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The rough guide to the Lake District
\"The Rough Guide to the Lake District is the best all-purpose guide to the English Lake District, beautifully illustrated with colour photos and full-colour maps. Comprehensive, lively reviews outline the finest places to stay and eat for every budget, based on personal inspection by a long-time Lakes expert. Whether you're looking for a walker's hostel or boutique hotel, simple cafe or swanky gastro-pub, farmhouse B&B or country-house hotel, The Rough Guide to the Lake District has the lowdown on all the best deals, detailed information on the best way to get around by public transport, while special features on the great outdoors focus on local walks, classic hikes, mountain climbs, lake cruises and family adventures. The Things Not to Miss section pinpoints some of the absolute must-sees, while author picks throughout The Rough Guide to the Lake District highlight personal favourites and special places that are less well known. Whether you're on a walking holiday or family break, you can discover all the facts you need - from full opening times and admission prices to festival dates and walking routes, plus history, culture, nature, and wildlife of the English lakes to help you make the most of your time in the Lake District.\" -- Provided by publisher.
Book
Antibiotic resistance breakers: can repurposed drugs fill the antibiotic discovery void?
Key Points
Concern over antibiotic resistance is growing. Resistance of up to 50% has been reported in some regions, including resistance to carbapenems, our current last line of defence.
New classes of antibiotics are needed, particularly against Gram-negative bacteria. However, even if the scientific hurdles can be overcome, it could take decades before sufficient numbers of such antibiotics become available.
As an interim solution, antibiotic resistance could be 'broken' by co-administering appropriate non-antibiotic drugs with failing antibiotics.
Several marketed drugs that do not currently have antibacterial indications can directly kill bacteria, reduce the antibiotic minimum inhibitory concentration when used in combination with existing antibiotics, modulate host defence through effects on host innate immunity, particularly inflammation and autophagy, or a combination of these three.
This article discusses how such 'antibiotic resistance breakers' (ARBs) could contribute to reducing the antibiotic resistance problem, and analyses a priority list of candidates for further investigation.
Drug resistance is threatening to sideline the currently available antibiotics, and new antibiotics are unlikely to become available before the current arsenal becomes ineffective. Brown proposes the use of approved drugs or neutraceuticals as antibiotic resistance breakers — compounds that could be administered alongside current antibiotics to prolong their useful lifespan — to bridge the gap.
Concern over antibiotic resistance is growing, and new classes of antibiotics, particularly against Gram-negative bacteria, are needed. However, even if the scientific hurdles can be overcome, it could take decades for sufficient numbers of such antibiotics to become available. As an interim solution, antibiotic resistance could be 'broken' by co-administering appropriate non-antibiotic drugs with failing antibiotics. Several marketed drugs that do not currently have antibacterial indications can either directly kill bacteria, reduce the antibiotic minimum inhibitory concentration when used in combination with existing antibiotics and/or modulate host defence through effects on host innate immunity, in particular by altering inflammation and autophagy. This article discusses how such 'antibiotic resistance breakers' could contribute to reducing the antibiotic resistance problem, and analyses a priority list of candidates for further investigation.
Journal Article
Emergency department triage prediction of clinical outcomes using machine learning models
Background
Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach—the Emergency Severity Index (ESI).
Methods
Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using
routinely
available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.
Results
Of 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85–0.87] in the deep neural network vs 0.74 [95%CI 0.72–0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82–0.83] in the deep neural network vs 0.69 [95%CI 0.68–0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit—a larger number of appropriate triages considering a trade-off with over-triages—across the range of clinical thresholds.
Conclusions
Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians’ triage decision making, thereby achieving better clinical care and optimal resource utilization.
