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Background: The critical issues of sustained memory immunity following ebolavirus disease among long-term survivors are still unclear., Methods: Here, we examine virus-specific immune and inflammatory responses following in vitro challengd in 12 Sudan virus (SUDV) long-term survivors from Uganda's 2000-2001 Gulu outbreak, 15 years after recovery. Total RNA from isolated SUDV-stimulated and unstimulated peripheral blood mononuclear cells was extracted and analyzed. Matched serum samples were also collected to determine SUDV IgG levels and functionality., Results: We detected persistent humoral (58%, 7 of 12) and cellular (33%, 4 of 12) immune responses in SUDV long-term survivors and identified critical molecular mechanisms of innate and adaptive immunity. Gene expression in immune pathways, the interferon signaling system, antiviral defense response, and activation and regulation of T- and B-cell responses were observed. SUDV long-term survivors also maintained robust virus-specific IgG antibodies capable of polyfunctional responses, including neutralizing and innate Fc effector functions., Conclusions: Data integration identified significant correlations among humoral and cellular immune responses and pinpointed a specific innate and adaptive gene expression signature associated with long-lasting immunity. This could help identify natural and vaccine correlates of protection against ebolavirus disease., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up and help beat coronavirus' digital survey alongside demographic, symptom and self-reported respiratory condition data. Digital survey submissions were linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,565 of 72,999 participants and 24,105 of 25,706 positive cases. Respiratory symptoms were reported by 45.6% of participants. This dataset has additional potential uses for bioacoustics research, with 11.3% participants self-reporting asthma, and 27.2% with linked influenza PCR test results., (© 2024. The Author(s).)

Schistosomiasis is a major neglected tropical disease targeted for elimination as a public health issue by 2030, however there is an urgent need for more sensitive and specific diagnostic tests suitable to resource-limited settings. Here we developed CATSH, a CRISPR-assisted diagnostic test for Schistosoma haematobium, utilising recombinase polymerase amplification, Cas12a-targeted cleavage and portable real-time fluorescence detection. CATSH showed high analytical sensitivity, consistent detection of a single parasitic egg and specificity for urogenital Schistosoma species. Thanks to a novel CRISPR-compatible sample preparation developed using simulated urine samples containing parasitic eggs, CATSH had a sample-to-result within 2 h. The components of CATSH can be lyophilised, reducing cold chain dependence and widening access to lower and middle-income countries. This work presents a new application of CRISPR diagnostics for highly sensitive and specific detection of parasitic pathogens in remote areas and could have a significant impact on the elimination of neglected tropical diseases., (© 2023. The Author(s).)

Background: We explore severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow immunoassay (LFIA) performance under field conditions compared to laboratory-based electrochemiluminescence immunoassay (ECLIA) and live virus neutralization., Methods: In July 2021, 3758 participants performed, at home, a self-administered Fortress LFIA on finger-prick blood, reported and submitted a photograph of the result, and provided a self-collected capillary blood sample for assessment of immunoglobulin G (IgG) antibodies using the Roche Elecsys® Anti-SARS-CoV-2 ECLIA. We compared the self-reported LFIA result to the quantitative ECLIA and checked the reading of the LFIA result with an automated image analysis (ALFA). In a subsample of 250 participants, we compared the results to live virus neutralization., Results: Almost all participants (3593/3758, 95.6%) had been vaccinated or reported prior infection. Overall, 2777/3758 (73.9%) were positive on self-reported LFIA, 2811/3457 (81.3%) positive by LFIA when ALFA-reported, and 3622/3758 (96.4%) positive on ECLIA (using the manufacturer reference standard threshold for positivity of 0.8 U mL-1). Live virus neutralization was detected in 169 of 250 randomly selected samples (67.6%); 133/169 were positive with self-reported LFIA (sensitivity 78.7%; 95% confidence interval [CI]: 71.8, 84.6), 142/155 (91.6%; 95% CI: 86.1, 95.5) with ALFA, and 169 (100%; 95% CI: 97.8, 100.0) with ECLIA. There were 81 samples with no detectable virus neutralization; 47/81 were negative with self-reported LFIA (specificity 58.0%; 95% CI: 46.5, 68.9), 34/75 (45.3%; 95% CI: 33.8, 57.3) with ALFA, and 0/81 (0%; 95% CI: 0, 4.5) with ECLIA., Conclusions: Self-administered LFIA is less sensitive than a quantitative antibody test, but the positivity in LFIA correlates better than the quantitative ECLIA with virus neutralization., Competing Interests: Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Protein-protein interactions are fundamental to life processes. Complementary computational, structural, and biophysical studies of these interactions enable the forces behind their specificity and strength to be understood. Antibody fragments such as single-chain antibodies have the specificity and affinity of full antibodies but a fraction of their size, expediting whole molecule studies and distal effects without exceeding the computational capacity of modeling systems. We previously reported the crystal structure of a high-affinity nanobody 59H10 bound to HIV-1 capsid protein p24 and deduced key interactions using all-atom molecular dynamics simulations. We studied the properties of closely related medium (37E7) and low (48G11) affinity nanobodies, to understand how changes of three (37E7) or one (48G11) amino acids impacted these interactions; however, the contributions of enthalpy and entropy were not quantified. Here, we report the use of qualitative and quantitative experimental and in silico approaches to separate the contributions of enthalpy and entropy. We used complementary circular dichroism spectroscopy and molecular dynamics simulations to qualitatively delineate changes between nanobodies in isolation and complexed with p24. Using quantitative techniques such as isothermal titration calorimetry alongside WaterMap and Free Energy Perturbation protocols, we found the difference between high (59H10) and medium (37E7) affinity nanobodies on binding to HIV-1 p24 is entropically driven, accounted for by the release of unstable waters from the hydrophobic surface of 59H10. Our results provide an exemplar of the utility of parallel in vitro and in silico studies and highlight that differences in entropic interactions between amino acids and water molecules are sufficient to drive orders of magnitude differences in affinity., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2022.)

