Analysis of fetal growth restriction in pregnancy in subjects attending in an obstetric clinic of a tertiary care teaching hospital

Background: Intrauterine growth restriction (IUGR) is defined as fetal growth less than the normal growth potential of a specific infant because of genetic or environmental factors. Fetal growth restriction or intrauterine growth restriction is one of the leading causes of perinatal mortality and morbidity in newborns. Fetal growth restriction is a complex multifactorial condition resulting from several fetal and maternal disorders. Objective of present study was to find out incidence of IUGR and assessment and evaluation of different important changes in IUGR.Methods: Women who attended the Obstetric OPD in their 1st trimester of pregnancy and those who were thought would be able to visit the antenatal clinic for their fortnightly check-up regularly were screened for intrauterine foetal growth retardation. Women with irregular and uncertain menstrual history and where the 1st trimester USG foetal crown rump length did not corroborate with the menstrual gestational age were excluded from this study.Results: Incidence of IUGR was 18.2% and 84% were found to be asymmetrical. IUGR was found to be double among primigravids and women above 30 years. It had been observed that IUGR was associated with certain conditions like short stature (52%), pregnancy induced hypertension (24%) and anaemia (12%).Conclusions: Thus, early USG screening along with robust screening for maternal BMI, nutritional status, and anaemia can assist the obstetric team in providing early diagnosis, prompt intervention, and better outcome in pregnancy with fetal growth restriction

The effects of combined therapy of myo-inositol and D-chiro inositol in reduction of the individual components of metabolic syndrome in overweight PCOS patients compared to myo-inositol supplementation alone: a prospective randomised controlled trial

Background: Polycystic ovarian syndrome (PCOS) is one of the most common endocrine disorder affecting five to ten percent women of reproductive age group. Variability of signs and symptoms along with metabolic syndrome as one of the long term complications make it worthy of early diagnosis and treatment. Medical management of PCOS is aimed at the treatment of metabolic derangements, anovulation, hirsutism, and menstrual irregularities.Methods: 140 patients, using inclusion and exclusion criteria, were selected and randomly divided into two groups (seventy in each) and age, BMI, waist hip ratio, blood pressure (systolic, diastolic), serum fasting insulin, fasting blood sugar, total cholesterol, HDL, LDL, triglycerides were measured. Study group were given {Myo-inositol (550 mg) + D-chiro-inositol (13.8 mg)} (MI+DCI) twice daily and the control group were given Myo-inositol (1 gm) (MI) twice daily for six months. Same variables were measured at the end of three and six months and compared with repeated measurement ANOVA using SPSS (version 20).Results: Comparison between these two groups before study was non-contributory. Combined drug therapy has provided statistically significant decrease in BMI, W:H ratio, Diastolic BP, Fasting blood sugar at the end of both 3rd and 6th month but in case of LDL it was at the end of 3 months. Combined drug therapy also increased the HDL level significantly in both the occasions.Conclusions: Combined medical therapy by (MI+DCI) is very much helpful in reducing the metabolic complications of PCOS without any major side effects

Multidimensional Liquidity: Evidences from Indian Stock Market

Various dimensions of liquidity including breadth, depth, resiliency, tightness, immediacy are examined using BSE 500 and NIFTY 500 indices from Indian Equity market. Liquidity dynamics of the stock markets were examined using trading volume, trading probability, spread, Market Efficiency coefficient, and turnover rate as they gauge different dimensions of market liquidity. We provide evidences on the order of importance of these liquidity measures in Indian stock market using machine learning tools like Artificial Neural Network (ANN) and Random Forest (RF). Findings reveal that liquidity variables collectively explains the movements of stock markets. Both these machine learning tools performs satisfactorily in terms of mean absolute percentage error. We also evidenced lower level of liquidity in Bombay Stock Exchange (BSE) than National Stock Exchange (NSE) and findings supports the liquidity enhancement program recently initiated by BSE

Analysis of fetal growth restriction in pregnancy in subjects attending in an obstetric clinic of a tertiary care teaching hospital

Background: Intrauterine growth restriction (IUGR) is defined as fetal growth less than the normal growth potential of a specific infant because of genetic or environmental factors. Fetal growth restriction or intrauterine growth restriction is one of the leading causes of perinatal mortality and morbidity in newborns. Fetal growth restriction is a complex multifactorial condition resulting from several fetal and maternal disorders. Objective of present study was to find out incidence of IUGR and assessment and evaluation of different important changes in IUGR.Methods: Women who attended the Obstetric OPD in their 1st trimester of pregnancy and those who were thought would be able to visit the antenatal clinic for their fortnightly check-up regularly were screened for intrauterine foetal growth retardation. Women with irregular and uncertain menstrual history and where the 1st trimester USG foetal crown rump length did not corroborate with the menstrual gestational age were excluded from this study.Results: Incidence of IUGR was 18.2% and 84% were found to be asymmetrical. IUGR was found to be double among primigravids and women above 30 years. It had been observed that IUGR was associated with certain conditions like short stature (52%), pregnancy induced hypertension (24%) and anaemia (12%).Conclusions: Thus, early USG screening along with robust screening for maternal BMI, nutritional status, and anaemia can assist the obstetric team in providing early diagnosis, prompt intervention, and better outcome in pregnancy with fetal growth restriction

Single Nucleotide Polymorphism Network: A Combinatorial Paradigm for Risk Prediction

<div><p>Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes <i>P2RY1</i> and <i>P2RY12</i>. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.</p> </div

The strategy of our work is described by the illustration.

<p>The chart shows the strategy employed in the present analysis. The way of doing the whole analyses is described sequentially through the chart. The methods involved in the network based analysis and further the consistency of the outcome of Network based approach and the conventional statistical methods are also described.</p

A network through which we represent the segregated pattern of combined genotypic data of case (acute coronary syndrome) and control population (healthy).

<p>The 5 SNPs of each individual are combined to form super-set genotypes in both case and control. Thirty five unique genotype combinations are observed of which 14 combinations are specific to cases (marked as red), 7 combinations are specific to controls (marked as green) and 14 combinations are present in both case and control (marked as brown). The number of occurrences of each particular genotype combinations is illustrated through its corresponding edge-width.</p

Case and control specific genotypic fractions after single locus omission.

<p>One SNP at a time is removed from all the genotypic set in the population to predict the probable risk genotype. The removed locus is denoted by * in the genotype supersets formed taking 4 loci at a time. The effect is studied in terms of the redistribution of number of unique genotypes (nodes) remaining after each SNP deletion in Case, Control and Common populations. The total number of genotype differs with different SNP combinations since once a particular SNP is removed; two genotypes may lose their variation and get collapsed to a single genotype.</p

Network representation of segregation of combined genotypic data of three population (oral cancer, leukoplakia and control).

<div><p>Five SNPs (studied with leukoplakia, cancer and control samples) of each individual are combined to form super-set genotypes. One hundred fourty three unique genotype-sets are observed of which 18 are specific to each control, leukoplakia and cancer individuals, 12 genotype-sets are present in both control and leukoplakia individuals, 18 genotype-sets are present in both control and cancer individuals, only 6 genotype-sets are common between leukoplakia and cancer individuals and as many as 53 genotype-sets are common to case, control and leukoplakia. The number of occurrences of each particular genotype-set is illustrated through its corresponding edge-width.</p> <p>The circles in i) red ii) violet iii) yellow iv) prussian blue v) grey vi) orange and vii) blue respectively represents the following groups i) cancer only ii) cancer and control iii) leukoplakia only iv) control only v) control and leukoplakia vi) cancer and leukoplakia and vii) cancer, leukoplakia and control.</p></div