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1.
Biol Lett ; 19(11): 20230358, 2023 11.
Article in English | MEDLINE | ID: mdl-37964576

ABSTRACT

Africa experiences frequent emerging disease outbreaks among humans, with bats often proposed as zoonotic pathogen hosts. We comprehensively reviewed virus-bat findings from papers published between 1978 and 2020 to evaluate the evidence that African bats are reservoir and/or bridging hosts for viruses that cause human disease. We present data from 162 papers (of 1322) with original findings on (1) numbers and species of bats sampled across bat families and the continent, (2) how bats were selected for study inclusion, (3) if bats were terminally sampled, (4) what types of ecological data, if any, were recorded and (5) which viruses were detected and with what methodology. We propose a scheme for evaluating presumed virus-host relationships by evidence type and quality, using the contrasting available evidence for Orthoebolavirus versus Orthomarburgvirus as an example. We review the wording in abstracts and discussions of all 162 papers, identifying key framing terms, how these refer to findings, and how they might contribute to people's beliefs about bats. We discuss the impact of scientific research communication on public perception and emphasize the need for strategies that minimize human-bat conflict and support bat conservation. Finally, we make recommendations for best practices that will improve virological study metadata.


Subject(s)
Chiroptera , Viruses , Animals , Humans , Disease Reservoirs , Africa
2.
RSC Med Chem ; 13(7): 831-839, 2022 Jul 20.
Article in English | MEDLINE | ID: mdl-35919336

ABSTRACT

By 2050, it is predicted that antimicrobial resistance will be responsible for 10 million global deaths annually, more deaths than cancer, costing the world economy $100 trillion. Clearly, strategies to address this problem are essential as bacterial evolution is rendering our current antibiotics ineffective. The discovery of an allosteric binding site on the established antibacterial target DNA gyrase offers a new medicinal chemistry strategy. As this site is distinct from the fluoroquinolone binding site, resistance is not yet documented. Using in silico molecular design methods, we have designed and synthesised a novel series of biphenyl-based inhibitors inspired by a published thiophene-based allosteric inhibitor. This series was evaluated in vitro against Escherichia coli DNA gyrase and E. coli topoisomerase IV with the most potent compounds exhibiting IC50 values towards the low micromolar range for DNA gyrase and only ∼2-fold less active against topoisomerase IV. The structure-activity relationships reported herein suggest insights to further exploit this allosteric site, offering a pathway to overcome developing fluoroquinolone resistance.

3.
PLoS One ; 17(3): e0261725, 2022.
Article in English | MEDLINE | ID: mdl-35358202

ABSTRACT

We investigated daily COVID-19 cases and deaths in the 337 lower tier local authority regions in England and Wales to better understand how the disease propagated over a 15-month period. Population density scaling models revealed residual variance and skewness to be sensitive indicators of the dynamics of propagation. Lockdowns and schools reopening coincided with increased variance indicative of conditions with local impact and country scale heterogeneity. University reopening and December holidays reduced variance indicative of country scale homogenisation which reached a minimum in mid-January 2021. Homogeneous propagation was associated with better correspondence with normally distributed residuals while heterogeneous propagation was more consistent with skewed models. Skewness varied from strongly negative to strongly positive revealing an unappreciated feature of community propagation. Hot spots and super-spreading events are well understood descriptors of regional disease dynamics that would be expected to be associated with positively skewed distributions. Positively skewed behaviour was observed; however, negative skewness indicative of "cold-spots" and "super-isolation" dominated for approximately 8 months during the period of study. In contrast, death metrics showed near constant behaviour in scaling, variance, and skewness metrics over the full period with rural regions preferentially affected, an observation consistent with regional age demographics in England and Wales. Regional positions relative to density scaling laws were remarkably persistent after the first 5-9 days of the available data set. The determinants of this persistent behaviour probably precede the pandemic and remain unchanged.


Subject(s)
COVID-19 , COVID-19/epidemiology , Communicable Disease Control , England/epidemiology , Humans , Population Density , Wales/epidemiology
4.
Sci Rep ; 10(1): 16863, 2020 10 08.
Article in English | MEDLINE | ID: mdl-33033349

ABSTRACT

The urban scaling hypothesis has improved our understanding of cities; however, rural areas have been neglected. We investigated rural-urban population density scaling in England and Wales using 67 indicators of crime, mortality, property, and age. Most indicators exhibited segmented scaling about a median critical density of 27 people per hectare. Above the critical density, urban regions preferentially attract young adults (25-40 years) and lose older people (> 45 years). Density scale adjusted metrics (DSAMs) were analysed using hierarchical clustering, networks, and self-organizing maps (SOMs) revealing regional differences and an inverse relationship between excess value of property transactions and a range of preventable mortality (e.g. diabetes, suicide, lung cancer). The most striking finding is that age demographics break the expected self-similarity underlying the urban scaling hypothesis. Urban dynamism is fuelled by preferential attraction of young adults and not a fundamental property of total urban population.


Subject(s)
Crime/statistics & numerical data , Mortality , Population Density , Population Dynamics , Rural Population/statistics & numerical data , Urban Population/statistics & numerical data , Adult , Age Factors , Aged , Aged, 80 and over , Cluster Analysis , England/epidemiology , Female , Humans , Male , Middle Aged , Wales/epidemiology
5.
PLoS One ; 15(9): e0239699, 2020.
Article in English | MEDLINE | ID: mdl-32966344

ABSTRACT

The current outbreak of the coronavirus disease 2019 (COVID-19) is an unprecedented example of how fast an infectious disease can spread around the globe (especially in urban areas) and the enormous impact it causes on public health and socio-economic activities. Despite the recent surge of investigations about different aspects of the COVID-19 pandemic, we still know little about the effects of city size on the propagation of this disease in urban areas. Here we investigate how the number of cases and deaths by COVID-19 scale with the population of Brazilian cities. Our results indicate small towns are proportionally more affected by COVID-19 during the initial spread of the disease, such that the cumulative numbers of cases and deaths per capita initially decrease with population size. However, during the long-term course of the pandemic, this urban advantage vanishes and large cities start to exhibit higher incidence of cases and deaths, such that every 1% rise in population is associated with a 0.14% increase in the number of fatalities per capita after about four months since the first two daily deaths. We argue that these patterns may be related to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas. We also find the initial growth rate of cases and deaths to be higher in large cities; however, these growth rates tend to decrease in large cities and to increase in small ones over time.


Subject(s)
Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Population Density , Age Distribution , Betacoronavirus , Brazil/epidemiology , COVID-19 , Cities/epidemiology , Health Services/supply & distribution , Health Services/trends , Humans , Pandemics/statistics & numerical data , SARS-CoV-2 , Time Factors
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