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Search Results (1,605)

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Keywords = cardiac arrhythmia

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7 pages, 1476 KiB  
Case Report
Mexiletine-Induced Esophageal Ulceration: Two Case Reports and a Review of the Literature
by Matteo Ghisa, Ilenia Barbuscio, Erica Bonazzi, Matteo Fassan, Brigida Barberio, Marco Senzolo and Edoardo V. Savarino
Reports 2025, 8(1), 9; https://doi.org/10.3390/reports8010009 (registering DOI) - 18 Jan 2025
Viewed by 196
Abstract
Background and Clinical Significance: Mexiletine is a class 1B antiarrhythmic drug commonly prescribed for ventricular arrhythmias and neuropathic pain. It works as a blocker of the sodium channel that modulates cardiac conduction and reduces aberrant nerve signaling. While it is generally well [...] Read more.
Background and Clinical Significance: Mexiletine is a class 1B antiarrhythmic drug commonly prescribed for ventricular arrhythmias and neuropathic pain. It works as a blocker of the sodium channel that modulates cardiac conduction and reduces aberrant nerve signaling. While it is generally well tolerated, gastrointestinal side effects, such as nausea, vomiting, and abdominal pain, are relatively common. Esophagitis and esophageal ulcerations have been described as rare side effects; however, they are poorly documented in the literature. Esophageal ulceration induced by oral medications, termed pill esophagitis, occurs due to prolonged contact between the medication and the esophageal mucosa. Factors contributing to this phenomenon include improper administration, such as swallowing without sufficient water, taking medication before lying down, or inherent irritant properties of the drug itself. Mexiletine-induced esophageal ulceration has not been extensively reported, making such cases clinically significant and worth investigating. In particular, the prompt diagnosis of mexiletine-induced esophageal injury is essential for timely treatment initiation or the discontinuation of the drug, preventing complications such as bleeding, strictures, or perforation. Altogether, these actions are important to prevent the onset of potentially serious complications, such as bleeding, strictures, and the perforation of the esophagus. Case Presentation: Two different patients were included in this case report on mexiletine-induced esophageal ulceration: a 78-year-old woman affected by primary dilated cardiomyopathy and atrial fibrillation with high ventricular response and a 19-year-old man affected by dilated cardiomyopathy and systemic sclerosis. Conclusions: This case report underscores the importance of recognizing mexiletine-induced esophageal ulceration, and it advocates for timely diagnosis and management to optimize patient outcomes. Full article
(This article belongs to the Section Gastroenterology)
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<p>EGD images with NBI showing linear ulcerations at the distal esophagus, 6 cm in length, covered by fibrin from the patient of case report #1. The blue color is due to the use of virtual chromoendoscopy.</p>
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<p>Multiple discontinuous distal esophageal ulcerations about 15 cm long, with fibrinous membranes, from the patient of case report #2.</p>
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<p>Representative histological pictures of case report #2. (<b>A</b>) Hyperplasia of the proliferative compartment and high-grade parakeratosis. (<b>B</b>,<b>C</b>) Ulceration of the mucosa associated with inflammatory granulation tissue and infiltration by leukocytes of the lamina propria and of the muscularis mucosae. (H&amp;E stain; original magnifications 10× and 20×).</p>
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10 pages, 1544 KiB  
Article
Snorkelling and Breath-Hold Diving Fatalities in Australia—A Review of 317 Deaths
by John M. Lippmann
Int. J. Environ. Res. Public Health 2025, 22(1), 119; https://doi.org/10.3390/ijerph22010119 (registering DOI) - 18 Jan 2025
Viewed by 246
Abstract
As snorkelling and breath-hold diving are conducted in a potentially hostile environment by participants with varying skills and health, fatalities occur. In this study, snorkelling and breath-hold diving fatalities were investigated in Australia from 2000 to 2021 to identify causes and countermeasures. The [...] Read more.
As snorkelling and breath-hold diving are conducted in a potentially hostile environment by participants with varying skills and health, fatalities occur. In this study, snorkelling and breath-hold diving fatalities were investigated in Australia from 2000 to 2021 to identify causes and countermeasures. The Australasian Diving Safety Foundation database and the National Coronial Information System were searched to identify snorkelling/breath-hold diving deaths from 2000 to 2021. Relevant data were extracted, recorded, and analysed. The median age of the 317 victims was 48 years, two-thirds were overweight or obese, and almost half had health conditions, including ischaemic heart disease (IHD) and left ventricular hypertrophy (LVH), predisposing them to an arrhythmia-related snorkelling incident. One-third of victims were likely disabled by cardiac arrhythmias and at least 137 deaths were from primary drowning, with 34 following apnoeic hypoxia. Pre-existing health conditions, particularly IHD and LVH, predispose to many snorkelling deaths in older participants and may be somewhat mitigated by targeted health screening. Drownings from apnoeic hypoxia persist in younger breath-hold divers who should avoid pushing their limits without close monitoring. Skills practice in a controlled environment, increased focus on the importance of an effective buddy, and improved supervision are necessary to mitigate risk in the inexperienced. Full article
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<p>Annual snorkelling-related deaths in Australia, 2000–2021.</p>
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<p>An “at risk” snorkeller with a marked snorkel, buoyancy aid, and close supervision.</p>
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<p>A large commercial snorkelling operation on the Great Barrier Reef. Of note, there is a marked snorkel area, rest stations, and several lookouts at multiple vantage points.</p>
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12 pages, 870 KiB  
Review
Detection of Deaths Caused by Hyperkalemia
by Małgorzata Żulicka, Kamila Sobczak, Dominik Kowalczyk, Sylwia Sikorska, Wioletta Arendt and Marta Hałas-Wiśniewska
Biomedicines 2025, 13(1), 222; https://doi.org/10.3390/biomedicines13010222 - 17 Jan 2025
Viewed by 282
Abstract
Under normal conditions, potassium is predominantly found within cells. The concentration gradient of sodium and potassium ions between intracellular and extracellular spaces enables signal transmission through membrane depolarization. The disruption of this transcellular process leads to elevated potassium ion levels in the extracellular [...] Read more.
Under normal conditions, potassium is predominantly found within cells. The concentration gradient of sodium and potassium ions between intracellular and extracellular spaces enables signal transmission through membrane depolarization. The disruption of this transcellular process leads to elevated potassium ion levels in the extracellular space, and thus in the blood, a condition known as hyperkalemia. Clinically, hyperkalemia may present as cardiac arrhythmias, muscle weakness, and palpitations. The post-mortem accumulation of potassium ions in various human tissues and organs, such as the heart, liver, kidneys, lungs, and vitreous body, particularly in cases of overdose, has been an area of research interest for years. Unfortunately, deaths caused by hyperkalemia are difficult to identify due to their non-specific symptoms and are often misinterpreted as cardiovascular-related. Furthermore, most potassium ion concentration tests developed in recent years are non-specific, have limitations, or are based on outdated techniques. Consequently, alternative methods, such as histopathological tissue analysis, potassium concentration assessment in the vitreous body, and aldosterone level measurement, show promise for improving the post-mortem detection of exogenous hyperkalemia. Full article
(This article belongs to the Section Molecular and Translational Medicine)
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<p>Diagram of potassium ion gradients: (<b>a</b>) ante-mortem and (<b>b</b>) post-mortem. Prepared based on [<a href="#B1-biomedicines-13-00222" class="html-bibr">1</a>]. Na<sup>+</sup>—sodium cation, K<sup>+</sup>—potassium cation, arrow—ion flow direction.</p>
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<p>Flow chart of publication selection for the manuscript.</p>
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15 pages, 534 KiB  
Article
Prenatally Diagnosed Cardiac Tumors and Tuberous Sclerosis Complex: A Single-Center Experience
by Matija Bakoš, Dora Jelinek, Ana Ćorić Ljoka, Nada Sindičić Dessardo, Dalibor Šarić and Ruža Grizelj
Children 2025, 12(1), 94; https://doi.org/10.3390/children12010094 (registering DOI) - 16 Jan 2025
Viewed by 363
Abstract
Background/Objectives: Cardiac rhabdomyoma (CR), the most frequently occurring fetal cardiac tumor, is often an early marker of tuberous sclerosis complex (TSC). This study evaluates outcomes of fetuses with prenatally diagnosed cardiac tumors managed at a single tertiary center. Methods: Medical records of fetuses [...] Read more.
