A simple cryogenic method for efficient analysis of triple oxygen isotopes in silicates.

RATIONALE: Oxygen isotopic ratios of silicates are excellent tools to reconstruct paleotemperature and isotopic composition of the precipitating fluid. However, the measurement of 17 O/16 O is difficult due to the low abundance of 17 O. The present study reports a simplified high-precision analytical technique for measuring the two oxygen isotope ratios, 17 O/16 O and 18 O/16 O, in silicates. METHODS: Silicate samples were ablated by a CO2 laser in a BrF5 environment. The released oxygen (O2 ) was then cryogenically trapped in a molecular sieve zeolite (MSZ). Associated contaminants such as BrF5 , F2 , NF3 etc. were cleaned by passing the gas through a NaCl trap followed by a cooled (-25°C) MSZ-packed U-tube trap. The purified O2 was analysed in a MAT 253 isotope ratio mass spectrometer for oxygen isotope ratios. RESULTS: The δδ17 O and δ18 O values of the working gas were calibrated by NBS-28 and crosschecked by inter-laboratory references UWG-2, SCO and IMAU-O2 . The average analytical precisions (using aliquots of NBS-28, UWG-2, SCO, and laboratory internal standards IIT-KGP-SQ quartz and IIT-KGP-NQ quartz) of the δ17 O, δ18 O and ∆'17 O values were 0.04‰, 0.08‰ and 4 per meg, respectively. CONCLUSIONS: A new cryogenic cleaning technique was developed that does not require GC but efficiently removes NF3 -contaminants from oxygen gas produced by laser fluorination of silicates. The technique is simple, quick and cost-effective and provides highly precise and accurate δ17 O, δ18 O and ∆'17 O values.

A CNN based neurobiology inspired approach for retinal image quality assessment.

Retinal image quality assessment (IQA) algorithms use different hand crafted features for training classifiers without considering the working of the human visual system (HVS) which plays an important role in IQA. We propose a convolutional neural network (CNN) based approach that determines image quality using the underlying principles behind the working of the HVS. CNNs provide a principled approach to feature learning and hence higher accuracy in decision making. Experimental results demonstrate the superior performance of our proposed algorithm over competing methods.

Segmentation of optic disc and optic cup in retinal fundus images using shape regression.

Glaucoma is one of the leading cause of blindness. The manual examination of optic cup and disc is a standard procedure used for detecting glaucoma. This paper presents a fully automatic regression based method which accurately segments optic cup and disc in retinal colour fundus image. First, we roughly segment optic disc using circular hough transform. The approximated optic disc is then used to compute the initial optic disc and cup shapes. We propose a robust and efficient cascaded shape regression method which iteratively learns the final shape of the optic cup and disc from a given initial shape. Gradient boosted regression trees are employed to learn each regressor in the cascade. A novel data augmentation approach is proposed to improve the regressors performance by generating synthetic training data. The proposed optic cup and disc segmentation method is applied on an image set of 50 patients and demonstrate high segmentation accuracy for optic cup and disc with dice metric of 0.95 and 0.85 respectively. Comparative study shows that our proposed method outperforms state of the art optic cup and disc segmentation methods.

A novel computer aided quantification method of focal arteriolar narrowing using colour retinal image.

We present a novel method for the quantification of focal arteriolar narrowing (FAN) in human retina, a precursor for hypertension, stroke and other cardiovascular diseases. A reliable and robust arteriolar boundary mapping method is proposed where intensity, gradient and spatial prior knowledge about the arteriolar shape is incorporated into a graph based optimization method to obtain the arteriolar boundary. Following the mapping of the arteriolar boundaries, arteriolar widths are analysed to quantify the severity of focal arteriolar narrowing (FAN). We evaluate our proposed method on a dataset of 116 retinal arteriolar segments which are manually graded by two expert graders. The experimental results indicate a strong correlation between the quantified FAN measurement scores provided by our method and two experts graded FAN severity levels. Our proposed FAN measurement score: percent narrowing (PN) shows high correlation (Spearman correlation coefficient of 0.82(p<0.0001) for grader-1 and 0.84(p<0.0001) for grade-2) with the manually graded FAN severity levels provided by two expert graders. In addition to that, the proposed method shows better reproducibility (Spearman correlation coefficient ρ=0.92(p<0.0001)) compared to two expert graders ( [Formula: see text] (p<0.0001) and [Formula: see text] ) in two successive sessions. The quantitative measurements provided by the proposed method can help us to establish a more reliable link between FAN and known systemic and eye diseases.

Multi-atlas label fusion using hybrid of discriminative and generative classifiers for segmentation of cardiac MR images.

Multi-atlas segmentation first registers each atlas image to the target image and transfers the label of atlas image to the coordinate system of the target image. The transferred labels are then combined, using a label fusion algorithm. In this paper, we propose a novel label fusion method which aggregates discriminative learning and generative modeling for segmentation of cardiac MR images. First, a probabilistic Random Forest classifier is trained as a discriminative model to obtain the prior probability of a label at the given voxel of the target image. Then, a probability distribution of image patches is modeled using Gaussian Mixture Model for each label, providing the likelihood of the voxel belonging to the label. The final label posterior is obtained by combining the classification score and the likelihood score under Bayesian rule. Comparative study performed on MICCAI 2013 SATA Segmentation Challenge demonstrates that our proposed hybrid label fusion algorithm is accurate than other five state-of-the-art label fusion methods. The proposed method obtains dice similarity coefficient of 0.94 and 0.92 in segmenting epicardium and endocardium respectively. Moreover, our label fusion method achieves more accurate segmentation results compared to four other label fusion methods.

Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field.

White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice.

An effective automated system for grading severity of retinal arteriovenous nicking in colour retinal images.

Retinal arteriovenous (AV) nicking is a precursor for hypertension, stroke and other cardiovascular diseases. In this paper, an effective method is proposed for the analysis of retinal venular widths to automatically classify the severity level of AV nicking. We use combination of intensity and edge information of the vein to compute its widths. The widths at various sections of the vein near the crossover point are then utilized to train a random forest classifier to classify the severity of AV nicking. We analyzed 47 color retinal images obtained from two population based studies for quantitative evaluation of the proposed method. We compare the detection accuracy of our method with a recently published four class AV nicking classification method. Our proposed method shows 64.51% classification accuracy in-contrast to the reported classification accuracy of 49.46% by the state of the art method.

Transfusion-related adverse events at the tertiary care center in North India: An institutional hemovigilance effort.

AIM: This study was designed to analyze the incidence and spectrum of adverse effects of blood transfusion so as to initiate measures to minimize risks and improve overall transfusion safety in the institute. MATERIALS AND METHODS: During the period from July 2002 to July 2003 all the adverse events related to transfusion of blood and blood components in various clinical specialties were recorded. They were analyzed and classified on the basis of their clinical features and laboratory tests. Attempt was also made to study the predisposing risk factors. RESULTS: During the study period 56,503 blood and blood components were issued to 29,720 patients. A total of 105 adverse reactions due to transfusion were observed during the study period. A majority of the adverse reactions was observed in hemato-oncology patients 43% (n = 45) and in presensitized patient groups 63% (n = 66). FNHTR 41% (n = 43) and allergic reactions 34% (n = 36) were the most common of all types of adverse transfusion reactions, followed by AcHTR 8.56% (n = 9). Majority of these AcHTR were due to unmonitored storage of blood in the refrigerator of wards resulting in hemolysis due to thermal injury. Less frequently observed reactions were anaphylactoid reactions (n = 4), bacterial sepsis (n = 4), hypervolemia (n = 2), hypocalcemia (n = 2), TRALI (n = 1), DHTR (n = 1), and TAGvHD (n = 1). CONCLUSION: Analysis of transfusion-related adverse outcomes is essential for improving safety. Factors such as improvement of blood storage conditions outside the blood bank, improvement in cross-matching techniques, careful donor screening, adherence to good manufacturing practices while component preparation, bedside monitoring of transfusion, and documentation of adverse events will help in reducing transfusion-related morbidity and mortality.

Statistical optimization of ranitidine HCl floating pulsatile delivery system for chronotherapy of nocturnal acid breakthrough.

Present work conceptualizes a specific technology, based on combining floating and pulsatile principles to develop drug delivery system, intended for chronotherapy in nocturnal acid breakthrough. This approach will be achieved by using a programmed delivery of ranitidine hydrochloride from a floating tablet with time-lagged coating. In this study, investigation of the functionality of the outer polymer coating to predict lag time and drug release was statistically analyzed using the response surface methodology (RSM). RSM was employed for designing of the experiment, generation of mathematical models and optimization study. The chosen independent variables, i.e. percentage weight ratios of ethyl cellulose to hydroxypropyl methyl cellulose in the coating formulation and coating level (% weight gain) were optimized with a 3(2) full factorial design. Lag time prior to drug release and cumulative percentage drug release in 7h were selected as responses. Results revealed that both, the coating composition and coating level, are significant factors affecting drug release profile. A second-order polynomial equation fitted to the data was used to predict the responses in the optimal region. The optimized formulation prepared according to computer-determined levels provided a release profile, which was close to the predicted values. The proposed mathematical model is found to be robust and accurate for optimization of time-lagged coating formulations for programmable pulsatile release of ranitidine hydrochloride, consistent with the demands of nocturnal acid breakthrough.

Multiparticulate formulation approach to pulsatile drug delivery: current perspectives.

In the body under physiological conditions, many vital functions are regulated by transient release of bioactive substances at a specific time and site. Thus, to mimic the function of living systems and in view of emerging chronotherapeutic approaches, pulsatile delivery, which is meant to release a drug following programmed lag phase, has attracted increasing interest in recent years. In pursuit of pulsatile release, various design strategies have been proposed, broadly categorized into single-unit and multiple-unit systems. However, in recent pharmaceutical applications involving pulsatile delivery, multiparticulate dosage forms are gaining much favor over single-unit dosage forms because of their potential benefits like predictable gastric emptying, no risk of dose dumping, flexible release patterns and increased bioavailability with less inter- and intra-subject variability. Based on these premises, the aim of the present review is to survey the main multiparticulate pulsatile delivery systems, for which the swelling and rupturing; dissolution or erosion; and changed permeability of the coating membrane are primarily involved in the control of release. The development of low density floating multiparticulate pulsed-release dosage forms possessing gastric retention capabilities has also been addressed with increasing focus on the upcoming multiparticulate-pulsatile technologies being exploited on an industrial scale.