BackgroundPredictive models for delayed graft function (DGF) after kidney transplantation are usually developed using logistic regression. We want to evaluate the value of machine learning methods in the prediction of DGF.Methods497 kidney transplantations from deceased donors at the Ghent University Hospital between 2005 and 2011 are included. A feature elimination procedure is applied to determine the optimal number of features, resulting in 20 selected parameters (24 parameters after conversion to indicator parameters) out of 55 retrospectively collected parameters. Subsequently, 9 distinct types of predictive models are fitted using the reduced data set: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs; using linear, radial basis function and polynomial kernels), decision tree (DT), random forest (RF), and stochastic gradient boosting (SGB). Performance of the models is assessed by computing sensitivity, positive predictive values and area under the receiver operating characteristic curve (AUROC) after 10-fold stratified cross-validation. AUROCs of the models are pairwise compared using Wilcoxon signed-rank test.ResultsThe observed incidence of DGF is 12.5 %. DT is not able to discriminate between recipients with and without DGF (AUROC of 52.5 %) and is inferior to the other methods. SGB, RF and polynomial SVM are mainly able to identify recipients without DGF (AUROC of 77.2, 73.9 and 79.8 %, respectively) and only outperform DT. LDA, QDA, radial SVM and LR also have the ability to identify recipients with DGF, resulting in higher discriminative capacity (AUROC of 82.2, 79.6, 83.3 and 81.7 %, respectively), which outperforms DT and RF. Linear SVM has the highest discriminative capacity (AUROC of 84.3 %), outperforming each method, except for radial SVM, polynomial SVM and LDA. However, it is the only method superior to LR.ConclusionsThe discriminative capacities of LDA, linear SVM, radial SVM and LR are the only ones above 80 %. None of the pairwise AUROC comparisons between these models is statistically significant, except linear SVM outperforming LR. Additionally, the sensitivity of linear SVM to identify recipients with DGF is amongst the three highest of all models. Due to both reasons, the authors believe that linear SVM is most appropriate to predict DGF.

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BACKGROUND Kidney transplantation is the preferred treatment for patients with end-stage renal disease. Delayed graft function (DGF) is a common complication and is associated with short- and long-term outcomes. Several predictive models for DGF have been developed. MATERIAL AND METHODS 497 kidney transplantations from deceased donors at our center between 2005-2011 are included. Firstly, the predictive accuracy of the existing models proposed by Irish et al. (M1), Jeldres et al. (M2), Chapal et al. (M3), and Zaza et al. (M4) was assessed. Secondly, the existing models were aggregated into a meta-model (MM) using stacked regressions. Finally, the association between 47 risk factors and DGF was studied in our -cohort-fitted model (CFM) using logistic regression. The accuracy of all models was assessed by area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow test. RESULTS M1, M2, M3, M4, MM, and CFM have AUROCs of 0.78, 0.65, 0.59, 0.67, 0.78, and 0.82, respectively. M1 (P=0.018), M2 (P<0.001), M3 (P<0.001), and M4 (P<0.001) overestimate the risk. MM (P=0.255) and CFM (P=0.836) are well calibrated. Donor subtype (P<0.001), recipient cardiac function (P<0.001), donor serum creatinine (P<0.001), donor age (P=0.006), duration of dialysis (P=0.02), recipient BMI (P=0.008), donor BMI (P=0.041), and recipient preoperative diastolic blood pressure (P=0.049) are associated with DGF in our CFM. CONCLUSIONS Four existing predictive models for DGF overestimate the risk in a cohort with a low incidence of DGF. We have identified 2 recipient parameters that are not included in previous models: cardiac function and preoperative diastolic blood pressure.

