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Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available.Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data.Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%–70% of real human stick falls up to 2.35 s in advance.Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

Abstract A new mathematical model for biological rhythms in the hypothalamic–pituitary–adrenal (HPA) axis is proposed. This model takes the form of a system of impulsive time-delay differential equations which include pulsatile release of adrenocorticotropin (ACTH) by the pituitary gland and a time delay for the release of glucocorticoid hormones by the adrenal gland. Numerical simulations demonstrate that the model’s response to periodic and circadian inputs from the hypothalamus are consistent with those generated by recent models which do not include a pulsatile pituitary. In contrast the oscillatory phenomena generated by the impulsive delay equation mode occur even if the time delay is zero. The observation that the time delay merely introduces a small phase shift suggesting that the effects of the adrenal gland are “downstream” to the origin of pulsativity. In addition, the model accounts for the occurrence of ultradian oscillations in an isolated pituitary gland. These observations suggest that principles of pulse modulated control, familiar to control engineers, may have an increasing role to play in understanding the HPA axis.

Abstract Background Cell therapies present an exciting potential but there is a long history of expensive translational failures in stroke research. Researchers engaged in cell therapy research would benefit from a practical framework that can help in planning research and development of investigational cell therapies into viable medical products. Methods We developed a checklist using a mixed methodology approach to evaluate the impact of study design, regulatory policy, ethical, and health economic considerations for efficient implementation of early phase cell therapy studies. Results The checklist comprises a series of questions arranged under four domains: the first concerns study design such as characterization of target study population, trial design, endpoints and operational fit of dosage, time, and route of administration to target populations. A second domain addresses the data package required for regulatory approval relevant to the intended use (allogeneic/autologous; homologous/non-homologous; nature of cell processing). The third domain comprises patient involvement to ensure relevant data is collected via targeted study design. The final domain requires the team to determine the critical data elements that could be built into study design to enable health economic data collection to be started at an early phase of the study. Conclusions The CT2S checklist can help to determine areas of expertise gaps and enable research groups to appropriately allocate resources for capacity building. Use of this checklist will allow identification of key areas where trial planning needs to be optimized, as well as helping to identify resources that need to be secured. The CT2S checklist can also serve as a general cell therapy research decision aid to improve research output and accelerate new cell therapy development.

Introduction Transient ischaemic attack (TIA) may be a warning sign of stroke and difficult to differentiate from minor stroke and TIA-mimics. Urgent evaluation and diagnosis is important as treating TIA early can prevent subsequent strokes. Recent improvements in mass spectrometer technology allow quantification of hundreds of plasma proteins and lipids, yielding large datasets that would benefit from different approaches including machine learning. Using plasma protein, lipid and radiological biomarkers, our study will develop predictive algorithms to distinguish TIA from minor stroke (positive control) and TIA-mimics (negative control). Analysis including machine learning employs more sophisticated modelling, allowing non-linear interactions, adapting to datasets and enabling development of multiple specialised test-panels for identification and differentiation.Methods and analysis Patients attending the Emergency Department, Stroke Ward or TIA Clinic at the Royal Adelaide Hospital with TIA, minor stroke or TIA-like symptoms will be recruited consecutively by staff-alert for this prospective cohort study. Advanced neuroimaging will be performed for each participant, with images assessed independently by up to three expert neurologists. Venous blood samples will be collected within 48 hours of symptom onset. Plasma proteomic and lipid analysis will use advanced mass spectrometry (MS) techniques. Principal component analysis and hierarchical cluster analysis will be performed using MS software. Output files will be analysed for relative biomarker quantitative differences between the three groups. Differences will be assessed by linear regression, one-way analysis of variance, Kruskal-Wallis H-test, χ2 test or Fisher’s exact test. Machine learning methods will also be applied including deep learning using neural networks.Ethics and dissemination Patients will provide written informed consent to participate in this grant-funded study. The Central Adelaide Local Health Network Human Research Ethics Committee approved this study (HREC/18/CALHN/384; R20180618). Findings will be disseminated through peer-reviewed publication and conferences; data will be managed according to our Data Management Plan (DMP2020-00062).

