PURPOSE OF REVIEW: Heart failure is not simply a pump that has grown tired; it is a tissue that adapts and then, too often, becomes locked into its adaptations. This review examines how epigenetic regulation - chromatin accessibility, histone modifications, DNA methylation and hydroxymethylation, and the reader complexes that interpret these marks - converts transient stress into durable transcriptional programs. We focus on the mechanisms by which the failing heart acquires regulatory "memory," and on the chromatin control nodes that are emerging as actionable targets for therapy and for biomarker development. RECENT FINDINGS: Three lines of evidence are reshaping the field. First, the failing-heart transcriptome is increasingly governed by a tractable set of chromatin control points: acetylation balance [histone deacetylases (HDACs) versus histone acetyltransferases (HATs) such as p300/CREB-binding protein (CBP)] and acetyl-lysine readers [bromodomain and extraterminal (BET) proteins, including bromodomain-containing protein 4 (BRD4)] that amplify hypertrophic, inflammatory, and profibrotic programs. Second, the adult human myocardium is not epigenetically inert: reproducible disease-associated methylation signatures and regulatory shifts are detectable across cardiomyopathy and ischemic heart failure, with early signals that some features may move with physiologic recovery. Third, acute stress phenotypes are being reframed as chromatin-state problems: Takotsubo syndrome compresses stress-to-dysfunction into days, and emerging mechanistic work supports an acetylation/deacetylation axis as a tractable regulatory lever in Takotsubo-like injury. SUMMARY: Epigenetics provides a mechanistic account of why cardiac injury can outlast its trigger and a rational route to intervention by targeting reader complexes, writer-eraser balance, and remodeler-mediated enhancer control. The translational mandate is precision: define causal regulatory nodes by cell type and disease phase and develop biomarkers that distinguish hemodynamic improvement from molecular reset.
1. INTRODUCTION 1.1 Principles The 2013 European Society of Hypertension/European Society of Cardiology (ESH/ESC) guidelines continue to adhere to some fundamental principles that inspired the 2003 and 2007 guidelines, namely to base recommendations on properly conducted studies identified from an extensive review of the literature; to consider, as the highest priority, data from randomized, controlled trials and their meta-analyses, but not to disregard the results of observational and other studies of appropriate scientific calibre; and to grade the level of scientific evidence and the strength of recommendations in order to more effectively alert physicians on recommendations that are based on the opinions of the experts rather than on evidence (Tables 1 and 2). When appropriately recognized, this can avoid guidelines being perceived as prescriptive and favour the performance of studies wherein opinion prevails and evidence is lacking.TABLE 1: Classes of recommendationsTABLE 2: Levels of evidenceThis shortened version of the ESH/ESC guidelines is for the practicing physician who often requires simplified information. However, whenever the physicians would like to know the source of the data upon which the recommendations are based, they are encouraged to consult the extensive version of the ESH/ESC guidelines wherein adequate references are given. These guidelines, however, do not override the individual responsibility of healthcare professionals to make appropriate decisions in the circumstances of the individual patient. 1.2 New aspects Because of new evidence on several diagnostic and therapeutic aspects of hypertension, the present guidelines differ from the 2007 ones in several points: Re-emphasis on integration of blood pressure (BP), cardiovascular risk factors, asymptomatic organ damage and clinical complications for total cardiovascular risk assessment. Update of the prognostic significance of out-of-office BP (both ambulatory and home BP), white-coat hypertension and masked hypertension. Initiation of antihypertensive drug treatment only in patients with SBP or DBP values at least 140 or 90 mmHg, independent of level of total cardiovascular risk. Unified target SBP (<140 mmHg) in both higher and lower cardiovascular risk patients. Revised recommendations on treatment of hypertension in young people and in the elderly. Liberal approach to initial monotherapy, without any all-ranking purpose scheme. Revised therapeutic algorithm for achieving target BP. Revised attention to resistant hypertension. 