Biocompatible Human Placental Extracellular Matrix Derived Hydrogels

Solubilizing extracellular matrix (ECM) materials and transforming them into hydrogels has expanded their potential applications both in vitro and in vivo. In this study, hydrogels are prepared by decellularization of human placental tissue using detergent and enzymes and by the subsequent creation of a homogenized acellular placental tissue powder (P-ECM). A perfusion-based decellularization approach is employed using detergent and enzymes. The P-ECM with and without gamma irradiation is then utilized to prepare P-ECM hydrogels. Physical and biological evaluations are conducted to assess the suitability of the P-ECM hydrogels for biocompatibility. The decellularized tissue has significantly reduced cellular content and retains the major ECM proteins. Increasing the concentration of P-ECM leads to improved mechanical properties of the P-ECM hydrogels. The biocompatibility of the P-ECM hydrogel is demonstrated through cell proliferation and viability assays. Notably, gamma-sterilized P-ECM does not support the formation of a stable hydrogel. Nonetheless, the use of HCl during the digestion process effectively decreases spore growth and bacterial bioburden. The study demonstrates that P-ECM hydrogels exhibit physical and biological attributes conducive to soft tissue reconstruction. These hydrogels establish a favorable microenvironment for cell growth and the need for investigating innovative sterilization methods.

Nomophobia: A rising concern among Indian students

BACKGROUND

Nomophobia is defined as "the fear of being without a mobile phone or unable to use it." Nowadays, it is considered a modern age phobia. It is to be considered as a form of behavioral addiction.

AIM

This study aims to determine the level of nomophobia in the Indian population aged between 15 and 35 years.

MATERIALS AND METHODS

A personalized questionnaire was designed in the Google Forms and distributed among the targeted audience. The questionnaire contained three parts: consent letter, sociodemographic details, and nomophobia questionnaire. A total of 2061 valid responses were analyzed in SPSS software.

RESULTS

Out of 2061, 52.9% of the respondents were male and 47.1% were female. 92.2% of the respondents were between 18 and 24 years of age group. Moreover, 79.1% of the respondents are undergraduate or pursuing their graduation. 35.5% of the respondents were from metropolitan city, 38.8% were from an urban city, and 12.15% were from a semiurban city, while the rest 13.6% were from rural areas. 74.8% of the respondents were moderate nomophobic, 18.9% were severe nomophobic, and 6.3% were mild nomophobic.

CONCLUSION

In this study, we observed that nomophobia is moderate to severe and that our "physical, mental, and social health" has a major concern.

An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score

Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.

Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model

Chronic liver diseases are fifth leading cause of fatality in developing countries. Their early diagnosis is extremely important for timely treatment and salvage life. To examine abnormalities of liver, ultrasound imaging is the most frequently used modality. However, the visual differentiation between chronic liver and cirrhosis, and presence of heptocellular carcinomas (HCC) evolved over cirrhotic liver is difficult, as they appear almost similar in ultrasound images. In this paper, to deal with this difficult visualization problem, a method has been developed for classifying four liver stages, that is, normal, chronic, cirrhosis, and HCC evolved over cirrhosis. The method is formulated with selected set of "handcrafted" texture features obtained after hierarchal feature fusion. These multiresolution and higher order features, which are able to characterize echotexture and roughness of liver surface, are extracted by using ranklet, gray-level difference matrix and gray-level co-occurrence matrix methods. Thereafter, these features are applied on proposed ensemble classifier that is designed with voting algorithm in conjunction with three classifiers, namely, k-nearest neighbor (k-NN), support vector machine (SVM), and rotation forest. The experiments are conducted to evaluate the (a) effectiveness of "handcrafted" texture features, (b) performance of proposed ensemble model, (c) effectiveness of proposed ensemble strategy, (d) performance of different classifiers, and (e) performance of proposed ensemble model based on Convolutional Neural Networks (CNN) features to differentiate four liver stages. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 96.6% is obtained by use of proposed classifier model.

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix

Chronic liver diseases are fifth leading cause of fatality in developing countries. Early diagnosis is important for timely treatment and to salvage life. Ultrasound imaging is frequently used to examine abnormalities of liver. However, ambiguity lies in visual interpretation of liver stages on ultrasound images. This difficult visualization problem can be solved by analysing extracted textural features from images. Grey-level difference matrix, a texture feature extraction method, can provide information about roughness of liver surface, sharpness of liver borders and echotexture of liver parenchyma. In this article, the behaviour of variants of grey-level difference matrix in characterizing liver stages is investigated. The texture feature sets are extracted by using variants of grey-level difference matrix based on two, three, five and seven neighbouring pixels. Thereafter, to take the advantage of complementary information from extracted feature sets, feature fusion schemes are implemented. In addition, hybrid feature selection (combination of ReliefF filter method and sequential forward selection wrapper method) is used to obtain optimal feature set in characterizing liver stages. Finally, a computer-aided system is designed with the optimal feature set to classify liver health in terms of normal, chronic liver, cirrhosis and hepatocellular carcinoma evolved over cirrhosis. In the proposed work, experiments are performed to (1) identify the best approximation of derivative (forward, central or backward); (2) analyse the performance of individual feature sets of variants of grey-level difference matrix; (3) obtain optimal feature set by exploiting the complementary information from variants of grey-level difference matrix and (4) analyse the performance of proposed method in comparison with existing feature extraction methods. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 94.5% is obtained by optimal feature set having complementary information from variants of grey-level difference matrix.

Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.