Search

Objective: To investigate sociodemographic and healthcare system barriers to access and utilization of alternative treatments to positive airway pressure (PAP) in the management of adult obstructive sleep apnea (OSA). Data Sources: PubMed, Embase, and Web of Science databases were searched from 2003 to 2023 for English‐language studies containing original data on sociodemographic and healthcare system barriers to PAP‐alternative treatments for adult OSA. Review Methods: Studies were assessed using the Preferred Reporting Items for Systematic Reviews and Meta‐analyses Extension for Scoping Reviews (PRISMA‐ScR) guidelines. Title and abstract screening, full‐text review, and data collection were conducted by two investigators independently. Results: Out of 1,615 studies screened, 13 studies met inclusion criteria and reported on a total of 1,206,115 patients who received PAP alternative treatments, including surgery (n = 9 studies), and oral appliances (OAs) (n = 3 studies). The chance of receiving a PAP‐alternative treatment such as surgery was greater among patients aged 39 years or younger, had body mass index below 30 kg/m2, fewer comorbidities, private insurance, and a higher occupational and income status. The decision of individuals to receive PAP alternative treatments was influenced by increased patient education from providers, as well as improvements in daytime sleepiness and partner perception of snoring and apnea. Conclusion: Cumulative evidence suggests that several sociodemographic and healthcare system factors are associated with decreased use of PAP alternatives when PAP therapy fails. Investigation of interventions to eliminate these potential barriers may improve access and treatment outcomes. Laryngoscope, 134:3903–3909, 2024 [ABSTRACT FROM AUTHOR]

In this work, green method to synthesize chromium-doped zinc oxide (ZnO) nanorods (NRs) using an aqueous flower extract from Rhododendron arboretum is explored. Herein, chromium-doped ZnO NRs were prepared with different amount of chromium doping, varied as 2–10%. The green synthesized products underwent substantial analysis through X-ray diffraction (XRD), spectroscopic such as ultraviolet spectroscopy(UV-Vis) and scanning electron microscopy (SEM) methods. All samples were found to have hexagonal wurtzite ZnO, with average particle sizes of 52.41, 56.6, 54.44, 53.05, and 56.99 nm, respectively, for 2, 4, 6, 8, and 10% chromium doping in ZnO NRs. The Cr-doped ZnO NRs exhibited remarkable photocatalytic degradation activity of cationic dyes under UV-light, i.e., Malachite Green and Fuchsin Basic with degradation of 99.604 and 99.881%, respectively in 90 min. The reusability tests for these green synthesized Cr-doped ZnO NRs have also been carried out, showed 9–11 cycles with 85% of degradation efficiency. In addition, the Cr-doped ZnO NRs exhibited high selectivity for cationic dyes when experiments against mixture of dyes were performed. Photodegradation kinetics followed the pseudo-first-order model. The flower-extract-stabilized chromium-doped ZnO NRs demonstrated high photocatalytic activity toward malachite green and fuchsin basic dyes, potential material for pollution remediation. Cr-doped ZnO NRs by green method using flower extract of Rhododendon arboretum were prepared for the first time under ambient reaction conditions. Effect of Dopant i.e. Cr on Photocatalytic activity have been exploited. Selective photocatalytic degradation of cationic dyes i.e. MG, and FB has been achieved in 60–90 minutes. Optimization of reaction condition and various parameters has also been carried out. Recyclability of Cr-doped ZnO NRs was also evaluated and were found to be reusable for 11 cycles for degradation. [ABSTRACT FROM AUTHOR]

Today's era of smart technologies has shifted the paradigm of how things work in almost every domain. This change has also affected the utmost need of human beings, that is, sustainability in healthcare. With the help of technologies like the Internet of medical things (IoMT) devices and cloud‐based applications, doctors can now monitor and diagnoses their patients remotely. In the data analytics process for health‐care data, more precision and challenges are present. Data must be transferred from a lower layer to final storage (cloud‐based) during data ingestion so that subsequent operations such as mining, accessing, and streaming can be performed on these data. This article addresses some of the important issues to maintain sustainability in healthcare like authentication, smart scheduling of devices, the removal of redundant data on the final layer, and improving access times for stored data. Bloom filter (a probabilistic data structure)‐based solutions are proposed at different layers of IoMT (edge‐fog‐cloud). The comparative analysis of state‐of‐art techniques with the proposed has shown significant improvement over various metrics. The proposed framework has been evaluated experimentally on real health‐care datasets. [ABSTRACT FROM AUTHOR]

