Successful robot-assisted laparoscopic resection of pheochromocytoma in a patient with dilated cardiomyopathy: A case report on extremely high-risk anesthesia management

The Role of Circular RNA in the Pathogenesis of Chemotherapy-Induced Cardiotoxicity in Cancer Patients: Focus on the Pathogenesis and Future Perspective

Cardiotoxicity is a serious challenge cancer patients face today. Various factors are involved in cardiotoxicity. Circular RNAs (circRNAs) are one of the effective factors in the occurrence and prevention of cardiotoxicity. circRNAs can lead to increased proliferation, apoptosis, and regeneration of cardiomyocytes by regulating the molecular pathways, as well as increasing or decreasing gene expression; some circRNAs have a dual role in cardiomyocyte regeneration or death. Identifying each of the pathways related to these processes can be effective on managing patients and preventing cardiotoxicity. In this study, an overview of the molecular pathways involved in cardiotoxicity by circRNAs and their effects on the downstream factors have been discussed.

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN

This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electrocardiogram (ECG) sensors for pattern generation and Convolution Neural Networks (CNN) for decision analytics. In health-related outbreaks, timely and early diagnosis of such complications is conclusive in reducing mortality rates and alleviating the burden on healthcare facilities. Existing methods rely on clinical assessments, medical history reviews, and hospital-based monitoring, which are valuable but have limitations in terms of accessibility, scalability, and timeliness, particularly during pandemics. The proposed scheme commences by deploying wearable ECG sensors on the patient's body. These sensors collect data by continuously monitoring the cardiac activity and respiratory patterns of the patient. The collected raw data is then transmitted securely in a wireless manner to a centralized server and stored in a database. Subsequently, the stored data is assessed using a preprocessing process which extracts relevant and important features like heart rate variability and respiratory rate. The preprocessed data is then used as input into the CNN model for the classification of normal and abnormal cardiorespiratory patterns. To achieve high accuracy in abnormality detection the CNN model is trained on labeled data with optimized parameters. The performance of the proposed scheme is evaluated and gauged using different scenarios, which shows a robust performance in detecting abnormal cardiorespiratory patterns with a sensitivity of 95% and specificity of 92%. Prominent observations, which highlight the potential for early interventions include subtle changes in heart rate variability and preceding respiratory distress. These findings show the significance of wearable ECG technology in improving pandemic management strategies and informing public health policies, which enhances preparedness and resilience in the face of emerging health threats.

Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement

Introduction

Global Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis.

Methods

This paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.

Results

On the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.

Discussion

These findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model's use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.

Stealth Nanocarriers in Cancer Therapy: a Comprehensive Review of Design, Functionality, and Clinical Applications

Nanotechnology has significantly transformed cancer treatment by introducing innovative methods for delivering drugs effectively. This literature review provided an in-depth analysis of the role of nanocarriers in cancer therapy, with a particular focus on the critical concept of the 'stealth effect.' The stealth effect refers to the ability of nanocarriers to evade the immune system and overcome physiological barriers. The review investigated the design and composition of various nanocarriers, such as liposomes, micelles, and inorganic nanoparticles, highlighting the importance of surface modifications and functionalization. The complex interaction between the immune system, opsonization, phagocytosis, and the protein corona was examined to understand the stealth effect. The review carefully evaluated strategies to enhance the stealth effect, including surface coating with polymers, biomimetic camouflage, and targeting ligands. The in vivo behavior of stealth nanocarriers and their impact on pharmacokinetics, biodistribution, and toxicity were also systematically examined. Additionally, the review presented clinical applications, case studies of approved nanocarrier-based cancer therapies, and emerging formulations in clinical trials. Future directions and obstacles in the field, such as advancements in nanocarrier engineering, personalized nanomedicine, regulatory considerations, and ethical implications, were discussed in detail. The review concluded by summarizing key findings and emphasizing the transformative potential of stealth nanocarriers in revolutionizing cancer therapy. This review enhanced the comprehension of nanocarrier-based cancer therapies and their potential impact by providing insights into advanced studies, clinical applications, and regulatory considerations.

