1. Male sex, B symptoms, bone marrow involvement, and genetic alterations as predictive factors in diffuse large B-cell lymphoma : Elektronski virMatej Panjan, Vita Šetrajčič Dragoš, Gorana Gašljević, Srdjan Novaković, Barbara Jezeršek Novaković, 2025, original scientific article Abstract: Approximately 40% of patients with diffuse large B-cell lymphoma (DLBCL) are not cured with first-line chemoimmunotherapy, resulting in poor prognosis. Schmitz et al. classified DLBCL into four prognostic genetic groups using whole-exome sequencing. We applied a simplified approach using a targeted next-generation sequencing assay (Archer FusionPlex Lymphoma Assay) to analyze samples from 105 patients—53 with a progression-free survival (PFS) < 2 years (the “Relapse group”) and 52 with a PFS > 5 years (the “Remission group”) following first-line systemic treatment. Patients were classified according to Schmitz et al. into the following categories: “MCD” (MYD88L265P and CD79B alteration), “N1” (NOTCH1 alteration), “BN2” (NOTCH2 alteration and BCL6 translocation), and “EZB” (EZH2 alteration and BCL2 translocation). The predictive value of this simplified genetic classification and of relevant clinical features were evaluated. The “Relapse group” included more patients classified as MCD and N1, while fewer were classified as EZB and BN2. Also, cell-of-origin (COO) characteristics and the size of N1 aligned with the classification of Schmitz et al. However, the limited sample size precludes definitive conclusions about the predictive value of our simplified approach. Additionally, male sex, B symptoms, and bone marrow involvement were associated with relapse. Therefore, these clinical features may be useful in predicting outcomes until an effective molecular classification is widely adopted.
Keywords: DLBCL, genetic classification, predictive, lymphoma
Published in DiRROS: 21.11.2025; Views: 181; Downloads: 52
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3. Building and applying quantitative adverse outcome pathway models for chemical hazard and risk assessmentEdward J. Perkins, Roman Ashauer, Lyle Burgoon, Rory Conolly, Brigitte Landesmann, Cameron Mackay, Cheryl A. Murphy, Nathan Pollesch, James R. Wheeler, Anže Županič, Stefan Scholz, 2019, review article Abstract: An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850–1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
Keywords: Quantitative Adverse Outcome pathways, TKTD modelling, alternatives to animal testing, predictive toxicology, species extrapolation, prioritization of chemicals
Published in DiRROS: 06.08.2024; Views: 918; Downloads: 556
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4. MsGEN : measuring generalization of nutrient value prediction across different recipe datasetsGordana Ispirova, Tome Eftimov, Sašo Džeroski, Barbara Koroušić-Seljak, 2023, original scientific article Abstract: In this study, we estimate the generalization of the performance of previously proposed predictive models for nutrient value prediction across different recipe datasets. For this purpose, we introduce a quantitative indicator that determines the level of generalization of using the developed predictive model for new unseen data not presented in the training process. On a predefined corpus of recipe embeddings from six publicly available recipe datasets (i.e., projecting them in the same meta-feature vector space), we train predictive models on one of the six recipe datasets and test the models on the rest of the datasets. In parallel, we define and calculate generalizability indexes which are numbers that indicate how generalizable a predictive model is i.e., how well will a predictive model learned on one dataset perform on another one not involved in the training. The evaluation results prove the validity of these indexes – their relation with the accuracy of the predictions. Further, we define three sampling techniques for selecting representative data instances that will cover all parts from the feature space uniformly (involving data from all datasets) and further will improve the generalization of a predictive model. We train predictive models with these generalized datasets and test them on instances from the six recipe datasets that are not selected and included in the generalized datasets. The results from the evaluation of these predictive models show improvement compared to the results from the predictive models trained on one recipe dataset and tested on the others separately.
Keywords: ML pipeline, predictive modeling, nutrient prediction, recipe datasets
Published in DiRROS: 25.09.2023; Views: 1427; Downloads: 878
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5. Assessing the generalizability of a performance predictive modelAna Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: algorithms, predictive models, machine learning
Published in DiRROS: 15.09.2023; Views: 2148; Downloads: 984
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7. Reconstruction of brown bear population dynamics in Slovenia in the period 1998-2019 : ǂa ǂnew approach combining genetics and long-term mortality dataKlemen Jerina, Andrés Ordiz, 2021, original scientific article Abstract: Reliable data and methods for assessing changes in wildlife population size over time are necessary for management and conservation. For most species, assessing abundance is an expensive and labor-intensive task that is not affordable on a frequent basis. We present a novel approach to reconstructing brown bear population dynamics in Slovenia in the period 1998-2019, based on the combination of two CMR non-invasive genetic estimates (in 2007 and 2015) and long-term mortality records, to show how the latter can help the study of population dynamics in combination with point-in-time estimates. The spring (i.e. including newborn cubs) population size estimate was 383 (CI: 336-432) bears in 1998 and 971 (CI: 825-1161) bears in 2019. In this period, the average annual population growth rate was 4.5 %. The predicted population size differed by just 7 % from the non-invasive genetic size estimate after eight years, suggesting that the method is reliable. It can predict the evolution of the population size under different management scenarios and provide information on key parameters, e.g. background mortality and the sex- and age-structure of the population. Our approach can be used for several other wildlife species, but it requires reliable mortality data over time.
Keywords: genetic estimates of population size, mortality records, population monitoring, population size, predictive modelling, brown bear
Published in DiRROS: 28.03.2021; Views: 5711; Downloads: 3177
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8. Lung cancer biomarker testing : perspective from EuropeErik Thunnissen, Birgit Weynand, Dalma Udovicic-Gagula, Luka Brčić, Malgorzata Szolkowska, Paul Hofman, Silvana Smojver-Ježek, Sisko Anttila, Fiorella Calabrese, Izidor Kern, 2020, review article Abstract: A questionnaire on biomarker testing previously used in central European countries was extended and distributed in Western and Central European countries to the pathologists participating at the Pulmonary Pathology Society meeting 26-28 June 2019 in Dubrovnik, Croatia. Each country was represented by one responder. For recent biomarkers the availability and reimbursement of diagnoses of molecular alterations in non-small cell lung carcinoma varies widely between different, also western European, countries. Reimbursement of such assessments varies widely between unavailability and payments by the health care system or even pharmaceutical companies. The support for testing from alternative sources, such as the pharmaceutical industry, is no doubt partly compensating for the lack of public health system support, but it is not a viable or long-term solution. Ideally, a structured access to testing and reimbursement should be the aim in order to provide patients with appropriate therapeutic options. As biomarker enabled therapies deliver a 50% better probability of outcome success, improved and unbiased reimbursement remains a major challenge for the future.
Keywords: lung neoplasms -- diagnosis -- therapy -- Europe, lung cancer, predictive testing
Published in DiRROS: 21.09.2020; Views: 3439; Downloads: 1706
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