Diffuse large B-cell lymphoma (DLBCL) is the most common histological subtype of non-Hodgkin’s lymphomas (NHL). DLBCL is an aggressive malignancy that displays a great heterogeneity in terms of morphology, genetics and biological behavior. While a sustained complete remission is obtained in the majority of patients with standard immunochemotherapy, patients with refractory of relapsed disease after first-line treatment have a poor prognosis. This patient group represents an important unmet need in lymphoma treatment. In recent years, improved understanding of the underlying molecular pathogenesis had led to new classification and prognostication tools, including the development of cell-free biomarkers in liquid biopsies. Although the majority of studies have focused on the use of cell-free fragments of DNA (cfDNA), there has been an increased interest in circulating-free coding and non-coding RNA, including messenger RNA (mRNA), microRNA (miRNA), long non-coding RNA (lncRNA) and circular RNA (circRNA), as well as RNA encapsulated in extracellular vesicles or tumor-educated platelets (TEPs). We performed a systematic search in PubMed to identify articles that evaluated circulating RNA as diagnostic, subtype, treatment response or prognostic biomarkers in a human DLBCL population. A total of 35 articles met the inclusion criteria. The aim of this systematic review is to present the current understanding of circulating RNA molecules as biomarker in DLBCL and to discuss their future potential.

Waldenström Macroglobulinemia (WM) is an indolent lymphoplasmacytic lymphoma, characterized by the production of excess immunoglobulin M monoclonal protein. WM belongs to the spectrum of IgM gammopathies, ranging from asymptomatic IgM monoclonal gammopathy of undetermined significance (IgM-MGUS), through IgM-related disorders and asymptomatic WM to symptomatic WM. In recent years, its complex genomic and transcriptomic landscape has been extensively explored, hereby elucidating the biological mechanisms underlying disease onset, progression and therapy response. An increasing number of mutations, cytogenetic abnormalities, and molecular signatures have been described that have diagnostic, phenotype defining or prognostic implications. Moreover, cell-free nucleic acid biomarkers are increasingly being investigated, benefiting the patient in a minimally invasive way. This review aims to provide an extensive overview of molecular biomarkers in WM and IgM-MGUS, considering current shortcomings, as well as potential future applications in a precision medicine approach.

Circulating nucleic acids in blood plasma form an attractive resource to study human health and disease. Here, we applied mRNA capture sequencing of blood plasma cell-free RNA from 266 cancer patients and cancer-free controls (discovery n=208, 25 cancer types; validation n=58, 3 types). We observed cancer-type specific as well as pan-cancer alterations in cell-free transcriptomes compared to controls. Differentially abundant RNAs were heterogenous among patients and among cohorts, hampering identification of robust cancer biomarkers. Therefore, we developed a novel method that compares each individual cancer patient to a reference control population to identify so-called biomarker tail genes. These biomarker tail genes discriminate ovarian, prostate, and uterine cancer patients from controls with very high accuracy (AUC = 0.980). Our results were confirmed in additional cohorts of 65 plasma donors (2 lymphoma types) and 24 urine donors (bladder cancer). Together, our findings demonstrate heterogeneity in cell-free RNA alterations among cancer patients and propose that case-specific alterations can be exploited for classification purposes.

Background Anisakiasis is an emerging zoonosis caused by the fish parasitic nematode Anisakis infecting the gastrointestinal tract. Case presentation We describe a case of a 58-year-old woman diagnosed with gastro-allergic anisakiasis, in which the patient developed an acute food-induced IgE-mediated hypersensitivity reaction as well as concurrent gastro-intestinal manifestations after consumption of raw fish. The patient presented with epigastric pain, anaphylaxis and acute dysphagia caused by eosinophilic oesophagitis. Discussion Anisakis allergy should be considered as causative agent in patients presenting with acute urticarial rash, anaphylaxis and/or abdominal manifestations, especially when symptoms occur after consumption of seafood. Moreover, eosinophilic oesophagitis may be a rare but important complication of Anisakis infection. Endoscopic evaluation with esophageal biopsies should therefore be considered if suggestive symptoms are present. Patients with confirmed gastroallergic anisakiasis are advised to properly freeze or cook fish prior to consumption, although caution is advised, since heat-stable allergen proteins have been described. An adrenaline auto-injector should be prescribed.

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