Towards an ultra-rapid smartphone- connected test for infectious diseases

The development is reported of an ultra-rapid, point-of-care diagnostic device which harnesses surface acoustic wave (SAW) biochips, to detect HIV in a finger prick of blood within 10 seconds (sample-in-result-out). The disposable quartz biochip, based on microelectronic components found in every consumer smartphone, is extremely fast because no complex labelling, amplification or wash steps are needed. A pocket-sized control box reads out the SAW signal and displays results electronically. High analytical sensitivity and specificity are found with model and real patient blood samples. The findings presented here open up the potential of consumer electronics to cut lengthy test waiting times, giving patients on the spot access to potentially life-saving treatment and supporting more timely public health interventions to prevent disease transmission

Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies

Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home but rely on subjective interpretation of a test line by eye, risking false positives and negatives. Here we report the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity. Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Automated analysis showed substantial agreement with human experts (Kappa 0.90-0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7-99.4%) and sensitivity (90.1-97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets). Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues. Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false positive and false negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests, to be a tool for improved accuracy for population-level community surveillance

EU agricultural reform fails on biodiversity

In December 2013, the European Union (EU) enacted the reformed Common Agricultural Policy (CAP) for 2014–2020, allocating almost 40% of the EU's budget and influencing management of half of its terrestrial area. Many EU politicians are announcing the new CAP as “greener,” but the new environmental prescriptions are so diluted that they are unlikely to benefit biodiversity. Individual Member States (MSs), however, can still use flexibility granted by the new CAP to design national plans to protect farmland habitats and species and to ensure long-term provision of ecosystem services

Assessing the ecological and societal impacts of alien parrots in Europe using a transparent and inclusive evidence-mapping scheme

Globally, the number of invasive alien species (IAS) continues to increase, and management and policy responses typically need to be adopted before conclusive empirical evidence on their environmental and socioeconomic impacts are available. Consequently, numerous protocols exist for assessing IAS impacts, and differ considerably in which evidence they include. However, inclusive strategies for building a transparent evidence base underlying IAS impact assessments are lacking, potentially affecting our ability to reliably identify priority IAS. Using alien parrots in Europe as a case study, here we apply an evidence-mapping scheme to classify impact evidence and evaluate the consequences of accepting different subsets of available evidence on impact assessment outcomes. We collected environmental and socioeconomic impact data in multiple languages using a “wiki-review” process, comprising a systematic evidence search and an online editing and consultation phase. Evidence was classified by parrot species, impact category (e.g. infrastructure), geographical area (e.g. native range), source type (e.g. peer-review), study design (e.g. experimental), and impact direction (deleterious, beneficial and no impact). Our comprehensive database comprised 386 impact entries from 233 sources. Most evidence was anecdotal (50%). 42% of entries reported damage to agriculture (mainly in native ranges), while within Europe most entries concerned interspecific competition (39%). We demonstrate that the types of evidence included in assessments can strongly influence impact severity scores. For example, including evidence from the native range or anecdotal evidence resulted in an overall switch from minimal-moderate to moderate-major overall impact scores. We advise using such an evidence-mapping approach to create an inclusive and updatable database as the foundation for more transparent IAS impact assessments. When openly shared, such evidence-mapping can help better inform IAS research, management and policy

Unravelling the Molecular Basis of High Affinity Nanobodies against HIV p24: In Vitro Functional, Structural, and in Silico Insights.

International audiencePreventing the spread of infectious diseases remains an urgent priority worldwide, and this is driving the development of advanced nanotechnology to diagnose infections at the point of care. Herein, we report the creation of a library of novel nanobody capture ligands to detect p24, one of the earliest markers of HIV infection. We demonstrate that these nanobodies, one tenth the size of conventional antibodies, exhibit high sensitivity and broad specificity to global HIV-1 subtypes. Biophysical characterization indicates strong 690 pM binding constants and fast kinetic on-rates, 1 to 2 orders of magnitude better than monoclonal antibody comparators. A crystal structure of the lead nanobody and p24 was obtained and used alongside molecular dynamics simulations to elucidate the molecular basis of these enhanced performance characteristics. They indicate that binding occurs at C-terminal helices 10 and 11 of p24, a negatively charged region of p24 complemented by the positive surface of the nanobody binding interface involving CDR1, CDR2, and CDR3 loops. Our findings have broad implications on the design of novel antibodies and a wide range of advanced biomedical applications

The effects of increasing land use intensity on soil nematodes: A turn towards specialism

Abstract The ecosystem services that humans obtain from the soil are strongly linked to the soil's biota. There is ample evidence that intensive agriculture has a negative effect on the soil's biological diversity. While in other ecosystems, habitat specialists are at a higher risk of extinction due to human impacts than generalists, we have no evidence of whether this holds true for soil biota. We calculated the realized niche width for soil nematodes using co-occurrence data. We compared these with ecological traits. We then calculated an index of community specialization and tested whether land use intensity leads to decreases in the index of community specialization, taxon richness, diversity and to changes in nematode abundance. The resulting realized niche widths did not correlate with ecological traits such as feeding group, body mass or c-p class. While it is possible that there are no relationships between these traits and the realized niche width, it is likelier that food availability, pH tolerance, or host breadth are more important factors in explaining niche width. Contrary to our expectations, the lowest community specialization levels were found in soils with the lowest human intervention (shrubland?woodland ecosystems), while grasslands, dairy farms and arable farms had an overall higher level of specialization. Weather variables and land use intensity explained 66% of the variation in the index of community specialization in sandy soils. We found highest richness and diversity at intermediate levels of disturbance (grasslands and dairy farms). The lowest abundances were found on shrubland?woodland systems. Dairy farms on sand and clay had similar indices of community specialization, whereas peaty soils fostered a higher proportion of habitat specialists. We argue that farmland supposes a stable environment for organisms with shorter life spans. Agricultural management strives to lower disturbances, allowing shorter lived organisms to escape pressures otherwise present in nature, such as drought or nutrient deficiencies during the growing season. In very disturbed systems, however, specialists may also suffer from negative effects of land use intensity. This co-occurrence method to assess niche width opens the door to estimating the soil community's niche breadth, for which resource-based methods are difficult to implement. A free Plain Language Summary can be found within the Supporting Information of this article