Assessment of the impact of a personalised nutrition intervention in impaired glucose regulation over 26 weeks: a randomised controlled trial

Dietary interventions can reduce progression to type 2 diabetes mellitus (T2DM) in people with non-diabetic hyperglycaemia. In this study we aimed to determine the impact of a DNA-personalised nutrition intervention in people with non-diabetic hyperglycaemia over 26 weeks. ASPIRE-DNA was a pilot study. Participants were randomised into three arms to receive either (i) Control arm: standard care (NICE guidelines) (n = 51), (ii) Intervention arm: DNA-personalised dietary advice (n = 50), or (iii) Exploratory arm: DNA-personalised dietary advice via a self-guided app and wearable device (n = 46). The primary outcome was the difference in fasting plasma glucose (FPG) between the Control and Intervention arms after 6 weeks. 180 people were recruited, of whom 148 people were randomised, mean age of 59 years (SD = 11), 69% of whom were female. There was no significant difference in the FPG change between the Control and Intervention arms at 6 weeks (- 0.13 mmol/L (95% CI [- 0.37, 0.11]), p = 0.29), however, we found that a DNA-personalised dietary intervention led to a significant reduction of FPG at 26 weeks in the Intervention arm when compared to standard care (- 0.019 (SD = 0.008), p = 0.01), as did the Exploratory arm (- 0.021 (SD = 0.008), p = 0.006). HbA1c at 26 weeks was significantly reduced in the Intervention arm when compared to standard care (- 0.038 (SD = 0.018), p = 0.04). There was some evidence suggesting prevention of progression to T2DM across the groups that received a DNA-based intervention (p = 0.06). Personalisation of dietary advice based on DNA did not result in glucose changes within the first 6 weeks but was associated with significant reduction of FPG and HbA1c at 26 weeks when compared to standard care. The DNA-based diet was effective regardless of intervention type, though results should be interpreted with caution due to the low sample size. These findings suggest that DNA-based dietary guidance is an effective intervention compared to standard care, but there is still a minimum timeframe of adherence to the intervention before changes in clinical outcomes become apparent.Trial Registration: www.clinicaltrials.gov.uk Ref: NCT03702465.

The Evolution of Affordable Technologies in Liquid Biopsy Diagnostics: The Key to Clinical Implementation

Cancer remains a leading cause of death worldwide, despite many advances in diagnosis and treatment. Precision medicine has been a key area of focus, with research providing insights and progress in helping to lower cancer mortality through better patient stratification for therapies and more precise diagnostic techniques. However, unequal access to cancer care is still a global concern, with many patients having limited access to diagnostic tests and treatment regimens. Noninvasive liquid biopsy (LB) technology can determine tumour-specific molecular alterations in peripheral samples. This allows clinicians to infer knowledge at a DNA or cellular level, which can be used to screen individuals with high cancer risk, personalize treatments, monitor treatment response, and detect metastasis early. As scientific understanding of cancer pathology increases, LB technologies that utilize circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) have evolved over the course of research. These technologies incorporate tumour-specific markers into molecular testing platforms. For clinical translation and maximum patient benefit at a wider scale, the accuracy, accessibility, and affordability of LB tests need to be prioritized and compared with gold standard methodologies in current use. In this review, we highlight the range of technologies in LB diagnostics and discuss the future prospects of LB through the anticipated evolution of current technologies and the integration of emerging and novel ones. This could potentially allow a more cost-effective model of cancer care to be widely adopted.

Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data

Mobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for personalisation, which has been proven effective in promoting behaviour change. In this work, we propose a theoretically driven and personalised behavioural intervention delivered through an adaptive knowledge-based system. The behavioural system design is guided by the Behavioural Change Wheel and the Capability-Opportunity-Motivation behavioural model. The system exploits the ever-increasing availability of health data from wearable devices, point-of-care tests and consumer genetic tests to issue highly personalised physical activity and sedentary behaviour recommendations. To provide the personalised recommendations, the system firstly classifies the user into one of four diabetes clusters based on their cardiometabolic profile. Secondly, it recommends activity levels based on their genotype and past activity history, and finally, it presents the user with their current risk of developing cardiovascular disease. In addition, leptin, a hormone involved in metabolism, is included as a feedback biosignal to personalise the recommendations further. As a case study, we designed and demonstrated the system on people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes, such as physical activity increase and sedentary behaviour reduction. We trained and simulated the system using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrate that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention.

The association between sedentary behaviour, physical activity and type 2 diabetes markers: A systematic review of mixed analytic approaches

The negative effect of sedentary behaviour on type 2 diabetes markers is established, but the interaction with measures of physical activity is still largely unknown. Previous studies have analysed associations with single-activity models, which ignore the interaction with other behaviours. By including results from various analytical approaches, this review critically summarises the effects of sedentary behaviour on diabetes markers and the benefits of substitutions and compositions of physical activity. Ovid Medline, Embase and Cochrane Library databases were systematically searched. Studies were selected if sedentary behaviour and physical activity were measured by accelerometer in the general population, and if associations were reported with glucose, insulin, HOMA-IR, insulin sensitivity, HbA1c, diabetes incidence, CRP and IL-6. Forty-five studies were included in the review. Conclusive detrimental associations with sedentary behaviour were determined for 2-h insulin (6/12 studies found associations), fasting insulin (15/19 studies), insulin sensitivity (4/6 studies), diabetes (3/4 studies) and IL-6 (2/3 studies). Reallocating sedentary behaviour to light or moderate-to-vigorous activity has a beneficial effect for 2-h glucose (1/1 studies), fasting insulin (3/3 studies), HOMA-IR (1/1 studies) and insulin sensitivity (1/1 studies). Compositional measures of sedentary behaviour were found to affect 2-h glucose (1/1 studies), fasting insulin (2/3 studies), 2-h insulin (1/1 studies), HOMA-IR (2/2 studies) and CRP (1/1 studies). Different analytical methods produced conflicting results for fasting glucose, 2-h glucose, 2-h insulin, insulin sensitivity, HOMA-IR, diabetes, hbA1c, CRP and IL-6. Studies analysing data by quartiles report independent associations between sedentary behaviour and fasting insulin, HOMA-IR and diabetes only for high duration of sedentary time (7-9 hours/day). However, this review could not provide sufficient evidence for a time-specific cut-off of sedentary behaviour for diabetes biomarkers. While substituting sedentary behaviour with moderate-to-vigorous activity brings greater improvements for health, light activity also benefits metabolic health. Future research should elucidate the effects of substituting and combining different activity durations and modalities.

