One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student’s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.

Introduction: The aim of this study was to assess the induction of solvents on the total phenol and flavonoid content and also the antioxidant activity of Ganoderma lucidum extracts. Materials & Methods: In this study, two concentrations (100% and 75%) of diethyl ether, ethanol, butanol, chloroform, and acetone were used as extractants of Ganoderma lucidum. Total phenol and flavonoid contents were measured by spectrophotometric methods and 1, 1-diphenyl-2-picrylhydrazyl (DPPH). Free radical scavenging assay was used for the investigation of antioxidant activity. Results & Discussion: Extractants significantly affected the % yield of extract, the quantity of phenol and flavonoids and antioxidant activity of Ganoderma lucidum mushroom. The highest extraction yield, around 38%, was achieved by 75% acetone, followed by 100% acetone (about 36%) and 75% chloroform (approximate 21%). Hydro-acetone extract exhibited the most significant antioxidative properties (EC50 value; 645.55 µg/mL) comprised of a higher total of phenol content. In conclusion, the total phenol content encouraged the antioxidative potential of Ganoderma lucidum mushroom. Conclusion: These findings indicate that the selective extraction of Ganoderma lucidum shows significant biological activities.

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IntroductionVirtual teaching sessions during the coronavirus disease 2019 pandemic were challenging for students and teachers but were also an opportunity to find creative ways to teach physical examination skills, including the neurologic examination. We examined expert opinions of the pros and cons that arise using a virtual platform to teach the neurologic examination and strategies to best address these challenges.MethodsThis was a qualitative study incorporating a focus group of faculty and resident neurologists. Data were coded using conventional content analysis. An interpretivist, social constructionist approach was used to look for interesting or novel ideas, rather than testing a specific hypothesis. Three independent auditors performed a dependability and confirmability audit to confirm that the themes accurately reflected the data.ResultsA single focus group was used. Four of the 6 participants were faculty neurologists and 2 were neurology residents. Five themes were identified: (1) learning the neurologic examination is complex, (2) lack of physical contact is the most important drawback of virtual teaching, (3) virtual teaching can effectively emphasize the organization of the examination, (4) virtual sessions can facilitate combined teaching of technique and demonstration of abnormalities, and (5) virtual platforms do not necessarily imply reduced participation.ConclusionTeaching the neurologic examination is a multifaceted process that should emphasize not only technique but also an overall approach to performing and documenting the examination. Many aspects of the neurologic examination can be appropriately taught virtually using various strategies, although there may always be some limitations. Virtual education can play a useful role for future curriculum design and global education.