Background/Objectives: Cardiac rhabdomyoma (CR), the most frequently occurring fetal cardiac tumor, is often an early marker of tuberous sclerosis complex (TSC). This study evaluates outcomes of fetuses with prenatally diagnosed cardiac tumors managed at a single tertiary center. Methods: Medical records of fetuses diagnosed with cardiac tumors between 2009 and 2024 were retrospectively reviewed. Results: Sixteen cases were identified, with a median follow-up of 6.7 years. TSC was confirmed in 14 cases (88%). Multiple tumors were observed in 13 cases (81%), while 3 cases (19%) had solitary tumors. Both non-TSC cases involved solitary tumors. Cardiac complications (arrhythmias, conduction disorders, and hemodynamic abnormalities) occurred in 38% of cases prenatally and 69% postnatally, with larger tumor diameters significantly associated with complications (p = 0.02). No fetal hydrops or mortality occurred; however, one child died at age five due to a seizure. Postnatal tumor regression occurred in 56% of cases and complete regression in 38% by a median age of 2.3 years (range: 0.6–4.4). One tumor remained stable. Brain MRI revealed TSC-related changes in all TSC-affected patients except one, who had a developmental brain anomaly. Most TSC patients experienced epilepsy (71%) and developmental delays. Conclusion: While CRs are typically benign and regress spontaneously, their strong association with TSC highlights the importance of early diagnosis and family counseling. TSC-related epilepsy and psychomotor delays significantly impair the quality of life. Early mTOR inhibitor therapy offers promise in mitigating TSC-related complications and improving outcomes. Full article
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<p>Flow diagram of neonates prenatally diagnosed with cardiac tumors.</p>
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12 pages, 773 KiB  
Article
Survey of Aconitum Alkaloids to Establish an Aconitum carmichaeli (Fu-Zi) Processing Procedure and Quality Index
by Kun-Teng Wang, Ming-Chung Lee and Wu-Chang Chuang
Chemistry 2025, 7(1), 8; https://doi.org/10.3390/chemistry7010008 - 14 Jan 2025
Viewed by 318
Abstract
Processed Fu-Zi (the lateral roots of Aconitum carmichaeli) is beneficial for the cardiac system, but, because it contains toxins, raw Fu-Zi produces arrhythmia and breathing difficulties. C19 diester diterpenoid alkaloids (DDAs), including aconitine, mesaconitine, and hypaconitine, are toxic Aconitum alkaloids found [...] Read more.
Processed Fu-Zi (the lateral roots of Aconitum carmichaeli) is beneficial for the cardiac system, but, because it contains toxins, raw Fu-Zi produces arrhythmia and breathing difficulties. C19 diester diterpenoid alkaloids (DDAs), including aconitine, mesaconitine, and hypaconitine, are toxic Aconitum alkaloids found in Fu-Zi and can be hydrolyzed to nontoxic monoester diterpenoid alkaloids (MDAs), including benzoylaconine, benzoylmesaconine, and benzoylhypaconine. In this study, six processed Fu-Zi decoction pieces and herbal medicines were analyzed. The highest DDA contents were found in Shengfupian, the raw Fu-Zi samples. A processing quality index (Grades A to D) was established to evaluate the processing quality of Fu-Zi. The data demonstrated that few Fu-Zi decoction pieces did not conform to the government regulation. The results of testing the inorganic elements showed that the calcium content increased by approximately 5 to 30 fold compared to raw Fu-Zi due to substances assisting with processing. Raw Fu-Zi processed by boiling, without additional substances, may have a decreased DDA content. This study provides a method of determining the quality status of pieces of Fu-Zi decoction and establishes a processing quality index for pieces of Fu-Zi decoction and herbal medicine. Furthermore, our results suggest that it is not necessary to use additional substance to assist with the processing of Fu-Zi. Through the established processing quality index, Fu-Zi may be used more safely and may demonstrate a greater consistency in quality. Full article
(This article belongs to the Section Biological and Natural Products)
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<p>Radar chart analysis of diester diterpenoid alkaloid (DDA) and monoester diterpenoid alkaloid (MDA) in SFP (<b>A</b>), DFP (<b>B</b>), BFP (<b>C</b>), HSP (<b>D</b>), PFP (<b>E</b>), and JZFP (<b>F</b>).</p>
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16 pages, 1769 KiB  
Review
Perioperative Risk: Short Review of Current Approach in Non Cardiac Surgery
by Andreea Boghean, Cristian Guțu and Dorel Firescu
J. Cardiovasc. Dev. Dis. 2025, 12(1), 24; https://doi.org/10.3390/jcdd12010024 - 13 Jan 2025
Viewed by 404
Abstract
The rate of major surgery is constantly increasing worldwide, and approximately 85% are non-cardiac surgery. More than half of patients over 45 years presenting for non-cardiac surgical interventions have cardiovascular risk factors, and the most common: chronic coronary syndrome and history of stroke. [...] Read more.
The rate of major surgery is constantly increasing worldwide, and approximately 85% are non-cardiac surgery. More than half of patients over 45 years presenting for non-cardiac surgical interventions have cardiovascular risk factors, and the most common: chronic coronary syndrome and history of stroke. The preoperative cardiovascular risk is determined by the comorbidities, the clinical condition before the intervention, the urgency, duration or type. Cardiovascular risk scores are necessary tools to prevent perioperative cardiovascular morbidity and mortality and the most frequently used are Lee/RCRI (Revised Cardiac Risk Index), APACHE II (Acute Physiology and Chronic Health Evaluation), POSSUM (Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity), The American University of Beirut (AUB)-HAS2. To reduce the perioperative risk, there is a need for an appropriate preoperative risk assessment, as well as the choice of the type and timing of surgical intervention. Quantification of surgical risk as low, intermediate, and high is useful in identifying the group of patients who are at risk of complications such as myocardial infarction, thrombosis, arrhythmias, heart failure, stroke or even death. Currently there are not enough studies that can differentiate the risk according to gender, race, elective versus emergency procedure, the value of cardiac biomarkers. Full article
(This article belongs to the Section Cardiovascular Clinical Research)
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<p>Total risk estimated by the interaction between the surgical risk and the patient’s cardiovascular risk. The red arrows signify the probability of perioperative cardiovascular complications (adapted according to the ESC 2022 guidelines).</p>
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<p>Pre-operative assessment before non-cardiac surgery.</p>
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<p>Level of evidence AHA/ACC and ESC.</p>
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17 pages, 10432 KiB  
Article
Mechanistic Insights into Melatonin’s Antiarrhythmic Effects in Acute Ischemia-Reperfusion-Injured Rabbit Hearts Undergoing Therapeutic Hypothermia
by Hui-Ling Lee, Po-Cheng Chang, Hung-Ta Wo, Shih-Chun Chou and Chung-Chuan Chou
Int. J. Mol. Sci. 2025, 26(2), 615; https://doi.org/10.3390/ijms26020615 - 13 Jan 2025
Viewed by 282
Abstract
The electrophysiological mechanisms underlying melatonin’s actions and the electrophysiological consequences of superimposed therapeutic hypothermia (TH) in preventing cardiac ischemia-reperfusion (IR) injury-induced arrhythmias remain largely unknown. This study aimed to unveil these issues using acute IR-injured hearts. Rabbits were divided into heart failure (HF), [...] Read more.
