Inflammation has been widely recognized as one of the major pathophysiological drivers of the development of atrial fibrillation (AF), which works in tandem with other risk factors of AF including obesity, diabetes, hypertension, and heart failure (HF). Our current understanding of the role of inflammation in the natural history of AF remains elusive; however, several key players, including the NLRP3 (NLR family pyrin domain containing 3) inflammasome, have been acknowledged to be heavily influential on chronic inflammation in the atrial myocardium, which leads to fibrosis and eventual degradation of its electrical function. Nevertheless, our current methods of pharmacological modalities with reported immunomodulatory properties, including well-established classes of drugs e.g., drugs targeting the renin–angiotensin–aldosterone system (RAAS), statins, and vitamin D, have proven effective in reducing the overall risk of developing AF, the onset of postoperative atrial fibrillation (POAF), and reducing overall mortality among patients with AF. This might bring hope for further progress in developing new treatment modalities targeting cellular checkpoints of the NLRP3 inflammasome pathway, or revisiting other well-known anti-inflammatory drugs e.g., colchicine, vitamin C, nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticosteroids, and antimalarial drugs. In our review, we aim to find relevant upstream anti-inflammatory treatment methods for the management of AF and present the most current real-world evidence of their clinical utility.

Pancreatic cancer is one of the most lethal neoplasms. Despite considerable research conducted in recent decades, not much has been achieved to improve its survival rate. That may stem from the lack of effective screening strategies in increased pancreatic cancer risk groups. One population that may be appropriate for screening is new-onset diabetes (NOD) patients. Such a conclusion stems from the fact that pancreatic cancer can cause diabetes several months before diagnosis. The most widely used screening tool for this population, the ENDPAC (Enriching New-Onset Diabetes for Pancreatic Cancer) model, has not achieved satisfactory results in validation trials. This provoked the first attempts at using artificial intelligence (AI) to create larger, multi-parameter models that could better identify the at-risk population, which would be suitable for screening. The results shown by the authors of these trials seem promising. Nonetheless, the number of publications is limited, and the downfalls of using AI are not well highlighted. This narrative review presents a summary of previous publications, recent advancements and feasible solutions for effective screening of patients with NOD for pancreatic cancer.