Advancements in tissue engineering for cardiovascular health: a biomedical engineering perspective

Myocardial infarction (MI) stands as a prominent contributor to global cardiovascular disease (CVD) mortality rates. Acute MI (AMI) can result in the loss of a large number of cardiomyocytes (CMs), which the adult heart struggles to replenish due to its limited regenerative capacity. Consequently, this deficit in CMs often precipitates severe complications such as heart failure (HF), with whole heart transplantation remaining the sole definitive treatment option, albeit constrained by inherent limitations. In response to these challenges, the integration of bio-functional materials within cardiac tissue engineering has emerged as a groundbreaking approach with significant potential for cardiac tissue replacement. Bioengineering strategies entail fortifying or substituting biological tissues through the orchestrated interplay of cells, engineering methodologies, and innovative materials. Biomaterial scaffolds, crucial in this paradigm, provide the essential microenvironment conducive to the assembly of functional cardiac tissue by encapsulating contracting cells. Indeed, the field of cardiac tissue engineering has witnessed remarkable strides, largely owing to the application of biomaterial scaffolds. However, inherent complexities persist, necessitating further exploration and innovation. This review delves into the pivotal role of biomaterial scaffolds in cardiac tissue engineering, shedding light on their utilization, challenges encountered, and promising avenues for future advancement. By critically examining the current landscape, we aim to catalyze progress toward more effective solutions for cardiac tissue regeneration and ultimately, improved outcomes for patients grappling with cardiovascular ailments.

Potential therapeutic effects of crocin

Crocin, a natural bioactive compound derived from saffron (Crocus sativus) and other Crocus genera, has gained significant attention recently due to its potential therapeutic properties. The multifaceted nature of crocin's biological effects has piqued the interest of researchers and health enthusiasts, prompting further investigations into its mechanisms of action and therapeutic applications. This review article comprehensively explores the emerging evidence supporting crocin's role as a promising ally in protecting against metabolic disorders. The review covers the molecular mechanisms underlying crocin's beneficial effects and highlights its potential applications in preventing and treating diverse pathological conditions. Understanding the mechanisms through which crocin exerts its protective effects could advance scientific knowledge and offer potential avenues for developing novel therapeutic interventions. As we uncover the potential of crocin as a valuable ally in the fight against disorders, it becomes evident that nature's palette holds remarkable solutions for enhancing our health.

An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines

Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.

Beyond the silence: A comprehensive exploration of long non-coding RNAs as genetic whispers and their essential regulatory functions in cardiovascular disorders

Long non-coding RNAs (lncRNAs) are RNA molecules that regulate gene expression at several levels, including transcriptional, post-transcriptional, and translational. They have a length of more than 200 nucleotides and cannot code. Many human diseases have been linked to aberrant lncRNA expression, highlighting the need for a better knowledge of disease etiology to drive improvements in diagnostic, prognostic, and therapeutic methods. Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide. LncRNAs play an essential role in the complex process of heart formation, and their abnormalities have been associated with several CVDs. This Review article looks at the roles and relationships of long non-coding RNAs (lncRNAs) in a wide range of CVDs, such as heart failure, myocardial infarction, atherosclerosis, and cardiac hypertrophy. In addition, the review delves into the possible uses of lncRNAs in diagnostics, prognosis, and clinical treatments of cardiovascular diseases. Additionally, it considers the field's future prospects while examining how lncRNAs might be altered and its clinical applications.

The applications of internet of things in smart healthcare sectors: a bibliometric and deep study

The recent attention garnered by Internet of Things (IoT) technology for its potential to alleviate challenges faced by healthcare systems, such as those resulting from an aging population and the rise in chronic illnesses, has underscored the significance of smart healthcare. Surprisingly, no bibliometric study has been conducted on this subject to date. Consequently, this investigation aims to provide a comprehensive overview of the longitudinal state and knowledge structure of IoT in smart healthcare. To achieve this, a content analysis tool is employed for academic research, facilitating the identification of key study themes, the growth trajectory of the research topic, the top journal sources, and the distribution of nations based on subject areas. The bibliometric evaluation encompasses 614 publications published in 14 journals spanning the period from 2016 to 2022. Employing bibliographic coupling analysis, the latest developments in IoT have been uncovered within the domain of smart healthcare. The findings reveal 11 primary research topic areas that have been the focus of scholarly discourse during this period. This study highlights that the computing paradigm and network connectivity emerge as the most prominent topics within this research domain. Blockchain-based security in healthcare closely follows as the second-largest topic discussed by scholars. Additionally, the analysis indicates a significant increase in total publications for the most popular topic, peaking around 2018.