Variability in detection of SARS-CoV-2-specific antibody responses following mild infection: a prospective multicentre cross-sectional study, London, United Kingdom, 17 April to 17 July 2020

IntroductionImmunoassays targeting different SARS-CoV-2-specific antibodies are employed for seroprevalence studies. The degree of variability between immunoassays targeting anti-nucleocapsid (anti-NP; the majority) vs the potentially neutralising anti-spike antibodies (including anti-receptor-binding domain; anti-RBD), particularly in mild or asymptomatic disease, remains unclear.AimsWe aimed to explore variability in anti-NP and anti-RBD antibody detectability following mild symptomatic or asymptomatic SARS-CoV-2 infection and analyse antibody response for correlation with symptomatology.MethodsA multicentre prospective cross-sectional study was undertaken (April-July 2020). Paired serum samples were tested for anti-NP and anti-RBD IgG antibodies and reactivity expressed as binding ratios (BR). Multivariate linear regression was performed analysing age, sex, time since onset, symptomatology, anti-NP and anti-RBD antibody BR.ResultsWe included 906 adults. Antibody results (793/906; 87.5%; 95% confidence interval: 85.2-89.6) and BR strongly correlated (ρ = 0.75). PCR-confirmed cases were more frequently identified by anti-RBD (129/130) than anti-NP (123/130). Anti-RBD testing identified 83 of 325 (25.5%) cases otherwise reported as negative for anti-NP. Anti-NP presence (+1.75/unit increase; p < 0.001), fever (≥ 38°C; +1.81; p < 0.001) or anosmia (+1.91; p < 0.001) were significantly associated with increased anti-RBD BR. Age (p = 0.85), sex (p = 0.28) and cough (p = 0.35) were not. When time since symptom onset was considered, we did not observe a significant change in anti-RBD BR (p = 0.95) but did note decreasing anti-NP BR (p < 0.001).ConclusionSARS-CoV-2 anti-RBD IgG showed significant correlation with anti-NP IgG for absolute seroconversion and BR. Higher BR were seen in symptomatic individuals, particularly those with fever. Inter-assay variability (12.5%) was evident and raises considerations for optimising seroprevalence testing strategies/studies.

Pooled sputum to optimise the efficiency and utility of rapid, point-of-care molecular SARS-CoV-2 testing

Background

As SARS-CoV-2 testing expands, particularly to widespread asymptomatic testing, high sensitivity point-of-care PCR platforms may optimise potential benefits from pooling multiple patients’ samples.

Method

We tested patients and asymptomatic citizens for SARS-CoV-2, exploring the efficiency and utility of CovidNudge (i) for detection in individuals’ sputum (compared to nasopharyngeal swabs), (ii) for detection in pooled sputum samples, and (iii) by modelling roll out scenarios for pooled sputum testing.

Results

Across 295 paired samples, we find no difference (p = 0.1236) in signal strength for sputum (mean amplified replicates (MAR) 25.2, standard deviation (SD) 14.2, range 0–60) compared to nasopharyngeal swabs (MAR 27.8, SD 12.4, range 6–56). At 10-sample pool size we find some drop in absolute strength of signal (individual sputum MAR 42.1, SD 11.8, range 13–60 vs. pooled sputum MAR 25.3, SD 14.6, range 1–54; p < 0.0001), but only marginal drop in sensitivity (51/53,96%). We determine a limit of detection of 250 copies/ml for an individual test, rising only four-fold to 1000copies/ml for a 10-sample pool. We find optimal pooled testing efficiency to be a 12–3-1-sample model, yet as prevalence increases, pool size should decrease; at 5% prevalence to maintain a 75% probability of negative first test, 5-sample pools are optimal.

Conclusion

We describe for the first time the use of sequentially dipped sputum samples for rapid pooled point of care SARS-CoV-2 PCR testing. The potential to screen asymptomatic cohorts rapidly, at the point-of-care, with PCR, offers the potential to quickly identify and isolate positive individuals within a population “bubble”.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06316-z.

Aptasensor for Quantification of Leptin Through PCR Amplification of Short DNA-Aptamers

Protein quantification is traditionally performed through enzyme-linked immunosorbent assay (ELISA), which involves long preparation times. To overcome this, new approaches use aptamers as an alternative to antibodies. In this paper, we present a new approach to quantify proteins with short DNA aptamers through polymerase chain reaction (PCR) resulting in shorter protocol times with comparatively improved limits of detection. The proposed method includes a novel way to quantify both the target protein and the corresponding short DNA-aptamers simultaneously, which also allows us to fully characterize the performance of aptasensors. Human leptin is used as a target protein to validate this technique, because it is considered an important biomarker for obesity-related studies. In our experiments, we achieved the lowest limit of detection of 100 pg/mL within less than 2 h, a limit affected by the dissociation constant of the leptin aptamer, which could be improved by selecting a more specific aptamer. Because of the simple and inexpensive approach, this technique can be employed for Lab-On-Chip implementations and for rapid "on-site" quantification of proteins.

A DNA-Based Intelligent Expert System for Personalised Skin-Health Recommendations

Intensive attention on personalised skin-health solutions is on account of incomparable love of skin and an urgent need for effective treatment. In the meanwhile, people have great expectations on how to utilise genetic knowledge of our body to provide a precise solution for different individuals, such as daily use of skin-health products, since the rapid development of genetic test services and skin-health science. However, the complexity of multi-modal data, the establishment of correlations between consumer genetic data and product ingredients are the main obstacles encountered today. Determining to settle such obstacles, a personalised recommendation expert system for selecting optimised skin-health product within the category based upon genetic phenotypes for each consumer was introduced in this article. Random Forests were implemented to achieve automatic product categorisation, the performance discussed and compared with SVM and Logistic Regression. Lastly, categorised skin-health product suggestion was made with an optimised recommendation model based on associated genetic phenotype information. Potential changes (up to 71.0% more phenotypic relevant ingredients) from experiments using real product data were demonstrated and compared with imitated cases of real-life human selections.

Assessing a novel, lab-free, point-of-care test for SARS-CoV-2 (CovidNudge): a diagnostic accuracy study

BACKGROUND

Access to rapid diagnosis is key to the control and management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Laboratory RT-PCR testing is the current standard of care but usually requires a centralised laboratory and significant infrastructure. We describe our diagnostic accuracy assessment of a novel, rapid point-of-care real time RT-PCR CovidNudge test, which requires no laboratory handling or sample pre-processing.

METHODS

Between April and May, 2020, we obtained two nasopharyngeal swab samples from individuals in three hospitals in London and Oxford (UK). Samples were collected from three groups: self-referred health-care workers with suspected COVID-19; patients attending emergency departments with suspected COVID-19; and hospital inpatient admissions with or without suspected COVID-19. For the CovidNudge test, nasopharyngeal swabs were inserted directly into a cartridge which contains all reagents and components required for RT-PCR reactions, including multiple technical replicates of seven SARS-CoV-2 gene targets (rdrp1, rdrp2, e-gene, n-gene, n1, n2 and n3) and human ribonuclease P (RNaseP) as sample adequacy control. Swab samples were tested in parallel using the CovidNudge platform, and with standard laboratory RT-PCR using swabs in viral transport medium for processing in a central laboratory. The primary analysis was to compare the sensitivity and specificity of the point-of-care CovidNudge test with laboratory-based testing.

