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A series of 50 derivatives of 1,4-naphthoquinone tested against human HL-60 leukaemic cells showed activity at a wide range of concentrations. A multivariate quantitative structure-activity relationship (QSAR) study of 45 compounds was performed through principal component analysis (PCA) and partial least squares (PLS) regression. A good PLS regression model was obtained with two factors describing 60.1% of the total variance, and the selected descriptors were partial atomic charge at carbons 1 and 10 (C1 and C10) and total dipole moment (DIP). The calibration model exhibited the determination coefficient r2 = 0.78 and the standard error of calibration = 0.29. For external validation, r2 and the standard error of prediction were 0.74 and 0.32, respectively. DIP and C1 were the main descriptors for PCA, as well as for PLS, such that the pIC50 value increases when C1 increases and DIP diminishes. The selected descriptors are in accordance with the literature, once C10 and C1 are bound or close to the quinone oxygens involved in the production of radical anions (O2-∙). From the QSAR analysis, the structures of two new naphthoquinones were proposed and their estimated IC50 values were 1.42 and 1.13 µmol L-1.

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PurposeAmbulatory surgery is a major area of surgical and anesthetic practice, and preoperative clinics are being increasingly used for low-risk surgical procedures. This study investigated the impact of preoperative evaluation on perioperative events in patients undergoing cataract surgery.MethodsThis was a retrospective cohort study of 968 consecutive patients undergoing cataract surgery. Details of medical conditions, surgical, anesthetic, and postoperative information were collected from medical records. A logistic regression model was developed using propensity score adjustment for baseline characteristics.ResultsOut 968 patients included, 240 (24.7%) underwent outpatient preoperative evaluation. There were no perioperative major cardiovascular events. Hypertension occurred in 319 (33%) patients, accounting for 79.7% of all adverse events. Preoperative evaluation resulted in a lower hypertension rate after adjustment for propensity score (OR=0.6; 95% CI 0.41-0.93); no effects were observed on posterior capsule rupture and emergency visits/hospitalization within 7 days of surgery. Eighty-nine patients (9.3%) had an initial systolic pressure ≥180 mm Hg, which was not associated with higher risk of posterior capsule rupture (P=0.158) or postoperative adverse events (P=0.902). Median waiting time to surgery was 6 and 2 months for evaluated and non-evaluated patients, respectively (P<0.001).ConclusionsIn the context of low-risk surgery and no major perioperative and postoperative outcomes, it appears that outpatient preoperative evaluation has no role in reducing adverse events in cataract surgery candidates. Despite fewer hypertensive episodes observed in evaluated patients, these episodes were not associated with any medical or surgical outcomes.

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The present study aims to identify the renewable resources available in Brazil such as açai seed, coconut husks, coffee husks, rice husks, eucalyptus sawdust, grass, soy peel, bamboo, banana stems and banana stalks. To identify such renewable energy sources, samples were examined for their physical and chemical characteristics using X-ray diffraction (XRD), proximate and ultimate analyses, thermogravimetric analysis (TGA), calorific value determination, near-infrared (NIR) spectroscopy, UV spectroscopy, high-pH anion-exchange chromatography (HPAEC-PAD) and accelerated solvent extraction (ASE). Among the biomasses, açai and coffee exhibited higher total sugar content, 67.70% and 62.55%, respectively. Sawdust exhibited low ash, along with the highest calorific value and lignin content. The highest glucose contents were observed in bamboo (44.65%) and sawdust (38.80%). The maximum yield for the bioproducts levulinic acid (LA), formic acid (FA) and furfural were estimated; açai exhibited the highest yield of LA and FA, while coffee exhibited the best furfural yield. All of these properties indicate that the residues are potential candidates for bioenergy production.

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Near infrared (NIR) spectroscopy was used to determine the content of Klason lignin, acid-soluble lignin, total lignin, extractives, ash, acid-insoluble residue, glucose, xylose, rhamnose, galactose, arabinose, mannose and total sugars in coconut residues. The samples were analyzed at several processing stages: wet unground (WU), dried unground (DU) and dried and sieved (DS). Partial least squares models were built, and the models for the analytes exhibited R(2)>0.80, with the exceptions of rhamnose, arabinose, galactose, mannose and ash from all fractions, and the lignin content from the WU fraction, which were predicted poorly (R(2)<0.70). There were some significant differences between the models for the main lignocellulosic components at the various stages of biomass. These results proved that NIR spectroscopy is useful for analysis at biorefineries, and it can be used as a faster and more economical alternative to the standard methods.

