We developed a sample-to-answer point-of-need molecular test for detecting bacteria toxin genes in milk, integrating a simple workflow that can be carried on the farm, demonstrating analytical performance in line with food safety recommendations.

Droplet microfluidics have found increasing applications across many fields. While droplet generation at a T-junction is a common method, its reliance on trial-and-error operation imposes undesirable constraints on its performance and applicability. In this study, we demonstrate a simple method for on-demand droplet formation at a T-junction with precise temporal control over individual droplet formation. Based on experimental observations, we also develop a physical model to describe the relationships among pressures, droplet generation, device geometry, and interfacial properties. Experimental validation demonstrates excellent performance of the model in predicting the pressure thresholds for switching droplet generation on and off. To address parameter uncertainties arising from real-world complexities, we show that monitoring droplet generation frequency provides a rapid, in situ approach for optimising experimental conditions. Our findings offer valuable guidelines for the design and automation of robust droplet-on-demand microfluidic systems, which can be readily implemented in conventional laboratories for a broad range of applications.

Despite progress made towards malaria control in endemic countries, key indicators have stalled since 2015. Achieving the World Health Organization's (WHO) Global Technical Strategy for Malaria 2016–2030 targets—aiming for 90

Synthetic biology aims to engineer genetic circuits for custom-designed behaviors in living systems, including sophisticated biosensing applications. The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) system has gained attention for its potential in genetic circuit design due to its modularity, programmability, precision, and orthogonality. Here we highlight the current CRISPR-based tools for gene regulation at both transcriptional and translational levels. We discuss how these CRISPR technologies facilitate the design and construction of complex genetic circuits that can perform customized logic computations within living systems. Furthermore, we summarize the applications of CRISPR-based genetic logic circuits in biosensing, emphasizing their potential for detecting diverse biological and environmental signals. Finally, we highlight the key challenges facing the development and application of CRISPR-enabled genetic logic circuits and propose directions for future research to overcome these bottlenecks.

Background The number of individuals living with multiple (≥2) long term conditions (MLTCs) is a growing global challenge. People with MLTCs experience reduced life expectancy, complex healthcare needs, higher healthcare utilisation, increased burden of treatment, poorer quality of life and higher mortality. Evolving technologies including artificial intelligence (AI) could address some of these challenges by enabling more preventive and better integrated care, however, they may also exacerbate inequities. Objective We aim to deliver an equity focused, action-ready plan for transforming MLTC prevention and care, co-designed by people with lived experience of MLTCs and delivered through an Innovation Hub: SysteMatic. Design Our Hub is being co-designed by people with lived experience of MLTCs, practitioners, academics and industry partners in Liverpool and Glasgow, UK. This work builds on research into mental-physical health interdependence across the life-course, and on mobilisation of large-scale quantitative data and technology validation in health and care systems serving deprived populations in Glasgow and Liverpool. We work with 3 population segments: 1) Children & Families: facing psychosocial and environmental challenges with lifetime impacts; 2). Working Life: people with poorly integrated mental, physical and social care; and 3) Pre-Frailty: older people with MLTCs. We aim to understand their experiences and in parallel look at routinely collected health data on people with MLTCs to help us identify targets for intervention. We are co-identifying opportunities for systems transformation with our patient partners, healthcare professionals and through discussion with companies and public-sector organisations. We are co-defining 3/5/7-year MLTC innovation/transition targets and sustainable learning approaches. Discussion SysteMatic will deliver an actionable MLTC Innovation Hub strategic plan, with investment from the UK National Health Service, civic health and care partners, universities, and industry, enabling feedback of well-translated, patient and public prioritised problems into the engineering, physical, health and social sciences to underpin future equitable innovation delivery.

The diagnosis of heart attacks in emergency settings needs to be fast and informative for clinical decisions at the point-of-care. However current technologies often require significant infrastructure, particularly for storage when they involve enzymes. This paper proposes an isothermal and non-enzymatic amplification reaction circuit to assay levels of brain-type natriuretic peptide (BNP), a biomarker of heart failure. In the presence of BNP, a matched aptamer pair binds simultaneously to the target, releasing complementary DNAs (cDNAs) which then trigger a cyclic amplification reaction. The secondary DNA structures created can be detected on a lateral flow format, providing convenient results close to the patient.

