Cellasys #8: A Microphysiometric Assay for Real-Time Cell Analysis Within 24 Hours

Traditional biochemical assays present a vital toolbox to identify the effects of a test substance on cells. However, current assays are single-point measurements, only reveal one parameter at a time, and introduce potential interferences with labels and fluorescent lights. We have addressed these limitations by introducing the cellasys #8 test which is a microphysiometric assay for real-time cell analysis. Within 24 h, the cellasys #8 test is able to identify not only the effect of a test substance but also measure recovery effects. Due to the multi-parametric read-out, the test provides insights into metabolic as well as morphological changes in real-time. The following protocol provides a detailed introduction to the materials as well as a step-by-step description to support scientists with protocol adoption. The automated and standardized assay opens up manifold new application areas for scientists to study biological mechanisms, develop new therapeutic approaches, and validate serum-free media formulation.

A Distributed Data Mesh Paradigm for an Event-based Smart Communities Monitoring Product

The recent pandemic events in Thailand, Covid-19 in 2018, demonstrated the need for an event-based smart monitoring system. While a distributed multi-level architecture has emerged as an architecture of choice for a larger-scale smart event-based system that requires better latency, security, scalability, and reliability, a recently introduced data mesh paradigm can add a few additional benefits. The paradigm enables each district to become an event-based smart monitoring mesh and handle its analytics and monitoring workload. Districts can form a set of domains in a network of event-based smart community monitoring systems and provide data products for others during a crisis. This paper presents a distributed data mesh paradigm for an event-based smart monitoring product in a given community with predefined domains. The paper presents smart monitoring as a data product between domains. Key considerations for designing an event-based smart monitoring data product are given. The author introduces three possible domains necessary for creating a smart monitoring system in each community. Each domain creates a data product for a given domain and shares data between domains. Finally, a three-layer analytics architecture for a smart monitoring product in each domain and a use case is presented.

Open-source software library for real-time inertial measurement unit data-based inverse kinematics using OpenSim

Background

Inertial measurements (IMUs) facilitate the measurement of human motion outside the motion laboratory. A commonly used open-source software for musculoskeletal simulation and analysis of human motion, OpenSim, includes a tool to enable kinematics analysis of IMU data. However, it only enables offline analysis, i.e., analysis after the data has been collected. Extending OpenSim's functionality to allow real-time kinematics analysis would allow real-time feedback for the subject during the measurement session and has uses in e.g., rehabilitation, robotics, and ergonomics.

Methods

We developed an open-source software library for real-time inverse kinematics (IK) analysis of IMU data using OpenSim. The software library reads data from IMUs and uses multithreading for concurrent calculation of IK. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. We published the code under an open-source license on GitHub.

Results

A standard desktop computer calculated full-body inverse kinematics from treadmill walking at 1.5 m/s with data from 12 IMUs in real-time with a mean delay below 55 ms and reached a throughput of more than 90 samples per second. A laptop computer had similar delays and reached a throughput above 60 samples per second with treadmill walking. Minimal walking kinematics, motion of lower extremities and torso, were calculated from treadmill walking data in real-time with a throughput of 130 samples per second on the laptop and 180 samples per second on the desktop computer, with approximately half the delay of full-body kinematics.

Conclusions

The software library enabled real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models. The performance results show that subject-specific full-body motion analysis is feasible in real-time, while a laptop computer and IMUs allowed the use of the method outside the motion laboratory.

Blood and cerebrospinal fluid flow oscillations measured with real-time phase-contrast MRI: breathing mode matters

BACKGROUND

Cervical blood and cerebrospinal fluid (CSF) flow rates can be quantified with Phase-contrast (PC) MRI, which is routinely used for clinical studies. Previous MRI studies showed that venous and CSF flow alterations are linked to various pathological conditions. Since it is well known that, besides the heart beating, the thoracic pump influences the blood and CSF dynamics, we studied the effect of different respiration modes on blood and CSF flow rates using a real-time (RT)-PC prototype.

