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Aims: Reduced left atrial PITX2 is associated with atrial cardiomyopathy and atrial fibrillation (AF). PITX2 is restricted to left atrial cardiomyocytes (aCMs) in the adult heart. The links between PITX2 deficiency, atrial cardiomyopathy, and AF are not fully understood., Methods and Results: To identify mechanisms linking PITX2 deficiency to AF, we generated and characterized PITX2-deficient human aCMs derived from human induced pluripotent stem cells (hiPSC) and their controls. PITX2-deficient hiPSC-derived atrial cardiomyocytes showed shorter and disorganized sarcomeres and increased mononucleation. Electron microscopy found an increased number of smaller mitochondria compared with isogenic controls. 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 AF compared with 43 patients with sinus rhythm. Dysregulation of left atrial mitochondrial (COX7C) and metabolic (FOXO1) genes was associated with PITX2 expression in human left atria., Conclusion: PITX2 deficiency causes atrial mitochondrial dysfunction and a metabolic shift to glycolysis in human aCMs. PITX2-dependent metabolic changes can contribute to the structural and functional defects found in PITX2-deficient atria., Competing Interests: Conflict of interest: L.F. has received institutional research grants and non-financial support from European Union, British Heart Foundation, Medical Research Council (U.K.), DFG, German Centre for Heart Research DZHK and several biomedical companies. P.K. has received additional support for research from the European Union, British Heart Foundation, Foundation Leducq, Medical Research Council (U.K.), and German Centre for Cardiovascular Research, from several drug and device companies active in AF, Honoria from several such companies, but not in the last three years. P.K. and L.F. are listed as inventors on two patents held by University of Birmingham (AFTherapy WO 2015140571, Markers for AF WO 2016021783). U.S. has received consultancy fees or honoraria from Università della Svizzera Italiana (USI, Switzerland), Roche Diagnostics (Switzerland), EP Solutions Inc. (Switzerland), Johnson & Johnson Medical Limited, (U.K.), Bayer Healthcare (Germany). U.S. is co-founder and shareholder of YourRhythmics BV, a spin-off company of the University Maastricht. K.G. has received additional support for research from the British Heart Foundation, Medical Research Council (U.K.), and Rocket Pharmaceuticals Inc. All other authors declare they have no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Colorectal cancer (CRC) is any cancer that starts in the colon or the rectum and presents a significant health concern. It is the third most diagnosed and the second deadliest cancer, with an estimated 153,020 new cases and 52,550 deaths in 2023. The severity of colon cancer may be attributed to its ability to avoid the host immune system and growth suppressors, its asymptomatic nature in the early stages, its association with a continually ageing population and unfavourable diet and obesity. The composition of the gut microbiome plays an important role in the development of CRC and presents as an important target in early detection and in predicting treatment outcomes in CRC. This study aims to identify microbiome-specific derived clusters in CRC patients and conduct subsequent survival analysis using the specific microbiome features within clusters., Methods: Consensus clustering and feature selection, involving a Kruskal-Wallis test, a random forest and least absolute shrinkage and selection operator (LASSO) were applied resulting in the identification of differently expressed microbiomes between clusters. Lastly, survival analysis was performed on the selected features using Kaplan-Meier curves and Cox regression. K-means clustering, as selected using consensus clustering interpretation, presented three distinct clusters with clear differences in alpha and beta diversity and baseline clinical variables., Results: A total 1311 of the 1406 microbes were selected using the Kruskal Wallis and passed to the random forest and LASSO, which narrowed the dataset to 140 features. Following the survival analysis, eight microbiome species, namely N4likevirus, Ambidensovirus, Synechococcus, Thermithiobacillus, Hydrocarboniphaga, Rhodovibrio, Gloeobacter and Candidatus Nitrosotenuis, were selected as significant in clustering and survival., Conclusion: This study reveals the heterogeneity of the CRC microbiome and its effect on disease prognosis and necessitates further exploration of the biological mechanisms of these selected microbiomes as well further investigation of whether the approach depicted here is applicable to other cancer types., (© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.)

Background: No robust data are available on the safety of primary bariatric and metabolic surgery (BMS) alone compared to primary BMS combined with other procedures., Objectives: The objective of this study is to collect a 30-day mortality and morbidity of primary BMS combined with cholecystectomy, ventral hernia repair, or hiatal hernia repair., Setting: This is as an international, multicenter, prospective, and observational audit of patients undergoing primary BMS combined with one or more additional procedures., Methods: The audit took place from January 1 to June 30, 2022. A descriptive analysis was conducted. A propensity score matching analysis compared the BLEND study patients with those from the GENEVA cohort to obtain objective evaluation between combined procedures and primary BMS alone., Results: A total of 75 centers submitted data on 1036 patients. Sleeve gastrectomy was the most commonly primary BMS (N = 653, 63%), and hiatal hernia repair was the most commonly concomitant procedure (N = 447, 43.1%). RYGB accounted for the highest percentage (20.6%) of a 30-day morbidity, followed by SG (10.5%). More than one combined procedures had the highest morbidities among all combinations (17.1%). Out of overall 134 complications, 129 (96.2%) were Clavien-Dindo I-III, and 4 were CD V. Patients who underwent a primary bariatric surgery combined with another procedure had a pronounced increase in a 30-day complication rate compared with patients who underwent only BMS (12.7% vs. 7.1%)., Conclusion: Combining BMS with another procedure increases the risk of complications, but most are minor and require no further treatment. Combined procedures with primary BMS is a viable option to consider in selected patients following multi-disciplinary discussion., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Deep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders., (© 2024. The Author(s).)