2. DEFINITIONS AND CLASSIFICATIONS The continuous relationship between BP and cardiovascular and renal events make the distinction between normotension and hypertension difficult. In practice, however, cut-off BP values are universally used to facilitate the decision about treatment (Table 3).TABLE 3: Definitions and classification of office blood pressure levels (mmHg)In order to help prognosis, total cardiovascular risk should be stratified in different categories (low, moderate, high and very high risk referred to the 10-year risk of cardiovascular mortality), based on BP category, cardiovascular risk factors, asymptomatic organ damage and presence of diabetes, and symptomatic cardiovascular disease or chronic kidney disease (CKD), as summarized in Fig. 1.FIGURE 1: Stratification of total cardiovascular risk in categories of low, moderate, high and very high risk according to SBP and DBP and presence of risk factors (RFs), asymptomatic organ damage (OD), diabetes, chronic kidney disease (CKD) stage or symptomatic cardiovascular disease (CVD). Individuals with a high normal office but a raised out-of-office BP (masked hypertension) have a cardiovascular risk in the hypertension range. Individuals with a high office BP but normal out-of-office BP (white-coat hypertension), particularly if there is no diabetes, OD, CVD or CKD, have lower risk than sustained hypertension for the same office BP. BP, blood pressure; CV, cardiovascular; DBP, diastolic blood pressure; HT, hypertension; SBP, systolic blood pressure.3. DIAGNOSTIC EVALUATION The initial evaluation of a patient with hypertension should confirm the diagnosis of hypertension; detect causes of secondary hypertension; and assess cardiovascular risk, organ damage and concomitant clinical conditions. This calls for BP measurement, medical history including family history, physical examination, laboratory investigation and further diagnostic tests. Some of the investigations are needed in all patients; others only in specific patient groups. 3.1 Blood pressure measurement 3.1.1 Office and out-of-office blood pressure Although conventional office BP measurement currently remains the ‘gold standard’ for screening, diagnosis and management of hypertension, it is generally accepted that out-of-office BP provides important adjunct information. At present, BP can no longer be estimated using a mercury manometer in many – although not all – European countries. Auscultatory or oscillometric semiautomatic sphygmomanometers are used instead, but these devices should be validated according to standardized protocols and their accuracy checked periodically. Table 4 gives instructions for correct office BP measurements, and Table 5 provides clinical indications for out-of-office BP measurement, namely measurements at home or over the 24 h.TABLE 4: Office blood pressure measurementTABLE 5: Clinical indications for out-of-office blood pressure measurement for diagnostic purposesOffice BP is usually higher than ambulatory and home BP and the difference increases as office BP increases. Cut-off values for the definition of hypertension by home and ambulatory BP are reported in Table 6.TABLE 6: Definitions of hypertension by office and out-of-office blood pressure levels3.1.2 White-coat and masked hypertension The term ‘white-coat’ or ‘isolated office’ hypertension refers to a condition in which BP is elevated in the office at repeated visits and normal out of the office either on ambulatory blood pressure monitoring or on home blood pressure monitoring. Conversely, BP may be normal in the office and abnormally high out of the medical environment, which is termed ‘masked’ or ‘isolated ambulatory’ hypertension. Cut-off values to be used are those in Table 6. 3.1.3 Central blood pressure Owing to the variable superposition of incoming and reflected pressure waves along the arterial tree, aortic BP (central BP) may be different from brachial BP. Central BP can be estimated indirectly by various methods. The current guidelines consider that, despite the growing interest in these methods, more investigation is needed before recommending the routine measurement of central BP for clinical use. 3.2 Medical history The information to be obtained at the time of the first diagnosis of hypertension is indicated in Table 7.TABLE 7: Personal and family medical history3.3 Physical examination Physical examination aims to establish or verify the diagnosis of hypertension, establish current BP, screen for secondary causes of hypertension and refine global cardiovascular risk. Procedures for BP measurement are indicated in Tables 4 and 5. Other information to be obtained by physical examination is in Table 8.TABLE 8: Physical examination for secondary hypertension, organ damage and obesity3.4 Laboratory investigations Laboratory investigations are directed at providing evidence for additional risk factors, searching for secondary hypertension and looking for organ damage. Investigations should proceed from the most simple to the more complicated