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Banana (stalk, leaf, rhizome, rachis and stem) and coffee (leaf and husks) residues are promising feedstock for fuel and chemical production. In this work we show the potential of near-infrared spectroscopy (NIR) and multivariate analysis to replace reference methods in the characterization of some constituents of coffee and banana residues. The evaluated parameters were Klason lignin (KL), acid soluble lignin (ASL), total lignin (TL), extractives, moisture, ash and acid insoluble residue (AIR) contents of 104 banana residues (B) and 102 coffee (C) residues from Brazil. PLS models were built for banana (B), coffee (C) and pooled samples (B+C). The precision of NIR methodology was better (p<0.05) than the reference method for almost all the parameters, being worse for moisture. With the exception of ash (B and C) and ASL (C) content, which was predicted poorly (R(2)<0.80), the models for all the analytes exhibited R(2)>0.80. The range error ratios varied from 4.5 to 16.0. Based on the results of external validation, the statistical tests and figures of merit, NIR spectroscopy proved to be useful for chemical prediction of banana and coffee residues and can be used as a faster and more economical alternative to the standard methodologies.

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In this work, soft modeling based on chemometric analyses of coffee beverage sensory data and the chromatographic profiles of volatile roasted coffee compounds is proposed to predict the scores of acidity, bitterness, flavor, cleanliness, body, and overall quality of the coffee beverage. A partial least squares (PLS) regression method was used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the compounds for the regression model of each sensory attribute in order to take only significant chromatographic peaks into account. The prediction errors of these models, using 4 or 5 latent variables, were equal to 0.28, 0.33, 0.35, 0.33, 0.34 and 0.41, for each of the attributes and compatible with the errors of the mean scores of the experts. Thus, the results proved the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of Brazilian Arabica coffee.

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High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

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Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.

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High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

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There are indications in the literature that delaying the period between ovarian superestimulation and ovum pick up (OPU) would induce follicles to a condition of initial atresia, which could be beneficial to oocyte development. In this work, we compared three protocols for OPU and in vitro production (IVP) of embryos, in Nellore cattle. Nellore cows (n = 18) were randomly allocated in three groups: Group 1 (OPU), Group 2 [Follicle stimulating hormone (FSH) and OPU] and Group 3 (FSH deprivation and OPU). Three OPUs were performed, and the animals were switched to a different group each time (crossover), in such a way that at the end of the experiment all cows received the 3 protocols. At random stage of the oestrous cycle (D-2), all follicles ≥ 6 mm were aspirated to induce a new follicular wave 2 days afterwards (D0). In Group 1, OPU was performed on D2 and oocytes were processed to IVP. In Group 2, starting on D0, cows were superstimulated (FSH, Folltropin(®), 30 mg administered daily, i.m., during three consecutive days, total dose = 180 mg), and 6 h after the last FSH dose, they received exogenous luteinizing hormone (LH) (12.5 mg, i.m., Lutropin(®), D3). The OPU was performed 6 h after LH administration, i.e. 12 h after the last dose of FSH. Animals in Group 3 received the same treatment as those in Group 2, except that LH was administered 42 h after the last dose of FSH, and OPU occurred 6 h later. Therefore, in this group, follicles were deprived of FSH at 48 h. Both cleavage and blastocyst rates were similar (p > 0.05, anova) among oocytes from Groups 1, 2 and 3, respectively: 77.4% (144/185) and 42.70% (79/185); 75.54% (105/139) and 31.65% (44/139); 63.52% (101/159) and 33.33% (53/159). However, hatched blastocyst rate was higher (p < 0.01) in Group 1 (30.27%, 56/185) when compared with Group 2 (11.51%, 16/139) or 3 (15.72%, 25/159). It is concluded that, contrary to previous work on European breeds (Bos taurus), ovarian superstimulation associated with deprivation of FSH and OPU (Group 3) did not increase IVP of Nellore embryos (Bos indicus). On the contrary, the highest hatched blastocyst rates were observed in oocytes from non-superstimulated cows.

