We develop a frame-covariant formulation of inflation in the slow-roll approximation by generalizing the inflationary attractor solution for scalar-curvature theories. Our formulation gives rise to new generalized forms for the potential slow-roll parameters, which enable us to examine the effect of conformal transformations and inflaton reparameterizations in scalar-curvature theories. We find that cosmological observables, such as the power spectrum, the spectral indices and their runnings, can be expressed in a concise manner in terms of the generalized potential slow-roll parameters which depend on the scalar-curvature coupling function, the inflaton wavefunction, and the inflaton potential. We show how the cosmological observables of inflation are frameinvariant in this generalized potential slow-roll formalism, as long as the end-of-inflation condition is appropriately extended to become frame-invariant as well. We then apply our formalism to specific scenarios, such as the induced gravity inflation, Higgs inflation and F (R) models of inflation, and obtain more accurate results, without making additional approximations to the potential. Our results are shown to be consistent to lowest order with those presented in the literature. Finally, we outline how our frame-covariant formalism can be naturally extended beyond the tree-level approximation, within the framework of the Vilkovisky-DeWitt effective action.

A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission during an emergency department attendance. We then evaluate our model’s performance on attendances occurring pre-pandemic (AUROC of 0.856 with 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC of 0.826 with 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature’s SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.

We revisit the calculation of matter quantum effects on the graviton self-energy on a flat Minkowski background, with the aim to acquire a deeper understanding of the mechanism that renders the graviton massless. To this end, we derive a low-energy theorem which directly relates the radiative corrections of the cosmological constant to those of the graviton mass to all orders in perturbation theory. As an illustrative example, we consider an Abelian Higgs model with minimal coupling to gravity and show explicitly how a suitable renormalization of the cosmological constant leads to the vanishing of the graviton mass at the one-loop level. In the same Abelian Higgs model, we also calculate the matter quantum corrections to the Newtonian potential and present analytical formulae in terms of modified Bessel and Struve functions of the particle masses in the loop. We show that the correction to the Newtonian potential exhibits an exponential fall-off dependence on the distance r, once the non-relativistic limit with respect to the non-zero loop mass is carefully considered. For massless scalars, fermions and gauge bosons in the loops, we recover the well-known results presented in the literature.

Clinical Microbiology and Infection xxx (xxxx) xxx Please cite this article as: Ptasinska A et al., Diagnostic accuracy of loop-mediated isothermal amplification coupled to nanopore sequencing (LamPORE) for the detection of SARS-CoV-2 infection at scale in symptomatic and asymptomatic populations, Clinical Microbiology and Infection,

Previous studies have described RT-LAMP methodology for the rapid detection of SARS-CoV-2 in nasopharyngeal (NP) and oropharyngeal (OP) swab and saliva samples. This study describes the validation of an improved sample preparation method for extraction free RT-LAMP and defines the clinical performance of four different RT-LAMP assay formats for detection of SARS-CoV-2 within a multisite clinical evaluation. Direct RT-LAMP was performed on 559 swabs and 86,760 saliva samples and RNA RT-LAMP on extracted RNA from 12,619 swabs and 12,521 saliva from asymptomatic and symptomatic individuals across healthcare and community settings. For Direct RT-LAMP, overall diagnostic sensitivity (DSe) of 70.35% (95% CI 63.48-76.60%) on swabs and 84.62% (79.50-88.88%) on saliva was observed, with diagnostic specificity (DSp) of 100% (98.98-100.00%) on swabs and 100% (99.72-100.00%) on saliva when compared to RT-qPCR; analysing samples with RT-qPCR ORF1ab C T values of < 25 and < 33, DSe of 100% (96.34-100%) and 77.78% (70.99-83.62%) for swabs were observed, and 99.01% (94.61-99.97%) and 87.61% (82.69-91.54%) for saliva, respectively. For RNA RT-LAMP, overall DSe and DSp were 96.06% (92.88-98.12%) and 99.99% (99.95-100%) for swabs, and 80.65% (73.54-86.54%) and 99.99% (99.95-100%) for saliva, respectively. These findings demonstrate that RT-LAMP is applicable to a variety of use-cases, including frequent, interval-based testing of saliva with Direct RT-LAMP from asymptomatic individuals that may otherwise be missed using symptomatic testing alone.

