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)

Developing Standard Treatment Workflows—way to universal healthcare in India

Primary healthcare caters to nearly 70% of the population in India and provides treatment for approximately 80–90% of common conditions. To achieve universal health coverage (UHC), the Indian healthcare system is gearing up by initiating several schemes such as National Health Protection Scheme, Ayushman Bharat, Nutrition Supplementation Schemes, and Inderdhanush Schemes. The healthcare delivery system is facing challenges such as irrational use of medicines, over- and under-diagnosis, high out-of-pocket expenditure, lack of targeted attention to preventive and promotive health services, and poor referral mechanisms. Healthcare providers are unable to keep pace with the volume of growing new scientific evidence and rising healthcare costs as the literature is not published at the same pace. In addition, there is a lack of common standard treatment guidelines, workflows, and reference manuals from the Government of India. Indian Council of Medical Research in collaboration with the National Health Authority, Govt. of India, and the WHO India country office has developed Standard Treatment Workflows (STWs) with the objective to be utilized at various levels of healthcare starting from primary to tertiary level care. A systematic approach was adopted to formulate the STWs. An advisory committee was constituted for planning and oversight of the process. Specialty experts' group for each specialty comprised of clinicians working at government and private medical colleges and hospitals. The expert groups prioritized the topics through extensive literature searches and meeting with different stakeholders. Then, the contents of each STW were finalized in the form of single-pager infographics. These STWs were further reviewed by an editorial committee before publication. Presently, 125 STWs pertaining to 23 specialties have been developed. It needs to be ensured that STWs are implemented effectively at all levels and ensure quality healthcare at an affordable cost as part of UHC

Application of the convolutional neural networks and supervised deep-learning methods for osteosarcoma bone cancer detection

Osteosarcoma is a cancerous tumor that occurs in bones. Although it can occur in any bone, it often occurs in long bones such as arms and legs. The exact cause of this cancerous tumor is still unknown, but according to experts, it occurs due to the deoxyribonucleic acid (DNA) mutations inside the bones. This creates immature, irregular, diseased bone and can destroy healthy body tissue. About 75 out of 100 people who have osteosarcoma can be cured if the cancer is not dispersed to the additional body parts. The bone X-ray is the initial test when a bone tumor is suspected. X-ray and imaging tests are the best way to identify osteosarcoma from the bones. A biopsy is the suggested method that can make a definitive diagnosis. This is a time-consuming and difficult procedure that can be automated. We propose several supervised deep-learning methods and select the most suitable model. The selection is made through the weightage from the users’ data to detect bone cancer. We show the model selected meets the expectations with the highest accuracy 90.36% using the residual neural network(ResNet101) algorithm and 89.51% precision in the prediction tasks

Advancements in quantum blockchain with real-time applications

The amalgamation of post-quantum cryptography in cyber-physical systems makes the computing system secure and also generates opportunities in areas like smart contracts, quantum blockchain, and smart security solutions. Sooner or later, all computing and security systems are going to adopt quantum-proof cryptography to safeguard these systems from quantum attacks. Post-quantum cryptography has tremendous potential in various domains and must be researched and explored further to be utilized successfully. Advancements in Quantum Blockchain With Real-Time Applications considers various concepts of computing such as quantum computing, post-quantum cryptography, quantum attack-resistant blockchain, quantum blockchains, and multidisciplinary applications and real-world use cases. The book also discusses solutions to various real-world problems within the industry. Covering key topics such as cybersecurity, data management, and smart society, this reference work is ideal for computer scientists, industry professionals, academicians, practitioners, scholars, researchers, instructors, and students.</p

Uromodulin rs4293393 T>C variation is associated with kidney disease in patients with type 2 diabetes

Background & objectives: Uromodulin, a UMOD gene encoded glycoprotein is synthesized exclusively in renal tubular cells and released into urine. Mutations lead to uromodulin misfolding and retention in the kidney, where it might stimulate cells of immune system to cause inflammation and progression of kidney disease. Genome-wide association studies (GWAS) have identified UMOD locus to be associated with hypertension and diabetic nephropathy (DN). In this study, we investigated the association between rs4293393 variation in UMOD gene and susceptibility to kidney disease in individuals with type 2 diabetes mellitus (T2DM). Methods: A total of 646 individuals, 208 with T2DM without evidence of kidney disease (DM), 221 with DN and 217 healthy controls (HC) were genotyped for UMOD variant rs4293393T>C by restriction fragment length polymorphism. Serum uromodulin levels were quantified by enzyme-linked immunosorbent assay. Results: A significant difference was found in genotype and allelic frequency among DM, DN and HC. TC+CC genotype and C allele were found more frequently in DN compared to HC (33.9 vs 23.0%, P=0.011 and 20.1 vs 12.9%, P=0.004, respectively). Compared to DM, C allele was found to be more frequent in individuals with DN (20.1 vs 14.7%, P=0.034). Those with DN had higher serum uromodulin levels compared to those with DM (P=0.001). Serum uromodulin levels showed a positive correlation with serum creatinine (r=0.431; P<0.001) and negative correlation with estimated glomerular filtration rate (r=−0.423; P<0.001). Interpretation & conclusions: The frequency of UMOD rs4293393 variant with C allele was significantly higher in individuals with DN. UMOD rs4293393 T>C variation might have a bearing on susceptibility to nephropathy in north Indian individuals with type 2 diabetes

Association of CTG repeat polymorphism in carnosine dipeptidase 1 (CNDP1) gene with diabetic nephropathy in north Indians

Background & objectives: CNDP1 gene, present on chromosome 18q22.3-23, encodes carnosinase, the rate-limiting enzyme in hydrolysis of carnosine to ß-alanine and L-histidine. Linkage of CTG trinucleotide (leucine) repeat polymorphism in CNDP1 gene with diabetic nephropathy has been observed in several populations. However, this association is conflicting and population-dependent. We investigated this association in type 2 diabetes mellitus (T2DM) patients with and without nephropathy in north India. Methods: A total of 564 individuals [199 T2DM without nephropathy (DM), 185 T2DM with nephropathy (DN) and 180 healthy individuals (HC)] were enrolled. CNDP1 CTG repeat analysis was done by direct sequencing of a 377 base pair fragment in exon 2. Results: The most frequent leucine (L) repeats were 5L-5L, 6L-5L and 6L-6L. 5L-5L genotype frequency was reduced in DN (24.3%) as compared to DM (34.7%, P=0.035) and HC (38.4%, P=0.005). Similarly, 5L allele frequency was lower in DN (46.8%) as compared to DM (57.3%, P=0.004) and HC (60.5%, P<0.001). The genotype and allelic frequencies were similar in DM and HC groups. No gender specific difference was observed in the genotype or allelic frequencies between groups. Interpretation & conclusions: Compared to healthy individuals and those with diabetes but no kidney disease, patients with diabetic nephropathy exhibited lower frequencies of 5L-5L genotype and 5L allele of CNDP1 gene, suggesting that this allele might confer protection against development of kidney disease in this population