The use of segmented regression in analysing interrupted time series studies : an example in pre-hospital ambulance care

Peer reviewedPublisher PD

Exosomal cancer immunotherapy is independent of MHC molecules on exosomes

Peptide-loaded exosomes are promising cancer treatment vehicles; however, moderate T cell responses in human clinical trials indicate a need to further understand exosome-induced immunity. We previously demonstrated that antigen-loaded exosomes carry whole protein antigens and require B cells for inducing antigen-specific T cells. Therefore, we investigated the relative importance of exosomal major histocompatibility complex (MHC) class I for the induction of antigen-specific T cell responses and tumour protection. We show that ovalbumin-loaded dendritic cell-derived exosomes from MHCI-/- mice induce antigen-specific T cells at the same magnitude as wild type exosomes. Furthermore, exosomes lacking MHC class I, as well as exosomes with both MHC class I and II mismatch, induced tumour infiltrating T cells and increased overall survival to the same extent as syngeneic exosomes in B16 melanoma. In conclusion, T cell responses are independent of exosomal MHC/peptide complexes if whole antigen is present. This establishes the prospective of using impersonalised exosomes, and will greatly increase the feasibility of designing exosome-based vaccines or therapeutic approaches in humans

β2-adrenergic signalling promotes cell migration by upregulating expression of the metastasis-associated molecule LYPD3

Metastasis is associated with poor prognosis in breast cancer. Although some studies suggest beta-blockers increase survival by delaying metastasis, others have been discordant. This study provides both insights into the anomalous findings and identifies potential biomarkers that may be treatment targets. Cell line models of basal-type and oestrogen receptor-positive breast cancer were profiled for basal levels of adrenoceptor gene/protein expression, and β2-adrenoceptor mediated cell behaviour including migration, invasion, adhesion, and survival in response to adrenoceptor agonist/antagonist treatment. Protein profiling and histology identified biomarkers and drug targets. Baseline levels of adrenoceptor gene expression are higher in basal-type rather than oestrogen receptor-positive cancer cells. Norepinephrine (NE) treatment increased invasive capacity in all cell lines but did not increase proliferation/survival. Protein profiling revealed the upregulation of the pro-metastatic gene Ly6/PLAUR Domain-Containing Protein 3 (LYPD3) in norepinephrine-treated MDA-MB-468 cells. Histology confirmed selective LYPD3 expression in primary and metastatic breast tumour samples. These findings demonstrate that basal-type cancer cells show a more aggressive adrenoceptor-β2-activated phenotype in the resting and stimulated state, which is attenuated by adrenoceptor-β2 inhibition. This study also highlights the first association between ADRβ2 signalling and LYPD3; its knockdown significantly reduced the basal and norepinephrine-induced activity of MCF-7 cells in vitro. The regulation of ADRβ2 signalling by LYPD3 and its metastasis promoting activities, reveal LYPD3 as a promising therapeutic target in the treatment of breast and other cancers

Exposure to parasitic infections determines features and phenotypes of active convulsive epilepsy in Africa

Background: Epilepsy affects 70 million people worldwide, 80% of whom are in low-and-middle income countries (LMICs). Infections of the central nervous system (CNS) contribute considerably to the burden of epilepsy in LMICs, but the nature and presentation of epilepsy following these infections is not fully understood. We examined if epilepsy foutcomes are associated with the exposure to parasitic infections. Methods: This was a case-comparison study nested in a cross-sectional survey of people with active convulsive epilepsy, with cases as those exposed to parasitic infections, and comparison as those unexposed. Associations of exposure to parasites with clinical and electroencephalographic features of epilepsy were done using a modified mixed effects Poisson regression model across five sites in Africa. Multiplicative and additive scale (RERI) interactions were explored to determine the effect of co-infections on epilepsy features. Population attributable fractions (PAF) were calculated to determine the proportion of severe clinical and electroencephalographic features of epilepsy attributable to CNS infections. Results: A total of 997 participants with active convulsive epilepsy from the five African sites were analyzed, 51% of whom were males. Exposure to parasitic infections was associated with more frequent seizures in adult epilepsy (relative risk (RR)=2.58, 95% confidence interval (95%CI):1.71-3.89). In children, exposure to any parasite was associated with convulsive status epilepticus (RR=4.68, (95%CI: 3.79-5.78), intellectual disabilities (RR=2.13, 95%CI: 1.35-3.34) and neurological deficits (RR=1.92, 95%CI: 1.42-2.61). Toxoplasma gondii and Onchocerca volvulus interacted synergistically to increase the risk of status epilepticus (RERI=0.91, 95%CI=0.48-1.35) in the data pooled across the sites. Exposure to parasitic infections contributed to 30% of severe features of epilepsy as shown by PAF. Conclusions: Parasitic infections may determine features and phenotypes of epilepsy through synergistic or antagonistic interactions, which can be different in children and adults. Interventions to control or manage infections may reduce complications and improve prognosis in epilepsy

