Jawaharlal Nehru Technological University

Controlling or eliminating N-nitrosamine impurities in pharmaceutical products has become a significant challenge for both drug manufacturers and regulatory authorities. This difficulty is particularly pronounced in light of the recent increase in nitrosamine drug substance-related impurities, which have raised concerns about the safety and efficacy of various medications. Additionally, the pharmaceutical industry faces the challenge of developing analytical methods that are not only sensitive and selective but also precise and accurate. These methods are crucial for the reliable quantification of low levels of N-nitrosamine impurities, ensuring compliance with stringent current regulatory guidelines. The intricate nature of detecting these impurities at such low levels necessitates the use of cutting-edge analytical techniques, such as liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry. In light of these challenges, this review article explores the latest regulatory guidelines and analytical methodologies concerning N-nitrosamine impurities in various pharmaceutical products for 2024. Findings from this review article help provide valuable insights for researchers and industry professionals to enhance the safety and quality of pharmaceutical products.

Skin cancer is a potentially fatal disease that can be successfully treated if identified at an early stage. However, early diagnosis is often difficult due to the visual similarity of malignant and benign skin lesions. To address this challenge, this paper proposes the Convolutional Neural Network-based Transfer Learning enhanced by Harmonic Brown Bear Optimization (CNN-TL_Hr-BOA) to improve the accuracy and robustness of skin cancer detection. The process begins with image denoising using the Medav filter to eliminate artifacts and noise. Next, skin lesion segmentation is performed using the Position and Context Information Fusion Network (PCF-Net), which effectively isolates lesion regions by combining positional and contextual cues. The segmented images are further improved through superpixel-mixing-based image augmentation, which diversifies the training data by preserving important structural details. Subsequently, image-level features and Reductant Discrete Wavelet Transform (RDWT) are extracted. These features are then fed into a CNN model initialized with pre-trained DenseNet weights under a transfer learning setup. To enhance model performance, Hr-BOA, a hybrid metaheuristic that combines Harmonic Analysis with the Brown Bear Optimization Algorithm, is introduced to fine-tune the CNN's hyperparameters efficiently. The proposed CNN-TL_Hr-BOA framework is evaluated using the SIIM-ISIC Melanoma Classification dataset, where it demonstrates superior detection capability. The model achieves an accuracy of 91.754 %, a True Positive Rate (TPR) of 93.755 %, and a True Negative Rate (TNR) of 89.766 % using 90 % of the dataset for training. These results confirm the effectiveness of the proposed approach in accurately identifying skin cancer at early stages.

Electrocardiogram (ECG) is essential for assessing heart function, but manual analysis is time-consuming and error-prone. Automated ECG analysis can improve early detection of cardiovascular diseases by accurately identifying abnormal beats despite signal irregularity and non-stationarity. In this work, a novel approach for accurate ECG beat classification is proposed, integrating a sequential approach with a fractional order differentiator, dual-tree complex wavelet transform (DTCWT) features, and machine learning (ML) classifiers. The methodology involves R-peak detection using a fractional order differentiator, feature extraction with DTCWT, and classification using various ML models. Evaluated on the MIT-BIH Arrhythmia Database, the proposed approach demonstrates superior performance, with the Random Forest classifier achieving an accuracy of 96.82%, sensitivity of 96.83%, specificity of 97.02%, positive predictive value (PPV) of 96.89%, and an F1-score of 96.85%. These results underscore the effectiveness of the proposed methodology in improving the accuracy of ECG beat classification, contributing to better clinical outcomes in heart disease diagnosis.