Cardiovascular computed tomography in cardiovascular disease:An overview of its applications from diagnosis to prediction

Cardiovascular computed tomography angiography(CTA)is a widely used imaging modality in the diagnosis of cardiovascular disease.Advancements in CT imaging technology have further advanced its applications from high diagnostic value to minimising radiation exposure to patients.In addition to the standard application of assessing vascular lumen changes,CTA-derived applications including 3D printed personalised models,3D visualisations such as virtual endoscopy,virtual reality,augmented reality and mixed reality,as well as CT-derived hemodynamic flow analysis and fractional flow reserve(FFRCT)greatly enhance the diagnostic performance of CTA in cardiovascular disease.The widespread application of artificial intelligence in medicine also significantly contributes to the clinical value of CTA in cardiovascular disease.Clinical value of CTA has extended from the initial diagnosis to identification of vulnerable lesions,and prediction of disease extent,hence improving patient care and management.In this review article,as an active researcher in cardiovascular imaging for more than 20 years,I will provide an overview of cardiovascular CTA in cardiovascular disease.It is expected that this review will provide readers with an update of CTA applications,from the initial lumen assessment to recent developments utilising latest novel imaging and visualisation technologies.It will serve as a useful resource for researchers and clinicians to judiciously use the cardiovascular CT in clinical practice.

Soluble ST2:A Novel Biomarker for Diagnosis and Prognosis of Cardiovascular Disease

The increasing incidence of cardiovascular disease(CVD)is a significant global health concern,affecting millions of individuals each year.Accurate diagnosis of acute CVD poses a formidable challenge,as misdiagnosis can significantly decrease patient survival rates.Traditional biomarkers have played a vital role in the diagnosis and prognosis of CVDs,but they can be influenced by various factors,such as age,sex,and renal function.Soluble ST2(sST2)is a novel biomarker that is closely associated with different CVDs.Its low reference change value makes it suitable for continuous measurement,unaffected by age,kidney function,and other confounding factors,facilitating risk stratification of CVDs.Furthermore,the combination of sST2 with other biomarkers can enhance diagnostic accuracy and prognostic value.This review aims to provide a comprehensive overview of sST2,focusing on its diagnostic and prognostic value as a myocardial marker for different types of CVDs and discussing the current limitations of sST2.

Nonalcoholic fatty liver disease:Updates in noninvasive diagnosis and correlation with cardiovascular disease

Nonalcoholic fatty liver disease(NAFLD)refers to the accumulation of fat(mainly triglycerides)within hepatocytes.Approximately 20%-30%of adults in the general population in developed countries have NAFLD;this trend is increasing because of the pandemicity of obesity and diabetes,and is becoming a serious public health burden.Twenty percent of individuals with NAFLD develop chronic hepatic inflammation[nonalcoholic steatohepatitis(NASH)],which can be associated with the development of cirrhosis,portal hypertension,and hepatocellular carcinoma in a minority of patients.And thus,the detection and diagnosis of NAFLD is important for general practitioners.Liver biopsy is the gold standard for diagnosing NAFLD and confirming the presence of NASH.However,the invasiveness of this procedure limits its application to screening the general population or patients with contraindications for liver biopsy.The development of noninvasive diagnostic methods for NAFLD is of paramount importance.This review focuses on the updates of noninvasive diagnosis of NAFLD.Besides,we review clinical evidence supporting a strong association between NAFLD and the risk of cardiovascular disease because of the cross link between these two disorders.

Nanomedicine: Tiny Particles and Machines, from Diagnosis to Treatment of Cardiovascular Disease, Provides Huge Achievements

Cardiovascular disease is one of many reverberating ailments that affect and kill hundreds of thousands of people around the world. To date treatments that offer improvement in the health condition of diseased people include the most promising nanomedicine although it is in its infancy, yet attaining attention from researchers of top notch day by day. In this current review importance is given on the application of nanomedicine in the diagnosis as well as treatment of cardiovascular disease.

Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

Diabetes and cardiac autonomic neuropathy: Clinical manifestations, cardiovascular consequences, diagnosis and treatment

Cardiac autonomic neuropathy(CAN) is a frequent chronic complication of diabetes mellitus with potentially life-threatening outcomes. CAN is caused by the impairment of the autonomic nerve fibers regulating heart rate, cardiac output, myocardial contractility, cardiac electrophysiology and blood vessel constriction anddilatation. It causes a wide range of cardiac disorders, including resting tachycardia, arrhythmias, intraoperative cardiovascular instability, asymptomatic myocardial ischemia and infarction and increased rate of mortality after myocardial infarction. Etiological factors associated with autonomic neuropathy include insufficient glycemic control, a longer period since the onset of diabetes, increased age, female sex and greater body mass index. The most commonly used methods for the diagnosis of CAN are based upon the assessment of heart rate variability(the physiological variation in the time interval between heartbeats), as it is one of the first findings in both clinically asymptomatic and symptomatic patients. Clinical symptoms associated with CAN generally occur late in the disease process and include early fatigue and exhaustion during exercise, orthostatic hypotension, dizziness, presyncope and syncope. Treatment is based on early diagnosis, life style changes, optimization of glycemic control and management of cardiovascular risk factors. Medical therapies, including aldose reductase inhibitors, angiotensin-converting enzyme inhibitors, prostoglandin analogs and alpha-lipoic acid, have been found to be effective in randomized controlled trials. The following article includes the epidemiology, clinical findings and cardiovascular consequences, diagnosis, and approaches to prevention and treatment of CAN.

Diagnosis and management of ischemic cardiomyopathy: Role of cardiovascular magnetic resonance imaging

Coronary artery disease(CAD) represents an important cause of mortality. Cardiovascular magnetic resonance(CMR) imaging evolved as an imaging modality that allows the assessment of myocardial function, perfusion, contractile reserve and extent of fibrosis in a single comprehensive exam. This review highlights the role of CMR in the differential diagnosis of acute chest pain by detecting the location of obstructive CAD or necrosis and identifying other conditions like stress cardiomyopathy or myocarditis that can present with acute chest pain. Besides, it underlines the prognostic implication of perfusion abnormalities in the setting of acute chest pain. Furthermore, the review addresses the role of CMR to detect significant CAD in patients with stable CAD. It elucidates the accuracy and clinical utility of CMR with respect to other imaging modalitieslike single-photon emission computed tomography and positron emission tomography. Besides, the prognostic value of CMR stress testing is discussed. Additionally, it summarizes the available CMR techniques to assess myocardial viability and describes algorithm to identify those patient who might profit from revascularization those who should be treated medically. Finally, future promising imaging techniques that will provide further insights into the fundamental disease processes in ischemic cardiomyopathy are discussed.

Age at diagnosis of type 2 diabetes and cardiovascular risk factor profile:A pooled analysis

BACKGROUND The diagnosis of type 2 diabetes(T2D)in younger adults,an increasingly common public health issue,is associated with a higher risk of cardiovascular complications and mortality,which may be due to a more adverse cardiovascular risk profile in individuals diagnosed at a younger age.AIM To investigate the association between age at diagnosis and the cardiovascular risk profile in adults with T2D.METHODS A pooled dataset was used,comprised of data from five previous studies of adults with T2D,including 1409 participants of whom 196 were diagnosed with T2D under the age of 40 years.Anthropometric and blood biomarker measurements included body weight,body mass index(BMI),waist circumference,body fat percentage,glycaemic control(HbA1c),lipid profile and blood pressure.Univariable and multivariable linear regression models,adjusted for diabetes duration,sex,ethnicity and smoking status,were used to investigate the association between age at diagnosis and each cardiovascular risk factor.RESULTS A higher proportion of participants diagnosed with T2D under the age of 40 were female,current smokers and treated with glucose-lowering medications,compared to participants diagnosed later in life.Participants diagnosed with T2D under the age of 40 also had higher body weight,BMI,waist circumference and body fat percentage,in addition to a more adverse lipid profile,compared to participants diagnosed at an older age.Modelling results showed that each one year reduction in age at diagnosis was significantly associated with 0.67 kg higher body weight[95%confidence interval(CI):0.52-0.82 kg],0.18 kg/m^(2) higher BMI(95%CI:0.10-0.25)and 0.32 cm higher waist circumference(95%CI:0.14-0.49),after adjustment for duration of diabetes and other confounders.Younger age at diagnosis was also significantly associated with higher HbA1c,total cholesterol,low-density lipoprotein cholesterol and triglycerides.CONCLUSION The diagnosis of T2D earlier in life is associated with a worse cardiovascular risk factor profile,compared to those diagnosed later in life.

