Rapid, Portable, and Electricity-free Sample Extraction Method for Enhanced Molecular Diagnostics in Resource-Limited Settings.

The COVID-19 pandemic has highlighted the need for rapid and reliable diagnostics that are accessible in resource-limited settings. To address this pressing issue, we have developed a rapid, portable, and electricity-free method for extracting nucleic acids from respiratory swabs (i.e. nasal, nasopharyngeal and buccal swabs), successfully demonstrating its effectiveness for the detection of SARS-CoV-2 in residual clinical specimens. Unlike traditional approaches, our solution eliminates the need for micropipettes or electrical equipment, making it user-friendly and requiring little to no training. Our method builds upon the principles of magnetic bead extraction and revolves around a low-cost plastic magnetic lid, called SmartLid, in combination with a simple disposable kit containing all required reagents conveniently prealiquoted. Here, we clinically validated the SmartLid sample preparation method in comparison to the gold standard QIAamp Viral RNA Mini Kit from QIAGEN, using 406 clinical isolates, including 161 SARS-CoV-2 positives, using the SARS-CoV-2 RT-qPCR assays developed by the US Centers for Disease Control and Prevention (CDC). The SmartLid method showed an overall sensitivity of 95.03% (95% CI: 90.44-97.83%) and a specificity of 99.59% (95% CI: 97.76-99.99%), with a positive agreement of 97.79% (95% CI: 95.84-98.98%) when compared to QIAGEN's column-based extraction method. There are clear benefits to using the SmartLid sample preparation kit: it enables swift extraction of viral nucleic acids, taking less than 5 min, without sacrificing significant accuracy when compared to more expensive and time-consuming alternatives currently available on the market. Moreover, its simplicity makes it particularly well-suited for the point-of-care where rapid results and portability are crucial. By providing an efficient and accessible means of nucleic acid extraction, our approach aims to introduce a step-change in diagnostic capabilities for resource-limited settings.

Applying artificial intelligence and digital health technologies, Viet Nam.

Problem: Direct application of digital health technologies from high-income settings to low- and middle-income countries may be inappropriate due to challenges around data availability, implementation and regulation. Hence different approaches are needed. Approach: Within the Viet Nam ICU Translational Applications Laboratory project, since 2018 we have been developing a wearable device for individual patient monitoring and a clinical assessment tool to improve dengue disease management. Working closely with local staff at the Hospital for Tropical Diseases, Ho Chi Minh City, we developed and tested a prototype of the wearable device. We obtained perspectives on design and use of the sensor from patients. To develop the assessment tool, we used existing research data sets, mapped workflows and clinical priorities, interviewed stakeholders and held workshops with hospital staff. Local setting: In Viet Nam, a lower middle-income country, the health-care system is in the nascent stage of implementing digital health technologies. Relevant changes: Based on patient feedback, we are altering the design of the wearable sensor to increase comfort. We built the user interface of the assessment tool based on the core functionalities selected by workshop attendees. The interface was subsequently tested for usability in an iterative manner by the clinical staff members. Lessons learnt: The development and implementation of digital health technologies need an interoperable and appropriate plan for data management including collection, sharing and integration. Engagements and implementation studies should be conceptualized and conducted alongside the digital health technology development. The priorities of end-users, and understanding context and regulatory landscape are crucial for success.

A dual paper-based nucleic acid extraction method from blood in under ten minutes for point-of-care diagnostics.

Nucleic acid extraction (NAE) plays a crucial role for diagnostic testing procedures. For decades, dried blood spots (DBS) have been used for serology, drug monitoring, and molecular studies. However, extracting nucleic acids from DBS remains a significant challenge, especially when attempting to implement these applications to the point-of-care (POC). To address this issue, we have developed a paper-based NAE method using cellulose filter papers (DBSFP) that operates without the need for electricity (at room temperature). Our method allows for NAE in less than 7 min, and it involves grade 3 filter paper pre-treated with 8% (v/v) igepal surfactant, 1 min washing step with 1× PBS, and 5 min incubation at room temperature in 1× TE buffer. The performance of the methodology was assessed with loop-mediated isothermal amplification (LAMP), targeting the human reference gene beta-actin and the kelch 13 gene from P. falciparum. The developed method was evaluated against FTA cards and magnetic bead-based purification, using time-to-positive (min) for comparative analysis. Furthermore, we optimised our approach to take advantage of the dual functionality of the paper-based extraction, allowing for elution (eluted disk) as well as direct placement of the disk in the LAMP reaction (in situ disk). This flexibility extends to eukaryotic cells, bacterial cells, and viral particles. We successfully validated the method for RNA/DNA detection and demonstrated its compatibility with whole blood stored in anticoagulants. Additionally, we studied the compatibility of DBSFP with colorimetric and lateral flow detection, showcasing its potential for POC applications. Across various tested matrices, targets, and experimental conditions, our results were comparable to those obtained using gold standard methods, highlighting the versatility of our methodology. In summary, this manuscript presents a cost-effective solution for NAE from DBS, enabling molecular testing in virtually any POC setting. When combined with LAMP, our approach provides sample-to-result detection in under 35 minutes.

Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools.

BACKGROUND: Dengue is a common viral illness and severe disease results in life-threatening complications. Healthcare services in low- and middle-income countries treat the majority of dengue cases worldwide. However, the clinical decision-making processes which result in effective treatment are poorly characterised within this setting. In order to improve clinical care through interventions relating to digital clinical decision-support systems (CDSS), we set out to establish a framework for clinical decision-making in dengue management to inform implementation. METHODS: We utilised process mapping and task analysis methods to characterise existing dengue management at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. This is a tertiary referral hospital which manages approximately 30,000 patients with dengue each year, accepting referrals from Ho Chi Minh city and the surrounding catchment area. Initial findings were expanded through semi-structured interviews with clinicians in order to understand clinical reasoning and cognitive factors in detail. A grounded theory was used for coding and emergent themes were developed through iterative discussions with clinician-researchers. RESULTS: Key clinical decision-making points were identified: (i) at the initial patient evaluation for dengue diagnosis to decide on hospital admission and the provision of fluid/blood product therapy, (ii) in those patients who develop severe disease or other complications, (iii) at the point of recurrent shock in balancing the need for fluid therapy with complications of volume overload. From interviews the following themes were identified: prioritising clinical diagnosis and evaluation over existing diagnostics, the role of dengue guidelines published by the Ministry of Health, the impact of seasonality and caseload on decision-making strategies, and the potential role of digital decision-support and disease scoring tools. CONCLUSIONS: The study highlights the contemporary priorities in delivering clinical care to patients with dengue in an endemic setting. Key decision-making processes and the sources of information that were of the greatest utility were identified. These findings serve as a foundation for future clinical interventions and improvements in healthcare. Understanding the decision-making process in greater detail also allows for development and implementation of CDSS which are suited to the local context.

Adaptive Filtering Framework to Remove Nonspecific and Low-Efficiency Reactions in Multiplex Digital PCR Based on Sigmoidal Trends.

Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics of amplification curves and the thermodynamics of melting curves, and it demonstrates that filtering out nonspecific or low-efficient reactions can significantly improve the classification accuracy for cutting-edge multiplexing methodologies.

A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems.

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (-4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.

A Real-world Evaluation of a Case-based Reasoning Algorithm to Support Antimicrobial Prescribing Decisions in Acute Care.