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Volatile compounds in fifty-eight Arabica roasted coffee samples from Brazil were analyzed by SPME-GC-FID and SPME-GC-MS, and the results were compared with those from sensory evaluation. The main purpose was to investigate the relationships between the volatile compounds from roasted coffees and certain sensory attributes, including body, flavor, cleanliness and overall quality. Calibration models for each sensory attribute based on chromatographic profiles were developed by using partial least squares (PLS) regression. Discrimination of samples with different overall qualities was done by using partial least squares-discriminant analysis (PLS-DA). The alignment of chromatograms was performed by the correlation optimized warping (COW) algorithm. Selection of peaks for each regression model was performed by applying the ordered predictors selection (OPS) algorithm in order to take into account only significant compounds. The results provided by the calibration models are promising and demonstrate the feasibility of using this methodology in on-line or routine applications to predict the sensory quality of unknown Brazilian Arabica coffee samples. According to the PLS-DA on chromatographic profiles of different quality samples, compounds 3-methypropanal, 2-methylfuran, furfural, furfuryl formate, 5-methyl-2-furancarboxyaldehyde, 4-ethylguaiacol, 3-methylthiophene, 2-furanmethanol acetate, 2-ethyl-3,6-dimethylpyrazine, 1-(2-furanyl)-2-butanone and three others not identified compounds can be considered as possible markers for the coffee beverage overall quality.

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Multidrug resistance activities pECr50 of diverse strains of pathogenic fungus Penicillium digitatum against seven toxicants were studied by Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Fungal growth data (radii, circumferences, surface areas of fungal colonies, radius differences and ratios) in absence and presence of toxicants were used to derive eight new descriptors for 35 fungal strains. This data set was studied by PCA and HCA, and was correlated with the genome descriptor PCR for expression of gene CYP51 by Partial Least Squares (PLS) regression. Both analyses of pECr50 data and of fungal growth data have identified baseline resistance character, origin and target fruits of the fungal strains. In addition, the analysis of fungal growth data shows that fungal growth morphology is multivariate by nature, meaning that experimental data can be explored more intensely than in usual practice. Fungal growth is directly related to the production of enzyme P45014DM as the main resistance mechanism of P. digitatum against demethylation inhibitors. This is visible from a parsimonious PLS model (two principal components, Q2 = 0.985, R2 = 0.991, SEV = 0.028), validated with eight strains in the external validation set (Q2ev = 0.982, R2ev = 0.990, SECev = 0.025). Chemometric methods in exploring bioassay data are promising approaches to obtain useful information on fungal resistance and to apply these findings in practice.

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Many studies have found clinical and metabolic alterations in subclinical hypothyroidism, however, there are disagreements about the benefits of levothyroxine therapy. The objective of the present study was to analyze the effects of 6 months of treatment on the lipid profile of patients with subclinical hypothyroidism. A randomized double blind, placebo-controlled clinical assay was conducted. Sixty patients were enrolled in stratified random allocation by TSH levels that generated similar groups in average: free thyroxine levels, lipid levels, age, clinical score, and sedentary. At 6 months, 18 patients in the levothyroxine and 20 in the placebo group were reevaluated and a fall in all atherogenic lipid variables was observed with treatment. The TC and LDL-c variations (-22.6+/-37.2 and -18.5+/-34.6 mg/dl, respectively) in the group that received LT4 were statistically different (p=0.023 and p=0.012) from those occurring in the placebo group (+7.3+/-37.1 and +14.7+/-40.6 mg/dl). Baseline characteristics associated with better improvement in the levels of TC and LDL-c were the presence of TPO-Ab, TSH levels >8.0 microUI/ml, Body Mass Index >or=25 kg/m2, and the presence of menopause. We concluded that treatment with dose-adjusted levothyroxine reduced atherogenic lipid levels in some patients. Further studies to determine the effects of LT4 replacement in specific subgroups of SH patients are still necessary, especially in patients with TSH <8.0 microUI/ml.

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Cassava starches are widely used in the production of biodegradable films, but their resistance to humidity migration is very low. In this work, commercial cassava starch films were studied and classified according to their physicochemical properties. A nondestructive method for water vapor permeability determination, which combines with infrared spectroscopy and multivariate calibration, is also presented. The following commercial cassava starches were studied: pregelatinized (amidomax 3550), carboxymethylated starch (CMA) of low and high viscosities, and esterified starches. To make the films, 2 different starch concentrations were evaluated, consisting of water suspensions with 3% and 5% starch. The filmogenic solutions were dried and characterized for their thickness, grammage, water vapor permeability, water activity, tensile strength (deformation force), water solubility, and puncture strength (deformation). The minimum thicknesses were 0.5 to 0.6 mm in pregelatinized starch films. The results were treated by means of the following chemometric methods: principal component analysis (PCA) and partial least squares (PLS) regression. PCA analysis on the physicochemical properties of the films showed that the differences in concentration of the dried material (3% and 5% starch) and also in the type of starch modification were mainly related to the following properties: permeability, solubility, and thickness. IR spectra collected in the region of 4000 to 600 cm(-1) were used to build a PLS model with good predictive power for water vapor permeability determination, with mean relative errors of 10.0% for cross-validation and 7.8% for the prediction set.