Abstract Background Stem cell research holds the potential for a paradigm shift in the management of diseases such as stroke. Patient and public involvement in research (PPIR) can bring a focus to issues of clinical relevance and accelerate translation to real‐world clinical practice. Objective A qualitative thematic analysis of the perspectives of stroke survivors regarding the conduct and design aspects of a proposed phase I clinical cell therapy study in stroke. Design Twelve stroke survivors were purposively recruited in July 2016–August 2017 and participated in semi‐structured, face‐to‐face interviews for input into the design of a proposed phase I clinical study of autologous dental pulp stem cells. Concurrent thematic analysis was conducted until data saturation was achieved. Discussion and conclusions Participants conveyed that the most relevant outcomes to them were regaining participation, decreased dependence on caregivers and improvement in cognition, memory, mood, pain and fatigue. The perception of risk vs. benefit was likely influenced by the time elapsed since stroke, with participants being more willing to accept a higher level of risk early in the post‐stroke disease course. They believed that all stroke survivors should be given an opportunity to participate in research, irrespective of their cognitive capacity. A relatively small sample population of 12 stroke survivors was studied as thematic saturation was achieved. PERSPECTIVES study applied principles of PPIR to early‐phase cell research. Incorporation of outcomes relevant to patients' need within the study design is critical to generate data that will enable personalized application of regenerative medicine in stroke.

Epigenetic mechanisms by altering the expression and, in turn, functions of target genes have potential to modify cellular processes that are characteristics of atherosclerosis, including inflammation, proliferation, migration and apoptosis/cell death. Butyrate, a natural epigenetic modifier and a histone deacetylase inhibitor (HDACi), is an inhibitor of vascular smooth muscle cell (VSMC) proliferation, a critical event in atherogenesis. Here, we examined whether glutathione peroxidases (GPxs), a family of antioxidant enzymes, are modulated by butyrate, contributing to its antiproliferation action on VSMC through the regulation of the inflammatory response by using western blotting, immunostaining methods and activity assay. Treatment of VSMC with butyrate not only upregulates glutathione peroxidase (GPx) 3 and GPx4, but also increases the overall catalytic activity of GPx supporting involvement of antioxidant effect in butyrate arrested VSMC proliferation. Moreover, analysis of the redox-sensitive NF-κB transcription factor system, the target of GPx, reveals that butyrate causes downregulation of IKKα, IKKβ, IkBα and NF-κBp65 expression and prevents NF-κBp65 phosphorylation at serine536 causing inhibition of the expression NF-κB target inflammatory genes, including inducible nitric oxide synthase, VCAM-1 and cyclooxygenase-2. Overall, these observations suggest a link between the antioxidant effect and anti-inflammatory response in butyrate-arrested VSMC proliferation, accentuating the atheroprotective and therapeutic potential of natural products, like butyrate, in vascular proliferative diseases.

The histone deacetylase (HDAC) inhibitors, butyrate and trichostatin A (TSA), are epigenetic histone modifiers and proliferation inhibitors by downregulating cyclin D1, a positive cell cycle regulator, and upregulating p21Cip1 and INK family of proteins, negative cell cycle regulators. Our recent study indicated cyclin D1 upregulation in vascular smooth muscle cells (VSMC) that are proliferation-arrested by butyrate. Here we investigate whether cyclin D1 upregulation is a unique response of VSMC to butyrate or a general response to HDAC inhibitors (HDACi) by evaluating the effects of butyrate and TSA on VSMC. While butyrate and TSA inhibit VSMC proliferation via cytostatic and cytotoxic effects, respectively, they downregulate cdk4, cdk6, and cdk2, and upregulate cyclin D3, p21Cip1 and p15INK4B, and cause similar effects on key histone H3 posttranslational modifications. Conversely, cyclin D1 is upregulated by butyrate and inhibited by TSA. Assessment of glycogen synthase 3-dependent phosphorylation, subcellular localization and transcription of cyclin D1 indicates that differential effects of butyrate and TSA on cyclin D1 levels are linked to disparity in cyclin D1 gene expression. Disparity in butyrate- and TSA-induced cyclin D1 may influence transcriptional regulation of genes that are associated with changes in cellular morphology/cellular effects that these HDACi confer on VSMC, as a transcriptional modulator.