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 Na V 1.5 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 Na V 1.5 clusters. 5α-Dihydrotestosterone, therefore, represents a stimulus aggravating the pro-arrhythmic phenotype in carriers of desmosomal mutations and can affect atrial electrical function., (© 2024 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

Background: There is little international data on morbidity and mortality of surgery for perforated peptic ulcer (PPU). This study aimed to understand the global 30-day morbidity and mortality of patients undergoing surgery for PPU and to identify variables associated with these., Method: We performed an international study of adults (≥ 18 years) who underwent surgery for PPU from 1st January 2022 to 30th June 2022. Patients who were treated conservatively or had an underlying gastric cancer were excluded. Patients were divided into subgroups according to age (≤ 50 and > 50 years) and time from onset of symptoms to hospital presentation (≤ 24 and > 24 h). Univariate and Multivariate analyses were carried out to identify factors associated with higher 30-day morbidity and mortality., Results:  1874 patients from 159 centres across 52 countries were included. 78.3% (n = 1467) of the patients were males and the median (IQR) age was 49 years (25). Thirty-day morbidity and mortality were 48.5% (n = 910) and 9.3% (n = 174) respectively. Median (IQR) hospital stay was 7 (5) days. Open surgery was performed in 80% (n = 1505) of the cohort. Age > 50 years [(OR = 1.7, 95% CI 1.4-2), (OR = 4.7, 95% CI 3.1-7.6)], female gender [(OR = 1.8, 95% CI 1.4-2.3), (OR = 1.9, 95% CI 1.3-2.9)], shock on admission [(OR = 2.1, 95% CI 1.7-2.7), (OR = 4.8, 95% CI 3.2-7.1)], and acute kidney injury [(OR = 2.5, 95% CI 1.9-3.2), (OR = 3.9), 95% CI 2.7-5.6)] were associated with both 30-day morbidity and mortality. Delayed presentation was associated with 30-day morbidity [OR = 1.3, 95% CI 1.1-1.6], but not mortality., Conclusions: This study showed that surgery for PPU was associated with high 30-day morbidity and mortality rate. Age, female gender, and signs of shock at presentation were associated with both 30-day morbidity and mortality., (© 2024. Crown.)

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 ., (© 2024. The Author(s).)

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., Competing Interests: The authors have no competing interests to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

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., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Liu, Bunting, Brind, Thorley, Karwath, Lu, Zhou, Wang, Mobley, Tica, Gkoutos, Kotecha and Duan.)

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., (© 2024. The Author(s).)

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)., Competing Interests: This work was supported by grants from the National Natural Science Foundation of China (Grant Number: 82202248), the Natural Science Foundation of Sichuan Province (Grant Number: 23NSFSC4589), and the 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (Grant numbers ZYJC18003). The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

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., Competing Interests: Conflict of interest: S.G. reports funding through the BigData@Heart Innovative Medicines Initiative [grant no. 116074]. A.B. reports funding from the BigData@Heart Innovative Medicines Initiative [grant no. 116074] during the conduct of the study. D.K. reports grants from the National Institute for Health Research (NIHR CDF-2015-08-074 RATE-AF; NIHR130280 DaRe2THINK; NIHR132974 D2T-NeuroVascular; and NIHR203326 BRC), the British Heart Foundation (PG/17/55/33087, AA/18/2/34218, and FS/CDRF/21/21032), the EU/EFPIA Innovative Medicines Initiative (BigData@Heart 116074), EU Horizon (HYPERMARKER 101095480), UK National Health Service—Data for R&D—Subnational Secure Data Environment programme, and the ESC supported by educational grants from Boehringer Ingelheim/BMS-Pfizer Alliance/Bayer/Daiichi Sankyo/Boston Scientific, the NIHR/University of Oxford Biomedical Research Centre, and British Heart Foundation/University of Birmingham Accelerator Award (STEEER-AF). In addition, he has received research grants and advisory board fees from Bayer, Amomed, and Protherics Medicines Development; all outside the submitted work. G.G. reports support from the NIHR Birmingham Experimental Cancer Medicine Centre, NIHR Birmingham Surgical Reconstruction and Microbiology Research Centre, Nanocommons H2020-EU (731032), and the MRC Heath Data Research UK (HDRUK/CFC/01). S.H. reports speaker fees from Alivercor, consultancy fees from BMS, a research grant from Abbott, and the NIHR grants (CASA and LOTO). G.A., I.K.H., Z.C., J.J., D.J., V.M., W.H., and T.W. have no relevant disclosures., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

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., (© 2024 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

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., (© 2023. Springer Nature Limited.)

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., (© 2023. Springer Nature Limited.)

Background: We aimed to predict response to biologics in inflammatory bowel disease (IBD) using computerized image analysis of probe confocal laser endomicroscopy (pCLE) in vivo and assess the binding of fluorescent-labeled biologics ex vivo. Additionally, we investigated genes predictive of anti-tumor necrosis factor (TNF) response., Methods: Twenty-nine patients (15 with Crohn's disease [CD], 14 with ulcerative colitis [UC]) underwent colonoscopy with pCLE before and 12 to 14 weeks after starting anti-TNF or anti-integrin α4β7 therapy. Biopsies were taken for fluorescein isothiocyanate-labeled infliximab and vedolizumab staining and gene expression analysis. Computer-aided quantitative image analysis of pCLE was performed. Differentially expressed genes predictive of response were determined and validated in a public cohort., Results: In vivo, vessel tortuosity, crypt morphology, and fluorescein leakage predicted response in UC (area under the receiver-operating characteristic curve [AUROC], 0.93; accuracy 85%, positive predictive value [PPV] 89%; negative predictive value [NPV] 75%) and CD (AUROC, 0.79; accuracy 80%; PPV 75%; NPV 83%) patients. Ex vivo, increased binding of labeled biologic at baseline predicted response in UC (UC) (AUROC, 83%; accuracy 77%; PPV 89%; NPV 50%) but not in Crohn's disease (AUROC 58%). A total of 325 differentially expressed genes distinguished responders from nonresponders, 86 of which fell within the most enriched pathways. A panel including ACTN1, CXCL6, LAMA4, EMILIN1, CRIP2, CXCL13, and MAPKAPK2 showed good prediction of anti-TNF response (AUROC >0.7)., Conclusions: Higher mucosal binding of the drug target is associated with response to therapy in UC. In vivo, mucosal and microvascular changes detected by pCLE are associated with response to biologics in inflammatory bowel disease. Anti-TNF-responsive UC patients have a less inflamed and fibrotic state pretreatment. Chemotactic pathways involving CXCL6 or CXCL13 may be novel targets for therapy in nonresponders., (© 2022 Crohn’s & Colitis Foundation. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation.)