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The surface topography of human hair, as defined by the outer layer of cellular sheets, termed cuticles, largely determines the cosmetic properties of the hair. The condition of the cuticles is of great cosmetic importance, but also has the potential to aid diagnosis in the medical and forensic sciences. Atomic force microscopy (AFM) has been demonstrated to offer unique advantages for analysis of the hair surface, mainly due to the high image resolution and the ease of sample preparation. This article presents an algorithm for the automatic analysis of AFM images of human hair. The cuticular structure is characterized using a series of descriptors, such as step height, tilt angle and cuticle density, allowing quantitative analysis and comparison of different images. The usefulness of this approach is demonstrated by a classification study. Thirty-eight AFM images were measured, consisting of hair samples from (a) untreated and bleached hair samples, and (b) the root and distal ends of the hair fibre. The multivariate classification technique partial least squares discriminant analysis is used to test the ability of the algorithm to characterize the images according to the properties of the hair samples. Most of the images (86%) were found to be classified correctly.

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Three hundred and nine carbon-carbon, carbon-nitrogen, and carbon-oxygen pi-bond lengths in high precision crystal structures of 31 purine and pyrimidine nucleobases were related to the Pauling pi-bond order, its analogues corrected to crystal packing effects, the numbers of non-hydrogen atoms around the bond, and the sum of atomic numbers of the bond atoms. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) demonstrated that the bond lengths in the nucleobases are three-dimensional phenomenon, characterized by nine distinct classes of bonds. Bond lengths predicted by Linear Regression models, Pauling Harmonic Potential Curves, Multiple Linear Regression, Principal Component, and Partial Least Squares Regression were compared to those calculated by molecular mechanics, semiempirical, and ab initio methods using PCA-HCA procedure on the calculated bond lengths, statistical parameters, and structural aromaticity indices. Incorporation of crystal packing effects into bond orders makes multivariate models to be competitive to semiempirical results, while further improvement of quantum chemical calculations can be achieved by geometry optimization of molecular clusters.

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Two hundred and twenty-three aromatic carbon-carbon bond lengths in high precision crystal structures containing 22 planar condensed benzenoid polycyclic aromatic hydrocarbons (PB-PAHs) were related to the Pauling pi-bond order, its analogue corrected to crystal packing effects, the number of hexagonal rings around the bond, and the numbers of carbons atoms around the bond at topological distance one and two. Principal Component Analysis (PCA) showed that the bond lengths in PB-PAHs are at least two-dimensional phenomenon, with well pronounced classification into 12 types of bonds, as confirmed with Hierachical Cluster Analysis (HCA). Consequently, Multiple Linear Regression (MLR) and Partial Least Squares (PLS) models were superior to univariate models, reducing the degeneration of the data set and improving the estimation of Julg's structural aromaticity index. The approximate regression models based on topological descriptors only were built for fast and easy prediction of bond lengths and bond orders in PB-PAHs.

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Radiofrequency ablation is successful in most supraventricular tachyarrhythmias and the first results in percutaneous applications in atrial fibrillation have been published. At present, all the surgical techniques for the treatment of atrial fibrillation use extensive atrial incisions. A case report is presented of a 67 year old woman with chronic atrial fibrillation and rheumatic mitral and tricuspid valve disease. At surgery, pulmonary vein isolation was performed using a continuous linear ablation around the pulmonary veins, instead of using extensive atrial incisions. Concomitantly, a mitral valve replacement and tricuspid valve annuloplasty were performed. Five months after surgery the patient remains with a stable sinus rhythm surgery and in functional class I (NYHA).

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This work deals with 48 substances composed exclusively of unsubstituted six-membered fused aromatic rings. In the first step, physicochemical properties which are relevant in environmental studies such as the boiling temperature (Tb), the retention index (RI), n-octanol/water partition coefficient (KOW) and solubility (S) are related with a series of electronic, geometric and topological descriptors. Among them are: electron affinity, the difference between electron affinity and ionization potential (GAP), Wiener, and connectivity indexes, volume, surface area, length-to-breadth ratio and enthalpy of formation. In a second step, these properties were incorporated into the descriptor matrix to build several quantitative structure-property relationships and to obtain prediction rules for the log KOC, log KOA, bioconcentration factor (BCF) and Henry's law constant (H). Finally, the photo-induced toxicity of freshwater organism Daphinia-Magna is modeled using the following transformed electronic descriptors: electron affinity, ionization potential and Gap.

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