The electrophysiological mechanisms underlying melatonin’s actions and the electrophysiological consequences of superimposed therapeutic hypothermia (TH) in preventing cardiac ischemia-reperfusion (IR) injury-induced arrhythmias remain largely unknown. This study aimed to unveil these issues using acute IR-injured hearts. Rabbits were divided into heart failure (HF), HF+melatonin, control, and control+melatonin groups. HF was induced by rapid right ventricular pacing. Melatonin was administered orally (10 mg/kg/day) for four weeks, and IR was created by 60-min coronary artery ligation and 30-min reperfusion. The hearts were then excised and Langendorff-perfused for optical mapping studies at normothermia, followed by TH. Melatonin significantly reduced ventricular fibrillation (VF) maintenance. In failing hearts, melatonin reduced the spatially discordant alternans (SDA) inducibility mainly by modulating intracellular Ca2+ dynamics via upregulation of sarcoplasmic reticulum Ca2+-ATPase (SERCA2a) and calsequestrin 2 and attenuating the downregulation of phosphorylated phospholamban protein expression. In control hearts, melatonin improved conduction slowing and reduced dispersion of action potential duration (APDdispersion) by upregulating phosphorylated connexin 43, attenuating the downregulation of SERCA2a and phosphorylated phospholamban and attenuating the upregulation of phosphorylated Ca2+/calmodulin-dependent protein kinase II. TH significantly retarded intracellular Ca2+ decay slowed conduction, and increased APDdispersion, thereby facilitating SDA induction, which counteracted the beneficial effects of melatonin in reducing VF maintenance. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>Effects of melatonin (Mel) and therapeutic hypothermia (TH) on intracellular Ca<sup>2+</sup> (Ca<sub>i</sub>) decay. (<b>A</b>,<b>C</b>) Summarized results of Ca<sub>i</sub> decay tau (τ) values in the ischemia–reperfusion (IR) and non-IR zones in heart failure (HF) and control groups, respectively. (<b>B</b>,<b>D</b>) Representative examples of Ca<sub>i</sub> decay in HF and control groups, respectively. Numbers indicate the mean values (in ms). TH significantly increased the tau values in the non-IR and IR zones. Melatonin significantly fastened Ca<sub>i</sub> decay in the IR zone at normothermia and TH in failing hearts.</p>
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<p>Effects of melatonin (Mel) and therapeutic hypothermia (TH) on conduction velocity (CV). (<b>A</b>,<b>C</b>) Summarized results of CV in the ischemia–reperfusion (IR) and non-IR zones in heart failure (HF) and control groups, respectively. (<b>B</b>,<b>D</b>) Representative examples of isochronal maps in HF and control groups, respectively. Numbers indicate the mean values of CV (in cm/s). Dashed black arrows indicate the directions of wavefront propagation.</p>
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<p>Effects of melatonin (Mel) on action potential duration (APD) at baseline and therapeutic hypothermia (TH). (<b>A</b>,<b>C</b>) Summarized results of maximal APD (APD<sub>max</sub>), minimal APD (APD<sub>min</sub>), and APD dispersion (APD<sub>dispersion</sub>) at baseline and TH with or without melatonin treatment in heart failure (HF) and control groups, respectively. (<b>B</b>,<b>D</b>) Representative APD maps in HF and control groups, respectively. Melatonin prolonged APD<sub>min</sub> and thereby reduced APD<sub>dispersion</sub> in failing hearts at TH and in control hearts at normothermia. Numbers indicate the values of APD at sites of APD<sub>max</sub> and APD<sub>min</sub> in each map (in ms).</p>
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<p>Effects of melatonin (Mel) and therapeutic hypothermia (TH) on spatially discordant alternans (SDA) induction. (<b>A</b>) Summarized results of SDA inducibility. (<b>B</b>) Representative examples of action potential duration (APD) and intracellular Ca<sup>2+</sup> (Ca<sub>i</sub>) alternans maps in heart failure (HF) (<b>upper</b>) and HF+Mel (<b>bottom</b>) groups. Black arrows indicate nodal lines. ΔAPD and ΔCa<sub>i</sub> are the differences between the two consecutive APD and Ca<sub>i</sub> amplitudes, respectively.</p>
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<p>Effects of melatonin and therapeutic hypothermia (TH) on ventricular fibrillation (VF) severity. (<b>A</b>,<b>B</b>) Summarized results of VF severity in heart failure (HF) and control groups, respectively. Dark grey, light grey, and white color represent VF severity scores 2, 1, and 0, respectively, and the number at the top of each bar represents the mean of VF severity score. Melatonin significantly reduced VF severity in HF and control groups at baseline, which was counteracted by TH.</p>
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<p>Ventricular fibrillation (VF) induction in a failing heart with acute ischemia-reperfusion (IR)-injury. (<b>A</b>) Photos of IR induction through obtuse marginal (OM) branch ligation and release. (<b>B</b>) Mapping field (<b>left</b>) and action potential duration alternans map (ΔAPD, <b>right</b>). (<b>C</b>) Pseudo-electrocardiograms show VF induction by extra stimulus pacing. The red arrow indicates a shock spike. The bottom pseudo-electrocardiogram corresponded to the period labeled by a red square in the upper pseudo-electrocardiogram. (<b>D</b>) Membrane voltage tracings during VF induction at sites “a” and “b” labeled in Panel (<b>B</b>). (<b>E</b>) Isochronal maps (<b>upper</b>) and phase maps (<b>bottom</b>) of VF induction (labeled in Panel (<b>D</b>)). White arrows indicate multiple impulses within the mapping field; black triangles indicate phase singularities. LAA, left atrial appendage; LV, left ventricle; RV, right ventricle.</p>
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<p>Melatonin pretreatment suppressed ventricular fibrillation induction in a failing heart with acute ischemia-reperfusion (IR) injury. (<b>A</b>) Photos of IR induction through obtuse marginal (OM) branch ligation and release. (<b>B</b>) Mapping field (<b>left</b>) and action potential duration alternans map (ΔAPD, <b>right</b>). (<b>C</b>) Pseudo-electrocardiograms show that extra stimulus pacing failed to induce VF. The bottom pseudo-electrocardiogram corresponded to the period labeled by a red square in the upper pseudo-electrocardiogram. (<b>D</b>) Membrane voltage tracings during extrastimulus pacing at sites “a” and “b” labeled in Panel (<b>B</b>). (<b>E</b>) Isochronal maps (<b>upper</b>) and phase maps (<b>bottom</b>) of extra stimulus pacing (labeled in Panel (<b>D</b>)). White arrows indicate impulse propagation within the mapping field. LAA, left atrial appendage; LV, left ventricle; RV, right ventricle.</p>
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<p>Results of protein analyses. Representative bands (upper subpanels) and summarized results of densitometric values normalized to the corresponding β-actin (lower subpanels). CaMKII, Ca<sup>2+</sup>-calmodulin-dependent protein kinase II; CaMKII-p, pThr286-CaMKII; CASQ2, calsequestrin 2; Cx43, connexin 43; Cx43-p, phosphorylated Cx43; HF, heart failure; IR, ischemia-reperfusion; NCX, sodium-calcium exchanger; PLN, phospholamban; PLN-s, pSer16-PLN; PLN-t, pThr17-PLN; RyR, ryanodine receptor 2; RyR-p, phosphorylated RyR2; SERCA2a, sarcoplasmic reticulum Ca<sup>2+</sup>-ATPase.</p>
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12 pages, 401 KiB  
Article
Differences in Arrhythmia Detection Between Harvard Step Test and Maximal Exercise Testing in a Paediatric Sports Population
by Massimiliano Bianco, Fabrizio Sollazzo, Riccardo Pella, Saverio Vicentini, Samuele Ciaffoni, Gloria Modica, Riccardo Monti, Michela Cammarano, Paolo Zeppilli and Vincenzo Palmieri
J. Cardiovasc. Dev. Dis. 2025, 12(1), 22; https://doi.org/10.3390/jcdd12010022 - 11 Jan 2025
Viewed by 352
Abstract
BACKGROUND: Sport practice may elevate the risk of cardiovascular events, including sudden cardiac death, in athletes with undiagnosed heart conditions. In Italy, pre-participation screening includes a resting ECG and either the Harvard Step Test (HST) or maximal exercise testing (MET), but the relative [...] Read more.