ones, as summarized in Table 9.TABLE 9: Laboratory investigations3.5 Searching for asymptomatic organ damage Owing to the importance of asymptomatic organ damage as an intermediate stage in the continuum of cardiovascular disease, and as a determinant of overall cardiovascular disease, signs of organ involvement should be sought carefully by appropriate techniques as indicated below.Figure3.6 Searching for secondary forms of hypertension A specific, potentially reversible cause of BP elevation can be identified in a relatively small number of adult patients with hypertension. However if basal work-up leads to the suspicion of a secondary form of hypertension, the patient should be referred to a specialized centre where specific diagnostic procedures may be performed. 4. TREATMENT APPROACH 4.1 Recommendations of previous guidelines revised The 2007 ESH/ESC Guidelines, like many other scientific guidelines, recommended the use of antihypertensive drugs in patients with Grade 1 hypertension even in the absence of other risk factors or organ damage after nonpharmacological treatment had proved unsuccessful. This recommendation also specifically included the elderly hypertensive patient. The 2007 Guidelines also suggested drug treatment of patients with diabetes, previous cardiovascular disease (CVD) or CKD even when their BP was in the high normal range (130–139/85–89 mmHg). Furthermore, a lower BP target was recommended for these high or very high risk patients (<130/80 mmHg) than in patients at low–moderate risk (<140/90 mmHg). These recommendations were reappraised in a 2009 ESH Task Force document on the basis of an extensive critical review of the evidence. The following now summarizes the conclusions for the current guidelines: attention should be directed to the Class of recommendation and the Level of evidence, in order to distinguish what is considered compelling and what simply prudent. Figure 2 also summarizes recommendations and suggestions for treatment initiation and BP targets in the context of total risk stratification of hypertensive individuals.FIGURE 2: Initiation of lifestyle changes and antihypertensive drug treatment. Targets of treatment are also indicated. Colours are as in Fig. 1. See 4.3 and 6.5 for evidence that, in patients with diabetes, the optimal DBP target is between 80 and 85 mmHg. In the high normal blood pressure (BP) range, drug treatment should be considered in the presence of a raised out-of-office BP (masked hypertension). See 4.2 and 6.3 for lack of evidence in favour of drug treatment in young individuals with isolated systolic hypertension. CKD, chronic kidney disease; CVD, cardiovascular disease; DBP, diastolic blood pressure; HT, hypertension; OD, organ damage; RF, risk factor; SBP, systolic blood pressure.4.2 When to initiate antihypertensive drug treatmentFigure4.3 Blood pressure treatment targetsFigure5. TREATMENT STRATEGIES 5.1 Lifestyle changes Appropriate lifestyle changes are the cornerstone for the prevention of hypertension. They are also important for its treatment, although they should never delay the initiation of drug therapy in patients at high level of risk. In addition to the BP-lowering effect, lifestyle changes contribute to the control of other cardiovascular risk factors and clinical conditions. The lifestyle measures that have been shown to be capable of reducing BP and therefore recommended are as follows: salt restriction to 5–6 g/day; moderation of alcohol consumption to no more than 20–30 g of ethanol per day in men and 10–20 g/day in women; high consumption of vegetables and fruits and low-fat dairy products; reduction of weight to a BMI of 25 kg/m2 and waist circumference to less than 102 cm in men and less than 88 cm in women; at least 30 min of moderate dynamic exercise on 5 to 7 days per week. 5.2 Pharmacological therapy 5.2.1 Choice of antihypertensive drugs The current guidelines reconfirm that all major classes of antihypertensive agents are suitable for the initiation and maintenance of antihypertensive treatment either in monotherapy or in some combinations, and that no all-purpose ranking of drugs for general antihypertensive usage is evidence based. All classes have their advantages but also contraindications, and may be preferentially used or avoided in specific conditions. Contraindications and preferred indications are listed in Tables 10 and 11.TABLE 10: Compelling and possible contraindications to the use of antihypertensive drugsTABLE 11: Drugs to be preferred in specific conditions5.2.2 Monotherapy and combination therapy The current guidelines share the 2007 Guidelines’ opinion that monotherapy can reduce BP to target only in a limited number of patients and that most patients require the combination of at least two drugs, and they reconfirm that initiation with a drug combination