Lateral flow tests, commonly based on metal plasmonic nanoparticles, are rapid, robust, and low-cost. However, improvements in analytical sensitivity are required to allow detection of low-abundance biomarkers, for example detection of low antigen concentrations for earlier or asymptomatic diagnosis of infectious diseases. Efforts to improve sensitivity often require changes to the assay. Here, we developed optical methods to improve the sensitivity of absorption-based lateral flow tests, requiring no assay modifications to existing tests. We experimentally compared five different lock-in and subtraction-based methods, exploiting the narrow plasmonic peak of gold nanoparticles for background removal by imaging at different light wavelengths. A statistical framework and three fitting models were used to compare limits of detection, giving a 2.0-5.4-fold improvement. We then demonstrated the broad applicability of the method to an ultrasensitive assay, designing 530 nm composite nanoparticles to increase the particle volume, and therefore light absorption per particle, whilst retaining the plasmonic peak to allow background removal and without adding any assay steps. This multifaceted, modular approach gave a combined 58-fold improvement in the fundamental limit of detection using a biotin-avidin model over 50 nm gold nanoparticles with single-wavelength imaging. Applying to a sandwich assay for the detection of HIV capsid protein gave a limit of detection of 170 fM. Additionally, we developed an open-source software tool for performing the detection limit analysis used in this work., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The COVID-19 pandemic has unveiled a pressing need to expand the diagnostic landscape to permit high-volume testing in peak demand. Rapid nucleic acid testing based on isothermal amplification is a viable alternative to real-time reverse transcription polymerase chain reaction (RT-PCR) and can help close this gap. With the emergence of SARS-CoV-2 variants of concern, clinical validation of rapid molecular tests needs to demonstrate their ability to detect known variants, an essential requirement for a robust pan-SARS-CoV-2 assay. To date, there has been no clinical validation of reverse transcription recombinase polymerase amplification (RT-RPA) assays for SARS-CoV-2 variants. We performed a clinical validation of a one-pot multi-gene RT-RPA assay with the E and RdRP genes of SARS-CoV-2 as targets. The assay was validated with 91 nasopharyngeal samples, with a full range of viral loads, collected at University College London Hospitals. Moreover, the assay was tested with previously sequenced clinical samples, including eleven lineages of SARS-CoV-2. The rapid (20 min) RT-RPA assay showed high sensitivity and specificity, equal to 96% and 97%, respectively, compared to gold standard real-time RT-PCR. The assay did not show cross-reactivity with the panel of respiratory pathogens tested. We also report on a semi-quantitative analysis of the RT-RPA results with correlation to viral load equivalents. Furthermore, the assay could detect all eleven SARS-CoV-2 lineages tested, including four variants of concern (Alpha, Beta, Delta, and Omicron). This variant-proof SARS-CoV-2 assay offers a significantly faster and simpler alternative to RT-PCR, delivering sensitive and specific results with clinical samples.

Phenotypic drug susceptibility testing (DST) for tuberculosis (TB) requires weeks to yield results. Although molecular tests rapidly detect drug resistance-associated mutations (DRMs), they are not scalable to cover the full genome and the many DRMs that can predict resistance. Whole-genome sequencing (WGS) methods are scalable, but if conducted directly on sputum, typically require a target enrichment step, such as nucleic acid amplification. We developed a targeted isothermal amplification-nanopore sequencing workflow for rapid prediction of drug resistance of TB isolates. We used recombinase polymerase amplification (RPA) to perform targeted isothermal amplification (37°C for 90 min) of three regions within the Mycobacterium tuberculosis genome, followed by nanopore sequencing on the MinION. We tested 29 mycobacterial genomic DNA extracts from patients with drug-resistant (DR) TB and compared our results to those of WGS by Illumina and phenotypic DST to evaluate the accuracy of prediction of resistance to rifampin and isoniazid. Amplification by RPA showed fidelity equivalent to that of high-fidelity PCR (100% concordance). Nanopore sequencing generated DRM predictions identical to those of WGS, with considerably faster sequencing run times of minutes rather than days. The sensitivity and specificity of rifampin resistance prediction for our workflow were 96.3% (95% confidence interval [CI], 81.0 to 99.9%) and 100.0% (95% CI, 15.8 to 100.0%), respectively. For isoniazid resistance prediction, the sensitivity and specificity were 100.0% (95% CI, 86.3 to 100.0%) and 100.0% (95% CI, 39.8 to 100.0%), respectively. The workflow consumable costs per sample are less than £100. Our rapid and low-cost drug resistance genotyping workflow provides accurate prediction of rifampin and isoniazid resistance, making it appropriate for use in resource-limited settings. IMPORTANCE Current methods for diagnosing drug-resistant tuberculosis are time consuming, resulting in delays in patients receiving treatment and in transmission onwards. They also require a high level of laboratory infrastructure, which is often only available at centralized facilities, resulting in further delays to diagnosis and additional barriers to deployment in resource-limited settings. This article describes a new workflow that can diagnose drug-resistant TB in a shorter time, with less equipment, and for a lower price than current methods. The amount of TB DNA is first increased without the need for bulky and costly thermocycling equipment. The DNA is then read using a portable sequencer called a MinION, which indicates whether there are tell-tale changes in the DNA that indicate whether the TB strain is drug resistant. Our workflow could play an important role in the future in the fight against the public health challenge that is TB drug resistance.

