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In sickle cell disease (SCD), heme released during intravascular hemolysis promotes oxidative stress, inflammation, and vaso-occlusion. Conversely, free heme can also activate expression of antioxidant and globin genes. Heme binds to the transcription factor BACH1, which represses NRF2-mediated gene transcription. ASP8731, is a selective small molecule inhibitor of BACH1. We investigated the ability of ASP8731 to modulate pathways involved in SCD pathophysiology. In HepG2 liver cells, ASP8731 increased HMOX1 and FTH1 mRNA. In pulmonary endothelial cells, ASP8731 decreased VCAM1 mRNA in response to TNF-α and blocked a decrease in glutathione in response to hemin. Townes-SS mice were gavaged once per day for 4 weeks with ASP8731, hydroxyurea (HU) or vehicle. Both ASP8731 and HU inhibited heme-mediated microvascular stasis and in combination, ASP8731 significantly reduced microvascular stasis compared to HU alone. In Townes-SS mice, ASP8731 and HU markedly increased heme oxygenase-1 and decreased hepatic ICAM-1, NF-kB phospho-p65 protein expression in the liver, and white blood cell counts. In addition, ASP8731 increased gamma-globin expression and HbF+ cells (F-cells) as compared to vehicle-treated mice. In human erythroid differentiated CD34+ cells, ASP8731 increased HGB mRNA and increased the percentage of F-cells 2-fold in manner similar to HU. ASP8731 and HU when given together induced more HbF+ cells compared to either drug alone. In CD34+ cells from one donor that was non-responsive to HU, ASP8731 induced HbF+ cells ~2-fold. ASP8731 and HU also increased HBG and HBA, but not HBB mRNA in erythroid differentiated CD34+ cells derived from SCD patients. These data indicate that BACH1 may offer a new therapeutic target to treat SCD.

Purpose: COVID-19-associated mucormycosis (CAM) was a serious public health problem during the second wave of COVID-19 in India. We planned to analyze public perceptions by sentiment analysis of Twitter data regarding CAM. Methods: In this observational study, the application programming interface (API) provided by the Twitter platform was used for extracting real-time conversations by using keywords related to mucormycosis (colloquially known as “black fungus”), from May 3 to August 29, 2021. Lexicon-based sentiment analysis of the tweets was done using the Vader sentiment analysis tool. To identify the overall sentiment of a user on any given topic, an algorithm to label a user “k” based on their sentiments was used. Results: A total of 4,01,037 tweets were collected between May 3 and August 29, 2021, and the peak frequency of 1,60,000 tweets was observed from May 17 to May 23, 2021. Positive sentiment tweets constituted a larger share as compared to negative sentiment tweets, with weekly variations. A temporal analysis of the demand for utilities showed that the demand was high in the initial period but decreased with time, which was associated with the availability of resources. Conclusion: Sentiment analysis using Twitter data revealed that social media platforms are gaining popularity to express one's emotions during the ongoing COVID-19 pandemic. In our study, time-based assessment of tweets showed a reduction over time in the frequency of negative sentiment tweets. The polarization in the retweet network of users, based on sentiment polarity, showed that the users were well connected, highlighting the fact that such issues bond our society rather than segregating it.

Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority, and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup, and Purity, whereas English tweets expressed more positive emotions for all moral dimensions. Considering moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority, and Purity for English and Ingroup, Authority and Purity for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English- and Japanese-speaking users.

Body weight variations are an integral part of a person's aging process. However, the lack of association between the age and the weight of an individual makes it challenging to model these variations for automatic face recognition. In this paper, we propose a regularizer-based approach to learn weight invariant facial representations using two different deep learning architectures, namely, sparse-stacked denoising autoencoders and deep Boltzmann machines. We incorporate a body-weight aware regularization parameter in the loss function of these architectures to help learn weight-aware features. The experiments performed on the extended WIT database show that the introduction of weight aware regularization improves the identification accuracy of the architectures both with and without dropout.

With the increase in age, there are changes in skeletal structure, muscle mass, and body fat. For recognizing faces with age variations, researchers have generally focused on the skeletal structure and muscle mass. However, the effect of change in body fat has not been studied with respect to face recognition. In this paper, we incorporate weight information to improve the performance of face recognition with age variations. The proposed algorithm utilizes neural network and random decision forest to encode age variations across different weight categories. The results are reported on the WhoIsIt database prepared by the authors containing 1109 images from 110 individuals with age and weight variations. The comparison with existing state-of-the-art algorithms and commercial system on WhoIsIt and FG-Net databases shows that the proposed algorithm outperforms existing algorithms significantly.