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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.

VANET (Vehicular Ad-hoc Network) is a developing technology, which is a combination of cellular technology, ad-hoc network & wireless LAN to improve the safety of vehicle as well as driver. VANET communication can be of two types, first one is broadcast and second one is unicast. Either communication may be broadcast or unicast both are sensitive to different types ofassaults, for example message forgery, (DOS) denial of service, Sybil assault, Greyhole, Blackhole & Wormhole assault. In this paper machine learning method is used to detect the wormhole assault in VANET’s multi-hop communication. We have created a scenario of VANET by using AODV routing protocol on NS-3.24.1 simulator, which utilizes the overall mobility traces generated by the simulator SUMO-0.32.0 to model the wormhole assault. The simulation is performed by using NS-3.24.1 simulator, and the statistics created by flow monitor are collected. The collected data is pre-processed and the k-NN & Random Forest algorithms are applied on this data, to make the model such type so that it can memorize the wormhole attack. The novelty of this research work is that with the help of proposed detection & prevention technique, vehicular ad-hoc network can be made free from wormhole assault by using ML approach. The performance of proposed machine learning models is compared with existing work. In this way it is clear that our proposed approach by using ML is powerful tool by which the wormhole assaults can be detected in VANETs. A scheme based on packet lease and cryptographic techniques is used to prevent the wormhole attack in VANET

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.

Epigenetic modifications are inherited differences in cellular phenotypes, such as cell gene expression alterations, that occur during somatic cell divisions (also, in rare circumstances, in germ line transmission), but no alterations to the DNA sequence are involved. Histone alterations, polycomb/trithorax associated proteins, short non-coding or short RNAs, long non—coding RNAs (lncRNAs), & DNA methylation are just a few biological processes involved in epigenetic events. These various modifications are intricately linked. The transcriptional potential of genes is closely conditioned by epigenetic control, which is crucial in normal growth and development. Epigenetic mechanisms transmit genomic adaptation to an environment, resulting in a specific phenotype. The purpose of this systematic review is to glance at the roles of Estrogen signalling, polycomb/trithorax associated proteins, DNA methylation in breast cancer progression, as well as epigenetic mechanisms in breast cancer therapy, with an emphasis on functionality, regulatory factors, therapeutic value, and future challenges.

A simple wet chemical method has been developed to synthesize selenium nanoparticles (size 40–100 nm), by the reaction of sodium selenosulphate precursor with different organic acids in aqueous medium, under ambient conditions. Polyvinyl alcohol has been used to stabilize the selenium nanoparticles. The synthesized nanoparticles can be separated from its sol by using a high-speed centrifuge and can be redispersed in aqueous medium with a sonicator. UV-visible optical absorption spectroscopy, X-ray diffraction, energy dispersive X-rays, differential scanning calorimetry, atomic force microscopy, and transmission electron microscopy techniques have been employed to characterize the synthesized selenium nanoparticles.