Bilateral ovarian serous cystadenocarcinoma metastasizing to cervix: a rare case report

Ovarian carcinoma is the second most common gynaecologic cancer and the leading cause of death from gynaecologic malignancy. Two-third of all malignant epithelial ovarian tumors are constituted by serous ovarian cystadenocarcinomas. It is generally observed that ovarian cancer tends to remain intraabdominal even in advanced cases and that dissemination is usually by invasion of adjacent viscera, diffuse intraperitoneal implantation, and metastatic involvement of aortic and pelvic lymph nodes. Metastasizes to the uterine cervix, vagina, or vulva in ovarian cancer is rare. The reverse i.e. ovarian metastasis from cervical tumor is rather more common. Published literature suggest that, patients with cervical metastases had associated malignant ascites, retroperitoneal lymph node involvement, and significant peritoneal carcinomatosis. Cervical metastasis in ovarian malignancies always indicates the advanced stage of tumor and multi-organ involvement, indirectly stating poor prognosis. The median survival in cases of ovarian cancer metastasizing to cervix is 4.4 months. Authors report a case of bilateral ovarian serous cystadenocarcinoma metastasising to posterior lip of cervix resulted in poor prognosis and proved fatal for the patient with review of published literature

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

Background: Refactoring is changing a software system without affecting the software functionality. The current researchers aim i to identify the appropriate method(s) or class(s) that needs to be refactored in object-oriented software. Ensemble learning helps to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level. Objective: This work aims to develop an ensemble-based refactoring prediction model with structural identification of source code metrics using different feature selection techniques and data sampling techniques to distribute the data uniformly. Our model finds the best classifier after achieving fewer errors during refactoring prediction at the class level. Methodology: At first, our proposed model extracts a total of 125 software metrics computed from object-oriented software systems processed for a robust multi-phased feature selection method encompassing Wilcoxon significant text, Pearson correlation test, and principal component analysis (PCA). The proposed multi-phased feature selection method retains the optimal features characterizing inheritance, size, coupling, cohesion, and complexity. After obtaining the optimal set of software metrics, a novel heterogeneous ensemble classifier is developed using techniques such as ANN-Gradient Descent, ANN-Levenberg Marquardt, ANN-GDX, ANN-Radial Basis Function; support vector machine with different kernel functions such as LSSVM-Linear, LSSVM-Polynomial, LSSVM-RBF, Decision Tree algorithm, Logistic Regression algorithm and extreme learning machine (ELM) model are used as the base classifier. In our paper, we have calculated four different errors i.e., Mean Absolute Error (MAE), Mean magnitude of Relative Error (MORE), Root Mean Square Error (RMSE), and Standard Error of Mean (SEM). Result: In our proposed model, the maximum voting ensemble (MVE) achieves better accuracy, recall, precision, and F-measure values (99.76, 99.93, 98.96, 98.44) as compared to the base trained ensemble (BTE) and it experiences less errors (MAE = 0.0057, MORE = 0.0701, RMSE = 0.0068, and SEM = 0.0107) during its implementation to develop the refactoring model. Conclusions: Our experimental result recommends that MVE with upsampling can be implemented to improve the performance of the refactoring prediction model at the class level. Furthermore, the performance of our model with different data sampling techniques and feature selection techniques has been shown in the form boxplot diagram of accuracy, F-measure, precision, recall, and area under the curve (AUC) parameters.publishedVersio

GC-MS ANALYSIS OF ESSENTIAL OIL OF SOME HIGH DRUG YIELDING GENOTYPES OF TURMERIC (CURCUMA LONGA L.)