Abstract Background Neisseria meningitidis can cause life-threatening meningococcal meningitis and meningococcemia. Old standard microbiological results from CSF/blood cultures are time consuming. This study aimed to combine the sensitivity of loop-mediated isothermal nucleic acid amplification (LAMP) with the specificity of CRISPR/Cas12a cleavage to demonstrate a reliable diagnostic assay for rapid detection of N. meningitidis. Methods A total of n = 139 samples were collected from patients with suspected meningococcal disease and were used for evaluation. The extracted DNA was subjected to qualitative real-time PCR, targeting capsular transporter gene (ctrA) of N. meningitidis. LAMP-specific primer pairs, also targeting the ctrA, were designed and the LAMP products were subjected to CRISPR/Cas12 cleavage reaction. the readout was on a lateral flow strip. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of LAMP-CRISPR/Cas was compared with real-time PCR assays. The limit of detection (LOD) was established with serial dilutions of the target N. meningitidis DNA and calculated by Probit regression analysis. Results Six LAMP assay-specific primers were developed targeting the ctrA gene of N. meningitidis, which is conserved in all meningococcal serogroups. The LAMP primers did not amplify DNA from other bacterial DNA tested, showing 100% specificity. The use of 0.4 M betaine increased the sensitivity and stability of the reaction. LAMP-CRISPR/Cas detected meningococcal serogroups (B, C, W). The assay showed no cross-reactivity and was specific for N. meningitidis. The LOD was 74 (95% CI: 47–311) N. meningitidis copies. The LAMP-CRISPR/Cas performed well compared to the gold standard. In the 139 samples from suspected patients, the sensitivity and specificity of the test were 91% and 99% respectively. Conclusion This developed and optimized method can complement for the available gold standard for the timely diagnosis of meningococcal meningitis and meningococcemia.

Introduction Diagnosis is a key step towards the provision of medical intervention and saving lives. However, in low- and middle-income countries, diagnostic services are mainly centralized in large cities and are costly. Point of care (POC) diagnostic technologies have been developed to fill the diagnostic gap for remote areas. The linkage of POC testing onto smartphones has leveraged the ever-expanding coverage of mobile phones to enhance health services in low- and middle-income countries. Tanzania, like most other middle-income countries, is poised to adopt and deploy the use of mobile phone-enabled diagnostic devices. However, there is limited information on the situation on the ground with regard to readiness and capabilities of the veterinary and medical professionals to make use of this technology. Methods In this study we survey awareness, digital literacy and prevalent health condition to focus on in Tanzania to guide development and future implementation of mobile phoned-enable diagnostic tools by veterinary and medical professionals. Data was collected using semi-structured questionnaire with closed and open-ended questions, guided in-depth interviews and focus group discussion administered to the participants after informed consent was obtained. Results A total of 305 participants from six regions of Tanzania were recruited in the study. The distribution of participants across the six regions was as follows: Kilimanjaro (37), Arusha (31), Tabora (68), Dodoma (61), Mwanza (58), and Iringa (50). Our analysis reveals that only 48.2% (126/255) of participants demonstrated significant awareness of mobile phone-enabled diagnostics. This awareness varies significantly across age groups, professions and geographical locations. Interestingly, while 97.4% of participants own and can operate a smartphone, 62% have never utilized their smartphones for health services, including disease diagnosis. Regarding prevalent health condition to focus on when developing mobile phone -enabled diagnostics tools for Tanzania; there was disparity between medical and veterinary professionals. For medical professionals the top 4 priority diseases were Malaria, Urinary Tract Infections, HIV and Diabetes, while for veterinary professionals they were Brucellosis, Anthrax, Newcastle disease and Rabies. Discussion Despite the widespread ownership of smartphones among healthcare providers (both human and animal), only a small proportion have utilized these devices for healthcare practices, with none reported for diagnostic purposes. This limited utilization may be attributed to factors such as a lack of awareness, absence of policy guidelines, limited promotion, challenges related to mobile data connectivity, and adherence to cultural practices. Conclusion The majority of medical and veterinary professionals in Tanzania possess the necessary digital literacy to utilize mobile phone-enabled diagnostics and demonstrate readiness to adopt digital technologies and innovations to enhance diagnosis. However, effective implementation will require targeted training and interventions to empower them to effectively apply such innovations for disease diagnosis and other healthcare applications.