FINDINGS

We obtained 386 paired samples: 280 (73%) from self-referred health-care workers, 15 (4%) from patients in the emergency department, and 91 (23%) hospital inpatient admissions. Of the 386 paired samples, 67 tested positive on the CovidNudge point-of-care platform and 71 with standard laboratory RT-PCR. The overall sensitivity of the point-of-care test compared with laboratory-based testing was 94% (95% CI 86-98) with an overall specificity of 100% (99-100). The sensitivity of the test varied by group (self-referred healthcare workers 94% [95% CI 85-98]; patients in the emergency department 100% [48-100]; and hospital inpatient admissions 100% [29-100]). Specificity was consistent between groups (self-referred health-care workers 100% [95% CI 98-100]; patients in the emergency department 100% [69-100]; and hospital inpatient admissions 100% [96-100]). Point of care testing performance was similar during a period of high background prevalence of laboratory positive tests (25% [95% 20-31] in April, 2020) and low prevalence (3% [95% 1-9] in inpatient screening). Amplification of viral nucleocapsid (n1, n2, and n3) and envelope protein gene (e-gene) were most sensitive for detection of spiked SARS-CoV-2 RNA.

INTERPRETATION

The CovidNudge platform was a sensitive, specific, and rapid point of care test for the presence of SARS-CoV-2 without laboratory handling or sample pre-processing. The device, which has been implemented in UK hospitals since May, 2020, could enable rapid decisions for clinical care and testing programmes.

FUNDING

National Institute of Health Research (NIHR) Imperial Biomedical Research Centre, NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with Public Health England, NIHR Biomedical Research Centre Oxford, and DnaNudge.

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study

BACKGROUND

Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital.

METHODS

An SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored.

RESULTS

One hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21-98)  years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20-0.40). ROC AUC was 0.84 (95% CI: 0.76-0.91).

CONCLUSIONS

An SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.

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 &lt; .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 &lt; .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.

A novel hotspot specific isothermal amplification method for detection of the common PIK3CA p.H1047R breast cancer mutation

Breast cancer (BC) is a common cancer in women worldwide. Despite advances in treatment, up to 30% of women eventually relapse and die of metastatic breast cancer. Liquid biopsy analysis of circulating cell-free DNA fragments in the patients' blood can monitor clonality and evolving mutations as a surrogate for tumour biopsy. Next generation sequencing platforms and digital droplet PCR can be used to profile circulating tumour DNA from liquid biopsies; however, they are expensive and time consuming for clinical use. Here, we report a novel strategy with proof-of-concept data that supports the usage of loop-mediated isothermal amplification (LAMP) to detect PIK3CA c.3140 A > G (H1047R), a prevalent BC missense mutation that is attributed to BC tumour growth. Allele-specific primers were designed and optimized to detect the p.H1047R variant following the USS-sbLAMP method. The assay was developed with synthetic DNA templates and validated with DNA from two breast cancer cell-lines and two patient tumour tissue samples through a qPCR instrument and finally piloted on an ISFET enabled microchip. This work sets a foundation for BC mutational profiling on a Lab-on-Chip device, to help the early detection of patient relapse and to monitor efficacy of systemic therapies for personalised cancer patient management.

Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring

Closed-loop or intelligent neuromodulation allows adjustable, personalized neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algorithm which decides the timing (when?) and strength (how much?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodulation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. However, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K⁺), sodium (Na⁺), calcium (Ca²⁺), chloride (Cl⁻), hydrogen (H⁺) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemical, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.

Adapting ISFETs for Epigenetics: An Overview

This paper gives an overview of how CMOS design methods can be applied to ion-sensitive field effect transistor (ISFETs) for pH-based DNA methylation and miRNA detection. Design specifications are fundamentally defined by the choice of analysis. As such, the focus for DNA methylation was on developing front-end analogue circuits to carry out Methylation-specific PCR (MSP) for Point-of-Care applications, and sequencing for detailed analysis. The use of MSP prompted the design of an ISFET weak inversion current mirror topology for differential sensing and reduction of drift and temperature sensitivities. The primary limitation in ion-semiconductor sequencing is base calling of repeated nucleotides known as homopolymers. Implementation of a switched current integrator can potentially increase both accuracy and window for detection, within the frequency region of DNA reactions. For quantifying miRNAs, digital back-end processing circuits were considered toward a fully portable platform that can carry out real-time monitoring of DNA amplification reactions. Two systems to evaluate threshold cycles were developed, based on the Derivative method and a new proposed 3-point exponential evaluation aim to reduce detection time simultaneously. Both implementations were tested with datasets from fluorescent qPCR reactions, as well as pH-LAMP experiments that have been optimized for on-chip amplifications. All designs were fabricated in unmodified CMOS with performance assessed based on functionality as well as pH-resolution required in practice.

Development of a patient-centred intervention to improve knowledge and understanding of antibiotic therapy in secondary care

Background

We developed a personalised antimicrobial information module co-designed with patients. This study aimed to evaluate the potential impact of this patient-centred intervention on short-term knowledge and understanding of antimicrobial therapy in secondary care.

Methods

Thirty previous patients who had received antibiotics in hospital within 12 months were recruited to co-design an intervention to promote patient engagement with infection management. Two workshops, containing five focus-groups were held. These were audio-recorded. Data were analysed using a thematic framework developed deductively based on previous work. Line-by-line coding was performed with new themes added to the framework by two researchers. This was used to inform the development of a patient information module, embedded within an electronic decision support tool (CDSS).

The intervention was piloted over a four-week period at Imperial College Healthcare NHS Trust on 30 in-patients. Pre- and post-intervention questionnaires were developed and implemented to assess short term changes in patient knowledge and understanding and provide feedback on the intervention. Data were analysed using SPSS and NVIVO software.

Results

Within the workshops, there was consistency in identified themes. The participants agreed upon and co-designed a personalised PDF document that could be integrated into an electronic CDSS to be used by healthcare professionals at the point-of-care. Their aim for the tool was to provide individualised practical information, signpost to reputable information sources, and enhance communication between patients and healthcare professionals.

Eighteen out of thirty in-patients consented to participant in the pilot evaluation with 15/18(83%) completing the study. Median (range) age was 66(22–85) years. The majority were male (10/15;66%). Pre-intervention, patients reported desiring further information regarding their infections and antibiotic therapy, including side effects of treatment. Deployment of the intervention improved short term knowledge and understanding of individuals infections and antibiotic management with median (IQR) scores improving from 3(2–5)/13 to 10(6–11)/13. 13/15(87%) reported that they would use the intervention again.

