Study of risk factors and perinatal outcome in meconium stained deliveries from a district of Uttar Pradesh, India

Background: The objective is to identify the risk factors of Meconium stained deliveries and evaluate the perinatal outcomes in Meconium Stained deliveries.Methods: This prospective observational study included those pregnant women who had completed 37 weeks of gestation, with singleton pregnancies with cephalic presentations and with no known fetal congenital anomalies. Among these, we selected 110 cases with Meconium stained amniotic fluid and they were compared with 110 randomly selected controls.Results: Regular antenatal visits were seen in 22.73 % of the cases while 77.27% cases had no previous visit. Majority of cases were primigravida and gestational ages of >40 weeks was seen in 55.45 % cases. 19.09% cases had meconium staining among pregnancies complicated with pregnancy induced hypertension, as compared to those among controls (5.45%). Fetal heart rate abnormalities were seen in 29.09% cases, and statistically significant fetal bradycardia was seen in cases. Caesarean section rates were nearly double in cases (54.55%). Poor perinatal outcome was found in cases as seen in results by low Apgar score (40 weeks, pregnancy induced hypertension and fetal bradycardia, increased cesarean section rates, low APGAR score and higher incidence of birth asphyxia and NICU admissions. Meconium aspiration syndrome was associated with early neonatal death

Meta-analysis of host response networks identifies a common core in tuberculosis

Tuberculosis remains a major global health challenge worldwide, causing more than a million deaths annually. To determine newer methods for detecting and combating the disease, it is necessary to characterise global host responses to infection. Several high throughput omics studies have provided a rich resource including a list of several genes differentially regulated in tuberculosis. An integrated analysis of these studies is necessary to identify a unified response to the infection. Such data integration is met with several challenges owing to platform dependency, patient heterogeneity, and variability in the extent of infection, resulting in little overlap among different datasets. Network-based approaches offer newer alternatives to integrate and compare diverse data. In this study, we describe a meta-analysis of host’s whole blood transcriptomic profiles that were integrated into a genome-scale protein–protein interaction network to generate response networks in active tuberculosis, and monitor their behaviour over treatment. We report the emergence of a highly active common core in disease, showing partial reversals upon treatment. The core comprises 380 genes in which STAT1, phospholipid scramblase 1 (PLSCR1), C1QB, OAS1, GBP2 and PSMB9 are prominent hubs. This network captures the interplay between several biological processes including pro-inflammatory responses, apoptosis, complement signalling, cytoskeletal rearrangement, and enhanced cytokine and chemokine signalling. The common core is specific to tuberculosis, and was validated on an independent dataset from an Indian cohort. A network-based approach thus enables the identification of common regulators that characterise the molecular response to infection, providing a platform-independent foundation to leverage maximum insights from available clinical data

Estimating population coefficient of variation using a single auxiliary variable in simple random sampling

This paper proposes an improved estimation method for the population coefficient of variation, which uses information on a single auxiliary variable. The authors derived the expressions for the mean squared error of the proposed estimators up to the first order of approximation. It was demonstrated that the estimators proposed by the authors are more efficient than the existing ones. The results of the study were validated by both empirical and simulation studies

EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

Background: In biological systems, diseases are caused by small perturbations in a complex network of interactions between proteins. Perturbations typically affect only a small number of proteins, which go on to disturb a larger part of the network. To counteract this, a stress-response is launched, resulting in a complex pattern of variations in the cell. Identifying the key players involved in either spreading the perturbation or responding to it can give us important insights. Results: We develop an algorithm, EpiTracer, which identifies the key proteins, or epicenters, from which a large number of changes in the protein-protein interaction (PPI) network ripple out. We propose a new centrality measure, ripple centrality, which measures how effectively a change at a particular node can ripple across the network by identifying highest activity paths specific to the condition of interest, obtained by mapping gene expression profiles to the PPI network. We demonstrate the algorithm using an overexpression study and a knockdown study. In the overexpression study, the gene that was overexpressed (PARK2) was highlighted as the most important epicenter specific to the perturbation. The other top-ranked epicenters were involved in either supporting the activity of PARK2, or counteracting it. Also, 5 of the identified epicenters showed no significant differential expression, showing that our method can find information which simple differential expression analysis cannot. In the second dataset (SP1 knockdown), alternative regulators of SP1 targets were highlighted as epicenters. Also, the gene that was knocked down (SP1) was picked up as an epicenter specific to the control condition. Sensitivity analysis showed that the genes identified as epicenters remain largely unaffected by small changes. Conclusions: We develop an algorithm, EpiTracer, to find epicenters in condition-specific biological networks, given the PPI network and gene expression levels. EpiTracer includes programs which can extract the immediate influence zone of epicenters and provide a summary of dysregulated genes, facilitating quick biological analysis. We demonstrate its efficacy on two datasets with differing characteristics, highlighting its general applicability. We also show that EpiTracer is not sensitive to minor changes in the network. The source code for EpiTracer is provided at Github (https://github.com/narmada26/EpiTracer)

EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

Diseases in biological systems may result from small perturbations in a complex network of protein-protein interactions (PPIs). The perturbations typically affect a small set of proteins, which then go on to disturb a larger part of the network. Biological systems attempt to counteract these perturbations by launching a stress-response, resulting in a complex pattern of variations in the cell. We present an algorithm, EpiTracer which identifies the key proteins, termed epicenters, from which a large number of the changes in PPI networks ripple out. We propose a new centrality measure, ripple centrality, that measures how effectively a change at a particular protein can ripple across the network, by identifying condition specific highest activity paths obtained by mapping gene expression profiles to the PPI network. We perform a case study on a dataset (E-GEOD-61973) where the gene PARK2 was intentionally overexpressed in human glioma (U251) cell line and analyze the top 10 ranked epicenters. We find that EpiTracer identifies PARK2 as the most important epicenter in the perturbed condition. Analysis of the other top-ranked epicenters showed that all of them were involved in either supporting the activity of PARK2 or counteracting it, indicating that the cell had activated a stress-response. We also find that 5 of the identified epicenters did not have significant differential expression, proving that our method is capable of finding information that simple differential expression analysis cannot

