Hydrogels are commonly used biomaterials for tissue engineering. With their high-water content, good biocompatibility and biodegradability they resemble the natural extracellular environment and have been widely used as scaffolds for 3D cell culture and studies of cell biology. The possible size of such hydrogel constructs with embedded cells is limited by the cellular demand for oxygen and nutrients. For the fabrication of large and complex tissue constructs, vascular structures become necessary within the hydrogels to supply the encapsulated cells. In this review, we discuss the types of hydrogels that are currently used for the fabrication of constructs with embedded vascular networks, the key properties of hydrogels needed for this purpose and current techniques to engineer perfusable vascular structures into these hydrogels. We then discuss directions for future research aimed at engineering of vascularized tissue for implantation.

Turmeric, a product of Curcuma longa, has a very long history of being used for the treatment of wounds in many Asian countries. Curcumin, the principal curcuminoid of turmeric, has recently been identified as a main mediator of turmeric's medicinal properties. However, the inherent limitations of the compound itself, such as hydrophobicity, instability, poor absorption and rapid systemic elimination, pose big hurdles for translation to wider clinical application. We present here an approach for engineering curcumin/gelatin-blended nanofibrous mats (NMs) by electrospinning to adequately enhance the bioavailability of the hydrophobic curcumin for wound repair. Curcumin was successfully formulated as an amorphous nanosolid dispersion and favorably released from gelatin-based biomimetic NMs that could be easily applied topically to experimental wounds. We show synergistic signaling by the released curcumin during the healing process: (i) mobilization of wound site fibroblasts by activating the Wnt signaling pathway, partly mediated through Dickkopf-related protein-1, and (ii) persistent inhibition of the inflammatory response through decreased expression of monocyte chemoattractant protein-1 by fibroblasts. With a combination of these effects, the curcumin/gelatin-blended NMs enhanced the regenerative process in a rat model of acute wounds, providing a method for translating this ancient medicine for use in modern wound therapy.

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-onlybased models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease.

At the time of writing this article, the world population is suffering from more than 2 million registered COVID-19 disease epidemic-induced deaths since the outbreak of the corona virus, which is now officially known as SARS-CoV-2. However, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of COVID-19 directly or its symptoms such as breathing, dry, and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient well-being. We quickly guide further through challenges that need to be faced for real-life usage and limitations also in comparison with non-audio solutions. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.

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Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its non-invasive and ubiquitous character by nature, CA based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the COVID-19 (coronavirus disease 2019), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On one hand, we have witnessed the power of 5G, internet of things, big data, computer vision, and artificial intelligence in applications of epidemiology modelling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multi-task speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i. e., three-category classification tasks for evaluating the physical and/or mental