This systematic review had three aims: i) to determine the frequency of anosmia (or other smell disorders) and dysgeusia (or other taste disorders) in COVID-19 patients; ii) to determine whether ...anosmia or dysgeusia are independently associated with COVID-19 diagnosis; and iii) to determine whether anosmia or dysgeusia are prognostic factors for impaired outcomes among COVID-19 patients.
On April 20
, 2020, we search MEDLINE, Embase, Global Health, Scopus, Web of Science and MedXriv. We used terms related to COVID-19, smell and taste disorders. We selected case series, cross-sectional, case-control and cohort studies. We included studies with COVID-19 patients describing their symptoms; studies that compared smell and taste disorders between COVID-19 patients and otherwise healthy subjects; and studies comparing smell and taste disorders between COVID-19 severe and mild/moderate cases. Because of methodological heterogeneity and the limited number of results, a qualitative synthesis is presented.
From 31 reports, we selected six (n=2,757). Six studies reported the proportion of smell and taste disorders among COVID-19 patients. Two reports studied whether smell and taste disorders were independently associated with COVID-19 diagnosis. No reports studied the association with impaired outcomes among COVID-19 patients. The frequency of anosmia ranged between 22%-68%. The definition of taste disorders varied greatly, with dysgeusia present in 33% and ageusia in 20%. People who reported loss of smell and taste had six-fold higher odds of being COVID-19 positive; similarly, anosmia and ageusia were associated with 10-fold higher odds of COVID-19 diagnosis.
The frequency of smell and taste disorders is as high as other symptoms, thus, at least anosmia for which the definition was more consistent, could be included in lists of COVID-19 symptoms. Although there is promising evidence, it is premature to conclude that smell and taste disorders are strongly associated with COVID-19 diagnosis.
PROSPERO CRD42020181308.
Background Despite recent improvements in cancer detection and survival rates, managing cancer-related pain remains a significant challenge. Compared to neuropathic and inflammatory pain conditions, ...cancer pain mechanisms are poorly understood, despite pain being one of the most feared symptoms by cancer patients and significantly impairing their quality of life, daily activities, and social interactions. The objective of this work was to select a panel of biomarkers of central pain processing and modulation and assess their ability to predict chronic pain in patients with cancer using predictive artificial intelligence (AI) algorithms. Methods We will perform a prospective longitudinal cohort, multicentric study involving 450 patients with a recent cancer diagnosis. These patients will undergo an in-person assessment at three different time points: pretreatment, 6 months, and 12 months after the first visit. All patients will be assessed through demographic and clinical questionnaires and self-report measures, quantitative sensory testing (QST), and electroencephalography (EEG) evaluations. We will select the variables that best predict the future occurrence of pain using a comprehensive approach that includes clinical, psychosocial, and neurophysiological variables. Discussion This study aimed to provide evidence regarding the links between poor pain modulation mechanisms at precancer treatment in patients who will later develop chronic pain and to clarify the role of treatment modality (modulated by age, sex and type of cancer) on pain. As a final output, we expect to develop a predictive tool based on AI that can contribute to the anticipation of the future occurrence of pain and help in therapeutic decision making. Keywords: Cancer pain, Chronic pain, EEG, QST, CHEPs
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Melatonin is a much conserved feature in vertebrates that plays a central role in the entrainment of daily and annual physiological rhythms. Investigations aiming at understanding how ...melatonin mediates the effects of photoperiod on crucial functions and behaviors have been very active in the last decades, particularly in mammals. In fish a clear-cut picture is still missing. Here we review the available data on (i) the sites of melatonin production in fish, (ii) the mechanisms that control its daily and annual rhythms of production and (iii) the characterization of its different receptor subtypes, their location and regulation. The in vivo and in vitro data on melatonin effects on crucial neuroendocrine regulations, including reproduction, growth, feeding and behavioral responses, are also reviewed. Finally we discuss how manipulation of the photic cues impact on fish circannual clock and annual cycle of reproduction, and how this can be used for aquaculture purposes.
Interest is growing in simple, fast and inexpensive systems to analyze urban wastewater quality in real time. In this research project, a methodology is presented for the characterization of COD, ...BOD5, TSS, TN, and TP of wastewater samples, without the need to alter the samples or use chemical reagents, from a few wavelengths, belonging to the different color groups that compose the visible spectrum in isolation: (380-700 nm): violet (380-427 nm), blue (427-476 nm), cyan (476-497 nm), green (497-570 nm), yellow (570-581 nm), orange (581-618 nm), and red (618-700 nm). In this study, about 650 raw and treated urban wastewater samples from over 43 WWTPs and a total of 36 estimation models based on genetic algorithms have been calculated. Seven models were calculated for each pollutant parameter; one model for each color group of the visible spectrum, except for TN, which includes an additional model combining the wavelengths of the violet and red region of the spectrum. All the calculated models showed high accuracy, with an R2 between 80 and 85 % for COD, BOD5 and TSS, and 66–74 % for TN and TP. The tests carried out have shown the accuracy of the models of the different color groups to be very close to each other. However, it is noted that the models making use of the wavelengths between 497 and 570 nm (green) were the ones that showed the best performance in all the parameters under study. This research work lays the foundations for the development of cheaper, faster, and simpler wastewater monitoring and characterization equipment.
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•Accurate and real-time wastewater characterization using reduced part of the visible spectrum.•36 models created using genetic algorithms for analyzing 650 samples from 43 WWTPs.•Prediction of COD, BOD5, TSS, TN, TP with a R2 of 80–85 %•Low-cost, high-precision wastewater monitoring using LED spectrophotometry.•Methodology enables real-time monitoring of wastewater pollutant levels at low cost.
•The average diameter of the ZnO and ZnO:Eu nanoparticles was in the range 14 a 20 nm.•Methylene blue degradation with ZnO and ZnO:Eu.•ZnO nanoparticles is not necessary to heat treatment to activate ...the photoactive.•1.62% Eu-ZnO has highest photocatalytic activity.
Photocatalysis using semiconductor materials, such as ZnO and TiO2, allows the degradation of organic compounds through the redox process when it is irradiated by UV or solar light. In the present work the nanoparticles of ZnO and ZnO:Eu were synthesized using the forced-hydrolysis method. It was determined the effect of the heat treatment on ZnO nanoparticles at 400, 450 and 500 °C. The percentages of doped ZnO with Europium (Eu) was 0.97, 1.62 and 3% weight. The materials were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), infrared spectroscopy (FT-IR), visible ultraviolet spectroscopy (UV–vis) and the photocatalytic activity was evaluated to degradation of methylene blue (MB). The ZnO:Eu with 1.62% show MB degradation of 99.3% in 80 min.
We realize a nationwide population-based retrospective study to analyze the characteristics and risk factors of fungal co-infections in COVID-19 hospitalized patients as well as describe their ...causative agents in the Spanish population in 2020 and 2021. Data were obtained from records in the Minimum Basic Data Set of the National Surveillance System for Hospital Data in Spain, provided by the Ministry of Health, and annually published with two years lag. The assessment of the risk associated with the development of healthcare-associated fungal co-infections was assessed using an adjusted logistic regression model. The incidence of fungal co-infection in COVID-19 hospitalized patients was 1.41%. The main risk factors associated were surgery, sepsis, age, male gender, obesity, and COPD. Co-infection was associated with worse outcomes including higher in-hospital and in ICU mortality, and higher length of stay. Candida spp. and Aspergillus spp. were the microorganisms more frequent. This is the first study analyzing fungal coinfection at a national level in hospitalized patients with COVID-19 in Spanish population and one of the few studies available that demonstrate that surgery was an independent risk factor of Aspergillosis coinfection in COVID-19 patients.
The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from ...open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic.
Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case.
The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate.
: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
•Systematic reviews support evidence-based medicine, but are expensive to produce.•Text classification can support the screening phase of Systematic Reviews.•Determining the best classification ...parameters is key for good results.•Selecting certain sections of the articles can make significant difference.•Positive results support this technology as a tool for systematic reviews.
Medical systematic reviews answer particular questions within a very specific domain of expertise by selecting and analysing the current pertinent literature. As part of this process, the phase of screening articles usually requires a long time and significant effort as it involves a group of domain experts evaluating thousands of articles in order to find the relevant instances. Our goal is to support this process through automatic tools. There is a recent trend of applying text classification methods to semi-automate the screening phase by providing decision support to the group of experts, hence helping reduce the required time and effort. In this work, we contribute to this line of work by performing a comprehensive set of text classification experiments on a corpus resulting from an actual systematic review in the area of Internet-Based Randomised Controlled Trials. These experiments involved applying multiple machine learning algorithms combined with several feature selection techniques to different parts of the articles (i.e., titles, abstract, or both). Results are generally positive in terms of overall precision and recall measurements, reaching values of up to 84%. It is also revealing in terms of how using only article titles provides virtually as good results as when adding article abstracts. Based on the positive results, it is clear that text classification can support the screening stage of medical systematic reviews . However, selecting the most appropriate machine learning algorithms, related methods, and text sections of articles is a neglected but important requirement because of its significant impact to the end results.
Summary
Type 2 diabetes mellitus (T2DM) is associated with a high mortality risk, although the magnitude of this association remains unknown in Latin America (LA). We aimed to assess the strength of ...the association between T2DM and all‐cause and cause‐specific mortality in population‐based cohort studies in LA.
Systematic review and meta‐analysis: inclusion criteria were (1) men and women 18 years old and above with T2DM; (2) study outcomes all‐cause and/or cause‐specific mortality; and (3) using people without T2DM as comparison group. Five databases (Scopus, Medline, Embase, Global Health, and LILACS) were searched. Risk of bias was evaluated with the ROBINS‐I criteria. Initially, there were 979 identified studies, of which 17 were selected for qualitative synthesis; 14 were included in the meta‐analysis (N = 416 821). Self‐reported T2DM showed a pooled relative risk (RR) of 2.49 for all‐causes mortality (I‐squared I2 = 85.7%, p < 0.001; 95% confidence interval CI, 1.96‐3.15). T2DM based on a composite definition was associated with a 2.26‐fold higher all‐cause mortality (I2 = 93.9%, p < 0.001; 95% CI, 1.36‐3.74). The pooled risk estimates were similar between men and women, although higher at younger ages. The pooled RR for cardiovascular mortality was 2.76 (I2 = 59.2%; p < 0.061; 95% CI, 1.99‐3.82) and for renal mortality 15.85 (I2 = 0.00%; p < 0.645; 95% CI, 9.82‐25.57). Using available population‐based cohort studies, this work has identified and estimated the strength of the association between T2DM and mortality in LA. The higher mortality risk compared with high‐income countries deserves close attention from health policies makers and clinicians to improve diabetes care and control hence preventing complications and delaying death.
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