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This study investigates the factors influencing the adoption of fintech AI among 397 government college students from middle-class families in Haryana, India. Structural equation modeling was employed to study the relationships between the adoption of fintech AI and its predictors, namely Perceived Usefulness, Perceived Privacy and Security, Perceived Ease of Use, and Satisfaction of Fintech AI. This study utilized a cross-sectional research design. In this study, a non-probability convenience sampling method was utilized. The researchers opted for a sample of 397 government college students, consisting of 200 males and 197 females, hailing from middle-class families in Haryana, India. This sampling approach was selected because of its simplicity and ease of access, as it enabled the researchers to effortlessly connect with participants without resorting to random selection. The participants were drawn from various subject streams to guarantee a diverse representation of the student body. Data collection occurred between November 2022 and January 2023. The findings suggest that as perceived usefulness, ease of use, and satisfaction with fintech AI increase, the likelihood of adoption also increases. Conversely, as perceived Privacy and Security increase, the likelihood of adoption decreases. It is important to note that the strength of these relationships varies, with the strongest positive relationship observed between Adoption and Satisfaction of Fintech AI, while the relationships with Perceived Usefulness, Perceived Privacy and Security, and Perceived Ease of Use are weaker. These insights provide valuable information for organizations and policymakers seeking to promote the adoption of fintech AI among college students. Strategies aimed at addressing Privacy and Security concerns, enhancing user satisfaction, and improving perceived usefulness and ease of use may prove effective in increasing the adoption of fintech AI within this demographic.

This study aimed to predict the bankruptcy risk of companies listed on the National Stock Exchange (NSE) in India using the Altman Z-score model. By examining the impact of variable factors such as market value equity/book value of total liabilities, retained earnings/total assets, working capital/total assets, earnings before interest and taxes/total assets, and sales/total assets, the research sought to assess their influence on the financial condition of listed companies. The Altman Z-score model is a widely accepted tool for predicting bankruptcy risk and has been utilized in various industries across different countries. The model comprises five financial ratios that capture a company's liquidity, profitability, leverage, solvency, and activity. By analyzing these ratios, the model calculates a composite Z-score, which can be used to classify companies into different risk categories. Using a sample of NSE-listed companies, this study employed the Altman Z-score model to predict bankruptcy risk and examined the impact of the five variable factors on the firms' financial health. The findings provided insights into the relationship between these financial ratios and the companies' bankruptcy risk, offering valuable information for investors, policymakers, and other stakeholders in the Indian financial market. The study demonstrated the applicability of the Altman Z-score model in predicting bankruptcy risk for NSE-listed companies in India and highlighted the importance of each variable factor in assessing a firm's financial condition. These findings can help stakeholders make informed decisions regarding investment, risk management, and policy formulation in the Indian financial market.

Midline astrocytic neoplasms have distinct molecular characteristics, quite different from astrocytic neoplasms with similar morphology but not located in the midline. It is imperative that neuropathologists should be aware of the existence of these tumors, so they can be correctly diagnosed. Here, we discuss the case of a 14-year-old boy who presented with acute onset of vomiting followed by loss of consciousness. Subsequent magnetic resonance imaging revealed an ill-defined exophytic lesion arising from the brainstem and extending into the left cerebellopontine angle, with areas of hemorrhage and patchy restricted diffusion. The tumor was resected. Microscopy revealed medium-sized tumor cells in diffuse sheets, having round nuclei, granular chromatin, and scant cytoplasm. Microvascular and endothelial cell proliferation in small necrotic areas were seen. Mitosis was 0–1 per high-power field. By routine histopathological analysis, all features were consistent with the diagnosis of glioblastoma. Tumor cells were immunopositive for glial fibrillary acidic protein and isocitrate dehydrogenase-1 mutation (R132H), immunonegative for p53, and retained alpha thalassemia/mental retardation syndrome X-linked. It also showed a strong immunopositivity for H3-K27M mutation. A diagnosis of a diffuse midline glioma with H3-K27M mutation corresponding to the World Health Organization Grade IV was made. This case highlights the importance of exploring signature mutations in well-defined tumor categories such as H3-K27M-mutant diffuse midline glioma.