Journal Article
Evidence for PMAT- and OCT-like biogenic amine transporters in a probiotic strain of Lactobacillus: Implications for interkingdom communication within the microbiota-gut-brain axis
The ability of prokaryotic microbes to produce and respond to neurochemicals that are more often associated with eukaryotic systems is increasingly recognized through the concept of microbial endocrinology. Most studies have described the phenomena of neurochemical production by bacteria, but there remains an incomplete understanding of the mechanisms by which microbe- or host-derived neuroactive substances can be recognized by bacteria. Based on the evolutionary origins of eukaryotic solute carrier transporters, we hypothesized that bacteria may possess an analogous uptake function for neuroactive biogenic amines. Using specific fluorescence-based assays, Lactobacillus salivarius biofilms appear to express both plasma membrane monoamine transporter (PMAT)- and organic cation transporter (OCT)-like uptake of transporter-specific fluorophores. This phenomenon is not distributed throughout the genus Lactobacillus as L. rhamnosus biofilms did not take up these fluorophores. PMAT probe uptake into L. salivarius biofilms was attenuated by the protonophore CCCP, the cation transport inhibitor decynium-22, and the natural substrates norepinephrine, serotonin and fluoxetine. These results provide the first evidence, to our knowledge, for the existence of PMAT- and OCT-like uptake systems in a bacterium. They also suggest the existence of a hitherto unrecognized mechanism by which a probiotic bacterium may interact with host signals and may provide a means to examine microbial endocrinology-based interactions in health and disease that are part of the larger microbiota-gut-brain axis.
Journal Article
Causes of encephalitis and differences in their clinical presentations in England: a multicentre, population-based prospective study
Encephalitis has many causes, but for most patients the cause is unknown. We aimed to establish the cause and identify the clinical differences between causes in patients with encephalitis in England.
Patients of all ages and with symptoms suggestive of encephalitis were actively recruited for 2 years (staged start between October, 2005, and November, 2006) from 24 hospitals by clinical staff. Systematic laboratory testing included PCR and antibody assays for all commonly recognised causes of infectious encephalitis, investigation for less commonly recognised causes in immunocompromised patients, and testing for travel-related causes if indicated. We also tested for non-infectious causes for acute encephalitis including autoimmunity. A multidisciplinary expert team reviewed clinical presentation and hospital tests and directed further investigations. Patients were followed up for 6 months after discharge from hospital.
We identified 203 patients with encephalitis. Median age was 30 years (range 0–87). 86 patients (42%, 95% CI 35–49) had infectious causes, including 38 (19%, 14–25) herpes simplex virus, ten (5%, 2–9) varicella zoster virus, and ten (5%, 2–9) Mycobacterium tuberculosis; 75 (37%, 30–44) had unknown causes. 42 patients (21%, 15–27) had acute immune-mediated encephalitis. 24 patients (12%, 8–17) died, with higher case fatality for infections from M tuberculosis (three patients; 30%, 7–65) and varicella zoster virus (two patients; 20%, 2–56). The 16 patients with antibody-associated encephalitis had the worst outcome of all groups—nine (56%, 30–80) either died or had severe disabilities. Patients who died were more likely to be immunocompromised than were those who survived (OR=3·44).
Early diagnosis of encephalitis is crucial to ensure that the right treatment is given on time. Extensive testing substantially reduced the proportion with unknown cause, but the proportion of cases with unknown cause was higher than that for any specific identified cause.
The Policy Research Programme, Department of Health, UK.
Journal Article
The Little Golden Book of jokes and riddles
Enjoy this unique book of kids' laugh-out-loud riddles and jokes!
Book
Toward crop variety recommender systems
Choosing an optimal crop variety to plant is one of the most important decisions faced by farmers. Nowadays, recommender systems are almost ubiquitous in several human activities, except in agriculture, and more deeply absent in crop variety decision‐making. This commentary presents the potential applications of crop variety recommender systems, describing the elements required for their implementation and adoption, and proposing new research areas to address the major research gaps. This work aims to motivate the scientific community at the intersection of agriculture, environmental science, data science, digital technologies, and computer science to work toward the development and application of recommender systems to support crop variety decision‐making. Core Ideas There is very little research on crop variety recommender systems. Potential applications of recommender systems for crop variety recommendations are proposed. Research gaps and challenges in recommender systems for crop varieties were identified. Digital crop variety catalogs are fundamental for crop variety recommender systems. Plain Language Summary Due to scarce supporting information, farmers often face the decision of what crop variety to plant with high uncertainty about the potential outcomes. Recommender systems are systems designed to support users in choosing a product or service from a given set of options. While recommender systems have been extensively applied to e‐commerce platforms and streaming services, their application to agricultural activities has been limited, and even more so in the case of recommendations of crop varieties. The purpose of this commentary is to provide a motivating perspective to increase the research activities around the application of recommender systems to crop variety decision‐making. After reviewing related literature, it is evident that the amount of research conducted applying recommender systems to crop variety decision‐making is very low and that one of the main elements required is the development of crop variety catalogs.
Journal Article