METHODS

Thirty healthy volunteers were examined with a 3 T scanner. A RT-PC sequence was acquired at the first cervical level to quantify the flow rates of internal carotid arteries, internal jugular veins (IJVs) and CSF. Each RT-PC acquisition was repeated three times, while the subjects were asked to breathe in three different ways for 60 s each: freely (F), with a constant rate (PN) and with deep and constant respiration rate (PD). The average flow rates were computed, they were removed from the respective signals and integrated in the inspiratory and expiratory phases (differential volumes). Finally, the power spectral density was computed for each detrended flow rate. High- and very-high frequency peaks were identified on the spectra while their frequencies were compared to the respiratory and cardiac frequencies estimated using a thoracic belt and a pulse oximeter. The area under the spectra was computed in four 0.5 Hz-wide ranges, centered on the high-frequency peak, on very-high frequency peak and its 2nd and 3rd harmonics, and then they were normalized by the flow rate variance. The effect of breathing patterns on average flow rates, on systolic and diastolic peaks, and on the normalized power was tested. Finally, the differential volumes of inspiration were compared to those of expiration.

RESULTS

The frequencies of the high- and very-high spectral peaks corresponded to the respiratory and cardiac frequencies. The average flow rate progressively decreased from F to PN to PD breathing, and the cardiac modulations were less predominant especially for the IJVs. The respiratory modulation increased with PD breathing. The average volumes displaced in the inspiratory phases were not significantly different from those of the expiratory one.

CONCLUSIONS

The spectral analyses demonstrated higher respiratory modulations in PD compared to free breathing, even prevailing the cardiac modulation in the IJVs, showing an increment of the thoracic pump affecting the flow rate shape.

An optotracer-based antibiotic susceptibility test specifically targeting the biofilm lifestyle of Salmonella

Antimicrobial resistance is a medical threat of global dimensions. Proper antimicrobial susceptibility testing (AST) for drug development, patient diagnosis and treatment is crucial to counteract ineffective drug use and resistance development. Despite the important role of bacterial biofilms in chronic and device-associated infections, the efficacy of antibiotics is determined using planktonic cultures. To address the need for antibiotics targeting bacteria in the biofilm lifestyle, we here present an optotracing-based biofilm-AST using Salmonella as model. Our non-disruptive method enables real-time recording of the extracellular matrix (ECM) components, providing specific detection of the biofilm lifestyle. Biofilm formation prior to antibiotic challenge can thus be confirmed and pre-treatment data collected. By introducing Kirby-Bauer discs, we performed a broad screen of the effects of antibiotics representing multiple classes, and identified compounds with ECM inhibitory as well as promoting effects. These compounds were further tested in agar-based dose-response biofilm-AST assays. By quantifying the ECM based on the amount of curli, and by visualizing the biofilm size and morphology, we achieved new information directly reflecting the treated biofilm. This verified the efficacy of several antibiotics that were effective in eradicating pre-formed biofilms, and it uncovered intriguing possible resistance mechanisms initiated in response to treatments. By providing deeper insights into the resistances and susceptibilities of microbes, expanded use of the biofilm-AST will contribute to more effective treatments of infections and reduced resistance development.

Dynamic landscape of multi-elements in PM2.5 revealed by real-time analysis

Metal components in fine particulate matter (PM_2.5) are closely associated with many adverse health outcomes. Dynamic changes of metals in PM_2.5 are critical for risk assessment due to their temporal variations. Herein, an online method for real-time determination of multi-elements (As, Cd, Cs, Cu, Fe, Mg, Mn, Pb, Rb, Sn, Tl, and V) in PM_2.5 was established by directly introducing air samples into inductively coupled plasma mass spectrometry (ICPMS). Meanwhile, a quantified method using metal standard aerosols (Cr, Mo, and W) and high time resolution for 3.3 min online measurement was developed and validated. The limits of detection were in the range of 0.001-6.30 ng/m³ for different metals. Subsequently, the real-time contents of multi-elements in PM_2.5 for 12 h over 33 days were measured at different air qualities. Temporal variations of crustal elements like Fe, Mg are similar to PM_2.5, whereas toxic elements (Pb, As and Cd) have upward trends at dusk. This denoted the association with various emission sources and different exposure concentrations of metals. In addition to the acquisition of real-time information, online analysis of multi-elements in PM_2.5 is beneficial for atmospheric monitoring and provides critical insights into the different exposure risks of metals in PM_2.5 at varying times.

