N-acetylcysteine attenuates the progression of chronic renal failure

N-acetylcysteine attenuates the progression of chronic renal failure.BackgroundLipid peroxidation impairs renal function. Aldosterone contributes to renal injury in the remnant kidney model. This study aimed to determine the effects of the antioxidant N-acetylcysteine (NAC) on renal function and aldosterone levels in chronic renal failure.MethodsAdult male Wistar rats were submitted to 5/6 nephrectomy or laparotomy (sham-operated) and received NAC (600 mg/L in drinking water, initiated on postoperative day 7 or 60), spironolactone (1.5 g/kg of diet initiated on postoperative day 7), the NAC-spironolactone combination or no treatment. Clearance studies were performed on postoperative days 21, 60, and 120.ResultsMean daily NAC and spironolactone ingestion was comparable among the treated groups. Mean weight gain was higher in NAC-treated rats than in untreated rats. A significant decrease in urinary thiobarbituric acid reactive substances (TBARS) concentrations, a lipid peroxidation marker, was observed in NAC-treated rats. By day 120, glomerular filtration rate (GFR), which dropped dramatically in untreated rats, was stable (albeit below normal) in NAC-treated rats, which also presented lower proteinuria, glomerulosclerosis index, and blood pressure, together with attenuated cardiac and adrenal hypertrophy. These beneficial effects, observed even when NAC was initiated on postnephrectomy day 60, were accompanied by a significant reduction in plasma aldosterone and urinary sodium/potassium ratio. The NAC-spironolactone combination lowered blood pressure and improved GFR protection.ConclusionThe NAC-spironolactone combination improves renal function more than does NAC alone. In the remnant kidney model, early or late NAC administration has a protective effect attributable to decreased plasma aldosterone and lower levels of lipid peroxidation

Main and moderated effects of multimorbidity and depressive symptoms on cognition

Objective: Multimorbidity, or the occurrence of two or more chronic conditions, is a global challenge, with implications for mortality, morbidity, disability, and life quality. Psychiatric disorders are common among the chronic diseases that affect patients with multimorbidity. It is still not well understood whether psychiatric symptoms, especially depressive symptoms, moderate the effect of multimorbidity on cognition. Methods: We used a large (n=2,681) dataset to assess whether depressive symptomatology moderates the effect of multimorbidity on cognition using structural equation modelling. Results: It was found that the more depressive symptoms and chronic conditions, the worse the cognitive performance, and the higher the educational level, the better the cognitive performance. We found a significant but weak (0.009; p = 0.04) moderating effect. Conclusion: We have provided the first estimate of the moderating effect of depression on the relation between multimorbidity and cognition, which was small. Although this moderation has been implied by many previous studies, it was never previously estimated

Is MR Spectroscopy Really the Best MR-Based Method for the Evaluation of Fatty Liver in Diabetic Patients in Clinical Practice?

Objective: To investigate if magnetic resonance spectroscopy (MRS) is the best Magnetic Resonance (MR)-based method when compared to gradient-echo magnetic resonance imaging (MRI) for the detection and quantification of liver steatosis in diabetic patients in the clinical practice using liver biopsy as the reference standard, and to assess the influence of steatohepatitis and fibrosis on liver fat quantification.Methods: Institutional approval and patient consent were obtained for this prospective study. Seventy-three patients with type 2 diabetes (60 women and 13 men; mean age, 5469 years) underwent MRI and MRS at 3.0 T. the liver fat fraction was calculated from triple-and multi-echo gradient-echo sequences, and MRS data. Liver specimens were obtained in all patients. the accuracy for liver fat detection was estimated by receiver operator characteristic (ROC) analysis, and the correlation between fat quantification by imaging and histolopathology was analyzed by Spearman's correlation coefficients.Results: the prevalence of hepatic steatosis was 92%. All gradient-echo MRI and MRS findings strongly correlated with biopsy findings (triple-echo, rho = 0.819; multi-echo, rho = 0.773; MRS, rho = 0.767). Areas under the ROC curves to detect mild, moderate, and severe steatosis were: triple-echo sequences, 0.961, 0.975, and 0.962; multi-echo sequences, 0.878, 0.979, and 0.961; and MRS, 0.981, 0.980, and 0.954. the thresholds for mild, moderate, and severe steatosis were: triple-echo sequences, 4.09, 9.34, and 12.34, multi-echo sequences, 7.53, 11.75, and 15.08, and MRS, 1.71, 11.69, and 14.91. Quantification was not significantly influenced by steatohepatitis or fibrosis.Conclusions: Liver fat quantification by MR methods strongly correlates with histopathology. Due to the wide availability and easier post-processing, gradient-echo sequences may represent the best imaging method for the detection and quantification of liver fat fraction in diabetic patients in the clinical practice.D'Or Institute for Research and EducationFundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)DOr Inst Res & Educ, Rio de Janeiro, BrazilUniv Fed Rio de Janeiro, Rio de Janeiro, BrazilUniv Estado Rio de Janeiro, Rio de Janeiro, BrazilUniv São Paulo, Inst Phys Sao Carlos, Sao Carlos, SP, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilUniv Paris Diderot Sorbonne, Paris, FranceUniversidade Federal de São Paulo, São Paulo, BrazilWeb of Scienc

