Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'

Rediscovering the Therapeutic Potential of Agarwood in the Management of Chronic Inflammatory Diseases

The inflammatory response is a central aspect of the human immune system that acts as a defense mechanism to protect the body against infections and injuries. A dysregulated inflammatory response is a major health concern, as it can disrupt homeostasis and lead to a plethora of chronic inflammatory conditions. These chronic inflammatory diseases are one of the major causes of morbidity and mortality worldwide and the need for them to be managed in the long term has become a crucial task to alleviate symptoms and improve patients’ overall quality of life. Although various synthetic anti-inflammatory agents have been developed to date, these medications are associated with several adverse effects that have led to poor therapeutic outcomes. The hunt for novel alternatives to modulate underlying chronic inflammatory processes has unveiled nature to be a plentiful source. One such example is agarwood, which is a valuable resinous wood from the trees of Aquilaria spp. Agarwood has been widely utilized for medicinal purposes since ancient times due to its ability to relieve pain, asthmatic symptoms, and arrest vomiting. In terms of inflammation, the major constituent of agarwood, agarwood oil, has been shown to possess multiple bioactive compounds that can regulate molecular mechanisms of chronic inflammation, thereby producing a multitude of pharmacological functions for treating various inflammatory disorders. As such, agarwood oil presents great potential to be developed as a novel anti-inflammatory therapeutic to overcome the drawbacks of existing therapies and improve treatment outcomes. In this review, we have summarized the current literature on agarwood and its bioactive components and have highlighted the potential roles of agarwood oil in treating various chronic inflammatory diseases.</jats:p

Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'