The COVID-19 pandemic is challenging diagnostic testing capacity worldwide. The mass testing needed to limit the spread of the virus requires new molecular diagnostic tests to dramatically widen access at the point-of-care in resource-limited settings. Isothermal molecular assays have emerged as a promising technology, given the faster turn-around time and minimal equipment compared to gold standard laboratory PCR methods. However, unlike PCR, they do not typically target multiple SARS-CoV-2 genes, risking sensitivity and specificity. Moreover, they often require multiple steps thus adding complexity and delays. Here we develop a multiplexed, 1-2 step, fast (20-30 min) SARS-CoV-2 molecular test using reverse transcription recombinase polymerase amplification to simultaneously detect two conserved targets - the E and RdRP genes. The agile multi-gene platform offers two complementary detection methods: real-time fluorescence or dipstick. The analytical sensitivity of the fluorescence test was 9.5 (95% CI: 7.0-18) RNA copies per reaction for the E gene and 17 (95% CI: 11-93) RNA copies per reaction for the RdRP gene. The analytical sensitivity for the dipstick method was 130 (95% CI: 82-500) RNA copies per reaction. High specificity was found against common seasonal coronaviruses, SARS-CoV and MERS-CoV model samples. The dipstick readout demonstrated potential for point-of-care testing in decentralised settings, with minimal or equipment-free incubation methods and a user-friendly prototype smartphone application. This rapid, simple, ultrasensitive and multiplexed molecular test offers valuable advantages over gold standard tests and in future could be configurated to detect emerging variants of concern., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Emergence of variants with specific mutations in key epitopes in the spike protein of SARS-CoV-2 raises concerns pertinent to mass vaccination campaigns and use of monoclonal antibodies. We aimed to describe the emergence of the B.1.1.7 variant of concern (VOC), including virological characteristics and clinical severity in contemporaneous patients with and without the variant., Methods: In this cohort study, samples positive for SARS-CoV-2 on PCR that were collected from Nov 9, 2020, for patients acutely admitted to one of two hospitals on or before Dec 20, 2020, in London, UK, were sequenced and analysed for the presence of VOC-defining mutations. We fitted Poisson regression models to investigate the association between B.1.1.7 infection and severe disease (defined as point 6 or higher on the WHO ordinal scale within 14 days of symptoms or positive test) and death within 28 days of a positive test and did supplementary genomic analyses in a cohort of chronically shedding patients and in a cohort of remdesivir-treated patients. Viral load was compared by proxy, using PCR cycle threshold values and sequencing read depths., Findings: Of 496 patients with samples positive for SARS-CoV-2 on PCR and who met inclusion criteria, 341 had samples that could be sequenced. 198 (58%) of 341 had B.1.1.7 infection and 143 (42%) had non-B.1.1.7 infection. We found no evidence of an association between severe disease and death and lineage (B.1.1.7 vs non-B.1.1.7) in unadjusted analyses (prevalence ratio [PR] 0·97 [95% CI 0·72-1·31]), or in analyses adjusted for hospital, sex, age, comorbidities, and ethnicity (adjusted PR 1·02 [0·76-1·38]). We detected no B.1.1.7 VOC-defining mutations in 123 chronically shedding immunocompromised patients or in 32 remdesivir-treated patients. Viral load by proxy was higher in B.1.1.7 samples than in non-B.1.1.7 samples, as measured by cycle threshold value (mean 28·8 [SD 4·7] vs 32·0 [4·8]; p=0·0085) and genomic read depth (1280 [1004] vs 831 [682]; p=0·0011)., Interpretation: Emerging evidence exists of increased transmissibility of B.1.1.7, and we found increased virus load by proxy for B.1.1.7 in our data. We did not identify an association of the variant with severe disease in this hospitalised cohort., Funding: University College London Hospitals NHS Trust, University College London/University College London Hospitals NIHR Biomedical Research Centre, Engineering and Physical Sciences Research Council., Competing Interests: Declaration of interests MS reports funding support for at-cost development and manufacture of UCL-Ventura continuous positive airway pressure device for patients with COVID-19 from the UK Department of Health and Social Care, during the conduct of the study; grants and advisory board fees paid to institution research fund from NewB; grants from DSTL; advisory board and speaking fees paid into institutional research fund from Amormed, Biotest, General ElectricBaxter, Baxter, Roche, Bayer, and Shionogi; and grants from Critical Pressure and Apollo Therapeutics, outside the submitted work. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