Patterns of multimorbidity and some psychiatric disorders : a systematic review of the literature

OBJECTIVE: The presence of two or more chronic diseases results in worse clinical outcomes than expected by a simple combination of diseases. This synergistic effect is expected to be higher when combined with some conditions, depending on the number and severity of diseases. Multimorbidity is a relatively new term, with the first fundamental definitions appearing in 2015. Studies usually define it as the presence of at least two chronic medical illnesses. However, little is known regarding the relationship between mental disorders and other non-psychiatric chronic diseases. This review aims at investigating the association between some mental disorders and non-psychiatric diseases, and their pattern of association. METHODS: We performed a systematic approach to selecting papers that studied relationships between chronic conditions that included one mental disorder from 2015 to 2021. These were processed using Covidence, including quality assessment. RESULTS: This resulted in the inclusion of 26 papers in this study. It was found that there are strong associations between depression, psychosis, and multimorbidity, but recent studies that evaluated patterns of association of diseases (usually using clustering methods) had heterogeneous results. Quality assessment of the papers generally revealed low quality among the included studies. CONCLUSIONS: There is evidence of an association between depressive disorders, anxiety disorders, and psychosis with multimorbidity. Studies that tried to examine the patterns of association between diseases did not find stable results. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021216101, identifier: CRD42021216101

Multimorbidity worsened anxiety and depression symptoms during the COVID-19 pandemic in Brazil

Multimorbidity is a global health issue impacting the quality of life of all ages. Multimorbidity with a mental disorder is little studied and is likely to have been affected by the COVID-19 pandemic. We used a survey of 14,007 respondents living in Brazil to investigate whether people who already had at least one chronic medical condition had more depression and anxiety symptoms during social distancing in 2020. Generalized linear models and structural equation modelling were used to estimate the effects. A 19 % and 15 % increase in depressive symptoms were found in females and males, respectively, for each unit of increase in the observed value of reported chronic disease. Older subjects presented fewer symptoms of depression and anxiety. There was a 16 % increase in anxiety symptoms in females for each unit increase in the reported chronic disease variable and a 14 % increase in males. Younger subjects were more affected by anxiety symptoms in a dose-response fashion. High income was significantly related to fewer depressive and anxiety symptoms in both males and females. Physical activity was significantly associated with fewer anxiety and depression symptoms. Structural equation modelling confirmed these results and provided further insight into the hypothesised paths

A list of land plants of Parque Nacional do Caparaó, Brazil, highlights the presence of sampling gaps within this protected area

Brazilian protected areas are essential for plant conservation in the Atlantic Forest domain, one of the 36 global biodiversity hotspots. A major challenge for improving conservation actions is to know the plant richness, protected by these areas. Online databases offer an accessible way to build plant species lists and to provide relevant information about biodiversity. A list of land plants of “Parque Nacional do Caparaó” (PNC) was previously built using online databases and published on the website "Catálogo de Plantas das Unidades de Conservação do Brasil." Here, we provide and discuss additional information about plant species richness, endemism and conservation in the PNC that could not be included in the List. We documented 1,791 species of land plants as occurring in PNC, of which 63 are cited as threatened (CR, EN or VU) by the Brazilian National Red List, seven as data deficient (DD) and five as priorities for conservation. Fifity-one species were possible new ocurrences for ES and MG states

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio