Senapati, Sidhartha Gautam

Hypertension has been traditionally known to be highlighted by mean blood pressure; however, emerging evidence exhibits that blood pressure variability (BPV), including short-term, day-to-day, and visit-to-visit fluctuations can have an implication across multiple body systems. Elevated BPV reflects repetitive hemodynamic stress, affecting the physiologic hemostasis contributing to vascular injury and end organ damage. This narrative review is a compilation of recent evidence on the prognostic value of BPV, explained by pathophysiology, various devices with its measurement approaches, and, essentially, the clinical implication of BPV and the use of such devices utilizing artificial intelligence. A comprehensive literature search across PubMed, Cochrane Library, Scopus, and Web of Science were conducted, focusing on observational studies, cohorts, randomized trials, and meta-analyses. Higher BPV has been associated with an increased risk of cardiovascular mortality, stroke, coronary events, and heart failure, the progression of chronic kidney disease, cognitive decline, and preeclampsia, among other end organ damage, despite mean blood pressure. The various pathophysiologic mechanisms include autonomic dysregulation, arterial stiffness, endothelial dysfunction, circadian rhythm alteration, and systemic inflammation, which result in vascular remodeling and multisystem damage. Antihypertensive medications such as calcium channel blockers and renin–angiotensin–aldosterone system inhibitors seem to reduce BPV; randomized trials have not specifically investigated their BPV-reducing effects. The aim of this review is to highlight that BPV is a dynamic marker of multisystem risk, and question how various AI-based devices can aid continuous BPV monitoring and patient specific risk stratification.

Heart failure, a significant global health burden, is divided into heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF), characterized by systolic dysfunction and diastolic stiffness, respectively. While HFrEF benefits from pharmacological and device-based therapies, HFpEF lacks effective treatments, with both conditions leading to high rehospitalization rates and reduced quality of life, especially in older adults with comorbidities. This review explores the role of artificial intelligence (AI) in advancing autonomic neuromodulation for heart failure management. AI enhances patient selection, optimizes stimulation strategies, and enables adaptive, closed-loop systems. In HFrEF, vagus nerve stimulation and baroreflex activation therapy improve functional status and biomarkers, while AI-driven models adjust stimulation dynamically based on physiological feedback. In HFpEF, AI aids in deep phenotyping to identify responsive subgroups for neuromodulatory interventions. Clinical tools support remote monitoring, risk assessment, and symptom detection. However, challenges like data integration, ethical oversight, and clinical adoption limit real-world application. Algorithm transparency, bias minimization, and equitable access are critical for success. Interdisciplinary collaboration and ethical innovation are essential to develop personalized, data-driven, patient-centered heart failure treatment strategies through AI-guided neuromodulation.

Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency and decision-making, particularly in managing VVs through improved risk factor identification, diagnosis, and treatment planning. Objective: This abstract explores the role of AI in VV management, focusing on its applications in risk detection, image analysis, treatment planning, and surgical interventions, while addressing challenges to its widespread adoption. Methods: AI leverages advanced techniques such as computer vision and deep learning to analyze patient data, including medical history, symptoms, physical examinations, and imaging (e.g., ultrasounds, venography). It identifies patterns in large datasets to support personalized treatment plans, early risk detection, and disease severity assessment. Results: AI demonstrates promise in automating VV detection and classification, assessing disease severity, and aiding treatment planning. It enhances surgical interventions through preoperative planning, intraoperative navigation, and recurrence risk prediction. However, its adoption is limited by a lack of large-scale studies, concerns over accuracy, and the need for regulatory and ethical oversight. Conclusion: AI has the potential to revolutionize VV management by improving diagnosis, treatment precision, and patient outcomes. Further research, validation, and integration are critical to overcoming current limitations and fully realizing AI’s capabilities in clinical practice.

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

The association and interaction between the central nervous system (CNS) and enteric nervous system (ENS) is well established. Essentially ENS is the second brain, as we call it. We tried to understand the structure and function, to throw light on the functional aspect of neurons, and address various disease manifestations. We summarized how various neurological disorders influence the gut via the enteric nervous system and/or bring anatomical or physiological changes in the enteric nervous system or the gut and vice versa. It is known that stress has an effect on Gastrointestinal (GI) motility and causes mucosal erosions. In our literature review, we found that stress can also affect sensory perception in the central nervous system. Interestingly, we found that mutations in the neurohormone, serotonin (5-HT), would result in dysfunctional organ development and further affect mood and behavior. We focused on the developmental aspects of neurons and cognition and their relation to nutritional absorption via the gastrointestinal tract, the development of neurodegenerative disorders in relation to the alteration in gut microbiota, and contrariwise associations between CNS disorders and ENS. This paper further summarizes the synergetic relation between gastrointestinal and neuropsychological manifestations and emphasizes the need to include behavioral therapies in management plans.

Introduction: Although the prevalence of atrial fibrillation (AF) is higher in men than in women, the onset of AF appears to diminish the survival advantage in women. The pathophysiologic factors underlying survival outcome in AF are not well understood. Methods: A cohort of 3480 Olmsted County, Minnesota residents (mean age 72.8±14 years, 51.8% female) with incident AF who underwent transthoracic echocardiography within 30 days of AF diagnosis [median (IQR) 1, 1-6 days] were retrospectively studied. We examined the association of clinical parameters and survival according to gender. Kaplan-Meier and Cox proportional hazards models were used to compare overall survival in males and females. Results: Females were older and slimmer than males and more frequently had hypertension, prior stroke, and had higher CHA 2 DS 2 vasc score, pulse pressure and effective arterial elastance. Women also had smaller LV end diastolic dimension with higher LV ejection fraction, LA volume index, E/e prime ratio, and PASP (43.6±13.8 vs 39.1±13.3; <.001). During a mean follow-up of 7.4±6.2 years (median 6.6 years), 2569 patients died. Women had a lower 5-year survival compared to men. The probability of 1- and 5-year survival rates for women vs men were 80.2% vs 84.1% at 1 year and 54.4% vs 63.7% at 5 years (log-rank P <.001). In multivariable Cox regression analysis, after accounting for potential confounders, the independent predictors of mortality were age (HR 1.07 [95% CI 1.06-1.08]), PASP (per 5 unit increment) (HR 1.11 [95% CI 1.09-1.12]), effective arterial elastance (HR 1.06 [95% CI 1.01-1.12]), previous heart failure (HR 1.34 [95% CI 1.20-1.49]), diabetes (HR 1.33 [95% CI 1.20-1.48]), prior stroke (HR 1.26 [95% CI 1.13-1.41]), smoking (HR 1.13 [95% CI 1.06-1.20]), chronic lung disease (HR 1.32 [95% CI 1.20-1.45]), peripheral arterial disease (HR, 1.32 [95% CI 1.18-1.48]), and chronic kidney disease (HR, 1.52 [95% CI 1.38-1.69]). Conclusions: In a community-based cohort of patients with incident AF, markers of arterial stiffness and diastolic dysfunction were more prevalent in women compared to men and were strongly predictive of mortality. Our findings suggest that vascular stiffness may underlie the increased risk of mortality observed in women after a new diagnosis of AF.