BACKGROUND: Sport practice may elevate the risk of cardiovascular events, including sudden cardiac death, in athletes with undiagnosed heart conditions. In Italy, pre-participation screening includes a resting ECG and either the Harvard Step Test (HST) or maximal exercise testing (MET), but the relative efficacy of the latter two tests for detecting arrhythmias and heart conditions remains unclear. METHODS: This study examined 511 paediatric athletes (8–18 years, 76.3% male) without known cardiovascular, renal, or endocrine diseases. All athletes underwent both HST and MET within 30 days. Absolute data and data relative to theoretical peak heart rates, arrhythmias (supraventricular and ventricular) and cardiovascular diagnoses were collected. RESULTS: HST resulted in a lower peak heart rate than MET (181.1 ± 9.8 vs. 187.5 ± 8.1 bpm, p < 0.001), but led to the detection of more supraventricular (18.6% vs. 13.1%, p < 0.001) and ventricular (30.5% vs. 22.7%, p < 0.001) arrhythmias, clustering during recovery (p = 0.014). This pattern was significant in males but not females. Among athletes diagnosed with cardiovascular diseases (22.3%), HST identified more ventricular arrhythmias (26.3% vs. 18.4%, p = 0.05), recovery-phase arrhythmias (20.2% vs. 14.0%, p = 0.035), and polymorphic arrhythmias (6.1% vs. 1.8%, p = 0.025). CONCLUSIONS: HST detects arrhythmias more effectively than MET in young male athletes, especially during recovery. More ventricular arrhythmias were highlighted even in athletes with cardiovascular conditions. Full article
(This article belongs to the Special Issue The Present and Future of Sports Cardiology and Exercise)
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<p>Total number of cardiovascular diseases found in the whole cohort of participants, subdivided on the basis of the resulting pathology (N.B. minor forms of heart disease have been grouped into a single class).</p>
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15 pages, 1103 KiB  
Article
Comorbidities Associated with Vitiligo: Results from the EpiChron Cohort
by Beatriz Clemente Hernández, Itziar Muelas Rives, Tamara Gracia Cazaña, Marcial Álvarez Salafranca, Beatriz Poblador-Plou, Clara Laguna-Berna, Aida Moreno Juste, Antonio Gimeno-Miguel and Yolanda Gilaberte
J. Clin. Med. 2025, 14(2), 432; https://doi.org/10.3390/jcm14020432 - 11 Jan 2025
Viewed by 356
Abstract
Background: Vitiligo is a pigmentation disorder that impacts approximately 0.5% to 2% of the global population. Growing interest surrounds the comorbidities associated with vitiligo. This study aimed to describe the socio-demographic characteristics of the patients with vitiligo in Aragón (Spain) and to investigate [...] Read more.
Background: Vitiligo is a pigmentation disorder that impacts approximately 0.5% to 2% of the global population. Growing interest surrounds the comorbidities associated with vitiligo. This study aimed to describe the socio-demographic characteristics of the patients with vitiligo in Aragón (Spain) and to investigate their associated comorbidities. Methods: A retrospective observational study was conducted using clinical data from individuals in the EpiChron Cohort (reference population of 1.3 million) who were diagnosed with vitiligo between 1 January and 31 December 2019. The prevalence of chronic comorbidities was calculated using logistic regression models, obtaining the odds ratio (OR) of each comorbidity (dependent variable) according to the presence or absence of vitiligo (independent variable). We used a cut-off point for a statistical significance of p-value < 0.05. Results: In total, 218 patients diagnosed with vitiligo were analyzed. The mean age was 44.0 years, and 56.42% were female. The largest proportion of patients (34.86%) were aged between 18 and 44 years. Among all vitiligo patients included, 71.5% presented multimorbidity, with an average of 3.21 diagnosed comorbidities. The conditions most frequently associated with vitiligo included thyroid disorders (OR: 3.01, p < 0.001), ocular and hearing abnormalities (OR: 1.54, p < 0.020), inflammatory skin disorders (OR: 2.21, p < 0.001), connective tissue diseases (OR: 1.84, p < 0.007), lower respiratory tract diseases (OR: 1.78, p < 0.014), urinary tract infections (OR: 1.69, p < 0.032), and cardiac arrhythmias (OR 1.84, p < 0.034). Conclusions: This research highlights the importance of understanding the broader health implications of vitiligo and provides a foundation for further exploration into the complex interplay between this dermatologic condition and a diverse range of comorbidities. Full article
(This article belongs to the Section Dermatology)
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<p>Age distribution of vitiligo patients.</p>
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<p>Proportion of multimorbidity among vitiligo patients.</p>
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<p>Prevalence of main comorbidities in vitiligo patients.</p>
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16 pages, 272 KiB  
Review
Anderson–Fabry Disease: An Overview of Current Diagnosis, Arrhythmic Risk Stratification, and Therapeutic Strategies
by Chiara Tognola, Giacomo Ruzzenenti, Alessandro Maloberti, Marisa Varrenti, Patrizio Mazzone, Cristina Giannattasio and Fabrizio Guarracini
Diagnostics 2025, 15(2), 139; https://doi.org/10.3390/diagnostics15020139 - 9 Jan 2025
Viewed by 316
Abstract
Anderson–Fabry disease (AFD) is a rare X-linked lysosomal storage disorder characterized by the accumulation of globotriaosylceramide, leading to multi-organ involvement and significant morbidity. Cardiovascular manifestations, particularly arrhythmias, are common and pose a considerable risk to affected individuals. This overview examines current approaches to [...] Read more.
