Claw lesion status in Brazilian commercial sow herds from 2013 to 2023.

Introduction: Claw lesions significantly contribute to lameness, greatly affecting sow welfare. This study investigated different factors that would impact the severity of claw lesions in the sows of Brazilian commercial herds. Methods: A total of 129 herds (n = 12,364 sows) were included in the study. Herds were in the Midwest, Southeast, or South regions of Brazil. Inventory sizes were stratified into 250-810 sows, 811-1,300 sows, 1,301-3,000 sows, and 3,001-10,000 sows. Herds belonged to Cooperative (Coop), Integrator, or Independent structures. The herd management was conducted either maintaining breeds from stock on-site (internal), or through purchase of commercially available genetics (external). Herds adopted either individual crates or group housing during gestation. Within each farm, one randomly selected group of sows was scored by the same evaluator (two independent experts evaluated a total of 129 herds) from 0 (none) to 3 (severe) for heel overgrowth and erosion (HOE), heel-sole crack (HSC), separation along the white line (WL), horizontal (CHW) and vertical (CVW) wall cracks, and overgrown toes (T), or dewclaws (DC) in the hind legs after parturition. The study assessed differences and similarities between herds using Principal Component Analysis (PCA) and Hierarchical Agglomerative Clustering (HAC) analysis. The effects of factors (i.e., production structure, management, housing during gestation, and region) were assessed using the partial least squares method (PLS). Results and discussion: Heel overgrowth and erosion had the highest prevalence, followed by WL and CHW, while the lowest scores were observed for T, DC, and CVW. Herds were grouped in three clusters (i.e., C1, C2, and C3). Heel overgrowth and erosion, HSC, WL, CHW, CVW, and T were decreased by 17, 25, 11, 25, 21, and 17%, respectively, in C3 compared to C1 and 2 combined. Independent structure increased the L-Index in all three clusters. Furthermore, individual housing increased the L-Index regardless of the cluster. The results suggest that shifting toward larger, more technologically advanced herds could potentially benefit claw health. Additionally, adopting group gestation housing appears to mitigate the adverse effects on claw health, although further validation is necessary, as Brazil has only recently transitioned from individual housing practices.

Colony identity clues for Syntermes grandis (Blattodea: Termitidae) individuals using near-infrared spectroscopy and PLS-DA approach.

Termites are social insects with high species diversity in tropical ecosystems. Multivariate analysis with near-infrared spectroscopy (NIRS) and data interpretation can separate social insects belonging to different colonies of the same species. The objective of this study was to propose the use of discriminant analysis by partial least squares (PLS-DA) combined with NIRS to identify the colonial origin of the Syntermes grandis (Rambur, 1842) (Blattodea: Termitidae) in 2 castes. Six ground S. grandis colonies were identified and mapped; 30 workers and 30 soldier termites in each colony were submitted to spectral measurement with NIRS. PLS-DA applied to the termites' spectral absorbance was used to detect a spectral pattern per S. grandis colony by caste. PLS-DA regression with NIRS proved to be an approach with 99.9% accuracy for identifying the colonial origin of S. grandis workers and 98.3% for soldiers. The methodology showed the importance of qualitatively characterizing the colonial phenotypic response of this species. NIRS is a high-precision approach to identifying the colony origin of S. grandis workers and soldiers. The PLS-DA can be used to design ecological field studies to identify colony territorial competition and foraging behavior of subterranean termite species.

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy.

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms-such as PLS, VIP, iPLS-VIP, GA, RF, and CARS-the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s-PLS were most significant for SWIR1 and SWIR2, while i-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Preference Drivers for Blackberry Nectar (Rubus spp., Rosaceae) with Different Sweeteners.

