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MicroRNAs (miRNAs) are small non-coding molecules that play a pivotal part in brain development and the processes of establishment and maintenance of dendrites and neurite outgrowth by modulating gene expression. Dysregulation of miRNAs has been linked with neurological disorders. Exogenous miRNAs are unstable in the plasma due to degradation by nucleases; hence, choosing a harmless and effective delivery mode is crucial in the quest for miRNA-based therapeutics to treat neurological disorders. This review aims to shed light on the emerging role of nanotechnology-based approaches using miRNAs to treat neurodegenerative disorders. Nanotechnology encompasses a broad spectrum of applications, one of which is its role in developing nanoscale drug delivery systems. Nanotechnology-based drug delivery systems have attracted the attention of researchers due to the superiority of this mode over conventional treatment systems in terms of their favorable attributes such as bio-compatibility, bio-degradability, extremely small size, and the ability to cross the blood-brain barrier. This review explores nanotechnology-based approaches using miRNAs highlighting the use of viral vectors as well as non-viral vectors (such as exosomes, liposome nanoparticles, gold and magnetic nanoparticles, dendrimer-based nanoparticles, polymeric nanoparticles) to treat neurodegenerative disorders.

Leukemoid Reaction (LR) signifies leukocytosis characterized by mature neutrophils. The incidence of LR is about 1 % among hospitalized patients. Mucormycosis is a rare, aggressive, fatal fungal infection that afflicts immune-compromised patients. This study discusses the case of concomitant leukemoid reaction and mucormycosis in a patient with severe COVID-19 infection.

In the earliest days of civilization, cosmetics evolved with the development of appealing and aesthetic senses. Even people’s perceptions of beauty and fashion have changed over period of time, the demand and craving for cosmetics has not diminished. Indeed, the market for this industry is always expanding. Green cosmetics are nothing but natural cosmetics or personal care products made only with natural ingredients, which stem from plants and minerals. Such cosmetics save the environment, eliminate pollution to a certain extent and ensure the cautious use of non-renewable resources. This research attempts to identify how the variables social media marketing effort and environmental attitude impact the intention towards purchase of Green Cosmetics by Indian female consumers. The role of Brand Attachment has also been examined as a part of this study. Data has been collected from 393 female working professionals from South India. Three states namely Tamilnadu, Karnataka and Kerala have been considered for primary data collection purpose. Stratified sampling is applied to collect the data. Data has been collected in Quantiative form and statistical analysis has been applied on the data. The results of the hypothesis testing using structural equation modelling analysis shows that Environmental attitude creates an impact on the intention of consumers to buy Green Cosmetic Products Online. At the same time, Brand attachment does not moderate the relationship between Environmental Attitude of the consumers and their intention to buy the products.

This work is aimed at investigating the potential risks linked to electroencephalography (EEG)-based person authentication and providing solutions to mitigate these issues. Authenticating a person by EEG involves verifying the legitimacy of the signals used for user identification. EEG signals serve as a biometric modality for authentication and verification. Additional biometric modalities, such as fingerprints or irises, are vulnerable to both fabrication and degradation over time, and illicit use of dead people’s biometrics has sometimes been seen. EEG’s intrinsic properties prohibit signal imitation or postmortem acquisition, making it more reliable than other biometric modalities. This research is aimed at investigating the most recent advancements in the domain of EEG signals, clarifying the current knowledge that impacts EEG-based authentication, and evaluating the emerging challenges. Many research publications have been collected to achieve this objective. By considering historical and recent efforts and achievements, this research also presents feasible resolutions to the emerging inquiries prompted by the ongoing advancements in EEG-based technology. The potential future application of EEG-based authentication has also been the subject of this scholarly discourse. A comprehensive collection of articles over the previous decade has been compiled to answer contemporary EEG signal research questions to get valuable insights. According to research findings, in February 2022, a significant milestone was achieved when the EEG signals of a deceased person were successfully captured for the first time in recorded history. However, this groundbreaking discovery may threaten EEG-based authentication. In addition, it is found that EEG-based authentication literature did not completely implement “liveness detection.” An updated approach for identifying liveness addresses novel challenges, that is, falsified EEG signals and a dead person’s EEG signals for EEG-based authentication that have not been discussed in the literature. The suggested solutions put forward in this study have the potential to stimulate additional research in this area.

