Lactoperoxidase potential in diagnosing subclinical mastitis in cows via image processing.

This report describes how image processing harnessed to multivariate analysis techniques can be used as a bio-analytical tool for mastitis screening in cows using milk samples collected from 48 animals (32 from Jersey, 7 from Gir, and 9 from Guzerat cow breeds), totalizing a dataset of 144 sequential images was collected and analyzed. In this context, this methodology was developed based on the lactoperoxidase activity to assess mastitis using recorded images of a cuvette during a simple experiment and subsequent image treatments with an R statistics platform. The color of the sample changed from white to brown upon its exposure to reagents, which is a consequence of lactoperoxidase enzymatic reaction. Data analysis was performed to extract the channels from the RGB (Red-Green-Blue) color system, where the resulting dataset was evaluated with Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Second-Order Regression (SO). Interesting results in terms of enzymatic activity correlation (R2 = 0.96 and R2 = 0.98 by MLR and SO, respectively) and of somatic cell count (R2 = 0.97 and R2 = 0.99 by MLR and SO, respectively), important mastitis indicators, were obtained using this simple method. Additionally, potential advantages can be accessed such as quality control of the dairy chain, easier bovine mastitis prognosis, lower cost, analytical frequency, and could serve as an evaluative parameter to verify the health of the mammary gland.

Tropical climate effect on the toxic heavy metal pollutant course of road-deposited sediments.

In modern society, the intense vehicle traffic and the lack of effective mitigating strategies may adversely impact freshwater systems. Road-deposited sediments (RDS) accumulate a variety of toxic substances which are transported into nature during hydrologic events, mainly affecting water bodies through stormwater runoff. The aim of this study was to evaluate the RDS metal enrichment ratio between the end of wet season and the middle of the dry season for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in samples from Natal, Brazil. Twenty RDS, drainage system and river sediment samples were collected in the wet and dry seasons using a stainless-steel pan, brush and spatula. In the laboratory, the samples were submitted to acid digestion and heavy metal concentrations were measured by atomic absorption spectrometry (AAS). A consistent RDS enrichment by heavy metals in dry season samples was followed by an increase in the finest particle size fraction (D < 63 µm). Maximum concentrations were 5, ND, 108, 23810, 83, ND, 77 and 150 mg kg-1 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn, respectively. The RDS enrichment ratio was Cr(1.3 × ), Cu(2.6 × ), Fe(3.3 × ), Mn(1.5 × ), Pb(1.5 × ) and Zn(2.1 × ). The Geo-accumulation Index values showed that RDS were moderately polluted for Cu and slighted polluted for Zn and Pb. Principal Component Analysis (PCA) showed that the accumulation of toxic heavy metals decreased according to water flow.

Rutin-modified silver nanoparticles as a chromogenic probe for the selective detection of Fe3+ in aqueous medium.

The development of nanoprobes for selective detection of metal ions in solution has attracted great attention due to their impact on living organisms. As a contribution to this field, this paper reports the synthesis of silver nanoparticles modified with rutin in the presence of ascorbic acid and their successful use as a chromogenic probe for the selective detection of Fe3+ in aqueous solution. Limits of detection and quantification were found to be 17 nmol L-1 and 56 nmol L-1, respectively. The sensing ability is proposed to proceed via an iron-induced nanoparticle growth/aggregation mechanism. A practical approach using image analysis for quantification of Fe3+ is also described.

On the synergy between silver nanoparticles and doxycycline towards the inhibition of Staphylococcus aureus growth.

In a previous paper (RSC Adv., 2015, 5, 66886-66893), we showed that the combination of silver nanoparticles (NanoAg) with doxycycline (DO) culminated in an increased bactericidal activity towards E. coli. Herein we further investigated the metabolic changes that occurred on Staphylococcus aureus upon exposure to NanoAg with the help of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) coupled with multivariate data analysis. It has been discovered that the combination of DO with NanoAg produced metabolic changes in S. aureus that were not simply the overlap of the treatments with DO and NanoAg separately. Our results suggest that DO and NanoAg act synergistically to impede protein synthesis by the bacteria.

Rapid discrimination of Klebsiella pneumoniae carbapenemase 2-producing and non-producing Klebsiella pneumoniae strains using near-infrared spectroscopy (NIRS) and multivariate analysis.

