Is the sigma-1 receptor a potential pharmacological target for cardiac pathologies? A systematic review.

Sigma-1 receptors are ligand-regulated chaperone proteins, involved in several cellular mechanisms. The aim of this systematic review was to examine the effects that the sigma-1 receptor has on the cardiovascular system. The interaction targets and proposed mechanisms of action of sigma-1 receptors were explored, with the aim of determining if the sigma-1 receptor is a potential pharmacological target for cardiac pathologies. This systematic review was conducted according to the PRISMA guidelines and these were used to critically appraise eligible studies. Pubmed and Scopus were systematically searched for articles investigating sigma-1 receptors in the cardiovascular system. Papers identified by the search terms were then subject to analysis against pre-determined inclusion criteria. 23 manuscripts met the inclusion criteria and were included in this review. The experimental platforms, experimental techniques utilised and the results of the studies were summarised. The sigma-1 receptor is found to be implicated in cardioprotection, via various mechanisms including stimulating the Akt-eNOS pathway, and reduction of Ca2 + leakage into the cytosol via modulating certain calcium channels. Sigma-1 receptors are also found to modulate other cardiac ion channels including different subtypes of potassium and sodium channels and have been shown to modulate intracardiac neuron excitability. The sigma-1 receptor is a potential therapeutic target for treatment of cardiac pathologies, particularly cardiac hypertrophy. We therefore suggest investigating the cardioprotective mechanisms of sigma-1 receptor function, alongside proposed potential ligands that can stimulate these functions

Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses

Introduction: Congenital long QT syndrome (LQTS) is a cardiac ion channelopathy that predisposes affected individuals to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD). The main aims of the study were to: (1) provide a description of the local epidemiology of LQTS, (2) identify significant risk factors of ventricular arrhythmias in this cohort, and (3) compare the performance of traditional Cox regression with that of random survival forests. / Methods: This was a territory-wide retrospective cohort study of patients diagnosed with congenital LQTS between 1997 and 2019. The primary outcome was spontaneous VT/VF. / Results: This study included 121 patients [median age of initial presentation: 20 (interquartile range: 8–44) years, 62% female] with a median follow-up of 88 (51–143) months. Genetic analysis identified novel mutations in KCNQ1, KCNH2, SCN5A, ANK2, CACNA1C, CAV3, and AKAP9. During follow-up, 23 patients developed VT/VF. Univariate Cox regression analysis revealed that age [hazard ratio (HR): 1.02 (1.01–1.04), P = 0.007; optimum cut-off: 19 years], presentation with syncope [HR: 3.86 (1.43–10.42), P = 0.008] or VT/VF [HR: 3.68 (1.62–8.37), P = 0.002] and the presence of PVCs [HR: 2.89 (1.22–6.83), P = 0.015] were significant predictors of spontaneous VT/VF. Only initial presentation with syncope remained significant after multivariate adjustment [HR: 3.58 (1.32–9.71), P = 0.011]. Random survival forest (RSF) model provided significant improvement in prediction performance over Cox regression (precision: 0.80 vs. 0.69; recall: 0.79 vs. 0.68; AUC: 0.77 vs. 0.68; c-statistic: 0.79 vs. 0.67). Decision rules were generated by RSF model to predict VT/VF post-diagnosis. / Conclusions: Effective risk stratification in congenital LQTS can be achieved by clinical history, electrocardiographic indices, and different investigation results, irrespective of underlying genetic defects. A machine learning approach using RSF can improve risk prediction over traditional Cox regression models

Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach

Aims Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure. Methods and results This was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co-morbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1–Q3: 71–87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five-fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively. Conclusions The electronic frailty index based on co-morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques

Arrhythmic Outcomes in Catecholaminergic Polymorphic Ventricular Tachycardia

Introduction Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a rare cardiac ion channelopathy. The aim of this study is to examine the genetic basis and identify pre-dictive factors for arrhythmic outcomes in CPVT patients from Hong Kong. Methods This was a territory-wide retrospective cohort study of consecutive patients diagnosed with CPVT at public hospitals or clinics in Hong Kong. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation (VT/VF). Results A total of 16 (mean presentation age=11±4 years old) patients were included. All patients presented at or before 19 years of age. Fifteen patients (93.8%) were initially symptomatic. Ten patients had both premature ventricular complexes (PVCs) and VT/VF, whereas one patient had PVCs without VT/VF. Genetic tests were performed in 14 patients (87.5%). Eight (57.1%) tested positive for the RyR2 gene. Seven variants have been described else-where (c.14848G>A, c.12475C>A, c.7420A>G, c.11836G>A, c.14159T>C, c.10046C>T and c.7202G>A). c.14861C>G is a novel RyR2 variant that has not been reported outside this cohort. All patients were treated with beta-blockers, three patients received amiodarone and two received verapamil. Sympathectomy (n=8), ablation (n=1) and implantable-cardioverter defibrillator implantation (n=3) were performed. Over a median follow-up of 127 (IQR: 97-143) months, six patients suffered from incident VT/VF. No significant predictors were identified on Cox regression. Nevertheless, a random survival forest model identified initial VT/VF/sudden cardiac death, palpitations, QTc, initially symptomatic and heart rate as important variables for estimating the probability of developing incident VT/VF. Conclusion All CPVT patients who are from Hong Kong presented at or before 19 years of age. Clinical and electrocardiographic findings can be used to predict arrhythmic outcomes. A nonparametric machine learning survival analysis achieved high accuracy for predicting the probability of incident VT/VF

Territory-wide cohort study of Brugada syndrome in Hong Kong: predictors of long-term outcomes using random survival forests and non-negative matrix factorisation

OBJECTIVES: Brugada syndrome (BrS) is an ion channelopathy that predisposes affected patients to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death. The aim of this study is to examine the predictive factors of spontaneous VT/VF. METHODS: This was a territory-wide retrospective cohort study of patients diagnosed with BrS between 1997 and 2019. The primary outcome was spontaneous VT/VF. Cox regression was used to identify significant risk predictors. Non-linear interactions between variables (latent patterns) were extracted using non-negative matrix factorisation (NMF) and used as inputs into the random survival forest (RSF) model. RESULTS: This study included 516 consecutive BrS patients (mean age of initial presentation=50±16 years, male=92%) with a median follow-up of 86 (IQR: 45-118) months. The cohort was divided into subgroups based on initial disease manifestation: asymptomatic (n=314), syncope (n=159) or VT/VF (n=41). Annualised event rates per person-year were 1.70%, 0.05% and 0.01% for the VT/VF, syncope and asymptomatic subgroups, respectively. Multivariate Cox regression analysis revealed initial presentation of VT/VF (HR=24.0, 95% CI=1.21 to 479, p=0.037) and SD of P-wave duration (HR=1.07, 95% CI=1.00 to 1.13, p=0.044) were significant predictors. The NMF-RSF showed the best predictive performance compared with RSF and Cox regression models (precision: 0.87 vs 0.83 vs. 0.76, recall: 0.89 vs. 0.85 vs 0.73, F1-score: 0.88 vs 0.84 vs 0.74). CONCLUSIONS: Clinical history, electrocardiographic markers and investigation results provide important information for risk stratification. Machine learning techniques using NMF and RSF significantly improves overall risk stratification performance

Cardiac Potassium Channels: Physiological Insights for Targeted Therapy.

The development of novel drugs specifically directed at the ion channels underlying particular features of cardiac action potential (AP) initiation, recovery, and refractoriness would contribute to an optimized approach to antiarrhythmic therapy that minimizes potential cardiac and extracardiac toxicity. Of these, K(+) channels contribute numerous and diverse currents with specific actions on different phases in the time course of AP repolarization. These features and their site-specific distribution make particular K(+) channel types attractive therapeutic targets for the development of pharmacological agents attempting antiarrhythmic therapy in conditions such as atrial fibrillation. However, progress in the development of such temporally and spatially selective antiarrhythmic drugs against particular ion channels has been relatively limited, particularly in view of our incomplete understanding of the complex physiological roles and interactions of the various ionic currents. This review summarizes the physiological properties of the main cardiac potassium channels and the way in which they modulate cardiac electrical activity and then critiques a number of available potential antiarrhythmic drugs directed at them