Pure shift amide detection in conventional and TROSY-type experiments of 13C,15N-labeled proteins

Large coupling networks in uniformly ¹³C,¹⁵N-labeled biomolecules induce broad multiplets that even in flexible proteins are frequently not recognized as such. The reason is that given multiplets typically consist of a large number of individual resonances that result in a single broad line, in which individual components are no longer resolved. We here introduce a real-time pure shift acquisition scheme for the detection of amide protons which is based on ¹³C-BIRD^r,X. As a result the full homo- and heteronuclear coupling network can be suppressed at low power leading to real singlets at substantially improved resolution and uncompromised sensitivity. The method is tested on a small globular and an intrinsically disordered protein (IDP) where the average spectral resolution is increased by a factor of ~ 2 and higher. Equally important, the approach works without saturation of water magnetization for solvent suppression and exchanging amide protons are not affected by saturation transfer.

NanoRTax, a real-time pipeline for taxonomic and diversity analysis of nanopore 16S rRNA amplicon sequencing data

Background

The study of microbial communities and their applications have been leveraged by advances in sequencing techniques and bioinformatics tools. The Oxford Nanopore Technologies long-read sequencing by nanopores provides a portable and cost-efficient platform for sequencing assays. While this opens the possibility of sequencing applications outside specialized environments and real-time analysis of data, complementing the existing efficient library preparation protocols with streamlined bioinformatic workflows is required.

Results

Here we present NanoRTax, a Nextflow pipeline for nanopore 16S rRNA gene amplicon data that features state-of-the-art taxonomic classification tools and real-time capability. The pipeline is paired with a web-based visual interface to enable user-friendly inspections of the experiment in progress. NanoRTax workflow and a simulated real-time analysis were used to validate the prediction of adult Intensive Care Unit patient mortality based on full-length 16S rRNA sequencing data from respiratory microbiome samples.

Conclusions

This constitutes a proof-of-concept simulation study of how real-time bioinformatic workflows could be used to shorten the turnaround times in critical care settings and provides an instrument for future research on early-response strategies for sepsis.

Real-time online point-of-view filming education for teaching clinical skills to medical students

PURPOSE

This study aimed to measure the educational satisfaction with and effectiveness of real-time online point-of-view filming (POVF) clinical skills education in medical students.

METHODS

Medical students participated in a 120-minute clinical skills education session. The session consisted of emergency procedures, wound management, and vascular access. The authors provided real-time online POVF using a smartphone. A questionnaire survey was issued to the students after the class, and their satisfaction with education, educational environment, and effectiveness were analyzed.

RESULTS

Responses about satisfaction with POVF education were very positive in all grades. However, approximately half of the students were satisfied with the smoothness of listening to a lecture and the video quality. More than half of the students responded positively to the question about educational effectiveness.

CONCLUSION

In these times of non-classroom teaching brought on by the coronavirus disease 2019 (COVID-19) pandemic, POVF clinical skills education is likely to be a very useful educational tool. If disadvantages such as insufficient feedback or environmental problems can be addressed, it could serve as an alternative method of clinical skills education even after the COVID-19 pandemic.

