Correlation of intra partum electronic fetal monitoring with neonatal outcome

Background: The importance of fetal monitoring during labour has been realized since long. The stress of uterine contractions may affect the fetus adversely especially if the fetus is already compromised, when the placental reserves are suboptimal, or when cord undergoes compression as in those associated with diminished liquor amnii or iatrogenic uterine hyperstimulation due to injudicious use of oxytocin. Even a fetus which is apparently normal in the antenatal period may develop distress during labour. Hence fetal monitoring during antepartum and intrapartum periods is of vital importance for timely detection of fetal distress so that appropriate management may be offered.Methods: This study was a prospective observational study included 100 patients of more than 34 weeks period of gestation were divided into two groups. Patients in labour were analyzed on an Electronic Monitor. Delivery conducted was either by vaginal route, instrumental or by caesarean section depending upon the fetal heart rate tracings and their interpretations as per the case. At the time of delivery umbilical cord blood was taken for the pH analysis. All new born babies were seen by the paediatrician immediately after the delivery and 1 and 5 minute APGAR score assessed for the delivered baby. The various EFM Patterns obtained were compared with the neonatal status at birth using the parameters already mentioned. The false positives and false negatives if any were tabulated. Data so obtained was analyzed statistically thereafter. Statistical Package for Social Sciences (SPSS) Version 13.0 was used for the purpose of analysis.Results: Results revealed that among the 50 subjects of the case group, 7 subjects showed the absence of the beat to beat variability, 12 subjects showed early deceleration, 32 subjects showed late deceleration, and 6 subjects showed the presence of variable deceleration. No significant association of beat to beat variability, early and variable deceleration could be established with meconium staining/NICU admissions/low APGAR. A significant positive association between persistent late deceleration with MSL, APGAR <7 at 1 min, and Instrumental/LSCS delivery was seen. A significant positive association between any CTG abnormality and APGAR at 1 min, type of delivery, and meconium staining was seen.Conclusions: EFM should be used judiciously. Cardiotocography machines are certainly required in the labour room. Equally important is the proper interpretation of the CTG tracings so that unjustified caesarean sections can be minimized, at the same time picking up cases of fetal distress in time which is likely to improve fetal outcome

Foetal umbilical artery doppler versus NST as predictors of adverse perinatal outcome in severe preeclampsia and foetal growth restriction

Background: With the advent of electronic foetal monitoring, a relationship between foetal movement and foetal heart rate was observed and that relationship formed the basis for non-stress test (NST). Doppler USG plays an important role in foetal growth restriction (FGR) pregnancies where hemodynamic rearrangements occur in response to foetal hypoxemia. It is now proved that significant Doppler changes occur with reduction in foetal growth at a time when other foetal well-being tests are still normal. This study was done to find out the comparative usefulness of Doppler and NST in the management of FGR and severe preeclampsia and subsequent correlation with perinatal outcome.Methods: This prospective study was conducted on pregnant women with severe preeclamsia and/or FGR beyond 30 weeks of gestation at AHRR Delhi. 50 pregnancies complicated with severe preeclampsia and/or FGR beyond 30 weeks of gestation were selected. Patients meeting the inclusion criteria were subjected to NST. Umbilical arterial Doppler flow was obtained at weekly or twice weekly interval depending on the severity by pulsed wave color doppler indices were measured during foetal apnea by the same examiner at the free loop site where the clearest waveform signal could be visualized. Of 3 measurements, the mean average of S/D ratio was recorded and followed up with serial Doppler assessment and non-stress test. Data was collected and statistical analysis was carried out.Results: The Doppler showed changes earlier than NST giving a significant lead time of up to 20 days with an average of 4.94 days. The UA S/D had the highest sensitivity (88%) and diagnostic accuracy (94%) in predicting the adverse perinatal outcome. The sensitivity and specificity of Doppler as compared to NST was 82.6% and 63.0% respectively with a diagnostic accuracy of 72%. The Doppler has negative predictive value of 80.95% and positive predictive value of 65.5%. Color Doppler has diagnostic accuracy of 72%. The mortality rate in reversal of diastolic flow was 77.77% and in absent UA flow was 16.66%. 12% foetuses were found to have AEDV in UA and among them 66.66% had both FGR+PE as maternal complication. There was 83.33% rate of LSCS, 16.66% neonatal mortality rate, 83.33% NICU stay rate and 66.66% complication rate in neonates. Whereas 18% had REDV and among that 88.88% had both FGR+PE as maternal complication, a similar rate of LSCS, 77.77% rate of neonatal mortality, 100 % NICU stay and 66.66% complication rate in the neonates.Conclusions: Combined foetal testing modalities such as Doppler, NST and biophysical profile provide a wealth of information regarding foetal health. Integrated foetal testing would be ideal for individualized care of the preterm compromised foetuses for timed intervention

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p

On the effects of large-scale transcriptomics datasets on gene functional analyses

The Guilt-by-Association (GBA) principle, according to which genes with similar expression profiles are functionally associated, is widely applied for functional analyses using large heterogeneous collections of transcriptomics data. In this thesis we show that using such large collections could hamper GBA functional analysis for genes whose expression is condition specific. In these cases a smaller set of condition related experiments should instead be used, but identifying such functionally relevant experiments from large collections based on literature knowledge alone is an impractical task. The study begins by discussing the basic principles underlying the definition of gene function and the use of large microarray collections for GBA based gene function analyses. We look at the effects of condition specific gene expression on GBA analyses and provide a mathematical and biological perspective. We show that using large microarray collections to calculate correlation can mask the effectiveness of the GBA principle. We suggest that using only those experiments that are relevant to the biological function under analysis can significantly improve GBA based gene functional analyses. We then present a semi-supervised algorithm that can select functionally relevant experiments from large collections of transcriptomics experiments. The algorithm is able to select experiments relevant to a given GO term, MIPS FunCat term or even KEGG pathways. We extensively test our algorithm on large dataset collections for Yeast and Arabidopsis. We demonstrate that: (i) using the selected experiments there is a statistically significant improvement both in correlation between genes in the functional category of interest and in GBA based function predictions; (ii) the effectiveness of the selected experiments increases with annotation specificity; (iii) our algorithm can be successfully applied to GBA based pathway reconstruction. We conclude by discussing the potential applications of our technique. We outline several developments that could be implemented in the future to improve the efficiency of the experiment selection procedure

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In bound volumes: Copyright Deposits 1820-186