Detection of carbon nanotubes in bovine raw milk through Fourier transform Raman spectroscopy.

The potential use of carbon-based methodologies for drug delivery and reproductive biology in cows raises concerns about residues in milk and food safety. This study aimed to assess the potential of Fourier transform Raman spectroscopy and discriminant analysis using partial least squares (PLS-DA) to detect functionalized multiwalled carbon nanotubes (MWCNT) in bovine raw milk. Oxidized MWCNT were diluted in milk at different concentrations from 25.00 to 0.01 µg/mL. Raman spectroscopy measurements and PLS-DA were performed to identify low concentrations of MWCNT in milk samples. The PLS-DA model was characterized by the analysis of the variable importance in projection (VIP) scores. All the training samples were correctly classified by the model, resulting in no false-positive or false-negative classifications. For test samples, only one false-negative result was observed, for 0.01 µg/mL MWCNT dilution. The association between Raman spectroscopy and PLS-DA was able to identify MWCNT diluted in milk samples up to 0.1 µg/mL. The PLS-DA model was built and validated using a set of test samples and spectrally interpreted based on the highest VIP scores. This allowed the identification of the vibrational modes associated with the D and G bands of MWCNT, as well as the milk bands, which were the most important variables in this analysis.

A model based on a multivariate classification for assessing impacts on water quality in a DOCE river watershed after the Fundão tailings dam failure.

The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.

Discrimination of dyslipidemia types with ATR-FTIR spectroscopy and chemometrics associated with multivariate analysis of the lipid profile, anthropometric, and pro-inflammatory biomarkers.

BACKGROUND: Obesity, dyslipidemia, and low-grade inflammatory state form a triad of self-sustaining metabolic dysfunction. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is a simple, rapid, and non-destructive technique that generates spectral fingerprints of biomolecules that can be correlated with metabolic changes. We verified the efficiency of ATR-FTIR spectroscopy in blood plasma (n = 74) to discriminate the types of dyslipidemias and suggest metabolic inflammatory changes. METHODS: Principal Component Analysis (PCA) was performed on the biochemical and anthropometric data to verify whether the dyslipidemia types share a similar biochemical profile plausible of discrimination in chemometric modeling. To discriminate the types of dyslipidemias based on spectral data, Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) was used and validated with leave-one-out cross-validation. RESULTS: Although no significant difference was obtained between the types of dyslipidemia and normal subjects by CRP, leptin, and cfDNA, there was a significant difference between normal subjects vs combined hyperlipidemia (CH) + hypercholesterolemia (HCL) + hypertriglyceridemia (HTG) (p < 0.05) by the 1245 cm-1 peak [νas(PO2-)] (possible indication of chronic inflammation by increased cfDNA). The area under the curve of the region between 1770 and 1720 cm-1 was significantly increased for CH in relation to other dyslipidemias and normal subjects. Furthermore, there were significant differences for the main representative peaks of lipids, proteins, carbohydrates, and nucleic acids between the types of dyslipidemias and between the types of dyslipidemias and normal subjects. The OPLS-DA model achieved 100 % accuracy with 1 latent variable and Standard Error of Cross-Validation (SECV) < 0.004 for all types of dyslipidemia  and the control group. CONCLUSIONS: Our results suggest that ATR-FTIR spectroscopy associated with chemometric modeling is a plausible applicant for screening the types of dyslipidemias. However, more extensive studies should be conducted to verify the real applicability in clinical analysis laboratories or medical clinics.

Using infrared spectroscopy of serum and chemometrics for diagnosis of paracoccidioidomycosis.

Paracoccidioidomycosis (PCM) is a systemic granulomatous mycosis endemic to Latin America, whose etiologic agents are fungi of the genus Paracoccidioides. PCM is usually diagnosed by microscopic observation of the fungus in biological samples, combined or not with other techniques such as serological methods. However, all currently used diagnostic methods have limitations. The objective of this study was to develop a method based on Fourier transform infrared spectroscopy (FTIR) and chemometric analysis for PCM diagnosis. We included 224 serum samples: 132 PCM sera, 24 aspergillosis sera, 10 cryptococcosis sera, 8 histoplasmosis sera, and 50 sera from healthy blood donors. Samples were analyzed by attenuated total reflection (ATR), and chemometric analyses including exploratory analysis through principal component analysis (PCA) and a classification method (PCM and non-PCM) through orthogonal partial least squares discriminant analysis (OPLS-DA). The spectra were similar, with the main bands up to approximately 1652 cm-1 and 1543 cm-1 (amide I and amide II bands). This same region was mainly responsible for the partial separation of the samples in PCA. The OPLS-DA model correctly classified all serum samples with only one latent variable, with a determination coefficient (R²) higher than 0.999 for both the calibration set and prediction set. Sensitivity and specificity were 100% for both sets, showing better performance than the reference diagnostic methods. Therefore, the use of FTIR/ATR together with OPLS-DA modeling proved to be a promising method for PCM diagnosis.

Machine learning and analysis of genomic diversity of "Candidatus Liberibacter asiaticus" strains from 20 citrus production states in Mexico.

Background: Huanglongbing (HLB, yellow shoot disease) is a highly destructive citrus disease associated with a nonculturable bacterium, "Candidatus Liberibacter asiaticus" (CLas), which is transmitted by Asian citrus psyllid (ACP, Diaphorina citri). In Mexico, HLB was first reported in Tizimin, Yucatán, in 2009 and is now endemic in 351 municipalities of 25 states. Understanding the population diversity of CLas is critical for HLB management. Current CLas diversity research is exclusively based on analysis of the bacterial genome, which composed two regions, chromosome (> 1,000 genes) and prophage (about 40 genes). Methods and results: In this study, 40 CLas-infected ACP samples from 20 states in Mexico were collected. CLas was detected and confirmed by PCR assays. A prophage gene(terL)-based typing system (TTS) divided the Mexican CLas strains into two groups: Term-G including four strains from Yucatán and Chiapas, as well as strain psy62 from Florida, USA, and Term-A included all other 36 Mexican strains, as well as strain AHCA1 from California, USA. CLas diversity was further evaluated to include all chromosomal and prophage genes assisted by using machine learning (ML) tools to resolve multidimensional data handling issues. A Term-G strain (YTMX) and a Term-A strain (BCSMX) were sequenced and analyzed. The two Mexican genome sequences along with the CLas genome sequences available in GenBank were studied. An unsupervised ML was implemented through principal component analysis (PCA) on average nucleotide identities (ANIs) of CLas whole genome sequences; And a supervised ML was implemented through sparse partial least squares discriminant analysis (sPLS-DA) on single nucleotide polymorphisms (SNPs) of coding genes of CLas guided by the TTS. Two CLas Geno-groups, Geno-group 1 that extended Term-A and Geno-group 2 that extended Term-G, were established. Conclusions: This study concluded that: 1) there were at least two different introductions of CLas into Mexico; 2) CLas strains between Mexico and USA are closely related; and 3) The two Geno-groups provide the basis for future CLas subspecies research.

Analysis of Malassezia Lipidome Disclosed Differences Among the Species and Reveals Presence of Unusual Yeast Lipids.

Malassezia yeasts are lipid dependent and part of the human and animal skin microbiome. However, they are also associated with a variety of dermatological conditions and even cause systemic infections. How these yeasts can live as commensals on the skin and switch to a pathogenic stage has long been a matter of debate. Lipids are important cellular molecules, and understanding the lipid metabolism and composition of Malassezia species is crucial to comprehending their biology and host-microbe interaction. Here, we investigated the lipid composition of Malassezia strains grown to the stationary phase in a complex Dixon medium broth. In this study, we perform a lipidomic analysis of a subset of species; in addition, we conducted a gene prediction analysis for the detection of lipid metabolic proteins. We identified 18 lipid classes and 428 lipidic compounds. The most commonly found lipids were triglycerides (TAG), sterol (CH), diglycerides (DG), fatty acids (FAs), phosphatidylcholine (PC), phosphatidylethanolamine (PE), ceramides, cholesteryl ester (CE), sphingomyelin (SM), acylcarnitine, and lysophospholipids. Particularly, we found a low content of CEs in Malassezia furfur, atypical M. furfur, and Malassezia pachydermatis and undetectable traces of these components in Malassezia globosa, Malassezia restricta, and Malassezia sympodialis. Remarkably, uncommon lipids in yeast, like diacylglyceryltrimethylhomoserine and FA esters of hydroxyl FAs, were found in a variable concentration in these Malassezia species. The latter are bioactive lipids recently reported to have antidiabetic and anti-inflammatory properties. The results obtained can be used to discriminate different Malassezia species and offer a new overview of the lipid composition of these yeasts. We could confirm the presence and the absence of certain lipid-biosynthesis genes in specific species. Further analyses are necessary to continue disclosing the complex lipidome of Malassezia species and the impact of the lipid metabolism in connection with the host interaction.