Tracking genomic sequences as microbial biomarkers in wastewater has been used to determine community prevalence of infectious diseases, contributing to public health surveillance programs worldwide. Here, we report upon a low-cost, rapid, and user-friendly paper microfluidic platform for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza detection, using loop- mediated isothermal amplification, with signal read using a mobile phone camera. Sample-to-answer results were collected in less than 1.5 h, providing rapid multiplexed detection of viruses in wastewater, with a detection limit of <20 copies mL(-1). The device was subsequently used for on-site testing of SARS-CoV-2 in wastewater samples from four quarantine hotels at London Heathrow Airport, showing comparable results to those obtained using polymerase chain reaction. This sensing platform, which enables rapid and localized testing without requiring samples to be sent to centralized laboratories, provides a potentially important public health tool for pandemic preparedness, with a variety of future wastewater surveillance applications in community settings.

Oximetry is used to quantify the presence of oxygen in soft tissues. It can be expressed as, for example, tissue oxygen saturation (StO2), arterial oxygen saturation (SaO2) and pulsatile oxygen saturation (SpO2), among others. Non-invasive medical devices are used to estimate (SaO2). Their accuracy is compromised in individuals with highly pigmented skin. The aim of this initial work is to go back few steps into the understanding of the light absorption for the DC component in pulse oximeters, by using a mixtures model for different hypothetical scenarios of normoxia and hyperoxia. Under hypoxic states, an initial and simple multi-wavelength approach could be established to identify the impact of eumelanin (EuM) and pheomelanin (PhM), which are directly related to skin pigmentation in dark skin colour individuals. We used public spectra for water (H2O), haemoglobin (HHb), oxy-haemoglobin (HbO2), eumelanin and pheomelanin, to create 1000 possible absorption combinations. These spectra simulations were used to understand the hypothetical limits, across a 450-800 nm wavelength range. These results have outlined the maximum oxy-haemoglobin concentrations that can be detected without interfering with eumelanin and pheomelanin. This initial and simple approach helped us to understand how eumelanin and pheomelanin absorption interferes and overlaps with low oxy-haemoglobin, which is a key biomarker for oxygen quantification in pulse oximeters and other non-invasive biomedical devices.

Fatigue can be a symptom of underlying health issues that may require medical attention and proper diagnosis. Human activity monitoring can be used to identify movements by using technology. Technology such as wearable devices can identify movements that indicate fatigue. However wearable devices have limitations such as battery life and the need to be worn regularly. Contactless solutions can remove these limitations. This paper makes use of RF sensing to be able to detect fatigue. The human movements consist of sitting and standing from a desk chair and a bed. Data is collected at a normal pace to simulate no fatigue and a slowed-down pace to simulate fatigue. A Convolutional Neural Network (CNN) is proposed to differentiate the different movements at the two positions and if fatigue is present or not. The CNN model achieved an accuracy score of 98.07 %. This result shows that RF sensing has the potential to observe fatigue in individuals within their own homes.

BackgroundIn countries where malaria is endemic, the use of rapid diagnostic tests(RDTs) has become routine, especially in rural settings. Such regions are characterised by often having other co-endemic infectious diseases, at high levels of prevalence.AimTo illustrate the potential added-value of "sentinel" screening for patients presenting for a routine diagnostic test for malaria, at healthcare facilities in Uganda.MethodsWe developed an economic model by combining two decision trees, one for malaria and a second for the co-endemic disease schistosomiasis. The integrated model was designed to inform policy strategies for the co-endemic disease in addition to malaria (i.e., whether to test opportunistically for schistosomiasis or use mass drug administration(MDA) as per usual practice).We performed the analysis on three comparators varying testing accuracy and costs.ResultsSentinel screening can provide added value to the testing of patients compared with the status quo: when schistosomiasis prevalence is high then MDA is preferential; if low prevalence, treating no one is preferred. If the disease has average levels of prevalence, then a strategy involving testing is preferred. Prevalence thresholds driving the dominant strategy are dependent upon the model parameters, which are highly context specific. At average levels of prevalence for schistosomiasis and malaria for Uganda, adding a sentinel screening was cost-effective when the accuracy of test was higher than current diagnostics and when economies of scope were generated(Expected value clinical Information = 0.65$ per DALY averted, 137.91$ per correct diagnoses).Protocols using diagnostics with current accuracy levels were preferred only for levels of MDA coverage below 75%.ConclusionThe importance of the epidemiological setting is crucial in determining the best cost-effective strategy for detecting endemic disease. Economies of scope can make sentinel screenings cost-effective strategies in specific contexts. Blanket thresholds recommended for MDA may not always be the preferred option for endemic diseases.

