Extraction and characterization of microcrystalline cellulose from Vachellia nilotica plant leaves: A biomass waste to wealth approach.

Biobased waste utilization is an intriguing area of research and an ecologically conscious approach. Plant-based materials can be used to render cellulose, which is an eco-friendly material that can be used in numerous aspects. In the current investigation, cellulose was extracted from the leaves of the Vachellia nilotica plant via acid hydrolysis. The application of this research is specifically directed toward the utilization of undesirable plant sources. To validate the extracted cellulose, FT-IR spectroscopy was applied. The cellulose was measured to have a density of 1.234 g/cm3. The crystallinity index (58.93%) and crystallinity size (11.56 nm) of cellulose are evaluated using X-ray diffraction spectroscopy analysis. The highest degradation temperature (320.8°C) was observed using thermogravimetry and differential scanning calorimetry curve analysis. The analysis of particle size was conducted utilizing images captured by scanning electron microscopy. Particle size of less than 30 µm was found and they exhibit non-uniform orientation. Additionally, atomic force microscopy analysis shows an improved average surface roughness (Ra), which increases the possibility of using extracted cellulose as reinforcement in biofilms.

H-Deep-Net: A deep hybrid network with stationary wavelet packet transforms for Retinal detachment classification through fundus images.

Nowadays, automated disease diagnosis has become a vital role in the medical field due to the significant population expansion. An automated disease diagnostic approach assists clinicians in the diagnosis of disease by giving exact, consistent, and prompt results, along with minimizing the mortality rate. Retinal detachment has recently emerged as one of the most severe and acute ocular illnesses, spreading worldwide. Therefore, an automated and quickest diagnostic model should be implemented to diagnose retinal detachment at an early stage. This paper introduces a new hybrid approach of best basis stationary wavelet packet transform and modified VGG19-Bidirectional long short-term memory to detect retinal detachment using retinal fundus images automatically. In this paper, the best basis stationary wavelet packet transform is utilized for image analysis, modified VGG19-Bidirectional long short-term memory is employed as the deep feature extractors, and then obtained features are classified through the Adaptive boosting technique. The experimental outcomes demonstrate that our proposed method obtained 99.67% sensitivity, 95.95% specificity, 98.21% accuracy, 97.43% precision, 98.54% F1-score, and 0.9985 AUC. The model obtained the intended results on the presently accessible database, which may be enhanced further when additional RD images become accessible. The proposed approach aids ophthalmologists in identifying and easily treating RD patients.

A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.

Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.

Conv-eRVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer's Disease Diagnosis.

As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the s-membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.

Lattice model on the rate of DNA hybridization.

We develop a lattice model on the rate of hybridization of the complementary single-stranded DNAs (c-ssDNAs). Upon translational diffusion mediated collisions, c-ssDNAs interpenetrate each other to form correct (cc), incorrect (icc), and trap correct contacts (tcc) inside the reaction volume. Correct contacts are those with exact registry matches, which leads to nucleation and zipping. Incorrect contacts are the mismatch contacts which are less stable compared to tcc, which can occur in the repetitive c-ssDNAs. Although tcc possess registry match within the repeating sequences, they are incorrect contacts in the view of the whole c-ssDNAs. The nucleation rate (k_{N}) is directly proportional to the collision rate and the average number of correct contacts (〈n_{cc}〉) formed when both c-ssDNAs interpenetrate each other. Detailed lattice model simulations suggest that 〈n_{cc}〉∝L/V where L is the length of c-ssDNAs and V is the reaction volume. Further numerical analysis revealed the scaling for the average radius of gyration of c-ssDNAs (R_{g}) with their length as R_{g}∝sqrt[L]. Since the reaction space will be approximately a sphere with radius equals to 2R_{g} and V∝L^{3/2}, one obtains k_{N}∝1/sqrt[L]. When c-ssDNAs are nonrepetitive, the overall renaturation rate becomes as k_{R}∝k_{N}L, and one finally obtains k_{R}∝sqrt[L] in line with the experimental observations. When c-ssDNAs are repetitive with a complexity of c, earlier models suggested the scaling k_{R}∝sqrt[L]/c, which breaks down at c=L. This clearly suggests the existence of at least two different pathways of renaturation in the case of repetitive c-ssDNAs, viz., via incorrect contacts and trap correct contacts. The trap correct contacts can lead to the formation of partial duplexes which can keep the complementary strands in the close proximity for a prolonged timescale. This is essential for the extended 1D slithering, inchworm movements, and internal displacement mechanisms which can accelerate the searching for the correct contacts. Clearly, the extent of slithering dynamics will be inversely proportional to the complexity. When the complexity is close to the length of c-ssDNAs, the pathway via incorrect contacts will dominate. When the complexity is much less than the length of c-ssDNA, pathway via trap correct contacts would be the dominating one.