Klebsiella pneumoniae Carbapenemase (KPC-2)-producing and non-producing Klebsiella pneumoniae (KP) have rapidly disseminated worldwide, challenging the diagnostics of Gram-negative infections. We evaluate the potential of a novel non-destructive and rapid method based on Near-Infrared Spectroscopic (NIRS) and multivariate analysis for distinguishing KPC-2-producing and non-producing KP. Thirty-nine NIRS spectra (24 KPC-2-producing KP, 15 KPC-2 non-producing KP) were acquired; different pre-processing methods such as baseline correction, derivative and Savitzky-Golay smoothing were performed. A spectral region fingerprint was achieved after using genetic algorithm-linear discriminant analysis (GA-LDA) and successive projection algorithm (SPA-LDA) algorithms for variable selection. The variables selected were then used for discriminating the microorganisms.Accuracy test results including sensitivity and specificity were determined. Sensitivity in KPC-2 producing and non-producing KP categories was 66.7% and 75%, respectively, using a SPA-LDA model with 66 wavenumbers. The resulting GA-LDA model successfully classified both microorganisms with respect to their "fingerprints" using only 39 wavelengths. Sensitivity in KPC-2 producing category was moderate(≈66.7%) using a GA-LDA model. However, sensitivity in KPC-2 non-producing category using GA-LDA accurately predicted the correct class (with 100% accuracy). As100% accuracy was achieved, this novel approach identifies potential biochemical markers that may have a relation with microbial functional roles and means of rapid identification of KPC-2 producing and non-producing KP strains.

Metabolomic assessment with CE-MS of the nutraceutical effect of Cystoseira spp extracts in an animal model.

There is a need of scientific evidence of claimed nutraceutical effects, but also there is a social movement towards the use of natural products and among them algae are seen as rich resources. Within this scenario, the development of methodology for rapid and reliable assessment of markers of efficiency and security of these extracts is necessary. The rat treated with streptozotocin has been proposed as the most appropriate model of systemic oxidative stress for studying antioxidant therapies. Cystoseira is a brown alga containing fucoxanthin and other carothenes whose pressure-assisted extracts were assayed to discover a possible beneficial effect on complications related to diabetes evolution in an acute but short-term model. Urine was selected as the sample and CE-TOF-MS as the analytical technique to obtain the fingerprints in a non-target metabolomic approach. Multivariate data analysis revealed a good clustering of the groups and permitted the putative assignment of compounds statistically significant in the classification. Interestingly a group of compounds associated to lysine glycation and cleavage from proteins was found to be increased in diabetic animals receiving vehicle as compared to control animals receiving vehicle (N6,N6,N6-trimethyl-L-lysine, N-methylnicotinamide, galactosylhydroxylysine, L-carnitine, N6-acetyl-N6-hydroxylysine, fructose-lysine, pipecolic acid, urocanic acid, amino-isobutanoate, formylisoglutamine. Fructoselysine significantly decreased after the treatment changing from a 24% increase to a 19% decrease. CE-MS fingerprinting of urine has provided a group of compounds different to those detected with other techniques and therefore proves the necessity of a cross-platform analysis to obtain a broad view of biological samples.

Capillary electrophoresis as a metabolomics tool for non-targeted fingerprinting of biological samples.

Metabolomics, understood as a data driven strategy trying to find markers of a situation under study without a priori hypothesis, has rapidly caught the attention and evolved from the simple pattern recognition strategy, which was a great innovation at its origins, to the interest for the final identification of markers responsible for class separation, i.e., from data to knowledge. Due to differences in physico-chemical properties and concentrations of the metabolites, but also due to differences in matrix properties, cross-platform approaches are proving to increase the capability of information. Once more techniques do not compete. This is the scene where capillary electrophoresis (CE) has its niche to provide information mainly on polar or ionic compounds in biological fluids. General advantages and disadvantages of CE for sample fingerprinting will be discussed and methods will be classified depending on the detection system (UV or MS) as this strongly affects all the conditions. Recent developments will be presented in different biological fluids, although urine is without a doubt the preferred sample for CE analysis.

Assessing the separation of neutral plant secondary metabolites by micellar electrokinetic chromatography.

In this work, partition coefficients (Pwm) and solute-micelle association constants per monomer (Km/N) were measured using micellar electrokinetic chromatography in tetraborate-sodium dodecylsulfate electrolytes for 18 important plant secondary metabolites (coumarin, verbenone, camphor, eucalyptol, carvone, alpha-terpineol, linalool, jasmone, bergapten, rose oxide, geraniol, t-anethole, citronellal, citronellol, p-cymene, limonene, caryophyllene and nerol) of wide occurrence in herbal extracts and essential oils. Caryophyllene presented a retention time longer than anthracene (micelle marker) and its set of constants could not be determined accurately. Pwm and Km/N were generated by the non-linear data fitting of both partition and solute-micelle association models for the 17 solutes under consideration (caryophyllene excluded). Pwm varied from 147 (coumarin) to 13175 (limonene) while Km/N varied from 37 (coumarin) to 3721 (limonene). Under optimal conditions, the separation of the selected compounds was attempted successfully in commercialized samples of rose, anise and geranium essential oils.