Microplastics provide a unique niche for viruses, promoting viral interactions with hosts and accelerating the rapid 'horizontal' spread of antibiotic resistance genes (ARGs). Currently, however, there is a lack of knowledge concerning the main drivers for viral distribution on microplastics and on the resulting patterns of viral biogeographic distributions and the spread of the associated ARGs. Here we performed metagenomic and virus enrichment-based viromic sequencings on both polyethylene and polypropylene microplastics along a river. Experimental results show that Proteobacteria, Firmicutes, Actinobacteria and Cyanobacteria were the potential hosts of viruses on microplastics, but only approximately 4.1% of viral variations were associated with a bacterial community. Notably, two shared ARGs and six metal resistance genes were identified in both viral and their host bacterial genomes, indicating the occurrence of horizontal gene transfer between viruses and bacteria. Furthermore, microplastics introduce more distinctive elements to viral ecology, fostering viral diversification and virus-host linkage while refraining from an escalated level of horizontal gene transfer of ARGs in contrast to natural matrixes. Our study provides comprehensive profiles of viral communities, virus-related ARGs and their driving factors on microplastics, highlighting how these anthropogenic niches provide unique interfaces that comprise highly defined viral ecological features in the environment.

Driver fatigue refers to the state of reduced mental and physical alertness, impairing a driver's ability to safely operate a vehicle, often caused by inadequate sleep or prolonged periods of driving. Radar is utilized for fatigue detection due to its non-intrusive nature and ability to continuously monitor physiological signals like respiration and movement, which are key indicators of a driver's alertness level. Traditional radars can only focus on 2D information. Using the novel 20*20 Multiple-Input Multiple-Output (MIMO) radar, the spatial information can be explored which is relavant to multiple human activities including the actions that represents fatigue. In the paper, we proposed a 3D based driver fatigue detection scheme (3D-DFD) with MIMO radar and achieved 81.54% of accuracy on pilot dataset.

In the field of smart healthcare, wearable and sensing devices are connected to the Internet of Things (IoT) to assess patients within their own homes. Fatigue is a multidimensional experience that can be characterised by exhaustion and reduced physical performance. Monitoring people within their homes to detect slower pace could be a promising method to objectively measure aspects of human fatigue. This paper makes use of both a wearable bracelet and Radio Frequency (RF) sensing to detect simulated fatigue from human activity monitoring. Different activities are collected at a normal pace to represent no fatigue and then repeated at a slower pace to represent fatigue. Artificial intelligence (AI) is used to detect if there is fatigue present or not regardless of activity taking place as well as identifying which activity took place and if fatigue is present in said activity. When using the data from both the bracelet and RF sensing, Random Forest and ResNet algorithms achieved 100 % in detecting fatigue as opposed to non-fatigue using algorithms. When using only the bracelet, only the Random Forest algorithm was able to achieve 100 % accuracy. Using only RF data, 94.80 % accuracy was achieved with a Convolutional Neural Network (CNN). When detecting individual activities with fatigue and no fatigue, the Random Forest algorithm achieved an accuracy score of 97.40 % using both the bracelet and RF sensing and with only the bracelet data. CNN was again the best algorithm for RF sensing only with an accuracy score of 89.84 %.

Although in vitro neuronal network models hold great potential for advancing neuroscience research, with the capacity to provide fundamental insights into mechanisms underlying neuronal functions, the dynamics of cell communication within such networks remain poorly understood. Here, we develop a customizable, polymer modified three-dimensional gold microelectrode array with sufficient stability for high signal-to-noise, long-term, neuronal recording of cultured networks. By using directed spatial and temporal patterns of electrical stimulation of cells to explore synaptic-based communication, we monitored cell network dynamics over 3 weeks, quantifying communication capability using correlation heatmaps and mutual information networks. Analysis of synaptic delay and signal speed between cells enabled us to establish a communication connectivity model. We anticipate that our discoveries of the dynamic changes in communication across the neuronal network will provide a valuable tool for future studies in understanding health and disease as well as in developing effective platforms for evaluating therapies.

