Household exposure to pesticides and bladder exstrophy in a newborn baby boy: a case report and review of the literature

© 2009 Martin et al; licensee Cases Network Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Methamphetamine withdrawal induces activation of CRF neurons in the brain stress system in parallel with an increased activity of cardiac sympathetic pathways.

Methamphetamine (METH) addiction is a major public health problem in some countries. There is evidence to suggest that METH use is associated with increased risk of developing cardiovascular problems. Here, we investigated the effects of chronic METH administration and withdrawal on the activation of the brain stress system and cardiac sympathetic pathways. Mice were treated with METH (2 mg/kg, i.p.) for 10 days and left to spontaneous withdraw for 7 days. The number of corticotrophin-releasing factor (CRF), c-Fos, and CRF/c-Fos neurons was measured by immunohistochemistry in the paraventricular nucleus of the hypothalamus (PVN) and the oval region of the bed nucleus of stria terminalis (ovBNST), two regions associated with cardiac sympathetic control. In parallel, levels of catechol-o-methyl-transferase (COMT), tyrosine hydroxylase (TH), and heat shock protein 27 (Hsp27) were measured in the heart. In the brain, chronic-METH treatment enhanced the number of c-Fos neurons and the CRF neurons with c-Fos signal (CRF+/c-Fos+) in PVN and ovBNST. METH withdrawal increased the number of CRF+neurons. In the heart, METH administration induced an increase in soluble (S)-COMT and membrane-bound (MB)-COMT without changes in phospho (p)-TH, Hsp27, or pHsp27. Similarly, METH withdrawal increased the expression of S- and MB-COMT. In contrast to chronic treatment, METH withdrawal enhanced levels of (p)TH and (p)Hsp27 in the heart. Overall, our results demonstrate that chronic METH administration and withdrawal activate the brain CRF systems associated with the heart sympathetic control and point towards a METH withdrawal induced activation of sympathetic pathways in the heart. Our findings provide further insight in the mechanism underlining the cardiovascular risk associated with METH use and proposes targets for its treatment

Effect of COMBinAtion therapy with remote ischemic conditioning and exenatide on the Myocardial Infarct size: a two-by-two factorial randomized trial (COMBAT-MI)

Remote ischemic conditioning (RIC) and the GLP-1 analog exenatide activate different cardioprotective pathways and may have additive effects on infarct size (IS). Here, we aimed to assess the efficacy of RIC as compared with sham procedure, and of exenatide, as compared with placebo, and the interaction between both, to reduce IS in humans. We designed a two-by-two factorial, randomized controlled, blinded, multicenter, clinical trial. Patients with ST-segment elevation myocardial infarction receiving primary percutaneous coronary intervention (PPCI) within 6 h of symptoms were randomized to RIC or sham procedure and exenatide or matching placebo. The primary outcome was IS measured by late gadolinium enhancement in cardiac magnetic resonance performed 3–7 days after PPCI. The secondary outcomes were myocardial salvage index, transmurality index, left ventricular ejection fraction and relative microvascular obstruction volume. A total of 378 patients were randomly allocated, and after applying exclusion criteria, 222 patients were available for analysis. There were no significant interactions between the two randomization factors on the primary or secondary outcomes. IS was similar between groups for the RIC (24 ± 11.8% in the RIC group vs 23.7 ± 10.9% in the sham group, P = 0.827) and the exenatide hypotheses (25.1 ± 11.5% in the exenatide group vs 22.5 ± 10.9% in the placebo group, P = 0.092). There were no effects with either RIC or exenatide on the secondary outcomes. Unexpected adverse events or side effects of RIC and exenatide were not observed. In conclusion, neither RIC nor exenatide, or its combination, were able to reduce IS in STEMI patients when administered as an adjunct to PPCI

Characteristics and Outcomes of People With Gout Hospitalized Due to COVID-19: Data From the COVID-19 Global Rheumatology Alliance Physician-Reported Registry

Objective: To describe people with gout who were diagnosed with coronavirus disease 2019 (COVID-19) and hospitalized and to characterize their outcomes. Methods: Data on patients with gout hospitalized for COVID-19 between March 12, 2020, and October 25, 2021, were extracted from the COVID-19 Global Rheumatology Alliance registry. Descriptive statistics were used to describe the demographics, comorbidities, medication exposures, and COVID-19 outcomes including oxygenation or ventilation support and death. Results: One hundred sixty-three patients with gout who developed COVID-19 and were hospitalized were included. The mean age was 63 years, and 85% were male. The majority of the group lived in the Western Pacific Region (35%) and North America (18%). Nearly half (46%) had two or more comorbidities, with hypertension (56%), cardiovascular disease (28%), diabetes mellitus (26%), chronic kidney disease (25%), and obesity (23%) being the most common. Glucocorticoids and colchicine were used pre-COVID-19 in 11% and 12% of the cohort, respectively. Over two thirds (68%) of the cohort required supplemental oxygen or ventilatory support during hospitalization. COVID-19-related death was reported in 16% of the overall cohort, with 73% of deaths documented in people with two or more comorbidities. Conclusion: This cohort of people with gout and COVID-19 who were hospitalized had high frequencies of ventilatory support and death. This suggests that patients with gout who were hospitalized for COVID-19 may be at risk of poor outcomes, perhaps related to known risk factors for poor outcomes, such as age and presence of comorbidity

Adherence to Interferon β-1b Treatment in Patients with Multiple Sclerosis in Spain

