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Introduction and Aim: Polycystic Ovarian Syndrome (PCOS) manifests with menstrual irregularities, infertility, galactorrhoea and hirsutism. Hormonal irregularities are found to be the pathogenesis behind PCOS. The study was aimed to evaluate whether obesity is associated with serum prolactin and insulin levels in PCOS women. Materials and Methods: This case control study was conducted in a tertiary care hospital in Chennai. Control group included 30 apparently healthy women and cases included 30 PCOS patients, diagnosis based on Rotterdam criteria. The individuals were of 20-40 years of age in both the groups. The serum prolactin and insulin were analysed by ELISA. The obtained data were checked for normality of distribution and subjected to statistical analysis. Receiver Operating Characteristics (ROC) curve was done. Statistical analyses were performed using SPSS version 16.0. P less than 0.05 was considered statistically significant. Results: The median and interquartile range (IQR) of serum insulin were 11.5 (1.8-70) and 5 (1.8-44) mIU/L in cases and controls respectively. The cut-off level of insulin was 7.0 mIU/L. The median and IQR of serum prolactin were 12.5 (4.5-53.0) and 12 (3.5-20) ng/mL in cases and controls respectively. BMI was positively correlated with age of the participants and prolactin. Conclusion: PCOS is prevalent among obese reproductive individuals. Insulin was markedly elevated in PCOS individuals. Insulin resistance alters levels of luteinizing hormone and sex hormone binging globulins. Hyperprolactinemia is associated with anovulation or oligoovulation, galactorrhoea and hirsutism. In obese PCOS women, prolactin and insulin could be diagnostic markers of PCOS.

Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright–Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.

INTRODUCTION : Tuberculosis is a disease of great antiquity. There is evidence of spinal tuberculosis in Neolithic, pre Columbian and early Egyptian remains. It was described in the Vedas and other Hindu texts as Rajyakshma-the king of diseases. Tuberculosis became a major problem during the industrial revolution, when crowded living conditions favoured its spread. Though the disease was known since ancient times, the organism causing tuberculosis was described only a century ago by Robert Koch on 24th March 1882. AIMS AND OBJECTIVES : 1. To analyse the risk factors that had led to retreatment of the study group. 2. To study the profile of drug sensitivity pattern of CAT II patients and correlate it with the socio demographic status of these patients. 3. To assess the influence of risk factors like socio demographic characteristics, DM, HIV status, smoking and previous treatment as a marker for the development of Drug Resistant TB in the study group. MATERIALS AND METHODS : The present study was conducted at the Department of Thoracic Medicine, Tirunelveli Medical College, Tirunelveli for a period of one year from May 2007 to April 2008 to assess the drug susceptibility profile of Category II patients registered under RNTCP. Inclusion criteria: The study population constituted; 1. Smear positive patients, with history of previous anti tuberculous treatment for more than one month comprising cases of Failure, treatment after Default and Relapse started on the CAT-II regimen. 2. Patients who had completed or defaulted Category II treatment, found to be still sputum positive and referred to Tuberculosis Research Centre, Chennai for Drug Susceptibility Testing. 3. The above cases that had been previously treated under RNTCP were alone included. Exclusion criteria: 1. Extra pulmonary cases of Category II and 2. Cases who had been previously treated privately were not included for the study. Socio demographic and clinical characteristics such as smear status, type of case, type of disease, category, treatment details such as drug regularity, number of doses taken by the patients and reasons for default were obtained from patient. Information on patient’s literacy, occupation, and personal habits like smoking, other diseases like diabetes and HIV were also obtained. Early morning sputum specimens were collected in a sterile container from the study group who were smear positive by Ziehl Neelsen method. Surface decontamination was done by immersing the specimen container in Lysol solution before transferring to the laboratory. All the laboratory works were carried out as per standard laboratory procedures and Bio-safety norms in Class II Biosafety cabinet. RESULTS : The collected data were edited for completeness, consistency and accuracy. They were analysed by the parameters like mean, median and percentages. The differences of above parameters were tested by the parametric tests like ‘Z’ and‘t’ and non-parametric test like c2 test, which was applicable wherever. The statistical package used for analysis and interpretation is SPSS (version-13) with the level of significance P=0.05. CONCLUSION : Age, sex, residence, education, income, diabetes were not risk factors for retreatment tuberculosis and multidrug resistant tuberculosis. Smoking was a significant risk factor among relapse, failure and MDR cases. There is a need to devise effective strategies for counselling patients about the impact of smoking on their cure. Irregular and interrupted treatment was a risk factor among defaulters, failures and multidrug resistant cases. Ensuring adherence to a full course of treatment is the key to cure TB patients and prevent the emergence of drug resistance. Drug related problems like nausea, vomiting, giddiness were the leading cause to default from treatment. The innovative strategies in health education are the need of the hour. HIV reactivity was noted among defaulters in the present study and not among MDR cases. As drug sensitivity testing is not routinely performed, NTM cases had been registered for retreatment unnecessarily. Though registered for retreatment, most of the isolates were sensitive to all the first line drugs and hence can be successfully treated with a category II regimen if they are compliant enough. Drug resistance was more among prior treatment failure cases, necessitating the need for timely culture and sensitivity testing for those who remain sputum positive during the course of treatment to curb the spread of multidrug resistant strains.