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 humans-experienced 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., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Introduction: The coronavirus (COVID-19) pandemic has caused significant global mortality and impacted lives around the world. Virus Watch aims to provide evidence on which public health approaches are most likely to be effective in reducing transmission and impact of the virus, and will investigate community incidence, symptom profiles and transmission of COVID-19 in relation to population movement and behaviours., Methods and Analysis: Virus Watch is a household community cohort study of acute respiratory infections in England and Wales and will run from June 2020 to August 2021. The study aims to recruit 50 000 people, including 12 500 from minority ethnic backgrounds, for an online survey cohort and monthly antibody testing using home fingerprick test kits. Nested within this larger study will be a subcohort of 10 000 individuals, including 3000 people from minority ethnic backgrounds. This cohort of 10 000 people will have full blood serology taken between October 2020 and January 2021 and repeat serology between May 2021 and August 2021. Participants will also post self-administered nasal swabs for PCR assays of SARS-CoV-2 and will follow one of three different PCR testing schedules based on symptoms., Ethics and Dissemination: This study has been approved by the Hampstead National Health Service (NHS) Health Research Authority Ethics Committee (ethics approval number 20/HRA/2320). We are monitoring participant queries and using these to refine methodology where necessary, and are providing summaries and policy briefings of our preliminary findings to inform public health action by working through our partnerships with our study advisory group, Public Health England, NHS and government scientific advisory panels., Competing Interests: Competing interests: AH serves on the UK New and Emerging Respiratory Virus Threats Advisory Group. AMJ was a governor of Wellcome Trust from 2011 to 2018 and is chair of the Committee for Strategic Coordination for Health of the Public Research., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Pyrethroid-impregnated nets have contributed significantly to halving the burden of malaria but resistance threatens their future efficacy and the pipeline of new insecticides is short. Here we report that an invertebrate automated phenotyping platform (INVAPP), combined with the algorithm Paragon, provides a robust system for measuring larval motility in Anopheles gambiae (and An. coluzzi) as well as Aedes aegypti with the capacity for high-throughput screening for new larvicides. By this means, we reliably quantified both time- and concentration-dependent actions of chemical insecticides faster than using the WHO standard larval assay. We illustrate the effectiveness of the system using an established larvicide (temephos) and demonstrate its capacity for library-scale chemical screening using the Medicines for Malaria Venture (MMV) Pathogen Box library. As a proof-of-principle, this library screen identified a compound, subsequently confirmed to be tolfenpyrad, as an effective larvicide. We have also used the INVAPP / Paragon system to compare responses in larvae derived from WHO classified deltamethrin resistant and sensitive mosquitoes. We show how this approach to monitoring larval response to insecticides can be adapted for use with a smartphone camera application and therefore has potential for further development as a simple portable field-assay with associated real-time, geo-located information to identify hotspots., Competing Interests: The authors have declared that no competing interests exist.

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest-as opposed to infections-using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2-23.2) and 22.1 (17.4-26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.

The quantum spin properties of nitrogen-vacancy defects in diamond enable diverse applications in quantum computing and communications 1 . However, fluorescent nanodiamonds also have attractive properties for in vitro biosensing, including brightness 2 , low cost 3 and selective manipulation of their emission 4 . Nanoparticle-based biosensors are essential for the early detection of disease, but they often lack the required sensitivity. Here we investigate fluorescent nanodiamonds as an ultrasensitive label for in vitro diagnostics, using a microwave field to modulate emission intensity 5 and frequency-domain analysis 6 to separate the signal from background autofluorescence 7 , which typically limits sensitivity. Focusing on the widely used, low-cost lateral flow format as an exemplar, we achieve a detection limit of 8.2 × 10 -19 molar for a biotin-avidin model, 10 5  times more sensitive than that obtained using gold nanoparticles. Single-copy detection of HIV-1 RNA can be achieved with the addition of a 10-minute isothermal amplification step, and is further demonstrated using a clinical plasma sample with an extraction step. This ultrasensitive quantum diagnostics platform is applicable to numerous diagnostic test formats and diseases, and has the potential to transform early diagnosis of disease for the benefit of patients and populations.

Growing antimicrobial resistance (AMR) is a serious global threat to human health. Current methods to detect resistance include phenotypic antibiotic sensitivity testing (AST), which measures bacterial growth and is therefore hampered by a slow time to obtain results (∼12-24 h). Therefore, new rapid phenotypic methods for AST are urgently needed. Nanomechanical cantilever sensors have recently shown promise for rapid AST but challenges of bacterial immobilization can lead to variable results. Herein, a novel cantilever-based method is described for detecting phenotypic antibiotic resistance within ∼45 min, capable of detecting single bacteria. This method does not require complex, variable bacterial immobilization and instead uses a laser and detector system to detect single bacterial cells in media as they pass through the laser focus. This provides a simple readout of bacterial antibiotic resistance by detecting growth (resistant) or death (sensitive), much faster than the current methods. The potential of this technique is demonstrated by determining the resistance in both laboratory and clinical strains of Escherichia coli ( E. coli) , a key species responsible for clinically burdensome urinary tract infections. This work provides the basis for a simple and fast diagnostic tool to detect antibiotic resistance in bacteria, reducing the health and economic burdens of AMR.

Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases.

