Factors Controlling The Deposition Of Silk Fibroin Nanofibrils During Layer-by-layer Assembly.

The layer-by-layer technique has been used as a powerful method to produce multilayer thin films with tunable properties. When natural polymers are employed, complicated phenomena such as self-aggregation and fibrilogenesis can occur, making it more difficult to obtain and characterize high-quality films. The weak acid and base character of such materials provides multilayer systems that may differ from those found with synthetic polymers due to strong self-organization effects. Specifically, LbL films prepared with chitosan and silk fibroin (SF) often involve the deposition of fibroin fibrils, which can influence the assembly process, surface properties, and overall film functionality. In this case, one has the intriguing possibility of realizing multilayer thin films with aligned nanofibers. In this article, we propose a strategy to control fibroin fibril formation by adjusting the assembly partner. Aligned fibroin fibrils were formed when chitosan was used as the counterpart, whereas no fibrils were observed when poly(allylamine hydrochloride) (PAH) was used. Charge density, which is higher in PAH, apparently stabilizes SF aggregates on the nanometer scale, thereby preventing their organization into fibrils. The drying step between the deposition of each layer was also crucial for film formation, as it stabilizes the SF molecules. Preliminary cell studies with optimized multilayers indicated that cell viability of NIH-3T3 fibroblasts remained between 90 and 100% after surface seeding, showing the potential application of the films in the biomedical field, as coatings and functional surfaces.

Avaliação do uso de identificadores eletrônicos em suínos

Nowadays the consumer market demands a gradually increase in the products' quality control. The manual control that exits, used in animal production, shows ineficiency in warrating an increasing percentual of the desirable quality, so this can only be reached when an effective animal tracebility system is applied, from birth to slaughter. Individual electronic identification presents high importance in this focus, providing information recorded directly from the animal. Electronic traceability uses electronic devices that emit a signal activated by a fixed reader placed where it is needed to record a certain event, or uses a manual reader which allows a higher independence of the operator. Knowing the importance of the electronic identification as a tool for applying traceability in animal production, this research had as objective to evaluate the use of transponders in order to garantee the manual reading as well as the fixed antenna reading. The following implant places were analized in piglet, just after their birth: 1) forehead, 2) external ear lobule, 3) the posterior auricular base, and 4) a transponder inserted in a earing implanted in the ear lobule. The factors of skin damage and migration were analized, as well as the reading efficiency. It was found that the best implant place was the posterior ear base.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.