Conclusion

A personalised, patient-centred intervention improved understanding and short-term knowledge of infections and antibiotic therapy in participating patients’. Long term impact on attitudes and behaviours post discharge will be further investigated.

Electronic supplementary material

The online version of this article (10.1186/s13756-018-0333-1) contains supplementary material, which is available to authorized users.

Influence of Cholecystokinin-8 on Compound Nerve Action Potentials from Ventral Gastric Vagus in Rats

OBJECTIVE

Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for a number of conditions, such as epilepsy, depression and for Neurometabolic Therapies, especially for treating obesity. The objective of this study was to characterize the left ventral subdiaphragmatic gastric trunk of vagus nerve (SubDiaGVN) and to analyze the influence of intravenous injection of gut hormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observed on the same branch, with the aim of understanding the impact of hormones on VNS and incorporating the methods and results into closed loop implant design.

METHODS

The cervical region of the left vagus nerve (CerVN) of male Wistar rats was stimulated with electric current and the elicited CNAPs were recorded on the SubDiaGVN under four different conditions: Control (no injection), Saline, CCK1 (100[Formula: see text]pmol/kg) and CCK2 (1000[Formula: see text]pmol/kg) injections.

RESULTS

We identified the presence of A[Formula: see text], B, C1, C2, C3 and C4 fibers with their respective velocity ranges. Intravenous administration of CCK in vivo results in selective, statistically significant reduction of CNAP components originating from A and B fibers, but with no discernible effect on the C fibers in [Formula: see text] animals. The affected CNAP components exhibit statistically significant ([Formula: see text] and [Formula: see text]) higher normalized stimulation thresholds.

CONCLUSION

This approach of characterizing the vagus nerve can be used in closed loop systems to determine when to initiate VNS and also to tune the stimulation dose, which is patient-specific and changes over time.

PERSON-Personalized Expert Recommendation System for Optimized Nutrition

The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.

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.

Injection moulded microneedle sensor for real-time wireless pH monitoring

This paper describes the development of an array of individually addressable pH sensitive microneedles using injection moulding and their integration within a portable device for real-time wireless recording of pH distributions in biological samples. The fabricated microneedles are subjected to gold patterning followed by electrodeposition of iridium oxide to sensitize them to 0.07 units of pH change. Miniaturised electronics suitable for the sensors readout, analog-to-digital conversion and wireless transmission of the potentiometric data are embodied within the device, enabling it to measure real-time pH of soft biological samples such as muscles. In this paper, real-time recording of the cardiac pH distribution, during ischemia followed by reperfusion cycles in cardiac muscles of male Wistar rats has been demonstrated by using the microneedle array.

Case-Based Reasoning for Insulin Bolus Advice

BACKGROUND

Insulin bolus calculators assist people with Type 1 diabetes (T1D) to calculate the amount of insulin required for meals to achieve optimal glucose levels but lack adaptability and personalization. We have proposed enhancing bolus calculators by the means of case-based reasoning (CBR), an established problem-solving methodology, by individualizing and optimizing insulin therapy for various meal situations. CBR learns from experiences of past similar meals, which are described in cases through a set of parameters (eg, time of meal, alcohol, exercise). This work discusses the selection, representation and effect of case parameters used for a CBR-based Advanced Bolus Calculator for Diabetes (ABC4D).

METHODS

We analyzed the usage and effect of selected parameters during a pilot study (n = 10), where participants used ABC4D for 6 weeks. Retrospectively, we evaluated the effect of glucose rate of change before the meal on the glycemic excursion. Feedback from study participants about the choice of parameters was obtained through a nonvalidated questionnaire.

RESULTS

Exercise and alcohol were the most frequently used parameters, which was congruent with the feedback from study participants, who found these parameters most useful. Furthermore, cases including either exercise or alcohol as parameter showed a trend in reduction of insulin at the end of the study. A significant difference ( P < .01) was found in glycemic outcomes for meals where glucose rate of change was rising compared to stable rate of change.

CONCLUSIONS

Results from the 6-week study indicate the potential benefit of including parameters exercise, alcohol and glucose-rate of change for insulin dosing decision support.

Novel pH sensing semiconductor for point-of-care detection of HIV-1 viremia

The timely detection of viremia in HIV-infected patients receiving antiviral treatment is key to ensuring effective therapy and preventing the emergence of drug resistance. In high HIV burden settings, the cost and complexity of diagnostics limit their availability. We have developed a novel complementary metal-oxide semiconductor (CMOS) chip based, pH-mediated, point-of-care HIV-1 viral load monitoring assay that simultaneously amplifies and detects HIV-1 RNA. A novel low-buffer HIV-1 pH-LAMP (loop-mediated isothermal amplification) assay was optimised and incorporated into a pH sensitive CMOS chip. Screening of 991 clinical samples (164 on the chip) yielded a sensitivity of 95% (in vitro) and 88.8% (on-chip) at >1000 RNA copies/reaction across a broad spectrum of HIV-1 viral clades. Median time to detection was 20.8 minutes in samples with >1000 copies RNA. The sensitivity, specificity and reproducibility are close to that required to produce a point-of-care device which would be of benefit in resource poor regions, and could be performed on an USB stick or similar low power device.

Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study

BACKGROUND

The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin bolus dose decision support system based on case-based reasoning (CBR). The system is implemented in a smartphone application to provide personalized and adaptive insulin bolus advice for people with type 1 diabetes. We aimed to assess proof of concept, safety, and feasibility of ABC4D in a free-living environment over 6 weeks.

METHODS

Prospective nonrandomized single-arm pilot study. Participants used the ABC4D smartphone application for 6 weeks in their home environment, attending the clinical research facility weekly for data upload, revision, and adaptation of the CBR case base. The primary outcome was postprandial hypoglycemia.

RESULTS

Ten adults with type 1 diabetes, on multiple daily injections of insulin, mean (standard deviation) age 47 (17), diabetes duration 25 (16), and HbA1c 68 (16) mmol/mol (8.4 (1.5) %) participated. A total of 182 and 150 meals, in week 1 and week 6, respectively, were included in the analysis of postprandial outcomes. The median (interquartile range) number of postprandial hypoglycemia episodes within 6-h after the meal was 4.5 (2.0-8.2) in week 1 versus 2.0 (0.5-6.5) in week 6 (P = 0.1). No episodes of severe hypoglycemia occurred during the study.

CONCLUSION

The ABC4D is safe for use as a decision support tool for insulin bolus dosing in self-management of type 1 diabetes. A trend suggesting a reduction in postprandial hypoglycemia was observed in the final week compared with week 1.