BACKGROUND: A locally developed case-based reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated. METHODS: Prescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in 2 patient populations: first, in patients with confirmed Escherichia coli blood stream infections ("E. coli patients"), and second in ward-based patients presenting with a range of potential infections ("ward patients"). Prescribing recommendations were compared against the Antimicrobial Spectrum Index (ASI) and the World Health Organization Essential Medicine List Access, Watch, Reserve (AWaRe) classification system. Appropriateness of a prescription was defined as the spectrum of the prescription covering the known or most-likely organism antimicrobial sensitivity profile. RESULTS: In total, 224 patients (145 E. coli patients and 79 ward patients) were included. Mean (standard deviation) age was 66 (18) years with 108/224 (48%) female sex. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (odds ratio [OR]: 1.24 95% confidence interval [CI]: .392-3.936; P = .71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0-13) compared to 8 (0-12) (P < .01). CBR recommendations were more likely to be classified as Access class antimicrobials compared to physicians' prescriptions at 110/224 (49%) vs. 79/224 (35%) (OR: 1.77; 95% CI: 1.212-2.588; P < .01). Results were similar for E. coli and ward patients on subgroup analysis. CONCLUSIONS: A CBR-driven decision support system provided appropriate recommendations within a narrower spectrum compared to current clinical practice. Future work must investigate the impact of this intervention on prescribing behaviors more broadly and patient outcomes.

High-Level Multiplexing in Digital PCR with Intercalating Dyes by Coupling Real-Time Kinetics and Melting Curve Analysis.

Digital polymerase chain reaction (dPCR) is a mature technique that has enabled scientific breakthroughs in several fields. However, this technology is primarily used in research environments with high-level multiplexing, representing a major challenge. Here, we propose a novel method for multiplexing, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic information in real-time amplification data and the thermodynamic melting profile using an affordable intercalating dye (EvaGreen). The method trains a system composed of supervised machine learning models for accurate classification, by virtue of the large volume of data from dPCR platforms. As a case study, we develop a new 9-plex assay to detect mobilized colistin resistant genes as clinically relevant targets for antimicrobial resistance. Over 100,000 amplification events have been analyzed, and for the positive reactions, the AMCA approach reports a classification accuracy of 99.33 ± 0.13%, an increase of 10.0% over using melting curve analysis. This work provides an affordable method of high-level multiplexing without fluorescent probes, extending the benefits of dPCR in research and clinical settings.

Complementary Metal-Oxide-Semiconductor Potentiometric Field-Effect Transistor Array Platform Using Sensor Learning for Multi-ion Imaging.

This work describes an array of 1024 ion-sensitive field-effect transistors (ISFETs) using sensor-learning techniques to perform multi-ion imaging for concurrent detection of potassium, sodium, calcium, and hydrogen. Analyte-specific ionophore membranes are deposited on the surface of the ISFET array chip, yielding pixels with quasi-Nernstian sensitivity to K+, Na+, or Ca2+. Uncoated pixels display pH sensitivity from the standard Si3N4 passivation layer. The platform is then trained by inducing a change in single-ion concentration and measuring the responses of all pixels. Sensor learning relies on offline training algorithms including k-means clustering and density-based spatial clustering of applications with noise to yield membrane mapping and sensitivity of each pixel to target electrolytes. We demonstrate multi-ion imaging with an average error of 3.7% (K+), 4.6% (Na+), and 1.8% (pH) for each ion, respectively, while Ca2+ incurs a larger error of 24.2% and hence is included to demonstrate versatility. We validate the platform with a brain dialysate fluid sample and demonstrate reading by comparing with a gold-standard spectrometry technique.

Amplification Curve Analysis: Data-Driven Multiplexing Using Real-Time Digital PCR.

