Hasil untuk "Diseases of the circulatory (Cardiovascular) system"

Shyon Parsa, Sulaiman Somani, Albert J. Rogers et al.

Background Atrial fibrillation (AF) is the most common arrhythmia worldwide, and patient perceptions significantly influence shared treatment decisions. Artificial intelligence–driven analysis of social media may offer valuable insights into contemporary public attitudes toward AF outside clinical settings. Methods This qualitative study used large language modeling and advanced artificial intelligence topic modeling techniques to analyze public perceptions of AF from Reddit discussions between April 2006 and November 2023. Results We curated 86 323 AF‐related conversations (18 754 posts, 67 569 comments) across 38 183 unique users by searching terms related to AF. Our topic modeling identified 65 distinct discussion topics organized into 9 thematic groups, with topics including personal experiences with treatments (eg, ablation, rate versus rhythm control), roles of health care providers and community support, AF triggers (diet, illicit substances, supplements, stress, caffeine), and anecdotes highlighting the difficulties of living with AF. Discussions commonly reflected 3 main themes: (1) advantages and limitations of wearable devices for AF monitoring, (2) hesitancy and misconceptions about AF treatment, and (3) patient‐centered challenges following an AF diagnosis. Conclusions The artificial intelligence–enabled analysis underscored substantial public discourse around patient experiences with AF detection and management. Leveraging social media data to understand patient perspectives on cardiovascular health may inform patient‐centered resources and future research directions to better support patients living with AF.

Jimmy Kang, Ryaan El-Andari, Nicholas Fialka et al.

Objective: The optimal revascularization strategy for patients with three-vessel coronary artery disease (3VD) and heart failure (HF) remains uncertain due to the absence of randomized trials directly comparing coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI). With few observational studies providing long-term follow-up, clinical equipoise persists. We therefore evaluated 10-year outcomes between CABG and PCI in patients with HF and 3VD. Methods: This retrospective population-based cohort study included adults with 3VD and HF undergoing isolated CABG or PCI in Edmonton, Alberta, Canada (2009–2018). Patients with STEMI, prior CABG, or concomitant procedures were excluded. The primary endpoint was all-cause mortality. Secondary endpoints included readmission for myocardial infarction (MI), stroke, repeat revascularization, and all-cause rehospitalization. Multivariable Cox regression was used to adjust for baseline characteristics. Results: Of 1774 screened patients, 632 met inclusion criteria (CABG: n = 97; PCI: n = 535). At 10 years, all-cause mortality was significantly lower in the CABG group (62.4 %) compared to PCI (71.8 %) (adjusted hazard ratio [aHR] 0.65, 95 % CI 0.47–0.92; p = 0.014). CABG was also associated with markedly lower rates of MI readmission (3.2 % vs. 23.7 %; aHR 0.11, 95 % CI 0.03–0.38; p < 0.001) and repeat revascularization (6.4 % vs. 21.6 %; aHR 0.22, 95 % CI 0.09–0.53; p = 0.001). Rates of stroke (p = 0.757) and all-cause rehospitalization (p = 0.157) were not significantly different. Conclusions: In patients with 3VD and HF, CABG is associated with significantly improved long-term survival, reduced MI readmissions, and fewer repeat revascularizations compared to PCI. These findings reinforce the need for a multidisciplinary Heart Team review to ensure the optimal intervention strategy.

Angelo Fabio d’Aiello, Francesca Bevilacqua, Angelo Micheletti et al.