Real-time allelic assays of SARS-CoV-2 variants to enhance sewage surveillance

To effectively control the ongoing outbreaks of fast-spreading SARS-CoV-2 variants, there is an urgent need to add rapid variant detection and discrimination methods to the existing sewage surveillance systems established worldwide. We designed eight assays based on allele-specific RT-qPCR for real-time allelic discrimination of eight SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Omicron, Lambda, Mu, and Kappa) in sewage. In silico analysis of the designed assays for identifying SARS-CoV-2 variants using more than four million SARS-CoV-2 variant sequences yielded ∼100% specificity and >90% sensitivity. All assays could sensitively discriminate and quantify target variants at levels as low as 10 viral RNA copy/µL with minimal cross-reactivity to the corresponding nontarget genotypes, even for sewage samples containing mixtures of SARS-CoV-2 variants with differential abundances. Integration of this method into the routine sewage surveillance in Hong Kong successfully identified the Beta variant in a community sewage. Complete concordance was observed between the results of viral whole-genome sequencing and those of our novel assays in sewage samples that contained exclusively the Delta variant discharged by a clinically diagnosed COVID-19 patient living in a quarantine hotel. Our assays in this method also provided real-time discrimination of the newly emerging Omicron variant in sewage two days prior to clinical test results in another quarantine hotel in Hong Kong. These novel allelic discrimination assays offer a rapid, sensitive, and specific way for detecting multiple SARS-CoV-2 variants in sewage and can be directly integrated into the existing sewage surveillance systems.

High-resolution CINE imaging of active guided knee motion using continuously acquired golden-angle radial MRI and rotary sensor information

To explore and extend on dynamic imaging of joint motion, an MRI-safe device guiding knee motion with an attached rotary encoder was used in MRI measurements of multiple knee flexion-extension cycles using radial gradient echo imaging with the golden-angle as azimuthal angle increment. Reproducibility of knee motion was investigated. Real-time and CINE mode anatomical images were reconstructed for different knee flexion angles by synchronizing the encoder information with the MRI data, and performing flexion angle selective gating across multiple motion cycles. When investigating the influence of the rotation angle window width on reconstructed CINE images, it was found that angle windows between 0.5° and 3° exhibited acceptable image sharpness without suffering from significant motion-induced blurring. Furthermore, due to flexible retrospective image reconstruction afforded by the radial golden-angle imaging, the number of motion cycles included in the reconstruction could be retrospectively reduced to investigate the corresponding influence of acquisition time on image quality. Finally, motion reproducibility between motion cycles and accuracy of the flexion angle selective gating were sufficient to acquire whole-knee 3D dynamic imaging with a retrospectively gated 3D cone UTE sequence.

Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

Water is the most important natural element present on earth for humans, yet the availability of pure water is becoming scarce and decreasing. An increase in population and rise in temperatures are two major factors contributing to the water crisis worldwide. Desalinated, brackish water from the sea, lake, estuary, or underground aquifers is treated to maximize freshwater availability for human consumption. However, mismanagement of water storage, distribution, or quality leads to serious threats to human health and ecosystems. Sensors, embedded and smart devices in water plants require proactive monitoring for optimal performance. Traditional quality and device management require huge investments in time, manual efforts, labour, and resources. This research presents an IoT-based real-time framework to perform water quality management, monitor, and alert for taking actions based on contamination and toxic parameter levels, device and application performance as the first part of the proposed work. Machine learning models analyze water quality trends and device monitoring and management architecture. The results display that the proposed method manages water monitoring and accessing water parameters efficiently than other works.

Making waves: Time for chemical surface water quality monitoring to catch up with its technical potential

A comprehensive real-time evaluation of the chemical status of surface water bodies is still utopian, but in our opinion, it is time to use the momentum delivered by recent advanced technical, infrastructural, and societal developments to get significantly closer. Procedures like inline and online analysis (in situ or in a bypass) with close to real-time analysis and data provision are already available in several industrial sectors. In contrast, atline and offline analysis involving manual sampling and time-decoupled analysis in the laboratory is still common practice in aqueous environmental monitoring. Automated tools for data analysis, verification, and evaluation are changing significantly, becoming more powerful with increasing degrees of automation and the introduction of self-learning systems. In addition, the amount of available data will most likely in near future be increased by societal awareness for water quality and by citizen science. In this analysis, we highlight the significant potential of surface water monitoring techniques, showcase "lighthouse" projects from different sectors, and pin-point gaps we must overcome to strike a path to the future of chemical monitoring of inland surface waters.

