Food quality and safety Technology Offers Page 4

Centre Technology Transfer CITTRU posted this:

Staphylococci (Staphylococcus) are one of the most important human bacterial pathogens. Many methods have been developed for the identification and typing of bacterial isolates of the genus Staphylococcus (Staphylococcus aureus) in recent years. These methods can be divided into two groups: phenotypic and genotypic methods. The results obtained using commercial tests, which are based on biochemical and immunological reactions profiles, are affected by mistakes due to variable expression of phenotypic features and the ambiguity from the interpreting the test results. The overall accuracy of conventional tests is low and it is in the range 50-70%. Among genotypic methods, DNA-DNA hybridisation and 16S rRNA gene sequence analysis have defined the species in the genus Staphylococcus and they are recommended to confirm the results of the new methods that are introduced into general use. However, there is still no universal method for staphylococcal species identification and typing at the same time, which would be widely used in all laboratories and allow to obtain high reproducibility of the results. The proposed method may determine the species identification and phylogenetic relationships in the genus Staphylococcus. It shows a high sensitivity and specificity. The results of this method can be interpret very easy, even in the case of the intraspecific polymorphisms. There is the possibility to differentiate between even very closely related species such as S. delphini, S. intermedius and S. pseudintermedius.

Universidad de Alicante posted this:

The research group "Electro catalysis and Electrochemistry of Polymers", Department of Physical Chemistry at the University of Alicante has developed a novel method that allows highly selective electrode manufacture biometrics to detect any biochemical substance of interest, food or environmental. The method is based on the electro assisted deposit of molecularly imprinted silica layers on different electrodes. This allows a fast and efficient detection of the molecule of interest, independently of the other interfering. In addition, allows the regeneration of the electrode in a very simple way and lets its usage almost indefinitely. Innovative aspects The biometrical electrode manufacturing method is based on a electro assisted method of molecularly imprinted silica layers on different electrodes. With this new procedure, we obtain uniform and consistent layers of silica that allow highly selective detection of any biochemical substance of interest, food or environmental when these electrodes are used as amperometric, voltammetric, impedimetric and potentiometric sensors. Main advantatges of the technology The main advantage of the electro assisted deposit respect to conventional methods of thin film deposition (spin-coating or dip-coating), lies in the control of consistency and porosity of the layers. Due to the prevention of uncontrolled pore formation, avoids the indiscriminate passage of species from the solution to the electrode surface, reducing the interference in the detection of the analyte of interest. It has a high specificity and affinity for the molecule of interest. High control on the deposition of silica when is done by electro assisted mode. The possibility of varying the thickness of the silica layer and layer morphology allows for a highly consistent and reproducible layer. Electro assisted deposit method is capable of "self-healing", i.e. prevents the formation of holes in the assisted layer that interfere with the detection of the molecule of interest. With continued use, the sensor loses its effectiveness by the collapse of the pores with the species to be determined. In this case, the regeneration process is very simple: just repeat the procedure for removing the template molecule to be performed after the gel layer (electrochemical extraction or cleaning solvents). Thus, the pores of the sensing phase are released for use again.

Servei de Gestió de la Innovació posted this:
Licensing Manager at Universitat Politècnica de Catalunya - UPC

We have developed a new algorithm for ordering anterior chamber OCT images in such a way that it is possible to classify them, in a fully unsupervised manner, in meaningful groups according to relevant features. We have tested the algorithm with a large set of images classified by two expert ophthalmologists, and with a larger set of annotated images. We have verified that the separation in the different classes defined by the ophthalmologists (closed, narrow, open, and wide open_ figure 6) is similar when using the manually extracted features, or when using the features that are returned by the unsupervised algorithm (View figures). Therefore, the abstract features generated by the algorithm provide novel tools for assessing OCT images of the anterior chamber. They can be used for direct classification of the images and, furthermore, they can be linked to established quantities used for characterizing diseased eyes (like chamber depth, iris-corneal angle) resulting in an automatic detection system. As the algorithm is fully unsupervised, it can be easily automated and set up in OCT imaging systems to aid technicians and doctors in an early diagnosis. The two main advantages of the algorithm demonstrated here over previous works are that it doesn’t need any ground truth or gold standard for training, and it does not rely on specific landmarks; thus, it can analyze images in which relevant landmarks are not visible or not easy to locate.
Image processing method for glaucoma detection and computer program