Mobile health, or 'mHealth', is the application of mobile devices, their components and related technologies to healthcare. It is already improving patients' access to treatment and advice. Now, in combination with internet-connected diagnostic devices, it offers novel ways to diagnose, track and control infectious diseases and to improve the efficiency of the health system. Here we examine the promise of these technologies and discuss the challenges in realizing their potential to increase patients' access to testing, aid in their treatment and improve the capability of public health authorities to monitor outbreaks, implement response strategies and assess the impact of interventions across the world.

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., Competing Interests: The authors declare no competing interests.

Effective management of foot and mouth disease (FMD) requires diagnostic tests to distinguish between infected and vaccinated animals (DIVA). To address this need, several enzyme-linked immunosorbent assay (ELISA) platforms have been developed, however, these tests vary in their sensitivity and specificity and are very expensive for developing countries. Camelid-derived single-domain antibodies fragments so-called Nanobodies, have demonstrated great efficacy for the development of serological diagnostics. This study describes the development of a novel Nanobody-based FMD 3ABC competitive ELISA, for the serological detection of antibodies against FMD Non-Structural Proteins (NSP) in Uganda cattle herds. This in-house ELISA was validated using more than 600 sera from different Uganda districts, and virus serotype specificities. The evaluation of the performance of the assay demonstrated high diagnostic sensitivity and specificity of 94 % (95 % CI: 88.9-97.2), and 97.67 % (95 % CI: 94.15-99.36) respectively, as well as the capability to detect NSP-specific antibodies against multiple FMD serotype infections. In comparison with the commercial PrioCHECK FMDV NSP-FMD test, there was a strong concordance and high correlation and agreement in the performance of the two tests. This new developed Nanobody based FMD 3ABC competitive ELISA could clearly benefit routine disease diagnosis, the establishment of disease-free zones, and the improvement of FMD management and control in endemically complex environments, such as those found in Africa.

One of the implications of climate change is a predicted increase in frequent and severe heatwaves. The impact of heatwaves on the health of the population is captured through real-time syndromic healthcare surveillance systems monitored daily in England during the summer months. Internet search data could potentially provide improved timeliness and help to assess the wider population health impact of heat by capturing a population sub-group who are symptomatic but do not seek healthcare. A retrospective observational study was carried out from June 2013 to September 2017 in England to compare daily trends in validated syndromic surveillance heat-related morbidity indicators against symptom-based heatwave related Google search terms. The degree of correlation was determined with Spearman correlation coefficients and lag assessment was carried out to determine timeliness. Daily increases in frequency in Google search terms during heatwave events correlated well with validated syndromic indicators. Correlation coefficients between search term frequency and syndromic indicators from 2013 to 2017 were highest with the telehealth service NHS 111 (range of 0.684-0.900 by search term). Lag analysis revealed a similar timeliness between the data sources, suggesting Google data did not provide a delayed or earlier signal in the context of England's syndromic surveillance systems. This work highlights the potential benefits for countries which lack established public health surveillance systems to monitor heat-related morbidity and the use of internet search data to assess the wider population health impact of exposure to heat., (Copyright © 2018. Published by Elsevier Inc.)

: Despite major advances in HIV testing, early detection of infection at the point of care (PoC) remains a key challenge. Although rapid antibody PoC and laboratory-based nucleic acid amplification tests dominate the diagnostics market, the viral capsid protein p24 is recognized as an alternative early virological biomarker of infection. However, the detection of ultra-low levels of p24 at the PoC has proven challenging. Here we review the landscape of p24 diagnostics to identify knowledge gaps and barriers and help shape future research agendas. Five hundred and seventy-four research articles to May 2018 that propose or evaluate diagnostic assays for p24 were identified and reviewed. We give a brief history of diagnostic development, and the utility of p24 as a biomarker in different populations such as infants, the newly infected, those on preexposure prophylaxis and self-testers. We review the performance of commercial p24 assays and consider elements such as immune complex disruption, resource-poor settings, prevalence, and assay antibodies. Emerging and ultrasensitive assays are reviewed and show a number of promising approaches but further translation has been limited. We summarize studies on the health economic benefits of using antigen testing. Finally, we speculate on the future uses of high-performance p24 assays, particularly, if available in self-test format.

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., Competing Interests: Competing interestsD.A., V.L., R.P., Z.C. and H.Y. are employed by OJ-Bio which produces the SH-SAW prototype. A studentship to V.T. was part-funded by OJ-Bio. The remaining authors declare no competing interests.

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., (© 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