An array of individually addressable micro-needles for mapping pH distributions

This work describes the preparation of an array of individually addressable pH sensitive microneedles which demonstrated suitable for measuring pH distribution during heart ischemia and reperfusion cycles.

Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning

This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from 166 ± 39 to 150 ± 16 in the adult populations (p = 0.03) and from 167 ± 25 to 162 ± 23 in the adolescent population (p = 0.06). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population.

Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes: A 24-Hour Randomized Controlled Crossover Study

BACKGROUND

The Bio-inspired Artificial Pancreas (BiAP) is a closed-loop insulin delivery system based on a mathematical model of beta-cell physiology and implemented in a microchip within a low-powered handheld device. We aimed to evaluate the safety and efficacy of the BiAP over 24 hours, followed by a substudy assessing the safety of the algorithm without and with partial meal announcement. Changes in lactate and 3-hydroxybutyrate concentrations were investigated for the first time during closed-loop.

METHODS

This is a prospective randomized controlled open-label crossover study. Participants were randomly assigned to attend either a 24-hour closed-loop visit connected to the BiAP system or a 24-hour open-loop visit (standard insulin pump therapy). The primary outcome was percentage time spent in target range (3.9-10 mmol/l) measured by sensor glucose. Secondary outcomes included percentage time in hypoglycemia (<3.9 mmol/l) and hyperglycemia (>10 mmol/l). Participants were invited to attend for an additional visit to assess the BiAP without and with partial meal announcements.

RESULTS

A total of 12 adults with type 1 diabetes completed the study (58% female, mean [SD] age 45 [10] years, BMI 25 [4] kg/m(2), duration of diabetes 22 [12] years and HbA1c 7.4 [0.7]% [58 (8) mmol/mol]). The median (IQR) percentage time in target did not differ between closed-loop and open-loop (71% vs 66.9%, P = .9). Closed-loop reduced time spent in hypoglycemia from 17.9% to 3.0% (P < .01), but increased time was spent in hyperglycemia (10% vs 28.9%, P = .01). The percentage time in target was higher when all meals were announced during closed-loop compared to no or partial meal announcement (65.7% [53.6-80.5] vs 45.5% [38.2-68.3], P = .12).

CONCLUSIONS

The BiAP is safe and achieved equivalent time in target as measured by sensor glucose, with improvement in hypoglycemia, when compared to standard pump therapy.

An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results

This paper presents the architecture and initial usability results of an advanced insulin bolus calculator for diabetes (ABC4D), which provides personalized insulin recommendations for people with diabetes by differentiating between various diabetes scenarios and automatically adjusting its parameters over time. The proposed platform comprises two main components: a smartphone-based patient platform allowing manual input of glucose and variables affecting blood glucose levels (e.g., meal carbohydrate content and exercise) and providing real-time insulin bolus recommendations; and a clinical revision platform to supervise the automatic adaptations of the bolus calculator parameters. The system implements a previously in silico validated bolus calculator algorithm based on case-based reasoning, which uses information from similar past events (i.e., cases) to suggest improved personalized insulin bolus recommendations and automatically learns from new events. Usability of ABC4D was assessed by analyzing the system usage at the end of a six-week pilot study (n = 10). Further feedback on the use of ABC4D has been obtained from each participant at the end of the study from a usability questionnaire. On average, each participant requested 115 ± 21 insulin recommendations, of which 103 ± 28 (90%) were accepted. The clinical revision software proposed a total of 754 case revisions, where 723 (96%) adaptations were approved by a clinical expert and updated in the patient platform.

A low-noise instrumentation amplifier with DC suppression for recording ENG signals

This paper presents an AC-coupled instrumentation amplifier for electroneurogram (ENG) activity recording. For this design, we evaluate gain and noise requirements based on interference sources (electrodes, power line, EMG). The circuit has been implemented in a commercially-available 0.35μm CMOS technology with total power consumption 460μW. The amplifier achieves CMRR 107 dB and integrated input referred noise 940 nV. The gain is 63 dB and the bandwidth is 0.5 Hz- 13 kHz. The chosen topology enables to minimise on-chip capacitance (only 27 pF), with a total chip area of 0.4mm2.

Method for automatic adjustment of an insulin bolus calculator: in silico robustness evaluation under intra-day variability

BACKGROUND AND OBJECTIVE

Insulin bolus calculators are simple decision support software tools incorporated in most commercially available insulin pumps and some capillary blood glucose meters. Although their clinical benefit has been demonstrated, their utilisation has not been widespread and their performance remains suboptimal, mainly because of their lack of flexibility and adaptability. One of the difficulties that people with diabetes, clinicians and carers face when using bolus calculators is having to set parameters and adjust them on a regular basis according to changes in insulin requirements. In this work, we propose a novel method that aims to automatically adjust the parameters of a bolus calculator. Periodic usage of a continuous glucose monitoring device is required for this purpose.

METHODS

To test the proposed method, an in silico evaluation under real-life conditions was carried out using the FDA-accepted Type 1 diabetes mellitus (T1DM) UVa/Padova simulator. Since the T1DM simulator does not incorporate intra-subject variability and uncertainty, a set of modifications were introduced to emulate them. Ten adult and ten adolescent virtual subjects were assessed over a 3-month scenario with realistic meal variability. The glycaemic metrics: mean blood glucose; percentage time in target; percentage time in hypoglycaemia; risk index, low blood glucose index; and blood glucose standard deviation, were employed for evaluation purposes. A t-test statistical analysis was carried out to evaluate the benefit of the presented algorithm against a bolus calculator without automatic adjustment.

RESULTS

The proposed method statistically improved (p<0.05) all glycemic metrics evaluating hypoglycaemia on both virtual cohorts: percentage time in hypoglycaemia (i.e. BG<70 mg/dl) (adults: 2.7±4.0 vs. 0.4±0.7, p=0.03; adolescents: 7.1±7.4 vs. 1.3±2.4, p=0.02) and low blood glucose index (LBGI) (adults: 1.1±1.3 vs. 0.3±0.2, p=0.002; adolescents: 2.0±2.19 vs. 0.7±1.4, p=0.05). A statistically significant improvement was also observed on the blood glucose standard deviation (BG SD mg/dL) (adults: 33.5±13.7 vs. 29.2±8.3, p=0.01; adolescents: 63.7±22.7 vs. 44.9±23.9, p=0.01). Apart from a small increase in mean blood glucose on the adult cohort (129.9±11.9 vs. 133.9±11.6, p=0.03), the rest of the evaluated metrics, despite showing an improvement trend, did not experience a statistically significant change.

CONCLUSIONS

A novel method for automatically adjusting the parameters of a bolus calculator has the potential to improve glycemic control in T1DM diabetes management.