Anderson–Fabry disease (AFD) is a rare X-linked lysosomal storage disorder characterized by the accumulation of globotriaosylceramide, leading to multi-organ involvement and significant morbidity. Cardiovascular manifestations, particularly arrhythmias, are common and pose a considerable risk to affected individuals. This overview examines current approaches to arrhythmic risk stratification in AFD, focusing on the identification, assessment, and management of cardiac arrhythmias associated with the disease. We explore advancements in diagnostic techniques, including echocardiography, cardiac MRI, and ambulatory ECG monitoring, to enhance the detection of arrhythmogenic substrate. Furthermore, we discuss the role of genetic and biochemical markers in predicting arrhythmic risk and the implications for personalized treatment strategies. Current therapeutic interventions, including enzyme replacement therapy and antiarrhythmic medications, are reviewed in the context of their efficacy and limitations. Finally, we highlight ongoing research and future directions with the aim of improving arrhythmic risk assessment and management in AFD. This overview underscores the need for a multidisciplinary approach to optimize care and outcomes for patients with AFD. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Treatment of Cardiac Arrhythmias 2025)
44 pages, 5390 KiB  
Article
Temporary Pacing Simulator: A Training Tool for Clinicians
by Ioana Cretu, Alexander Tindale, Maysam Abbod, Wamadeva Balachandran, Ashraf W. Khir and Hongying Meng
Appl. Sci. 2025, 15(2), 573; https://doi.org/10.3390/app15020573 - 9 Jan 2025
Viewed by 294
Abstract
Cardiovascular diseases (CVDs) are the leading global cause of death, impacting nations worldwide. Despite medical advancements, managing patients who require temporary pacing (TP) after cardiac surgery remains challenging. TP devices are essential for stabilizing patients with unstable arrhythmias or post-surgical complications but demand [...] Read more.
Cardiovascular diseases (CVDs) are the leading global cause of death, impacting nations worldwide. Despite medical advancements, managing patients who require temporary pacing (TP) after cardiac surgery remains challenging. TP devices are essential for stabilizing patients with unstable arrhythmias or post-surgical complications but demand manual adjustments and precise clinician management, unlike permanent pacemakers. There is an urgent need for improved TP training and standardisation, hindered by a lack of formal guidelines and adequate protocols. Existing simulators often omit crucial haemodynamic parameters and complex clinical scenarios, limiting their effectiveness. This paper introduces an advanced Temporary Cardiac Pacing Simulator (TCPS) that provides comprehensive physiological signals and complex scenarios for realistic training. It simulates various pacing modes—atrial, ventricular, and dual-chamber—while modeling pacing failures and haemodynamic changes. Sophisticated algorithms replicate clinical responses, offering real-time feedback and dynamic visualizations to enhance learning. Additionally, an innovative feature optimises atrioventricular (AV) delay settings, crucial for improving patient outcomes in both acute and postoperative care. Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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<p>Flowchart diagram of the TCPS user interface.</p>
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<p>The TCPS welcome window.</p>
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<p>The TCPS form window, where each user will have to add their details in order to use the TCPS app.</p>
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<p>The main window of the TCPS panel before the user starts the TP settings selection.</p>
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<p>The Atrial Pacing Flow—This flowchart illustrates the sequence of steps and choices a user can make when selecting an atrial pacing mode. In AAI mode, all elements are enabled, whereas in AOO mode, only the atrial capture threshold failures and atrial capture threshold flow are enabled.</p>
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<p>The Ventricular Pacing Flow—This flowchart illustrates the sequence of steps and choices a user can make when selecting a ventricular pacing mode.</p>
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<p>The Dual Pacing Flow—This flowchart illustrates the sequence of steps and choices a user can make when selecting a dual pacing mode.</p>
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<p>The Capture Threshold Flows- This flowchart illustrates the sequence of steps and choices a user can make when testing the capture threshols for both atria and ventricles.</p>
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<p>Sensitivity Threshold Flowchart: This diagram illustrates the sensitivity flows for both atrial and ventricular responses. Each flow triggers the activation of its respective sensitivity buttons—atrial or ventricular.</p>
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<p>AV Delay Flowchart—This diagram illustrates the AV delay functions and buttons that are enabled when the user chooses a DDI pacing mode.</p>
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<p>Example of signal displayed when the user chooses the baseline signals, in this case SR rhtyhm.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, SR, and chooses the VVI pacing mode.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, SR, and chooses the VVI pacing mode with a pacing rate under 70 bpm, causing the intrinsic conduction to show on the signals and the inhibition of the pacing spikes in the ventricle, where intrinsic conduction occurs.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, SR, and chooses the AAI pacing mode.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, SR, and chooses the DDI pacing mode.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, SR, and chooses the DDI pacing mode with a pacing rate under 70 bpm, causing the intrinsic conduction to show on the signals and the inhibition of the pacing spikes in both the atria and the ventricles, where intrinsic conduction occurs.</p>
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<p>Example of the signal displayed when the user selects the SR baseline rhythm and chooses the DOO pacing mode with a pacing rate of 80 bpm, while also selecting VLOC from the failure mode dropdown menu.</p>
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<p>Example of the signal displayed when the user selects the SR baseline rhythm and chooses the DOO pacing mode with a pacing rate of 80 bpm, while also selecting ALOC from the failure mode dropdown menu.</p>
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<p>Example of the signal displayed when the user selects the SR with LBBB baseline rhythm and chooses the VVI pacing mode with a pacing rate of 80 bpm, while also selecting “undersensing” from the failure mode dropdown menu. In undersensing failure mode, the TCPS shows pacing spikes erroneously superimposed on the T-waves. These inappropriate spikes occur due to the TP’s failure to correctly sense the heart’s intrinsic activity, leading to the delivery of pacing stimuli during the repolarization phase.</p>
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<p>DDI Pacing with Atrial Undersensing: This figure depicts the signal output when the user selects a sinus rhythm (SR) baseline and sets the pacing mode to DDI at a rate of 80 bpm. The user inputs a value for the atrial sensitivity threshold and clicks the “Insert A Sensitivity Threshold” button. The TCPS compares this input against a generated maximal atrial sensitivity threshold. In this scenario, the system fails to detect atrial activity, causing the failure mode to switch to undersensing. A warning message is displayed, notifying the user of atrial undersensing due to the inputted threshold.</p>
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<p>DDI Pacing with Ventricular Undersensing: This figure illustrates the signal output when the user selects a sinus rhythm (SR) baseline and sets the pacing mode to DDI at a rate of 80 bpm. The user inputs a value for the ventricular sensitivity threshold and clicks the “Insert V Sensitivity Threshold” button. The TCPS compares this input to a generated maximal ventricular sensitivity threshold. In this scenario, the system fails to detect ventricular activity, triggering the failure mode to switch to undersensing. A warning message is displayed, notifying the user of ventricular undersensing due to the inputted threshold.</p>
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<p>DDI Pacing with Atrial and Ventricular Undersensing: This figure shows the signal output when the user selects a sinus rhythm (SR) baseline and sets the pacing mode to DDI at a rate of 80 bpm. The user inputs values for both atrial and ventricular sensitivity thresholds, then clicks either the “Insert V Sensitivity Threshold” or the “Insert A Sensitivity Threshold” button. The TCPS compares these values against generated maximal atrial and ventricular sensitivity thresholds. Here, both the atrial and ventricular activity go undetected, causing the failure mode to switch to undersensing. The issues a warning, informing the user of undersensing in both chambers due to the specified thresholds.</p>
