Discovery, validation, and diagnostic ability of multiple protein-based biomarkers in saliva and gingival crevicular fluid to distinguish between health and periodontal diseases.

AIM: To discover and validate differential protein biomarker expression in saliva and gingival crevicular fluid (GCF) to discriminate objectively between periodontal health and plaque-induced periodontal disease states. MATERIALS AND METHODS: One-hundred and ninety participants were recruited from two centres (Birmingham and Newcastle upon Tyne, UK) comprising healthy, gingivitis, periodontitis, and edentulous donors. Samples from the Birmingham cohort were analysed by quantitative mass spectrometry proteomics for biomarker discovery. Shortlisted candidate proteins were then verified by enzyme-linked immunosorbent assay in both cohorts. Leave-one-out cross validation logistic regression analysis was used to identify the best performing biomarker panels. RESULTS: Ninety-five proteins were identified in both GCF and saliva samples, and 15 candidate proteins were selected based upon differences discovered between the donor groups. The best performing panels to distinguish between: health or gingivitis and periodontitis contained matrix metalloproteinase-9 (MMP9), S100A8, alpha-1-acid glycoprotein (A1AGP), pyruvate kinase, and age (area under the curve [AUC] 0.970); health and gingivitis contained MMP9, S100A8, A1AGP, and pyruvate kinase, but not age (AUC 0.768); and mild to moderate and advanced periodontitis contained MMP9, S100A8, A1AGP, pyruvate kinase, and age (AUC 0.789). CONCLUSIONS: Biomarker panels containing four proteins with and without age as a further parameter can distinguish between periodontal health and disease states.

Forecasting Time Series in Healthcare With Gaussian Processes and Dynamic Time Warping Based Subset Selection.

Modelling real-world time series can be challenging in the absence of sufficient data. Limited data in healthcare, can arise for several reasons, namely when the number of subjects is insufficient or the observed time series is irregularly sampled at a very low sampling frequency. This is especially true when attempting to develop personalised models, as there are typically few data points available for training from an individual subject. Furthermore, the need for early prediction (as is often the case in healthcare applications) amplifies the problem of limited availability of data. This article proposes a novel personalised technique that can be learned in the absence of sufficient data for early prediction in time series. Our novelty lies in the development of a subset selection approach to select time series that share temporal similarities with the time series of interest, commonly known as the test time series. Then, a Gaussian processes-based model is learned using the existing test data and the chosen subset to produce personalised predictions for the test subject. We will conduct experiments with univariate and multivariate data from real-world healthcare applications to show that our strategy outperforms the state-of-the-art by around 20%.

Feature selection for unbiased imputation of missing values: A case study in healthcare.

Datasets in healthcare are plagued with incomplete information. Imputation is a common method to deal with missing data where the basic idea is to substitute some reasonable guess for each missing value and then continue with the analysis as if there were no missing data. However unbiased predictions based on imputed datasets can only be guaranteed when the missing mechanism is completely independent of the observed or missing data. Often, this promise is broken in healthcare dataset acquisition due to unintentional errors or response bias of the interviewees. We highlight this issue by studying extensively on an annual health survey dataset on infant mortality prediction and provide a systematic testing for such assumption. We identify such biased features using an empirical approach and show the impact of wrongful inclusion of these features on the predictive performance.Clinical relevance- We show that blind analysis along with plug and play imputation of healthcare data is a potential pitfall that clinicians and researchers want to avoid in finding important markers of disease.

Gestational weight gain prediction using privacy preserving federated learning.

Gestational weight gain prediction in expecting women is associated with multiple risks. Manageable interventions can be devised if the weight gain can be predicted as early as possible. However, training the model to predict such weight gain requires access to centrally stored privacy sensitive weight data. Federated learning can help mitigate this problem by sending local copies of trained models instead of raw data and aggregate them at the central server. In this paper, we present a privacy preserving federated learning approach where the participating users collaboratively learn and update the global model. Furthermore, we show that this model updation can be done incrementally without having the need to store the local updates eternally. Our proposed model achieves a mean absolute error of 4.455 kgs whilst preserving privacy against 2.572 kgs achieved in a centralised approach utilising individual training data until day 140.Clinical relevance- Privacy preserving training of machine learning algorithm for early gestational weight gain prediction with minor tradeoff to performance.

Finite difference time domain electroacoustic model for synthetic jet actuators including nonlinear flow resistance.

A lumped element electroacoustic model for a synthetic jet actuator is presented. The model includes the nonlinear flow resistance associated with flow separation and employs a finite difference scheme in the time domain. As opposed to more common analytical frequency domain electroacoustic models, in which the nonlinear resistance can only be considered as a constant, it allows the calculation of higher harmonics, i.e., distortion components, generated as a result of this nonlinear resistance. Model calculations for the time-averaged momentum flux of the synthetic jet as well as the radiated sound power spectrum are compared to experimental results for various configurations. It is shown that a significantly improved prediction of the momentum flux-and thus flow velocity-of the jet is obtained when including the nonlinear resistance. Here, the current model performs slightly better than an analytical model. For the power spectrum of radiated sound, a reasonable agreement is obtained when assuming a plausible slight asymmetry in the nonlinear resistance. However, results suggest that loudspeaker nonlinearities play a significant role as well in the generation of the first few higher harmonics.

PREgDICT : Early prediction of gestational weight gain for pregnancy care.

Excessive or inadequate Gestational Weight Gain (GWG) is considered to not only put the mothers, but also the infants at increased risks with a number of adverse outcomes. In this paper, we use self-reported weight measurements from the early days of pregnancy to predict and classify the end-of-pregnancy weight gain into an underweight, normal or obese category in accordance with the Institute of Medicine recommended guidelines. Self-reported weight measurements suffer from issues such as lack of enough data and non-uniformity. We propose and compare two novel parametric and non-parametric approaches that utilise self-training data along with population data to tackle limited data availability. We, dynamically find the subset of closest time series from the population weight-gain data to a given subject. Then, a non-parametric Gaussian Process (GP) regression model, learnt on the selected subset is used to forecast the self-reported weight measurements of given subject. Our novel approach produces mean absolute error (MAE) of 2.572 kgs in forecasting end-of-pregnancy weight gain and achieves weight-category-classification accuracy of 63.75% mid-way through the pregnancy, whereas a state-of-the-art approach is only 53.75% accurate and produces high MAE of 16.22 kgs. Our method ensures reliable prediction of the end-of-pregnancy weight gain using few data points and can assist in early intervention that can prevent gaining or losing excessive weight during pregnancy.