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.

Convergence Of Soil Nitrogen Isotopes Across Global Climate Gradients.

Quantifying global patterns of terrestrial nitrogen (N) cycling is central to predicting future patterns of primary productivity, carbon sequestration, nutrient fluxes to aquatic systems, and climate forcing. With limited direct measures of soil N cycling at the global scale, syntheses of the (15)N:(14)N ratio of soil organic matter across climate gradients provide key insights into understanding global patterns of N cycling. In synthesizing data from over 6000 soil samples, we show strong global relationships among soil N isotopes, mean annual temperature (MAT), mean annual precipitation (MAP), and the concentrations of organic carbon and clay in soil. In both hot ecosystems and dry ecosystems, soil organic matter was more enriched in (15)N than in corresponding cold ecosystems or wet ecosystems. Below a MAT of 9.8°C, soil δ(15)N was invariant with MAT. At the global scale, soil organic C concentrations also declined with increasing MAT and decreasing MAP. After standardizing for variation among mineral soils in soil C and clay concentrations, soil δ(15)N showed no consistent trends across global climate and latitudinal gradients. Our analyses could place new constraints on interpretations of patterns of ecosystem N cycling and global budgets of gaseous N loss.

Development Of Egg Pc/cholesterol/α-tocopherol Liposomes With Ionic Gradients To Deliver Ropivacaine.

Ropivacaine (RVC) is an aminoamide local anesthetic widely used in surgical procedures. Studies with RVC encapsulated in liposomes and complexed in cyclodextrins have shown good results, but in order to use RVC for lengthy procedures and during the postoperative period, a still more prolonged anesthetic effect is required. This study therefore aimed to provide extended RVC release and increased upload using modified liposomes. Three types of vesicles were studied: (i) large multilamellar vesicle (LMV), (ii) large multivesicular vesicle (LMVV) and (iii) large unilamellar vesicle (LUV), prepared with egg phosphatidylcholine/cholesterol/α-tocopherol (4:3:0.07 mol%) at pH 7.4. Ionic gradient liposomes (inside: pH 5.5, pH 5.5 + (NH4)2SO4 and pH 7.4 + (NH4)2SO4) were prepared and showed improved RVC loading, compared to conventional liposomes (inside: pH 7.4). An high-performance liquid chromatography analytical method was validated for RVC quantification. The liposomes were characterized in terms of their size, zeta potential, polydispersion, morphology, RVC encapsulation efficiency (EE(%)) and in vitro RVC release. LMVV liposomes provided better performance than LMV or LUV. The best formulations were prepared using pH 5.5 (LMVV 5.5in) or pH 7.4 with 250 mM (NH4)2SO4 in the inner aqueous core (LMVV 7.4in + ammonium sulfate), enabling encapsulation of as much as 2% RVC, with high uptake (EE(%) ∼70%) and sustained release (∼25 h). The encapsulation of RVC in ionic gradient liposomes significantly extended the duration of release of the anesthetic, showing that this strategy could be a viable means of promoting longer-term anesthesia during surgical procedures and during the postoperative period.

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.