Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model.

BACKGROUND: Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP). METHODS: Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures. RESULTS: The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56). CONCLUSIONS: Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.

Analysis of translesion polymerases in colorectal cancer cells following cetuximab treatment: A network perspective.

INTRODUCTION: Adaptive mutagenesis observed in colorectal cancer (CRC) cells upon exposure to EGFR inhibitors contributes to the development of resistance and recurrence. Multiple investigations have indicated a parallel between cancer cells and bacteria in terms of exhibiting adaptive mutagenesis. This phenomenon entails a transient and coordinated escalation of error-prone translesion synthesis polymerases (TLS polymerases), resulting in mutagenesis of a magnitude sufficient to drive the selection of resistant phenotypes. METHODS: In this study, we conducted a comprehensive pan-transcriptome analysis of the regulatory framework within CRC cells, with the objective of identifying potential transcriptome modules encompassing certain translesion polymerases and the associated transcription factors (TFs) that govern them. Our sampling strategy involved the collection of transcriptomic data from tumors treated with cetuximab, an EGFR inhibitor, untreated CRC tumors, and colorectal-derived cell lines, resulting in a diverse dataset. Subsequently, we identified co-regulated modules using weighted correlation network analysis with a minKMEtostay threshold set at 0.5 to minimize false-positive module identifications and mapped the modules to STRING annotations. Furthermore, we explored the putative TFs influencing these modules using KBoost, a kernel PCA regression model. RESULTS: Our analysis did not reveal a distinct transcriptional profile specific to cetuximab treatment. Moreover, we elucidated co-expression modules housing genes, for example, POLK, POLI, POLQ, REV1, POLN, and POLM. Specifically, POLK, POLI, and POLQ were assigned to the "blue" module, which also encompassed critical DNA damage response enzymes, for example. BRCA1, BRCA2, MSH6, and MSH2. To delineate the transcriptional control of this module, we investigated associated TFs, highlighting the roles of prominent cancer-associated TFs, such as CENPA, HNF1A, and E2F7. CONCLUSION: We found that translesion polymerases are co-regulated with DNA mismatch repair and cell cycle-associated factors. We did not, however, identified any networks specific to cetuximab treatment indicating that the response to EGFR inhibitors relates to a general stress response mechanism.

PITX2 deficiency leads to atrial mitochondrial dysfunction.

AIM: Reduced left atrial PITX2 is associated with atrial cardiomyopathy and atrial fibrillation. PITX2 is restricted to left atrial cardiomyocytes in the adult heart. The links between PITX2 deficiency, atrial cardiomyopathy and atrial fibrillation are not fully understood. METHODS AND RESULTS: To identify mechanisms linking PITX2 deficiency to atrial fibrillation, we generated and characterized PITX2-deficient human atrial cardiomyocytes derived from human induced pluripotent stem cells (hiPSC) and their controls. PITX2-deficient hiPSC-derived atrial cardiomyocytes showed shorter and disorganised sarcomeres and increased mononucleation. Electron microscopy found an increased number of smaller mitochondria compared to the control. Mitochondrial protein expression was altered in PITX2-deficient hiPSC-derived atrial cardiomyocytes. Single-nuclear RNA-sequencing found differences in cellular respiration pathways and differentially expressed mitochondrial and ion channel genes in PITX2-deficient hiPSC-derived atrial cardiomyocytes. PITX2 repression in hiPSC-derived atrial cardiomyocytes replicated dysregulation of cellular respiration. Mitochondrial respiration was shifted to increased glycolysis in PITX2-deficient hiPSC-derived atrial cardiomyocytes. PITX2-deficient human hiPSC-derived atrial cardiomyocytes showed higher spontaneous beating rates. Action potential duration was more variable with an overall prolongation of early repolarization, consistent with metabolic defects. Gene expression analyses confirmed changes in mitochondrial genes in left atria from 42 patients with atrial fibrillation compared to 43 patients in sinus rhythm. Dysregulation of left atrial mitochondrial (COX7C) and metabolic (FOXO1) genes was associated with PITX2 expression in human left atria. CONCLUSIONS: In summary, PITX2 deficiency causes mitochondrial dysfunction and a metabolic shift to glycolysis in human atrial cardiomyocytes. PITX2-dependent metabolic changes can contribute to the structural and functional defects found in PITX2-deficient atria.

Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers: the RATE-AF randomized trial.

Consumer-grade wearable technology has the potential to support clinical research and patient management. Here, we report results from the RATE-AF trial wearables study, which was designed to compare heart rate in older, multimorbid patients with permanent atrial fibrillation and heart failure who were randomized to treatment with either digoxin or beta-blockers. Heart rate (n = 143,379,796) and physical activity (n = 23,704,307) intervals were obtained from 53 participants (mean age 75.6 years (s.d. 8.4), 40% women) using a wrist-worn wearable linked to a smartphone for 20 weeks. Heart rates in participants treated with digoxin versus beta-blockers were not significantly different (regression coefficient 1.22 (95% confidence interval (CI) -2.82 to 5.27; P = 0.55); adjusted 0.66 (95% CI -3.45 to 4.77; P = 0.75)). No difference in heart rate was observed between the two groups of patients after accounting for physical activity (P = 0.74) or patients with high activity levels (≥30,000 steps per week; P = 0.97). Using a convolutional neural network designed to account for missing data, we found that wearable device data could predict New York Heart Association functional class 5 months after baseline assessment similarly to standard clinical measures of electrocardiographic heart rate and 6-minute walk test (F1 score 0.56 (95% CI 0.41 to 0.70) versus 0.55 (95% CI 0.41 to 0.68); P = 0.88 for comparison). The results of this study indicate that digoxin and beta-blockers have equivalent effects on heart rate in atrial fibrillation at rest and on exertion, and suggest that dynamic monitoring of individuals with arrhythmia using wearable technology could be an alternative to in-person assessment. ClinicalTrials.gov identifier: NCT02391337 .

Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations.

Fecal metabolites effectively discriminate inflammatory bowel disease (IBD) and show differential associations with diet. Metabolomics and AI-based models, including explainable AI (XAI), play crucial roles in understanding IBD. Using datasets from the UK Biobank and the Human Microbiome Project Phase II IBD Multi'omics Database (HMP2 IBDMDB), this study uses multiple machine learning (ML) classifiers and Shapley additive explanations (SHAP)-based XAI to prioritize plasma and fecal metabolites and analyze their diet correlations. Key findings include the identification of discriminative metabolites like glycoprotein acetyl and albumin in plasma, as well as nicotinic acid metabolites andurobilin in feces. Fecal metabolites provided a more robust disease predictor model (AUC [95%]: 0.93 [0.87-0.99]) compared to plasma metabolites (AUC [95%]: 0.74 [0.69-0.79]), with stronger and more group-differential diet-metabolite associations in feces. The study validates known metabolite associations and highlights the impact of IBD on the interplay between gut microbial metabolites and diet.

Creating a next-generation phenotype library: the health data research UK Phenotype Library.

Objective: To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms. Materials and Methods: We undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly. Results: We created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups. Discussion: This platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes. Conclusions: We have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.

Remote monitoring of atrial fibrillation recurrence using mHealth technology (REMOTE-AF).