Outcomes of teleconsultation services and patient satisfaction among pregnant women delivering at a tertiary care center in South India during coronavirus disease 2019 pandemic.

Coronavirus disease 2019 pandemic has disrupted the antenatal care in low- and middle-income countries such as India. Telemedicine was introduced for the first time in India for continuing antenatal care. Hence, a questionnaire-based descriptive cross-sectional study is done to assess the outcomes of teleconsultation services, factors influencing it, and patient's perceived satisfaction. Three hundred and fifty-five women who delivered the following teleconsultation from July 2020 to October 2020 were included in the study. Thirty-two percent were high-risk pregnancies and 15% of the babies required neonatal intensive care unit admission. Ninety-eight percent could convey their health concerns, 18% had a referral to other departments, and 25% had visited casualty. Sixty-three percent procured medicine through e-prescription. Seventy-six percent were happy with teleconsultation overcrowded clinic, 82% were happy about saving travel expenditure, whereas overall satisfaction was 50%. Fourteen percent did not have access to smartphone and 9% did not receive the call at scheduled time. Telemedicine has a vital role in managing pregnancy concerns during this pandemic.

Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection.

Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.

Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE Features.

Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes and elevations as retinal fundus images frequently have irregular illumination and are poorly contrasting. Manual detection of EX is a time-consuming process to diagnose a mass number of diabetic patients. In the domain of signal processing, both SIFT (scale-invariant feature transform) and SURF (speed-up robust feature) methods are predominant in scale-invariant location retrieval and have shown a range of advantages. But, when extended to medical images with corresponding weak contrast between reference features and neighboring areas, these methods cannot differentiate significant features. Considering these, in this paper, a novel method is proposed based on modified KAZE features, which is an emerging technique to extract feature points and extreme learning machine autoencoders(ELMAE) for robust and fast localization of the EX in fundus images. The main stages of the proposed method are pre-processing, OD localization, dimensionality reduction using ELMAE, and EX localization. The proposed method is evaluated based on the freely accessible retinal database DIARETDB0, DIARETDB1, e-Ophtha, MESSIDOR, and local retinal database collected from Silchar Medical College and Hospital(SMCH). The sensitivity, specificity, and accuracy obtained by the proposed method are 96.5%, 96.4%, and 97%, respectively, with the processing time of 3.19 seconds per image. The results of this study are satisfactory with state-of-the-art methods. The results indicate that the approach taken can detect EX with less processing time and accurately from the fundus images.

Multi-COVID-Net: Multi-objective optimized network for COVID-19 diagnosis from chest X-ray images.

Coronavirus Disease 2019 (COVID-19) had already spread worldwide, and healthcare services have become limited in many countries. Efficient screening of hospitalized individuals is vital in the struggle toward COVID-19 through chest radiography, which is one of the important assessment strategies. This allows researchers to understand medical information in terms of chest X-ray (CXR) images and evaluate relevant irregularities, which may result in a fully automated identification of the disease. Due to the rapid growth of cases every day, a relatively small number of COVID-19 testing kits are readily accessible in health care facilities. Thus it is imperative to define a fully automated detection method as an instant alternate treatment possibility to limit the occurrence of COVID-19 among individuals. In this paper, a two-step Deep learning (DL) architecture has been proposed for COVID-19 diagnosis using CXR. The proposed DL architecture consists of two stages, "feature extraction and classification". The "Multi-Objective Grasshopper Optimization Algorithm (MOGOA)" is presented to optimize the DL network layers; hence, these networks have named as "Multi-COVID-Net". This model classifies the Non-COVID-19, COVID-19, and pneumonia patient images automatically. The Multi-COVID-Net has been tested by utilizing the publicly available datasets, and this model provides the best performance results than other state-of-the-art methods.

WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images.

Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.

OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19.

The quick spread of coronavirus disease (COVID-19) has become a global concern and affected more than 15 million confirmed patients as of July 2020. To combat this spread, clinical imaging, for example, X-ray images, can be utilized for diagnosis. Automatic identification software tools are essential to facilitate the screening of COVID-19 using X-ray images. This paper aims to classify COVID-19, normal, and pneumonia patients from chest X-ray images. As such, an Optimized Convolutional Neural network (OptCoNet) is proposed in this work for the automatic diagnosis of COVID-19. The proposed OptCoNet architecture is composed of optimized feature extraction and classification components. The Grey Wolf Optimizer (GWO) algorithm is used to optimize the hyperparameters for training the CNN layers. The proposed model is tested and compared with different classification strategies utilizing an openly accessible dataset of COVID-19, normal, and pneumonia images. The presented optimized CNN model provides accuracy, sensitivity, specificity, precision, and F1 score values of 97.78%, 97.75%, 96.25%, 92.88%, and 95.25%, respectively, which are better than those of state-of-the-art models. This proposed CNN model can help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

A novel four-step feature selection technique for diabetic retinopathy grading.

Diabetic retinopathy is a microvascular complication of diabetes mellitus that develops over time. Diabetic retinopathy is one of the retinal disorders. Early detection of diabetic retinopathy reduces the chances of permanent vision loss. However, the identification and regular diagnosis of diabetic retinopathy is a time-consuming task and requires expert ophthalmologists and radiologists. In addition, an automatic diabetic retinopathy detection technique is necessary for real-time applications to facilitate and minimize potential human errors. Therefore, we propose an ensemble deep neural network and a novel four-step feature selection technique in this paper. In the first step, the preprocessed entropy images improve the quality of the retinal features. Second, the features are extracted using a deep ensemble model include InceptionV3, ResNet101, and Vgg19 from the retinal fundus images. Then, these features are combined to create an ample feature space. To reduce the feature space, we propose four-step feature selection techniques: minimum redundancy, maximum relevance, Chi-Square, ReliefF, and F test for selecting efficient features. Further, appropriate features are chosen from the majority voting techniques to reduce the computational complexity. Finally, the standard machine learning classifier, support vector machines, is used in diabetic retinopathy classification. The proposed method is tested on Kaggle, MESSIDOR-2, and IDRiD databases, available publicly. The proposed algorithm provided an accuracy of 97.78%, a sensitivity of 97.6%, and a specificity of 99.3%, using top 300 features, which are better than other state-of-the-art methods.

Measurement of uranium isotope ratios in Fukushima-accident contaminated soil samples using multi collector inductively coupled plasma mass spectrometry.

In the present study, 137Cs and 238U activity concentrations, 234U/238U activity ratio, and 235U/238U isotope ratio were measured in fifteen soil samples collected from the exclusion zone around the Fukushima Daiichi Nuclear Power Station (FDNPS). The 137Cs activity concentrations of Fukushima-accident contaminated soil samples ranged from 29.9 to 4780 kBq kg-1 with a mean of 2007 kBq kg-1. On the other hand, the 238U activity concentrations of these soil samples ranged from 5.2 to 22.4 Bq kg-1 with a mean of 13.2 Bq kg-1. The activity ratios of 234U/238U ranged from 0.973 to 1.023. The 235U/238U isotope ratios of these exclusion zone soil samples varied from 0.007246 to 0.007260, and they were similar to the natural terrestrial ratio confirming the natural origin. Using isotope dilution technique, the 235U/137Cs activity ratio was theoretically estimated for highly 137Cs contaminated soil samples from Fukushima exclusion zone ranged from 5.01 × 10-8 - 6.16 × 10-7 with a mean value of 2.51 × 10-7.