Adherence to interferon β-1b (INFβ-1b) therapy is essential to maximize the beneficial effects of treatment in multiple sclerosis (MS). For that reason, the main objectives of this study are to assess adherence to INFβ-1b in patients suffering from MS in Spain, and to identify the factors responsible for adherence in routine clinical practice.This was an observational, retrospective, cross-sectional study including 120 Spanish patients with MS under INFβ-1b treatment. Therapeutic adherence was assessed with Morisky-Green test and with the percentage of doses received. The proportion of adherent patients assessed by Morisky-Green test was 68.3%, being indicative of poor adherence. Nevertheless, the percentage of doses received, which was based on the number of injected medication, was 94.3%. The main reason for missing INFβ-1b injections was forgetting some of the administrations (64%). Therefore, interventions that diminish forgetfulness might have a positive effect in the proportion of adherent patients and in the percentage of doses received. In addition, age and comorbidities had a significant effect in the number of doses injected per month, and should be considered in the management of adherence in MS patients.Among all the available methods for assessing adherence, the overall consumption of the intended dose has to be considered when addressing adherence

Quantifying unpredictability: A multiple-model approach based on satellite imagery data from Mediterranean ponds.

Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study included 20 Mediterranean saline ponds and lakes, and the focal variable was the water-surface area. This study first aimed to produce a method for accurately estimating the water-surface area from satellite images. Saline ponds can develop salt-crusted areas that make it difficult to distinguish between soil and water. This challenge was addressed using a novel pipeline that combines band ratio water indices and the short near-infrared band as a salt filter. The study then extracted the predictable and unpredictable components of variation in the water-surface area. Two different approaches, each showing variations in the parameters, were used to obtain the stochastic variation around a regular pattern with the objective of dissecting the effect of assumptions on predictability estimations. The first approach, which is based on Colwell's predictability metrics, transforms the focal variable into a nominal one. The resulting discrete categories define the relevant variations in the water-surface area. In the second approach, we introduced General Additive Model (GAM) fitting as a new metric for quantifying predictability. Both approaches produced a wide range of predictability for the studied ponds. Some model assumptions-which are considered very different a priori-had minor effects, whereas others produced predictability estimations that showed some degree of divergence. We hypothesize that these diverging estimations of predictability reflect the effect of fluctuations on different types of organisms. The fluctuation analysis described in this manuscript is applicable to a wide variety of systems, including both aquatic and nonaquatic systems, and will be valuable for quantifying and characterizing predictability, which is essential within the expected global increase in the unpredictability of environmental fluctuations. We advocate that a priori information for organisms of interest should be used to select the most suitable metrics estimating predictability, and we provide some guidelines for this approach

Resource limitation drives spatial organization in microbial groups.

Dense microbial groups such as bacterial biofilms commonly contain a diversity of cell types that define their functioning. However, we have a limited understanding of what maintains, or purges, this diversity. Theory suggests that resource levels are key to understanding diversity and the spatial arrangement of genotypes in microbial groups, but we need empirical tests. Here we use theory and experiments to study the effects of nutrient level on spatio-genetic structuring and diversity in bacterial colonies. Well-fed colonies maintain larger well-mixed areas, but they also expand more rapidly compared with poorly-fed ones. Given enough space to expand, therefore, well-fed colonies lose diversity and separate in space over a similar timescale to poorly fed ones. In sum, as long as there is some degree of nutrient limitation, we observe the emergence of structured communities. We conclude that resource-driven structuring is central to understanding both pattern and process in diverse microbial communities

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry

OBJECTIVE: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. METHODS: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. RESULTS: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. CONCLUSION: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression

Inhibition of PbGP43 expression may suggest that gp43 is a virulence factor in Paracoccidioides brasiliensis

ABSTARCT: Glycoprotein gp43 is an immunodominant diagnostic antigen for paracoccidioidomycosis caused by Paracoccidioides brasiliensis. It is abundantly secreted in isolates such as Pb339. It is structurally related to beta-1,3-exoglucanases, however inactive. Its function in fungal biology is unknown, but it elicits humoral, innate and protective cellular immune responses; it binds to extracellular matrix-associated proteins. In this study we applied an antisense RNA (aRNA) technology and Agrobacterium tumefaciens-mediated transformation to generate mitotically stable PbGP43 mutants (PbGP43 aRNA) derived from wild type Pb339 to study its role in P. brasiliensis biology and during infection. Control PbEV was transformed with empty vector. Growth curve, cell vitality and morphology of PbGP43 aRNA mutants were indistinguishable from those of controls. PbGP43 expression was reduced 80-85% in mutants 1 and 2, as determined by real time PCR, correlating with a massive decrease in gp43 expression. This was shown by immunoblotting of culture supernatants revealed with anti-gp43 mouse monoclonal and rabbit polyclonal antibodies, and also by affinity-ligand assays of extracellular molecules with laminin and fibronectin. In vitro, there was significantly increased TNF-α production and reduced yeast recovery when PbGP43 aRNA1 was exposed to IFN-γ-stimulated macrophages, suggesting reduced binding/uptake and/or increased killing. In vivo, fungal burden in lungs of BALB/c mice infected with silenced mutant was negligible and associated with decreased lung ΙΛ-10 and IL-6. Therefore, our results correlated low gp43 expression with lower pathogenicity in mice, but that will be definitely proven when PbGP43 knockouts become available.