Information about the kinetics of PCR reactions is encoded in the amplification curve. However, in digital PCR (dPCR), this information is typically neglected by collapsing each amplification curve into a binary output (positive/negative). Here, we demonstrate that the large volume of raw data obtained from real-time dPCR instruments can be exploited to perform data-driven multiplexing in a single fluorescent channel using machine learning methods, by virtue of the information in the amplification curve. This new approach, referred to as amplification curve analysis (ACA), was shown using an intercalating dye (EvaGreen), reducing the cost and complexity of the assay and enabling the use of melting curve analysis for validation. As a case study, we multiplexed 3 carbapenem-resistant genes to show the impact of this approach on global challenges such as antimicrobial resistance. In the presence of single targets, we report a classification accuracy of 99.1% (N = 16188), which represents a 19.7% increase compared to multiplexing based on the final fluorescent intensity. Considering all combinations of amplification events (including coamplifications), the accuracy was shown to be 92.9% (N = 10383). To support the analysis, we derived a formula to estimate the occurrence of coamplification in dPCR based on multivariate Poisson statistics and suggest reducing the digital occupancy in the case of multiple targets in the same digital panel. The ACA approach takes a step toward maximizing the capabilities of existing real-time dPCR instruments and chemistries, by extracting more information from data to enable data-driven multiplexing with high accuracy. Furthermore, we expect that combining this method with existing probe-based assays will increase multiplexing capabilities significantly. We envision that once emerging point-of-care technologies can reliably capture real-time data from isothermal chemistries, the ACA method will facilitate the implementation of dPCR outside of the lab.

Rapid Detection of Azole-Resistant Aspergillus fumigatus in Clinical and Environmental Isolates by Use of a Lab-on-a-Chip Diagnostic System.

Aspergillus fumigatus has widely evolved resistance to the most commonly used class of antifungal chemicals, the azoles. Current methods for identifying azole resistance are time-consuming and depend on specialized laboratories. There is an urgent need for rapid detection of these emerging pathogens at point-of-care to provide the appropriate treatment in the clinic and to improve management of environmental reservoirs to mitigate the spread of antifungal resistance. Our study demonstrates the rapid and portable detection of the two most relevant genetic markers linked to azole resistance, the mutations TR34 and TR46, found in the promoter region of the gene encoding the azole target cyp51A. We developed a lab-on-a-chip platform consisting of: (i) tandem-repeat loop-mediated isothermal amplification; (ii) state-of-the-art complementary metal-oxide-semiconductor microchip technology for nucleic acid amplification detection; and (iii) a smartphone application for data acquisition, visualization, and cloud connectivity. Specific and sensitive detection was validated with isolates from clinical and environmental samples from 6 countries across 5 continents, showing a lower limit of detection of 10 genomic copies per reaction in less than 30 min. When fully integrated with a sample preparation module, this diagnostic system will enable the detection of this ubiquitous fungus at the point-of-care, and could help to improve clinical decision making, infection control, and epidemiological surveillance.

An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning.

(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70-180 mg/dL) from 74.1%±8.4% to 80.9%±6.9% (p<0.01) and 54.9%±12.4% to 61.6%±14.1% (p<0.01) in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation.

Simultaneous Single-Channel Multiplexing and Quantification of Carbapenem-Resistant Genes Using Multidimensional Standard Curves.

Multiplexing and quantification of nucleic acids, both have, in their own right, significant and extensive use in biomedical related fields. Currently, the ability to detect several nucleic acid targets in a single-reaction scales linearly with the number of targets; an expensive and time-consuming feat. Here, we propose a new methodology based on multidimensional standard curves that extends the use of real-time PCR data obtained by common qPCR instruments. By applying this novel methodology, we achieve simultaneous single-channel multiplexing and enhanced quantification of multiple targets using only real-time amplification data. This is obtained without the need of fluorescent probes, agarose gels, melting curves or sequencing analysis. Given the importance and demand for tackling challenges in antimicrobial resistance, the proposed method is applied to four of the most prominent carbapenem-resistant genes: blaOXA-48, blaNDM, blaVIM, and blaKPC, which account for 97% of the UK's reported carbapenemase-producing Enterobacteriaceae.

Framework for DNA Quantification and Outlier Detection Using Multidimensional Standard Curves.