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]., Competing Interests: All authors declare no competing interests., (© 2023 The Author(s).)

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 (CHA 2 DS 2 -VASc score ≥4); (2) ERC and lower comorbidity burden (CHA 2 DS 2 -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 (CHA 2 DS 2 -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 (CHA 2 DS 2 -VASc score ≥4)., Competing Interests: Disclosures Dr Kirchhof is listed as inventor on 2 patents held by University of Birmingham (Atrial Fibrillation Therapy WO 2015140571, Markers for Atrial Fibrillation WO 2016012783). Dr Kirchhof receives research support for basic, translational, and clinical research projects from European Union, British Heart Foundation, Leducq Foundation, Medical Research Council (United Kingdom), and German Center for Cardiovascular Research, from several drug and device companies active in atrial fibrillation and has received honoraria from several such companies in the past, but not in the last 3 years. Dr Rillig received consultant fees from Medtronic, KODEX-EPD, Biosense Webster and travel grants and lecture fees from Medtronic, Cardiofocus, Biosense Webster, Abbott, Boehringer Ingelheim, Philips KODEX-EPD, Ablamap, Bayer and Novartis. Dr Noseworthy is a study investigator in an ablation trial sponsored by Medtronic. Dr Noseworthy and Mayo Clinic are involved in potential equity/royalty relationship with AliveCor. Dr Noseworthy has served on an expert advisory panel for OptumLabs. Dr Noseworthy and Mayo Clinic have filed patents related to the application of AI to the ECG for diagnosis and risk stratification. Dr Ellinor has received sponsored research support from Bayer AG and IBM Research and he has served on advisory boards or consulted for Bayer AG, MyoKardia, and Novartis. The other authors report no conflicts.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive lethal diseases among other cancer types. Gut microbiome and its metabolic regulation play a crucial role in PDAC. Metabolic regulation in the gut is a complex process that involves microbiome and microbiome-derived short-chain fatty acids (SCFAs). SCFAs regulate inflammation, as well as lipid and glucose metabolism, through different pathways. This review aims to summarize recent developments in PDAC in the context of gut and oral microbiota and their associations with short-chain fatty acid (SCFA). In addition to this, we discuss possible therapeutic applications using microbiota in PDAC., (© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: Oesophageal perforation is an uncommon surgical emergency associated with high morbidity and mortality. The timing and type of intervention is crucial and there has been a major paradigm shift towards minimal invasive management over the last 15 years. Herein, we review our management of spontaneous and iatrogenic oesophageal perforations and assess the short- and long-term outcomes., Methods: We performed a retrospective review of consecutive patients presenting with intra-thoracic oesophageal perforation between January 2004 and Dec 2020 in a single tertiary hospital., Results: Seventy-four patients were identified with oesophageal perforations: 58.1% were male; mean age of 68.28 ± 13.67 years. Aetiology was spontaneous in 42 (56.76%), iatrogenic in 29 (39.2%) and foreign body ingestion/related to trauma in 3 (4.1%). The diagnosis was delayed in 29 (39.2%) cases for longer than 24 h. There was change in the primary diagnostic modality over the period of this study with CT being used for diagnosis for 19 of 20 patients (95%). Initial management of the oesophageal perforation included a surgical intervention in 34 [45.9%; primary closure in 28 (37.8%), resection in 6 (8.1%)], endoscopic stenting in 18 (24.3%) and conservative management in 22 (29.7%) patients. On multivariate analysis, there was an effect of pathology (malignant vs. benign; p = 0.003) and surgical treatment as first line (p = 0.048) on 90-day mortality. However, at 1-year and overall follow-up, time to presentation (≤ 24 h vs. > 24 h) remained the only significant variable (p = 0.017 & p = 0.02, respectively)., Conclusion: Oesophageal perforation remains a condition with high mortality. The paradigm shift in our tertiary unit suggests the more liberal use of CT to establish an earlier diagnosis and a higher rate of oesophageal stenting as a primary management option for iatrogenic perforations. Time to diagnosis and management continues to be the most critical variable in the overall outcome., (© 2022. The Author(s).)

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., Competing Interests: Funding Support and Author Disclosures This work was supported by a grant from the 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (grant number ZYJC18003). Dr Hosadurg was funded by the National Institutes of Health T32 grant (grant number T32EB003841), and Dr Wang was funded by the National Natural Science Foundation of China (grant number 82202248). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose, (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction and Aim: Artificial Intelligence (AI) is already being successfully employed to aid the interpretation of multiple facets of burns care. In the light of the growing influence of AI, this systematic review and diagnostic test accuracy meta-analyses aim to appraise and summarise the current direction of research in this field., Method: A systematic literature review was conducted of relevant studies published between 1990 and 2021, yielding 35 studies. Twelve studies were suitable for a Diagnostic Test Meta-Analyses., Results: The studies generally focussed on burn depth (Accuracy 68.9%-95.4%, Sensitivity 90.8% and Specificity 84.4%), burn segmentation (Accuracy 76.0%-99.4%, Sensitivity 97.9% and specificity 97.6%) and burn related mortality (Accuracy >90%-97.5% Sensitivity 92.9% and specificity 93.4%). Neural networks were the most common machine learning (ML) algorithm utilised in 69% of the studies. The QUADAS-2 tool identified significant heterogeneity between studies., Discussion: The potential application of AI in the management of burns patients is promising, especially given its propitious results across a spectrum of dimensions, including burn depth, size, mortality, related sepsis and acute kidney injuries. The accuracy of the results analysed within this study is comparable to current practices in burns care., Conclusion: The application of AI in the treatment and management of burns patients, as a series of point of care diagnostic adjuncts, is promising. Whilst AI is a potentially valuable tool, a full evaluation of its current utility and potential is limited by significant variations in research methodology and reporting., Competing Interests: Declaration of Competing Interest None of the authors have any conflicts of interest to declare., (Crown Copyright © 2022. Published by Elsevier Ltd. All rights reserved.)