Pi (π) interactions originating from the N-H bond of amine and ethylene moiety have been explored on the mechanism of aromatic nucleophilic substitution (ArSN) of the nitro group of 4-methylene-5,7-dinitroquinazoline with methylamine in the gas phase and solvent media within DFT framework. The free energy profiles confirmed that the amination should take place at peri-position and calculations support the one-step mechanism through a transition state with no intermediate in the reaction route. Stabilisation of peri-transition state by intramolecular weak interaction akin to hydrogen bonding N-H—CH2 = C leads to the regioselective amination at peri-position of 4-methylene-5,7-dinitroquinazoline. The intramolecular hydrogen bond interactions at N-H—CH2 = C in the peri-transition state are strongly supported by a red shift in N-H stretching vibrations in simulated IR spectra and are confirmed by studying energetics of amination of structural/electronic analogues of 4-methylene-5,7-dinitroquinazoline. The present study established that ethylene plays an important role as an efficient H-bond acceptor in fixing the regioselectivity at peri-position in the amination of 4-methylene-5,7-dinitroquinazoline. This is the first-ever report for the exploration of the role of ethylene moiety as a hydrogen bond acceptor in mediating the regioselectivity of organic reactions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Polyps are tumorous growths in the colon or rectal area which can turn into cancer at later stages, thus detection of polyps is very important for timely prevention of colorectal cancer. The aim of the study is to develop a framework to accurately detect polyp images in colonoscopy images. OBJECTIVE: Development of an intelligent framework for classification of colorectal cancer from colon and rectal images. The standard machine learning, convolutional neural networks and ensemble models with nature inspired approach were implemented for this study. Model optimization was performed by varying hyper parameters. The main objective was to find an optimal model with high accuracy, optimized weights and less parameters. METHODS: The deep learning Convolutional Neural Network (CNN) models such as VGG19, ResNet50, EfficientNet, Ensemble Model (EM), and Modified Ensemble CNN with Genetic Algorithm (MEGANET) were implemented for the classification of colon images. RESULTS: Ensemble model was also created with two best performing deep learning models to further achieve higher accuracy of 96%. The ensemble model outperformed the other models in terms of accuracy, precision, recall, and F1 score. But this model has more complexity. The MEGANET, nature inspired evolutionary ensemble CNN model was implemented with transfer learning and genetic algorithms for weights optimization and parameter reduction. It achieved accuracy of 95%, on training data. CONCLUSION: The MEGANET performed similar to EM with less number of parameters on training, validation and test dataset. In future different methods will be implemented to further reduce the parameters and attain reasonable accuracy using MEGANET. [ABSTRACT FROM AUTHOR]

Named Data Networking (NDN) has gained importance in today's era due to a paradigm shift in the Internet usage pattern which revolves around the content rather than the respective host addresses. Three important data structures in NDN are Content Store (CS), Pending Interest Table (PIT) and Forwarding Information Base (FIB). The search time of PIT is quite high since its size grows with the addition of new content names, and the Interest packets which are not served by CS are searched in millions of existing entries in the PIT. Hence, lookup time can be improved if, instead of checking all the available entries, initial scanning is done to determine whether the required content name exists in the PIT or not. In this paper, we propose a Stable Bloom Filter (SBF) based PIT called S-PIT, to minimize the PIT search time by identifying the existence of query content through SBF. The various experiments performed show that S-PIT outperforms existing data structures in terms of memory consumption, content insertion time, average search time and false positive rate. [ABSTRACT FROM AUTHOR]

Mental disorders are major problems and influence the thought of suicide, so it needs to be diagnosed and treated earlier. Detecting the symptoms of mental disorders is highly significant as they are considered a life-threatening task. Hence, this paper plans to implement new mental depression recognition by analyzing the tweet media text to secure the lives of the affected individuals. In the standard dataset, the text tweet data is collected. The gathered text data are used in the bags of n-grams and glove embedding for extracting the text features. Here, the extracted features are considered for the weighted feature selection, where the weight optimization takes place using the Awareness Probability-based Crow Black Widow Search algorithm. Finally, the weighted features are used for the recognition phase using the Convolutional Neural Network-Ensemble learning, where the final layer of CNN is replaced by the "Support Vector Machine (SVM), Random Forest (RF), Long Short Term Memory (LSTM) and Artificial Neural Network (ANN)" for classification. Here, parameter optimization occurs in the classifiers to improve the evaluation of the designed method. The experimental outcomes show the efficient performance of the developed method when compared with the other baseline approaches. [ABSTRACT FROM AUTHOR]

Membrane proteins (MPs) are functionally important but structurally complex. In particular, MPs often carry three structural features, i.e., transmembrane domains (TMs), disulfide bonds (SSs), and N-glycosylation (N-GLYCO). All three features have been intensively studied; however, how the three features potentially correlate has been less addressed in the literature. With the growing accuracy from computational prediction, we used publicly available information on SSs and N-GLYCO and analyzed the potential relationships among post-translational modifications (PTMs) and the predicted membrane topology in the human proteome. Our results suggested a very close relationship between SSs and N-GLYCO that behaved similarly, whereas a complementary relation between the TMs and the two PTMs was also revealed, in which the high SS and/or N-GLYCO presence is often accompanied by a low TM occurrence in a protein. Furthermore, the occurrence of SSs and N-GLYCO in a protein heavily relies on the protein length; however, TMs seem not to possess such length dependence. Finally, SSs exhibits larger potential dynamics than N-GLYCO, which is confined by the presence of sequons. The special classes of proteins possessing extreme or unique patterns of the three structural features are comprehensively identified, and their structural features and potential dynamics help to identify their susceptibility to different physiological and pathophysiological insults, which could help drug development and protein engineering. [ABSTRACT FROM AUTHOR]