Aims: This proof-of-concept study sought to evaluate changes in heart rate (HR) obtained from a consumer wearable device and compare against implantable loop recorder (ILR)-detected recurrence of atrial fibrillation (AF) and atrial tachycardia (AT) after AF ablation. Methods and results: REMOTE-AF (NCT05037136) was a prospectively designed sub-study of the CASA-AF randomized controlled trial (NCT04280042). Participants without a permanent pacemaker had an ILR implanted at their index ablation procedure for longstanding persistent AF. Heart rate and step count were continuously monitored using photoplethysmography (PPG) from a commercially available wrist-worn wearable. Photoplethysmography-recorded HR data were pre-processed with noise filtration and episodes at 1-min interval over 30 min of HR elevations (Z-score = 2) were compared with corresponding ILR data. Thirty-five patients were enrolled, with mean age 70.3 ± 6.8 years and median follow-up 10 months (interquartile range 8-12 months). Implantable loop recorder analysis revealed 17 out of 35 patients (49%) had recurrence of AF/AT. Compared with ILR recurrence, wearable-derived elevations in HR ≥ 110 beats per minute had a sensitivity of 95.3%, specificity 54.1%, positive predictive value (PPV) 15.8%, negative predictive value (NPV) 99.2%, and overall accuracy 57.4%. With PPG-recorded HR elevation spikes (non-exercise related), the sensitivity was 87.5%, specificity 62.2%, PPV 39.2%, NPV 92.3%, and overall accuracy 64.0% in the entire patient cohort. In the AF/AT recurrence only group, sensitivity was 87.6%, specificity 68.3%, PPV 53.6%, NPV 93.0%, and overall accuracy 75.0%. Conclusion: Consumer wearable devices have the potential to contribute to arrhythmia detection after AF ablation. Study Registration: ClinicalTrials.gov Identifier: NCT05037136 https://clinicaltrials.gov/ct2/show/NCT05037136.

The Prognostic Value of Left Ventricular Entropy From T1 Mapping in Patients With Hypertrophic Cardiomyopathy.

Background: The prognostic value of left ventricular (LV) entropy in hypertrophic cardiomyopathy (HCM) is unclear. Objectives: This study aimed to assess the prognostic value of LV entropy from T1 mapping in HCM. Methods: A total of 748 participants with HCM, who underwent cardiovascular magnetic resonance (CMR), were consecutively enrolled. LV entropy was quantified by native T1 mapping. A competing risk analysis and a Cox proportional hazards regression analysis were performed to identify potential associations of LV entropy with sudden cardiac death (SCD) and cardiovascular death (CVD), respectively. Results: A total of 40 patients with HCM experienced SCD, and 65 experienced CVD during a median follow-up of 43 months. Participants with increased LV entropy (≥4.06) were more likely to experience SCD and CVD (all P < 0.05) in the entire study cohort or the subgroup with low late gadolinium enhancement (LGE) extent (<15%). After adjustment for the European Society of Cardiology predictors and the presence of high LGE extent (≥15%), LV mean entropy was an independent predictor for SCD (HR: 1.03; all P < 0.05) by the multivariable competing risk analysis and CVD (HR: 1.06; 95% CI: 1.03-1.09; P < 0.001) by multivariable Cox regression analysis. Conclusions: LV mean entropy derived from native T1 mapping, reflecting myocardial tissue heterogeneity, was an independent predictor of SCD and CVD in participants with HCM. (Cardiac Magnetic Resonance Imaging Clinical Application Registration Study; ChiCTR1900024094).

Network dynamics and therapeutic aspects of mRNA and protein markers with the recurrence sites of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that typically manifests late patient presentation and poor outcomes. Furthermore, PDAC recurrence is a common challenge. Distinct patterns of PDAC recurrence have been associated with differential activation of immune pathway-related genes and specific inflammatory responses in their tumour microenvironment. However, the molecular associations between and within cellular components that underpin PDAC recurrence require further development, especially from a multi-omics integration perspective. In this study, we identified stable molecular associations across multiple PDAC recurrences and utilised integrative analytics to identify stable and novel associations via simultaneous feature selection. Spatial transcriptome and proteome datasets were used to perform univariate analysis, Spearman partial correlation analysis, and univariate analyses by Machine Learning methods, including regularised canonical correlation analysis and sparse partial least squares. Furthermore, networks were constructed for reported and new stable associations. Our findings revealed gene and protein associations across multiple PDAC recurrence groups, which can provide a better understanding of the multi-layer disease mechanisms that contribute to PDAC recurrence. These findings may help to provide novel association targets for clinical studies for constructing precision medicine and personalised surveillance tools for patients with PDAC recurrence.

Development of automated neural network prediction for echocardiographic left ventricular ejection fraction.

Introduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey's method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline's accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. Results: This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. Conclusion: The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function.

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.

Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.

Reduced plakoglobin increases the risk of sodium current defects and atrial conduction abnormalities in response to androgenic anabolic steroid abuse.