Background: Liver cancer is the fourth leading cause of cancer-related death globally which is estimated to reach more than 1 million deaths a year by 2030. Among liver cancer types, hepatocellular carcinoma (HCC) accounts for approximately 90% of the cases and is known to have a tumour promoting inflammation regardless of its underlying aetiology. However, current promising treatment approaches, such as immunotherapy, are partially effective for most of the patients due to the immunosuppressive nature of the tumour microenvironment (TME). Therefore, there is an urgent need to fully understand TME in HCC and discover new immune markers to eliminate resistance to immunotherapy., Methods: We analyse three microarray datasets, using unsupervised and supervised methods, in an effort to discover signature genes. First, univariate, and multivariate, feature selection methods, such as the Boruta algorithm, are applied. Subsequently, an optimisation procedure, which utilises random forest algorithm with three dataset pairs combinations, is performed. The resulting optimal gene sets are then combined and further subjected to network analysis and pathway enrichment analysis so as to obtain information related to their biological relevance. The microarray datasets were analysed via the MCP-counter, CIBERSORT, TIMER, EPIC, and quanTIseq deconvolution methods and an estimation of cell type abundances for each dataset sample were identified. The differences in the cell type abundances, between the adjacent and tumour sample groups, were then assessed using a Wilcoxon Rank Sum test (p-value < 0.05)., Results: The optimal gene signature sets, derived from each of the data pairs combination, achieved AUC values ranging from 0.959 to 0.988 in external validation sets using Random Forest model. CLEC1B and PTTG1 genes are retrieved across each optimal set. Among the signature genes, PTTG1, AURKA, and UBE2C genes are found to be involved in the regulation of mitotic sister chromatid separation and anaphase-promoting complex (APC) dependent catabolic process (adjusted p-value < 0.001). Additionally, the application of deconvolution algorithms revealed significant changes in cell type abundances of Regulatory T (Treg) cells, M0 and M1 macrophages, and T CD8 + cells between adjacent and tumour samples., Conclusion: We identified ECM1 gene as a potential immune-related marker acting through immune cell migration and macrophage polarisation. Our results indicate that macrophages, such as M0 macrophage and M1 macrophage cells, undergo significant changes in HCC TME. Moreover, our immune deconvolution approach revealed significant infiltration of Treg cells and M0 macrophages, and a significant decrease in T CD8 + cells and M1 macrophages in tumour samples., (© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models., (© 2022. The Author(s).)

Objective: The Early Treatment of Atrial Fibrillation for Stroke Prevention (EAST-AFNET4) trial showed a clinical benefit of early rhythm-control therapy in patients with recently diagnosed atrial fibrillation (AF). The generalisability of the results in the general population is not known., Methods: Participants in the population-based UK Biobank were assessed for eligibility based on the EAST-AFNET4 inclusion/exclusion criteria. Treatment of all eligible participants was classified as early rhythm-control (antiarrhythmic drug therapy or AF ablation) or usual care. To assess treatment effects, primary care data and Hospital Episode Statistics were merged with UK Biobank data.Efficacy and safety outcomes were compared between groups in the entire cohort and in a propensity-matched data set., Results: AF was present in 35 526/502 493 (7.1%) participants, including 8340 (988 with AF <1 year) with AF at enrolment and 27 186 with incident AF during follow-up. Most participants (22 003/27 186; 80.9%) with incident AF were eligible for early rhythm-control.Eligible participants were older (70 years vs 63 years) and more likely to be female (42% vs 21%) compared with ineligible patients. Of 9004 participants with full primary care data, 874 (9.02%) received early rhythm-control. Safety outcomes were not different between patients receiving early rhythm-control and controls. The primary outcome of EAST-AFNET 4, a composite of cardiovascular death, stroke/transient ischaemic attack and hospitalisation for heart failure or acute coronary syndrome occurred less often in participants receiving early rhythm-control compared with controls in the entire cohort (HR 0.82, 95% CI 0.71 to 0.94, p=0.005). In the propensity-score matched analysis, early rhythm-control did not significantly decrease of the primary outcome compared with usual care (HR 0.87, 95% CI 0.72 to 1.04, p=0.124)., Conclusion: Around 80% of participants diagnosed with AF in the UK population are eligible for early rhythm-control. Early rhythm-control therapy was safe in routine care., Competing Interests: Competing interests: RS has received lecture fees and advisory board fees from BMS/Pfizer outside this work. LF and PK are listed as inventor on two patents held by University of Birmingham (Atrial Fibrillation Therapy WO 2015140571, Markers for Atrial Fibrillation WO 2016012783). LF has received institutional research grants and non-financial support from European Union, DFG, British Heart Foundation, Medical Research Council (UK), National Institute of Health Research NIHR and several biomedical companies. PK receives research support for basic, translational and clinical research projects from European Union, British Heart Foundation, Leducq Foundation, Medical Research Council (UK) and German Centre for Cardiovascular Research, from several drug and device companies active in atrial fibrillation and has received honoraria from several such companies in the past but not in the last 3 years. All other authors declared no conflict of interest., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes., (This article has been co-published with permission in The BMJ, the Lancet Digital Health, and the European Heart Journal © the Authors 2022.)