EpiTracer - an algorithm for identifying epicenters in condition-specific biological networks

Diseases in biological systems may result from small perturbations in a complex network of protein-protein interactions (PPIs). The perturbations typically affect a small set of proteins, which then go on to disturb a larger part of the network. Biological systems attempt to counteract these perturbations by launching a stress-response, resulting in a complex pattern of variations in the cell. We present an algorithm, EpiTracer which identifies the key proteins, termed epicenters, from which a large number of the changes in PPI networks ripple out. We propose a new centrality measure, ripple centrality, that measures how effectively a change at a particular protein can ripple across the network, by identifying condition specific highest activity paths obtained by mapping gene expression profiles to the PPI network. We perform a case study on a dataset (E-GEOD-61973) where the gene PARK2 was intentionally overexpressed in human glioma (U251) cell line and analyze the top 10 ranked epicenters. We find that EpiTracer identifies PARK2 as the most important epicenter in the perturbed condition. Analysis of the other top-ranked epicenters showed that all of them were involved in either supporting the activity of PARK2 or counteracting it, indicating that the cell had activated a stress-response. We also find that 5 of the identified epicenters did not have significant differential expression, proving that our method is capable of finding information that simple differential expression analysis cannot. The source code is available at Github (https://github.com/narmada26/EpiTracer)

A comparative study to evaluate the role of Letrozole and low dose human menopausal gonadotrophin on ovulation induction in polycystic ovarian syndrome patients

Background: Polycystic ovarian syndrome (PCOS) is a multifaceted syndrome that affects multipleorgan systems with significant metabolic and reproductive manifestations. Treatment must be individualized on the basis of patient’s presentation, and desire for the pregnancy. Objective: To study the role of letrozole (LE) and low dose human menopausal gonadotrophen (HMG) on ovulation induction in the polycystic ovarian syndrome patients. Material & Methods: The present hospital based double blind clinical trial, recruited total 105 patients’ of age 21 to 35 years women with PCOS among those attending the gynecology outpatient’s clinic in Hind Institute of Medical Sciences Safedabad Barabanki in the period from Sept 2018 to Sept 2021 were enrolled. Observation: Endometrial thickness, and number of the mature follicles of the LE+HMG group were higher significantly than other two groups (P<0.001). Effects of different regimens on pregnancy. Pregnancy rate of LE+HMG group was 54.3%, which was insignificantly higher than LE group (31.4%) and HMG group (34.3%) (P>0.05). There were also no statistically significant differences in rate of abortion, and rates of multiple pregnancy among the three groups. All the three groups suffered from OHSS, but the incidences rates were comparable.&nbsp

Comparative analysis of conventional X-ray chest Vs. NCCT chest in patients of blunt trauma chest: An observational study

Background: Chest X-Ray (CXR) is routinely used as the primary diagnostic technique in chest trauma but some possibly life-threatening injuries are repeatedly missed on CXR. Non contrast computed tomography (NCCT) scan is a superior diagnostic device in blunt trauma chest. Aim: To compare efficacy of the x-ray versus NCCT chest in diagnosis of blunt trauma chest. Methodology: The present cross sectional study was performed in the admitted patients in causality in Surgery Department of Hind Medical College, Safedabad, Barabanki (U.P). The patients who were treated in level 1 trauma centre for blunt chest trauma and received both Chest X-ray and CT chest scan during study period 2020 - 2021. Identification of the patients was done from the hospital’s registry. Results: while 39 (24.38%) patients were undetected on chest X-ray chest. However, the fractures could not be detected in only 2 (1.25%) patients on NCCT scan chest. Statistically significant variance was found in circumstances of sternum fracture, rib fracture, scapula fracture, lung contusion and pneumothorax. The sensitivity of CXR for sternum fracture, rib fracture, lung contusion and pneumothorax were 100.00% and other injuries like clavicle fracture, scapula fracture, diaphragmatic rupture, and hemothorax were 88.89%, 87.50%, 66.67% and 90.91%%, respectively

Comparative analysis of conventional X-ray chest Vs. NCCT chest in patients of blunt trauma chest: An observational study

Background: Chest X-Ray (CXR) is routinely used as the primary diagnostic technique in chest trauma but some possibly life-threatening injuries are repeatedly missed on CXR. Non contrast computed tomography (NCCT) scan is a superior diagnostic device in blunt trauma chest. Aim: To compare efficacy of the x-ray versus NCCT chest in diagnosis of blunt trauma chest. Methodology: The present cross sectional study was performed in the admitted patients in causality in Surgery Department of Hind Medical College, Safedabad, Barabanki (U.P). The patients who were treated in level 1 trauma centre for blunt chest trauma and received both Chest X-ray and CT chest scan during study period 2020 - 2021. Identification of the patients was done from the hospital’s registry. Results: while 39 (24.38%) patients were undetected on chest X-ray chest. However, the fractures could not be detected in only 2 (1.25%) patients on NCCT scan chest. Statistically significant variance was found in circumstances of sternum fracture, rib fracture, scapula fracture, lung contusion and pneumothorax. The sensitivity of CXR for sternum fracture, rib fracture, lung contusion and pneumothorax were 100.00% and other injuries like clavicle fracture, scapula fracture, diaphragmatic rupture, and hemothorax were 88.89%, 87.50%, 66.67% and 90.91%%, respectively