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Journal of Assisted Reproduction and... Aug 2021To study the use of in silica model to better understand and propose new markers of ovarian response to controlled ovarian stimulation before IVF.
PURPOSE
To study the use of in silica model to better understand and propose new markers of ovarian response to controlled ovarian stimulation before IVF.
METHODS
A systematic review and in silica model using bioinformatics. After the selection of 103 papers from a systematic review process, we performed a GRADE qualification of all included papers for evidence-based quality evaluation. We included 57 genes in the silica model using a functional protein network interaction. Moreover, the construction of protein-protein interaction network was done importing these results to Cytoscape. Therefore, a cluster analysis using MCODE was done, which was exported to a plugin BINGO to determine Gene Ontology. A p value of < 0.05 was considered significant, using a Bonferroni correction test.
RESULTS
In silica model was robust, presenting an ovulation-related gene network with 87 nodes (genes) and 348 edges (interactions between the genes). Related to the network centralities, the network has a betweenness mean value = 102.54; closeness mean = 0.007; and degree mean = 8.0. Moreover, the gene with a higher betweenness was PTPN1. Genes with the higher closeness were SRD5A1 and HSD17B3, and the gene with the lowest closeness was GDF9. Finally, the gene with a higher degree value was UBB; this gene participates in the regulation of TP53 activity pathway.
CONCLUSIONS
This systematic review demonstrated that we cannot use any genetic marker before controlled ovarian stimulation for IVF. Moreover, in silica model is a useful tool for understanding and finding new markers for an IVF individualization.
PROSPERO
CRD42020197185.
Topics: Computational Biology; Computer Simulation; Female; Fertilization in Vitro; Gene Regulatory Networks; Humans; Ovary; Ovulation Induction; Prognosis; Protein Interaction Maps
PubMed: 33788133
DOI: 10.1007/s10815-021-02141-0 -
The Cochrane Database of Systematic... Oct 2019Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. Early intervention for those with high cardiovascular risk is crucial in improving... (Review)
Review
BACKGROUND
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. Early intervention for those with high cardiovascular risk is crucial in improving patient outcomes. Traditional prevention strategies for CVD have focused on conventional risk factors, such as overweight, dyslipidaemia, diabetes, and hypertension, which may reflect the potential for cardiovascular insult. Natriuretic peptides (NPs), including B-type natriuretic peptide (BNP) and N-terminal pro B-type natriuretic peptide (NT-proBNP), are well-established biomarkers for the detection and diagnostic evaluation of heart failure. They are of interest for CVD prevention because they are secreted by the heart as a protective response to cardiovascular stress, strain, and damage. Therefore, measuring NP levels in patients without heart failure may be valuable for risk stratification, to identify those at highest risk of CVD who would benefit most from intensive risk reduction measures.
OBJECTIVES
To assess the effects of natriuretic peptide (NP)-guided treatment for people with cardiovascular risk factors and without heart failure.
SEARCH METHODS
Searches of the following bibliographic databases were conducted up to 9 July 2019: CENTRAL, MEDLINE, Embase, and Web of Science. Three clinical trial registries were also searched in July 2019.
SELECTION CRITERIA
We included randomised controlled trials enrolling adults with one or more cardiovascular risk factors and without heart failure, which compared NP-based screening and subsequent NP-guided treatment versus standard care in all settings (i.e. community, hospital).
DATA COLLECTION AND ANALYSIS
Two review authors independently screened titles and abstracts and selected studies for inclusion, extracted data, and evaluated risk of bias. Risk ratios (RRs) were calculated for dichotomous data, and mean differences (MDs) with 95% confidence intervals (CIs) were calculated for continuous data. We contacted trial authors to obtain missing data and to verify crucial study characteristics. Using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, two review authors independently assessed the quality of the evidence and GRADE profiler (GRADEPRO) was used to import data from Review Manager to create a 'Summary of findings' table.
MAIN RESULTS
We included two randomised controlled trials (three reports) with 1674 participants, with mean age between 64.1 and 67.8 years. Follow-up ranged from 2 years to mean 4.3 years.For primary outcome measures, effect estimates from a single study showed uncertainty for the effect of NP-guided treatment on cardiovascular mortality in patients with cardiovascular risk factors and without heart failure (RR 0.33, 95% CI 0.04 to 3.17; 1 study; 300 participants; low-quality evidence). Pooled analysis demonstrated that in comparison to standard care, NP-guided treatment probably reduces the risk of cardiovascular hospitalisation (RR 0.52, 95% CI 0.40 to 0.68; 2 studies; 1674 participants; moderate-quality evidence). This corresponds to a risk of 163 per 1000 in the control group and 85 (95% CI 65 to 111) per 1000 in the NP-guided treatment group.When secondary outcome measures were evaluated, evidence from a pooled analysis showed uncertainty for the effect of NP-guided treatment on all-cause mortality (RR 0.90, 95% CI 0.60 to 1.35; 2 studies; 1354 participants; low-quality evidence). Pooled analysis indicates that NP-guided treatment probably reduces the risk of all-cause hospitalisation (RR 0.83, 95% CI 0.75 to 0.92; 2 studies; 1354 participants; moderate-quality evidence). This corresponds to a risk of 601 per 1000 in the control group and 499 (95% CI 457 to 553) per 1000 in the NP-guided treatment group. The effect estimate from a single study indicates that NP-guided treatment reduced the risk of ventricular dysfunction (RR 0.61, 95% CI 0.41 to 0.91; 1374 participants; high-quality evidence). The risk in this study's control group was 87 per 1000, compared with 53 (95% CI 36 to 79) per 1000 with NP-guided treatment. Results from the same study show that NP-guided treatment does not affect change in NP level at the end of follow-up, relative to standard care (MD -4.06 pg/mL, 95% CI -15.07 to 6.95; 1 study; 1374 participants; moderate-quality evidence).
AUTHORS' CONCLUSIONS
This review shows that NP-guided treatment is likely to reduce ventricular dysfunction and cardiovascular and all-cause hospitalisation for patients who have cardiovascular risk factors and who do not have heart failure. Effects on mortality and natriuretic peptide levels are less certain. Neither of the included studies were powered to evaluate mortality. Available evidence shows uncertainty regarding the effects of NP-guided treatment on both cardiovascular mortality and all-cause mortality; very low event numbers resulted in a high degree of imprecision in these effect estimates. Evidence also shows that NP-guided treatment may not affect NP level at the end of follow-up.As both trials included in our review were pragmatic studies, non-blinding of patients and practices may have biased results towards a finding of equivalence. Further studies with more adequately powered sample sizes and longer duration of follow-up are required to evaluate the effect of NP-guided treatment on mortality. As two trials are ongoing, one of which is a large multi-centre trial, it is hoped that future iterations of this review will benefit from larger sample sizes across a wider geographical area.
PubMed: 31613983
DOI: 10.1002/14651858.CD013015.pub2