Feasibility study of a bio-inspired artificial pancreas in adults with type 1 diabetes

BACKGROUND

This study assesses proof of concept and safety of a novel bio-inspired artificial pancreas (BiAP) system in adults with type 1 diabetes during fasting, overnight, and postprandial conditions. In contrast to existing glucose controllers in artificial pancreas systems, the BiAP uses a control algorithm based on a mathematical model of β-cell physiology. The algorithm is implemented on a miniature silicon microchip within a portable hand-held device that interfaces the components of the artificial pancreas.

MATERIALS AND METHODS

In this nonrandomized open-label study each subject attended for a 6-h fasting study followed by a 13-h overnight and post-breakfast study on a separate occasion. During both study sessions the BiAP system was used, and microboluses of insulin were recommended every 5 min by the control algorithm according to subcutaneous sensor glucose levels. The primary outcome was percentage time spent in the glucose target range (3.9-10.0 mmol/L).

RESULTS

Twenty subjects (55% male; mean [SD] age, 44 [10] years; duration of diabetes, 22 [12] years; glycosylated hemoglobin, 7.4% [0.7%] [57 (7) mmol/mol]; body mass index, 25 [4] kg/m(2)) participated in the fasting study, and the median (interquartile range) percentage time in target range was 98.0% (90.8-100.0%). Seventeen of these subjects then participated in the overnight/postprandial study, where 70.7% (63.9-77.4%) of time was spent in the target range and, reassuringly, 0.0% (0.0-2.3%) of time was spent in hypoglycemia (<3.9 mmol/L).

CONCLUSIONS

The BiAP achieves safe glycemic control during fasting, overnight, and postprandial conditions.

A low power sub- μW chemical gilbert cell for ISFET differential reaction monitoring

This paper presents a low power current-mode method for monitoring differentially derived changes in pH from ion-sensitive field-effect transistor (ISFET) sensors, by adopting the Chemical Gilbert Cell. The fabricated system, with only a few transistors, achieves differential measurements and therefore drift minimisation of continuously recorded pH signals obtained from biochemical reactions such as DNA amplification in addition to combined gain tunability using only a single current. Experimental results are presented, demonstrating the capabilities of the front-end at a microscopic level through integration in a lab-on-chip (LoC) setup combining a microfluidic assembly, suitable for applications that require differential monitoring in small volumes, such as DNA detection where more than one gene needs to be studied. The system was designed and fabricated in a typical 0.35 μ m CMOS process with the resulting topology achieving good differential pH sensitivity with a measured low power consumption of only 165 nW due to weak inversion operation. A tunable gain is demonstrated with results confirming 15.56 dB gain at 20 nA of ISFET bias current and drift reduction of up to 100 times compared to a single-ended measurement is also reported due to the differential current output, making it ideal for robust, low-power chemical measurement.

RF communication with implantable wireless device: effects of beating heart on performance of miniature antenna

The frequency response of an implantable antenna is key to the performance of a wireless implantable sensor. If the antenna detunes significantly, there are substantial power losses resulting in loss of accuracy. One reason for detuning is because of a change in the surrounding environment of an antenna. The pulsating anatomy of the human heart constitutes such a changing environment, so detuning is expected but this has not been quantified dynamically before. Four miniature implantable antennas are presented (two different geometries) along with which are placed within the heart of living swine the dynamic reflection coefficients. These antennas are designed to operate in the short range devices frequency band (863-870 MHz) and are compatible with a deeply implanted cardiovascular pressure sensor. The measurements recorded over 27 seconds capture the effects of the beating heart on the frequency tuning of the implantable antennas. When looked at in the time domain, these effects are clearly physiological and a combination of numerical study and posthumous autopsy proves this to be the case, while retrospective simulation confirms this hypothesis. The impact of pulsating anatomy on antenna design and the need for wideband implantable antennas is highlighted.

Parylene C-based flexible electronics for pH monitoring applications

Emerging materials in the field of implantable sensors should meet the needs for biocompatibility; transparency; flexibility and integrability. In this work; we present an integrated approach for implementing flexible bio-sensors based on thin Parylene C films that serve both as flexible support substrates and as active H⁺ sensing membranes within the same platform. Using standard micro-fabrication techniques; a miniaturized 40-electrode array was implemented on a 5 μm-thick Parylene C film. A thin capping film (1 μm) of Parylene on top of the array was plasma oxidized and served as the pH sensing membrane. The sensor was evaluated with the use of extended gate discrete MOSFETs to separate the chemistry from the electronics and prolong the lifetime of the sensor. The chemical sensing array spatially maps the local pH levels; providing a reliable and rapid-response (<5 s) system with a sensitivity of 23 mV/pH. Moreover; it preserves excellent encapsulation integrity and low chemical drifts (0.26–0.38 mV/min). The proposed approach is able to deliver hybrid flexible sensing platforms that will facilitate concurrent electrical and chemical recordings; with application in real-time physiological recordings of organs and tissues.

Ngram-derived pattern recognition for the detection and prediction of epileptic seizures

This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure). The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.

Bio-Inspired glucose control in diabetes based on an analogue implementation of a β-cell model

This paper presents a bio-inspired method for in-vivo control of blood glucose based on a model of the pancreatic β-cell. The proposed model is shown to be implementable using low-power analogue integrated circuits in CMOS, realizing a biologically faithful implementation which captures all the behaviours seen in physiology. This is then shown to be capable of glucose control using an in silico population of diabetic subjects achieving 93% of the time in tight glycemic target (i.e., [70, 140] mg/dl) . The proposed controller is then compared with a commonly used external physiological insulin delivery (ePID) controller for glucose control. Results confirm equivalent, or superior, performance in comparison with ePID. The system has been designed in a commercially available 0.35 μm CMOS process and achieves an overall power consumption of 1.907 mW.

Continuous in vivo blood pressure measurements using a fully implantable wireless SAW sensor

In this paper, the development of a fully implantable wireless sensor able to provide continuous real-time accurate pressure measurements is presented. Surface Acoustic Wave (SAW) technology was used to deposit resonators on crystalline quartz wafers; the wafers were then assembled to produce a pressure sensitive device. Excitation and reading via a miniature antenna attached to the pressure sensor enables continuous external interrogation. The main advantages of such a configuration are the long term stability of quartz and the low power necessary for the interrogation, which allows 24/7 interrogation by means of a hand-held, battery powered device. Such data are of vital importance to clinicians monitoring and treating the effects of hypertension and heart failure. A prototype was designed and tested using both a bio-phantom test rig and an animal model. The pressure traces for both compare very well with a commercially available catheter tip pressure transducer. The work presented in this paper is the first known wireless pressure data from the left ventricle of the heart of a living swine.

