scholarly journals Chromosome 17: neXt-MP50 C-HPP Challenge Completed

2019 ◽  
Author(s):  
Hongjiu Zhang ◽  
Omer Siddiqui ◽  
Yuanfang Guan ◽  
Gilbert S. Omenn
Keyword(s):  
Protein Interaction ◽  
Protein Level ◽  
Chromosome 17 ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
High Quality ◽  
Protein Protein Interaction ◽  
High Quality Evidence ◽  
Quality Evidence

ABSTRACTThe C-HPP Team for Chromosome 17 has documented meeting the neXt-MP50 Missing Protein Challenge. Protein-level PE1 evidence including MS and protein-protein interaction data from the global proteomics community has reduced the number of PE2,3,4 proteins coded on Chromosome 17 by 50 since the 2016 baseline in neXtProt. As a follow-up to our analysis and prioritization of Chr 17 missing proteins last year, we describe what predictions and guidance were successful in this update and we explain the dynamics of a net reduction of PE2,3,4 Missing Proteins. This analysis can serve as a guide for other C-HPP chromosome teams seeking high-quality evidence for the 2129 remaining missing proteins.

scholarly journals Cannabis and breastfeeding

2020 ◽  
Vol 25 (Supplement_1) ◽  
pp. S26-S28 ◽  
Author(s):  
Lisa Graves
Keyword(s):  
Health Care Providers ◽  
Small Sample ◽  
Adverse Outcomes ◽  
Care Providers ◽  
High Quality ◽  
High Quality Evidence ◽  
Small Sample Sizes ◽  
The Impact ◽  
Quality Evidence

Abstract Cannabis is one of the most commonly used substances in Canada with 15% of Canadians reporting use in 2019. There is emerging evidence that cannabis is linked to an impact on the developing brain in utero and adverse outcomes in infants, children, and adolescents. The impact of cannabis during breastfeeding has been limited by studies with small sample sizes, follow-up limited to 1 year and the challenge of separating prenatal exposure from that during breastfeeding. In the absence of high-quality evidence, health care providers need to continue to engage women in conversation about the potential concerns related to breastfeeding and cannabis use.

scholarly journals The effectiveness of M-Health associated with cardiac rehabilitation on functional capacity and cardiovascular risk factors: a systematic review and meta-analysis

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
A C Campagnolo Goncalves Toledo ◽  
N Soares De Almeida ◽  
A Pierucci ◽  
A Straioto Salomao ◽  
I Ribeiro Lemes ◽  
...  
Keyword(s):  
Functional Capacity ◽  
Meta Analysis ◽  
Medium Term ◽  
Short Term ◽  
High Quality ◽  
Quality Of Evidence ◽  
High Quality Evidence ◽  
Quality Evidence

Abstract Introduction Smartphone applications for health (M-Health) seem to overcome barriers to access Cardiovascular Rehabilitation Programs (CRP), because of their high degree of acceptance and also their potential to influence the frequency of physical exercise in weight loss. Objective To analyze the effectiveness of the combination of M-Health and CRP compared to CRP alone on functional capacity, adherence to CRP, management of cardiovascular risk factors in cardiac patients. Design Systematic Review and Meta-Analysis. Methods The following databases were used Medline via Ovid, EMBASE, Central, PEDro and SPORTDiscus via EBSCOhost from their inception until July/2020. We included randomized controlled trials investigating the effects of M-Health in combination with CRP compared to CRP alone in adults with heart disease, and the interventions with M-Health consisted of text messages, e-mails, and applications. The primary outcome of this review was functional capacity, measured by VO2peak, or self-reported physical activity (METs.min/week). PEDro scale was used to assess the methodological quality of the studies and the GRADE approach to assess the overall quality of evidence. Pooled estimates were calculated using a random effect model to obtain mean difference (MD) or standardized mean differences (SMD) and their respectives 95% confidence intervals (CI). Results Nineteen RCTs were eligible, the median risk of bias was 7 out of 10 points. The primary endpoint was analyzed by subgroups, time of intervention and kind of type CRP, eigthteen of these studies assessed functional capacity. CRP in combination with a m-health intervention was more effective than CRP alone in improving VO2peak, ml/min/kg, (MD: 0.84, CI: 0.30 to 1.38; I2=0%, high quality evidence, 12 trials, n=1889) at short-term follow-up, but at medium-term follow-up (MD: 0.84, CI: −0.26 to 1.41; I2=0%, high quality evidence, 8 trials, n=927,). Similarly, CRP associated with m-health was superior to CRP alone in increasing self-reported at short-term, METs.min/week, (MD:1.31, CI: −0.24 to 2.37; I2 = not aplicable, very low quality evidence, 1 trial, n=18), and at medium-term follow-up (MD: 0.18, CI: −0.01 to 0.36; I2=56%, moderate quality evidence, 4 trials, n=1107). Conclusion High quality of evidence shows that M-Health improves cardiorespiratory fitness at short-term follow-up. In addition, supervised program showed to be more effective than non-supervised. Funding Acknowledgement Type of funding sources: None.

Discovering Interaction Motifs from Protein Interaction Networks

2009 ◽  
pp. 99-116 ◽  
Author(s):  
Hugo Willy
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Patterns ◽  
Protein Protein Interaction ◽  
Family Based ◽  
High Throughput Experiments

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

Assessment of prediction accuracy of protein function from protein-protein interaction data

Yeast ◽  
2001 ◽  
Vol 18 (6) ◽  
pp. 523-531 ◽  
Author(s):  
Haretsugu Hishigaki ◽  
Kenta Nakai ◽  
Toshihide Ono ◽  
Akira Tanigami ◽  
Toshihisa Takagi
Keyword(s):  
Protein Interaction ◽  
Protein Function ◽  
Prediction Accuracy ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interaction
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