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<p>Example of the signal displayed when the user selects the “SR with VEs” baseline rhythm and chooses the VVI pacing mode with a pacing rate of 90 bpm, while also selecting “oversensing” from the failure mode dropdown menu. In oversensing failure mode, the TCPS reverts to the patient’s baseline rhythm, ignoring any pacing settings configured by the user. As a result, the ECG signal shows no pacing spikes and exhibits irregular RR intervals.</p>
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<p>DDI Pacing with Atrial Oversensing: This figure illustrates the signal output when the sinus rhythm (SR) baseline is selected. The user inputs a value for the atrial sensitivity threshold and activates it by clicking the “Insert A Sensitivity Threshold” button. The TCPS compares this input against a generated minimal atrial sensitivity threshold. In this scenario, the TCPS detects oversensing in the atria, triggering the failure mode to switch to oversensing. A warning message alerts the user to the issue, indicating that the inputted threshold has caused atrial oversensing.</p>
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<p>DDI Pacing with Atrial and Ventricular Oversensing: This figure shows the signal output when the user selects a sinus rhythm (SR) baseline and sets the pacing mode to DDI. The user inputs values for both atrial and ventricular sensitivity thresholds, then clicks on the “Insert V Sensitivity Threshold” and on the “Insert A Sensitivity Threshold” buttons. The TCPS compares these values against generated minimal atrial and ventricular sensitivity thresholds. Here, both the atrial and ventricular activity are oversensed, causing the failure mode to switch to oversensing in both chambers. The issues a warning, informing the user of oversensing in both chambers due to the specified thresholds.</p>
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<p>Ventricular Capture Threshold Test: This figure illustrates the signal generated when the user selects SR with LBBB as the baseline rhythm, and sets the pacemaker to VVI mode at a rate of 80 bpm. After inputting a value for the ventricular capture threshold and clicking the “Insert V Capture Threshold” button, the TCPS system compares the input against a predefined minimal ventricular capture threshold. In this scenario, the entered threshold successfully captures the ventricles, and the system confirms this by displaying an informational message on the screen.</p>
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<p>Ventricular Capture Threshold Test: This figure shows the signal output when the user selects SR with LBBB as the baseline rhythm, and chooses the VVI pacing mode with a rate of 80 bpm. After entering a value for the ventricular capture threshold and clicking the “Insert V Capture Threshold” button, the TCPS system evaluates the input against the randomly generated minimal ventricular capture threshold. In this case, the entered threshold fails to capture the ventricles, prompting the TCPS to automatically switch the failure mode to VLOC. A warning message is then displayed, informing the user that the selected threshold has caused VLOC.</p>
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<p>Atrial Sensitivity Threshold Test: This figure depicts the signal output when the user selects LBBB as baseline rhythm and sets the pacing mode to AAI at a rate of 80 bpm. The user inputs a value for the atrial sensitivity threshold and clicks the “Insert A Sensitivity Threshold” button. The TCPS system compares this input against a predefined maximal atrial sensitivity threshold. In this scenario, the entered threshold successfully senses the atria, and the system confirms this by displaying an informational message on the screen.</p>
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<p>Atrial Sensitivity Threshold Test: This figure depicts the signal output when the user selects LBBB as baseline rhythm and sets the pacing mode to AAI at a rate of 80 bpm. The user inputs a value for the atrial sensitivity threshold and clicks the “Insert A Sensitivity Threshold” button. The TCPS system compares this input against a predefined maximal atrial sensitivity threshold. In this case, the entered threshold fails to capture the atria, prompting the TCPS to automatically switch the failure mode to undersensing. A warning message is then displayed, informing the user that the selected threshold has caused undersensing in the atria.</p>
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<p>Ventricular Sensitivity Threshold Test: This figure depicts the signal output when the user selects SR as baseline rhythm and sets the pacing mode to DDI. The user inputs a value for the ventricular sensitivity threshold and clicks the “Insert V Sensitivity Threshold” button. The TCPS system compares this input against a predefined minima ventricular sensitivity threshold. In this case, the entered threshold oversenses the ventricles and inappropriately inhibits pacing, prompting the TCPS to automatically switch the failure mode to oversensing. A warning message is then displayed, informing the user that the selected threshold has caused oversensing in the ventricle.</p>
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<p>AV Delay Test: An example of how the user can test different AV delay for a chosen baseline, when the selected pacing mode is set in dual pacing mode.</p>
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<p>AV Delay Test: An example of how the user inserts a value for optimal AV delay and clicks the “Validate AV Delay” button for a chosen baseline, when the selected pacing mode is set in dual pacing mode. In this scenario, the introduced value is optimal, and the TCPS confirms that by displaying a feedback message on the screen.</p>
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<p>AV Delay Test: An example of how the user inserts a value for optimal AV delay and clicks the “Validate AV Delay” button for a chosen baseline, when the selected pacing mode is set in dual pacing mode. In this scenario, the introduced value is not optimal, and the TCPS confirms that by displaying an error message on the screen which asks the user to analyse the signals and try again.</p>
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<p>AV Delay Test: An example of how the user can visualise the effects of different AV delays by clicking on the “Plot AV Delay” button. The plot generates a visual representation of how blood pressure varies with different AV delay values, using 120 ms as a reference point for the optimal window length of one respiratory cycle for ABP and two respiratory cycles for CVP.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, sinus rhythm (SR), and chooses the VOO pacing mode with a pacing rate of 90 bpm.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, sinus rhythm (SR), and chooses the AOO pacing mode with the default pacing rate of 80 bpm.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, sinus rhythm (SR), and chooses the DOO pacing mode with the default pacing rate of 80 bpm.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, atrial fibrillation (AF).</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, atrial fibrillation (AF), and chooses the VVI pacing mode with the default pacing rate of 80 bpm.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, tachycardia (T).</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, LBBB.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, LBBB, and chooses the VVI pacing mode with the default pacing rate of 80 bpm.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, LBBB, and chooses the VVI pacing mode with a pacing rate under 70 bpm, causing the intrinsic conduction to show on the signals and the inhibition of the pacing spikes in the ventricle, where intrinsic conduction occurs.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, SR with VEs.</p>
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<p>Example of the signal displayed when the user selects the baseline signal, in this case, SR wit VEs, and chooses the VVI pacing mode with the default pacing rate of 80 bpm.</p>
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Review
PET-CT Imaging in Hypertrophic Cardiomyopathy: A Narrative Review on Risk Stratification and Prognosis
by Patrícia Marques-Alves, Lino Gonçalves and Maria João Ferreira
Diagnostics 2025, 15(2), 133; https://doi.org/10.3390/diagnostics15020133 - 8 Jan 2025
Viewed by 315
Abstract
Hypertrophic cardiomyopathy (HCM) is a heterogeneous cardiac disease and one of its major challenges is the limited accuracy in stratifying the risk of sudden cardiac death (SCD). Positron emission tomography (PET), through the evaluation of myocardial blood flow (MBF) and metabolism using fluorodeoxyglucose [...] Read more.