Real-time PCR is a highly sensitive and powerful technology for the quantification of DNA and has become the method of choice in microbiology, bioengineering, and molecular biology. Currently, the analysis of real-time PCR data is hampered by only considering a single feature of the amplification profile to generate a standard curve. The current "gold standard" is the cycle-threshold ( Ct) method which is known to provide poor quantification under inconsistent reaction efficiencies. Multiple single-feature methods have been developed to overcome the limitations of the Ct method; however, there is an unexplored area of combining multiple features in order to benefit from their joint information. Here, we propose a novel framework that combines existing standard curve methods into a multidimensional standard curve. This is achieved by considering multiple features together such that each amplification curve is viewed as a point in a multidimensional space. Contrary to only considering a single-feature, in the multidimensional space, data points do not fall exactly on the standard curve, which enables a similarity measure between amplification curves based on distances between data points. We show that this framework expands the capabilities of standard curves in order to optimize quantification performance, provide a measure of how suitable an amplification curve is for a standard, and thus automatically detect outliers and increase the reliability of quantification. Our aim is to provide an affordable solution to enhance existing diagnostic settings through maximizing the amount of information extracted from conventional instruments.

Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes-An In Silico Proof-of-Concept.

In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (± 13.89) to 67.00 (± 11.54; p < 0.01) without increasing hypoglycemia.

Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal.

(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e.g., >60 min), which are usually needed in applications such as precision insulin dosing (e.g., an artificial pancreas), still remain a challenge. In this paper, we present a novel glucose forecasting algorithm that is well-suited for long-term prediction horizons. The proposed algorithm is currently being used as the core component of a modular safety system for an insulin dose recommender developed within the EU-funded PEPPER (Patient Empowerment through Predictive PERsonalised decision support) project. (2) Methods: The proposed blood glucose forecasting algorithm is based on a compartmental composite model of glucose-insulin dynamics, which uses a deconvolution technique applied to the continuous glucose monitoring (CGM) signal for state estimation. In addition to commonly employed inputs by glucose forecasting methods (i.e., CGM data, insulin, carbohydrates), the proposed algorithm allows the optional input of meal absorption information to enhance prediction accuracy. Clinical data corresponding to 10 adult subjects with T1D were used for evaluation purposes. In addition, in silico data obtained with a modified version of the UVa-Padova simulator was used to further evaluate the impact of accounting for meal absorption information on prediction accuracy. Finally, a comparison with two well-established glucose forecasting algorithms, the autoregressive exogenous (ARX) model and the latent variable-based statistical (LVX) model, was carried out. (3) Results: For prediction horizons beyond 60 min, the performance of the proposed physiological model-based (PM) algorithm is superior to that of the LVX and ARX algorithms. When comparing the performance of PM against the secondly ranked method (ARX) on a 120 min prediction horizon, the percentage improvement on prediction accuracy measured with the root mean square error, A-region of error grid analysis (EGA), and hypoglycaemia prediction calculated by the Matthews correlation coefficient, was 18.8 % , 17.9 % , and 80.9 % , respectively. Although showing a trend towards improvement, the addition of meal absorption information did not provide clinically significant improvements. (4) Conclusion: The proposed glucose forecasting algorithm is potentially well-suited for T1D management applications which require long-term glucose predictions.

Allele-Specific Isothermal Amplification Method Using Unmodified Self-Stabilizing Competitive Primers.

Rapid and specific detection of single nucleotide polymorphisms (SNPs) related to drug resistance in infectious diseases is crucial for accurate prognostics, therapeutics and disease management at point-of-care. Here, we present a novel amplification method and provide universal guidelines for the detection of SNPs at isothermal conditions. This method, called USS-sbLAMP, consists of SNP-based loop-mediated isothermal amplification (sbLAMP) primers and unmodified self-stabilizing (USS) competitive primers that robustly delay or prevent unspecific amplification. Both sets of primers are incorporated into the same reaction mixture, but always targeting different alleles; one set specific to the wild type allele and the other to the mutant allele. The mechanism of action relies on thermodynamically favored hybridization of totally complementary primers, enabling allele-specific amplification. We successfully validate our method by detecting SNPs, C580Y and Y493H, in the Plasmodium falciparum kelch 13 gene that are responsible for resistance to artemisinin-based combination therapies currently used globally in the treatment of malaria. USS-sbLAMP primers can efficiently discriminate between SNPs with high sensitivity (limit of detection of 5 × 101 copies per reaction), efficiency, specificity and rapidness (<35 min) with the capability of quantitative measurements for point-of-care diagnosis, treatment guidance, and epidemiological reporting of drug-resistance.