Background: Survival rates for patients with congenital heart disease (CHD) have improved, but complications like pulmonary regurgitation (PR) often require re-interventions. Transcatheter pulmonary valve implantation (TPVI) with self-expandable valves, such as the Venus P-Valve, has broadened treatment options. Accurate procedural planning, particularly valve sizing, remains a significant challenge. Mixed Reality (MxR) technology enables a patient-specific approach that enhances procedural planning accuracy. Aim: To evaluate the use of MxR in planning Venus-P valve implantation. Materials and methods: This study included patients undergoing Venus P-Valve implantation with holographic models generated from CT data using ARTICOR® software from January 2023 to June 2024. Two independent operators used these models for procedural planning. Concordance between operators was assessed. Predictions were compared with implanted valve dimensions to evaluate concordance. Results: Of 29 eligible patients, 26 underwent successful Venus-P valve implantation. Concordance between the operators reached 60 % (n = 15/26) for valve diameter and over 88 % (n = 23/26) for valve length. Holographic models achieved 96 % (n = 25/26) concordance in predicting valve length, type of the approach (92 %) and 50 % (n = 13/26) concordance for diameter. Discussion: Holographic models enhanced procedural planning, enabling better visualization and collaborative decision-making. While highly effective for valve length predictions, and type of the approach limitations in predicting valve diameter highlight the need for improved methods, such as computational modelling or machine learning. Conclusion: Patient-specific holographic models are promising tools for TPVI planning. Advancements in technology and interdisciplinary collaboration are critical to overcoming current limitations and advancing procedural planning and related outcomes in interventional cardiology. Condensed abstract: This study assesses the use of mixed reality (MxR) technology for procedural planning in transcatheter pulmonary valve implantation (TPVI) with the Venus P-Valve. Holographic models were created from CT data to aid in valve sizing and implantation strategies, with two operators comparing measurements for valve diameter and length. Among 26 patients who underwent successful implantation, concordance between operators was 60 % for valve diameter and 88 % for valve length. Predictions using holographic models showed 96 % concordance for implanted valve length, type of the approach (92 %) and 50 % concordance for valve diameter. These results highlight the potential of MxR for improving TPVI planning, though the limited accuracy for valve diameter suggests a need for further advancements, such as computational modeling or machine learning, to optimize procedural outcomes in interventional cardiology.

Omar Faruq Shikdar, Fahad Ahammed, B. M. Shahria Alam et al.

Tea is among the most widely consumed drinks globally. Tea production is a key industry for many countries. One of the main challenges in tea harvesting is tea leaf diseases. If the spread of tea leaf diseases is not stopped in time, it can lead to massive economic losses for farmers. Therefore, it is crucial to identify tea leaf diseases as soon as possible. Manually identifying tea leaf disease is an ineffective and time-consuming method, without any guarantee of success. Automating this process will improve both the efficiency and the success rate of identifying tea leaf diseases. The purpose of this study is to create an automated system that can classify different kinds of tea leaf diseases, allowing farmers to take action to minimize the damage. A novel dataset was developed specifically for this study. The dataset contains 5278 images across seven classes. The dataset was pre-processed prior to training the model. We deployed three pretrained models: DenseNet, Inception, and EfficientNet. EfficientNet was used only in the ensemble model. We utilized two different attention modules to improve model performance. The ensemble model achieved the highest accuracy of 85.68%. Explainable AI was introduced for better model interpretability.

Ahmed El-Barbary, Mohamed Farouk Abd-Ulqader, Engy F. Madyan et al.

Abubakr A.A. Al-shoaibi, Christopher M. Lee, Julia H. Raney et al.

The associations of adverse childhood experiences (ACEs) with blood pressure in adulthood are inconclusive. Similarly, the association between ACEs and blood pressure earlier in the life course is understudied. This study aims to assess the associations of ACEs with blood pressure among early adolescents. We utilized data collected at baseline (age: 9–10 years) and Year 2 follow-up from 4077 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We used adjusted multiple linear regression models to estimate the associations of ACEs (cumulative score and subtypes) at baseline with systolic blood pressure (SBP) and diastolic blood pressure (DBP) at year 2 of follow-up. Experiencing ≥4 ACEs (compared to 0) was significantly associated with higher SBP (B = 3.31, 95 % CI 0.03, 6.57, p = 0.048). Of the ACEs subtypes, household substance use (B = 2.28, 95 % CI 0.28, 4.28, p = 0.028) and divorce or separation (B = 2.08, 95 % CI 0.01, 4.15, p = 0.048) were both significantly associated with a higher SBP while household mental illness (B = 2.57, 95 % CI 1.32, 3.81, p < 0.001) was significantly associated with a higher DBP. Our findings suggest that exposure to multiple ACEs is associated with higher blood pressure in adolescence.

Silvia Andaloro, Stefano Cacciatore, Maria Anna Nicolazzi et al.

