Local Convexity Properties of Quasihyperbolic Balls in Punctured Space

This paper deals with local convexity properties of the quasihyperbolic metric in the punctured space. We consider convexity and starlikeness of quasihyperbolic balls.Comment: Submitted to Journal of Mathematical Analysis and Application

Local Convexity Properties of j-metric Balls

This paper deals with local convexity properties of the j-metric. We consider convexity and starlikeness of the j-metric balls in convex, starlike and general subdomains of R^n.Comment: To appear in Annales Academiae Scientiarum Fennicae Mathematic

PASI: A novel pathway method to identify delicate group effects

Pathway analysis is a common approach in diverse biomedical studies, yet the currently-available pathway tools do not typically support the increasingly popular personalized analyses. Another weakness of the currently-available pathway methods is their inability to handle challenging data with only modest group-based effects compared to natural individual variation. In an effort to address these issues, this study presents a novel pathway method PASI (Pathway Analysis for Sample-level Information) and demonstrates its performance on complex diseases with different levels of group-based differences in gene expression. PASI is freely available as an R package

Improving patient identification for advanced cardiac imaging through machine learning-integration of clinical and coronary CT angiography data

Background: Standard computed tomography angiography (CTA) outputs a myriad of interrelated variables in the evaluation of suspected coronary artery disease (CAD). But an important proportion of obstructive lesions does not cause significant myocardial ischemia. Nowadays, machine learning (ML) allows integration of numerous variables through complex interdependencies that optimize classification and prediction at the individual level. We evaluated ML performance in integrating CTA and clinical variables to identify patients that demonstrate myocardial ischemia through PET and those who ultimately underwent early revascularization. Methods and results: 830 patients with CTA and selective PET were analyzed. Nine clinical and 58 CTA variables were integrated through ensemble-boosting ML to identify patients with ischemia and those who underwent early revascularization. ML performance was compared against expert CTA interpretation, calcium score and clinical variables. While ML using all CTA variables achieved an AUC = 0.85, it was outperformed by expert CTA interpretation (AUC = 0.87, p < 0.01 for comparison), comparable to ML integration of CTA variables with clinical variables. However, the best performance was achieved by ML integration of expert CTA interpretation and clinical variables for both dependent variables (AUCs = 0.91 and 0.90, p < 0.001). Conclusions: Machine learning integration of diagnostic CTA and clinical data may improve identification of patients with myocardial ischemia and those requiring early revascularization at the individual level. This could potentially aid in sparing the need for subsequent advanced imaging and better identifying patients in ultimate need for revascularization. While ML integrating all CTA variables did not outperform expert CTA interpretation, ML data integration from different sources consistently improves diagnostic performance. (C) 2021 The Authors. Published by Elsevier B.V

L1TD1-a prognostic marker for colon cancer

BackgroundPrognostic markers specific to a particular cancer type can assist in the evaluation of survival probability of patients and help clinicians to assess the available treatment modalities.MethodsGene expression data was analyzed from three independent colon cancer microarray gene expression data sets (N=1052). Survival analysis was performed for the three data sets, stratified by the expression level of the LINE-1 type transposase domain containing 1 (L1TD1). Correlation analysis was performed to investigate the role of the interactome of L1TD1 in colon cancer patients.ResultsWe found L1TD1 as a novel positive prognostic marker for colon cancer. Increased expression of L1TD1 associated with longer disease-free survival in all the three data sets. Our results were in contrast to a previous study on medulloblastoma, where high expression of L1TD1 was linked with poor prognosis. Notably, in medulloblastoma L1TD1 was co-expressed with its interaction partners, whereas our analysis revealed lack of co-expression of L1TD1 with its interaction partners in colon cancer.ConclusionsOur results identify increased expression of L1TD1 as a prognostic marker predicting longer disease-free survival in colon cancer patients.Peer reviewe

Novel effects of the gastrointestinal hormone secretin on cardiac metabolism and renal function

The cardiac benefits of gastrointestinal hormones have been of interest in recent years. The aim of this study was to explore the myocardial and renal effects of the gastrointestinal hormone secretin in the GUTBAT trial (NCT03290846). A placebo-controlled crossover study was conducted on 15 healthy males in fasting conditions, where subjects were blinded to the intervention. Myocardial glucose uptake was measured with [F-18]2-fluoro-2-deoxy-o-glucose ([F-18]FDG) positron emission tomography. Kidney function was measured with [F-18]FDG renal clearance and estimated glomerular filtration rate (eGFR). Secretin increased myocardial glucose uptake compared with placebo (secretin vs. placebo, means +/- SD, 15.5 +/- 7.4 vs. 9.7 +/- 4.9 gmol/100 g/min, 95% confidence interval (CI) [2.2, 9.4], P = 0.004). Secretin also increased [F-18]FDG renal clearance (44.5 +/- 5.4 vs. 39.5 8.5 mL/min, 95%CI [1.9, 8.1], P = 0.004), and eGFR was significantly increased from baseline after secretin, compared with placebo (17.8 +/- 9.8 vs. 6.0 +/- 5.2 Delta mL/min/1.73 m(2),( ) 95%CI [6.0, 17.6], P = 0.001). Our results implicate that secretin increases heart work and renal filtration, making it an interesting drug candidate for future studies in heart and kidney failure. NEW & NOTEWORTHY Secretin increases myocardial glucose uptake compared with placebo, supporting a previously proposed inotropic effect. Secretin also increased renal filtration rate.Peer reviewe

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0.90-0.96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries

Risk factors for revision due to prosthetic joint infection following total knee arthroplasty based on 62,087 knees in the Finnish Arthroplasty Register from 2014 to 2020

Peer reviewe