Androgenic anabolic steroids (AAS) are commonly abused by young men. Male sex and increased AAS levels are associated with earlier and more severe manifestation of common cardiac conditions, such as atrial fibrillation, and rare ones, such as arrhythmogenic right ventricular cardiomyopathy (ARVC). Clinical observations suggest a potential atrial involvement in ARVC. Arrhythmogenic right ventricular cardiomyopathy is caused by desmosomal gene defects, including reduced plakoglobin expression. Here, we analysed clinical records from 146 ARVC patients to identify that ARVC is more common in males than females. Patients with ARVC also had an increased incidence of atrial arrhythmias and P wave changes. To study desmosomal vulnerability and the effects of AAS on the atria, young adult male mice, heterozygously deficient for plakoglobin (Plako+/-), and wild type (WT) littermates were chronically exposed to 5α-dihydrotestosterone (DHT) or placebo. The DHT increased atrial expression of pro-hypertrophic, fibrotic and inflammatory transcripts. In mice with reduced plakoglobin, DHT exaggerated P wave abnormalities, atrial conduction slowing, sodium current depletion, action potential amplitude reduction and the fall in action potential depolarization rate. Super-resolution microscopy revealed a decrease in NaV1.5 membrane clustering in Plako+/- atrial cardiomyocytes after DHT exposure. In summary, AAS combined with plakoglobin deficiency cause pathological atrial electrical remodelling in young male hearts. Male sex is likely to increase the risk of atrial arrhythmia, particularly in those with desmosomal gene variants. This risk is likely to be exaggerated further by AAS use. KEY POINTS: Androgenic male sex hormones, such as testosterone, might increase the risk of atrial fibrillation in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC), which is often caused by desmosomal gene defects (e.g. reduced plakoglobin expression). In this study, we observed a significantly higher proportion of males who had ARVC compared with females, and atrial arrhythmias and P wave changes represented a common observation in advanced ARVC stages. In mice with reduced plakoglobin expression, chronic administration of 5α-dihydrotestosterone led to P wave abnormalities, atrial conduction slowing, sodium current depletion and a decrease in membrane-localized NaV1.5 clusters. 5α-Dihydrotestosterone, therefore, represents a stimulus aggravating the pro-arrhythmic phenotype in carriers of desmosomal mutations and can affect atrial electrical function.

Safety outcomes of bariatric surgery in patients with advanced organ disease: the ONWARD study: a prospective cohort study.

INTRODUCTION: Increasing numbers of patients with advanced organ disease are being considered for bariatric and metabolic surgery (BMS). There is no prospective study on the safety of BMS in these patients. This study aimed to capture outcomes for patients with advanced cardiac, renal, or liver disease undergoing BMS. MATERIALS AND METHODS: This was a multinational, prospective cohort study on the safety of elective BMS in adults (≥18 years) with advanced disease of the heart, liver, or kidney. RESULTS: Data on 177 patients with advanced diseases of heart, liver, or kidney were submitted by 75 centres in 33 countries. Mean age and BMI was 48.56±11.23 years and 45.55±7.35 kg/m 2 , respectively. Laparoscopic sleeve gastrectomy was performed in 124 patients (70%). The 30-day morbidity and mortality were 15.9% ( n =28) and 1.1% ( n =2), respectively. Thirty-day morbidity was 16.4%, 11.7%, 20.5%, and 50.0% in patients with advanced heart ( n =11/61), liver ( n =8/68), kidney ( n =9/44), and multi-organ disease ( n =2/4), respectively. Cardiac patients with left ventricular ejection fraction less than or equal to 35% and New York Heart Association classification 3 or 4, liver patients with model for end-stage liver disease score greater than or equal to 12, and patients with advanced renal disease not on dialysis were at increased risk of complications. Comparison with a propensity score-matched cohort found advanced disease of the heart, liver, or kidney to be significantly associated with higher 30-day morbidity. CONCLUSION: Patients with advanced organ disease are at increased risk of 30-day morbidity following BMS. This prospective study quantifies that risk and identifies patients at the highest risk.

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions.

Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n = 933) and validated in the remaining patients (n = 552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation.

Tafamidis treatment in patients with transthyretin amyloid cardiomyopathy: a systematic review and meta-analysis.

