Fumagilina mikotoxinak aspergilosi inbaditzailearen garapenean duen rola aztertzeko SPE-UHPLC-DAD metodo analitikoa

Invasive aspergillosis caused by Aspergillus fumigatus is a threat for immunocompromised patients. According to recent studies, fumagillin, a mycotoxin produced by this fungus, has been associated with the propagation of the disease. Therefore, this molecule might help to understand the mechanisms of this disease and to study the use of fumagillin as a potential biomarker of invasive aspergillosis. In spite of the relevance of fumagillin analysis in microbiological research, no quanti-tative method has been developed so far for its determination in cell culture media. Here, we present the first validated method for the quantitative analysis of fumagillin in RPMI-1640. The sample treatment consists of a mixed-mode anion exchange Solid Phase Extraction that effectively removes potential interferences and offered a recovery of 83 ± 7%. The analysis was carried out by Ultra High Performance Liquid Chromatography coupled to Diode Array Detection at 336 nm. The method fulfilled all the validation criteria established by EMA (European Medicine Agency) and FDA (Food and Drug Administration) guidelines for bioanalysis. Finally, the method was satisfactorily applied to the quantification of the fumagillin produced by different strains of Aspergillus fumigatus and it was observed that they had a different micotoxin production capacity.; Aspergillus fumigatus onddoak sortutako aspergilosi inbaditzailea mehatxua da immunoeskasia duten gai-xoentzat. Azkeneko ikerketa batzuen arabera, fumagilinak, onddoak sortutako mikotoxinak, gaixotasunaren hedapenarekin zerikusia duela ikusi da. Hori dela eta, konposatu honen determinazioa lagungarria izan daiteke bai gaixotasunaren mekanismoak hobeto ulertzeko eta baita aspergilosi inbaditzailearen biomarkatzaile gisa erabili ahal izateko ere. Ikerketa mikro-biologikoetan fumagilinaren analisiak garrantzia izan arren, oraindik ez da haren determinaziorako metodo kuantitatiborik garatu zelula-hazkuntzako inguruneetan. Beraz, lan honetan fumagilinaren determinazio kuantitatiborako lehenengo metodo analitikoa balidatu da RPMI-1640 zelula-hazkuntzako ingurunean. Laginaren tratamendua fase solidoko erauzketarekin egin da, anioi trukatzaile sendoak diren modu mistoko kartutxoak erabiliz. Horrela, egon daitezkeen interferentziak modu eraginkorrean ezabatu dira, eta % 83 ± 7ko berreskurapena lortu da. Analisia fotodiodo detektagailuari akoplaturiko bereizmen oso altuko likido kromatografia erabiliz egin da 336 nm-ko uhin-luzeran. Horrela, metodoak EMA (Europako Medikamentuen Agentzia) eta FDA (Elikagai eta Sendagaien Administrazioa) agentziek balidazio bioanalitikoetarako zehazten dituzten parametro guztien onartze-irizpideak betetzen dituela egiaztatu da. Gero, metodoa A. fumigatus-en lau andui analizatzeko aplikatu da, eta bakoitzak mikotoxinaren kantitate desberdina ekoizteko gaitasuna daukala ikusi da

Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data

The k-nearest neighbours algorithm is characterised as a simple yet effective data mining technique. The main drawback of this technique appears when massive amounts of data -likely to contain noise and imperfections - are involved, turning this algorithm into an imprecise and especially inefficient technique. These disadvantages have been subject of research for many years, and among others approaches, data preprocessing techniques such as instance reduction or missing values imputation have targeted these weaknesses. As a result, these issues have turned out as strengths and the k-nearest neighbours rule has become a core algorithm to identify and correct imperfect data, removing noisy and redundant samples, or imputing missing values, transforming Big Data into Smart Data - which is data of sufficient quality to expect a good outcome from any data mining algorithm. The role of this smart data gleaning algorithm in a supervised learning context will be investigated. This will include a brief overview of Smart Data, current and future trends for the k-nearest neighbour algorithm in the Big Data context, and the existing data preprocessing techniques based on this algorithm. We present the emerging big data-ready versions of these algorithms and develop some new methods to cope with Big Data. We carry out a thorough experimental analysis in a series of big datasets that provide guidelines as to how to use the k-nearest neighbour algorithm to obtain Smart/Quality Data for a high quality data mining process. Moreover, multiple Spark Packages have been developed including all the Smart Data algorithms analysed