can be considered in patients at high cardiovascular risk or with markedly high BP. The algorithm of Fig. 3, however, is a modification of the 2007 one, to emphasize that adding drugs to drugs should be done with attention to results and any compound overtly ineffective or minimally effective should be replaced, rather than retained in an automatic step-up multiple-drug approach. Combinations to be preferred or avoided are illustrated in Fig. 4.FIGURE 3: Monotherapy vs. drug combination strategies to achieve target blood pressure (BP). CV, cardiovascular.FIGURE 4: Possible combinations of classes of antihypertensive drugs. Green continuous lines: preferred combinations; green dashed line: useful combination (with some limitations); black dashed lines: possible but less well tested combinations; red continuous line: not recommended combination. Although verapamil and diltiazem are sometimes used with a β-blocker to improve ventricular rate control in permanent atrial fibrillation, only dihydropyridine calcium antagonists should normally be combined with β-blockers. ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker. *Thiazide diuretics also include thiazide-like compounds (chlorthalidone) and indapamide.Strengths of recommendations about choice of drugs and combinations of antihypertensive agents are given in the summary table below.Figure6. TREATMENT STRATEGIES IN SPECIAL CONDITIONS Summary recommendations for antihypertensive treatment strategies in various conditions are listed below. 6.1 White-coat and masked hypertensionFigure6.2 ElderlyFigure6.3 Young adultsFigure6.4 WomenFigure6.5 Diabetes mellitusFigure6.6 Metabolic syndromeFigure6.7 Diabetic and nondiabetic nephropathyFigure6.8 Cerebrovascular diseaseFigure6.9 Heart diseaseFigure6.10 Atherosclerosis, arteriosclerosis and peripheral artery diseaseFigure6.11 Resistant hypertensionFigure7. TREATMENT OF ASSOCIATED RISK FACTORSFigure8. FOLLOW-UP 8.1 Follow-up visits After initiation of antihypertensive drug therapy, it is important to see the patient at 2-week to 4-week intervals to evaluate the effects on BP and to assess possible side-effects. Some medications will have an effect within days or weeks but a continued delayed response may occur during the first 2 months. Once the target is reached, a visit interval of a few months is reasonable. 8.2 Elevated blood pressure at control visits Patients and physicians have a tendency to interpret an uncontrolled BP at a given visit as due to occasional factors and thus to downplay its clinical significance. Due attention should be given to poor adherence or irregular consumption of drugs (sometimes because of adverse effects), to the white-coat effect and to substances or drugs opposing the antihypertensive effect of treatment. 8.3 Can antihypertensive medication be stopped? In some patients, in whom treatment is accompanied by an effective BP control for an extended period, it may be possible to reduce the number and dosage of drugs. This may be particularly the case if BP control is accompanied by healthy lifestyle changes. Reduction of medications should be made gradually and the patient should frequently be checked because of the risk of reappearance of hypertension. 9. IMPROVEMENT OF BLOOD PRESSURE CONTROL IN HYPERTENSION Despite overwhelming evidence that hypertension is a major cardiovascular risk factor and that BP-lowering substantially reduce the risk, there is evidence that all over the world a noticeable proportion of hypertensive individuals are unaware of this condition or, if aware, do not undergo treatment; target BP values are seldom achieved; failure to achieve BP control is associated with persistence of an elevated cardiovascular risk; and the rate of awareness of hypertension and BP control is improving slowly or not at all. As a consequence, high BP remains a leading cause of death and cardiovascular morbidity in Europe, as elsewhere in the world. Overall, three main causes of the low rate of BP control in real life have been identified: physician inertia; patient low adherence to treatment; and deficiencies of healthcare systems in their approach to chronic diseases. Methods to improve adherence to physicians’ recommendations are listed in Table 12.TABLE 12: Methods to improve adherence to physicians’ recommendations