Background: Routine influenza surveillance, based on laboratory confirmation of viral infection, often fails to estimate the true burden of influenza-like illness (ILI) in the community because those with ILI often manage their own symptoms without visiting a health professional. Internet-based surveillance can complement this traditional surveillance by measuring symptoms and health behavior of a population with minimal time delay. Flusurvey, the UK's largest crowd-sourced platform for surveillance of influenza, collects routine data on more than 6000 voluntary participants and offers real-time estimates of ILI circulation. However, one criticism of this method of surveillance is that it is only able to assess ILI, rather than virologically confirmed influenza., Objective: We designed a pilot study to see if it was feasible to ask individuals from the Flusurvey platform to perform a self-swabbing task and to assess whether they were able to collect samples with a suitable viral content to detect an influenza virus in the laboratory., Methods: Virological swabbing kits were sent to pilot study participants, who then monitored their ILI symptoms over the influenza season (2014-2015) through the Flusurvey platform. If they reported ILI, they were asked to undertake self-swabbing and return the swabs to a Public Health England laboratory for multiplex respiratory virus polymerase chain reaction testing., Results: A total of 700 swab kits were distributed at the start of the study; from these, 66 participants met the definition for ILI and were asked to return samples. In all, 51 samples were received in the laboratory, 18 of which tested positive for a viral cause of ILI (35%)., Conclusions: This demonstrated proof of concept that it is possible to apply self-swabbing for virological laboratory testing to an online cohort study. This pilot does not have significant numbers to validate whether Flusurvey surveillance accurately reflects influenza infection in the community, but highlights that the methodology is feasible. Self-swabbing could be expanded to larger online surveillance activities, such as during the initial stages of a pandemic, to understand community transmission or to better assess interseasonal activity., (©Clare Wenham, Eleanor R Gray, Candice E Keane, Matthew Donati, Daniela Paolotti, Richard Pebody, Ellen Fragaszy, Rachel A McKendry, W John Edmunds. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 01.03.2018.)

Paper-based lateral flow immunoassays (LFIAs) are one of the most widely used point-of-care (PoC) devices; however, their application in early disease diagnostics is often limited due to insufficient sensitivity for the requisite sample sizes and the short time frames of PoC testing. To address this, we developed a serum-stable, nanoparticle catalyst-labeled LFIA with a sensitivity surpassing that of both current commercial and published sensitivities for paper-based detection of p24, one of the earliest and most conserved biomarkers of HIV. We report the synthesis and characterization of porous platinum core-shell nanocatalysts (PtNCs), which show high catalytic activity when exposed to complex human blood serum samples. We explored the application of antibody-functionalized PtNCs with strategically and orthogonally modified nanobodies with high affinity and specificity toward p24 and established the key larger nanoparticle size regimes needed for efficient amplification and performance in LFIA. Harnessing the catalytic amplification of PtNCs enabled naked-eye detection of p24 spiked into sera in the low femtomolar range (ca. 0.8 pg·mL -1 ) and the detection of acute-phase HIV in clinical human plasma samples in under 20 min. This provides a versatile absorbance-based and rapid LFIA with sensitivity capable of significantly reducing the HIV acute phase detection window. This diagnostic may be readily adapted for detection of other biomolecules as an ultrasensitive screening tool for infectious and noncommunicable diseases and can be capitalized upon in PoC settings for early disease detection.

Ebola virus disease causes widespread and highly fatal epidemics in human populations. Today, there is still great need for point-of-care tests for diagnosis, patient management and surveillance, both during and post outbreaks. We present a point-of-care test comprising an immunochromatographic strip and a smartphone reader, which detects and semiquantifies Ebola-specific antibodies in human survivors. We developed a Sudan virus glycoprotein monoplex platform and validated it using sera from 90 human survivors and 31 local noninfected controls. The performance of the glycoprotein monoplex was 100% sensitivity and 98% specificity compared to standard whole antigen enzyme-linked immunosorbent assay (ELISA), and it was validated with freshly collected patient samples in Uganda. Moreover, we constructed a multiplex test for simultaneous detection of antibodies against three recombinant Sudan virus proteins. A pilot study comprising 15 survivors and 5 noninfected controls demonstrated sensitivity and specificity of 100% compared to standard ELISA. Finally, we developed a second multiplex subtype assay for the identification of exposure to three related EVD species: Sudan virus, Bundibugyo virus and Ebola virus (formerly Zaire) using recombinant viral glycoprotein. This multiplex test could distinguish between the host's immunity to specific viral species and identify cross-reactive immunity. These developed serological platforms consisted of capture ligands with high specificity and sensitivity, in-house developed strips and a compatible smartphone application. These platforms enabled rapid and portable testing, data storage and sharing as well as geographical tagging of the tested individuals in Uganda. This platform holds great potential as a field tool for diagnosis, vaccine development, and therapeutic evaluation.

We report the development of a tuneable plasmonic nanochain immunoassay with increased sensitivity over traditional monodisperse nanoparticle lateral flow tests. Our approach takes advantage of the unique self-assembling properties of polyamidoamine dendrimers with gold nanoparticles in aqueous media to create one-dimensional nanochains, with a distinct red to blue colour change, attributable to a longitudinal plasmon resonance, which can be readily detected by eye and a digital camera. We optimise and characterise nanochain formation and stability using UV-visible spectroscopy, transmission electron microscopy and dynamic light scattering. As a proof-of-principle we focus on the application of nanochains for point-of-care diagnostics for p24, an important biomarker of early HIV infections and successfully detect p24 with a limit of detection of 5 ng ml -1 in pseudo-serum, 4 fold more sensitive than comparable studies with gold nanoparticles. These findings and underlying concepts highlight the potential of advanced functional organic-inorganic composite nanomaterials to diagnose infections, with broad applicability to non-communicable 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.

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.

