First described in 1958, the human monkeypox virus (hMPXV) is a neglected zoonotic pathogen closely associated with the smallpox virus. The virus usually spreads via close contact with the infected animal or human and has been endemic mostly in parts of the African continent. However, with the recent increase in trade, tourism, and travel, the virus has caused outbreaks in countries outside Africa. The recent outbreak in 2022 has been puzzling given the lack of epidemiological connection and the possible sexual transmission of the virus. Furthermore, there is limited understanding of the structural and pathogenetic mechanisms that are employed by the virus to invade the host cells. Henceforth, it is critical to understand the working apparatus governing the viral-immune interactions to develop effective therapeutical and prophylactic modalities. Hence, in the present short communication, we summarize the previously reported research findings regarding the virology of the human monkeypox virus.

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Context: The third generation of cryptocurrencies gathers cryptocurrencies that are as diverse as the market is big (e.g., Dogecoin or Litecoin). While Dogecoin is seen as a memecoin, the other gathers a very different category of investors. To our knowledge, no study has independently assessed the crypto community’s economical impact on these cryptocurrencies. Furthermore, various methodological possibilities exist to forecast cryptocurrency price—mainly coming from online communities.Method: Our study has retrospectively studied (from 01/01/2015 to 03/11/2021)—using open access data—the association strength (using normalized mutual information) and the linear correlation (using Pearson’s correlation) between Twitter activity and cryptocurrency economical attributes. In addition, we have computed different models (ADF, ARIMA, and Interpretable MultiVvariable Long Short-Term Memory recurrent neural network) that forecast past price values and assessed their precision.Findings and conclusions: While the average Dogecoin transaction value is impacted by tweets, tweets are impacted by Litecoin transactions number and average Litecoin transaction value. Tweet number is impacted by Dogecoin whale behavior, but no significant relationship was found between Litecoin whales and tweets. The forecasting error resulting from our ARIMA (0,0,0) models was 0.08% (with Litecoin) and 0.22% (with Dogecoin). Therefore, those are just the beginning of scientific findings that may lead to building a trading robot based on these results. However, in itself, this study is only for academic discussion, and conclusions need to be drawn by further research. The authors cannot be liable if any financial investment is made based on its conclusions.