Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance

Background: Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe. Methods : We modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present. Results: The distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups. Conclusions: The duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain

What is the best spectroscopic method for simultaneous analysis of organic acids and (poly)saccharides in biological matrices: Example of Aloe vera extracts?

Several species of (poly)saccharides and organic acids can be found often simultaneously in various biological matrices, e.g., fruits, plant materials, and biological fluids. The analysis of such matrices sometimes represents a challenging task. Using Aloe vera (A. vera) plant materials as an example, the performance of several spectroscopic methods (80 MHz benchtop NMR, NIR, ATR-FTIR and UV–vis) for the simultaneous analysis of quality parameters of this plant material was compared. The determined parameters include (poly)saccharides such as aloverose, fructose and glucose as well as organic acids (malic, lactic, citric, isocitric, acetic, fumaric, benzoic and sorbic acids). 500 MHz NMR and high-performance liquid chromatography (HPLC) were used as the reference methods.UV–vis data can be used only for identification of added preservatives (benzoic and sorbic acids) and drying agent (maltodextrin) and semiquantitative analysis of malic acid. NIR and MIR spectroscopies combined with multivariate regression can deliver more informative overview of A. vera extracts being able to additionally quantify glucose, aloverose, citric, isocitric, malic, lactic acids and fructose. Low-field NMR measurements can be used for the quantification of aloverose, glucose, malic, lactic, acetic, and benzoic acids. The benchtop NMR method was successfully validated in terms of robustness, stability, precision, reproducibility and limit of detection (LOD) and quantification (LOQ), respectively.All spectroscopic techniques are useful for the screening of (poly)saccharides and organic acids in plant extracts and should be applied according to its availability as well as information and confidence required for the specific analytical goal. Benchtop NMR spectroscopy seems to be the most feasible solution for quality control of A. vera products

Correction: Co-infection and ICU-acquired infection in COVID-19 ICU patients: a secondary analysis of the UNITE-COVID data set (Critical care (London, England) (2022) 26 1 (236))

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Correction: Co-infection and ICU-acquired infection in COVID-19 ICU patients: a secondary analysis of the UNITE-COVID data set (Critical Care, (2022), 26, 1, (236), 10.1186/s13054-022-04108-8)

Following publication of the original article [1], an error was identified in the article title: COVID‑19 was incorrectly captured as COIVD‑19. The article title has been updated above and in the original article

Correction: Co-infection and ICU-acquired infection in COVID-19 ICU patients: a secondary analysis of the UNITE-COVID data set.

BACKGROUND: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients. METHODS: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson’s Chi-squared and continuous variables by Mann–Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the “full” matching method. RESULTS: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO. CONCLUSIONS: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021). GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-04108-8

Natalizumab Treatment Reduces Fatigue in Multiple Sclerosis : Results from the TYNERGY Trial: A Study in the Real Life Setting

Fatigue is a significant symptom in multiple sclerosis (MS) patients. First-generation disease modifying therapies (DMTs) are at best moderately effective to improve fatigue. Observations from small cohorts have indicated that natalizumab, an antibody targeting VLA-4, may reduce MS-related fatigue. The TYNERGY study aimed to further evaluate the effects of natalizumab treatment on MS-related fatigue. In this one-armed clinical trial including 195 MS patients, natalizumab was prescribed in a real-life setting, and a validated questionnaire, the Fatigue Scale for Motor and Cognitive functions (FSMC), was used both before and after 12 months of treatment to evaluate a possible change in the fatigue experienced by the patients. In the treated cohort all measured variables, that is, fatigue score, quality of life, sleepiness, depression, cognition, and disability progression were improved from baseline (all p values<0.0001). Walking speed as measured by the six-minute walk-test also increased at month 12 (p = 0.0016). All patients were aware of the nature of the treatment agent, and of the study outcomes. Conclusion: Natalizumab, as used in a real-life setting, might improve MS-related fatigue based on the results from this one-armed un-controlled stud. Also other parameters related to patients' quality of life seemed to improve with natalizumab treatment

Fatigue as a symptom or comorbidity of neurological diseases

Fatigue, best described as an overwhelming feeling of tiredness and exhaustion, occurs in the context of various neurological diseases. The high prevalence of fatigue as either a symptom or a comorbidity of neurological disease must be taken seriously, as fatigue interferes with patients' activities of daily living, has a remarkable negative impact on quality of life, and is a major reason for early retirement. The tremendous consequences of fatigue are consistent across neurological diseases, as is the uncertainty concerning its underlying pathophysiological mechanisms. Inconsistencies in defining fatigue contribute to the present situation, in which fatigue represents one of the least-studied and least- understood conditions. Tools for assessing fatigue abound, but few can be recommended for clinical or research use. To make matters worse, evidence-based pharmacological treatment options are scarce. However, non-pharmacological approaches are currently promising and likely to become of increasing importance. In sum, fatigue is challenging for both health-care professionals and patients. The present article aims to provide a comprehensive review of the literature on fatigue in neurological disease, and to reveal its complexity, as well as weaknesses in the concept of fatigue itself