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states.At the same time,latest develop-ments of artificial intelligence(AI)techniques have the ability to manage and ana-lyzing massive amounts of biomedical datasets results in clinical decisions and real time applications.They can be employed for medical imaging;however,the 1D biomedical signal recognition process is still needing to be improved.Electrocardiogram(ECG)is one of the widely used 1-dimensional biomedical sig-nals,which is used to diagnose cardiovascular diseases.Computer assisted diag-nostic modelsfind it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals.To resolve these issues,this study designs automated deep learning based 1D biomedical ECG sig-nal recognition for cardiovascular disease diagnosis(DLECG-CVD)model.The DLECG-CVD model involves different stages of operations such as pre-proces-sing,feature extraction,hyperparameter tuning,and classification.At the initial stage,data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing.In addition,deep belief network(DBN)model is applied to derive a set of feature vectors.Besides,improved swallow swarm optimization(ISSO)algorithm is used for the hyper-parameter tuning of the DBN model.Lastly,extreme gradient boosting(XGBoost)classifier is employed to allocate proper class labels to the test ECG signals.In order to verify the improved diagnostic performance of the DLECG-CVD model,a set of simulations is carried out on the benchmark PTB-XL dataset.A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy,sensitivity,specificity,kappa,Mathew correlation coefficient,and Hamming loss.

Movement analysis in the diagnosis and management of Parkinson’s disease

Challenges in the diagnosis and treatment of Parkinson’s disease:Parkinson’s disease(PD)is an increasingly prevalent neurodegenerative disease,at first sight primarily characterized by motor symptoms,although non-motor symptoms also constitute a major part of the overall phenotype.Clinically,this disease cannot be diagnosed reliably until a large part of the vulnerable dopaminergic neurons has been irretrievably lost,and the disease progresses inexorably.New biological criteria for PD have been proposed recently and might eventually improve early diagnosis,but they require further validation,and their use will initially be restricted to a research environment(Darweesh et al.,2024).

Visualizing traumatic brain injury:ocular clues for diagnosis and assessment

Traumatic brain injury (TBI) is defined as damage to the brain resulting from an external sudden physical force or shock to the head.It is considered a silent public health epidemic causing significant death and disability globally.There were 64,000 TBI related deaths reported in the USA in 2020,with about US$76 billion in direct and indirect medical costs annually.

Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies

Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota composition.Satisfactory results are lacking for conventional treatments for epilepsy.Surgical resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy.Non-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research focus.This review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of epilepsy.It also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation.Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures.Additionally,many new technologies for the diagnosis and treatment of epilepsy are being explored.However,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.

Association between age at diagnosis of diabetes and ocular disease:Insights from a recent article

In this article,we discuss Ye et al's recent article on the association between age at diabetes diagnosis and subsequent risk of age-related ocular diseases.The study,which utilized United Kingdom Biobank data,highlighted a strong link between early diabetes onset and major eye conditions,such as cataracts,glaucoma,agerelated macular degeneration,and vision loss,independent of glycemic control and disease duration.This finding challenges the previous belief that diabetic eye disease primarily correlates with hyperglycemia.As lifestyles evolve and the age of diabetes diagnosis decreases,understanding this relationship may reveal the complex pathogenesis underlying diabetes-related complications.This editorial summarizes potential mechanisms connecting the age of diabetes onset with four types of ocular diseases,emphasizing the significance of early diagnosis.