The alarming increase of pathogenic bacteria that are resistant to multiple antibiotics is now recognized as a major health issue fuelling demand for new drugs. Bacterial resistance is often caused by molecular changes at the bacterial surface, which alter the nature of specific drug-target interactions. Here, we identify a novel mechanism by which drug-target interactions in resistant bacteria can be enhanced. We examined the surface forces generated by four antibiotics; vancomycin, ristomycin, chloroeremomycin and oritavancin against drug-susceptible and drug-resistant targets on a cantilever and demonstrated significant differences in mechanical response when drug-resistant targets are challenged with different antibiotics although no significant differences were observed when using susceptible targets. Remarkably, the binding affinity for oritavancin against drug-resistant targets (70 nM) was found to be 11,000 times stronger than for vancomycin (800 μM), a powerful antibiotic used as the last resort treatment for streptococcal and staphylococcal bacteria including methicillin-resistant Staphylococcus aureus (MRSA). Using an exactly solvable model, which takes into account the solvent and membrane effects, we demonstrate that drug-target interactions are strengthened by pronounced polyvalent interactions catalyzed by the surface itself. These findings further enhance our understanding of antibiotic mode of action and will enable development of more effective therapies., Competing Interests: The authors declare no competing financial interests.

We explore and exploit diffraction effects that have been previously neglected when modelling optical measurement techniques for the bending of micro-mechanical transducers such as cantilevers for atomic force microscopy. The illumination of a cantilever edge causes an asymmetric diffraction pattern at the photo-detector affecting the calibration of the measured signal in the popular optical beam deflection technique (OBDT). The conditions that avoid such detection artefacts conflict with the use of smaller cantilevers. Embracing diffraction patterns as data yields a potent detection technique that decouples tilt and curvature and simultaneously relaxes the requirements on the illumination alignment and detector position through a measurable which is invariant to translation and rotation. We show analytical results, numerical simulations and physiologically relevant experimental data demonstrating the utility of the diffraction patterns. We offer experimental design guidelines and quantify possible sources of systematic error in OBDT. We demonstrate a new nanometre resolution detection method that can replace OBDT, where diffraction effects from finite sized or patterned cantilevers are exploited. Such effects are readily generalized to cantilever arrays, and allow transmission detection of mechanical curvature, enabling instrumentation with simpler geometry. We highlight the comparative advantages over OBDT by detecting molecular activity of antibiotic Vancomycin.

Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such open data can shed light on the causes of disease and effects of treatment, including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual's characteristics, known as personalized or "stratified medicine." Developments, such as crowdsourcing, participatory surveillance, and individuals pledging to become "data donors" and the "quantified self" movement (where citizens share data through mobile device-connected technologies), have great potential to contribute to our knowledge of disease, improving diagnostics, and delivery of -healthcare and treatment. There is not only a great potential but also major concerns over privacy, confidentiality, and control of data about individuals once it is shared. Issues, such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, are becoming increasingly scrutinized at national and international levels. This can be seen in the recent backlash over the proposed implementation of care.data, which enables individuals' NHS data to be linked, retained, and shared for other uses, such as research and, more controversially, with businesses for commercial exploitation. By way of contrast, through increasing popularity of social media, GPS-enabled mobile apps and tracking/wearable devices, the IT industry and MedTech giants are pursuing new projects without clear public and policy discussion about ownership and responsibility for user-generated data. In the absence of transparent regulation, this paper addresses the opportunities of Big Data in healthcare together with issues of responsibility and accountability. It also aims to pave the way for public policy to support a balanced agenda that safeguards personal information while enabling the use of data to improve public health.

Cantilever arrays have been used to monitor biochemical interactions and their associated stress. However, it is often necessary to passivate the underside of the cantilever to prevent unwanted ligand adsorption, and this process requires tedious optimization. Here, we show a way to immobilize membrane receptors on nanomechanical cantilevers so that they can function without passivating the underlying surface. Using equilibrium theory, we quantitatively describe the mechanical responses of vancomycin, human immunodeficiency virus type 1 antigens and coagulation factor VIII captured on the cantilever in the presence of competing stresses from the top and bottom cantilever surfaces. We show that the area per receptor molecule on the cantilever surface influences ligand-receptor binding and plays an important role on stress. Our results offer a new way to sense biomolecules and will aid in the creation of ultrasensitive biosensors.

Background: The escalating cost of global health care is driving the development of new technologies to identify early indicators of an individual's risk of disease. Traditionally, epidemiologists have identified such risk factors using medical databases and lengthy clinical studies but these are often limited in size and cost and can fail to take full account of diseases where there are social stigmas or to identify transient acute risk factors., Objective: Here we report that Web search engine queries coupled with information on Wikipedia access patterns can be used to infer health events associated with an individual user and automatically generate Web-based risk markers for some of the common medical conditions worldwide, from cardiovascular disease to sexually transmitted infections and mental health conditions, as well as pregnancy., Methods: Using anonymized datasets, we present methods to first distinguish individuals likely to have experienced specific health events, and classify them into distinct categories. We then use the self-controlled case series method to find the incidence of health events in risk periods directly following a user's search for a query category, and compare to the incidence during other periods for the same individuals., Results: Searches for pet stores were risk markers for allergy. We also identified some possible new risk markers; for example: searching for fast food and theme restaurants was associated with a transient increase in risk of myocardial infarction, suggesting this exposure goes beyond a long-term risk factor but may also act as an acute trigger of myocardial infarction. Dating and adult content websites were risk markers for sexually transmitted infections, such as human immunodeficiency virus (HIV)., Conclusions: Web-based methods provide a powerful, low-cost approach to automatically identify risk factors, and support more timely and personalized public health efforts to bring human and economic benefits.