Background: Previous studies have reported that tafamidis treatment was associated with better outcomes in patients with transthyretin amyloid cardiomyopathy (ATTR-CM) compared with those without tafamidis treatment. Therefore, we aimed to systematically assess the association of tafamidis treatment with outcomes in patients with ATTR-CM. Methods: The protocol for this systematic review and meta-analysis was registered in the PROSPERO (CRD42022381985). Pubmed, Ovid Embase, Scopus, Cochrane Library, and Web of Science were interrogated to identify studies that evaluated the impact of tafamidis on prognosis in ATTR-CM, from January 1, 2000 to June 1, 2023. A random-effects model was used to determine the pooled risk ratio (RR) for the adverse endpoints. In addition, the main outcomes included all-cause death or heart transplantation, the composite endpoints included all-cause death, heart transplantation, cardiac-assist device implantation, heart failure exacerbations, and hospitalization. Findings: Fifteen studies comprising 2765 patients (mean age 75.9 ± 9.3 years; 83.7% male) with a mean follow-up duration of 18.7 ± 17.1 months were included in the meta-analysis. There was a decrease in left ventricular ejection fraction (LVEF) (standard mean differences (SMD: -0.17; 95% confidence interval (CI), -0.31 to -0.03; P = 0.02) but were no significant differences in intraventricular septum (IVS) thickness or global longitudinal strain (GLS) after tafamidis treatment. However, subgroup analysis showed no significant deterioration in LVEF in the patients with wild-type ATTR after tafamidis treatment (SMD: -0.11; 95% CI, -0.34 to 0.12, P = 0.34). In addition, the group with tafamidis treatment had a decreased risk for all-cause death or heart transplantation compared to patients without treatment (the pooled RR, 0.44; 95% CI, 0.31-0.65; P < 0.01). Subgroup analysis showed that there was no significant difference of tafamidis on the outcomes in patients with wild-type or hereditary ATTR (RR, 0.44; 95% CI, 0.27-0.73 versus 0.21, 95% CI, 0.11-0.40, P = 0.08). Furthermore, tafamidis treatment was associated with a lower risk of the composite endpoint (RR, 0.57; 95% CI, 0.42-0.77; P < 0.01). Interpretation: Our findings suggested that there was no significant deterioration in LVEF in the patients with wild-type ATTR after tafamidis treatment. In addition, tafamidis treatment was associated with a low risk of all-cause death and adverse cardiovascular events. Funding: This work was supported by grants from the Natural Science Foundation of Sichuan Province [Grant Number: 23NSFSC4589] and the National Natural Science Foundation of China [Grant Number: 82202248].

Link prediction in complex network using information flow.

Link prediction in complex networks has recently attracted a great deal of attraction in diverse scientific domains, including social and biological sciences. Given a snapshot of a network, the goal is to predict links that are missing in the network or that are likely to occur in the near future. This problem has both theoretical and practical significance; it not only helps us to identify missing links in a network more efficiently by avoiding the expensive and time consuming experimental processes, but also allows us to study the evolution of a network with time. To address the problem of link prediction, numerous attempts have been made over the recent years that exploit the local and the global topological properties of the network to predict missing links in the network. In this paper, we use parametrised matrix forest index (PMFI) to predict missing links in a network. We show that, for small parameter values, this index is linked to a heat diffusion process on a graph and therefore encodes geometric properties of the network. We then develop a framework that combines the PMFI with a local similarity index to predict missing links in the network. The framework is applied to numerous networks obtained from diverse domains such as social network, biological network, and transport network. The results show that the proposed method can predict missing links with higher accuracy when compared to other state-of-the-art link prediction methods.

Correction: Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models.

[This corrects the article DOI: 10.1371/journal.pone.0263390.].

Outcomes of Early Rhythm Control Therapy in Patients With Atrial Fibrillation and a High Comorbidity Burden in Large Real-World Cohorts.

