storage modulus (optimal concentration of 0.5 % w/v) twofold without modifying the gelling temperature. Moreover, including 0.5% w/v of GG also increased 6 times the degradation time of the hydrogel. Regarding the inclusion complex, the addition of free CD decreased the viscosity and the gel strength since polymer chains were included in CD cavity without affecting the gelling temperature. Contrarily, the inclusion complex CD-FOH did not significantly modify any property of the formulation because the FOH was hosted in the CD. Furthermore, a mathematical model was developed to adjust the degradation time. This model highlights that the addition of the GG decreases the number of released chains from the polymeric network (which coincides with an increase in the storage modulus) and that the free CD reduces the degradation rate, protecting the polymeric chains. Finally, FOH release was quantified with a specific device, that was designed and printed for this type of system, observing a sustainable drug release (similar to FOH aqueous solubility, 8 µM) dependent on polymer degradation.

Changes in Sensitization Patterns in the Last 25 Years in 619 Patients with Confirmed Diagnoses of Immediate Hypersensitivity Reactions to Beta-Lactams.

Beta-lactam (BL) drugs are the antibiotics most prescribed worldwide due to their broad spectrum of action. They are also the most frequently implied in hypersensitivity reactions with a known specific immunological mechanism. Since the commercialization of benzylpenicillin, allergic reactions have been described; over the years, other new BL drugs provided alternative treatments to penicillin, and amoxicillin is now the most prescribed BL in Europe. Diagnosis of BL allergy is mainly based on skin tests and drug provocation tests, defining different sensitization patterns or phenotypes. In this study, we evaluated 619 patients with a confirmed diagnosis of BL-immediate allergy during the last 25 years, using the same diagnostic procedures with minor adaptations to the successive guidelines. The initial eliciting drug was benzylpenicillin, which changed to amoxicillin with or without clavulanic acid and cephalosporins in recent years. In skin tests, we found a decrease in sensitivity to major and minor penicillin determinants and an increase in sensitivity to amoxicillin and others; this might reflect that the changes in prescription could have influenced the sensitization patterns, thus increasing the incidence of specific reactions to side-chain selective reactions.

Prediction of the type of milk and degree of ripening in cheeses by means of artificial neural networks with data concerning fatty acids and near infrared spectroscopy.

The present study addresses the prediction of the time of ripening and type of mixtures of milk (cow's, ewe's and goat's) in cheeses of varying composition using artificial neural networks (ANN). To accomplish this aim, neural networks were designed using as input data the content of 19 fatty acids obtained with GC-FID of the cheese fat and scores obtained from principal component analysis (PCA) of NIR spectra. The best model of neuronal networks for the identification of the type of mixtures of milk was obtained using the information concerning the fatty acid concentration (80% of correct results in the training phase and 75% in the validation phase). Regarding the information of the near-infrared (NIR) spectra a neural network was designed. The aforesaid neural network predicted the ripening of cheeses with 100% accuracy in both training and in validation.