In traditional models of opinion dynamics, each agent in a network has an opinion and changes in opinions arise from pairwise (i.e., dyadic) interactions between agents. However, in many situations, groups of individuals possess a collective opinion that can differ from the opinions of its constituent individuals. In this paper, we study the effects of group opinions on opinion dynamics. We formulate a hypergraph model in which both individual agents and groups of 3 agents have opinions, and we examine how opinions evolve through both dyadic interactions and group memberships. In some parameter regimes, we find that the presence of group opinions can lead to oscillatory and excitable opinion dynamics. In the oscillatory regime, the mean opinion of the agents in a network has self-sustained oscillations. In the excitable regime, finite-size effects create large but short-lived opinion swings (as in social fads). We develop a mean-field approximation of our model and obtain good agreement with direct numerical simulations. We also show -- both numerically and via our mean-field description -- that oscillatory dynamics occur only when the number of dyadic and polyadic interactions per
Deterministic dynamics is a mathematical model used to describe the temporal evolution of a system, generally expressed as dx/dt = F(x), where x represents the system's state, and F(x) determines its dynamics. It is employed to understand long-term system behavior, including opinion formation and polarization in online communities. Opinion dynamics models, like the Katz model and the logistic map, help analyze how individual opinions are influenced within social networks and exhibit chaotic behavior. These models are crucial for studying opinion formation and collective behavior on social media, especially in conjunction with branching theory. For instance, Galam's Ising model applies principles from physics to social sciences, representing individual opinions as "spins" and illustrating how local interactions influence consensus formation. The Bounding Confidence model considers opinions within a confidence interval, showing how opinions converge or polarize. These models effectively analyze opinion dynamics in online communities, aiding in understanding trends and viral phenomena on social media. This research aims to analyze discourse flow and opinion evolution, predicting futur
Opinion summarisation is a task that aims to condense the information presented in the source documents while retaining the core message and opinions. A summary that only represents the majority opinions will leave the minority opinions unrepresented in the summary. In this paper, we use the stance towards a certain target as an opinion. We study bias in opinion summarisation from the perspective of opinion diversity, which measures whether the model generated summary can cover a diverse set of opinions. In addition, we examine opinion similarity, a measure of how closely related two opinions are in terms of their stance on a given topic, and its relationship with opinion diversity. Through the lens of stances towards a topic, we examine opinion diversity and similarity using three debatable topics under COVID-19. Experimental results on these topics revealed that a higher degree of similarity of opinions did not indicate good diversity or fairly cover the various opinions originally presented in the source documents. We found that BART and ChatGPT can better capture diverse opinions presented in the source documents.
Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining. Tweets are often informal, unstructured and lacking labeled data such as categories and ratings, making it challenging for product opinion mining. In this paper, we propose an LDA-based opinion model named Twitter Opinion Topic Model (TOTM) for opinion mining and sentiment analysis. TOTM leverages hashtags, mentions, emoticons and strong sentiment words that are present in tweets in its discovery process. It improves opinion prediction by modeling the target-opinion interaction directly, thus discovering target specific opinion words, neglected in existing approaches. Moreover, we propose a new formulation of incorporating sentiment prior information into a topic model, by utilizing an existing public sentiment lexicon. This is novel in that it learns and updates with the data. We c
Opinions are central to almost all human activities and are key influencers of our behaviors. In current times due to growth of social networking website and increase in number of e-commerce site huge amount of opinions are now available on web. Given a set of evaluative statements that contain opinions (or sentiments) about an Entity, opinion mining aims to extract attributes and components of the object that have been commented on in each statement and to determine whether the comments are positive, negative or neutral. While lot of research recently has been done in field of opinion mining and some of it dealing with ranking of entities based on review or opinion set, classifying opinions into finer granularity level and then ranking entities has never been done before. In this paper method for opinion mining from statements at a deeper level of granularity is proposed. This is done by using fuzzy logic reasoning, after which entities are ranked as per this information.
