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 (samplein-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.Ebola and Zika viruses offer a stark reminder that infectious diseases rank among the gravest threats to human health, and can spread rapidly and unpredictably. New infections will continue to emerge each year, and old enemies re-emerge, increasingly with acquired-drug resistance (e.g. gonorrhoea and HIV). Rapid diagnosis plays a crucial role in any outbreak situation, empowering patients to gain faster access to potentially life-saving treatment, and informing prevention strategies to protect the wider public. However, routine diagnostic tests based on enzyme linked immunosorbent assays (ELISAs) and polymerase chain reaction (PCR) are confined to centralized laboratories often requiring large, sophisticated, costly instrumentation and highly trained staff. Inherent delays occur between taking samples, conveying them to the laboratory, waiting for results to come back and subsequent follow up appointments 1-3 . This means that a patient often has to make multiple visits to a clinic in order to receive treatment, potentially over long distances. This delays prescribing of treatment with increased risk of suffering, mortality, and also incorrect prescription of antimicrobials.Recent policy drivers aim to widen access to testing using so called 'rapid' point-of-care tests (POCT) but the performance and implementation of these tests still remain a challenge 4 . The most common tests based on lateral flow technology are still relatively slow, requiring a 10-20 minute waiting time for results 5 . This exceeds a typical doctor's appointment (8-10 mins in the UK 6 ) necessitating changes to patient pathways within a clinic with additional on-costs and staffing implications. It is also notoriously difficult to interpret a faint lateral flow test line by eye, particularly for non-experts (e.g. self-testers) 1 . Those tests that are currently available are insensitive to recent (acute) infections 7 and lack the ability to automatically capture test results electronically, risking an incorrect reading, missed opportunities to link patients to care pathways and potential data loss for public health (e.g. during an Ebola outbreak) 8 . Alternatively, uneccesary treatment may be initi...

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humansexperienced nurses and newly trained community health worker staff-and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low-and middle-income countries, termed REASSURED diagnostics 1 , an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections.Rapid diagnostic tests (RDTs) save lives by informing case management, treatment, screening, disease control and elimination programs 1 . Lateral flow tests are among the most common RDTs, and hundreds of millions of these tests are performed worldwide each year. They have the potential to support near-person testing and decentralized management of a range of clinically important diseases (including malaria, HIV, syphilis, tuberculosis, influenza and noncommunicable diseases 2 ), making it convenient for the end user and more affordable for health systems 3 . However, RDTs also present some issues, namely: errors in performing the test and interpreting the result 4,5 , quality control and lack of electronic data capture records of the test and results within health systems and surveillance. Many of these would be overcome with the real-time connectivity associated with REASSURED-the new criterion for an ideal test to reflect the importance of digital connectivity, coined by Peeling and coworkers 1 . Real-time connectivity involves the use of mobile-phone-connected RDTs. To date there have been few peer-reviewed studies or evaluations of the effectiveness of connected lateral flow tests at scale in populations in low-and middle-income countries.

Preventing 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.

Despite widened access to HIV testing, around half of those infected worldwide are unaware of their HIV-positive status and linkage to care remains a major challenge. Current rapid HIV tests are typically analogue risking incorrect interpretation, no facile electronic data capture, poor linkage to care and data loss for public health. Smartphone-connected diagnostic devices have potential to dramatically improve access to testing and patient retention with electronic data capture and wireless connectivity. We report a pilot clinical study of surface acoustic wave biosensors based on low-cost components found in smartphones to diagnose HIV in 133 patient samples. We engineered a small, portable, laboratory prototype and dual-channel biochips, with in-situ reference control coating and miniaturised configuration, requiring only 6 µL plasma. The dual-channel biochips were functionalized by ink-jet printing with capture coatings to detect either anti-p24 or anti-gp41 antibodies, and a reference control. Biochips were tested with 31 plasma samples from patients with HIV, and 102 healthy volunteers. SH-SAW biosensors showed excellent sensitivity, specificity, low sample volumes and rapid time to result, and were benchmarked to commercial rapid HIV tests. Testing for individual biomarkers found sensitivities of 100% (anti-gp41) and 64.5% (anti-p24) (combined sensitivity of 100%) and 100% specificity, within 5 min. All positive results were recorded within 60 s of sample addition with an electronic readout. Next steps will focus on a smartphone-connected device prototype and user-friendly app interface for larger scale evaluation and field studies, towards our ultimate goal of a new generation of affordable, connected point-of-care HIV tests.

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Background 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 false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity. Methods 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. Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues. Results Automated analysis showed substantial agreement with human experts (Cohen’s 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). Conclusions 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 as a tool for improved accuracy for population-level community surveillance.

A novel ultra‐low‐cost biochemical analysis platform to quantify protein dissociation binding constants and kinetics using paper microfluidics is reported. This approach marries video imaging with one of humankind's oldest materials: paper, requiring no large, expensive laboratory equipment, complex microfluidics or external power. Temporal measurements of nanoparticle–antibody conjugates binding on paper is found to follow the Langmuir Adsorption Model. This is exploited to measure a series of antibody–antigen dissociation constants on paper, showing excellent agreement with a gold‐standard benchtop interferometer. The concept is demonstrated with a camera and low‐end smartphone, 500‐fold cheaper than the reference method, and can be multiplexed to measure ten reactions in parallel. These findings will help to widen access to quantitative analytical biochemistry, for diverse applications spanning disease diagnostics, drug discovery, and environmental analysis in resource‐limited settings.

Mechanical signaling involved in molecular interactions lies at the heart of materials science and biological systems, but the mechanisms involved are poorly understood. Here we use nanomechanical sensors and intact human cells to provide unique insights into the signaling pathways of connectivity networks, which deliver the ability to probe cells to produce biologically relevant, quantifiable and reproducible signals. We quantify the mechanical signals from malignant cancer cells, with 10 cells per ml in 1000-fold excess of non-neoplastic human epithelial cells. Moreover, we demonstrate that a direct link between cells and molecules creates a continuous connectivity which acts like a percolating network to propagate mechanical forces over both short and long length-scales. The findings provide mechanistic insights into how cancer cells interact with one another and with their microenvironments, enabling them to invade the surrounding tissues. Further, with this system it is possible to understand how cancer clusters are able to co-ordinate their migration through narrow blood capillaries.