Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE)

A phase II study of sequential neoadjuvant gemcitabine plus doxorubicin followed by gemcitabine plus cisplatin in patients with operable breast cancer: prediction of response using molecular profiling

This study examined the pathological complete response (pCR) rate and safety of sequential gemcitabine-based combinations in breast cancer. We also examined gene expression profiles from tumour biopsies to identify biomarkers predictive of response. Indian women with large or locally advanced breast cancer received 4 cycles of gemcitabine 1200 mg m−2 plus doxorubicin 60 mg m−2 (Gem+Dox), then 4 cycles of gemcitabine 1000 mg m−2 plus cisplatin 70 mg m−2 (Gem+Cis), and surgery. Three alternate dosing sequences were used during cycle 1 to examine dynamic changes in molecular profiles. Of 65 women treated, 13 (24.5% of 53 patients with surgery) had a pCR and 22 (33.8%) had a complete clinical response. Patients administered Gem d1, 8 and Dox d2 in cycle 1 (20 of 65) reported more toxicities, with G3/4 neutropenic infection/febrile neutropenia (7 of 20) as the most common cycle-1 event. Four drug-related deaths occurred. In 46 of 65 patients, 10-fold cross validated supervised analyses identified gene expression patterns that predicted with ⩾73% accuracy (1) clinical complete response after eight cycles, (2) overall clinical complete response, and (3) pCR. This regimen shows strong activity. Patients receiving Gem d1, 8 and Dox d2 experienced unacceptable toxicity, whereas patients on other sequences had manageable safety profiles. Gene expression patterns may predict benefit from gemcitabine-containing neoadjuvant therapy

Burden of malaria in pregnancy in Jharkhand State, India

<p>Abstract</p> <p>Background</p> <p>Past studies in India included only symptomatic pregnant women and thus may have overestimated the proportion of women with malaria. Given the large population at risk, a cross sectional study was conducted in order to better define the burden of malaria in pregnancy in Jharkhand, a malaria-endemic state in central-east India.</p> <p>Methods</p> <p>Cross-sectional surveys at antenatal clinics and delivery units were performed over a 12-month period at two district hospitals in urban and semi-urban areas, and a rural mission hospital. Malaria was diagnosed by Giemsa-stained blood smear and/or rapid diagnostic test using peripheral or placental blood.</p> <p>Results</p> <p>2,386 pregnant women were enrolled at the antenatal clinics and 718 at the delivery units. 1.8% (43/2382) of the antenatal clinic cohort had a positive diagnostic test for malaria (53.5% <it>Plasmodium falciparum</it>, 37.2% <it>Plasmodium vivax</it>, and 9.3% mixed infections). Peripheral parasitaemia was more common in pregnant women attending antenatal clinics in rural sites (adjusted relative risk [aRR] 4.31, 95%CI 1.84-10.11) and in those who were younger than 20 years (aRR 2.68, 95%CI 1.03-6.98). Among delivery unit participants, 1.7% (12/717) had peripheral parasitaemia and 2.4% (17/712) had placental parasitaemia. Women attending delivery units were more likely to be parasitaemic if they were in their first or second pregnancy (aRR 3.17, 95%CI 1.32-7.61), had fever in the last week (aRR 5.34, 95%CI 2.89-9.90), or had rural residence (aRR 3.10, 95%CI 1.66-5.79). Malaria control measures including indoor residual spraying (IRS) and untreated bed nets were common, whereas insecticide-treated bed nets (ITN) and malaria chemoprophylaxis were rarely used.</p> <p>Conclusion</p> <p>The prevalence of malaria among pregnant women was relatively low. However, given the large at-risk population in this malaria-endemic region of India, there is a need to enhance ITN availability and use for prevention of malaria in pregnancy, and to improve case management of symptomatic pregnant women.</p

Controlled synthesis and characteristics of antireflection coatings of TiO2 produced from a organometallic colloid

Antireflection titanium dioxide (TiO2) coatings have been developed on monocrystalline silicon by a sol–gel spin-coating process using titanium di-isopropoxidebis(acetylacetonate) colloidal precursor solution. The effect of titanium content in the precursor, spin rate, sintering duration and temperature have been studied and their effect on coating thickness and optical properties (i.e., refractive index and reflectivity) were investigated. The influence of post-deposition sintering temperature on the optical characteristics, composition and the microstructure of the coatings have been evaluated by UV–vis spectroscopy, ellipsometry, X-ray photoelectron spectroscopy, atomic force microscopy and X-ray diffraction techniques. Solar cells made on silicon wafers with TiO2 as antireflection layer showed enhancement of more than 20% in short circuit current density in comparison to a cell devoid of the TiO2 coating