Many models of opinion dynamics include measures of distance between opinions. Such models are susceptible to boundary effects where the choice of the topology of the opinion space may influence the dynamics. In this paper we study an opinion dynamics model following the seminal model by Axelrod, with the goal of understanding the effect of a toroidal opinion space. To do this we systematically compare two versions of the model: one with toroidal opinion space and one with cubic opinion space. In their most basic form the two versions of our model result in similar dynamics (consensus is attained eventually). However, as we include bounded confidence and eventually per agent weighting of opinion elements the dynamics become quite contrasting. The toroidal opinion space consistently allows for a greater number of groups in steady state than the cubic opinion space model. Furthermore, the outcome of the dynamics in the toroidal opinion space model are more sensitive to the inclusion of extensions than in the cubic opinion space model.
This paper revises previous work and introduces changes in spatio-temporal scales. The paper presents a model that includes layers A and B with varying degrees of forgetting and dependence over time. We also model changes in dependence and forgetting in layers A, A', B, and B' under certain conditions. In addition, to discuss the formation of opinion clusters that have reinforcing or obstructive behaviors of forgetting and dependence and are conservative or brainwashing or detoxifying and less prone to filter bubbling, new clusters C and D that recommend, obstruct, block, or incite forgetting and dependence over time are Introduction. This introduction allows us to test hypotheses regarding the expansion of opinions in two dimensions over time and space, the state of development of opinion space, and the expansion of public opinion. Challenges in consensus building will be highlighted, emphasizing the dynamic nature of opinions and the need to consider factors such as dissent, distrust, and media influence. The paper proposes an extended framework that incorporates trust, distrust, and media influence into the consensus building model. We introduce network analysis using dimerizing
Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon. Highlighting all details in EHRs helps reduce the likelihood of missing crucial information by drawing attention to key content. This study proposes the design of a Cardiology Interface Terminology (CIT) to accurately highlight all details in EHR notes of cardiology patients. We introduce an innovative Machine Learning (ML) technique for the design of CIT. The ML technique requires training data. Manual preparation of such training data is time-consuming and expensive. The process of the CIT design includes three phases. In the first two phases, we innovatively derive a training data CIT to be used by the third phase, ML technique. We start by designing an initial CIT, composed of several components: the cardiology-related sub-hierarchies of SNOMED, other SNOMED concepts mined from EHRs of build set, and necessary components of terms e.g., medical abbreviations and medications. Utilizing an iterative process, fine-grained phrases containing initial CIT concepts are extracted from build set as CIT concept candidates. The candidate concep
Opinion Dynamics (OD) models are a particular case of Agent-Based Models in which the evolution of opinions within a population is studied. In most OD models, opinions evolve as a consequence of interactions between agents, and the opinion fusion rule defines how those opinions are updated. In consequence, despite being simplistic, OD models provide an explainable and interpretable mechanism for understanding the underlying dynamics of opinion evolution. Unfortunately, existing OD models mainly focus on explaining the evolution of (usually synthetic) opinions towards consensus, fragmentation, or polarization, but they usually fail to analyze scenarios of (real-world) highly oscillating opinions. This work overcomes this limitation by studying the ability of several OD models to reproduce highly oscillating dynamics. To this end, we formulate an optimization problem which is further solved using Evolutionary Algorithms, providing both quantitative results on the performance of the optimization and qualitative interpretations on the obtained results. Our experiments on a real-world opinion dataset about immigration from the monthly barometer of the Spanish Sociological Research Cente
Biomedical text embeddings have primarily been developed using research literature from PubMed, yet clinical cardiology practice relies heavily on procedural knowledge and specialized terminology found in comprehensive textbooks rather than research abstracts. This research practice gap limits the effectiveness of existing embedding models for clinical applications incardiology. This study trained CardioEmbed, a domain-specialized embedding model based on Qwen3-Embedding-8B, using contrastive learning on a curated corpus of seven comprehensive cardiology textbooks totaling approximately 150,000 sentences after deduplication. The model employs InfoNCE loss with in-batch negatives and achieves 99.60% retrieval accuracy on cardiac-specific semantic retrieval tasks, a +15.94 percentage point improvement over MedTE, the current state-of-the-art medical embedding model. On MTEB medical benchmarks, the model obtained BIOSSES 0.77 Spearman and SciFact 0.61 NDCG@10, indicating competitive performance on related biomedical domains. Domain-specialized training on comprehensive clinical textbooks yields near-perfect cardiology retrieval (99.60% Acc@1), improving over MedTE by +15.94 percentage