This study determined the dynamic sensory profile and consumer acceptance of blackberry nectar with different sweeteners. The ideal scale was used to determine the ideal sweetness of the sucrose and the magnitude estimation method for the equivalent sweetness of the sweeteners. The sensory profile was determined by time-intensity analyses with trained panelists. This study determined the dynamic sensory profile and consumer acceptance of blackberry nectar with different sweeteners. First, to determine the concentration of sucrose to promote optimal sweetness in blackberry nectar, a study was carried out by consumers, who used an unstructured 9 cm "Ideal Scale", ranging from the extreme left as "extremely less sweet than ideal" to the extreme right as "extremely sweet than ideal", with the center of the scale being the ideal sweetness point. Then, the magnitude estimation method was applied to determine the concentration of each sweetener studied in order to obtain the same sensation of ideal sweetness in the blackberry nectar. The sensory profile of blackberry nectar in the same equi-sweetness was determined by time-intensity analysis with trained assessors and CATA (Check-All-that-Apply) with consumers. According to our results and the opinion of the involved consumers, the optimal sucrose concentration in blackberry nectar was 9.3%, and the sweetener concentrations equivalent to sucrose were 0.015% of sucralose, 0.052% of aspartame and 0.09% of stevia with different rebaudioside A concentrations. Time intensity and overall liking data were statistically analyzed by partial least squares regression (PLSR), thus generating the temporal preference drivers for blackberry nectar. The results showed that the sucralose and tasteva sweeteners have a temporal profile closer to sucrose, being characterized by a lower intensity and duration of sweet and bitter taste, with a positive impact on consumer acceptance. Concomitant results were found by the CATA analysis, indicating that the attributes of blackberry aroma, blackberry flavor, sweet taste, and brightness also have a positive impact and stand out in the samples with sucrose, sucralose, and tasteva. The samples sweetened with stevia were characterized by a greater intensity of bitter taste and the presence of a sweet and bitter aftertaste, with a negative impact on acceptance. The different rebaudioside A concentrations in stevia (78%, 92%, and 97%) did not interfere with consumer acceptance.

Application of hydride generation - microwave plasma - atomic emission spectrometry and partial least squares regression for the determination of antimony directly in water and in PET after alkaline methanolysis.

Antimony is present in different types of plastics as a catalyzer residue and/or as a synergistic fire retardant; relatively high concentrations of this element reported in polyethylene terephthalate (PET) bottles and wrappers as well as its migration to the edible products or to different environment compartments are of concern. In this work, Sb determination is such products had been undertaken using hydride generation - microwave plasma - atomic emission spectrometry. To avoid harsh conditions typically reported for the digestion of PET, alkaline methanolysis was introduced whereas water samples were analyzed directly. Another original approach was to perform quantification by partial least squares regression (PLS1), taking spectral data from 2-nm range that comprised two emission lines (217.581 nm and less intense 217.919 nm). For PET, the calibration solutions contained Sb-free digest and covered the Sb concentration range 80-230 µg L-1. For the analysis of water, the calibration range was 0.5-10 µg L-1 and aqueous standard solutions were used. PLS1 provided reliable prediction, eliminating spectral interferences detected in the presence of PET digests and compensating for the spectral changes observed at low Sb concentrations. After standard addition to the real-world samples, the percentage recoveries were in the range 93.8-99.3% and 68-102% for PET and for bottled water, respectively. The method quantification limit for PET was 10 mg kg-1 and for water it corresponded to 0.20 µg L-1. The concentrations of Sb found in the analyzed samples were: 154-279 mg kg-1 for PET bottles and <0.5-5.30 µg L-1 for water.

Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients.

The accurate recognition of activities is fundamental for following up on the health progress of people with dementia (PwD), thereby supporting subsequent diagnosis and treatments. When monitoring the activities of daily living (ADLs), it is feasible to detect behaviour patterns, parse out the disease evolution, and consequently provide effective and timely assistance. However, this task is affected by uncertainties derived from the differences in smart home configurations and the way in which each person undertakes the ADLs. One adjacent pathway is to train a supervised classification algorithm using large-sized datasets; nonetheless, obtaining real-world data is costly and characterized by a challenging recruiting research process. The resulting activity data is then small and may not capture each person's intrinsic properties. Simulation approaches have risen as an alternative efficient choice, but synthetic data can be significantly dissimilar compared to real data. Hence, this paper proposes the application of Partial Least Squares Regression (PLSR) to approximate the real activity duration of various ADLs based on synthetic observations. First, the real activity duration of each ADL is initially contrasted with the one derived from an intelligent environment simulator. Following this, different PLSR models were evaluated for estimating real activity duration based on synthetic variables. A case study including eight ADLs was considered to validate the proposed approach. The results revealed that simulated and real observations are significantly different in some ADLs (p-value < 0.05), nevertheless synthetic variables can be further modified to predict the real activity duration with high accuracy (R2(pred)>90%).

Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics.