Background: The prevalence of combined heart failure (HF) and atrial fibrillation (AF) is rising, and these patients suffer from high rates of mortality. This study aims to provide robust data on factors associated with death, uniquely supported by post-mortem examination., Methods: A retrospective cohort study of hospitalized adults with a clinical diagnosis of HF and AF at a tertiary centre in Romania between 2014 and 2017. A standardized post-mortem examination was performed where death occurred within 24 h of admission, when the cause of death was not clear or by physician request. National records were used to collect mortality data, subsequently categorized and analysed as HF-related death, vascular death and non-cardiovascular death using Cox proportional hazards regression., Results: A total of 1009 consecutive patients with a mean age of 73 ± 11 years, 47% women, NYHA class 3.0 ± 0.9, left ventricular ejection fraction (LVEF) 40.1 ± 11.0% and 100% anticoagulated were followed up for 1.5 ± 0.9 years. A total of 291 (29%) died, with post-mortems performed on 186 (64%). Baseline factors associated with mortality were dependent on the cause of death. HF-related death in 136 (47%) was associated with higher NYHA class (hazard ratio [HR] 2.45 per one class increase, 95% CI 1.73-3.46; p < 0.001) and lower LVEF (0.95 per 1% increase, 0.93-0.97; p < 0.001). Vascular death occurred in 75 (26%) and was associated with hypertension (HR 2.83, 1.36-5.90; p = 0.005) and higher LVEF (1.08 per 1% increase, 1.05-1.11; p < 0.001). Non-cardiovascular death in 80 (28%) was associated with clinical obesity (HR 2.20, 1.21-4.00; p = 0.010) and higher LVEF (1.10 per 1% increase, 1.06-1.13; p < 0.001). Across all causes, there was no relationship between mortality and AF type (p = 0.77), HF type (p = 0.85) or LVEF (p = 0.58)., Conclusions: Supported by post-mortem data, the cause of death in HF and AF patients is heterogeneous, and the relationships with typical markers of mortality are critically dependent on the mode of death. The poor prognosis in this group demands further attention to improve management beyond anticoagulation., (© 2022. The Author(s).)

