Bovine Coronavirus: Variability,Evolution, and Dispersal Patterns of a No Longer Neglected Betacoronavirus

Bovine coronavirus (BoCV) is an important pathogen of cattle, causing severe enteric disease and playing a role in the bovine respiratory disease complex. Similar to other coronaviruses, a remarkable variability characterizes both its genome and biology. Despite their potential relevance, different aspects of the evolution of BoCV remain elusive. The present study reconstructs the history and evolution of BoCV using a phylodynamic approach based on complete genome and spike protein sequences. The results demonstrate high mutation and recombination rates affecting different parts of the viral genome. In the spike gene, this variability undergoes significant selective pressures—particularly episodic pressure—located mainly on the protein surface, suggesting an immune-induced selective pressure. The occurrence of compensatory mutations was also identified. On the contrary, no strong evidence in favor of host and/or tissue tropism affecting viral evolution has been proven. The well-known plasticity is thus ascribable to the innate broad viral tropism rather than mid- or long-term adaptation. The evaluation of the geographic spreading pattern clearly evidenced two clusters: a European cluster and an American–Asian cluster. While a relatively dense and quick migration network was identified in the former, the latter was dominated by the primary role of the United States (US) as a viral exportation source. Since the viral spreading pattern strongly mirrored the cattle trade, the need for more intense monitoring and preventive measures cannot be underestimated as well as the need to enforce the vaccination of young animals before international trade, to reduce not only the clinical impact but also the transferal and mixing of BoCV strains.     

Prefrontal Cortex Activated Bilaterally by a Tilt Board Balance Task: A Functional Near-Infrared Spectroscopy Study in a Semi-Immersive Virtual Reality Environment

The aim of this study was to assess the prefrontal cortex (PFC) oxygenation response to a 5-min incremental tilt board balance task (ITBBT) in a semi-immersive virtual reality (VR) environment driven by a depth-sensing camera. It was hypothesized that the PFC would be bilaterally activated in response to the increase of the ITBBT difficulty, given the PFC involvement in the allocation of the attentional resources to maintain postural control. Twenty-two healthy male subjects were asked to use medial–lateral postural sways to maintain their equilibrium on a virtual tilt board (VTB) balancing over a pivot. When the subject was unable to maintain the VTB angle within ±35° the VTB became red (error). An eight-channel fNIRS system was employed for measuring changes in PFC oxygenated-deoxygenated hemoglobin (O₂Hb-HHb, respectively). Results revealed that the number of the performed board sways and errors augmented with the increasing of the ITBBT difficulty. A PFC activation was observed with a tendency to plateau for both O₂Hb-HHb changes within the last 2 min of the task. A significant main effect of the level of difficulty was found in O₂Hb and HHb (p < 0.001). The study has demonstrated that the oxygenation increased over the PFC while the subject was performing an ITBBT in a semi-immersive VR environment. This increase was modulated by the task difficulty, suggesting that the PFC is bilaterally involved in attention-demanding tasks. This task could be considered useful for diagnostic testing and functional neurorehabilitation given its adaptability in elderly and in patients with movement disorders.     

Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Several studies have shown a significant adenoma miss rate up to 35% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence(AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate(ADR) and polyp detection rate(PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network(DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos. Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR. In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.