Medical Diagnosis Based on Multi-Attribute Group Decision-Making Using Extension Fuzzy Sets,Aggregation Operators and Basic Uncertainty Information Granule

Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.

MicroRNAs as potential biomarkers for diagnosis of post-traumatic stress disorder

Post-traumatic stress disorder is a mental disorder caused by exposure to severe traumatic life events.Currently,there are no validated biomarkers or laboratory tests that can distinguish between trauma survivors with and without post-traumatic stress disorder.In addition,the heterogeneity of clinical presentations of post-traumatic stress disorder and the overlap of symptoms with other conditions can lead to misdiagnosis and inappropriate treatment.Evidence suggests that this condition is a multisystem disorder that affects many biological systems,raising the possibility that peripheral markers of disease may be used to diagnose post-traumatic stress disorder.We performed a PubMed search for microRNAs(miRNAs)in post-traumatic stress disorder(PTSD)that could serve as diagnostic biomarkers and found 18 original research articles on studies performed with human patients and published January 2012 to December 2023.These included four studies with whole blood,seven with peripheral blood mononuclear cells,four with plasma extracellular vesicles/exosomes,and one with serum exosomes.One of these studies had also used whole plasma.Two studies were excluded as they did not involve microRNA biomarkers.Most of the studies had collected samples from adult male Veterans who had returned from deployment and been exposed to combat,and only two were from recently traumatized adult subjects.In measuring miRNA expression levels,many of the studies had used microarray miRNA analysis,miRNA Seq analysis,or NanoString panels.Only six studies had used real time polymerase chain reaction assay to determine/validate miRNA expression in PTSD subjects compared to controls.The miRNAs that were found/validated in these studies may be considered as potential candidate biomarkers for PTSD and include miR-3130-5p in whole blood;miR-193a-5p,-7113-5p,-125a,-181c,and-671-5p in peripheral blood mononuclear cells;miR-10b-5p,-203a-3p,-4488,-502-3p,-874-3p,-5100,and-7641 in plasma extracellular vesicles/exosomes;and miR-18a-3p and-7-1-5p in blood plasma.Several important limitations identified in the studies need to be taken into account in future studies.Further studies are warranted with war veterans and recently traumatized children,adolescents,and adults having PTSD and use of animal models subjected to various stressors and the effects of suppressing or overexpressing specific microRNAs.

Autism spectrum disorder:difficulties in diagnosis and microRNA biomarkers

We performed a PubMed search for microRNAs in autism spectrum disorder that could serve as diagnostic biomarkers in patients and selected 17 articles published from January 2008 to December 2023,of which 4 studies were performed with whole blood,4 with blood plasma,5 with blood serum,1 with serum neural cell adhesion molecule L1-captured extracellular vesicles,1 with blood cells,and 2 with peripheral blood mononuclear cells.Most of the studies involved children and the study cohorts were largely males.Many of the studies had performed microRNA sequencing or quantitative polymerase chain reaction assays to measure microRNA expression.Only five studies had used real-time polymerase chain reaction assay to validate microRNA expression in autism spectrum disorder subjects compared to controls.The microRNAs that were validated in these studies may be considered as potential candidate biomarkers for autism spectrum disorder and include miR-500a-5p,-197-5p,-424-5p,-664a-3p,-365a-3p,-619-5p,-664a-3p,-3135a,-328-3p,and-500a-5p in blood plasma and miR-151a-3p,-181b-5p,-320a,-328,-433,-489,-572,-663a,-101-3p,-106b-5p,-19b-3p,-195-5p,and-130a-3p in blood serum of children,and miR-15b-5p and-6126 in whole blood of adults.Several important limitations were identified in the studies reviewed,and need to be taken into account in future studies.Further studies are warranted with children and adults having different levels of autism spectrum disorder severity and consideration should be given to using animal models of autism spectrum disorder to investigate the effects of suppressing or overexpressing specific microRNAs as a novel therapy.

Role of photon-counting computed tomography in pediatric cardiovascular imaging

Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.