Geochemical characterization of monazite sands based on rare earth elements, thorium and uranium from a natural high background radiation area in Tamil Nadu, India.

The Kanyakumari coastal area in the southernmost part of Tamil Nadu, India is a well-known natural high background radiation area due to the abundance of monazite in beach placer deposits. In the present study, the concentrations of major oxides, rare earth elements (REEs), Th and U were measured to understand geochemical characteristics of these monazite sands. Based on the ambient dose rate, 23 locations covering an area of about 60 km along the coast were selected for sample collection. The concentrations of U and Th ranged from 1.1 to 737.8 µg g-1 and 25.2-12250.6 µg g-1, respectively. The Th/U ratio ranged from 2.2 to 61.6, which clearly indicated that Th was the dominant contributing radionuclide to the enhanced natural radioactivity in this coastal region. The chondrite-normalized REEs pattern of the placer deposits showed enrichment in light REEs and depletion in heavy REEs with a negative Eu anomaly that indicated the monazite sands were derived from granite, charnockite, and granitoid rocks from the Nagercoil and the Trivandrum Blocks of the Southern Granulite Terrain.

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network.

The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network.

The novel coronavirus disease (COVID-19) spread quickly worldwide, changing the everyday lives of billions of individuals. The preliminary diagnosis of COVID-19 empowers health experts and government professionals to break the chain of change and level the epidemic curve. The regular sort of COVID-19 detection test, be that as it may, requires specific hardware and generally has low sensitivity. Chest X-ray images to be used to diagnosis the COVID-19. In this work, a dataset of X-ray images with COVID-19, bacterial pneumonia, and normal was used to diagnose the COVID-19 automatically. This work to assess the execution of best in class Convolutional Neural Network (CNN) models proposed over ongoing years for clinical image classification. In particular, the modified pre-trained CNN-ResNet50 based Extreme Learning Machine classifier (ELM) has proposed for different diagnosis abnormalities such as COVID-19, Pneumonia, and normal. The proposed CNN method has trained and tested with the publicly available COVID-19, pneumonia, and normal datasets. The presented pre-trained ResNet CNN model provides accuracy, sensitivity, specificity, recall, precision, and F1 score values of 94.07, 98.15, 91.48, 85.21, 98.15, and 91.22, respectively, which is the best classification performance than other states of the art methods. This study introduced a computationally productive and exceptionally exact model for multi-class grouping of three diverse contamination types from alongside Normal people. This CNN model can help in the automatic diagnosis of COVID-19 cases and help decrease the burden on medicinal services frameworks.

Extracorporeal Plating of High-Level Condyle fracture Through Retromandibular Approach - A Technical Note.

The complex anatomy of the mandibular condyle makes its fracture management challenging and debatable. Apart from this, the approaches to condyle are also challenging as most of them depend on the surgical expertise. The retromandibular approach which was initially proposed for the vertical sub condylar osteotomies was later popularized for condyle fracture management. It is considered to be a gold standard approach in the management of low condylar fractures. Although it has its own demerits in managing high condylar fracture due to its poor access and visibility, the major complications of temporary facial nerve paresis and sialocele are very less compared to other approaches. However, modified extracorporeal plating combined with retromandibular approach proves to be effective in managing high condylar fracture. In this article, we discuss about a case of bilateral neck of condyle fracture that has been managed with the combined modified extracorporeal plating with retromandibular approach and has been followed with no complications for about 1 year.

A lattice model on the rate of in vivo site-specific DNA-protein interactions.

