Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error. The method measures the distinction of the integral formulations of the parameter coefficients at the proposed sampling times. It can thus predict the susceptibility of the parameters to the effects of measurement error. The method is tested in-silico with Monte Carlo analyses of a number of insulin sensitivity test applications. The method successfully captured the analogous nature of identifiability observed in Monte Carlo analyses of a number of cases including protocol alterations, parameter changes and differences in participant behaviour. However, due to the numerical nature of the analyses, prediction was not perfect in all cases. Thus although the current method has valuable and significant capabilities in terms of study or test protocol design, additional developments would further strengthen the predictive capability of the method. Finally, the method captures the experimental reality that sampling error and timing can negate assumed parameter identifiability and that identifiability is a continuous rather than discrete phenomenon.

Polycystic ovary syndrome (PCOS) affects up to 20% of women but remains poorly understood. It is a heterogeneous condition with many potential comorbidities. This review offers an overview of the dysregulation of the reproductive and metabolic systems associated with PCOS. Review of the literature informed the development of a comprehensive summarizing ‘wiring’ diagram of PCOS-related features. This review provides a justification for each diagram aspect from the relevant academic literature, and explores the interactions between the hypothalamus, ovarian follicles, adipose tissue, reproductive hormones and other organ systems. The diagram will provide an efficient and useful tool for those researching and treating PCOS to understand the current state of knowledge on the complexity and variability of PCOS.

Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (E_drs) model that can be used in spontaneously breathing patients to determine their respiratory mechanics. A time-varying respiratory elastance model is developed with a negative elastic component (E_demand), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E_drs of every breathing cycle for each patient at different ventilation modes are presented for comparison. At the start of every breathing cycle initiated by patient, E_drs is < 0. This negativity is attributed from the E_demand due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of E_drs trajectories was able to give unique information to patients’ breathing variability under different ventilation modes. The area under the curve of E_drs (AUCE_drs) for most patients is > 25 cmH₂Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator. The E_drs model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.