Effect Of Particles Of Ashes Produced From Sugarcane Burning On The Respiratory System Of Rats.

The practice of burning sugarcane obtained by non-mechanized harvesting exposes workers and the people of neighboring towns to high concentrations of particulate matter (PM) that is harmful to health, and may trigger a series of cardiorespiratory diseases. The aim of this study was to analyze the chemical composition of the micro-particles coming from sugarcane burning residues and to verify the effects of this micro-particulate matter on lung and tracheal tissues. Micro-particulate matter (PM10) was obtained by dissolving filter paper containing burnt residues in NaCl solution. This material was instilled into the Wistar rats' nostrils. Histological analyses (hematoxylin and eosin - HE) of cardiac, lung and tracheal tissues were performed. Inflammatory mediators were measured in lung tissues by using ELISA. The chemical composition of the particulate material revealed a large quantity of the phthalic acid ester, high concentrations of phenolic compounds, anthracene and polycyclic aromatic hydrocarbons (PAH). Histological analysis showed a reduction in subjacent conjunctive tissue in the trachea, lung inflammation with inflammatory infiltrate formation and reduction of alveolar spaces and a significant increase (p<0.05) in the release of IL-1α, IL-1β, IL-6, and INF-γ in the group treated with PM10 when compared to the control group. We concluded that the burning sugarcane residues release many particles, which have toxic chemical compounds. The micro-particulate matter can induce alterations in the respiratory system.

Effects Of Misalignments In The Retinal Nerve Fiber Layer Thickness Measurements With Spectral Domain Optical Coherence Tomography.

Purpose. To investigate misalignments (MAs) on retinal nerve fiber layer thickness (RNFLT) measurements obtained with Cirrus(©) SD-OCT. Methods. This was a retrospective, observational, cross-sectional study. Twenty-seven healthy and 29 glaucomatous eyes of 56 individuals with one normal exam and another showing MA were included. MAs were defined as an improper alignment of vertical vessels in the en face image. MAs were classified in complete MA (CMA) and partial MA (PMA), according to their site: 1 (superior, outside the measurement ring (MR)), 2 (superior, within MR), 3 (inferior, within MR), and 4 (inferior, outside MR). We compared RNFLT measurements of aligned versus misaligned exams in all 4 sectors, in the superior area (sectors 1 + 2), inferior area (sectors 3 + 4), and within the measurement ring (sectors 2 + 3). Results. RNFLT measurements at 12 clock-hour of eyes with MAs in the superior area (sectors 1 + 2) were significantly lower than those obtained in the same eyes without MAs (P = 0.043). No significant difference was found in other areas (sectors 1 + 2 + 3 + 4, sectors 3 + 4, and sectors 2 + 3). Conclusion. SD-OCT scans with superior MAs may present lower superior RNFLT measurements compared to aligned exams.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.