Deterministic Classifiers Accuracy Optimization for Cancer Microarray Data

The objective of this study was to improve classification accuracy in cancer microarray gene expression data using a collection of machine learning algorithms available in WEKA. State of the art deterministic classification methods, such as: Kernel Logistic Regression, Support Vector Machine, Stochastic Gradient Descent and Logistic Model Trees were applied on publicly available cancer microarray datasets aiming to discover regularities that provide insights to help characterization and diagnosis correctness on each cancer typology. The implemented models, relying on 10-fold cross-validation, parameterized to enhance accuracy, reached accuracy above 90%. Moreover, although the variety of methodologies, no significant statistic differences were registered between them, at significance level 0.05, confirming that all the selected methods are effective for this type of analysis.info:eu-repo/semantics/publishedVersio

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society

Synthesis, characterization, thermal and chelation properties of new polymeric hydrazone based on 2, 4-dihydroxy benzaldehyde

A chelating polymer poly(2-hydroxy-4-methacryloyloxy benzaldehyde hydrazone) poly(2H4MBH) was prepared in N,N-dimethylformamide (DMF) at 70°C using benzoyl peroxide as initiator. Poly (2H4MBH) was characterized by infra-red and 1H-NMR spectroscopic techniques. The molecular weight of the polymer was determined by gel permeation chromatography. Polychelates were obtained when the DMF solution of the polymer containing a few drops of ammonia was treated with the aqueous solution of Cu(II)/Ni(II). Elemental analysis of the polychelates indicates that the metal-ligand ratio was about 1: 2. The infrared spectra of polychelates suggest that the metals were coordinated through the oxygen of the phenolic—OH group and nitrogen of the azomethine group. The EPR (Electron paramagnetic resonance) and magnetic moment data indicate a square planar structure for Cu(II) complex whereas octahedral structure for Ni(II) complex. The thermogravimetric analysis, differential calorimetry, and X-ray diffraction data indicated that the incorporation of the metal ions significantly enhanced the degree of crystallinity. The sorption properties of the chelate-forming polymer towards various divalent metal ions Cu(II) and Ni(II) were studied as a function of pH, nature, and concentration of electrolytes. </jats:p