A new era of semiconductor genetics using ion-sensitive field-effect transistors: the gene-sensitive integrated cell

Semiconductor genetics is now disrupting the field of healthcare owing to the rapid parallelization and scaling of DNA sensing using ion-sensitive field-effect transistors (ISFETs) fabricated using commercial complementary metal -oxide semiconductor technology. The enabling concept of DNA reaction monitoring introduced by Toumazou has made this a reality and we are now seeing relentless scaling with Moore's law ultimately achieving the $100 genome. In this paper, we present the next evolution of this technology through the creation of the gene-sensitive integrated cell (GSIC) for label-free real-time analysis based on ISFETs. This device is derived from the traditional metal-oxide semiconductor field-effect transistor (MOSFET) and has electrical performance identical to that of a MOSFET in a standard semiconductor process, yet is capable of incorporating DNA reaction chemistries for applications in single nucleotide polymorphism microarrays and DNA sequencing. Just as application-specific integrated circuits, which are developed in much the same way, have shaped our consumer electronics industry and modern communications and memory technology, so, too, do GSICs based on a single underlying technology principle have the capacity to transform the life science and healthcare industries.

Selective hydrophilic modification of Parylene C films: a new approach to cell micro-patterning for synthetic biology applications

We demonstrate a simple, accurate and versatile method to manipulate Parylene C, a material widely known for its high biocompatibility, and transform it to a substrate that can effectively control the cellular microenvironment and consequently affect the morphology and function of the cells in vitro. The Parylene C scaffolds are fabricated by selectively increasing the material's surface water affinity through lithography and oxygen plasma treatment, providing free bonds for attachment of hydrophilic biomolecules. The micro-engineered constructs were tested as culture scaffolds for rat ventricular fibroblasts and neonatal myocytes (NRVM), toward modeling the unique anisotropic architecture of native cardiac tissue. The scaffolds induced the patterning of extracellular matrix compounds and therefore of the cells, which demonstrated substantial alignment compared to typical unstructured cultures. Ca(2+) cycling properties of the NRVM measured at rates of stimulation 0.5-2 Hz were significantly modified with a shorter time to peak and time to 90% decay, and a larger fluorescence amplitude (p < 0.001). The proposed technique is compatible with standard cell culturing protocols and exhibits long-term pattern durability. Moreover, it allows the integration of monitoring modalities into the micro-engineered substrates for a comprehensive interrogation of physiological parameters.

Effectiveness of mobile phone messaging in prevention of type 2 diabetes by lifestyle modification in men in India: a prospective, parallel-group, randomised controlled trial

BACKGROUND

Type 2 diabetes can often be prevented by lifestyle modification; however, successful lifestyle intervention programmes are labour intensive. Mobile phone messaging is an inexpensive alternative way to deliver educational and motivational advice about lifestyle modification. We aimed to assess whether mobile phone messaging that encouraged lifestyle change could reduce incident type 2 diabetes in Indian Asian men with impaired glucose tolerance.

METHODS

We did a prospective, parallel-group, randomised controlled trial between Aug 10, 2009, and Nov 30, 2012, at ten sites in southeast India. Working Indian men (aged 35-55 years) with impaired glucose tolerance were randomly assigned (1:1) with a computer-generated randomisation sequence to a mobile phone messaging intervention or standard care (control group). Participants in the intervention group received frequent mobile phone messages compared with controls who received standard lifestyle modification advice at baseline only. Field staff and participants were, by necessity, not masked to study group assignment, but allocation was concealed from laboratory personnel as well as principal and co-investigators. The primary outcome was incidence of type 2 diabetes, analysed by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT00819455.

RESULTS

We assessed 8741 participants for eligibility. 537 patients were randomly assigned to either the mobile phone messaging intervention (n=271) or standard care (n=266). The cumulative incidence of type 2 diabetes was lower in those who received mobile phone messages than in controls: 50 (18%) participants in the intervention group developed type 2 diabetes compared with 73 (27%) in the control group (hazard ratio 0·64, 95% CI 0·45-0·92; p=0·015). The number needed to treat to prevent one case of type 2 diabetes was 11 (95% CI 6-55). One patient in the control group died suddenly at the end of the first year. We recorded no other serious adverse events.

INTERPRETATION

Mobile phone messaging is an effective and acceptable method to deliver advice and support towards lifestyle modification to prevent type 2 diabetes in men at high risk.

FUNDING

The UK India Education and Research Initiative, the World Diabetes Foundation.

A composite model of glucagon-glucose dynamics for in silico testing of bihormonal glucose controllers

BACKGROUND

The utility of simulation environments in the development of an artificial pancreas for type 1 diabetes mellitus (T1DM) management is well established. The availability of a simulator that incorporates glucagon as a counterregulatory hormone to insulin would allow more efficient design of bihormonal glucose controllers. Existing models of the glucose regulatory system that incorporates glucagon action are difficult to identify without using tracer data. In this article, we present a novel model of glucagon-glucose dynamics that can be easily identified with standard clinical research data.

METHODS

The minimal model of plasma glucose and insulin kinetics was extended to account for the action of glucagon on net endogenous glucose production by incorporating a new compartment. An existing subcutaneous insulin absorption model was used to account for subcutaneous insulin delivery. The same model of insulin pharmacokinetics was employed to model the pharmacokinetics of subcutaneous glucagon absorption. Finally, we incorporated an existing gastrointestinal absorption model to account for meal intake. Data from a closed-loop artificial pancreas study using a bihormonal controller on T1DM subjects were employed to identify the composite model. To test the validity of the proposed model, a bihormonal controller was designed using the identified model.

RESULTS

Model parameters were identified with good precision, and an excellent fitting of the model with the experimental data was achieved. The proposed model allowed the design of a bihormonal controller and demonstrated its ability to improve glycemic control over a single-hormone controller.

CONCLUSIONS

A novel composite model, which can be easily identified with standard clinical data, is able to account for the effect of exogenous insulin and glucagon infusion on glucose dynamics. This model represents another step toward the development of a bihormonal artificial pancreas.

Simultaneous DNA amplification and detection using a pH-sensing semiconductor system

We developed an integrated chip for real-time amplification and detection of nucleic acid using pH-sensing complementary metal-oxide semiconductor (CMOS) technology. Here we show an amplification-coupled detection method for directly measuring released hydrogen ions during nucleotide incorporation rather than relying on indirect measurements such as fluorescent dyes. This is a label-free, non-optical, real-time method for detecting and quantifying target sequences by monitoring pH signatures of native amplification chemistries. The chip has ion-sensitive field effect transistor (ISFET) sensors, temperature sensors, resistive heating, signal processing and control circuitry all integrated to create a full system-on-chip platform. We evaluated the platform using two amplification strategies: PCR and isothermal amplification. Using this platform, we genotyped and discriminated unique single-nucleotide polymorphism (SNP) variants of the cytochrome P450 family from crude human saliva. We anticipate this semiconductor technology will enable the creation of devices for cost-effective, portable and scalable real-time nucleic acid analysis.