This work presents the simultaneous quantification of four non-steroidal anti-inflammatory drugs (NSAIDs), paracetamol, diclofenac, naproxen, and aspirin, in mixture solutions, by a laboratory-made working electrode based on carbon paste modified with multi-wall carbon nanotubes (MWCNT-CPE) and Differential Pulse Voltammetry (DPV). Preliminary electrochemical analysis was performed using cyclic voltammetry, and the sensor morphology was studied by scanning electronic microscopy and electrochemical impedance spectroscopy. The sample set ranging from 0.5 to 80 µmol L-1 was prepared using a complete factorial design (34) and considering some interferent species such as ascorbic acid, glucose, and sodium dodecyl sulfate to build the response model and an external randomly subset of samples within the experimental domain. A data compression strategy based on discrete wavelet transform was applied to handle voltammograms' complexity and high dimensionality. Afterward, Partial Least Square Regression (PLS) and Artificial Neural Networks (ANN) predicted the drug concentrations in the mixtures. PLS-adjusted models (n = 12) successfully predicted the concentration of paracetamol and diclofenac, achieving correlation values of R ≥ 0.9 (testing set). Meanwhile, the ANN model (four layers) obtained good prediction results, exhibiting R ≥ 0.968 for the four analyzed drugs (testing stage). Thus, an MWCNT-CPE electrode can be successfully used as a potential sensor for voltammetric determination and NSAID analysis.

A lettuce moisture detection method based on terahertz time-domain spectroscopy / Um método de detecção de umidade de alface baseado em THz-TDS

For non-destructive detection of water stress in lettuce, terahertz time-domain spectroscopy (THz-TDS) was used to quantitatively analyze water content in lettuce. Four gradient lettuce water contents were used . Spectral data of lettuce were collected by a THz-TDS system, and denoised using the S-G derivative, Savitzky-Golay (S-G) smoothing and normalization filtering. The fitting effect of the pretreatment method was better than that of regression fitting, and the S-G derivative fitting effect was obtained. Then a calibration set and a verification set were divided by the Kennan-Stone algorithm, sample set partitioning based on joint X-Y distance (SPXY) algorithm, and the random sampling (RS) algorithm, and the parameters of RS were optimized by regression fitting. The stability competitive adaptive reweighted sampling, iteratively retained information variables and interval combination optimization were used to select characteristic wavelengths, and then continuous projection was used on basis of the three algorithms above. After the successive projection algorithm was re-screened, partial least squares regression was used into modeling. The regression coefficients R²c and RMSEC reach 0.8962 and 412.5% respectively, and R²p and RMSEP of the verification set are 0.8757 and 528.9% respectively.

Para a detecção não destrutiva de estresse hídrico da alface, espectroscopia no domínio do tempo em terahertz (THz-TDS) foi usada para analisar quantitativamente o conteúdo de água na alface. Quatro gradientes de conteúdo de água de alface foram usados. Os dados espectrais da alface foram coletados por um sistema THz-TDS e denoised usando o derivado S-G, Savitzky-Golay (S-G) suavização e filtragem de normalização. O efeito de ajuste do método de pré-tratamento foi melhor do que o do ajuste de regressão, e o efeito de ajuste da derivada S-G foi obtido. Em seguida, um conjunto de calibração e um conjunto de verificação foram divididos pelo algoritmo Kennan-Stone, particionamento do conjunto de amostra com base no algoritmo de distância X-Y conjunta (SPXY) e o algoritmo de amostragem aleatória (RS), e os parâmetros de RS foram otimizados por ajuste de regressão. A amostragem adaptativa de estabilidade competitiva reponderada, variáveis de informação retidas iterativamente e otimização de combinação de intervalo foram usadas para selecionar comprimentos de onda característicos e, em seguida, a projeção contínua foi usada com base nos três algoritmos acima. Depois que o algoritmo de projeção sucessivo foi reprojetado, a regressão de mínimos quadrados parcial foi usada na modelagem. Os coeficientes de regressão R2 e erro quadrático médio (RMSEP) atingem 0,8962 e 412,50%, respectivamente, e R2 e RMSEP do conjunto de verificação são 0,8757 e 528,93%, respectivamente.

Discrimination of Genetically Very Close Accessions of Sweet Orange (Citrus sinensis L. Osbeck) by Laser-Induced Breakdown Spectroscopy (LIBS).

