An ovarian mucinous cystadenoma with adnexal tuberculosis: a case report

We report the occurrence of a case of a benign ovarian tumour- mucinous cystadenoma ovary with adnexal tuberculosis. Our case was a middle-aged Indian woman who presented with abdominal distension and discomfort at the gynaecology clinic of M.Y. hospital, Indore. The data were collected by history-taking, clinical examination, laboratory investigations, transabdominal ultrasonographic examination, and by histopathological study of the excised surgical specimen. It was reported as ovarian mucinous cystadenoma with adnexal TB. This case report emphasizes the significance of thorough evaluation of all women presenting with vague abdominal pains and thorough search of any other pathology in the specimen, like in our case it was tuberculosis. With the increasing awareness of such conditions, more and more cases could be detected and reported.

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Not AvailableClassification and prediction in agricultural systems are quite useful for effective planning. In this paper, logistic regression modeling has been employed for classification purposes on data pertaining to the area of agricultural ergonomics. Presence or absence of discomfort for the farm labourers in operating farm machineries has been considered as the dependent variable and associated quantitative and qualitative variables as regressors. From the different possible subsets of regressors, appropriate logistic regression models that best describe the dependent variable have been selected. Appropriate goodness of fit and predictive ability measures have been utilized for evaluating the performance of the fitted models. A single best regressor i.e., load given to the farm machinery during operation has been identified by employing variable selection based on collinearity diagnostics and stepwise logistic regression. Results of classifications of the test datasets revealed that logistic regression performed better than the conventionally used discriminant function analysis approach. The study revealed that logistic regression modeling can be employed as a viable alternative for classification purposes in the field of agricultural ergonomic

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Impact of pharmacometrics on drug approval and labeling decisions: A survey of 42 new drug applications

The value of quantitative thinking in drug development and regulatory review is increasingly being appreciated. Modeling and simulation of data pertaining to pharmacokinetic, pharmacodynamic, and disease progression is often referred to as the pharmacometrics analyses. The objective of the current report is to assess the role of pharmacometrics at the US Food and Drug Administration (FDA) in making drug approval and labeling decisions. The New Drug Applications (NDAs) submitted between 2000 and 2004 to the Cardio-renal, Oncology, and Neuropharmacology drug products divisions were surveyed. For those NDA reviews that included a pharmacometrics consultation, the clinical pharmacology scientists ranked the impact on the regulatory decision(s). Of about a total of 244 NDAs, 42 included a pharmacometrics component. Review of NDAs involved independent, quantitative evaluation by FDA pharmacometricians, even when such analysis was not conducted by the sponsor. Pharmacometric analyses were pivotal in regulatory decision making in more than half of the 42 NDAs. Of the 14 reviews that were pivotal to approval related decisions, 5 identified the need for additional trials, whereas 6 reduced the burden of conducting additional trials. Collaboration among the FDA clinical pharmacology, medical, and statistical reviewers and effective communication with the sponsors was critical for the impact to occur. The survey and the case studies emphasize the need for early interaction between the FDA and sponsors to plan the development more efficiently by appreciating the regulatory expectations better