Fast discrimination of bacteria using a filter paper-based SERS platform and PLS-DA with uncertainty estimation.

Rapid and reliable identification of bacteria is an important issue in food, medical, forensic, and environmental sciences; however, conventional procedures are time-consuming and often require extensive financial and human resources. Herein, we present a label-free method for bacterial discrimination using surface-enhanced Raman spectroscopy (SERS) and partial least squares discriminant analysis (PLS-DA). Filter paper decorated with gold nanoparticles was fabricated by the dip-coating method and it was utilized as a flexible and highly efficient SERS substrate. Suspensions of bacterial samples from three genera and six species were directly deposited on the filter paper-based SERS substrates before measurements. PLS-DA was successfully employed as a multivariate supervised model to classify and identify bacteria with efficiency, sensitivity, and specificity rates of 100% for all test samples. Variable importance in projection was associated with the presence/absence of some purine metabolites, whereas confidence intervals for each sample in the PLS-DA model were calculated using a resampling bootstrap procedure. Additionally, a potential new species of bacteria was analyzed by the proposed method and the result was in agreement with that obtained via 16S rRNA gene sequence analysis, thereby indicating that the SERS/PLS-DA approach has the potential to be a valuable tool for the discovery of novel bacteria. Graphical abstract This paper describes the discrimination of bacteria at the genus and species levels, after minimal sample preparation, using paper-based SERS substrates and PLS-DA with uncertainty estimation.

Screening method for rapid classification of psychoactive substances in illicit tablets using mid infrared spectroscopy and PLS-DA.

Several new psychoactive substances (NPS) have reached the illegal drug market in recent years, and ecstasy-like tablets are one of the forms affected by this change. Cathinones and tryptamines have increasingly been found in ecstasy-like seized samples as well as other amphetamine type stimulants. A presumptive method for identifying different drugs in seized ecstasy tablets (n=92) using ATR-FTIR (attenuated total reflectance - Fourier transform infrared spectroscopy) and PLS-DA (partial least squares discriminant analysis) was developed. A hierarchical strategy of sequential modeling was performed with PLS-DA. The main model discriminated four classes: 5-MeO-MIPT, methylenedioxyamphetamines (MDMA and MDA), methamphetamine, and cathinones. Two submodels were built to identify drugs present in MDs and cathinones classes. Models were validated through the estimate of figures of merit. The average reliability rate (RLR) of the main model was 96.8% and accordance (ACC) was 100%. For the submodels, RLR and ACC were 100%. The reliability of the models was corroborated through their spectral interpretation. Thus, spectral assignments were performed by associating informative vectors of each specific modeled class to the respective drugs. The developed method is simple, fast, and can be applied to the forensic laboratory routine, leading to objective results reports useful for forensic scientists and law enforcement.

Effects of obesity, energy restriction and neutering on the faecal microbiota of cats.

