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Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast cancer research and clinical practice tools in recent years. Various projects are underway in early detection, diagnosis, prognosis, drug discovery, advanced image analysis, precision medicine, predictive modeling, and personalized treatment planning using artificial intelligence and machine learning. These projects use different algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and deep learning methods, to analyze and improve different types of data, such as clinical, genomic, and imaging data for breast cancer management. The success of these projects has the potential to transform breast cancer care, and continued research and development in this area is likely to lead to more accurate and personalized breast cancer diagnosis, treatment, and outcomes.
RESUMO
BACKGROUND: Paratuberculosis is a worldwide endemic infectious disease of ruminants that results in high economic losses. Public health concerns are also being raised with human Crohn's disease. Therefore, control is becoming priority for governments. Control is largely dependent on "Test and Cull" or "Test and Segregate" policy. Hence, it is critical to assure the infection before making the decision. Commercial kits are costly especially in view of resource limited areas. Present study analyzed the performance various in house DNA isolation methods and PCR master mix combinations to optimize a protocol for confirmation of paratuberculosis bacilli shedding in feces. METHODS AND RESULTS: Present study included five protocols of fecal DNA isolation (chemical, bio-chemical, physio-chemical and physical) and three reaction mixes (based on Qiagen, Genei and Thermo 2X master mixes) in nine different combinations using additives and tested their performance for IS900 PCR. Spiked fecal samples were used to select the best combination of DNA isolation method and PCR master mix (PRM). Selected combination was used to test reference (positive and negative) fecal samples and field samples. Findings revealed that combination physical method of DNA isolation and Genei based PRM (with additives; betaine DMSO and BSA) had lowest limit of detection. Sensitivity was 83% and specificity was 100% in comparison to fecal culture. High prevalence (23%) was reported for paratuberculosis on field samples. CONCLUSION: Optimized protocol has acceptable sensitivity and can easily be adopted in resource-limited laboratories. High prevalence of paratuberculosis needs immediate implementation of the control strategies.