The ongoing Russian invasion of Ukraine has taken a tremendous toll on the physical and mental wellbeing of the Ukrainian people. Accordingly, medical trainees and institutions must adapt to a high degree of uncertainty and turmoil. In 2022, Ukraine has 23 medical institutions filled with not only Ukrainian nationals but medical students from around the world. It is estimated that approximately 18,000 students from India alone study in Ukraine, many of whom are medical learners. Many of these national and foreign medical students are now displaced refugees looking to escape to Western Europe or their country of origin. Even by escaping the ongoing conflict, the question remains as to how many of these students will complete their medical education and cope with the trauma of political unrest. This is the first time that the International Journal of Medical Students Editorial Team has commented on a political conflict. We have learned in our tenure that medical students and their education worldwide is affected by many factors, including global warming and conflict. We have raised our voice before for actions to prevent climate change. We are adding our voice of support to those suffering from political unrest and acts of violence globally, with specific focus on the Ukraine. The path of war and vengeance is an easy one when compared to political dialogue and collaboration. We, the new generation of scientists of the world, claim for the use of reason over emotions to keep us all safe and promote progress worldwide.

Acute cholangitis is a potentially life-threatening bacterial infection of the intra and/or extrahepatic bile ducts. It remains the second and third cause of community-acquired and hospital-acquired bacteremia, respectively, and is associated with mortality rates of up to 15%, despite advances in broad-spectrum antimicrobial therapy and improved access to emergency biliary tract decompression procedures. Even though not much has changed in recent years in terms of diagnosis or treatment, new data have emerged regarding multidrug-resistant bacteria that serve as etiologic agents of cholangitis. Moreover, different approaches in antibiotic regimes depending on severity grading and bile sample cultures as well as novel minimally invasive endoscopic procedures that can help when consecrated treatments such as endoscopic retrograde cholangiopancreatography (ERCP) fail, cannot be performed, or are unavailable have been proposed. This state-of-the-art review aims to offer a complete and updated assessment of the epidemiology, novel diagnostic and therapeutic methods, complications, and prognostic variables of acute cholangitis. The authors will review the prognostic implications of unusual complications, the relevance of regular bile samples and antibiograms, and their new role in guiding antibiotic therapy and limiting antibiotic resistance to present an organized and comprehensive approach to the care of acute cholangitis.

Multimodal monitoring (MMM) in the intensive care unit (ICU) has become increasingly sophisticated with the integration of neurophysical principles. However, the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes. This manuscript reviewed current neuromonitoring tools, focusing on intracranial pressure, cerebral electrical activity, metabolism, and invasive and noninvasive autoregulation monitoring. In addition, the integration of advanced machine learning and data science tools within the ICU were discussed. Invasive monitoring includes analysis of intracranial pressure waveforms, jugular venous oximetry, monitoring of brain tissue oxygenation, thermal diffusion flowmetry, electrocorticography, depth electroencephalography, and cerebral microdialysis. Noninvasive measures include transcranial Doppler, tympanic membrane displacement, near-infrared spectroscopy, optic nerve sheath diameter, positron emission tomography, and systemic hemodynamic monitoring including heart rate variability analysis. The neurophysical basis and clinical relevance of each method within the ICU setting were examined. Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools, helping clinicians make more accurate and timely decisions. These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies. MMM, grounded in neurophysics, offers a more nuanced understanding of cerebral physiology and disease in the ICU. Although each modality has its strengths and limitations, its integrated use, especially in combination with machine learning algorithms, can offer invaluable information for individualized patient care.

Background: Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. Methods: Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. Results: A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4–96.8%) was found using the random-effects model on four studies that showed significant heterogeneity (P<0.05) in the Cochrane’s Q test. Further, a pooled sensitivity of 93.9% (CI 92.4–95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane’s Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane’s Q test and determined as 93.1% (CI 90.7–95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3–95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4–96.8%). Conclusion: AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.

Attention deficit hyperactivity disorder (ADHD) has a growing incidence and prevalence in the United States and throughout the world, much of which is contributed to increased awareness of the condition and solidified diagnostic criteria. Substance use disorder (SUD) similarly has seen a sharp increase, particularly with the rising cases of opioid abuse. Management of ADHD is done primarily with pharmacologic therapy, often stimulants and with psychosocial interventions (i.e., exercise, meditation, peer-to-peer intervention, etc.) for adjunctive management. Management of SUD involves cessation and treatment based on the underlying drug of abuse. Many clinicians are uncomfortable treating ADHD in patients with SUD based on concerns the intervention may lead to an adverse event, including drug relapse, and the development of other psychiatric comorbidities. Concerns also arise about stimulants acting as a gateway drug in adolescents leading to the onset of SUD. Thus, in this narrative review, we aim to shed light on ADHD in relation to SUD and to provide clinical insight based on the current scientific literature on the topic. ADHD causes lesions in subcortical structures in the basal ganglia and limbic system. Treatment of ADHD with stimulants has been shown to normalize malformed neuroanatomical variations and lead to improved long-term outcomes compared to non-treatment of ADHD. Based on current scientific literature, it is recommended to treat ADHD with guideline-directed pharmacologic agents including stimulants along with non-pharmacologic interventions primarily exercise. There may be some improvement in reducing risky behavior, such as substance abuse, and may even help prevent the development of SUD.