Accurate prediction of the efficacy of immunotherapy for cancer patients through the characterization of both genetic and phenotypic heterogeneity in individual patient cells holds great promise in informing targeted treatments, and ultimately in improving care pathways and clinical outcomes. Here, we describe the nanoplatform for interrogating living cell host - gene and (micro - )environment (NICHE) relationships, that integrates micro - and nanofluidics to enable highly efficient capture of circulating tumor cells (CTCs) from blood samples. The platform uses a unique nanopore - enhanced electrodelivery system that efficiently and rapidly integrates stable multichannel fluorescence probes into living CTCs for in situ quantification of target gene expression, while on - chip coculturing of CTCs with immune cells allows for the real - time correlative quantification of their phenotypic heterogeneities in response to immune checkpoint inhibitors (ICI). The NICHE microfluidic device provides a unique ability to perform both gene expression and phenotypic analysis on the same single cells in situ, allowing us to generate a predictive index for screening patients who could benefit from ICI. This index, which simultaneously integrates the heterogeneity of single cellular responses for both gene expression and phenotype, was validated by clinically tracing 80 non-small cell lung cancer patients, demonstrating significantly higher AUC (area under the curve) (0.906) than current clinical reference for immunotherapy prediction.

Smartphone-based colorimetry has been widely applied in clinical analysis, although significant challenges remain in its practical implementation, including the need to consider biases introduced by the ambient imaging environment, which limit its potential within a clinical decision pathway. In addition, most commercial devices demonstrate variability introduced by manufacturer-to-manufacturer differences. Here, we undertake a systematic characterization of the potential imaging interferences that lead to this limited performance in conventional smartphones and, in doing so, provide a comprehensive new understanding of smartphone color imaging. Through derivation of a strongly correlated parameter for sample quantification, we enable real-time imaging, which for the first time, takes the first steps to turning the mobile phone camera into an analytical instrument - irrespective of model, software, and the operating systems used. We demonstrate clinical applicability through the imaging of patients' skin, enabling rapid and convenient diagnosis of cyanosis and measurement of local oxygen concentration to a level that unlocks clinical decision-making for monitoring cardiovascular disease and anemia. Importantly, we show that our solution also accounts for the differences in individuals' skin tones as measured across the Fitzpatrick scale, overcoming potential clinically significant errors in current optical oximetry.

Foot-and-mouth disease (FMD) is a highly contagious viral disease of cloven-hoofed animals responsible for economic losses that amount to >$20 billion annually. Rapid recognition of FMD cases provides vital information to guide control programmes. A range of point-of-need amplification technologies have been developed which allow sensitive detection of the causative virus (FMDV) in the field at locations remote from laboratories. Here we describe a novel system to detect FMDV RNA using loop-mediated isothermal amplification (LAMP). This test was evaluated using a panel of FMDV isolates (n = 79) and RNA standards demonstrating capability to amplify viral genome directly from clinical material in the absence of nucleic acid extraction. This extraction-free RT-LAMP assay was transferred to a bespoke closed-system lateral flow test (LFT) that was used in combination with a low-cost hand-held heater. Our results show that the RT-LAMP-LFT assay retains a high level of diagnostic and analytical sensitivity when using direct clinical material, with a limit of detection under 80 copies per reaction. Together, our data support the potential for the use of this assay at the point-of-need to facilitate rapid feedback on the status of suspect cases.

Abstract Despite its importance, the functional heterogeneity surrounding the dynamics of interactions between mycobacterium tuberculosis and human immune cells in determining host immune strength and tuberculosis (TB) outcomes, remains far from understood. This work now describes the development of a new technological platform to elucidate the immune function differences in individuals with TB, integrating single‐cell RNA sequencing and cell surface antibody sequencing to provide both genomic and phenotypic information from the same samples. Single‐cell analysis of 23 990 peripheral blood mononuclear cells from a new cohort of primary TB patients and healthy controls enables to not only show four distinct immune phenotypes (TB, myeloid, and natural killer (NK) cells), but also determine the dynamic changes in cell population abundance, gene expression, developmental trajectory, transcriptomic regulation, and cell–cell signaling. In doing so, TB‐related changes in immune cell functions demonstrate that the immune response is mediated through host T cells, myeloid cells, and NK cells, with TB patients showing decreased naive, cytotoxicity, and memory functions of T cells, rather than their immunoregulatory function. The platform also has the potential to identify new targets for immunotherapeutic treatment strategies to restore T cells from dysfunctional or exhausted states.