Real-time visual analytics for in-home medical rehabilitation of stroke patient-systematic review

This paper is focused on real-time visual analytics for home-based rehabilitation dedicated for brain stroke survivors. This research is at the intersection of three main domains: visual analytics for time-oriented data and dynamic visual analytics with specific focus on data analytics for rehabilitation systems. This study has emphasized the analysis of the most important research works in these domains. The studies included in this review are published between January 2008 and December 2020 that met eligibility criteria. From 243 papers retrieved from research including the Google Scholar database and manual research, 69 papers were finally included. This paper presents a classification of the reviewed research based on key features required by the visual analytics for real-time monitoring of patients. The findings suggested that real-time monitoring visual analytics for biodata captured during the rehabilitation sessions was not sufficiently addressed by previous research. To provide real-time monitoring visual analytics of biodata, the concept of a unified framework that combines the processing of batch and stream data in a distributed architecture is proposed. The system is currently under development; its validation will be carried out by an experimental study and the evaluation of the system performance.

A Neuromorphic Brain Interface based on RRAM Crossbar Arrays for High Throughput Real-time Spike Sorting

Real-time spike sorting and processing are crucial for closed-loop brain-machine interfaces and neural prosthetics. Recent developments in high-density multi-electrode arrays with hundreds of electrodes have enabled simultaneous recordings of spikes from a large number of neurons. However, the high channel count imposes stringent demands on real-time spike sorting hardware regarding data transmission bandwidth and computation complexity. Thus, it is necessary to develop a specialized real-time hardware that can sort neural spikes on the fly with high throughputs while consuming minimal power. Here, we present a real-time, low latency spike sorting processor that utilizes high-density CuO_x resistive crossbars to implement in-memory spike sorting in a massively parallel manner. We developed a fabrication process which is compatible with CMOS BEOL integration. We extensively characterized switching characteristics and statistical variations of the CuO_x memory devices. In order to implement spike sorting with crossbar arrays, we developed a template matching-based spike sorting algorithm that can be directly mapped onto RRAM crossbars. By using synthetic and in vivo recordings of extracellular spikes, we experimentally demonstrated energy efficient spike sorting with high accuracy. Our neuromorphic interface offers substantial improvements in area (~1000× less area), power (~200× less power), and latency (4.8μs latency for sorting 100 channels) for real-time spike sorting compared to other hardware implementations based on FPGAs and microcontrollers.

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

BACKGROUND

Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.

METHODS

We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.

RESULTS

All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.

CONCLUSIONS

Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

Evaluation of the real-time fluorescence loop-mediated isothermal amplification assay for the detection of Ureaplasma urealyticum

Ureaplasma urealyticum (UU) is commonly present in human reproductive tract, which frequently leads to genital tract infection. Hence, there is an urgent need to develop a rapid detection method for UU. In our study, a real-time fluorescence loop-mediated isothermal amplification (LAMP) assay was developed and evaluated for the detection of UU. Two primers were specifically designed based on the highly conserved regions of ureaseB genes. The reaction was carried out for 60 min in a constant temperature system using Bst DNA polymerase, and the process was monitored by real-time fluorescence signal, while polymerase chain reaction (PCR) was performed simultaneously. In real-time fluorescence LAMP reaction system, positive result was only obtained for UU among 9 bacterial strains, with detection sensitivity of 42 pg/μL (4.2 × 10⁵ CFU/mL), and all 16 clinical samples of UU could be detected. In conclusion, real-time fluorescence LAMP is a simple, sensitive, specific and effective method compared with conventional PCR, which shows great promise in the rapid detection of UU.