Recent research has developed the Ising model from physics, especially statistical mechanics, and it plays an important role in quantum computing, especially quantum annealing and quantum Monte Carlo methods. The model has also been used in opinion dynamics as a powerful tool for simulating social interactions and opinion formation processes. Individual opinions and preferences correspond to spin states, and social pressure and communication dynamics are modeled through interactions between spins. Quantum computing makes it possible to efficiently simulate these interactions and analyze more complex social networks.Recent research has incorporated concepts from quantum information theory such as Graph State, Stabilizer State, and Surface Code (or Toric Code) into models of opinion dynamics. The incorporation of these concepts allows for a more detailed analysis of the process of opinion formation and the dynamics of social networks. The concepts lie at the intersection of graph theory and quantum theory, and the use of Graph State in opinion dynamics can represent the interdependence of opinions and networks of influence among individuals. It helps to represent the local stability
This study introduces a new numerical model to simulate how information is comprehended and processed on social networks, using continuous "Phase Field Modeling" variables (phiA, phiB, phiC) to represent individual users' opinions. It captures the immediate and two-way nature of social media interactions, reproducing the spread and feedback of information. The model incorporates psychological and social factors like confirmation bias and opinion rigidity to analyze information processing and opinion development among users. It also explores the dynamics of opinion segregation and interaction in and out of filter bubbles, offering a quantitative view of opinion dynamics on platforms like social networking services (SNS). This approach combines theoretical models with real-world social network data to study the effects of information concentration on opinion formation and the phenome Phase Field Modeling of opinion polarization and echo chamber effects on SNS. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.
Importance: Clinical decisions are ideally based on evidence generated from multiple randomized controlled trials (RCTs) evaluating clinical outcomes, but historically, few clinical guideline recommendations have been based entirely on this type of evidence. Objective: To determine the class and level of evidence (LOE) supporting current major cardiovascular society guideline recommendations, and changes in LOE over time. Data Sources: Current American College of Cardiology/American Heart Association (ACC/AHA) and European Society of Cardiology (ESC) clinical guideline documents (2008-2018), as identified on cardiovascular society websites, and immediate predecessors to these guideline documents (1999-2014), as referenced in current guideline documents. Study Selection: Comprehensive guideline documents including recommendations organized by class and LOE. Data Extraction and Synthesis: The number of recommendations and the distribution of LOE (A [supported by data from multiple RCTs or a single, large RCT], B [supported by data from observational studies or a single RCT], and C [supported by expert opinion only]) were determined for each guideline document. Main Outcomes and Measures: The proportion of guideline recommendations supported by evidence from multiple RCTs (LOE A). Results: Across 26 current ACC/AHA guidelines (2930 recommendations; median, 121 recommendations per guideline [25th-75th percentiles, 76-155]), 248 recommendations (8.5%) were classified as LOE A, 1465 (50.0%) as LOE B, and 1217 (41.5%) as LOE C. The median proportion of LOE A recommendations was 7.9% (25th-75th percentiles, 0.9%-15.2%). Across 25 current ESC guideline documents (3399 recommendations; median, 130 recommendations per guideline [25th-75th percentiles, 111-154]), 484 recommendations (14.2%) were classified as LOE A, 1053 (31.0%) as LOE B, and 1862 (54.8%) as LOE C. When comparing current guidelines with prior versions, the proportion of recommendations that were LOE A did not increase in either ACC/AHA (median, 9.0% [current] vs 11.7% [prior]) or ESC guidelines (median, 15.1% [current] vs 17.6% [prior]). Conclusions and Relevance: Among recommendations in major cardiovascular society guidelines, only a small percentage were supported by evidence from multiple RCTs or a single, large RCT. This pattern does not appear to have meaningfully improved from 2008 to 2018.
