The application of computational approaches in medical science for diagnosis is made possible by the development in technical advancements connected to computer and biological sciences. The current cancer diagnosis system is becoming outmoded due to the new and rapid growth in cancer cases, and new, effective, and efficient methodologies are now required. Accurate cancer-type prediction is essential for cancer diagnosis and treatment. Understanding, diagnosing, and identifying the various types of cancer can be greatly aided by knowledge of the cancer genes. The Convolution Neural Network (CNN) and neural pattern recognition (NPR) approaches are used in this study paper to detect and predict the type of cancer. Different Convolution Neural Networks (CNNs) have been proposed by various researchers up to this point. Each model concentrated on a certain set of parameters to simulate the expression of genes. We have developed a novel CNN-NPR architecture that predicts cancer type while accounting for the tissue of origin using high-dimensional gene expression inputs. The 5000-person sample of the 1-D CNN integrated with NPR is trained and tested on the gene profile, mapping with various cancer kinds. The proposed model's accuracy of 94% suggests that the suggested combination may be useful for long-term cancer diagnosis and detection. Fewer parameters are required for the suggested model to be efficiently trained before prediction.
Antisnake venom (ASV) is the only specific and standard treatment for snakebite envenoming worldwide. The knowledge of antivenom dosage, mode of administration, availability, and logistics is essential to the healthcare practitioners (HCPs) in the management of snakebites. It is vital for the HCPs involved in the handling of ASVs to have its basic knowledge. The ASV contains proteins and can, therefore, easily get denatured if not handled appropriately, leading to poor therapeutic outcome. It is also essential for clinicians to be aware of the tendency of ASV to cause a severe life-threatening hypersensitivity reaction. There is currently no validated tool for assessing the knowledge of ASV among HCPs. Therefore, we developed and validated a tool for evaluating the HCPs knowledge of ASV. The items included in the tool were first generated from a comprehensive literature review. Face validity were conducted by presenting the drafted tool to ten experts on the subject matter. A validation study was conducted among doctors, pharmacists, nurses, pharmacy technicians, and the general public. The objectives of the study were to test the tool for content validity using the content validity index (CVI), construct validity using contrast group approach, difficulty index, readability, and reliability test using the test-retest method. We developed and validated a final tool containing thirty-three items. The tool was valid for face validity and had a scale-level (average) content validity (S-CVI/Ave) of 0.91. The ASV knowledge of pharmacists was higher than that of doctors, pharmacy technicians, nurses, and the general public (p tool using the Simple Measure of Gobbledygook (SMOG) was determined to be grade level 7. The test-retest analysis showed no significant difference between the mean knowledge scores measured at four weeks interval (p = 0.916), implying excellent reliability. The AKAT has demonstrated good psychometrical properties that would enable its application among a wide range of healthcare practitioners.