Does bariatric surgery improve cardiac autonomic modulation assessed by heart rate variability? A systematic review

Objectives. Our study aimed to explore the influence of Bariatric Surgery (BS) on heart rate (HR) variability (HRV) through a systematic review. Data Sources. Manuscripts were selected based on electronic searches of MEDLINE, EMBASE and CINAHL databases from the inception of each database up to year 2020 and followed the PRISMA protocol. Searching of these studies was systematized using the PICOS strategy. Eligibility Criteria for Selecting Studies. We selected randomized and non-randomized controlled trials and cohorts’ prospective studies that reported the influence of BS on HRV. We assessed the quality rating using the Black and Downs questionnaire. Results. Following the screening and eligibility stages, 14 studies were included in the review. All studies agreed that BS promotes an increase in parasympathetic HR control and HRV and, a decrease in HR. Yet, the literature does not provide evidence that this outcome was directly caused by the surgical procedure. There is limited evidence to support that patients with type 2 Diabetes Mellitus (TDM2) have greater improvement in HRV as an interim measure, to individuals without. The decrease in insulin resistance was correlated with the increase in HRV in some studies, but, other studies are unsupportive of this outcome. Improvements in two metabolic parameters (e.g., Leptin, NT-proBNP) were connected with a superior increase in HRV

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

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

Beetroot (Beta Vulgaris L.) extract acutely improves heart rate variability recovery following strength exercise: A randomized, double-blind, placebo-controlled crossover trial-pilot study

Objective: We assessed the acute effect of beetroot extract intake on cardiovascular and autonomic recovery subsequent to strength exercise.Methods: This is a crossover, randomized, double-blind and placebo-controlled trial. We assessed 16 subjects but only 12 healthy male adults completed the two protocols in two randomized days: Beetroot extract (600 mg in capsule) and placebo (600 mg starch in capsule). Beetroot extract or placebo was ingested, the subjects endured 120 minutes seated at rest, followed by a 75% 1RM strength exercise and then remained seated for 60 minutes at rest. Cardiorespiratory parameters, heart rate (HR) variability (HRV) (SDNN, rMSSD, pNN50, SD1, SD2 HF [ms(2)]) were estimated before, during exercise and during recovery from exercise.Results: ingestion of beetroot extract before exercise: accelerates the recovery of SBP following physical effort; improves HR recovery to baseline resting levels (beetroot protocol: change in ∼62% vs. placebo protocol: change in ∼80%), and intensifies the return of vagal HR control during recovery after exercise.Conclusions: Beetroot extract acutely improved cardiovascular and autonomic recovery after exercise

Pancreatic surgery outcomes: multicentre prospective snapshot study in 67 countries

Background: Pancreatic surgery remains associated with high morbidity rates. Although postoperative mortality appears to have improved with specialization, the outcomes reported in the literature reflect the activity of highly specialized centres. The aim of this study was to evaluate the outcomes following pancreatic surgery worldwide.Methods: This was an international, prospective, multicentre, cross-sectional snapshot study of consecutive patients undergoing pancreatic operations worldwide in a 3-month interval in 2021. The primary outcome was postoperative mortality within 90 days of surgery. Multivariable logistic regression was used to explore relationships with Human Development Index (HDI) and other parameters.Results: A total of 4223 patients from 67 countries were analysed. A complication of any severity was detected in 68.7 percent of patients (2901 of 4223). Major complication rates (Clavien-Dindo grade at least IIIa) were 24, 18, and 27 percent, and mortality rates were 10, 5, and 5 per cent in low-to-middle-, high-, and very high-HDI countries respectively. The 90-day postoperative mortality rate was 5.4 per cent (229 of 4223) overall, but was significantly higher in the low-to-middle-HDI group (adjusted OR 2.88, 95 per cent c.i. 1.80 to 4.48). The overall failure-to-rescue rate was 21 percent; however, it was 41 per cent in low-to-middle-compared with 19 per cent in very high-HDI countries.Conclusion: Excess mortality in low-to-middle-HDI countries could be attributable to failure to rescue of patients from severe complications. The authors call for a collaborative response from international and regional associations of pancreatic surgeons to address management related to death from postoperative complications to tackle the global disparities in the outcomes of pancreatic surgery (NCT04652271; ISRCTN95140761)