Dermatophytes are fungi responsible for a disease known as dermatophytosis. Biofilms are sessile microbial communities surrounded by extracellular polymeric substances (EPS) with increased resistance to antimicrobial agents and host defenses. This paper describes, for the first time, the characteristics of Trichophyton rubrum and T. mentagrophytes biofilms. Biofilm formation was analyzed by light microscopy, scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) as well as by staining with crystal violet and safranin. Metabolic activity was determined using the XTT reduction assay. Both species were able to form mature biofilms in 72 h. T. rubrum biofilm produced more biomass and EPS and was denser than T. mentagrophytes biofilm. The SEM results demonstrated a coordinated network of hyphae in all directions, embedded within EPS in some areas. Research and characterization of biofilms formed by dermatophytes may contribute to the search of new drugs for the treatment of these mycoses and might inform future revisions with respect to the dose and duration of treatment of currently available antifungals.
Abstract. Several studies conducted worldwide report an inverse association between caffeine/coffee consumption and the risk of developing Parkinson's disease (PD). However, heterogeneity and conflicting results between studies preclude a correct estimation of the strength of this association. We conducted a systematic review and meta-analysis of published epidemiological studies to better estimate the effect of caffeine exposure on the incidence of PD. Data sources searched included Medline, LILACS, Scopus, Web of Science and reference lists, up to September 2009. Cohort, case-control and cross-sectional studies were included. Three independent reviewers selected the studies and extracted the data on to standardized forms. Twenty-six studies were included: 7 cohort, 2 nested case-control, 16 case-control, and 1 cross-sectional study. Quantitative data synthesis of the most precise estimates from each study was accomplished through random effects meta-analysis. Heterogeneity was quantified using the I 2 statistic. The summary RR for the association between caffeine intake and PD was 0.75 [95% Confidence Interval (95%CI): 0.68-0.82], with low to moderate heterogeneity (I 2 = 28.8%). Publication bias for case-control/cross-sectional studies may exist (Egger's test, p = 0.053). When considering only the cohort studies, the RR was 0.80 (95%CI: 0.71-90; I 2 = 8.1%). The negative association was weaker when only women were considered (RR = 0.86, 95%CI: 0.73-1.02; I 2 = 12.9%). A linear relation was observed between levels of exposure to caffeine and the RR estimates: RR of 0.76 (95%CI: 0.72-0.80; I 2 = 35.1%) per 300 mg increase in caffeine intake. This study confirm an inverse association between caffeine intake and the risk of PD, which can hardly by explained by bias or uncontrolled confounding.
Abstract.A recent meta-analysis of 4 studies published up to January 2004 suggests a negative association between coffee consumption and Alzheimer's disease, despite important heterogeneity in methods and results. Several epidemiological studies on this issue have been published since then, warranting an update of the insights on this topic. We conducted a systematic review and meta-analysis of published studies quantifying the relation between caffeine intake and cognitive decline or dementia. Data sources searched included Medline, LILACS, Scopus, Web of Science and reference lists, up to September 2009. Cohort and case-control studies were included. Three independent reviewers selected the studies and extracted the data on to standardized forms. Nine cohort and two case-control studies were included. Quantitative data synthesis of the most precise estimates from each study was accomplished through random effects meta-analysis. Heterogeneity was quantified using the I 2 statistic. The outcomes of the studies considered for meta-analysis were Alzheimer's disease in four studies, dementia or cognitive impairment in two studies, and cognitive decline in three studies. The summary relative risk (RR) for the association between caffeine intake and different measures of cognitive impairment/decline was 0.84 [95% Confidence Interval (95% CI): 0.72-0.99; I 2 = 42.6%]. When considering only the cohort studies, the summary RR was 0.93 (95% CI: 0.83-1.04, I 2 = 0.0%), and 0.77 (95% CI: 0.63-0.95, I 2 = 34.7%), if the most influential study was excluded. This systematic review and meta-analysis found a trend towards a protective effect of caffeine, but the large methodological heterogeneity across a still limited number of epidemiological studies precludes robust and definite statements on this topic.
Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed. Objectives: To develop and validate a deep learning method to improve the management of IPNs. Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions. Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4–90.7%) and 91.9% (95% CI, 88.7–94.7%), compared with 78.1% (95% CI, 68.7–86.4%) and 81.9 (95% CI, 76.1–87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts. Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.
Abstract. Alzheimer's disease has emerged in recent decades as a major health problem and the role of lifestyles in the modulation of risk has been increasingly recognized. Recent epidemiological studies suggest a protective effect for caffeine intake in dementia. We aimed to quantify the association between caffeine dietary intake and cognitive decline, in a cohort of adults living in Porto. A cohort of 648 subjects aged 65 years was recruited between 1999 . Follow-up evaluation (2005-2008 was carried out on 58.2% of the eligible participants and 10.9% were deceased. Caffeine exposure in the year preceding baseline evaluation was assessed with a validated food frequency questionnaire. Cognitive evaluation consisted of baseline and follow-up Mini-Mental State Examination (MMSE). Cognitive decline was defined by a decrease 2 points in the MMSE score between evaluations. Relative risk (RR) and 95% confidence interval (95%CI) estimates adjusted for age, education, smoking, alcohol drinking, body mass index, hypertension, and diabetes were computed using Poisson regression. Caffeine intake (> 62 mg/day [3rd third] vs. < 22 mg/day [1st third]) was associated with a lower risk of cognitive decline in women (RR = 0.49, 95%CI 0.24-0.97), but not significantly in men (RR = 0.65, 95%CI 0.27-1.54). Our study confirms the negative association between caffeine and cognitive decline in women.
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