This study attempts to investigate Saudi EFL learners' perceptions, perspectives, and the prospects for AI-enabled learning and teaching in higher education in Saudi Arabia. As the study deals with how certain independent variables (levels of study, gender, and employment profile of learners' parents) shape the dependent variables (perceptions, perspectives, and prospects), a quantitative approach (descriptive quantitative design) was selected, and a questionnaire was prepared with partial adoption of modified items from existing studies. A simple random sampling method was used to collect responses from 258 Saudi EFL learners studying at different levels of the undergraduate programs at the College of Business Administration and College of Science and Humanities, Prince Sattam bin Abdulaziz University, KSA. The findings revealed that Saudi EFL learners were highly positive in their perceptions and perspectives toward AI-enabled learning, and they strongly favored the adoption of AI-powered tools for learning and teaching a language. It was also revealed that the Saudi higher education system offered great prospects for the integration of AI-powered technology into EFL classrooms. One-way ANOVA analysis revealed that there is no statistically significant difference among the study participants based on gender and parents' employment profile. However, a certain difference of statistical significance was found among them based on their study levels. The study holds implications as the findings will help the course instructors and administrators for effective integrations of AIenabled technology to equip students with effective English skills. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown promise in enhancing English as a second language (ESL) writing skills by providing personalized feedback and targeted corrections, thereby facilitating improved grammar and composition proficiency. Despite the potential of AI tools like ChatGPT, their impact on common writing errors in ESL contexts has yet to be explored. This study employed a quasi-experimental design to compare the efficacy of ChatGPT's mobile application feedback against traditional teacher feedback in a senior secondary public school in India. Over eight weeks, the experimental group received feedback on their writing error corrections through the ChatGPT application, while the control group received feedback from teachers. Additionally, participants' attitudes towards using ChatGPT for language learning were assessed through a questionnaire administered post-intervention to 132 students. Data was collected using pre- and post-tests that involved writing stories based on pictures. The study results demonstrated that the experimental group significantly improved writing proficiency, showing a reduction in common errors (third-person singular present, past tense, progressive, past participle, plural, possessive, comparative, and superlative) compared to the control group. Furthermore, most students preferred AI feedback, associating it with noticeable improvements in their writing skills and grammatical accuracy. These findings support the integration of AI tools like ChatGPT into language learning curricula as effective supplements to traditional teaching methods, offering personalized and immediate corrections that enhance learning outcomes. [ABSTRACT FROM AUTHOR]

Abstract We hypothesized that the gut microbiome in patients with diabetes secondary to chronic pancreatitis (Type 3c) is different from those with Type 1 and Type 2 diabetes. This was a cross-sectional preliminary study that included 8 patients with Type 1, 10 with Type 2, 17 with Type 3c diabetes and 9 healthy controls. Demographic, clinical, biochemical, imaging and treatment data were recorded and sequencing of the V3–V4 region of the bacterial 16SrRNA was done on fecal samples. Bioinformatics and statistical analyses was performed to evaluate the differences in the diversity indices, distance matrices, relative abundances and uniqueness of organisms between the types of diabetes. There was significant difference in the species richness. Beta diversity was significantly different between patients with Type 3c diabetes and the other groups. 31 genera were common to all the three types of diabetes. There was significant differences in the species level taxa between Type 3c diabetes and the other groups. The unique bacterial species signature in Type 3c diabetes compared to Type 1 and Type 2 diabetes included Nesterenkonia sp. AN1, Clostridium magnum, Acinetobacter lwoffii, Clostridium septicum, Porphyromonas somerae, Terrabacter tumescens, and Synechococus sp.