Notes

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The results of using this adaptive flexible blended learning approach provided an autonomous independent learning, self-study approach that broadened student performance such that we have more students scoring between 80 and 89%, whereas the in-person learning resulted in most of the students scoring >90% in the medical neuroscience laboratory activities.

An adaptive flexible blended learning approach that combined virtual face-to-face instruction using digital technology with online learning of neuroscience laboratory activities provided a unique educational experience for Year 1 medical students to learn neuroscience laboratory activities during the COVID-19 pandemic.
An adaptive flexible blended learning approach that combined virtual face-to-face instruction using digital technology with online learning of neuroscience laboratory activities provided a unique educational experience for Year 1 medical students to learn neuroscience laboratory activities during the COVID-19 pandemic.Coronavirus disease 2019 (COVID-19) pandemic has proven to be tenacious and shows that the global community is still poorly prepared to handling such emerging pandemics. Enhancing global solidarity in emergency preparedness and response, and the mobilization of conscience and cooperation, can serve as an excellent source of ideas and measures in a timely manner. The article provides an overview of the key components of risk communication and community engagement (RCCE) strategies at the early stages in vulnerable nations and populations, and highlight contextual recommendations for strengthening coordinated and sustainable RCCE preventive and emergency response strategies against COVID-19 pandemic. Global solidarity calls for firming governance, abundant community participation and enough trust to boost early pandemic preparedness and response. Promoting public RCCE response interventions needs crucially improving government health systems and security proactiveness, community to individual confinement, trust and resilience solutions. To better understand population risk and vulnerability, as well as COVID-19 transmission dynamics, it is important to build intelligent systems for monitoring isolation/quarantine and tracking by use of artificial intelligence and machine learning systems algorithms. Experiences and lessons learned from the international community is crucial for emerging pandemics prevention and control programs, especially in promoting evidence-based decision-making, integrating data and models to inform effective and sustainable RCCE strategies, such as local and global safe and effective COVID-19 vaccines and mass immunization programs.Mucus consistency affects voice physiology and is connected to voice disorders. Nevertheless, the rheological characteristics of human laryngeal mucus from the vocal folds remain unknown. Knowledge about mucus viscoelasticity enables fabrication of artificial mucus with natural properties, more realistic ex-vivo experiments and promotes a better understanding and improved treatment of dysphonia with regard to mucus consistency. We studied human laryngeal mucus samples from the vocal folds with two complementary approaches 19 samples were successfully applied to particle tracking microrheology (PTM) and five additional samples to oscillatory shear rheology (OSR). Mucus was collected by experienced laryngologists from patients together with demographic data. The analysis of the viscoelasticity revealed diversity among the investigated mucus samples according to their rigidity (absolute G' and G″). Cryptotanshinone order Moreover some samples revealed throughout solid-like character (G' > G″), whereas some underwent a change from solid-like to liquid-like (G' less then G″). This led to a subdivision into three groups. We assume that the reason for the differences is a variation in the hydration level of the mucus, which affects the mucin concentration and network formation factors of the mucin mesh. The demographic data could not be correlated to the differences, except for the smoking behavior. Mucus of predominant liquid-like character was associated with current smokers.Smart nanoparticles for medical applications have gathered considerable attention due to an improved biocompatibility and multifunctional properties useful in several applications, including advanced drug delivery systems, nanotheranostics and in vivo imaging. Among nanomaterials, zinc oxide nanoparticles (ZnO NPs) were deeply investigated due to their peculiar physical and chemical properties. The large surface to volume ratio, coupled with a reduced size, antimicrobial activity, photocatalytic and semiconducting properties, allowed the use of ZnO NPs as anticancer drugs in new generation physical therapies, nanoantibiotics and osteoinductive agents for bone tissue regeneration. However, ZnO NPs also show a limited stability in biological environments and unpredictable cytotoxic effects thereof. To overcome the abovementioned limitations and further extend the use of ZnO NPs in nanomedicine, doping seems to represent a promising solution. This review covers the main achievements in the use of doped ZnO NPs for nanomedicine applications. Sol-gel, as well as hydrothermal and combustion methods are largely employed to prepare ZnO NPs doped with rare earth and transition metal elements. For both dopant typologies, biomedical applications were demonstrated, such as enhanced antimicrobial activities and contrast imaging properties, along with an improved biocompatibility and stability of the colloidal ZnO NPs in biological media. The obtained results confirm that the doping of ZnO NPs represents a valuable tool to improve the corresponding biomedical properties with respect to the undoped counterpart, and also suggest that a new application of ZnO NPs in nanomedicine can be envisioned.In spite of machine learning has been successfully used in a wide range of healthcare applications, there are several parameters that could influence the performance of a machine learning system. One of the big issues for a machine learning algorithm is related to imbalanced dataset. An imbalanced dataset occurs when the distribution of data is not uniform. This makes harder the implementation of accurate models. In this paper, intelligent models are implemented to predict the hematocrit level of blood starting from visible spectral data. The aim of this work is to show the effects of two balancing techniques (SMOTE and SMOTE+ENN) on the imbalanced dataset of blood spectra. Four different machine learning systems are fitted with imbalanced and balanced datasets and their performances are compared showing an improvement, in terms of accuracy, due to the use of balancing.
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