Recent advances in the biosensors application for the detection of bacteria and viruses in wastewater

The presence of disease-causing pathogens in wastewater can provide an excellent diagnostic tool for infectious diseases. Biosensors are far superior to conventional methods used for regular infection screening and surveillance testing. They are rapid, sensitive, inexpensive portable and carry no risk of exposure in their detection schemes. In this context, this review summarizes the most recently developed biosensors for the detection of bacteria and viruses in wastewater. The review also provides information on the new detection methods aimed at screening for SARS-CoV-2, which has now caused more than 4 million deaths. In addition, the review highlights the potential behind on-line and real-time detection of pathogens in wastewater pipelines. Most of the biosensors reported were not targeted to wastewater samples due to the complexity of the matrix. However, this review highlights on the performance factors of recently developed biosensors and discusses the importance of nanotechnology in amplifying the output signals, which in turn increases the accuracy and reliability of biosensors. Current research on the applicability of biosensors in wastewater promises a dramatic change to the conventional approach in the field of medical screening.

Real-time and simultaneous assay of monophenolase and diphenolase activity in tyrosinase catalyzed cascade reactions by combination of three-way calibration and excitation-emission matrix fluorescence

A real-time assay for multiple enzyme activities in cascade reactions is required for research on metabolism and bioengineering. Tyrosinase has the bifunctional activity of monophenolase and diphenolase. A combined strategy of three-way calibration with excitation-emission matrix (EEM) fluorescence was developed for real-time and simultaneous determination of monophenolase and diphenolase activity with tyrosine as a substrate. Mathematical separation and second-order advantage were utilized to solve spectral overlapping and uncalibrated interferents during complex dynamic enzymatic processes. Kinetic evolution profiles of EEM were monitored to stack a fusion three-way data array together with static samples. Using a parallel factor analysis (PARAFAC) algorithm, pseudo-univariate calibration curves with limits of detection (LODs) of 3.00 μM and 0.85 μM were established to simultaneously and real-time measure tyrosine and DOPA. Progress curves for tyrosine consumption by monophenolase and DOPA consumption by diphenolase were obtained using the law of mass conservation to calculate the initial velocity. The LODs for monophenolase and diphenolase were 0.0232 U⋅mL^-1 and 0.0316 U⋅mL^-1. The method achieved real-time and simultaneous assays of multiple enzyme activities in cascade reactions. It showed potential application in the metabolic pathway and biochemical industry.

MultiFRET: A Detailed Protocol for High-Throughput Multiplexed Ratiometric FRET

The newly generated software plugin MultiFRET allows for real-time measurements of multiplexed fluorescent biosensors in a near high-throughput fashion. Here we describe a detailed protocol for setup and use of this software for any purpose requiring instant feedback during fluorescence measurement experiments. We further describe its non-primary features including beam splitter misalignment correction, custom calculations through input of simple equations typed in a .txt format, customizable Excel output, and offline bulk analysis of image stacks. Finally, we supply a usage example of a cAMP measurement in cultured rat neonatal cardiomyocytes.

Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis

BACKGROUND

Brazil is one of the countries worst affected by the COVID-19 pandemic with over 20 million cases and 557,000 deaths reported by August 2021. Comparison of real-time local COVID-19 data between areas is essential for understanding transmission, measuring the effects of interventions, and predicting the course of the epidemic, but are often challenging due to different population sizes and structures.

METHODS

We describe the development of a new app for the real-time visualisation of COVID-19 data in Brazil at the municipality level. In the CLIC-Brazil app, daily updates of case and death data are downloaded, age standardised and used to estimate the effective reproduction number (Rt ). We show how such platforms can perform real-time regression analyses to identify factors associated with the rate of initial spread and early reproduction number. We also use survival methods to predict the likelihood of occurrence of a new peak of COVID-19 incidence.

FINDINGS

After an initial introduction in São Paulo and Rio de Janeiro states in early March 2020, the epidemic spread to northern states and then to highly populated coastal regions and the Central-West. Municipalities with higher metrics of social development experienced earlier arrival of COVID-19 (decrease of 11·1 days [95% CI:8.9,13.2] in the time to arrival for each 10% increase in the social development index). Differences in the initial epidemic intensity (mean Rt ) were largely driven by geographic location and the date of local onset.

INTERPRETATION

This study demonstrates that platforms that monitor, standardise and analyse the epidemiological data at a local level can give useful real-time insights into outbreak dynamics that can be used to better adapt responses to the current and future pandemics.