We develop a lattice model of site-specific DNA-protein interactions under in vivo conditions where DNA is modelled as a self-avoiding random walk that is embedded in a cubic lattice box resembling the living cell. The protein molecule searches for its cognate site on DNA via a combination of three dimensional (3D) and one dimensional (1D) random walks. Hopping and intersegmental transfers occur depending on the conformational state of DNA. Results show that the search acceleration ratio (= search time in pure 3D route/search time in 3D and 1D routes) asymptotically increases towards a limiting value as the dilution factor of DNA (= volume of the cell/the volume of DNA) tends towards infinity. When the dilution ratio is low, then hopping and intersegmental transfers significantly enhance the search efficiency over pure sliding. At high dilution ratio, hopping does not enhance the search efficiency much since under such situation DNA will be in a relaxed conformation that favors only sliding. In the absence of hopping and intersegmental transfers, there exists an optimum sliding time at which the search acceleration ratio attains a maximum in line with the current theoretical results. However, existence of such optimum sliding length disappears in the presence of hopping. When the DNA is confined in a small volume inside the cell resembling a natural cell system, then there exists an optimum dilution and compression ratios (= total cell volume/volume in which DNA is confined) at which the search acceleration factor attains a maximum especially in the presence of hopping and intersegmental transfers. These optimum values are consistent with the values observed in the Escherichia coli cell system. In the absence of confinement of DNA, position of the specific binding site on the genomic DNA significantly influences the search acceleration. However, such position dependent changes in the search acceleration ratio will be nullified in the presence of hopping and intersegmental transfers especially when the DNA is confined in a small volume that is embedded in an outer cell.

Influence of Ni Doping in SnO2 Nanoparticles with Enhanced Visible Light Photocatalytic Activity for Degradation of Methylene Blue Dye.

In this article, Nickel doped rutile structure tin oxide (SnO2) nanoparticles have been prepared by simple chemical co-precipitation method and prepared samples were characterized by Powder X-ray Diffraction, Fourier transform infrared Spectroscopy, Microraman analysis, Photoluminescene Spectroscopy, UV-Visible Spectroscopy, Energy dispersive analysis and Field emission scanning electron microscope. XRD studies revealed the single phase tetragonal rutile structure with space group of P42/mnm. The average crystallite size of the particles was decreased from 27 to 22 nm with increasing Ni doping concentration. FTIR spectra confirmed the presence of various bands such as O-H, C-H, Sn-O-Sn. Raman modes Eg, A1g and B2g were assigned at 478, 630 and 740 cm-1 which confirmed the single phase of pure and Ni doped SnO2 nanoparticles. The photoluminescence spectra confirmed that the defect related emissions increased with increasing of Ni concentration. The UV absorption spectra showed that the absorption of the particles decreased with increasing Ni concentration and the band gap values decreased from 3.7 to 3.4 eV. EDX spectra confirmed the presence of Sn, Ni, O in pure and doped samples. The photocatalytic activity of the pure and Ni doped SnO2 nanoparticles were analyzed by using methylene blue dye under visible light irradiation. It is concluded Ni (7%) doped SnO2 nanoparticles have higher degradation efficiency compared to pure SnO2 nanoparticles.

Comparative study of evaluation of the oral stereognostic ability between diabetic and nondiabetic complete denture wearers with and without denture.

BACKGROUND: The diabetic subjects would have impaired oral stereognostic ability (OSA) compared with normal subjects due to diabetic neuropathy and microcirculatory disturbances. This study was conducted to compare the OSA between diabetic and nondiabetic complete denture wearers with and without denture. MATERIALS AND METHODS: In this in vivo study the present comparative study comprised of seventy edentulous subjects (36 males and 34 females), aged from 35 to 84 rehabilitated with complete dentures (among them 35 were diabetic and 35 subjects were nondiabetic complete denture wearer). The OSA tests were conducted using acrylic test samples of 12 shaped forms, which were placed in patient's mouth for a given period of time for identification and scored according to three-point scale as OSA score and the identification time was also recorded. The data obtained were analyzed using Chi-square test, t-test, and Pearson's correlation coefficient (P < 0.05). RESULTS: In this study, diabetic complete denture wearers got the mean OSA score of 12.43 ± 3.93 without dentures, which was lower than nondiabetic complete denture wearer group (14.82 ± 4.44). There was a significant difference (P = 0.020*) in the identification of test pieces. CONCLUSION: Within limitations of this study, diabetic complete denture wearers showed decreased OSA than nondiabetic subjects, particularly it was significant while not wearing dentures. Oral stereognosis may be used as one of the clinical aids in predicting patient's performance to a prosthesis. Based on their response, we can educate the patient about the prognosis.