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes., Competing Interests: Declaration of interests The BigData@Heart project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (grant 116074) which receives support from the Horizon 2020 research and innovation programme of the European Union and the European Federation of Pharmaceutical Industries and Associations. The funders had no role in considering the study design; in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the Review for publication. DK reports grants from the European Union and the European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative (116074), the National Institute for Health Research (NIHR CDF-2015-08-074 RATE-AF; NIHR130280 DaRe2THINK; and NIHR132974 D2T-NeuroVascular), the British Heart Foundation (PG/17/55/33087, AA/18/2/34218 and FS/CDRF/21/21032), and the ESC. DK is supported by educational grants from Boehringer Ingelheim, Bristol Myers Squibb (BMS)-Pfizer Alliance, Bayer, Daiichi Sankyo, Boston Scientific, the NIHR and University of Oxford Biomedical Research Centre, the British Heart Foundation and University of Birmingham Accelerator Award (STEEER-AF NCT04396418), Amomed Pharma, Istituto Di Ricovero e Cura a Carattere Scientifico San Raffaele, and Menarini (NCT0083244). DK receives advisory board personal fees from Bayer, Amomed, Protherics Medicines Development, and Myokardia. FWA reports grants from IMI BigData@Heart. SDA reports grants and personal fees from Vifor Pharma, Abbott, and Abbott Vascular; and personal fees from Bayer, Boehringer Ingelheim, Servier, Cardiac Dimensions, Actimed, AstraZeneca, Amgen, Bioventrix, Janssen, Respicardia, V-Wave, Brahms, Cordio, and Occlutech. CB reports grants from Medical Research Council, Boehringer Ingelheim, and NIHR. AB reports grants from AstraZeneca. BB reports grants from the European Commission. GB reports grants from the European Commission (IMI project support) and Bayer. BC reports non-financial support from Roche Diagnostics and iRhythm. CC reports grants from the European Commission. MRC reports personal fees from AstraZeneca. FC reports personal fees from Amgen, AstraZeneca, Servier, and BMS; and advisory board fees from GlyCardial Diagnostics. MC reports personal fees from Vifor Pharma. AD reports salary from Bayer. MF reports salary from AstraZeneca. CPG reports personal fees from AstraZeneca, Amgen, Bayer, Boehringer Ingelheim, Daiichi Sankyo, Vifor Pharma, Menarini, Wondr Medical, Raisio Group, and Oxford University Press; and grants from BMS, Abbott, British Heart Foundation, NIHR, and ESC. MI reports grants from World Economic Forum, Swedish Innovation Agency, and the European Commission; and collaboration with Frisq. SL reports grants from the European Commission. TFL reports grants from Abbott, Amgen, Novartis, Boehringer Ingelheim, Servier, Vifor Pharma, Sanofi, and AstraZeneca; and personal fees from Daichi Sankyo, Pfizer, and Menarini. LR reports salary from Bayer. CS reports personal fees from Bayer. BT reports personal fees from Servier. GvT reports grants from IMI. KvB reports grants from IMI BigData@Heart. PEV reports personal fees from Hygeia Hospitals Group, Hellenic Healthcare Group, ESC, and Servier. TV is working as an editor at The BMJ and Acta Médica Portuguesa and is Vice President of the European Union of General Practitioners. MV reports grants from the European Commission. SW reports grants from Abbott, Amgen, AstraZeneca, BMS, Bayer, Biotronik, Boston Scientific, Cardinal Health, CardioValve, CSL Behring, Daiichi Sankyo, Edwards Lifesciences, Guerbet, InfraRedx, Johnson & Johnson, Medicure, Medtronic, Novartis, Polares, OrPha Suisse, Pfizer, Regeneron, Sanofi-Aventis, Sinomed, Terumo, and V-Wave. SW serves as an unpaid advisory board member or unpaid member of the steering or executive group of trials funded by Abbott, Abiomed, Amgen, AstraZeneca, BMS, Boston Scientific, Biotronik, Cardiovalve, Edwards Lifesciences, MedAlliance, Medtronic, Novartis, Polares, Sinomed, V-Wave, and Xeltis. He has not received personal payments by pharmaceutical companies or device manufacturers. SW is a member of the steering or executive committee group of several investigator-initiated trials that receive funding by industry without effect on his personal remuneration. SW is an unpaid member of the Pfizer Research Award selection committee in Switzerland and of the Women as One Awards Committee. SW is a member of the Clinical Study Group of the Deutsches Zentrum für Herz Kreislauf-Forschung and of the Advisory Board of the Australian Victorian Heart Institute. He is Chairperson of the ESC Congress Program Committee, former Chairperson of the ESC Clinical Practice Guidelines Committee, and Deputy Editor of the Journal of the American College of Cardiology Cardiovascular Interventions. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection., Methods: This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool., Results: 28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias., Conclusion: PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection., Competing Interests: Competing interests: Dr Gill reports funding through the BigData@Heart Innovative Medicines Initiative, grant no.116074. Dr Bunting reports a grant from the University of Birmingham’s British Heart Foundation Accelerator Award (BHF AA/18/2/34218). Dr Sartini reports that Bayer AG is one of the partners that have funded the IMI framework and is employed by Bayer AG in this IMI collaboration. Mr Roth Cardoso has nothing to disclose. Ms Narges Ghoreishi has nothing to disclose. Dr Uh reports grants and personal fees from Innovative Medicines Initiative 2 BigData@Heart, grant no. 116074, during the conduct of the study. Dr Williams has nothing to disclose. Dr Kiliana Suzart-Woischnik has nothing to disclose. Dr Banerjee reports grants from AstraZeneca, outside the submitted work. Professor Asselbergs reports grants from Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No (116074), during the conduct of the study and is supported by UCL Hospitals NIHR Biomedical Research Centre. Professor MJC Eijkemans has nothing to disclose. Professor Gkoutos has nothing to disclose. Professor Kotecha reports grants from the National Institute for Health Research (NIHR CDF-2015-08-074 RATE-AF; NIHR HTA-130280 DaRe2THINK; NIHR EME-132974 D2T-NV), the British Heart Foundation (PG/17/55/33087 and AA/18/2/34218), EU/EFPIA Innovative Medicines Initiative (BigData@Heart 116074), the European Society of Cardiology supported by educational grants from Boehringer Ingelheim/BMS-Pfizer Alliance/Bayer/Daiichi Sankyo/Boston Scientific, the NIHR/University of Oxford Biomedical Research Centre and British Heart Foundation/University of Birmingham Accelerator Award (STEEER-AF NCT04396418), Amomed Pharma and IRCCS San Raffaele/Menarini (Beta-blockers in Heart Failure Collaborative Group NCT0083244); in addition to personal fees from Bayer (Advisory Board), AtriCure (Speaker fees), Protherics Medicines Development (Advisory Board) and Myokardia (Advisory Board)., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Real-world evidence (RWE) plays an increasingly important role within global regulatory and reimbursement processes. RWE generation can be enhanced by collecting and using patient-reported outcomes (PROs), which can provide valuable information on the effectiveness, safety, and tolerability of health interventions from the patient perspective. This analysis aims to examine and summarise the utilisation of patient-reported outcomes measures (PROMs) in real-world studies., Methods: Descriptions of phase IV trials were downloaded on July 22, 2021 from the Clinicaltrials.gov database since its inception. An automated algorithm was built to detect trials utilising PROMs and composite measures including patient-reported components. Search terms were developed based on the PROQOLID database., Results: Of 27,976 phase IV clinical trials posted on Clinicaltrials.gov between 1999 and July 2021, 21% and 4% used PROMs and composite measures, respectively. Recent years demonstrated a steady increase in the utilisation of PROMs in phase IV trials., Conclusions: The use of PROMs in phase IV trials seems to be lower than its use in earlier phases of clinical research. Increased uptake of PROMs in RWE studies can be facilitated in a number of ways including the development of standards for their collection, analysis and use., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: MC is Director of the Birmingham Health Partners Centre for Regulatory Science and Innovation, Director of the Centre for Patient-Reported Outcomes Research and is a National Institute for Health and Care Research (NIHR) Senior Investigator. She receives funding from the NIHR Birmingham Biomedical Research Centre, the NIHR Surgical Reconstruction and Microbiology Research Centre and NIHR Applied Research Collaboration (ARC) West Midlands at the University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Health Data Research UK, Innovate UK (part of UK Research and Innovation), Macmillan Cancer Support, UCB and GSK. MC has received personal fees from Astellas, Takeda, Merck, Daiichi Sankyo, Glaukos, CIS Oncology, Aparito Ltd, GSK, Genentech and the Patient-Centered Outcomes Research Institute (PCORI) outside the submitted work. OLA receives funding from the NIHR Birmingham Biomedical Research Centre (BRC), NIHR Applied Research Collaboration (ARC) West Midlands at the University of Birmingham and University Hospitals Birmingham NHS Foundation, Innovate UK (part of UK Research and Innovation), Gilead Sciences Ltd, Janssen Pharmaceuticals, Inc., and Sarcoma UK. OLA declares personal fees from Gilead Sciences Ltd, GSK and Merck outside the submitted work. TK is an employee and shareholder of GSK Ltd. KM is the holder of the GSK PhD grant. Other authors declare no competing interests. The views expressed in this article are those of the authors and not necessarily those of the NIHR, or the Department of Health and Social Care., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer., (© 2022 the author(s), published by De Gruyter, Berlin/Boston.)

