The Efficacy and Biopharmaceutical Properties of a Fixed-Dose Combination of Disulfiram and Benzyl Benzoate

Scabies and hair lice are parasitic diseases that affect human skin and hair, respectively. The incidence and resistances of these infections are increasing. Tenutex^® (disulfiram and benzyl benzoate emulsion) is an alternative to standard insecticides to avoid resistances. The aim of the work is to evaluate the transdermal absorption and the in vitro efficacy against scabies and hair lice after different exposition times. Dermatomed human skin was used to assess the dermal absorption using a validated High Performance Liquid Chromatography (HPLC) method. HEK001 keratinocytes were used to evaluate the cytotoxicity of benzyl benzoate. Only benzyl benzoate was able to cross the skin, but it did not show cytotoxicity at any of the tested concentrations. The product efficacy was tested on Psoroptes ovis after direct contact and after administration on sheep skin explants at different contact times. Permethrin/malathion-resistant strains of Pediculus humanis capitis adults and eggs were directly exposed to Tenutex, and the vitality and hatchability, respectively, were evaluated. The anti-scabies study demonstrated that exposure for 6 or 24 h completely eradicated the parasite. The pediculicidal activity of Tenutex exhibited superior efficacy than standard treatment on resistant lice. The positive results obtained suggest that Tenutex^® is a good treatment option, especially in drug resistance situations.     

Multiplexed Biosensing Diagnostic Platforms Detecting Autoantibodies to Tumor-Associated Antigens from Exosomes Released by CRC Cells and Tissue Samples Showed High Diagnostic Ability for Colorectal Cancer

Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. The five-year survival rate of CRC patients depends on the stage at diagnosis, being higher than 80% when CRC is diagnosed in the early stages but lower than 10% when CRC is diagnosed in advanced stages. Autoantibodies against specific CRC autoantigens (tumor-associated antigens (TAAs)) in the sera of patients have been widely demonstrated to aid in early diagnosis. Thus, we herein aim to identify autoantigens target of autoantibodies specific to CRC that possess a significant ability to discriminate between CRC patients and healthy individuals by means of liquid biopsy. To that end, we examined the protein content of the exosomes released by five CRC cell lines and tissue samples from CRC patients by means of immunoprecipitation coupled with mass spectrometry analysis. A total of 103 proteins were identified as potential autoantigens specific to CRC. After bioinformatics and meta-analysis, we selected 15 proteins that are more likely to be actual CRC autoantigens in order to evaluate their role in CRC prognosis by Western blot (WB) and immunohistochemistry (IHC). We found dysregulation at the protein level for 11 of these proteins in both tissue and plasma exosome samples from patients, along with an association of nine of these proteins with CRC prognosis. After validation, all but one showed a statistically significant high diagnostic ability to distinguish CRC patients and individuals with premalignant lesions from healthy individuals, either by luminescence Halotag-based beads, or by a multiplexed biosensing platform involving the use of magnetic microcarriers as solid support modified with covalently immobilized Halotag fusion proteins constructed for CRC detection. Taken together, our results highlight the usefulness of the approach defined here to identify the TAAs specific to chronic diseases; they also demonstrate that the measurement of autoantibody levels in plasma against the TAAs identified here could be integrated into a point-of-care (POC) device for CRC detection with high diagnostic ability.     

Evolutionary selection of interesting class association rules using genetic relation algorithm

During the last years, several association rule‐based classification methods have been proposed, these algorithms may quickly generate accurate rules. However, the generated rules are often very large in terms of the number of rules and usually complex and hardly understandable for users. Among all the rules generated by the algorithms, only some of them are likely to be of any interest to the domain expert analyzing the data. Most of the rules are either redundant, irrelevant or obvious. In this paper, a new method for selecting the interesting class association rules is proposed by an evolutionary method named genetic relation algorithm. The algorithm evaluates the relevance and interestingness of the discovered association rules by the relationships between the rules in each generation using a specific measure of distance among them giving a reduced set of rules as the result in the final generation. This small rule set has the following properties: (i) accurate as it has at least the same classification accuracy as the complete association rule set, (ii) interesting because of the diversity of rules and (iii) comprehensible because it is more understandable for the users as the number of attributes involved in the rules is also small. The efficiency of the proposed method is compared with other conventional methods including genetic network programming‐based mining using ten databases and the experimental results show that it outperforms others keeping a good balance between the classification accuracy and the comprehensibility of the rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.