The correct recognition of sweet orange (Citrus sinensis L. Osbeck) variety accessions at the nursery stage of growth is a challenge for the productive sector as they do not show any difference in phenotype traits. Furthermore, there is no DNA marker able to distinguish orange accessions within a variety due to their narrow genetic trace. As different combinations of canopy and rootstock affect the uptake of elements from soil, each accession features a typical elemental concentration in the leaves. Thus, the main aim of this work was to analyze two sets of ten different accessions of very close genetic characters of three varieties of fresh citrus leaves at the nursery stage of growth by measuring the differences in elemental concentration by laser-induced breakdown spectroscopy (LIBS). The accessions were discriminated by both principal component analysis (PCA) and a classifier based on the combination of classification via regression (CVR) and partial least square regression (PLSR) models, which used the elemental concentrations measured by LIBS as input data. A correct classification of 95.1% and 80.96% was achieved, respectively, for set 1 and set 2. These results showed that LIBS is a valuable technique to discriminate among citrus accessions, which can be applied in the productive sector as an excellent cost-benefit tool in citrus breeding programs.

FT-NIR spectroscopy and RP-HPLC combined with multivariate analysis reveals differences in plant cell suspension cultures of Thevetia peruviana treated with salicylic acid and methyl jasmonate.

Plant cell suspension culture of T. peruviana is a feasible biotechnological platform for the production of secondary metabolites with anti-proliferative/cytotoxic activity, as phenolic compounds (PC); however, different in in vitro growth conditions may affect the production, demanding strategies to increase the metabolite biosynthesis, as well as the development of sensitive and rapid analytical methods for metabolite monitoring. The Fourier transform near-infrared (FT-NIR) spectroscopy and Reversed-phase high-performance liquid chromatography (RP-HPLC) combined with Multivariate analysis (MVA) were used to detect significant differences in the PC production in cultures treated with two elicitors. The results suggest that the FT-NIR-MVA is useful for discriminating samples according to the treatment, showed significant influence of the PC signal. RP-HPLC-MVA showed that the elicitor effect occurs at 72 h post-elicitation. Detection of dihydroquercetin (maximum concentration = 12.59 mg/L), a flavonoid with anti-cancer properties, is highlighted. Future studies will be aimed at scaling this culture to increase the productivity of dihydroquercetin.

Simultaneous metal determination in artisanal cachaça by using voltammetry and multivariate calibration.

In this study, square wave anodic stripping voltammetry using two different types of electrodes (carbon nanotube electrode and graphite electrode) was combined with chemometric methods - partial least squares (PLS) and artificial neural networks (ANN) for determining copper, zinc, cadmium and lead in cachaça. The objectives were comparison of methods developed and the verification of the quality of artisanal cachaças in terms of metal content. For the development of the methodology, inductively coupled plasma optical emission spectrometry (ICP OES) was used as reference technique. The performance of multivariate models obtained was evaluated by the coefficient of determination (R2) and root mean square error of prediction (RMSEP). F test was utilized for comparing methods at confidence level of 95%. Better results were observed by using carbon nanotube electrode regardless of the multivariate method proposed. The methodology is simple, fast, and inexpensive and it can be used in quality control laboratories.

Identifying the factors impacting on farmers' intention to adopt animal friendly practices.

Brazil, one of the leading countries in livestock production, has not yet developed legislation considering animal welfare issues and most of the actions to improve farm animal welfare (FAW) standards are developed by livestock industries and government focusing in meet the demands of exporting countries. Such actions resulted in FAW protocols and manuals for adoption of best management practices. In this context, farmers' decisions are of particular importance as they may comply with current FAW protocols or even decide to provide better FAW standards than required. A present example of farmers' decisions to provide better FAW standards than required by FAW protocols is in the adoption of environmental enrichment in pig farming. This practice is widely available to pig farmers, however, anecdotal evidence shows that the adoption rate is low. This study uses the theory of planned behavior (TPB) as a framework to identify the socio-psychological factors that influence pig farmers' intention to adopt environmental enrichment on their farms. The TPB hypothesizes that intention is determined by three psychological constructs: attitude, subjective norms, and perceived behavioral control. These three constructs are derived from behavioral, normative, and control beliefs, respectively. Self-identity was added as an additional construct to explain intention. A survey with 185 farmers was conducted. We used Partial-Least-Square Structural Equation Modeling (PLS-SEM) to identify the impact of attitude, subjective norms, perceived behavioral control, and self-identity on farmers' intention to adopt environmental enrichment on their farms. We used MIMIC models to identify the most important beliefs underlying farmers' intention to adopt environmental enrichment in their farms. Results show that the intention of farmers to adopt was mainly determined by their positive perceptions about their own capability to adopt environmental enrichment (perceived behavioral control), followed by their perceptions about the social pressure to adopt it (subjective norms), their positive evaluations of adoption (attitude), and self-identity. The most important behavioral beliefs were 'increase productivity', and 'decrease animals stress'. The most important normative beliefs were 'family', 'neighbor farmers', 'pig buyers', and 'experts'. The most important control belief was 'receive bonus when selling pigs'. These results revealed important implications to design public and private interventions aimed to stimulate the adoption of animal friendly practices.

Evaluation of efficiency and selectivity in the sorption process assisted by chemometric approaches: Removal of emerging contaminants from water.

This paper describes, by the first time, a chemometric approach that combines a simple set of the UV-Vis spectra and partial least square regression (PLSR) for measuring the removal of five pharmaceuticals present in simulated hospital effluents by sorption using activated carbon. The use of multivariate calibration allowed the quantification of the remaining concentrations of the studied drugs present in a complex mixture with high accuracy, avoiding the need for the use of sophisticated methodologies based on chromatography. Isothermal sorption studies were performed on single-component solutions containing amoxicillin, paracetamol, propranolol, sodium diclofenac, or tetracycline as well as on a solution containing a mixture of all these 5 compounds. The isotherm data obtained were fitted to the Langmuir, Freundlich and Liu models. It was observed that for each pharmaceutical, the maximum sorption capacity of the activated carbon was higher for the single component than in the mixture. It was observed that the removal of paracetamol, propranolol, and tetracycline, the removal was complete (100%) and for amoxicillin and sodium diclofenac it was at least 92.71 ±â€¯3.15% and 91.82 ±â€¯0.95% respectively, indicating that the avocado seed activated carbon is an adsorbent with high sorption capacity that can remove five pharmaceuticals from simulated hospital effluents.

Infrared spectroscopy and multivariate methods as a tool for identification and quantification of fuels and lubricant oils in soil.

Environmental contamination caused by leakage of fuels and lubricant oils at gas stations is of great concern due to the presence of carcinogenic compounds in the composition of gasoline, diesel, and mineral lubricant oils. Chromatographic methods or non-selective infrared methods are usually used to assess soil contamination, which makes environmental monitoring costly or not appropriate. In this perspective, the present work proposes a methodology to identify the type of contaminant (gasoline, diesel, or lubricant oil) and, subsequently, to quantify the contaminant concentration using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and multivariate methods. Firstly, gasoline, diesel, and lubricating oil samples were acquired from gas stations and analyzed by gas chromatography to determine the total petroleum hydrocarbon (TPH) fractions (gasoline range organics, diesel range organics, and oil range organics). Then, solutions of these contaminants in hexane were prepared in the concentration range of about 5-10,000 mg kg-1. The infrared spectra of the solutions were obtained and used for the development of the pattern recognition model and the calibration models. The partial least square discriminant analysis (PLS-DA) model could correctly classify 100% of the samples of each type of contaminant and presented selectivity equal to 1.00, which provides a suitable method for the identification of the source of contamination. The PLS regression models were developed using multivariate filters, such as orthogonal signal correction (OSC) and general least square weighting (GLSW), and selection variable by genetic algorithm (GA). The validation of the models resulted in correlation coefficients above 0.96 and root-mean-square error of prediction values below the maximum permissible contamination limit (1000 mg kg-1). The methodology was validated through the addition of fuels and lubricating oil in soil samples and quantification of the TPH fractions through the developed models after the extraction of the analytes by the EPA 3550 method adapted by the authors. The recovery percentage of the analytes was within the acceptance limits of ASTM D7678 (70-130%), except for one sample (69% of recovery). Therefore, the methodology proposed here provides faster and less costly analyses than the chromatographic methods and it is adequate for the environmental monitoring of soil contamination by gas stations.

Association Between Short-Term Systolic Blood Pressure Variability and Carotid Intima-Media Thickness in ELSA-Brasil Baseline.