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is associated with a range of persistent symptoms impacting everyday functioning, known as post-COVID-19 condition or long COVID. We undertook a retrospective matched cohort study using a UK-based primary care database, Clinical Practice Research Datalink Aurum, to determine symptoms that are associated with confirmed SARS-CoV-2 infection beyond 12 weeks in non-hospitalized adults and the risk factors associated with developing persistent symptoms. We selected 486,149 adults with confirmed SARS-CoV-2 infection and 1,944,580 propensity score-matched adults with no recorded evidence of SARS-CoV-2 infection. Outcomes included 115 individual symptoms, as well as long COVID, defined as a composite outcome of 33 symptoms by the World Health Organization clinical case definition. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) for the outcomes. A total of 62 symptoms were significantly associated with SARS-CoV-2 infection after 12 weeks. The largest aHRs were for anosmia (aHR 6.49, 95% CI 5.02-8.39), hair loss (3.99, 3.63-4.39), sneezing (2.77, 1.40-5.50), ejaculation difficulty (2.63, 1.61-4.28) and reduced libido (2.36, 1.61-3.47). Among the cohort of patients infected with SARS-CoV-2, risk factors for long COVID included female sex, belonging to an ethnic minority, socioeconomic deprivation, smoking, obesity and a wide range of comorbidities. The risk of developing long COVID was also found to be increased along a gradient of decreasing age. SARS-CoV-2 infection is associated with a plethora of symptoms that are associated with a range of sociodemographic and clinical risk factors., (© 2022. The Author(s).)

Importance: Previous in vitro and postmortem research suggests that inflammation may lead to structural brain changes via activation of microglia and/or astrocytic dysfunction in a range of neuropsychiatric disorders., Objective: To investigate the relationship between inflammation and changes in brain structures in vivo and to explore a transcriptome-driven functional basis with relevance to mental illness., Design, Setting, and Participants: This study used multistage linked analyses, including mendelian randomization (MR), gene expression correlation, and connectivity analyses. A total of 20 688 participants in the UK Biobank, which includes clinical, genomic, and neuroimaging data, and 6 postmortem brains from neurotypical individuals in the Allen Human Brain Atlas (AHBA), including RNA microarray data. Data were extracted in February 2021 and analyzed between March and October 2021., Exposures: Genetic variants regulating levels and activity of circulating interleukin 1 (IL-1), IL-2, IL-6, C-reactive protein (CRP), and brain-derived neurotrophic factor (BDNF) were used as exposures in MR analyses., Main Outcomes and Measures: Brain imaging measures, including gray matter volume (GMV) and cortical thickness (CT), were used as outcomes. Associations were considered significant at a multiple testing-corrected threshold of P < 1.1 × 10-4. Differential gene expression in AHBA data was modeled in brain regions mapped to areas significant in MR analyses; genes were tested for biological and disease overrepresentation in annotation databases and for connectivity in protein-protein interaction networks., Results: Of 20 688 participants in the UK Biobank sample, 10 828 (52.3%) were female, and the mean (SD) age was 55.5 (7.5) years. In the UK Biobank sample, genetically predicted levels of IL-6 were associated with GMV in the middle temporal cortex (z score, 5.76; P = 8.39 × 10-9), inferior temporal (z score, 3.38; P = 7.20 × 10-5), fusiform (z score, 4.70; P = 2.60 × 10-7), and frontal (z score, -3.59; P = 3.30 × 10-5) cortex together with CT in the superior frontal region (z score, -5.11; P = 3.22 × 10-7). No significant associations were found for IL-1, IL-2, CRP, or BDNF after correction for multiple comparison. In the AHBA sample, 5 of 6 participants (83%) were male, and the mean (SD) age was 42.5 (13.4) years. Brain-wide coexpression analysis showed a highly interconnected network of genes preferentially expressed in the middle temporal gyrus (MTG), which further formed a highly connected protein-protein interaction network with IL-6 (enrichment test of expected vs observed network given the prevalence and degree of interactions in the STRING database: 43 nodes/30 edges observed vs 8 edges expected; mean node degree, 1.4; genome-wide significance, P = 4.54 × 10-9). MTG differentially expressed genes that were functionally enriched for biological processes in schizophrenia, autism spectrum disorder, and epilepsy., Conclusions and Relevance: In this study, genetically determined IL-6 was associated with brain structure and potentially affects areas implicated in developmental neuropsychiatric disorders, including schizophrenia and autism.

Background: Several common conditions have been widely recognised as risk factors for COVID-19 related death, but risks borne by people with rare diseases are largely unknown. Therefore, we aim to estimate the difference of risk for people with rare diseases comparing to the unaffected., Method: To estimate the correlation between rare diseases and COVID-19 related death, we performed a retrospective cohort study in Genomics England 100k Genomes participants, who tested positive for Sars-Cov-2 during the first wave (16-03-2020 until 31-July-2020) of COVID-19 pandemic in the UK (n = 283). COVID-19 related mortality rates were calculated in two groups: rare disease patients (n = 158) and unaffected relatives (n = 125). Fisher's exact test and logistic regression was used for univariable and multivariable analysis, respectively., Results: People with rare diseases had increased risk of COVID19-related deaths compared to the unaffected relatives (OR [95% CI] = 3.47 [1.21- 12.2]). Although, the effect was insignificant after adjusting for age and number of comorbidities (OR [95% CI] = 1.94 [0.65-5.80]). Neurology and neurodevelopmental diseases was significantly associated with COVID19-related death in both univariable (OR [95% CI] = 4.07 [1.61-10.38]) and multivariable analysis (OR [95% CI] = 4.22 [1.60-11.08])., Conclusions: Our results showed that rare disease patients, especially ones affected by neurology and neurodevelopmental disorders, in the Genomics England cohort had increased risk of COVID-19 related death during the first wave of the pandemic in UK. The high risk is likely associated with rare diseases themselves, while we cannot rule out possible mediators due to the small sample size. We would like to raise the awareness that rare disease patients may face increased risk for COVID-19 related death. Proper considerations for rare disease patients should be taken when relevant policies (e.g., returning to workplace) are made., (© 2022. The Author(s).)