Objective: Li-Fraumeni Syndrome (LFS) is a rare autosomal-dominant syndrome caused by a heterozygous germline mutation of the TP53 gene. It is characterized by early-onset malignancies and high penetrance. Non-bacterial thrombotic endocarditis (NBTE) is an uncommon condition for which cancer is a significant risk factor. Here we present a complex case of LFS unveiled by a NBTE-related ischemic stroke. Case presentation: A 41-year-old woman was admitted following a syncopal episode, preceded by a history of ischemic stroke. She had a notable family history of cancers. Imaging studies revealed ischemic damage and hemorrhagic infarcts, indicating a possible embolic origin or neoplastic involvement. Subsequent examinations revealed a NBTE on the aortic valve as well as multiple primary malignancies including high-grade invasive ductal carcinoma in the breast and primary lung adenocarcinoma. Genetic testing confirmed the presence of a pathogenic variant in TP53. Conclusion: This case underscores the intricate interplay between LFS, oncological manifestations, and thrombotic complications leading to ischemic stroke through NBTE embolization.

Robert Semco, BS, Ross G. McFall, MD, Jay Khambhati, MD et al.

Transcatheter aortic valve replacement may be performed with a transcarotid approach when peripheral vascular disease is prohibitive for transfemoral access. In this case, a patient who presented in cardiogenic shock secondary to severe aortic stenosis developed electroencephalographic changes during transcarotid TAVR. A temporary extracorporeal femoro-carotid shunt permitted successful TAVR.

Mohit Tomar, Abhisek Tiwari, Sriparna Saha

With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor-patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor's effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants.

Jiayu Chang, Shiyu Wang, Chen Ling et al.

The intricate relationship between genetic variation and human diseases has been a focal point of medical research, evidenced by the identification of risk genes regarding specific diseases. The advent of advanced genome sequencing techniques has significantly improved the efficiency and cost-effectiveness of detecting these genetic markers, playing a crucial role in disease diagnosis and forming the basis for clinical decision-making and early risk assessment. To overcome the limitations of existing databases that record disease-gene associations from existing literature, which often lack real-time updates, we propose a novel framework employing Large Language Models (LLMs) for the discovery of diseases associated with specific genes. This framework aims to automate the labor-intensive process of sifting through medical literature for evidence linking genetic variations to diseases, thereby enhancing the efficiency of disease identification. Our approach involves using LLMs to conduct literature searches, summarize relevant findings, and pinpoint diseases related to specific genes. This paper details the development and application of our LLM-powered framework, demonstrating its potential in streamlining the complex process of literature retrieval and summarization to identify diseases associated with specific genetic variations.

Ivana Škrlec

Jihen Amara, Birgitta König-Ries, Sheeba Samuel

Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the increasing global digitalization and the recent advances in computer vision based on deep learning. In fact, plant disease classification based on deep convolutional neural networks has shown impressive performance. However, these methods have yet to be adopted globally due to concerns regarding their robustness, transparency, and the lack of explainability compared with their human experts counterparts. Methods such as saliency-based approaches associating the network output to perturbations of the input pixels have been proposed to give insights into these algorithms. Still, they are not easily comprehensible and not intuitive for human users and are threatened by bias. In this work, we deploy a method called Testing with Concept Activation Vectors (TCAV) that shifts the focus from pixels to user-defined concepts. To the best of our knowledge, our paper is the first to employ this method in the field of plant disease classification. Important concepts such as color, texture and disease related concepts were analyzed. The results suggest that concept-based explanation methods can significantly benefit automated plant disease identification.

Armando D. Diaz Gonzalez, Kevin S. Hughes, Songhui Yue et al.

Published biomedical information has and continues to rapidly increase. The recent advancements in Natural Language Processing (NLP), have generated considerable interest in automating the extraction, normalization, and representation of biomedical knowledge about entities such as genes and diseases. Our study analyzes germline abstracts in the construction of knowledge graphs of the of the immense work that has been done in this area for genes and diseases. This paper presents SimpleGermKG, an automatic knowledge graph construction approach that connects germline genes and diseases. For the extraction of genes and diseases, we employ BioBERT, a pre-trained BERT model on biomedical corpora. We propose an ontology-based and rule-based algorithm to standardize and disambiguate medical terms. For semantic relationships between articles, genes, and diseases, we implemented a part-whole relation approach to connect each entity with its data source and visualize them in a graph-based knowledge representation. Lastly, we discuss the knowledge graph applications, limitations, and challenges to inspire the future research of germline corpora. Our knowledge graph contains 297 genes, 130 diseases, and 46,747 triples. Graph-based visualizations are used to show the results.

Emmanouil Mavrogiannis, Quint A. J. Hagdorn, Venetia Bazioti et al.