Exploring the Use of C-Reactive Protein to Estimate the Pharmacodynamics of Vancomycin.

BACKGROUND: C-reactive protein (CRP) pharmacodynamic (PD) models have the potential to provide adjunctive methods for predicting the individual exposure response to antimicrobial therapy. We investigated CRP PD linked to a vancomycin pharmacokinetic (PK) model using routinely collected data from noncritical care adults in secondary care. METHODS: Patients receiving intermittent intravenous vancomycin therapy in secondary care were identified. A 2-compartment vancomycin PK model was linked to a previously described PD model describing CRP response. PK and PD parameters were estimated using a Non-Parametric Adaptive Grid technique. Exposure-response relationships were explored with vancomycin area-under-the-concentration-time-curve (AUC) and EC50 (concentration of drug that causes a half maximal effect) using the index, AUC:EC50, fitted to CRP data using a sigmoidal Emax model. RESULTS: Twenty-nine individuals were included. Median age was 62 (21-97) years. Fifteen (52%) patients were microbiology confirmed. PK and PD models were adequately fitted (r 0.83 and 0.82, respectively). There was a wide variation observed in individual Bayesian posterior EC50 estimates (6.95-48.55 mg/L), with mean (SD) AUC:EC50 of 31.46 (29.22). AUC:EC50 was fitted to terminal CRP with AUC:EC50 >19 associated with lower CRP value at 96-120 hours of therapy (100 mg/L versus 44 mg/L; P < 0.01). CONCLUSIONS: The use of AUC:EC50 has the potential to provide in vivo organism and host response data as an adjunct for in vitro minimum inhibitory concentration data, which is currently used as the gold standard PD index for vancomycin therapy. This index can be estimated using routinely collected clinical data. Future work must investigate the role of AUC:EC50 in a prospective cohort and explore linkage with direct patient outcomes.

Towards a minimally invasive device for beta-lactam monitoring in humans.

Antimicrobial resistance is a leading patient safety issue. There is a need to develop novel mechanisms for monitoring and subsequently improving the precision of how we use antibiotics. A surface modified microneedle array was developed for monitoring beta-lactam antibiotic levels in human interstitial fluid. The sensor was fabricated by anodically electrodepositing iridium oxide (AEIROF) onto a platinum surface on the microneedle followed by fixation of beta-lactamase enzyme within a hydrogel. Calibration of the sensor was performed to penicillin-G in buffer solution (PBS) and artificial interstitial fluid (ISF). Further calibration of a platinum disc electrode was undertaken using amoxicillin and ceftriaxone. Open-circuit potentials were performed and data analysed using the Hill equation and log(concentration [M]) plots. The microneedle sensor demonstrated high reproducibility between penicillin-G runs in PBS with mean Km (±1SD) = 0.0044 ± 0.0013 M and mean slope function of log(concentration plots) 29 ± 1.80 mV/decade (r2=0.933). Response was reproducible after 28 days storage at 4°C. In artificial ISF, the sensors response was Km (±1SD) = 0.0077 ± 0.0187 M and a slope function of 34 ± 1.85 mv/decade (r2=0.995). Our results suggest that microneedle array based beta-lactam sensing may be a future application of this AEIROF based enzymatic sensor.

Supervised learning for infection risk inference using pathology data.

BACKGROUND: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions. METHODS: From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using ten-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions. RESULTS: More than 50% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available. CONCLUSION: The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clinical Decision Support Systems could benefit from these findings to assist clinicians in deciding whether or not to initiate antimicrobial therapy to improve prescription practices.