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Background: In today's digital age, various diseases drastically reduce people's quality of life. Arthritis is one amongst the most common and debilitating maladies. Osteoarthritis affects several joints, including the hands, knees, spine, and hips. This study focuses on the medical disorder underlying Knee Osteoarthritis (KOA) which severely impairs people's quality of life. KOA is characterised by restricted mobility, stiffness, and terrible pain and can be caused by a range of factors such as ageing, obesity, and traumas. This degenerative disorder leads to progressive wear and tear of the knee joint., Methods: To combat arthritis in the kneecap, this study employs a 12-layer Convolutional Neural Network (CNN) to reach deep learning capabilities. A collection of data from the Osteoarthritis Initiative (OAI) is used to classify KOA. Through the use of medical image processing; the study ascertains whether an individual has this ailment. A sophisticated CNN architecture created especially for binary classification and KOA severity utilising deep learning algorithms is the main component of this work., Results: The cross-entropy loss function is an important component of the model's laborious design that classifies data into two groups. The remaining section uses the Kellgren-Lawrence (KL) grade to classify the disease's severity. In the binary classification, the proposed algorithm outperforms previous methods with an accuracy rate of 92.3%, and in the multiclassification, its accuracy rate is 78.4% which is superior to the previous findings., Conclusion: Looking ahead, the research broadens the scope of this work by gathering information from various sources and using these methods on a wider range of datasets and situations. The potential for major advancements in the field of osteoarthritis detection and classification is highlighted by this forward-looking approach. Furthermore, this method reduces the intervention of medical practitioners and ultimately results in accurate diagnosis., Clinical Trial Number: Not applicable., (© 2024. The Author(s).)

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient's internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance., Competing Interests: The authors declare that they have no conflict of interest., (Copyright © 2022 Sheeba Praveen et al.)

Multiwalled carbon nanotubes (MWCNTs) were incorporated into highly concentrated water-in-oil emulsions with the ultimate aim of achieving a uniform and effective dispersion of MWCNTs within the emulsion matrix. The emulsion was formulated in such a way, wherein the internal phase consists of higher than 90 wt %. By keeping the same aqueous-to-oil phase ratio, the amount of MWCNTs in the oil phase was systematically adjusted to investigate their effects on the microstructure development and rheological behavior of the emulsion. The addition of MWCNTs led to a reduced droplet size and also resulted in a narrower distribution of the droplet size. The rheological behavior of nanotube-incorporated emulsions was characterized with varying MWCNT concentrations and also as a function of the emulsification time. The rheological characteristics of the nanotube-incorporated emulsions were identical to those of the neat emulsion and were primarily governed by the variation in the droplet size and droplet-size distribution. However, the yield strain and cross-over strain were independent of the mean droplet size and polydispersity of the emulsion. Emulsions that have smaller droplets exhibited higher storage modulus ( G '), yield stress (τ Y ), and apparent viscosity (η). For all refining times investigated, nanotube-incorporated emulsions have higher G ', τ Y , and η values when compared to the neat emulsion, and these values further increased with the MWCNT concentration. This was primarily due to the decrease in the droplet size with MWCNT addition. Furthermore, our findings suggest that the incorporated MWCNTs did not induce any significant change in the rheological behavior of emulsions with identical droplet sizes, and it remained essentially unchanged with the concentration of MWCNTs. However, the nanotube-incorporated emulsions possessed solidlike behavior up to a higher applied stress when compared to a neat emulsion of identical droplet size., Competing Interests: The authors declare no competing financial interest.

Background: Low/middle-income countries need a large-scale improvement in the quality of care (QoC) around the time of childbirth in order to reduce high maternal, fetal and neonatal mortality. However, there is a paucity of scalable models., Methods: We conducted a stepped-wedge cluster-randomised trial in 15 primary health centres (PHC) of the state of Haryana in India to test the effectiveness of a multipronged quality management strategy comprising capacity building of providers, periodic assessments of the PHCs to identify quality gaps and undertaking improvement activities for closure of the gaps. The 21-month duration of the study was divided into seven periods (steps) of 3  months each. Starting from the second period, a set of randomly selected three PHCs (cluster) crossed over to the intervention arm for rest of the period of the study. The primary outcomes included the number of women approaching the PHCs for childbirth and 12 directly observed essential practices related to the childbirth. Outcomes were adjusted with random effect for cluster (PHC) and fixed effect for 'months of intervention'., Results: The intervention strategy led to increase in the number of women approaching PHCs for childbirth (26 vs 21 women per PHC-month, adjusted incidence rate ratio: 1.22; 95% CI 1.17 to 1.28). Of the 12 practices, 6 improved modestly, 2 remained near universal during both intervention and control periods, 3 did not change and 1 worsened. There was no evidence of change in mortality with a majority of deaths occurring either during referral transport or at the referral facilities., Conclusion: A multipronged quality management strategy enhanced utilisation of services and modestly improved key practices around the time of childbirth in PHCs in India., Trial Registration Number: CTRI/2016/05/006963., Competing Interests: Competing interests: None declared.

We report this peculiar case of ocular cysticercosis with intermittent blindness which is never reported to our knowledge. This case highlights use of Ultrasound for examination of the posterior segment of eye globe. In limited facilities Ultrasound is the main modality to reach the diagnosis. We also wish to enforce the point that in endemic regions and in young population primary cysticercosis and its complications must be kept in differentials of cystic mass or calcified mass, as in this case, we have seen both the stages of cysticercosis progression.