Notch signaling is a primitive signaling pathway having various roles in the normal origin and development of each multicellular organisms. Therefore, any aberration in the pathway will inevitably lead to deadly outcomes such as cancer. It has now been more than two decades since Notch was acknowledged as an oncogene in mouse mammary tumor virus-infected mice. Since that discovery, activated Notch signaling and consequent up-regulation of tumor-promoting Notch target genes have been observed in human breast cancer. Moreover, consistent over-expression of Notch ligands and receptors has been shown to correlate with poor prognosis in human breast cancer. Notch regulates a number of key processes during breast carcinogenesis, of which, one key phenomenon is epithelial–mesenchymal transition (EMT). EMT is a key process for large-scale cell movement during morphogenesis at the time of embryonic development. Cancer cells aided by transcription factors usurp this developmental program to execute the multi-step process of tumorigenesis and metastasis. In this review, we recapitulate recent progress in breast cancer research that has provided new perceptions into the molecular mechanisms behind Notch-mediated EMT regulation during breast tumorigenesis.

Alzheimer’s disease (AD) is a deadly brain degenerative disorder that leads to brain shrinkage and dementia. AD is manifested with hyperphosphorylated tau protein levels and amyloid beta (Aβ) peptide buildup in the hippocampus and cortex regions of the brain. The nervous tissue of AD patients also contains fungal proteins and DNA which are linked to bacterial infections, suggesting that polymicrobial infections also occur in the brains of those with AD. Both immunohistochemistry and next-generation sequencing (NGS) techniques were employed to assess fungal and bacterial infections in the brain tissue of AD patients and non-AD controls, with the most prevalent fungus genera detected in AD patients being Alternaria, Botrytis, Candida, and Malassezia. Interestingly, Fusarium was the most common genus detected in the control group. Both AD patients and controls were also detectable for Proteobacteria, followed by Firmicutes, Actinobacteria, and Bacteroides for bacterial infection. At the family level, Burkholderiaceae and Staphylococcaceae exhibited higher levels in the brains of those with AD than the brains of the control group. Accordingly, there is thought to be a viscous cycle of uncontrolled neuroinflammation and neurodegeneration in the brain, caused by agents such as the herpes simplex virus type 1 (HSV1), Chlamydophilapneumonia, and Spirochetes, and the presence of apolipoprotein E4 (APOE4), which is associated with an increased proinflammatory response in the immune system. Systemic proinflammatory cytokines are produced by microorganisms such as Cytomegalovirus, Helicobacter pylori, and those related to periodontal infections. These can then cross the blood–brain barrier (BBB) and lead to the onset of dementia. Here, we reviewed the relationship between the etiology of AD and microorganisms (such as bacterial pathogens, Herpesviridae viruses, and periodontal pathogens) according to the evidence available to understand the pathogenesis of AD. These findings might guide a targeted anti-inflammatory therapeutic approach to AD.

This paper introduces an LLM-Enhanced Agent-Based Influence Diffusion Simulation (LLM-AIDSim) framework that integrates large language models (LLMs) into agent-based modelling to simulate influence diffusion in social networks. The proposed framework enhances traditional influence diffusion models by allowing agents to generate language-level responses, providing deeper insights into user agent interactions. Our framework addresses the limitations of probabilistic models by simulating realistic, context-aware user behaviours in response to public statements. Using real-world news topics, we demonstrate the effectiveness of LLM-AIDSim in simulating topic evolution and tracking user discourse, validating its ability to replicate key aspects of real-world information propagation. Our experimental results highlight the role of influence diffusion in shaping collective discussions, revealing that, over time, diffusion narrows the focus of conversations around a few dominant topics. We further analyse regional differences in topic clustering and diffusion behaviours across three cities, Sydney, Auckland, and Hobart, revealing how demographics, income, and education levels influence topic dominance. This work underscores the potential of LLM-AIDSim as a decision-support tool for strategic communication, enabling organizations to anticipate and understand public sentiment trends.

The rapid growth in Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy. Recently, research has begun to explore automatic explanations as an integral part of the accuracy analysis process. However, the explainability within AFV is lagging compared to the wider field of explainable AI (XAI), which aims at making AI decisions more transparent. This study looks at the notion of explainability as a topic in the field of XAI, with a focus on how it applies to the specific task of Automated Fact Verification. It examines the explainability of AFV, taking into account architectural, methodological, and dataset-related elements, with the aim of making AI more comprehensible and acceptable to general society. Although there is a general consensus on the need for AI systems to be explainable, there a dearth of systems and processes to achieve it. This research investigates the concept of explainable AI in general and demonstrates its various aspects through the particular task of Automated Fact Verification. This study explores the topic of faithfulness in the context of local and global explainability. This paper concludes by highlighting the gaps and limitations in current data science practices and possible recommendations for modifications to architectural and data curation processes, contributing to the broader goals of explainability in Automated Fact Verification.

Stock market is a well-known option for investments and has impact in today’s economy. Due to high returns and losses, it will be an investor’s dream to predict the rise and fall accurately. But future price is very unpredictable. Although there are different analysis present for years like fundamental analysis, technical analysis etc. Now a days different algorithms are used to forecast the future price. Stock price prediction is very important as it can be seen as the guideline used by the traders and investors about the future trend of the market. So, it will help them to take entry or exit in the market in right time. In this paper, we have predicted the stock price of five NIFTY 50 shares and then compared it with the actual value to analyze the accuracy of price prediction. Here Long-Short-Term Memory, a deep learning (DL) model is used. It is found that the DL model performed very well in predicting the price of required stock. Quantification of the performance of the model is done using mean absolute percentage error.