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript, we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosting decision tree model trained to identify patients at risk of reattendance within 72 h of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8847 attendances by 7237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI 0.722–0.773) and an average precision of 0.233 (95% CI 0.194–0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques (the UMAP algorithm) can be used to investigate the different sub-groups of explanations present in our patient cohort.

Background Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary disease by using data self-reported to a digital health app. Objective The aim of this study was to evaluate if data self-reported to a digital health app can be used to predict acute exacerbation events in the near future. Methods This is a retrospective study evaluating the use of symptom and chronic obstructive pulmonary disease assessment test data self-reported to a digital health app (myCOPD) in predicting acute exacerbation events. We include data from 2374 patients who made 68,139 self-reports. We evaluated the degree to which the different variables self-reported to the app are predictive of exacerbation events and developed both heuristic and machine learning models to predict whether the patient will report an exacerbation event within 3 days of self-reporting to the app. The model’s predictive ability was evaluated based on self-reports from an independent set of patients. Results Users self-reported symptoms, and standard chronic obstructive pulmonary disease assessment tests displayed correlation with future exacerbation events. Both a baseline model (area under the receiver operating characteristic curve [AUROC] 0.655, 95% CI 0.689-0.676) and a machine learning model (AUROC 0.727, 95% CI 0.720-0.735) showed moderate ability in predicting exacerbation events, occurring within 3 days of a given self-report. Although the baseline model obtained a fixed sensitivity and specificity of 0.551 (95% CI 0.508-0.596) and 0.759 (95% CI 0.752-0.767) respectively, the sensitivity and specificity of the machine learning model can be tuned by dichotomizing the continuous predictions it provides with different thresholds. Conclusions Data self-reported to health care apps designed to remotely monitor patients with chronic obstructive pulmonary disease can be used to predict acute exacerbation events with moderate performance. This could increase personalization of care by allowing preemptive action to be taken to mitigate the risk of future exacerbation events.

Previous studies have described RT-LAMP methodology for the rapid detection of SARS-CoV-2 in nasopharyngeal/oropharyngeal swab and saliva samples. Here we describe the validation of an improved simple sample preparation method for Direct SARS-CoV-2 RT-LAMP, removing the need for RNA extraction, using 559 swabs and 86,760 saliva samples from asymptomatic and symptomatic individuals across multiple healthcare settings. Using this improved method we report a diagnostic sensitivity (DSe) of 70.35% (95% CI 63.48-76.60%) on swabs and 84.62% (79.50-88.88%) on saliva, with diagnostic specificity (DSp) 100% (98.98-100.00%) on swabs and 100% (99.72-100.00%) on saliva when compared to RT-qPCR. Analysing samples with RT-qPCR ORF1ab CT values of <25 and <33 (high and medium-high viral loads, respectively), we found DSe of 100% (96.34-100%) and 77.78% (70.99-83.62%) for swabs, and 99.01% (94.61-99.97%) and 87.32% (80.71-92.31%) for saliva. We also describe RNA RT-LAMP (on extracted RNA) performed on 12,619 swabs and 12,521 saliva samples to provide updated performance data with DSe and DSp of 95.98% (92.74-98.06%) and 99.99% (99.95-100%) for swabs, and 80.65% (73.54-86.54%) and 99.99% (99.95-100%) for saliva, respectively. We also report on daily samples collected from one individual from symptom onset where both Direct and RNA RT-LAMP detected SARS-CoV-2 in saliva collected on all six days where symptoms were recorded, with RNA RT-LAMP detecting SARS-CoV-2 for an additional further day. The findings from these studies demonstrate that RT-LAMP testing of swabs and saliva is potentially applicable to a variety of use-cases, including frequent, interval-based testing of saliva from asymptomatic individuals via Direct RT-LAMP that may be missed using symptomatic testing alone.