Proteomics: in pursuit of effective traumatic brain injury therapeutics

Effective traumatic brain injury (TBI) therapeutics remain stubbornly elusive. Efforts in the field have been challenged by the heterogeneity of clinical TBI, with greater complexity among underlying molecular phenotypes than initially conceived. Future research must confront the multitude of factors comprising this heterogeneity, representing a big data challenge befitting the coming informatics age. Proteomics is poised to serve a central role in prescriptive therapeutic development, as it offers an efficient endpoint within which to assess post-TBI biochemistry. We examine rationale for multifactor TBI proteomic studies and the particular importance of temporal profiling in defining biochemical sequences and guiding therapeutic development. Lastly, we offer perspective on repurposing biofluid proteomics to develop theragnostic assays with which to prescribe, monitor and assess pharmaceutics for improved translation and outcome for TBI patients

On the Deformation of a Hyperelastic Tube Due to Steady Viscous Flow Within

In this chapter, we analyze the steady-state microscale fluid--structure interaction (FSI) between a generalized Newtonian fluid and a hyperelastic tube. Physiological flows, especially in hemodynamics, serve as primary examples of such FSI phenomena. The small scale of the physical system renders the flow field, under the power-law rheological model, amenable to a closed-form solution using the lubrication approximation. On the other hand, negligible shear stresses on the walls of a long vessel allow the structure to be treated as a pressure vessel. The constitutive equation for the microtube is prescribed via the strain energy functional for an incompressible, isotropic Mooney--Rivlin material. We employ both the thin- and thick-walled formulations of the pressure vessel theory, and derive the static relation between the pressure load and the deformation of the structure. We harness the latter to determine the flow rate--pressure drop relationship for non-Newtonian flow in thin- and thick-walled soft hyperelastic microtubes. Through illustrative examples, we discuss how a hyperelastic tube supports the same pressure load as a linearly elastic tube with smaller deformation, thus requiring a higher pressure drop across itself to maintain a fixed flow rate.Comment: 19 pages, 3 figures, Springer book class; v2: minor revisions, final form of invited contribution to the Springer volume entitled "Dynamical Processes in Generalized Continua and Structures" (in honour of Academician D.I. Indeitsev), eds. H. Altenbach, A. Belyaev, V. A. Eremeyev, A. Krivtsov and A. V. Porubo

Training emergency services’ dispatchers to recognise stroke: an interrupted time-series analysis

