Mohammad Mansour

Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.

Background: Addiction disorders pose significant challenges to public health, necessitating innovative treatments. This assesses deep brain stimulation (DBS) as a potential intervention for addiction disorders. Methods: A literature review was carried out with a focus on the role of DBS in addiction disorders and its future implications in neurosurgical research. Results: The online literature shows that DBS precisely modulates certain brain regions to restore addiction-related neural circuits and promote behavioral control. Conclusion: Preclinical evidence demonstrates DBS’s potential to rebalance neural circuits associated with addiction, and early clinical trials provide encouraging outcomes in enhancing addiction-related outcomes. Ethical considerations, long-term safety, and personalized patient selection require further investigation.

The life expectancy of people living with HIV (PLWH) has greatly increased due to advancements in combination antiretroviral treatment (cART). However, this longer life has also increased the prevalence of age-related comorbidities, such as frailty, which now manifest sooner in this group. Frailty, a term coined by the insurance industry, has been broadened to include physical, cognitive, and emotional elements and has been recognized as a critical predictor of negative health outcomes. With the median age of PLWH now in the mid-50s, treating frailty is critical given its link to chronic diseases, cognitive decline, and even death. Frailty assessment tools, such as the Frailty Phenotype (FP) and the Frailty Index (FI), are used to identify vulnerable people. Understanding the pathophysiology of frailty in PLWH indicates the role of immunological mechanisms. Frailty screening and management in this group have progressed, with specialized clinics and programs concentrating on multidisciplinary care. Potential pharmacotherapeutic solutions, as well as novel e-health programs and sensors, are in the future of frailty treatment, but it is critical to ensure that frailty evaluation is not exploited to perpetuate ageist healthcare practices. This narrative review investigates the changing healthcare environment for older people living with HIV (OPLWH), notably in high-income countries. It emphasizes the significance of identifying and managing frailty as a crucial feature of OPLWH's holistic care and well-being.

Stroke is the second most common cause of death worldwide, with 50% of survivors experiencing long-term disability. For more than two decades, treatment with intravenous thrombolysis (IVT) and mechanical endovascular thrombectomy (MET), the only approved stroke reperfusion therapies, was restricted to patients within the 4.5-6 hour time window, respectively. Therefore, patients who presented with acute ischemic stroke (AIS) beyond the conventional time window were excluded from reperfusion treatment. This narrative review aims to review the scientific literature on the possibilities of reperfusion therapies for patients who present with an unknown time of stroke onset, and those with stroke onset beyond the conventional 4.5-6 hour time window. Beyond the conventional time window, the eligibility of patients for IVT or MET, the two main therapeutic procedures, is decided based on the concept of penumbral imaging. Penumbral imaging identifies patients with hypoperfused but viable brain tissue, who could benefit from reperfusion. On the other hand, clock-based DWI-fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can detect stroke that has occurred within 4.5 hours in patients with an unknown time of onset, including patients who awaken with stroke. The introduction of penumbral imaging and MRI-based tissue clocking as imaging biomarkers for stroke has revolutionized stroke therapy, potentially allowing for personalized treatment of eligible stroke patients.