Stroke in Practice: From Diagnosis to Evidence-Based Management

Stroke is the leading cause of disability and the third leading cause of death in the developed world. The past decade has witnessed a paradigm shift in the management of stroke, with the understanding that care of acute stroke patients by specialists working in dedicated stroke units greatly improves patient outcomes; as a consequence, stroke units now exist in all major hospitals in the UK and Europe. Interventions to treat stroke have also increased in complexity, and the discipline of stroke medicine is now recognised as a medical specialty. This book is a concise, accessible and authoritative source of relevant and focussed information about stroke disease. It contains a thorough review of the management of cerebrovascular disease - everything you need to function effectively on an acute stroke unit. Tables and diagrams aid quick reference and easy comprehension. The most up-to-date and clinically relevant resource on the market, Stroke in Practice equips all medical professionals with evidence-based, practical knowledge and a comprehensive understanding of treatment of stroke. 'This text is directed at the non-specialist and emphasises practicality over academic niceties. If it inspires enthusiasm for a fascinating condition and convinces the reader that stroke is preventable, treatable and yet potentially devastating for patients and their families, it will justify its existence.' from the foreword by Kennedy R Lee

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and software were followed for data acquisition and analysis. The effectiveness of PLSR, bagged trees, and TLNN in predicting the firmness, SSC, DM, and tannins of kiwifruit was assessed via statistical metrics, including R squared (R²) values and the root mean square error (RMSE). The obtained results indicate varying degrees of efficiency for each model in predicting kiwifruit quality parameters. The study concludes that machine learning algorithms, especially neural networks, offer substantial accuracy, surpassing traditional methods for evaluating kiwifruit quality traits. Overall, the current study highlights the potential of such non-destructive techniques in revolutionizing quality assessment during postharvest by yielding rapid and reliable predictions regarding the critical quality attributes of fruits

Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy

Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%

Alopecia areata and risk of common infections:a population-based cohort study

Background. It is not known whether alopecia areata (AA) is associated with a greater or reduced risk for infection. Aim. We undertook a population-based study exploring associations between AA and common infections. Methods. We extracted primary care records from the UK Oxford-Royal College of General Practitioners Research and Surveillance Centre database (trial registration: NCT04239521). The incidence of common and viral infection composite outcomes, and individual respiratory, gastrointestinal (GI), skin, urinary tract, genital and herpes infections, were compared in people with AA (AA group, n=10 391) and a propensity-matched control group (n=41 564). Adjusted hazard ratios (aHRs), controlling for sociodemographic and clinical covariates, and comorbidities were used to estimate the association between AA and each infection over 5 years. Results. The incidence (per 100 person-years) of common infections was slightly higher in the AA group [14.2, 95% confidence interval (CI) 13.8–14.6] than the control group (11.7, 95% CI 11.5–11.9). In adjusted analysis, positive associations were observed for composite outcomes (common infections aHR 1.13, 95% CI 1.09–1.17; viral infections aHR 1.11, 95% CI 1.07–1.16) and with respiratory tract, GI, skin and herpes simplex infections (aHR range 1.09–1.32). Excluding people in the control group without a recent consultation with their general practitioner showed no association between AA and infection (common infections aHR 1.01, 95% CI 0.98–1.05, viral infections aHR 0.99, 95% CI 0.95–1.03). Conclusions. The association between AA and common infection may represent a higher propensity of people with AA to engage with healthcare services (and thereby to have infections recorded), rather than a true association between AA and infection. Overall our findings suggest that AA is not associated with a clinically significantly increased or decreased incidence of common infections.</p