Abstract Pulmonary arterial hypertension (PAH) is a fatal disease characterized by increased pulmonary arterial pressure, inflammation, and neointimal remodeling of pulmonary arterioles. Serum levels of interleukin (IL)‐1β and IL‐18 are elevated in PAH patients and may enhance proinflammatory neointimal remodeling. NLRP3 inflammasome activation induces cleavage of the cytokines IL‐1β and IL‐18, required for their secretion. Pirfenidone (PFD), an antifibrotic and anti‐inflammatory drug, has been suggested to inhibit NLRP3 inflammasome activation. We hypothesized that PFD delays the progression of PAH by suppressing NLRP3 inflammasome activation. We assessed the effects of PFD treatment in a rat model for neointimal PAH induced by monocrotaline and aortocaval shunt using echocardiographic, hemodynamic, and vascular remodeling parameters. We measured inflammasome activation by NLRP3 immunostaining, Western blots for caspase‐1, IL‐1β, and IL‐18 cleavage, and macrophage IL‐1β secretion. PFD treatment ameliorated pulmonary arterial pressure, pulmonary vascular resistance, and pulmonary vascular remodeling in PAH rats. In PAH rats, immunostaining of NLRP3 in pulmonary arterioles and caspase‐1, IL‐1β, and IL‐18 cleavage in lung homogenates were increased compared to controls, reflecting NLRP3 inflammasome activation in vivo. PFD decreased IL‐1β and IL‐18 cleavage, as well as macrophage IL‐1β secretion in vitro. Our studies show that PFD ameliorates pulmonary hemodynamics and vascular remodeling in experimental PAH. Although PFD did not affect all NLRP3 inflammasome parameters, it decreased IL‐1β and IL‐18 cleavage, the products of NLRP3 inflammasome activation that are key to its downstream effects. Our findings thus suggest a therapeutic benefit of PFD in PAH via suppression of NLRP3 inflammasome activation.

Claire J. Peet, Dorota Rowczenio, Ebun Omoyinmi et al.

Background Idiopathic recurrent pericarditis (IRP) is an orphan disease that carries significant morbidity, partly driven by corticosteroid dependence. Innate immune modulators, colchicine and anti‐interleukin‐1 agents, pioneered in monogenic autoinflammatory diseases, have demonstrated remarkable efficacy in trials, suggesting that autoinflammation may contribute to IRP. This study characterizes the phenotype of patients with IRP and monogenic autoinflammatory diseases, and establishes whether autoinflammatory disease genes are associated with IRP. Methods and Results We retrospectively analyzed the medical records of patients with IRP (n=136) and monogenic autoinflammatory diseases (n=1910) attending a national center (London, UK) between 2000 and 2021. We examined 4 genes (MEFV, MVK, NLRP3, TNFRSF1A) by next‐generation sequencing in 128 patients with IRP and compared the frequency of rare deleterious variants to controls obtained from the Genome Aggregation Database. In this cohort of patients with IRP, corticosteroid dependence was common (39/136, 28.7%) and was associated with chronic pain (adjusted odds ratio 2.8 [95% CI, 1.3–6.5], P=0.012). IRP frequently manifested with systemic inflammation (raised C‐reactive protein [121/136, 89.0%] and extrapericardial effusions [68/136, 50.0%]). Pericarditis was observed in all examined monogenic autoinflammatory diseases (0.4%–3.7% of cases). Rare deleterious MEFV variants were more frequent in IRP than in ancestry‐matched controls (allele frequency 9/200 versus 2932/129 200, P=0.040). Conclusions Pericarditis is a feature of interleukin‐1 driven monogenic autoinflammatory diseases and IRP is associated with variants in MEFV, a gene involved in interleukin‐1β processing. We also found that corticosteroid dependence in IRP is associated with chronic noninflammatory pain. Together these data implicate autoinflammation in IRP and support reducing reliance on corticosteroids in its management.

Vu Hoang Tran, Jordy Mehawej, Donna M. Abboud et al.