Hypertrophic cardiomyopathy (HCM) is a heterogeneous cardiac disease and one of its major challenges is the limited accuracy in stratifying the risk of sudden cardiac death (SCD). Positron emission tomography (PET), through the evaluation of myocardial blood flow (MBF) and metabolism using fluorodeoxyglucose (FDG) uptake, can reveal microvascular dysfunction, ischemia, and increased metabolic demands in the hypertrophied myocardium. These abnormalities are linked to several factors influencing disease progression, including arrhythmia development, ventricular dilation, and myocardial fibrosis. Fibroblast activation can also be evaluated using PET imaging, providing further insights into early-stage myocardial fibrosis. Conflicting findings underscore the need for further research into PET’s role in risk stratification for HCM. If PET can establish a connection between parameters such as abnormal MBF or increased FDG uptake and SCD risk, it could enhance predictive accuracy. Additionally, PET holds significant potential for monitoring therapeutic outcomes. The aim of this review is to provide a comprehensive overview of the most significant data on disease progression, risk stratification, and prognosis in patients with HCM using cardiac PET-CT imaging. Full article
(This article belongs to the Special Issue Latest Advances and Prospects in Cardiovascular Imaging)
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<p>Predictors and outcomes of microvascular dysfunction in HCM. References: * [<a href="#B21-diagnostics-15-00133" class="html-bibr">21</a>,<a href="#B23-diagnostics-15-00133" class="html-bibr">23</a>,<a href="#B36-diagnostics-15-00133" class="html-bibr">36</a>,<a href="#B39-diagnostics-15-00133" class="html-bibr">39</a>]; <sup>+</sup> [<a href="#B35-diagnostics-15-00133" class="html-bibr">35</a>,<a href="#B41-diagnostics-15-00133" class="html-bibr">41</a>,<a href="#B42-diagnostics-15-00133" class="html-bibr">42</a>,<a href="#B48-diagnostics-15-00133" class="html-bibr">48</a>]; <sup>†</sup> [<a href="#B22-diagnostics-15-00133" class="html-bibr">22</a>,<a href="#B24-diagnostics-15-00133" class="html-bibr">24</a>,<a href="#B49-diagnostics-15-00133" class="html-bibr">49</a>]. Abbreviations: CV: cardiovascular; HCM: hypertrophic cardiomyopathy; LVOT: left ventricular obstruction outflow tract; MBF: myocardial blood flow; MFR: myocardial flow reserve; MWT: maximum wall thickness.</p>
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<p>Patterns of <sup>18</sup>F-FDG uptake and LGE in different types of HCM. References: * [<a href="#B52-diagnostics-15-00133" class="html-bibr">52</a>,<a href="#B55-diagnostics-15-00133" class="html-bibr">55</a>,<a href="#B58-diagnostics-15-00133" class="html-bibr">58</a>,<a href="#B59-diagnostics-15-00133" class="html-bibr">59</a>]; <sup>+</sup> [<a href="#B29-diagnostics-15-00133" class="html-bibr">29</a>,<a href="#B57-diagnostics-15-00133" class="html-bibr">57</a>]; <sup>†</sup> [<a href="#B57-diagnostics-15-00133" class="html-bibr">57</a>]. Abbreviations: AHCM—apical hypertrophic cardiomyopathy; LGE—late gadolinium enhancement; NOHCM—non-obstructive hypertrophic cardiomyopathy; OHCM—obstructive hypertrophic cardiomyopathy; ↑increase; ↓ decrease; x no effect.</p>
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14 pages, 583 KiB  
Article
Risk Stratification of QTc Prolongations in Hospitalized Cardiology and Gastroenterology Patients Using the Tisdale Score—A Retrospective Analysis
by Julian Steinbrech, Ute Amann, Michael Irlbeck, Sebastian Clauß and Dorothea Strobach
J. Clin. Med. 2025, 14(2), 339; https://doi.org/10.3390/jcm14020339 - 8 Jan 2025
Viewed by 362
Abstract
Background/Objectives: QTc prolongation can result in lethal arrhythmia. Risk scores like the Tisdale score can be used for risk stratification for targeted pharmaceutical interventions. However, the practical usability across different medical specialties has not been sufficiently investigated. The aim of this study [...] Read more.
Background/Objectives: QTc prolongation can result in lethal arrhythmia. Risk scores like the Tisdale score can be used for risk stratification for targeted pharmaceutical interventions. However, the practical usability across different medical specialties has not been sufficiently investigated. The aim of this study was to compare relevant risk factors for QTc prolongation and to investigate the use of the Tisdale score in cardiology and gastroenterology patients. Methods: For patients on a cardiology and a gastroenterology ward receiving a weekly pharmaceutical electronic chart review, risk factors for QTc prolongation, QTc-prolonging drugs, and electrocardiograms (ECGs) were retrospectively collected for a four-month period (07-10/2023), and the Tisdale score and its sensitivity and specificity were calculated. Results: A total of 627 chart reviews (cases) (335 cardiology, 292 gastroenterology) were performed. The median age was 66 (range 20–94) years, and 39% (245) of patients were female. The presence of established risk factors (hypokalemia, renal impairment, age ≥ 68 years, cardiac diseases) differed significantly between the specialties. A median of 2 (range 0–5) QTc-prolonging drugs were prescribed in both groups. Baseline and follow-up ECG were recorded in 166 (50%) cardiology cases, of which prolonged QTc intervals were detected in 38 (23%) cases. In the 27 (9%) gastroenterology cases with baseline and follow-up ECG, no QTc prolongations were detected. Across both specialties, the Tisdale score achieved a sensitivity of 74% and a specificity of 30%. Conclusions: The presence of established risk factors for QTc prolongation differed significantly between cardiology and gastroenterology cases. The Tisdale score showed acceptable sensitivity for risk stratification; however, the limited availability of ECGs for gastroenterology cases was a limiting factor. Full article
(This article belongs to the Section Cardiovascular Medicine)
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<p>Study flow diagram.</p>
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22 pages, 2440 KiB  
Review
Cardiotoxicity of Chemotherapy: A Multi-OMIC Perspective
by Yan Ma, Mandy O. J. Grootaert and Raj N. Sewduth
J. Xenobiot. 2025, 15(1), 9; https://doi.org/10.3390/jox15010009 - 8 Jan 2025
Viewed by 654
Abstract
Chemotherapy-induced cardiotoxicity is a critical issue in cardio-oncology, as cancer treatments often lead to severe cardiovascular complications. Approximately 10% of cancer patients succumb to cardiovascular problems, with lung cancer patients frequently experiencing arrhythmias, cardiac failure, tamponade, and cardiac metastasis. The cardiotoxic effects of [...] Read more.