Background: Stroke is a time-dependent medical emergency in which early presentation to specialist care reduces death and dependency. Up to 70% of all stroke patients obtain first medical contact from the Emergency Medical Services (EMS). Identifying ‘true stroke’ from an EMS call is challenging, with over 50% of strokes being misclassified. The aim of this study was to evaluate the impact of the training package on the recognition of stroke by Emergency Medical Dispatchers (EMDs). Methods: This study took place in an ambulance service and a hospital in England using an interrupted time-series design. Suspected stroke patients were identified in one week blocks, every three weeks over an 18 month period, during which time the training was implemented. Patients were included if they had a diagnosis of stroke (EMS or hospital). The effect of the intervention on the accuracy of dispatch diagnosis was investigated using binomial (grouped) logistic regression. Results: In the Pre-implementation period EMDs correctly identified 63% of stroke patients; this increased to 80% Post-implementation. This change was significant (p=0.003), reflecting an improvement in identifying stroke patients relative to the Pre-implementation period both the During-implementation (OR=4.10 [95% CI 1.58 to 10.66]) and Post-implementation (OR=2.30 [95% CI 1.07 to 4.92]) periods. For patients with a final diagnosis of stroke who had been dispatched as stroke there was a marginally non-significant 2.8 minutes (95% CI −0.2 to 5.9 minutes, p=0.068)reduction between Pre- and Post-implementation periods from call to arrival of the ambulance at scene. Conclusions: This is the first study to develop, implement and evaluate the impact of a training package for EMDs with the aim of improving the recognition of stroke. Training led to a significant increase in the proportion of stroke patients dispatched as such by EMDs; a small reduction in time from call to arrival at scene by the ambulance also appeared likely. The training package has been endorsed by the UK Stroke Forum Education and Training, and is free to access on-line

Code-free deep learning for multi-modality medical image classification

© 2021, The Author(s). A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches

How do Zimbabweans value health states?

Background Quality of life weights based on valuations of health states are often used in cost utility analysis and population health measures. This paper reports on an attempt to develop quality of life weights within the Zimbabwe context. Methods 2,384 residents in randomly selected small residential plots of land in a high-density suburb of Harare valued descriptors of 38 health states based on different combinations of the five domains of the EQ-5D (mobility, self-care, usual activities, pain or discomfort and anxiety or depression). The English version of the EQ-5D was used. The time trade-off method was used to determine the values, and 19,020 individual preferences for health states were analysed. A residual maximum likelihood linear mixed model was used to estimate a function for predicting the values of all possible combinations of levels on the five domains. The model was fit to a random subset of two-thirds of the observations, with the remaining observations reserved for analysis of predictive validity. The results were compared to a similar study undertaken in the United Kingdom. Results A credible model was developed to predict the values of states that were not valued directly. In the subset of observations reserved for validation, the mean absolute difference between predicted and observed values was 0.045. All domains of the EQ-5D were found to contribute significantly to the model, both at the moderate and severe levels. Severe pain was found to have the largest negative coefficient, followed by the inability to wash and dress oneself. Conclusion Despite a generally lower education level than their European counterparts, urban Zimbabweans appear to value health states in a consistent manner, and the determination of a global method of establishing quality of life weights may be feasible and valid. However, as the relative weightings of the different domains, although correlated, differed from the standard set of weights recommended by the EuroQol Group, the locally determined coefficients should be used within the Zimbabwean context

A mathematical model for breath gas analysis of volatile organic compounds with special emphasis on acetone

Recommended standardized procedures for determining exhaled lower respiratory nitric oxide and nasal nitric oxide have been developed by task forces of the European Respiratory Society and the American Thoracic Society. These recommendations have paved the way for the measurement of nitric oxide to become a diagnostic tool for specific clinical applications. It would be desirable to develop similar guidelines for the sampling of other trace gases in exhaled breath, especially volatile organic compounds (VOCs) which reflect ongoing metabolism. The concentrations of water-soluble, blood-borne substances in exhaled breath are influenced by: (i) breathing patterns affecting gas exchange in the conducting airways; (ii) the concentrations in the tracheo-bronchial lining fluid; (iii) the alveolar and systemic concentrations of the compound. The classical Farhi equation takes only the alveolar concentrations into account. Real-time measurements of acetone in end-tidal breath under an ergometer challenge show characteristics which cannot be explained within the Farhi setting. Here we develop a compartment model that reliably captures these profiles and is capable of relating breath to the systemic concentrations of acetone. By comparison with experimental data it is inferred that the major part of variability in breath acetone concentrations (e.g., in response to moderate exercise or altered breathing patterns) can be attributed to airway gas exchange, with minimal changes of the underlying blood and tissue concentrations. Moreover, it is deduced that measured end-tidal breath concentrations of acetone determined during resting conditions and free breathing will be rather poor indicators for endogenous levels. Particularly, the current formulation includes the classical Farhi and the Scheid series inhomogeneity model as special limiting cases.Comment: 38 page