Although American traditional medicine (ATM) has been practiced for millennia, its complex multi-target mechanisms of therapeutic action remain poorly understood. Animal models are widely used to elucidate the therapeutic effects of various ATMs, including their modulation of brain and behavior. Complementing rodent models, the zebrafish (Danio rerio) is a promising novel organism in translational neuroscience and neuropharmacology research. Here, we emphasize the growing value of zebrafish for testing neurotropic effects of ATMs and outline future directions of research in this field. We also demonstrate the developing utility of zebrafish as complementary models for probing CNS mechanisms of ATM action and their potential to treat brain disorders. Withania somnifera (WS), also known as Ashwagandha, is commonly used in Ayurveda and other traditional medicine systems. WS has seen an increase in public use worldwide due to its reputation as an adaptogen. This popularity has elicited increased scientific study of its biological effects, including a potential application for neuropsychiatric and neurodegenerative disorders. This review aims to provide a comprehensive summary of preclinical and clinical studies examining the neuropsychiatric effects of WS, specifically its application in stress, anxiety, depression, and insomnia. Reports of human trials and animal studies of WS were collected primarily from the PubMed, Scopus, and Google Scholar databases. WS root and leaf extracts exhibited noteworthy anti-stress and anti-anxiety activity in animal and human studies. WS also improved symptoms of depression and insomnia, though fewer studies investigated these applications. WS may alleviate these conditions predominantly through modulation of the hyportant to investigate potential herb-drug interactions involving WS if used alongside pharmaceutical interventions. Further elucidation of active compounds of WS is also needed.Sleep is an evolutionarily conserved phenomenon, being an essential biological necessity for the learning process and memory consolidation. The brain displays two types of electrical activity during sleep slow-wave activity or non-rapid eye movement (NREM) sleep and desynchronized brain wave activity or rapid eye movement (REM) sleep. There are many theories about "Why we need to sleep?" among them the synaptic homeostasis. This theory proposes that the role of sleep is the restoration of synaptic homeostasis, which is destabilized by synaptic strengthening triggered by learning during waking and by synaptogenesis during development. Sleep diminishes the plasticity load on neurons and other cells to normalize synaptic strength. In contrast, it re-establishes neuronal selectivity and the ability to learn, leading to the consolidation and integration of memories. The use of zebrafish as a tool to assess sleep and its disorders is growing, although sleep in this animal is not yet divided, for example, into REM and NREM states. However, zebrafish are known to have a regulated daytime circadian rhythm. Their sleep state is characterized by periods of inactivity accompanied by an increase in arousal threshold, preference for resting place, and the "rebound sleep effect" phenomenon, which causes an increased slow-wave activity after a forced waking period. In addition, drugs known to modulate sleep, such as melatonin, nootropics, and nicotine, have been tested in zebrafish. In this review, we discuss the use of zebrafish as a model to investigate sleep mechanisms and their regulation, demonstrating this species as a promising model for sleep research.Internal carotid artery dissection (ICAD) represents the cause of ictus cerebral in about 20% of all cases of cerebral infarction among the young adult population. ICAD could involve both the extracranial and intracranial internal carotid artery (ICA). It could be spontaneous (SICAD) or traumatic (TICAD). It has been estimated that carotid injuries could complicate the 0,32% of cases of general blunt trauma and the percentage seems to be higher in severe multiple traumas. TICAD is diagnosed when neurological symptoms have already occurred, and it could have devastating consequences, from permanent neurological impairment to death. Thus, even if it is a rare condition, a prompt diagnosis is essential. There are no specific guidelines regarding TICAD screening. TICAD is mainly correlated to motor vehicle accidents (94/227), specifically to car accidents (39/94), and to direct or indirect head and cervical trauma (76/227). Nevertheless, TICAD should be considered when a young adult or middle-aged patient presents after severe blunt trauma. Understanding which kind of traumatic event is most associated with TICAD could help clinicians direct their diagnostic process. Herein, a review of the literature concerning TICAD has been carried out to highlights its correlation with specific traumatic events. As well, a case report is presented to discuss TICAD forensic implications.Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. https://www.selleckchem.com/products/AP24534.html A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories semi-supervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.