A bionics chemical synapse

Implementation of the current mode CMOS circuit for chemical synapses (AMPA and NMDA receptors) with dynamic change of glutamate as the neurotransmitter input is presented in this paper. Additionally, circuit realisation for receptor GABA(A) and GABA(B) with an electrical signal which symbolises γ-Aminobutyric Acid (GABA) perturbation is introduced. The chemical sensor for glutamate sensing is the modified ISFET with enzyme (glutamate oxidase) immobilisation. The measured results from these biomimetics chemical synapse circuits closely match with the simulation result from the mathematical model. The total power consumption of the whole chip (four chemical synapse circuits and all auxiliary circuits) is 168.3 μW. The total chip area is 3 mm(2) in 0.35-μm AMS CMOS technology.

The spatial pattern of light determines the kinetics and modulates backpropagation of optogenetic action potentials

Optogenetics offers an unprecedented ability to spatially target neuronal stimulations. This study investigated via simulation, for the first time, how the spatial pattern of excitation affects the response of channelrhodopsin-2 (ChR2) expressing neurons. First we described a methodology for modeling ChR2 in the NEURON simulation platform. Then, we compared four most commonly considered illumination strategies (somatic, dendritic, axonal and whole cell) in a paradigmatic model of a cortical layer V pyramidal cell. We show that the spatial pattern of illumination has an important impact on the efficiency of stimulation and the kinetics of the spiking output. Whole cell illumination synchronizes the depolarization of the dendritic tree and the soma and evokes spiking characteristics with a distinct pattern including an increased bursting rate and enhanced back propagation of action potentials (bAPs). This type of illumination is the most efficient as a given irradiance threshold was achievable with only 6 % of ChR2 density needed in the case of somatic illumination. Targeting only the axon initial segment requires a high ChR2 density to achieve a given threshold irradiance and a prolonged illumination does not yield sustained spiking. We also show that patterned illumination can be used to modulate the bAPs and hence spatially modulate the direction and amplitude of spike time dependent plasticity protocols. We further found the irradiance threshold to increase in proportion to the demyelination level of an axon, suggesting that measurements of the irradiance threshold (for example relative to the soma) could be used to remotely probe a loss of neural myelin sheath, which is a hallmark of several neurodegenerative diseases.

Inductive and ultrasonic multi-tier interface for low-power, deeply implantable medical devices

We report the development of a novel multi-tier interface which enables the wireless, noninvasive transfer of sufficient amounts of power as well as the collection and transmission of data from low-power, deeply implantable analog sensors. The interface consists of an inductive coupling subsystem and an ultrasonic subsystem. The designed and experimentally verified inductive subsystem ensures that 5 W of power is transferred across 10 mm of air gap between a single pair of PCB spiral coils with an efficiency of 83% using our prototype CMOS logic gate-based driver circuit. The implemented ultrasonic subsystem, based on ultrasonic PZT ceramic discs driven in their low-frequency, radial/planar-excitation mode, further ensures that 29 μW of power is delivered 70 mm deeper inside a homogenous liquid environment-with no acoustic matching layer employed-with an efficiency of 1%. Overall system power consumption is 2.3 W. The implant is intermittently powered every 800 msec; charging a capacitor which provides sufficient power for a duration of ~ 18&nbsp;msec; sufficient for an implant μC operating at a frequency of 500 KHz to transmit a nibble (4 bits) of digitized sensed data.

High-frequency limit of neural stimulation with ChR2

Optogenetic technology based on light activation of genetically targeted single component opsins such as Channelrhodopsin-2 (ChR2) has been changing the way neuroscience research is conducted. This technology is becoming increasingly important for neural engineering as well. The efficiency of neural stimulation with ChR2 drops at high frequencies, often before the natural limit of the neuron is reached. This study aims to investigate the underlying mechanisms that limit the efficiency of the stimulation at high frequencies. The study analyzes the dynamics of the spikes induced by ChR2 in comparison to control stimulations using patch clamp current injection. It shows that the stimulation dynamics is limited by two mechanisms: 1) a frequency independent reduction in the conductance-to-irradiance yield due to the ChR2 light adaptation process and 2) a frequency dependent reduction in the conductance-to-current yield due to a decrease in membrane re-polarization level between spikes that weakens the ionic driving force. The effect of the first mechanism can be minimized by using ChR2 mutants with lower irradiance threshold. In contrast the effect of the second mechanism is fundamentally limited by the rate the native ion channels re-polarize the membrane potential.

A robust microfluidic in vitro cell perifusion system

We present here a robust microfluidic cell perifusion device for in vitro primary tissue cell secretion studies. This system increases the sample concentration to perifusion volume ratio by an order of magnitude compared with standard multi-well plate static incubation assays. Further, this device achieves physiologically relevant flow rates, pressures, and temperature. It has been manufactured with typical machining facilities, principally drilling and milling. No specialist clean room equipment is required to replicate it. We show its capability here with hormone perifusion experiments on primary pancreatic tissue from mice. This device can increase cell secretion concentrations by up to a factor of 20, allowing for the first time the direct measurement of islet glucagon using mass spectrometry.

A bio-inspired glucose controller based on pancreatic β-cell physiology

INTRODUCTION

Control algorithms for closed-loop insulin delivery in type 1 diabetes have been mainly based on control engineering or artificial intelligence techniques. These, however, are not based on the physiology of the pancreas but seek to implement engineering solutions to biology. Developments in mathematical models of the β-cell physiology of the pancreas have described the glucose-induced insulin release from pancreatic β cells at a molecular level. This has facilitated development of a new class of bio-inspired glucose control algorithms that replicate the functionality of the biological pancreas. However, technologies for sensing glucose levels and delivering insulin use the subcutaneous route, which is nonphysiological and introduces some challenges. In this article, a novel glucose controller is presented as part of a bio-inspired artificial pancreas.

METHODS

A mathematical model of β-cell physiology was used as the core of the proposed controller. In order to deal with delays and lack of accuracy introduced by the subcutaneous route, insulin feedback and a gain scheduling strategy were employed. A United States Food and Drug Administration-accepted type 1 diabetes mellitus virtual population was used to validate the presented controller.

RESULTS

Premeal and postmeal mean ± standard deviation blood glucose levels for the adult and adolescent populations were well within the target range set for the controller [(70, 180) mg/dl], with a percent time in range of 92.8 ± 7.3% for the adults and 83.5 ± 14% for the adolescents.

CONCLUSIONS

This article shows for the first time very good glucose control in a virtual population with type 1 diabetes mellitus using a controller based on a subcellular β-cell model.