Feasibility study of treatment of mixed astigmatism with small-incision lenticule extraction (SMILE) by using research software.

PURPOSE: To report the preliminary experience and initial clinical results following SMILE for the treatment of mixed astigmatism. METHODS: Thirteen eyes of nine patients with a mean age of 27 ± 4.36 years were included in the series. In 8/13 eyes, myopic SMILE license and in 4/13 eyes, hyperopic SMILE license (available as part of an open/research software) was used for the treatment. The mean follow-up was 9.5 ± 8.7 (0.5-24) months, and the median follow-up was 6 months. SETTING: Nethradhama Superspeciality Eye Hospital, Bangalore, India. DESIGN: Exploratory study. RESULTS: The mean preoperative sphere, cylinder, and spherical equivalent (SE) were 1.44 ± 1.63, -2.70 ± 2.30, and -0.24 ± 1.14 D, which changed to -0.03 ± 0.30, -0.28 ± 0.48, and -0.18 ± 0.49 D, respectively, 6 months postoperatively. Furthermore, 85% (11/13) eyes were within ± 0.50 D, 92% (12/13) eyes were within ± 1.00 D, while all eyes were within ± 1.50 D of SE correction. All eyes were within ± 1.00 D of cylinder correction. In addition, 92% (12/13) eyes had UDVA better than 20/32, with 54% (7/13) eyes having UDVA 20/20 or better. Safety and efficacy indices were 1.08 and 0.92, respectively. No eyes lost more than 1 line of CDVA. The mean corneal higher order aberrations (HOA) increased from 0.111 ± 0.048 to 0.209 ± 0.056 (P < 0.001). The mean objective scatter index (OSI) did not show a significant change (pre = 0.71 ± 0.69, 6 months = 0.89 ± 0.20; P = 0.35). CONCLUSION: Early experience showed that SMILE was feasible for the management of eyes with mixed astigmatism, without any intraoperative complications, unique to the procedure.

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples.

BACKGROUND: Machine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments. Hence, machine learning techniques are widely used in cancer detection and prognosis. METHODS: In this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets to generate sub-datasets, each of which contains a subset of the most relevant/informative attributes of the original dataset. The MFSAC method is a feature selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets. RESULTS: To assess the performance of the proposed MFSAC-EC model, it is applied on different high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing models to establish its effectiveness with respect to other models. From the experimental results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes.

Laccase production by the white-rot fungus Termitomyces clypeatus.

Laccase was detected in the culture filtrate of white-rot fungus Termitomyces clypeatus. The enzyme was found at the late phase of submerged growth in a medium containing glucose or cellulose as the carbon source. The present study indicates that laccase produced by T. clypeatus is an intracellular enzyme, released in the medium due to cell lysis at the end of the growing phase. Laccase produced by T. clypeatus is different from the extracellular polyphenol oxidase of T. albuminosus, also produced at the late phase of growth. This is the first report of laccase production by a Termitomyces sp.