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The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.

SummaryDonor HLA‐specific antibodies (DSAs) can cause rejection and graft loss after renal transplantation, but their levels measured by the current assays are not fully predictive of outcomes. We investigated whether IgG subclasses of DSA were associated with early rejection and graft failure. DSA levels were determined pretreatment, at the day of peak pan‐IgG level and at 30 days post‐transplantation in eighty HLA antibody‐incompatible kidney transplant recipients using a modified microbead assay. Pretreatment IgG4 levels were predictive of acute antibody‐mediated rejection (P = 0.003) in the first 30 days post‐transplant. Pre‐treatment presence of IgG4 DSA (P = 0.008) and day 30 IgG3 DSA (P = 0.03) was associated with poor graft survival. Multivariate regression analysis showed that in addition to pan‐IgG levels, total IgG4 levels were an independent risk factor for early rejection when measured pretreatment, and the presence of pretreatment IgG4 DSA was also an independent risk factor for graft failure. Pretreatment IgG4 DSA levels correlated independently with higher risk of early rejection episodes and medium‐term death‐censored graft survival. Thus, pretreatment IgG4 DSA may be used as a biomarker to predict and risk stratify cases with higher levels of pan‐IgG DSA in HLA antibody‐incompatible transplantation. Further investigations are needed to confirm our results.

Experimental datasets in bioengineering are commonly limited in size, thus rendering Machine Learning (ML) impractical for predictive modelling. Novel techniques of multiple runs for model development and surrogate data analysis for model validation are suggested for prediction of biomedical outcomes based on small datasets for classification and regression tasks. The proposed framework was applied to designing a Neural Network model for osteoarthritic bone fracture risk stratification, and a Decision Tree model for prediction of antibody-mediated kidney transplant rejection. Despite the small datasets (35 bone specimens and 80 kidney transplants), the two models achieved high accuracy of 98.3% and 85%, respectively.

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Understanding the complex dynamics of cardio-respiratory coupling sheds light on the underlying mechanisms governing the communication between these two physiological systems. Previous research has predominantly considered the coupling at respiratory rates slower than the heart rate and shown that respiratory oscillations lead to modulation and/or synchronization of the heart rate. Whereas the mechanisms of cardio-respiratory communication are still under discussion, peripheral nervous regulation is considered to be the predominant factor. This work offers a novel experimental design and applies the concept of instantaneous phase to detect cardio-respiratory entrainment at elevated respiration rates, close to the resting heart rate. If such 1:1 entrainment exists, it would suggest direct neuronal communication between the respiration and heart centres in the brain. We have observed 1:1 entrainment in all volunteers, with consistently longer synchronization episodes seen in physically fitter people, and demonstrated that cardio-respiratory synchronization at both low and high respiration rates is associated with a common underlying communication mechanism.

The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles.

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