Background: There is a paucity of data comparing 30-day morbidity and mortality of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one anastomosis gastric bypass (OAGB). This study aimed to compare the 30-day safety of SG, RYGB, and OAGB in propensity score-matched cohorts., Materials and Methods: This analysis utilised data collected from the GENEVA study which was a multicentre observational cohort study of bariatric and metabolic surgery (BMS) in 185 centres across 42 countries between 01/05/2022 and 31/10/2020 during the Coronavirus Disease-2019 (COVID-19) pandemic. 30-day complications were categorised according to the Clavien-Dindo classification. Patients receiving SG, RYGB, or OAGB were propensity-matched according to baseline characteristics and 30-day complications were compared between groups., Results: In total, 6770 patients (SG 3983; OAGB 702; RYGB 2085) were included in this analysis. Prior to matching, RYGB was associated with highest 30-day complication rate (SG 5.8%; OAGB 7.5%; RYGB 8.0% (p = 0.006)). On multivariate regression modelling, Insulin-dependent type 2 diabetes mellitus and hypercholesterolaemia were associated with increased 30-day complications. Being a non-smoker was associated with reduced complication rates. When compared to SG as a reference category, RYGB, but not OAGB, was associated with an increased rate of 30-day complications. A total of 702 pairs of SG and OAGB were propensity score-matched. The complication rate in the SG group was 7.3% (n = 51) as compared to 7.5% (n = 53) in the OAGB group (p = 0.68). Similarly, 2085 pairs of SG and RYGB were propensity score-matched. The complication rate in the SG group was 6.1% (n = 127) as compared to 7.9% (n = 166) in the RYGB group (p = 0.09). And, 702 pairs of OAGB and RYGB were matched. The complication rate in both groups was the same at 7.5 % (n = 53; p = 0.07)., Conclusions: This global study found no significant difference in the 30-day morbidity and mortality of SG, RYGB, and OAGB in propensity score-matched cohorts., (© 2021. The Author(s).)

Motivation: Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data but also the lack of understanding of microbial interactions which can be indicative of the disease., Results: We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-aware correlation inference, a collection of posterior correlation networks can be drawn and used for graph-level classification, thus incorporating uncertainty in the estimates. CACONET then employs a deep learning approach for graph classification, achieving excellent performance metrics by exploiting the correlation structure. We test the framework on both simulated data and a large real-world dataset pertaining to microbiome samples of colorectal cancer (CRC) and healthy subjects, and identify potential network substructure characteristic of CRC microbiota. CACONET is customizable and can be adapted to further improve its utility., Availability and Implementation: CACONET is available at https://github.com/yuanwxu/corr-net-classify., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC's mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets., Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs., Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes., Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.

Background: Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics., Methods: A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation., Results: For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB., Competing Interests: The authors have declared that no competing interests exist.

Background: Semantic similarity is a valuable tool for analysis in biomedicine. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance 'patient-like me' analyses, automated coding, differential diagnosis, and outcome prediction. While a large body of work exists exploring the use of semantic similarity for multiple tasks, including protein interaction prediction, and rare disease differential diagnosis, there is less work exploring comparison of patient phenotype profiles for clinical tasks. Moreover, there are no experimental explorations of optimal parameters or better methods in the area., Methods: We develop a platform for reproducible benchmarking and comparison of experimental conditions for patient phentoype similarity. Using the platform, we evaluate the task of ranking shared primary diagnosis from uncurated phenotype profiles derived from all text narrative associated with admissions in the medical information mart for intensive care (MIMIC-III)., Results: 300 semantic similarity configurations were evaluated, as well as one embedding-based approach. On average, measures that did not make use of an external information content measure performed slightly better, however the best-performing configurations when measured by area under receiver operating characteristic curve and Top Ten Accuracy used term-specificity and annotation-frequency measures., Conclusion: We identified and interpreted the performance of a large number of semantic similarity configurations for the task of classifying diagnosis from text-derived phenotype profiles in one setting. We also provided a basis for further research on other settings and related tasks in the area., (© 2022. The Author(s).)

Background: People presenting with first-episode psychosis (FEP) have heterogenous outcomes. More than 40% fail to achieve symptomatic remission. Accurate prediction of individual outcome in FEP could facilitate early intervention to change the clinical trajectory and improve prognosis., Aims: We aim to systematically review evidence for prediction models developed for predicting poor outcome in FEP., Method: A protocol for this study was published on the International Prospective Register of Systematic Reviews, registration number CRD42019156897. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance, we systematically searched six databases from inception to 28 January 2021. We used the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Prediction Model Risk of Bias Assessment Tool to extract and appraise the outcome prediction models. We considered study characteristics, methodology and model performance., Results: Thirteen studies reporting 31 prediction models across a range of clinical outcomes met criteria for inclusion. Eleven studies used logistic regression with clinical and sociodemographic predictor variables. Just two studies were found to be at low risk of bias. Methodological limitations identified included a lack of appropriate validation, small sample sizes, poor handling of missing data and inadequate reporting of calibration and discrimination measures. To date, no model has been applied to clinical practice., Conclusions: Future prediction studies in psychosis should prioritise methodological rigour and external validation in larger samples. The potential for prediction modelling in FEP is yet to be realised.