COVID-19 vaccine hesitancy and anti-vaxxers – supporting healthcare workers to navigate the unvaccinated: Reflections from clinical practice

An important step in preparation for the fourth COVID-19 wave is to provide healthcare workers (HCWs) with skills to facilitate behaviour change in vaccine-hesitant patients. Convincing members of the public who are vaccine hesitant rather than anti-vaxxers should be the focus of our efforts. Our experience is that vaccine-hesitant individuals and anti-vaxxers are generally distinct cohorts, with differing reasons for their vaccine reluctance. If we are to truly address hesitancy, we must take time to understand the reasons for an individual’s hesitancy. Developing a conceptual framework and skills for HCWs during encounters with unvaccinated individuals will be important not only for shifting some to get vaccinated, but also to manage the complex emotions that HCWs will undoubtedly be forced to confront during the fourth wave

A chain rule for the expected suprema of Gaussian processes

The expected supremum of a Gaussian process indexed by the image of an index set under a function class is bounded in terms of separate properties of the index set and the function class. The bound is relevant to the estimation of nonlinear transformations or the analysis of learning algorithms whenever hypotheses are chosen from composite classes, as is the case for multi-layer models

Well-to-Well Log Correlation Using Knowledge-Based Systems and Dynamic Depth Warping

We present a novel system for well-to-well log correlation using knowledge-based systems and dynamic depth warping techniques. This approach overcomes a major drawback inherent in previous methods, namely the difficulty in correlating missing or discontinuous rock units. The system has three components: (1) A Dynamic Programming algorithm to correlate the logs and to find the minimum-cost or "best" match; (2) A set of "rules" to guide the correlation; (3) A data base that contains the logs and other relevant geologic and seismic information. The Dynamic Programming algorithm calculates the cost of correlating each point in the first well with each of the points in the second well. The resulting matrix of dissimilarity contains cost information about every possible operation which matches the well logs. The cost of matching the two wells is measured by the difference in the log values. The dynamic programming approach allows correlation across geologic structures, thinning beds, and missing or discontinuous units. A path finding algorithm then traces through the matrix to define a function which maps the first well onto the second. The minimum cost path is the optimal correlation between the wells. The system's database contains the well logs themselves and other relevant data including information about the geologic setting, seismic ties, interpreted lithologies, and dipmeter information. Rules operating on the data affect the dynamic programming and path finding algorithms in several ways: (1) Seismic ties or marker beds define a point in the warping path, thereby removing calculations over large portions of the search space; (2) Dipmeter results and knowledge of geologic structure further constrain the path to certain global areas and save calculation time; (3) The system assigns weights to different logs based on log quality and sensitivity; (4) Knowledge of the paleoenvironment allows the program to choose a set of rules (model) which accounts for changes in sediment type or thickness within a field. For example, when the program is operating in a deltaic environment, it will correlate the shales before attempting to correlate the sands. We demonstrate the method with synthetic examples in which the program successfully correlates across geologic structures and pinch-outs. We also applied the program to field examples from two widely separated oil provinces. In both cases, the automated correlation agreed very well with correlations provided by geologic experts

COVID-19 antibody testing: From hype to immunological reality

The potential role for serological tests in the current COVID-19 pandemic has generated very considerable recent interest across many sectors worldwide, inter alia pathologists seeking additional weapons for their armoury of diagnostic tests; epidemiologists seeking tools to gain seroprevalence data that will inform improved models of the spread of disease; research scientists seeking tools to study the natural history of COVID-19 disease; vaccine developers seeking tools to assess vaccine efficacy in clinical trials; and companies and governments seeking tools to aid return-to-work decision-making. However, much of the local debate to date has centred on questions surrounding whether regulatory approval processes are limiting access to serological tests, and has not paused to consider the intrinsically limiting impact of underlying fundamental biology and immunology on where and how different COVID-19 serological tests can usefully be deployed in the response to the current pandemic. We review, from an immunological perspective, recent experimental evidence on the time-dependency of adaptive immune responses following SARS-CoV-2 infection and the impact of this on the sensitivity and specificity of COVID-19 antibody tests made at different time points post infection. We interpret this scientific evidence in terms of mooted clinical applications for current COVID-19 antibody tests in identifying acute infections, in confirming recent or past infections at the individual and population level, and in detecting re-infection and protective immunity. We conclude with guidance on where current COVID-19 antibody tests can make a genuine impact in the pandemic

Investigation of photoplethysmographs from the anterior fontanelle of neonates

Photoplethysmography (PPG) signals have been investigated at a new anatomical site, the anterior fontanelle (ANTF), on the hypothesis that blood supply at this location is preferentially preserved during cases of poor peripheral circulation which might cause the commercial pulse oximeters to fail to estimate accurately arterial blood oxygen saturation (SpO2). Two custom built reflectance PPG sensors have been developed, one for placement on the fontanelle and one on the periphery (foot). A PPG processing system and software were also developed to process the raw PPG signals and to estimate SpO2. A pilot study on sixteen babies, (9 male, 7 female) with a median age of 15.5 d (interquartile range = 46.8 d) and a median weight of 3.15 kg (SD = 0.93 kg), on a neonatal intensive care unit (NICU) has been carried out. PPG signals from the ANTF were of good quality and high signal-to-noise ratio. The amplitudes of the ANTF PPGs were found to be sensitive to changes in amplitude when amplitudes were observed at the reference PPG site. Bland-Altman analysis of the gold standard blood gas analysis reveals that all three sensors are inaccurate at SaO2 < 85–90 %, but the ANTF sensor shows better mean difference than the commercial device

Case Report: Acute generalised exanthematous pustulosis secondary to cotrimoxazole or tenofovir

Cutaneous adverse drug reactions are a common complication of antiretroviral therapy and of drugs used to treat opportunistic infections. We present a rare case of acute generalised exanthematous pustulosis secondary to&#160; cotrimoxazole or tenofovir

Acute generalised exanthematous pustulosis secondary to cotrimoxazole or tenofovir

Cutaneous adverse drug reactions are a common complication of antiretroviral therapy and of drugs used to treat opportunistic infections. We present a rare case of acute generalised exanthematous pustulosis secondary to cotrimoxazole or tenofovir

The Viscous Nonlinear Dynamics of Twist and Writhe

Exploiting the "natural" frame of space curves, we formulate an intrinsic dynamics of twisted elastic filaments in viscous fluids. A pair of coupled nonlinear equations describing the temporal evolution of the filament's complex curvature and twist density embodies the dynamic interplay of twist and writhe. These are used to illustrate a novel nonlinear phenomenon: ``geometric untwisting" of open filaments, whereby twisting strains relax through a transient writhing instability without performing axial rotation. This may explain certain experimentally observed motions of fibers of the bacterium B. subtilis [N.H. Mendelson, et al., J. Bacteriol. 177, 7060 (1995)].Comment: 9 pages, 4 figure

Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset