The study was undertaken to determine the correlation of serum LDH in pre eclampsia and eclampsia and perinatal outcome. This is a hospital based comparative case control study done in Department of Obstetrics & Gynaecology SMS Medical College, Jaipur between March 2014 to Oct 2015 on 140 subjects including normotensive , mild pre eclamptic , severe pre eclamptic and eclamptic pregnant women after 28 weeks of gestation before termination of pregnancy. Serum LDH levels were recorded and perinatal outcomes observed. The mean value of serum LDH in control group was 391.4 ± 10.9 IU/L, in mild pre eclampsia 531.5 ± 24.5 IU/L, in severe preeclampsia 922.1± 515.5 IU/L and in eclampsia 1497.6 ± 602.1 IU/L. The difference in serum LDH level was highly significant (P<0.001).Higher LDH levels were associated with High Blood Pressure and had significant correlation with poor perinatal outcome. Thus we conclude that High serum LDH levels correlate well with poor maternal and perinatal outcomes in patients of Preeclampsia and Eclampsia.

No abstract

Cancer begins in cells, the building blocks that make up tissues. Tissues make up the organs of the body. The buildup of extra cells often forms a mass of tissue called a growth, polyp or tumor. Tumors can be benign (non cancerous) or malignant (cancerous). Benign tumors are not as harmful as malignant tumors. The transformation of normal cells into cancer cells is called Carcinogenesis.Cancer is one of the major health problems persisting world-wide. Urbanization, industrialization, changes in lifestyles, population growth and ageing all have contributed for epidemiological transition in the country. The absolute number of new cancer cases is increasing rapidly due to growth in size of the population The stages of cancer are considered as different states of a Markov Process. Discrete-time Markov chains have been successfully used to investigate treatment programs and health care protocols for chronic diseases like HIV, AIDS, Hypertension etc. In this study, the process of carcinogenesis was classified into 6 states. The history of every patient is recorded in the form of a data segment starting from initial state.The transitional states and absorbing states are well defined. Since all the patients under study do not reach the last state at a given point of time, the process was studied as a Semi Markov Process. Maximum likelihood estimation of the transitional probabilities, the survival function, the hazard function and the waiting time distribution of patients in different states were studied. This kind of statistical methodology used to study the prognosis of cancer can be applied to real-time data of cancer patients.

No abstract

There are several methods to forecast the values of any time series. The best model is one which has minimum error. If the given time series is stationary, we can use the autoregressive integrated moving average (ARIMA) model. If it is not stationary, it has to be made stationary by differencing the time series. ARIMA model was identified using the Bayesian Information Criteria. The Auto Covariance Function (ACF) and Partial Auto Covariance Function (PACF) were studied along with the stationarity of the time series. The future values were forecasted and the past values were predicted using recursive method.The actual time series data may not be linear. Most of time series have non-stationary behaviour. Thus, we cannot fit these time series adequately using autoregressive M. S. Dalabanjan (B) DBIT,