BACKGROUND: A recent subanalysis of the EAST-AFNET 4 (Early Treatment of Atrial Fibrillation for Stroke Prevention Trial) suggests a stronger benefit of early rhythm control (ERC) in patients with atrial fibrillation and a high comorbidity burden when compared to patients with a lower comorbidity burden. METHODS: We identified 109 739 patients with newly diagnosed atrial fibrillation in a large United States deidentified administrative claims database (OptumLabs) and 11 625 patients in the population-based UKB (UK Biobank). ERC was defined as atrial fibrillation ablation or antiarrhythmic drug therapy within the first year after atrial fibrillation diagnosis. Patients were classified as (1) ERC and high comorbidity burden (CHA2DS2-VASc score ≥4); (2) ERC and lower comorbidity burden (CHA2DS2-VASc score 2-3); (3) no ERC and high comorbidity burden; and (4) no ERC and lower comorbidity burden. Patients without an elevated comorbidity burden (CHA2DS2-VASc score 0-1) were excluded. Propensity score overlap weighting and cox proportional hazards regression were used to balance patients and compare groups for the primary composite outcome of all-cause mortality, stroke, or hospitalization with the diagnoses heart failure or myocardial infarction as well as for a primary composite safety outcome of death, stroke, and serious adverse events related to ERC. RESULTS: In both cohorts, ERC was associated with a reduced risk for the primary composite outcome in patients with a high comorbidity burden (OptumLabs: hazard ratio, 0.83 [95% CI 0.72-0.95]; P=0.006; UKB: hazard ratio, 0.77 [95% CI, 0.63-0.94]; P=0.009). In patients with a lower comorbidity burden, the difference in outcomes was not significant (OptumLabs: hazard ratio, 0.92 [95% CI, 0.54-1.57]; P=0.767; UKB: hazard ratio, 0.94 [95% CI, 0.83-1.06]; P=0.310). The comorbidity burden interacted with ERC in the UKB (interaction- P=0.027) but not in OptumLabs (interaction-P=0.720). ERC was not associated with an increased risk for the primary safety outcome. CONCLUSIONS: ERC is safe and may be more favorable in a population-based sample of patients with high a comorbidity burden (CHA2DS2-VASc score ≥4).

Prognostic Value of RV Abnormalities on CMR in Patients With Known or Suspected Cardiac Sarcoidosis.

BACKGROUND: Left ventricular abnormalities in cardiac sarcoidosis (CS) are associated with adverse cardiovascular events, whereas the prognostic value of right ventricular (RV) involvement found on cardiac magnetic resonance is unclear. OBJECTIVES: This study aimed to systematically assess the prognostic value of right ventricular ejection fraction (RVEF) and RV late gadolinium enhancement (LGE) in known or suspected CS. METHODS: This study was prospectively registered in PROSPERO (CRD42022302579). PubMed, Embase, and Web of Science were searched to identify studies that evaluated the association between RVEF or RV LGE on clinical outcomes in CS. A composite endpoint of all-cause death, cardiovascular events, or sudden cardiac death (SCD) was used. A meta-analysis was performed to determine the pooled risk ratio (RR) for these adverse events. The calculated sensitivity, specificity, and area under the curve with 95% CIs were weighted and summarized. RESULTS: Eight studies including a total of 899 patients with a mean follow-up duration of 3.2 ± 0.7 years were included. The pooled RR of RV systolic dysfunction was 3.1 (95% CI: 1.7-5.5; P < 0.01) for composite events and 3.0 (95% CI: 1.3-7.0; P < 0.01) for SCD events. In addition, CS patients with RV LGE had a significant risk for composite events (RR: 4.8 [95% CI: 2.4-9.6]; P < 0.01) and a higher risk for SCD (RR: 9.5 [95% CI: 4.4-20.5]; P < 0.01) than patients without RV LGE. Furthermore, the pooled area under the curve, sensitivity, and specificity of RV LGE for identifying patients with CS who were at highest SCD risk were 0.8 (95% CI: 0.8-0.9), 69% (95% CI: 50%-84%), and 90% (95% CI: 70%-97%), respectively. CONCLUSIONS: In patients with known or suspected CS, RVEF and RV LGE were both associated with adverse events. Furthermore, RV LGE shows good discrimination in identifying CS patients at high risk of SCD.