Background Few studies have examined age and sex differences in the receipt of cardiac diagnostic and interventional procedures in patients hospitalized with acute myocardial infarction and trends in these possible differences during recent years. Methods and Results Data from patients hospitalized with a first acute myocardial infarction at the major medical centers in the Worcester, Massachusetts, metropolitan area were utilized for this study. Logistic regression analysis was used to examine age (<55, 55–64, 65–74, and ≥75 years) and sex differences in the receipt of echocardiography, exercise stress testing, coronary angiography, percutaneous coronary interventions, and coronary artery bypass graft surgery, and trends in the use of those procedures during patients' acute hospitalization, between 2005 and 2018, while adjusting for important confounding factors. The study population consisted of 1681 men and 1154 women with an initial acute myocardial infarction who were hospitalized on an approximate biennial basis between 2005 and 2018. A smaller proportion of women underwent cardiac catheterization, percutaneous coronary intervention, and coronary artery bypass graft surgery, while there were no sex differences in the receipt of echocardiography and exercise stress testing. Patients aged ≥75 years were less likely to undergo cardiac catheterization, percutaneous coronary intervention, and coronary artery bypass graft surgery, but were more likely to receive echocardiography compared with younger patients. Between 2005 and 2018, the use of echocardiography and coronary artery bypass graft surgery nonsignificantly increased among all age groups and both sexes, while the use of cardiac catheterization and percutaneous coronary intervention increased nonsignificantly faster in women and older patients. Conclusions We observed a continued lower receipt of invasive cardiac procedures in women and patients aged ≥75 years with acute myocardial infarction, but age and sex gaps associated with these procedures have narrowed during recent years.

Nisar Ahmed, Hafiz Muhammad Shahzad Asif, Gulshan Saleem et al.

Crop diseases are a major threat to food security and their rapid identification is important to prevent yield loss. Swift identification of these diseases are difficult due to the lack of necessary infrastructure. Recent advances in computer vision and increasing penetration of smartphones have paved the way for smartphone-assisted disease identification. Most of the plant diseases leave particular artifacts on the foliar structure of the plant. This study was conducted in 2020 at Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan to check leaf-based plant disease identification. This study provided a deep neural network-based solution to foliar disease identification and incorporated image quality assessment to select the image of the required quality to perform identification and named it Agricultural Pathologist (Agro Path). The captured image by a novice photographer may contain noise, lack of structure, and blur which result in a failed or inaccurate diagnosis. Moreover, AgroPath model had 99.42% accuracy for foliar disease identification. The proposed addition can be especially useful for application of foliar disease identification in the field of agriculture.

Jabir Al Nahian, Abu Kaisar Mohammad Masum, Sheikh Abujar et al.

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.

Mahboobeh Jafari, Afshin Shoeibi, Navid Ghassemi et al.

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed.

Irina K. Petrukhina, Petr A. Lebedev, Tatyana K. Ryazanova et al.

Aim. Study of multi-year sales statistics in pharmacy market segment to assess the volume and structure of consumed cardiovascular and antithrombotic drugs under COVID-19 pandemic conditions. Material and methods. Data on nomenclature and sales volumes of drugs in pharmacy segment of Samara region in 20152020. Results. It is shown that the share of basic cardiovascular drugs in physical terms is insignificant (5.2-3%). The largest volume of sales are drugs of angiotensin-converting enzyme inhibitor group (28%) and b-adrenoblockers (23.5%). Fixed combinations of hypotensive drugs account for only 13% of the volume of sold basic cardiovascular drugs, and the share of statins is 7.6%, which does not correspond to their role as the most effective drugs in primary and secondary cardiac prevention. The share of antithrombotic drugs in total sales in volume terms was 0.45% in 20152019. In absolute terms, sales of anticoagulants and disaggregants increased in 20182020, which was accompanied by an average 15% increase in the cost per daily dose. Among disaggregants, acetylsalicylic acid (66.6%), clopidogrel (21.6%), and dipyridamole (10.2%) are most frequently sold. The share of disaggregants in sales fell from 74.8 to 57.6% at the expense of acetylsalicylic acid drugs. In 2020, consumption of dipyridamole increased significantly to 16.6% due to inclusion of the drug in protocols for managing patients with COVID-19. Among anticoagulants, the proportion of new oral medications is characterized by a progressive increase from 7.8% in 2015 to 27% in 2020. During the COVID-19 pandemic, the most demanded group of Xa factor blockers is dominated by apixaban (63.8%), which can be explained by the lower cost (by 16%) of a daily equivalent dose compared to rivaroxaban. Conclusion. Low consumption of basic cardiovascular drugs among the population of Samara region, especially statins and combined hypotensive drugs was observed. During COVID-19 pandemic there was an increase in consumption of antithrombotic drugs, due to dipyridamole and new oral anticoagulants.