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BACKGROUND: Chronic heart failure (HF) is frequently associated with various comorbidities. These comorbid conditions, such as anemia, diabetes mellitus, renal insufficiency, and sleep apnea, can significantly impact the prognosis of patients with HF. OBJECTIVE: This review aims to synthesize current evidence on the prevalence, impact, and management of comorbidities in patients with chronic HF. METHODS: A comprehensive review was conducted, with a rigorous selection process. Out of an initial pool of 59,030 articles identified across various research modalities, 134 articles were chosen for inclusion. The selection spanned various research methods, from randomized controlled trials to observational studies. RESULTS: Comorbidities are highly prevalent in patients with HF and contribute to increased hospitalization rates and mortality. Despite advances in therapies for HF with reduced ejection fraction, options for treating HF with preserved ejection fraction remain sparse. Existing treatment protocols often lack standardization, reflecting a limited understanding of the intricate relationships between HF and associated comorbidities. CONCLUSION: There is a pressing need for a multidisciplinary, tailored approach to manage HF and its intricate comorbidities. This review underscores the importance of ongoing research efforts to devise targeted treatment strategies for HF patients with various comorbid conditions.
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Heart failure (HF) with preserved ejection fraction (HFpEF) is a clinical syndrome in which patients have signs and symptoms of HF due to high left ventricular (LV) filling pressure despite normal or near normal LV ejection fraction. It is more common than HF with reduced ejection fraction (HFrEF), and its diagnosis and treatment are more challenging than HFrEF. Although hypertension is the primary risk factor, coronary artery disease and other comorbidities, such as atrial fibrillation (AF), diabetes, chronic kidney disease (CKD), and obesity, also play an essential role in its formation. This review summarizes current knowledge about HFpEF, its pathophysiology, clinical presentation, diagnostic challenges, current treatments, and promising novel treatments. It is essential to continue to be updated on the latest treatments for HFpEF so that patients always receive the most therapeutic treatments. The use of GnRH agonists in the management of HFpEF, infusion of Apo a-I nanoparticle, low-level transcutaneous vagal stimulation (LLTS), and estrogen only in post-menopausal women are promising strategies to prevent diastolic dysfunction and HFpEF; however, there is still no proven curative treatment for HFpEF yet.
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Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Breastfeeding has been extensively studied in relation to breast cancer risk. The results of the reviewed studies consistently show a decreased risk of breast cancer associated with breastfeeding, especially for 12 months or longer. This protective effect is attributed to hormonal, immunological, and physiological changes during lactation. Breastfeeding also appears to have a greater impact on reducing breast cancer risk in premenopausal women and specific breast cancer subtypes. Encouraging breastfeeding has dual benefits: benefiting infants and reducing breast cancer risk long-term. Healthcare professionals should provide evidence-based guidance on breastfeeding initiation, duration, and exclusivity, while public health policies should support breastfeeding by creating enabling environments. This review examines the existing literature and analyzes the correlation between breastfeeding and breast cancer risk.
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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.
