Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques.

Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.

Emerging Developments in Separation Techniques and Analysis of Chiral Pharmaceuticals.

Chiral separation, the process of isolating enantiomers from a racemic mixture, holds paramount importance in diverse scientific disciplines. Using chiral separation methods like chromatography and electrophoresis, enantiomers can be isolated and characterized. This study emphasizes the significance of chiral separation in drug development, quality control, environmental analysis, and chemical synthesis, facilitating improved therapeutic outcomes, regulatory compliance, and enhanced industrial processes. Capillary electrophoresis (CE) has emerged as a powerful technique for the analysis of chiral drugs. This review also highlights the significance of CE in chiral drug analysis, emphasizing its high separation efficiency, rapid analysis times, and compatibility with other detection techniques. High-performance liquid chromatography (HPLC) has become a vital technique for chiral drugs analysis. Through the utilization of a chiral stationary phase, HPLC separates enantiomers based on their differential interactions, allowing for the quantification of individual enantiomeric concentrations. This study also emphasizes the significance of HPLC in chiral drug analysis, highlighting its excellent resolution, sensitivity, and applicability. The resolution and enantiomeric analysis of nonsteroidal anti-inflammatory drugs (NSAIDs) hold great importance due to their chiral nature and potential variations in pharmacological effects. Several studies have emphasized the significance of resolving and analyzing the enantiomers of NSAIDs. Enantiomeric analysis provides critical insights into the pharmacokinetics, pharmacodynamics, and potential interactions of NSAIDs, aiding in drug design, optimization, and personalized medicine for improved therapeutic outcomes and patient safety. Microfluidics systems have revolutionized chiral separation, offering miniaturization, precise fluid control, and high throughput. Integration of microscale channels and techniques provides a promising platform for on-chip chiral analysis in pharmaceuticals and analytical chemistry. Their applications in techniques such as high-performance liquid chromatography (HPLC) and capillary electrochromatography (CEC) offer improved resolution and faster analysis times, making them valuable tools for enantiomeric analysis in pharmaceutical, environmental, and biomedical research.