Background: Mass gatherings, such as music festivals and religious events, pose a health care challenge because of the risk of transmission of communicable diseases. This is exacerbated by the fact that participants disperse soon after the gathering, potentially spreading disease within their communities. The dispersion of participants also poses a challenge for traditional surveillance methods. The ubiquitous use of the Internet may enable the detection of disease outbreaks through analysis of data generated by users during events and shortly thereafter., Objective: The intent of the study was to develop algorithms that can alert to possible outbreaks of communicable diseases from Internet data, specifically Twitter and search engine queries., Methods: We extracted all Twitter postings and queries made to the Bing search engine by users who repeatedly mentioned one of nine major music festivals held in the United Kingdom and one religious event (the Hajj in Mecca) during 2012, for a period of 30 days and after each festival. We analyzed these data using three methods, two of which compared words associated with disease symptoms before and after the time of the festival, and one that compared the frequency of these words with those of other users in the United Kingdom in the days following the festivals., Results: The data comprised, on average, 7.5 million tweets made by 12,163 users, and 32,143 queries made by 1756 users from each festival. Our methods indicated the statistically significant appearance of a disease symptom in two of the nine festivals. For example, cough was detected at higher than expected levels following the Wakestock festival. Statistically significant agreement (chi-square test, P<.01) between methods and across data sources was found where a statistically significant symptom was detected. Anecdotal evidence suggests that symptoms detected are indeed indicative of a disease that some users attributed to being at the festival., Conclusions: Our work shows the feasibility of creating a public health surveillance system for mass gatherings based on Internet data. The use of multiple data sources and analysis methods was found to be advantageous for rejecting false positives. Further studies are required in order to validate our findings with data from public health authorities.

There is a growing appreciation that mechanical signals can be as important as chemical and electrical signals in biology. To include such signals in a systems biology description for understanding pathobiology and developing therapies, quantitative experiments on how solution-phase and surface chemistry together produce biologically relevant mechanical signals are needed. Because of the appearance of drug-resistant hospital 'superbugs', there is currently great interest in the destruction of bacteria by bound drug-target complexes that stress bacterial cell membranes. Here, we use nanomechanical cantilevers as surface-stress sensors, together with equilibrium theory, to describe quantitatively the mechanical response of a surface receptor to different antibiotics in the presence of competing ligands in solution. The antibiotics examined are the standard, Food and Drug Administration-approved drug of last resort, vancomycin, and the yet-to-be approved oritavancin, which shows promise for controlling vancomycin-resistant infections. The work reveals variations among strong and weak competing ligands, such as proteins in human serum, that determine dosages in drug therapies. The findings further enhance our understanding of the biophysical mode of action of the antibiotics and will help develop better treatments, including choice of drugs as well as dosages, against pathogens.

The cantilever sensor, which acts as a transducer of reactions between model bacterial cell wall matrix immobilized on its surface and antibiotic drugs in solution, has shown considerable potential in biochemical sensing applications with unprecedented sensitivity and specificity. The drug-target interactions generate surface stress, causing the cantilever to bend, and the signal can be analyzed optically when it is illuminated by a laser. The change in surface stress measured with nano-scale precision allows disruptions of the biomechanics of model bacterial cell wall targets to be tracked in real time. Despite offering considerable advantages, multiple cantilever sensor arrays have never been applied in quantifying drug-target binding interactions. Here, we report on the use of silicon multiple cantilever arrays coated with alkanethiol self-assembled monolayers mimicking bacterial cell wall matrix to quantitatively study antibiotic binding interactions. To understand the impact of vancomycin on the mechanics of bacterial cell wall structures. We developed a new model(1) which proposes that cantilever bending can be described by two independent factors; i) namely a chemical factor, which is given by a classical Langmuir adsorption isotherm, from which we calculate the thermodynamic equilibrium dissociation constant (Kd) and ii) a geometrical factor, essentially a measure of how bacterial peptide receptors are distributed on the cantilever surface. The surface distribution of peptide receptors (p) is used to investigate the dependence of geometry and ligand loading. It is shown that a threshold value of p ~10% is critical to sensing applications. Below which there is no detectable bending signal while above this value, the bending signal increases almost linearly, revealing that stress is a product of a local chemical binding factor and a geometrical factor combined by the mechanical connectivity of reacted regions and provides a new paradigm for design of powerful agents to combat superbug infections.

The alarming rise in drug-resistant hospital 'superbugs' and the associated increase in fatalities is driving the development of technologies to search for new antibiotics and improve disease diagnostics. One of the most successful drug targets is the bacterial cell wall, an evolutionary feature of virtually all prokaryotes and vital for their survival by providing mechanical strength. The recent discovery of bacterial cytoskeletal proteins analogous to the key force-bearing machinery in eukaryotes also provides new opportunities for drug discovery, but little is known about their mechanical role in bacteria. In the present short article, I review recent developments in the field of nanotechnology to investigate the mechanical mechanisms of action of potent antibiotics on cell wall and cytoskeletal targets with unprecedented spatial, temporal and force resolution and the development of a new generation of nanomechanical devices to detect pathogens for point-of-care diagnostics.