Avoiding misdiagnosis of multilocular thymic cysts as malignant tumors on computer tomography

This editorial provides insights from a case report by Sun et al published in the World Journal of Clinical Cases.The case report focuses on a case where a multilocular thymic cyst(MTC)was misdiagnosed as a thymic tumor,resulting in an unnecessary surgical procedure.Both MTCs and thymic tumors are rare conditions that heavily rely on radiological imaging for accurate diagnosis.However,the similarity in their imaging presentations can lead to misinterpretation,resulting in unnecessary surgical procedures.Due to the ongoing lack of comprehensive knowledge about MTCs and thymic tumors,we offer a summary of diagnostic techniques documented in recent literature and examine potential causes of misdiagnosis.When computer tomography(CT)values surpass 20 Hounsfield units and display comparable morphology,there is a risk of misdiagnosing MTCs as thymic tumors.Employing various differential diagnostic methods like biopsy,molecular biology,multi-slice CT,CT functional imaging,positron emission tomography/CT molecular functional imaging,magnetic resonance imaging and radiomics,proves advantageous in reducing clinical misdiagnosis.A deeper understanding of these conditions requires increased attention and exploration by healthcare providers.Moreover,the continued advancement and utilization of various diagnostic methods are expected to enhance precise diagnoses,provide appropriate treatment options,and improve the quality of life for patients with thymic tumors and MTCs in the future.continued advancement and utilization of various diagnostic methods are expected to enhance precise diagnoses,provide appropriate treatment options,and improve the quality of life for patients with thymic tumors and MTCs in the future.

The Importance of Setting Treatment Goals for Cardiovascular Diseases

Background: Guidelines are issued by most major organizations that focus on a specific disease entity. Guidelines should be a significant help to the practicing physician who may not be up-to-date with the recent medical literature. Unfortunately, when conflicting guidelines for a specific disease are published, confusion results. Purpose: This article provides a suggested guideline outcome measure that would benefit the physician and patient. Methods: A review of 19 different guidelines for cardiovascular disease treatment is one example of the lack of specific outcomes that currently exist. The basic problem with most guidelines is that they do not state the expected end result (i.e., the benefit to the patient) if that guideline is followed. When guidelines use cardiovascular disease risk factors to dictate therapy, the end benefit is never stated so that the patient can make an appropriate choice of which (if any) guideline to follow. Results: A good example is guidelines published by the American Heart Association for reducing cardiovascular disease. These guidelines are risk factor based and only indicate that cardiovascular disease would be reduced if followed. No specific percentage in the reduction of the incidence of disease is given. In contrast, when elimination of the disease is the stated goal of the guideline, the end result is clear. To date, this goal has been stated by only one organization devoted to eliminating cardiovascular disease. Conclusion: Guidelines need to be written to provide the physician and the patient with a specific end point that is expected when the guideline is followed. Patient acceptance and compliance will be much improved if the patient knows the risk/benefit of following the guideline’s recommendations.

Delayed diagnosis in inflammatory bowel disease:Time to consider solutions

In this editorial,we discuss a recently published manuscript by Blüthner et al in the World Journal of Gastroenterology,with a specific focus on the delayed diagnosis of inflammatory bowel disease(IBD).IBD,which includes Crohn's disease and ulcerative colitis,is a chronic intestinal disorder.A time lag may exist between the onset of inflammation and the appearance of signs and symptoms,potentially leading to an incorrect or delayed diagnosis,a situation referred to as the delayed diagnosis of IBD.Early diagnosis is crucial for effective patient treatment and prognosis,yet delayed diagnosis remains common.The reasons for delayed diagnosis of IBD are numerous and not yet fully understood.One key factor is the nonspecific nature of IBD symptoms,which can easily be mistaken for other conditions.Additionally,the lack of specific diagnostic methods for IBD contributes to these delays.Delayed diagnosis of IBD can result in numerous adverse consequences,including increased intestinal damage,fibrosis,a higher risk of colorectal cancer,and a decrease in the quality of life of the patient.Therefore,it is essential to diagnose IBD promptly by raising physician awareness,enhancing patient education,and developing new diagnostic methods.