Feasibility of using intermittent active monitoring of vital signs by smartphone users to predict SARS-CoV-2 PCR positivity.

Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant's vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.

The cholinergic anti-inflammatory pathway inhibits inflammation without lymphocyte relay.

The magnitude of innate inflammatory immune responses is dependent on interactions between peripheral neural and immune cells. In particular, a cholinergic anti-inflammatory pathway (CAP) has been identified in the spleen whereby noradrenaline (NA) released by splenic nerves binds to ß2-adrenergic receptors (ß2-AR) on CD4+ T cells which, in turn, release acetylcholine (ACh). The binding of ACh to α7 acetylcholine receptors (α7-AChR) expressed by splenic macrophages inhibits the production of inflammatory cytokines, including tumor necrosis factor (TNF). However, the role of ACh-secreting CD4+ T-cells in the CAP is still controversial and largely based on the absence of this anti-inflammatory pathway in mice lacking T-cells (nude, FoxN1-/-). Using four conscious, non-lymphopenic transgenic mouse models, we found that, rather than acting on CD4+ T-cells, NA released by splenic nerve terminals acts directly onto ß2-AR on splenic myeloid cells to exert this anti-inflammatory effect. We also show that, while larger doses of LPS are needed to trigger CAP in nude mouse strain compared to other strains, TNF production can be inhibited in these animals lacking CD4+ T-cell by stimulating either the vagus or the splenic nerve. We demonstrate that CD4+ T-cells are dispensable for the CAP after antibody-mediated CD4+ T-cell depletion in wild type mice. Furthermore, we found that NA-mediated inhibition of in vitro LPS-induced TNF secretion by human or porcine splenocytes does not require α7-AChR signaling. Altogether our data demonstrate that activation of the CAP by stimulation of vagus or splenic nerves in mice is mainly mediated by direct binding of NA to ß2-AR on splenic macrophages, and suggest that the same mechanism is at play in larger species.

Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population.

The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 167 variables selected from UKB, processed into 429 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.7772 and 0.7720 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.7810 and 0.7728. After feature selection, the depression model contained 39 predictors and the concordance index was 0.7769 for Cox and 0.7772 for DeepSurv. The reduced anxiety model, with 53 predictors, achieved concordance of 0.7699 for Cox and 0.7710 for DeepSurv. The final models showed good discrimination and calibration in the test datasets. We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable via smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.

Accelerating cryoprotectant diffusion kinetics improves cryopreservation of pancreatic islets.

Cryopreservation offers the potential to increase the availability of pancreatic islets for treatment of diabetic patients. However, current protocols, which use dimethyl sulfoxide (DMSO), lead to poor cryosurvival of islets. We demonstrate that equilibration of mouse islets with small molecules in aqueous solutions can be accelerated from > 24 to 6 h by increasing incubation temperature to 37 °C. We utilize this finding to demonstrate that current viability staining protocols are inaccurate and to develop a novel cryopreservation method combining DMSO with trehalose pre-incubation to achieve improved cryosurvival. This protocol resulted in improved ATP/ADP ratios and peptide secretion from ß-cells, preserved cAMP response, and a gene expression profile consistent with improved cryoprotection. Our findings have potential to increase the availability of islets for transplantation and to inform the design of cryopreservation protocols for other multicellular aggregates, including organoids and bioengineered tissues.

Splenic Nerve Neuromodulation Reduces Inflammation and Promotes Resolution in Chronically Implanted Pigs.

Neuromodulation of the immune system has been proposed as a novel therapeutic strategy for the treatment of inflammatory conditions. We recently demonstrated that stimulation of near-organ autonomic nerves to the spleen can be harnessed to modulate the inflammatory response in an anesthetized pig model. The development of neuromodulation therapy for the clinic requires chronic efficacy and safety testing in a large animal model. This manuscript describes the effects of longitudinal conscious splenic nerve neuromodulation in chronically-implanted pigs. Firstly, clinically-relevant stimulation parameters were refined to efficiently activate the splenic nerve while reducing changes in cardiovascular parameters. Subsequently, pigs were implanted with a circumferential cuff electrode around the splenic neurovascular bundle connected to an implantable pulse generator, using a minimally-invasive laparoscopic procedure. Tolerability of stimulation was demonstrated in freely-behaving pigs using the refined stimulation parameters. Longitudinal stimulation significantly reduced circulating tumor necrosis factor alpha levels induced by systemic endotoxemia. This effect was accompanied by reduced peripheral monocytopenia as well as a lower systemic accumulation of CD16+CD14high pro-inflammatory monocytes. Further, lipid mediator profiling analysis demonstrated an increased concentration of specialized pro-resolving mediators in peripheral plasma of stimulated animals, with a concomitant reduction of pro-inflammatory eicosanoids including prostaglandins. Terminal electrophysiological and physiological measurements and histopathological assessment demonstrated integrity of the splenic nerves up to 70 days post implantation. These chronic translational experiments demonstrate that daily splenic nerve neuromodulation, via implanted electronics and clinically-relevant stimulation parameters, is well tolerated and is able to prime the immune system toward a less inflammatory, pro-resolving phenotype.

Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study.

Aims: Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Predictive scores providing personalized risk of developing CVD are increasingly used in clinical practice. Most scores, however, utilize a homogenous set of features and require the presence of a physician. The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments. Methods and results: Across 466 052 participants, Cox proportional hazards (CPH) and DeepSurv models were trained using 608 variables derived from the UK Biobank to investigate the 10-year risk of developing a CVD. Data-driven feature selection reduced the number of features to 47, after which reduced models were trained. Both models were compared to the Framingham score. The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score. Minimal difference was observed when cholesterol and blood pressure were excluded from the models (CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and discrimination on the test data. Conclusion: We developed a cardiovascular risk model that has very good predictive capacity and encompasses new variables. The score could be incorporated into clinical practice and utilized in a remote setting, without the need of including cholesterol. Future studies will focus on external validation across heterogeneous samples.

Quantification of clinically applicable stimulation parameters for precision near-organ neuromodulation of human splenic nerves.

Neuromodulation is a new therapeutic pathway to treat inflammatory conditions by modulating the electrical signalling pattern of the autonomic connections to the spleen. However, targeting this sub-division of the nervous system presents specific challenges in translating nerve stimulation parameters. Firstly, autonomic nerves are typically embedded non-uniformly among visceral and connective tissues with complex interfacing requirements. Secondly, these nerves contain axons with populations of varying phenotypes leading to complexities for axon engagement and activation. Thirdly, clinical translational of methodologies attained using preclinical animal models are limited due to heterogeneity of the intra- and inter-species comparative anatomy and physiology. Here we demonstrate how this can be accomplished by the use of in silico modelling of target anatomy, and validation of these estimations through ex vivo human tissue electrophysiology studies. Neuroelectrical models are developed to address the challenges in translation of parameters, which provides strong input criteria for device design and dose selection prior to a first-in-human trial.