Objective: The aim of this investigation was to carry out the qualitative evaluation of selected high drug yielding elite genotypes of turmeric to add to their eliteness.Methods: 131 turmeric genotypes collected from 10 different agroclimatic zones were analysed for curcumin content. Leaves and rhizomes of these plants were collected for extraction of essential oil. Curcumin percentage of the sample was estimated according to the ASTA method. Essential oil was extracted by hydro-distillation of fresh leaves and rhizomes following the method of Guenther (1972). Initial screening of elite genotypes was done on the basis of curcumin content (≥5%), rhizome oil content (≥1.5%) and leaf oil content (≥0.5%). Selected elite genotypes were subjected to qualitative evaluation of essential oil through GC-MS analysis.Results: The five high rhizome oil yielding genotypes, TR1, TR2, TR3 and TR5 containing high rhizome oil yield of 2.1%, 1.7%, 1.6% and 1.5% respectively were considered to be elite clones containing tumerone as the major constituent of rhizome essential oil along with all desirable constituents. On the basis of leaf oil yield, genotypes TL1 and TL2 with 1.9% and 1.1% leaf oil were proved as elite clones with α–phellandrene as the major constituent along with other desirable constituents. GC-MS analysis of 3 selected high curcumin yielding genotypes TC1, TC2 and TC3 with curcumin content 7.3, 7.2 and 7.0% respectively revealed TC1 and TC2 as elite genotypes containing high quality rhizome and leaf oil.Conclusion: The present investigation reveals that eight genotypes of turmeric selected with high drug yield and high quality essential oil would have enough significance for boosting the production and export of value added products in the national and international market.Â

Rapid multiplication and in vitro production of leaf biomass in Kaempferia galanga through tissue culture

An efficient protocol has been established for rapid multiplication and in vitro production of leaf biomass in Kaempferia galanga L, a rare medicinal plant. Different plant growth regulators like Benzyladenine (BA), Indoleacetic acid (IAA), Indolebutyric acid (IBA), Napthaleneacetic acid (NAA) and adenine sulphates (Ads) have been tried for induction of multiple shoots using lateral bud of rhizome as explants. The highest rate of shoot multiplication (11.5 \ub1 0.6) shoot/explant as well as leaf biomass production (7.4 \ub1 0.3) gram/explant was observed on Murashige and Skoog medium supplemented with Benzyladenine (1 mg/l) and Indoleacetic acid (0.5 mg/l). Data of shoot multiplication and leaf biomass production were statistically analysed. Upon excission of leaves after 2 months of culture under sterile condition, the base of each plantlet was transferred to fresh media which could produce the same leaf biomass within another 2 months in a 50 ml culture tube containing 20 ml and 250 ml conical flasks containing 30 ml Murashige and Skoog medium. The rate of multiplication and leaf biomass production remained unchanged in subsequent subcultures. The regenerated plantlets were acclimatized in greenhouse and subsequently transferred to the field. Survival rate of the plantlets under ex vitro condition was 95 percent. Genetic fidelity of the regenerants was confirmed using random amplified polymorphic DNA (RAPD) marker. The protocol could be commercially utilized for large scale production of true-to-type plantlets and as an alternative method of leaf biomass production in Kaempferia galanga

Quantitative Structure–Property Relationship Studies on Ostwald Solubility and Partition Coefficients of Organic Solutes in Ionic Liquids

Article discussing quantitative structure-property relationship studies on Ostwald solubility and partition coefficients of organic solutes in ionic liquids

Correlation of critical micelle concentration of nonionic surfactants with molecular descriptors

113-118<span style="font-size:12.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"times="" roman";mso-ansi-language:="" en-in;mso-fareast-language:en-in;mso-bidi-language:ar-sa"="" lang="EN-IN">The critical micelle concentration (CMC) values of three sets. of fortysix nonionic surfactants with oxyethylene groups as the hydrophilic group have been subjected to quantitative structure-property relationships (QSPR) studies. The hydrophobic groups have varied number of carbon atoms with linear, octyl phenol and branched alkyl chain derivatives. Molecular descriptors derived from the chemical graphs have been used for the studies. A general regression model has been proposed to predict the CMC of non ionic surfactants. Plots related to principal components of molecular descriptors resulted in the ordination of non ionic surfactants.</span

QSAR studies on biological oxygen demand of alcohols

766-772A model for predicting the biological oxygen demand, and hence biodegradibility of chemical substances, is proposed for some alcohols. A novel parameter, ThOD, has been used along with some other topological indices for construction of regression models. Statistical evaluation of the agreement between model predictions show that the compounds can be classified as highly degradable, moderately degradable and low-degradable alcohols