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.

A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR.

This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system's modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings.

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".

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 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.

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.

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.

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.

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.

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.

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.

Extracellular pH monitoring for use in closed-loop vagus nerve stimulation.

OBJECTIVE: Vagal nerve stimulation (VNS) has shown potential benefits for obesity treatment; however, current devices lack physiological feedback, which limit their efficacy. Changes in extracellular pH (pHe) have shown to be correlated with neural activity, but have traditionally been measured with glass microelectrodes, which limit their in vivo applicability. APPROACH: Iridium oxide has previously been shown to be sensitive to fluctuations in pH and is biocompatible. Iridium oxide microelectrodes were inserted into the subdiaphragmatic vagus nerve of anaesthetised rats. Introduction of the gut hormone cholecystokinin (CCK) or distension of the stomach was used to elicit vagal nerve activity. MAIN RESULTS: Iridium oxide microelectrodes have sufficient pH sensitivity to readily detect changes in pHe associated with both CCK and gastric distension. Furthermore, a custom-made Matlab script was able to use these changes in pHe to automatically trigger an implanted VNS device. SIGNIFICANCE: This is the first study to show pHe changes in peripheral nerves in vivo. In addition, the demonstration that iridium oxide microelectrodes are sufficiently pH sensitive as to measure changes in pHe associated with physiological stimuli means they have the potential to be integrated into closed-loop neurostimulating devices.

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.