The field of opinion dynamics has evolved steadily since the earliest studies applying magnetic physics methods to better understand social opinion formation. However, in the real world, complete agreement of opinions is rare, and biaxial consensus, especially on social issues, is rare. To address this challenge, Ishii and Kawabata (2018) proposed an extended version of the Bounded Confidence Model that introduces new parameters indicating dissent and distrust, as well as the influence of mass media. Their model aimed to capture more realistic social opinion dynamics by introducing coefficients representing the degree of trust and distrust, rather than assuming convergence of opinions. In this paper, we propose a new approach to opinion dynamics based on this Trust-Distrust Model (TDM), applying the dimer allocation and Ising model. Our goal is to explore how the interaction between trust and distrust affects social opinion formation. In particular, we analyze through mathematical models how various external stimuli, such as mass media, third-party opinions, and economic and political factors, affect people's opinions. Our approach is to mathematically represent the dynamics of tru
The field of opinion dynamics has its roots in early research that applied methods from magnetic physics to gain insights into the formation of social opinions. A central challenge in this field lies in modeling how diverse opinions coexist and exert influence on each other. In the realm of social issues, it's In this study, we leverage the dimer construct and the dimer model to establish a theoretical framework. Through numerical simulations, we demonstrate how this proposed model can be applied to real-world scenarios of social opinion formation. The model involves the computation of the Castellain matrix (K), the distribution function (Z), and the probability of dimer configuration (P(D)) for convex regions with varying positions and distances. It explores how alterations in convex regions impact the probability of dimer configuration. Furthermore, our model takes into account two critical factors: "dependence" and "forgetting" in the process of opinion formation. It also delves into the concepts of "distance" and "location" of opinions. The results of numerical simulations shed light on how our model effectively captures the processes involved in real-world social opinion forma
This paper delves into the history and integration of quantum theory into areas such as opinion dynamics, decision theory, and game theory, offering a novel framework for social simulations. It introduces a quantum perspective for analyzing information transfer and decision-making complexity within social systems, employing a toric code-based method for error discrimination.Central to this research is the use of toric codes, originally for quantum error correction, to detect and correct errors in social simulations, representing uncertainty in opinion formation and decision-making processes. Operator and error syndrome measurement, vital in quantum computation, help identify and analyze errors and uncertainty in social simulations. The paper also discusses fault-tolerant computation employing transversal gates, which protect against errors during quantum computation. In social simulations, transversal gates model protection from external interference and misinformation, enhancing the fidelity of decision-making and strategy formation processes.
In e-commerce, opinion tags refer to a ranked list of tags provided by the e-commerce platform that reflect characteristics of reviews of an item. To assist consumers to quickly grasp a large number of reviews about an item, opinion tags are increasingly being applied by e-commerce platforms. Current mechanisms for generating opinion tags rely on either manual labelling or heuristic methods, which is time-consuming and ineffective. In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews. The abstractive opinion tagging task comes with three main challenges: (1) the noisy nature of reviews; (2) the formal nature of opinion tags vs. the colloquial language usage in reviews; and (3) the need to distinguish between different items with very similar aspects. To address these challenges, we propose an abstractive opinion tagging framework, named AOT-Net, to generate a ranked list of opinion tags given a large number of reviews. First, a sentence-level salience estimation component estimates each review's salience score. Next, a
Opinion formation in the population has attracted extensive research interest. Various models have been introduced and studied, including the ones with individuals' free will allowing them to change their opinions. Such models, however, have not taken into account the fact that individuals with different opinions may have different levels of loyalty, and consequently, different probabilities of changing their opinions. In this work, we study on how the non-uniform distribution of the opinion changing probability may affect the final state of opinion distribution. By simulating a few different cases with different symmetric and asymmetric non-uniform patterns of opinion changing probabilities, we demonstrate the significant effects that the different loyalty levels of different opinions have on the final state of the opinion distribution.