Surveys report that 25-57 % of cats are overweight or obese. The most evinced cause is neutering. Weight loss often fails; thus, new strategies are needed. Obesity has been associated with altered gut bacterial populations and increases in microbial dietary energy extraction, body weight and adiposity. This study aimed to determine whether alterations in intestinal bacteria were associated with obesity, energy restriction and neutering by characterising faecal microbiota using 16S rRNA gene sequencing in eight lean intact, eight lean neutered and eight obese neutered cats before and after 6 weeks of energy restriction. Lean neutered cats had a bacterial profile similar to obese rodents and humans, with a greater abundance (P<0·05) of Firmicutes and lower abundance (P<0·05) of Bacteroidetes compared with the other groups. The greater abundance of Firmicutes in lean neutered cats was due to a bloom in Peptostreptococcaceae. Obese cats had an 18 % reduction in fat mass after energy restriction (P<0·05). Energy reduction was concurrent with significant shifts in two low-abundance bacterial genera and trends in four additional genera. The greatest change was a reduction in the Firmicutes genus, Sarcina, from 4·54 to 0·65 % abundance after energy restriction. The short duration of energy restriction may explain why few bacterial changes were observed in the obese cats. Additional work is needed to understand how neutering, obesity and weight loss are related to changes in feline microbiota and how these microbial shifts affect host physiology.

Classification of biodegradable materials using QSAR modelling with uncertainty estimation.

The ability to determine the biodegradability of chemicals without resorting to expensive tests is ecologically and economically desirable. Models based on quantitative structure-activity relations (QSAR) provide some promise in this direction. However, QSAR models in the literature rarely provide uncertainty estimates in more detail than aggregated statistics such as the sensitivity and specificity of the model's predictions. Almost never is there a means of assessing the uncertainty in an individual prediction. Without an uncertainty estimate, it is impossible to assess the trustworthiness of any particular prediction, which leaves the model with a low utility for regulatory purposes. In the present work, a QSAR model with uncertainty estimates is used to predict biodegradability for a set of substances from a publicly available data set. Separation was performed using a partial least squares discriminant analysis model, and the uncertainty was estimated using bootstrapping. The uncertainty prediction allows for confidence intervals to be assigned to any of the model's predictions, allowing for a more complete assessment of the model than would be possible through a traditional statistical analysis. The results presented here are broadly applicable to other areas of modelling as well, because the calculation of the uncertainty will clearly demonstrate where additional tests are needed.

Craniofacial Secular Change in Recent Mexican Migrants.

Research by economists suggests that recent Mexican migrants are better educated and have higher socioeconomic status (SES) than previous migrants. Because factors associated with higher SES and improved education can lead to positive secular changes in overall body form, secular changes in the craniofacial complex were analyzed within a recent migrant group from Mexico. The Mexican group represents individuals in the act of migration, not yet influenced by the American environment, and thus can serve as a starting point for future studies of secular change in this population group. The excavation of a historic Hispanic cemetery in Tucson, Arizona, also allows for a comparison between historic Hispanics and recent migrants to explore craniofacial trends over a broad time period, as both groups originate from Mexico. The present research addresses two main questions: (1) Are cranial secular changes evident in recent Mexican migrants? (2) Are historic Hispanics and recent Mexican migrants similar? By studying secular changes within a migrant population group, secular trends may be detected, which will be important for understanding the biological variation of the migrants themselves and will serve as a preliminary investigation of secular change within Mexican migrants. The comparison of a sample of recent Mexican migrants with a historic Hispanic sample, predominantly of Mexican origin, allows us to explore morphological similarities and differences between early and recent Mexicans within the United States. Vault and face size and a total of 82 craniofacial interlandmark distances were used to explore secular changes within the recent Mexican migrants (females, n = 38; males, n = 178) and to explore the morphological similarities between historic Hispanics (females, n = 54; males, n = 58) and recent migrants. Sexes were separated, and multivariate adaptive regression splines and basis splines (quadratic with one knot) were used to assess the direction and magnitude of secular trends for the recent Mexican migrants. Because dates of birth were unavailable for the historic sample, partial least squares discriminant analysis (PLS-DA) was used to evaluate morphological differences between historic and recent Mexican migrant samples. The data were separated into a training data set and a testing data set to ensure realistic results. Males had eight variables (four positive and four negative) and females had six variables (two positive and four negative) that demonstrated significant differences over time. In the PLS-DA, three components were identified as important in model creation and resulted in a classification accuracy of 87% when applied to a testing sample. The high classification accuracy demonstrates significant morphological differences between the two groups, with the historic Hispanic sample displaying overall larger craniofacial dimensions. While differences in cranial morphology are evident between historic Hispanics and recent Mexican migrants, relatively few positive and negative secular trends were detected within the recent migrant sample.

Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers.

Paper spray mass spectrometry (PS-MS) combined with partial least squares discriminant analysis (PLS-DA) was applied for the first time in a forensic context to a fast and effective differentiation of beers. Eight different brands of American standard lager beers produced by four different breweries (141 samples from 55 batches) were studied with the aim at performing a differentiation according to their market prices. The three leader brands in the Brazilian beer market, which have been subject to fraud, were modeled as the higher-price class, while the five brands most used for counterfeiting were modeled as the lower-price class. Parameters affecting the paper spray ionization were examined and optimized. The best MS signal stability and intensity was obtained while using the positive ion mode, with PS(+) mass spectra characterized by intense pairs of signals corresponding to sodium and potassium adducts of malto-oligosaccharides. Discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). However, supervised classification models provided high rates of sensitivity and specificity. A PLS-DA model using full scan mass spectra were improved by variable selection with ordered predictors selection (OPS), providing 100% of reliability rate and reducing the number of variables from 1701 to 60. This model was interpreted by detecting fifteen variables as the most significant VIP (variable importance in projection) scores, which were therefore considered diagnostic ions for this type of beer counterfeit.

Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine.

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000-4000 per cm. Using PLS-DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2% w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples.

Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA.

This paper proposed a new screening method for the simultaneous detection of five common adulterants in raw cow milk by using attenuated total reflectance (ATR) mid infrared spectroscopy and multivariate supervised classification (partial least squares discrimination analysis - PLSDA). The method was able to detect the presence of the adulterants water, starch, sodium citrate, formaldehyde and sucrose in milk samples containing from one up to five of these analytes, in the range of 0.5-10% w/v. A multivariate qualitative validation was performed, estimating specific figures of merit, such as false positive and false negative rates, selectivity, specificity and efficiency rates, accordance and concordance. The proposed method does not need any sample pretreatment, requires a small amount of sample (30 µL), is fast and simple, being suitable for the control of raw milk in a dairy industry or for the quality inspection of commercialized milk.

A proton nuclear magnetic resonance-based metabonomics study of metabolic profiling in immunoglobulin a nephropathy

OBJECTIVES: Immunoglobulin A nephropathy is the most common cause of chronic renal failure among primary glomerulonephritis patients. The ability to diagnose immunoglobulin A nephropathy remains poor. However, renal biopsy is an inconvenient, invasive, and painful examination, and no reliable biomarkers have been developed for use in routine patient evaluations. The aims of the present study were to identify immunoglobulin A nephropathy patients, to identify useful biomarkers of immunoglobulin A nephropathy and to establish a human immunoglobulin A nephropathy metabolic profile. METHODS: Serum samples were collected from immunoglobulin A nephropathy patients who were not using immunosuppressants. A pilot study was undertaken to determine disease-specific metabolite biomarker profiles in three groups: healthy controls (N = 23), low-risk patients in whom immunoglobulin A nephropathy was confirmed as grades I-II by renal biopsy (N = 23), and high-risk patients with nephropathies of grades IV-V (N = 12). Serum samples were analyzed using proton nuclear magnetic resonance spectroscopy and by applying multivariate pattern recognition analysis for disease classification. RESULTS: Compared with the healthy controls, both the low-risk and high-risk patients had higher levels of phenylalanine, myo-Inositol, lactate, L6 lipids ( = CH-CH2-CH = O), L5 lipids (-CH2-C = O), and L3 lipids (-CH2-CH2-C = O) as well as lower levels of β -glucose, α-glucose, valine, tyrosine, phosphocholine, lysine, isoleucine, glycerolphosphocholine, glycine, glutamine, glutamate, alanine, acetate, 3-hydroxybutyrate, and 1-methylhistidine. CONCLUSIONS: These metabolites investigated in this study may serve as potential biomarkers of immunoglobulin A nephropathy. Point scoring of pattern recognition analysis was able to distinguish immunoglobulin A nephropathy patients from healthy controls. However, there were no obvious differences between the low-risk and high-risk groups in our research. These results offer new, sensitive and specific, noninvasive approaches that may be of great benefit to immunoglobulin A nephropathy patients by enabling earlier diagnosis.