Virtual medical research mentoring

Background Medical research is important for professional advancement, and mentoring is a key means by which students and early-career doctors can engage in research. Contrasting international research collaborations, research mentoring programmes are often geographically limited. As the COVID-19 pandemic has led to increased use of online technology for classes and conferences, a virtual, international approach to medical research mentoring may be valuable. Approach We hereby describe our experience at the Cardiovascular Analytics Group, a virtual international medical research mentoring group established in 2015. We make use of virtual platforms in multi-level mentoring with peer mentoring and emphasise active participation, early leadership, an open culture, accessible research support and a distributed research workflow. Evaluation With 63 active members from 14 different countries, the Group has been successful in training medical students and early-career medical graduates in academic medicine. Our members have led over 100 peer-reviewed publications of original research and reviews since 2015, winning 13 research prizes during this time. Implications Our accessible-distributed model of virtual international medical research collaboration and multi-level mentoring is viable and efficient and caters to the needs of contemporary healthcare. Others should consider building similar models to improve medical research mentoring globally

Life-threatening intoxication with methylene bis(thiocyanate): clinical picture and pitfalls. A case report

BACKGROUND: Methylene bis(thiocyanate) (MBT) is a microbiocidal agent mainly used in industrial water cooling systems and paper mills as an inhibitor of algae, fungi, and bacteria. CASE PRESENTATION: We describe the first case of severe intoxication following inhalation of powder in an industrial worker. Profound cyanosis and respiratory failure caused by severe methemoglobinemia developed within several minutes. Despite immediate admission to the intensive care unit, where mechanical ventilation and hemodialysis for toxin elimination were initiated, multi-organ failure involving liver, kidneys, and lungs developed. While liver failure was leading, the patient was successfully treated with the MARS (molecular adsorbent recirculating system) procedure. CONCLUSION: Intoxication with MBT is a potentially life-threatening intoxication causing severe methemoglobinemia and multi-organ failure. Extracorporeal liver albumin dialysis (MARS) appears to be an effective treatment to allow recovery of hepatic function

A Distinct Urinary Biomarker Pattern Characteristic of Female Fabry Patients That Mirrors Response to Enzyme Replacement Therapy

Female patients affected by Fabry disease, an X-linked lysosomal storage disorder, exhibit a wide spectrum of symptoms, which renders diagnosis, and treatment decisions challenging. No diagnostic test, other than sequencing of the alpha-galactosidase A gene, is available and no biomarker has been proven useful to screen for the disease, predict disease course and monitor response to enzyme replacement therapy. Here, we used urine proteomic analysis based on capillary electrophoresis coupled to mass spectrometry and identified a biomarker profile in adult female Fabry patients. Urine samples were taken from 35 treatment-naive female Fabry patients and were compared to 89 age-matched healthy controls. We found a diagnostic biomarker pattern that exhibited 88.2% sensitivity and 97.8% specificity when tested in an independent validation cohort consisting of 17 treatment-naive Fabry patients and 45 controls. The model remained highly specific when applied to additional control patients with a variety of other renal, metabolic and cardiovascular diseases. Several of the 64 identified diagnostic biomarkers showed correlations with measures of disease severity. Notably, most biomarkers responded to enzyme replacement therapy, and 8 of 11 treated patients scored negative for Fabry disease in the diagnostic model. In conclusion, we defined a urinary biomarker model that seems to be of diagnostic use for Fabry disease in female patients and may be used to monitor response to enzyme replacement therapy

Quantum biology: an update and perspective

This is the final version. Available from MDPI via the DOI in this record. Data Availability Statement: Not applicable.Understanding the rules of life is one of the most important scientific endeavours and has revolutionised both biology and biotechnology. Remarkable advances in observation tech-niques allow us to investigate a broad range of complex and dynamic biological processes in which living systems could exploit quantum behaviour to enhance and regulate biological functions. Recent evidence suggests that these non-trivial quantum mechanical effects may play a crucial role in maintaining the non-equilibrium state of biomolecular systems. Quantum biology is the study of such quantum aspects of living systems. In this review, we summarise the latest progress in quantum biology, including the areas of enzyme-catalysed reactions, photosynthesis, spin-dependent reactions, DNA, fluorescent proteins, and ion channels. Many of these results are expected to be fundamental building blocks towards understanding the rules of life.Leverhulme Trus