FUNDING

This project was supported by a Medical Research Council UK (MRC-UK) -São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP 18/14389-0).

An Ultracompact Real-Time Fluorescence Loop-Mediated Isothermal Amplification (LAMP) Analyzer

Low-cost access to the highly sensitive and specific detection of the pathogen in the field is a crucial attribute for the next generation point-of-care (POC) platforms. In this work, we developed a real-time fluorescence nucleic acid testing device with automated and scalable sample preparation capability for field malaria diagnosis. The palm-sized battery-powered analyzer equipped with a disposable microfluidic reagent compact disc described in the companion Chap. 16 which facilitates four isothermal nucleic acid tests in parallel from raw blood samples to answer. The platform has a user-friendly interface such as touchscreen LCD and smartphone data connectivity for on-site and remote healthcare delivery, respectively. The chapter mainly focuses on describing integration procedures of the real-time fluorescence LAMP analyzer and the validation of its subsystems. The device cost is significantly reduced compared to the commercial benchtop real-time machine and other existing POC platforms. As a platform technology, self-sustainable, portable, low-cost, and easy-to-use analyzer design should create a new paradigm of molecular diagnosis toward a variety of infectious diseases at the point of need.

A real-time recombinase polymerase amplification assay for fast and accurate detection of Ditylenchus destructor

Ditylenchus destructor is a plant-parasitic nematode that seriously infests sweet potato crop in China. Thus, fast and accurate detection of D. destructor in soil and plant tissue samples is of great significance. In this study, a real-time recombinase polymerase amplification (RPA) assay was developed for the rapid and accurate detection of D. destructor in various samples. The RPA assay could be easily operated and detected as low as 1/500 individual J4 nematode DNA per reaction in 20 min at 39 °C with high specificity. The assay meets the requirements of rapid detection prior to port quarantine as well as on-site real-time detection and can be applied to detect the parasite in soil and plant samples. The modified gDNA extraction method for a single nematode established in this study significantly reduced the time of detection and improved the applicability of the real-time RPA assay for on-site detection in different environments. The real-time RPA assay to detect D. destructor will be useful for epidemiological investigations in the field as well as for quarantine processes in the sweet potato and potato trade.

A sandwich-based evanescent wave fluorescent biosensor for simple, real-time exosome detection†

Exosomes are regarded as a promising biomarker for the noninvasive diagnosis and treatment of diseases. The value of exosomes for medical research has promoted the search for a fast, efficient, and sensitive detection method. This study reported a sandwich-based evanescent wave fluorescent biosensor (S-EWFB) for exosome detection. A two-step strategy was implemented to take advantages of the simple binding of fluorescent probes with exosomes via the hydrophobic interaction between the cholesteryl and phospholipid bilayer membrane, as well as real-time detection on an evanescent wave liquid-solid interface based on CD63 aptamer-specific capture to form an exosome@fluorescence probe/aptamer sandwich structure. The one-to-many connection between exosomes and signal molecules and the aptamer-modified evanescent wave optical fiber detection platform reduced the detection limit of exosomes to 7.66 particles/mL, with a linear range of 47.5-4.75 × 10⁶ particles/mL. The entire detection process was simple, rapid, and real-time and lasted about 1 h while requiring no separation and purification. Additionally, this platform showed excellent surface regeneration capability and exhibited good performance during the analysis of tumor and non-tumor-derived exosomes.

The development of real-time digital PCR technology using an improved data classification method

For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established. In addition, an enhanced process-based classification model (PAM) was built and trained. And then the device and the analytical model were employed in classification tasks for different concentrations of Epstein-Barr Virus (EBV) plasmid quantification assays. The results indicated that the real-time analysis device achieved a linearity of 0.97, the classification method was able to distinguish the false-positive curves, and the recognition error of positive wells was decreased by 64.4% compared with typical static analysis techniques when low concentrations of samples were tested. With these advantages, it is supposed that the real-time digital PCR analysis apparatus and the improved classification method can be employed to enhance the performance of digital PCR technology.