BACKGROUND: Blood pressure (BP) is associated with carotid intima-media thickness (CIMT), but few studies have explored the association between BP variability and CIMT. We aimed to investigate this association in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) baseline. METHODS: We analyzed data from 7,215 participants (56.0% women) without overt cardiovascular disease (CVD) or antihypertensive use. We included 10 BP readings in varying positions during a 6-hour visit. We defined BP variability as the SD of these readings. We performed a 2-step analysis. We first linearly regressed the CIMT values on main and all-order interaction effects of the variables age, sex, body mass index, race, diabetes diagnosis, dyslipidemia diagnosis, family history of premature CVD, smoking status, and ELSA-Brasil site, and calculated the residuals (residual CIMT). We used partial least square path analysis to investigate whether residual CIMT was associated with BP central tendency and BP variability. RESULTS: Systolic BP (SBP) variability was significantly associated with residual CIMT in models including the entire sample (path coefficient [PC]: 0.046; P < 0.001), and in women (PC: 0.046; P = 0.007) but not in men (PC: 0.037; P = 0.09). This loss of significance was probably due to the smaller subsample size, as PCs were not significantly different according to sex. CONCLUSIONS: We found a small but significant association between SBP variability and CIMT values. This was additive to the association between SBP central tendency and CIMT values, supporting a role for high short-term SBP variability in atherosclerosis.

Study of advanced rheumatoid arthritis

INTRODUCTION: Rheumatic diseases are considered public health problems affecting millions of people worldwide resulting in high and rising health-care costs. In this work, Fourier Transform Infrared spectroscopy associated to Partial Least Square regression (PLS) analysis was used to diagnose rheumatoid arthritis (RA) from human serum. METHODS: The sera of 94 individuals were collected, which included 47 from rheumatic patients and 47 from healthy individuals. The results from PLS analysis were compared to standard clinical trials such as anti-citrullinated peptide antibodies, C- Reactive protein, and Rheumatoid factor. RESULTS: For clinical diagnosis, the anti-citrullinated peptide antibodies of second generation proved to be the most specific to diagnosis rheumatoid arthritis even after long periods of drug therapy. CONCLUSIONS: The qualitative PLS analysis has shown higher values of IgM of RA group, but the difference was very small. The RA patients were under medication, which interfered with the IgM concentration.

Determination of amorphous/rubbery states in freeze-dried prebiotic sugars using a combined approach of near-infrared spectroscopy and multivariate analysis.

Galacto-oligosaccharides (GOS) and lactulose are well-recognized prebiotics widely used in functional food and pharmaceutical products, but there is still a lack of knowledge regarding their physical-chemical properties. In this study, a physical-chemical approach on two GOS of different composition (GOS Cup Oligo H-70® and GOS Biotempo) and lactulose was assessed. Mid infrared and Raman spectra of the freeze-dried sugars allowed their structural characterization in the amorphous state, lactulose, showing the main spectral differences. Freeze-dried sugars were then equilibrated at 4°C at relative humidity (RH) ranging from 11% to 80%. Near-infrared reflectance spectra were registered in each condition in the 900- to 1700-nm region. A principal component analysis (PCA) was performed on the three sugars equilibrated at different RH. In all the three sugars, the groups observed explained more than 95% of the variance and were related with the RH of the samples. According to the loading plots of PC1, the main differences related with RH were observed in the 1380- to 1500-nm region. As the amorphous states are very sensitive to changes in temperature and moisture content, and the moisture content is related with the parameter T-Tg (T: storage temperature; Tg: vitreous transition temperature), an effort was made to determine this parameter directly from the NIR spectra. To this aim, a partial least square model (PLS) was defined. Tg values obtained by differential scanning calorimetry (DSC) were used to calculate the T-Tg values of reference. The model was validated with an independent set of data. The mean of predicted values fitted nicely T-Tg obtained from DSC (correlation=0.966; R2=0.934), thus supporting the use of the PLS model to investigate unknown samples. The stability of amorphous sugars in foods and pharmaceuticals is of practical and economical importance because it affects different quality attributes of foods, including texture, aroma retention and shelf life. Therefore, predicting T-Tg, a parameter that is independent on the sugar investigated, directly from their NIR spectra is of utmost importance to determine the shelf life of food and food-related products and up to our knowledge has never been determined hereto.