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In the present study, an ultra-sensitive surface plasmon resonance biosensor is developed. The device is composed of a CaF2-prism, silver, silicon nitride, nickel, and black phosphorus have been deposited. The setup is grounded in the Kretschmann configuration and theoretically analyzed by means of transfer matrix method and Fresnel equations. For selective diagnosis of liver tissue and its mutation in terms of variation in RI, the biosensor provides an ultrahigh angular sensitivity of 383.62°/RIU for MET tissue and 403.92°/RIU for HCC tissue. The optimization of angular sensitivity and other sensor parameters in terms of thickness and number of layers has also been utilized. To validate the reflectance curve, electric field intensity enhancement factor and phase interrogation have also been conducted. The acquired results state that the proposed biosensor yields an enhanced sensitivity when compared to other reported results. Due to its advantage of fast, accurate and early-stage detection of liver mutations with a very small number of samples, this report may expedite a sensing device for commercial applications. [ABSTRACT FROM AUTHOR]

Background: There is limited understanding of how universal health coverage (UHC) schemes such as publicly-funded health insurance (PFHI) benefit women as compared to men. Many of these schemes are gender-neutral in design but given the existing gender inequalities in many societies, their benefits may not be similar for women and men. We contribute to the evidence by conducting a gender analysis of the enrolment of individuals and households in India's national PFHI scheme, Rashtriya Swasthya Bima Yojana (RSBY). Methods: We used data from a cross-sectional household survey on RSBY eligible families across eight Indian states and studied different outcome variables at both the individual and household levels to compare enrolment among women and men. We applied multivariate logistic regressions and controlled for several demographic and socio-economic characteristics. Results: At the individual level, the analysis revealed no substantial differences in enrolment between men and women. Only in one state were women more likely to be enrolled in RSBY than men (AOR: 2.66, 95% CI: 1.32-5.38), and this pattern was linked to their status in the household. At the household level, analyses revealed that female-headed households had a higher likelihood to be enrolled (AOR: 1.36, 95% CI: 1.14-1.62), but not necessarily to have all household members enrolled. Conclusion: Findings are surprising in light of India's well-documented gender bias, permeating different aspects of society, and are most likely an indication of success in designing a policy that did not favour participation by men above women, by mandating spouse enrolment and securing enrolment of up to five family members. Higher enrolment rates among female-headed households are also an indication of women's preferences for investments in health, in the context of a conducive policy environment. Further analyses are needed to examine if once enrolled, women also make use of the scheme benefits to the same extent as men do. India is called upon to capitalise on the achievements of RSBY and apply them to newer schemes such as PM-JAY. [ABSTRACT FROM AUTHOR]

The brain‐computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared to traditional methods integrated with machine learning (ML) approaches that were initially explored in the past decade, there is a relatively limited number of studies utilizing deep learning (DL) approaches in BCI applications. A novel approach has been developed for the automatic recognition of motor imagery (MI) tasks. This article introduces the improved sparrow search algorithm with deep transfer learning‐based EEG motor imagery classification (ISSADTL‐EEGMIC) algorithm for BCIs. The presented ISSADTL‐EEGMIC algorithm primarily focuses on the automated classification of motor imagery tasks. To achieve this, the ISSADTL‐EEGMIC method first preprocesses EEG signals using the wavelet packet decomposition (WPD) technique. Additionally, it employs the neural architecture search network (NASNet) technique for feature extraction. Moreover, the ISSADTL‐EEGMIC model performs the classification process using the stacked sparse autoencoder (SSAE) model. Furthermore, hyperparameter optimization of the SSAE model is performed using the ISSA technique, which helps achieve maximum classification performance. The results from the simulation of the ISSADTL‐EEGMIC algorithm have been examined using two benchmark datasets. The experimental findings suggest that it outperforms recent approaches in terms of performance. [ABSTRACT FROM AUTHOR]

Biomedical imaging devices, in general, have been made and used a lot lately to examine the insides of the body during diagnostic and analytic procedures. Biomedical imaging gives accurate information about metabolites, which can be used to find and classify diseases because it is not invasive. For the study of cervical cancer (CC), the pap smear is a crucial type of biological imaging. CC is a crucial reason to enhance the rate of women's mortalities. Proper screening of pap smear images is critical for assisting in the early detection and analysis of CC. Computer‐aided systems for cancerous cell recognition need well established artificial intelligence (AI) methods. In this study, we introduce an automated Cervical Precancerous Lesion Classification using Quantum Invasive Weed Optimization with Deep Learning (CPLC‐QIWODL) on biomedical pap smear images. The presented CPLC‐QIWODL technique examines the pap smear images for cervical cancer classification. To do so, the presented CPLC‐QIWODL technique pre‐processes the biomedical images using a Gabor filtering (GF) approach. Moreover, the CPLC‐QIWODL technique uses a deep convolutional neural network‐based SqueezeNet system for feature extraction. Furthermore, the hyperparameter tuning of the SqueezeNet methodology takes place using the QIWO technique, showing the novelty of the work. Finally, to classify CC, the deep variational autoencoder (DVAE) model is applied. The experimental result analysis of the CPLC‐QIWODL technique is tested using a benchmark medical image database. Extensive comparative results demonstrated the enhanced outcomes of the CPLC‐QIWODL technique over other existing algorithms, with a maximum accuracy of 99.07%. [ABSTRACT FROM AUTHOR]

A few ubiquitin ligases have been shown to target Runx2, the key osteogenic transcription factor and thereby regulate bone formation. The regulation of Runx2 expression and function are controlled both at the transcriptional and posttranslational levels. Really interesting new gene (RING) finger ubiquitin ligases of which RNF138 is a member are important players in the ubiquitin‐proteasome system, contributing to the regulation of protein turnover and cellular processes. Here, we demonstrated that RNF138 negatively correlated with Runx2 protein levels in osteopenic ovariectomized rats which implied its role in bone loss. Accordingly, RNF138 overexpression potently inhibited osteoblast differentiation of mesenchyme‐like C3H10T1/2 as well primary rat calvarial osteoblast (RCO) cells in vitro, whereas overexpression of catalytically inactive mutant RNF138Δ18‐58 (lacks RING finger domain) had mild to no effect. Contrarily, RNF138 depletion copiously enhanced endogenous Runx2 levels and augmented osteogenic differentiation of C3H10T1/2 as well as RCOs. Mechanistically, RNF138 physically associates within multiple regions of Runx2 and ubiquitinates it leading to its reduced protein stability in a proteasome‐dependent manner. Moreover, catalytically active RNF138 destabilized Runx2 which resulted in inhibition of its transactivation potential and physiological function of promoting osteoblast differentiation leading to bone loss. These findings underscore the functional involvement of RNF138 in bone formation which is primarily achieved through its modulation of Runx2 by stimulating ubiquitin‐mediated proteasomal degradation. Thus, our findings indicate that RNF138 could be a promising novel target for therapeutic intervention in postmenopausal osteoporosis. [ABSTRACT FROM AUTHOR]