Chemotherapy-induced cardiotoxicity is a critical issue in cardio-oncology, as cancer treatments often lead to severe cardiovascular complications. Approximately 10% of cancer patients succumb to cardiovascular problems, with lung cancer patients frequently experiencing arrhythmias, cardiac failure, tamponade, and cardiac metastasis. The cardiotoxic effects of anti-cancer treatments manifest at both cellular and tissue levels, causing deformation of cardiomyocytes, leading to contractility issues and fibrosis. Repeated irradiation and chemotherapy increase the risk of valvular, pericardial, or myocardial diseases. Multi-OMICs analyses reveal that targeting specific pathways as well as specific protein modifications, such as ubiquitination and phosphorylation, could offer potential therapeutic alternatives to current treatments, including Angiotensin converting enzymes (ACE) inhibitors and beta-blockers that mitigate symptoms but do not prevent cardiomyocyte death, highlighting the need for more effective therapies to manage cardiovascular defects in cancer survivors. This review explores the xenobiotic nature of chemotherapy agents and their impact on cardiovascular health, aiming to identify novel biomarkers and therapeutic targets to mitigate cardiotoxicity. Full article
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<p>Cardiovascular events in cancer patients after chemotherapy or radiotherapy (<b>A</b>,<b>B</b>) (adapted from [<a href="#B17-jox-15-00009" class="html-bibr">17</a>]) as well as relevance of multi-OMICs analyses to improve their understanding (<b>C</b>).</p>
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<p>TrancriptOMICs analysis of cardiac disease initiation in cardiomyocytes (<b>A</b>) Pathway analysis of RNA sequencing from isolated cardiomyocytes after exposition to doxorubicin (GSE226116) comparing differentially expressed genes (q value &lt; 0.005) using ENRICHR (Hallmark analysis). N = 3. Odds ratios are graphed, and the bars sorted from the most significant adjusted <span class="html-italic">p</span> value. (<b>B</b>) CRISPR screen comparing inhibited sgRNA (CRISPRi) when comparing the Doxocyclin-treated vs. Vehicle IPSC-derived cardiomyocytes conditions (Dataset: GSE276161). Positively and negatively enriched sgRNAs in red and blue respectively, grey indicate sgRNAs that do not show effects. (<b>C</b>,<b>D</b>) Reactome analysis performed on the positively and negatively enriched sgRNAs. The color of the dot corresponds to the adjusted <span class="html-italic">p</span>-value for each pathway (green for the most significant adjusted <span class="html-italic">p</span> value to dark blue for the less significant ones) and the size of the dot corresponds to the number of inhibited sgRNA for each group.</p>
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<p>Transcriptomic analysis of cardiac disease initiation in endothelial cells. (<b>A</b>,<b>B</b>) Pathway analysis of RNA sequencing from isolated cardiac ECs after exposition to doxorubicin (GSE226116) comparing differentially expressed genes (q value &lt; 0.005) using ENRICHR N = 3. Odds ratios are graphed, and the bars are sorted from the most significant corrected <span class="html-italic">p</span> value. TRRUST analysis and Wikipathways analysis respectively.</p>
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<p>Transcriptomic analysis of cardiac disease initiation upon Sorafenib treatment in cardiomyocytes adapted from Series GSE222642 comparing differentially expressed genes (q value &lt; 0.005), where male rats were gavaged with 50 mg/kg sorafenib (heart tissues collected at 14 days after treatment). ENCODE analysis (<b>A</b>) and Wikipathways analysis (<b>B</b>) respectively (ENRICHR, software developed by Ma’ayan lab, Computational Systems Biology, New York, NY, USA).</p>
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<p>Transcriptomic analysis of cardiac disease initiation upon Indisulam treatment in cardiomyocytes from Query DataSets for GSE213311 comparing differentially expressed genes (q value &lt; 0.005), RNA-seq analysis on cardiomyocytes treated with vehicle or indisulam. ENCODE analysis (<b>A</b>) and Wikipathways analysis (<b>B</b>) respectively (ENRICHR, Ma’ayan lab).</p>
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<p>Transcriptomic analysis of cardiac disease initiation upon Trastuzumab treatment in cardiomyocytes adapted from GSE264120 comparing differentially expressed genes (q value &lt; 0.005), ENCODE analysis (<b>A</b>) and MCF7 GEO UP signatures analysis (<b>B</b>) respectively (ENRICHR, Ma’ayan lab).</p>
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<p>Transcriptomic analysis of cardiac disease initiation in rats upon 5-FU treatment in cardiomyocytes adapted from GSE166957 comparing differentially expressed genes (q value &lt; 0.005), TRRUST analysis (<b>A</b>) and Wikipathways analysis (<b>B</b>) respectively (ENRICHR, Ma’ayan lab).</p>
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<p>Transcriptomic analysis of cardiomyocyte response to doxorubicin in mice with OTUB1 heterozygous knockout according to Data obtained from GSE240959 and comparing differentially expressed genes (q value &lt; 0.005), using ENRICHR. Odds ratios are graphed, and corrected <span class="html-italic">p</span> values are indicated. TRRUST analysis (<b>A</b>) and Wikipathways analysis (<b>B</b>) respectively.</p>
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<p>Transcriptomic analysis of cardiomyocyte response to doxorubicin in mice with ADAM17 knockout. Data obtained from GSE276325 comparing differentially expressed genes (q value &lt; 0.005), Odds ratios are graphed, and bars are sorted from the most significant corrected <span class="html-italic">p</span>-value. TRRUST analysis (<b>A</b>) and Wikipathways analysis (<b>B</b>) respectively.</p>
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8 pages, 1424 KiB  
Proceeding Paper
A Convolutional Neural Network for Early Supraventricular Arrhythmia Identification
by Emilio J. Ochoa and Luis C. Revilla
Eng. Proc. 2025, 83(1), 8; https://doi.org/10.3390/engproc2025083008 - 8 Jan 2025
Viewed by 194
Abstract
Supraventricular arrhythmias (SVAs), including the often-asymptomatic supraventricular extrasystole (SVE), pose significant challenges in early detection and precise diagnosis. These challenges are of paramount importance, as recurrent SVEs may elevate the risk of developing severe SVAs, potentially resulting in cardiac weakening and subsequent heart [...] Read more.
Supraventricular arrhythmias (SVAs), including the often-asymptomatic supraventricular extrasystole (SVE), pose significant challenges in early detection and precise diagnosis. These challenges are of paramount importance, as recurrent SVEs may elevate the risk of developing severe SVAs, potentially resulting in cardiac weakening and subsequent heart failure. In the study conducted, an innovative approach was introduced that combined a convolutional neural network (CNN) architecture to enable the early identification and characterization of SVEs within electrocardiogram (ECG) signals. The analysis leveraged a dataset comprising 78 half-hour recordings from the highly regarded MIT-BIH Arrhythmia Database, which included annotation headers serving as labels for each recording. Signals were down-sampled by a factor of 2 and split into windows of 512 samples, with 12,288 observations for training. Following the methodology, classic signal preprocessing techniques (filtering and data normalization) were used. The proposed model was based on the UNET 1D model. A binary cross-entropy loss function, Adam optimizer, and a batch size of 128 were obtained after a hyperparameter tuning. As a training-validation methodology, a 50-fold cross-validation technique was used. The approach demonstrated a Dice coefficient of 79.01%, a precision of 80.96%, and a recall rate of 86.60% in detecting SVE events. These findings were corroborated through meticulous comparison with the annotations provided by the MIT-BIH database. The results underscore the immense potential of CNN and deep learning techniques in the early detection of supraventricular arrhythmias. This approach not only offers a valuable tool for healthcare professionals engaged in telemonitoring and early intervention strategies but also represents a significant contribution to the field of cardiac health monitoring. By facilitating efficient and precise identification of SVEs, our research sets the stage for improved patient outcomes and the prevention of severe SVAs, marking substantial advancements in this critical domain. Full article
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<p>Architecture of the proposed convolutional neural network model.</p>
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<p>Project flowchart. It starts with the choice of databases, a preprocessing of the dataset, the preparation of the neural network, its evaluation, and, finally, its validation.</p>
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<p>Results of neural network segmentation for the detection of supraventricular extrasystole in an electrocardiogram signal. Interval detected.</p>
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<p>Results of neural network segmentation for the detection of supraventricular extrasystole in an electrocardiogram signal. ECG signal superposed.</p>
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<p>Probability level of recognition of an SVE (<b>left</b>) and its respective segmented signal in an ECG: red (training), green (validation), and brown (coincidence) (<b>right</b>).</p>
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