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BackgroundTesticular cancer is the most common malignancy among young men. Vitamin D has pluripotent effects on cancer pathogenesis and plays a role in the metastatic cascade. The aim of this study is to analyze plasma vitamin D in association with clinico-pathological findings and prognosis in patients with germ-cell tumors (GCTs).MethodsThis study included 120 newly diagnosed and/or relapsed GCT patients treated from April 2013 to July 2020, for whom plasma was available in the biobank. Blood samples were drawn the 1st chemotherapy cycle as well as before the 2nd cycle. Plasma vitamin D was measured using ELISA and correlated with disease characteristics and the outcome. For survival analysis, the cohort was dichotomized into “low” and “high” based on median vitamin D.ResultsThere was no significant difference in vitamin D plasma levels between healthy donors and GCT patients (p = 0.71). Vitamin D level was not associated with disease characteristics except for brain metastases, where patients with brain metastases had a vitamin D level that was 32% lower compared to patients without brain metastases, p = 0.03. Vitamin D was also associated with response to chemotherapy, with an approximately 32% lower value in patients with an unfavorable response compared to a favorable response, p = 0.02. Moreover, low plasma levels of vitamin D were significantly associated with disease recurrence and inferior progression-free survival (PFS), but not with overall survival (OS) (HR = 3.02, 95% CI 1.36–6.71, p = 0.01 for PFS and HR = 2.06, 95% CI 0.84–5.06, p = 0.14 for OS, respectively).ConclusionOur study suggests the prognostic value of pretreatment vitamin D concentrations in GCT patients. Low plasma vitamin D was associated with an unfavorable response to therapy and disease recurrence. However, it remains to be determined whether the biology of the disease confirms a causative role for low vitamin D and whether its supplementation affects the outcome.

BackgroundSurvivors of testicular germ cell tumors (GCT) may suffer from late cognitive impairment. We hypothesized that disruption of intestinal barrier during chemotherapy and/or radiotherapy may be a contributing factor of cognitive dysfunction within the gut-blood-brain axis.MethodsGCT survivors (N = 142) from National Cancer Institute of Slovakia completed the Functional Assessment of Cancer Therapy Cognitive Function questionnaires during their annual follow-up visit at 9-year median (range 4-32). Biomarkers of gut microbial translocation and dysbiosis high mobility group box-1 (HMGB-1), lipopolysaccharide, d-lactate and sCD14 were measured from peripheral blood obtained during the same visit. Each questionnaire score was correlated with biomarkers. Survivors were treated with orchiectomy only (N = 17), cisplatin-based chemotherapy (N = 108), radiotherapy to the retroperitoneum (N = 11) or both (N = 6).ResultsGCT survivors with higher sCD14 (above median) had worse cognitive function perceived by others (CogOth domain) (mean ± SEM; 14.6 ± 0.25 vs 15.4 ± 0.25, p = 0.019), lower perceived cognitive abilities (CogPCA domain) (20.0 ± 0.74 vs 23.4 ± 0.73, p = 0.025) and lower overall cognitive function score (109.2 ± 0.74 vs 116.7 ± 1.90, p = 0.021). There were no significant cognitive declines associated with HMGB-1, d-lactate and lipopolysaccharide. Survivors treated with ≥ 400mg/m2 vs < 400mg/m2 of cisplatin-based chemotherapy had a higher lipopolysaccharide (567.8 μg/L ± 42.7 vs 462.9 μg/L ± 51.9, (p = 0.03).ConclusionssCD14 is a marker of monocytic activation by lipopolysaccharide and may also serve as a promising biomarker of cognitive impairment in long-term cancer survivors. While chemotherapy and radiotherapy-induced intestinal injury may be the underlying mechanism, further research using animal models and larger patient cohorts are needed to explore the pathogenesis of cognitive impairment in GCT survivors within the gut-brain axis.

The paper deals with the problem of solving the nonresponse problem in a realized census. The purpose is to modify the method of poststratification using weights to compensate for nonresponse, which is known in sample surveys, for its application in censuses. The suggested approach offers more accurate estimates because of compensation for nonresponse and the possibility to formulate broader conclusions based on the census data. The approach is advised in all surveys in which the costs of realization the survey by the census, are practically the same as for sample survey and the list of all units of the population is available.

Statistical surveys are often used in shaping managerial policy and practice. In this paper we study, how to analyze the association between two categorical variables based on census data with a high level of nonresponse. The purpose is to discuss the suggested approach to the investigation. We used the census data from the survey executed at one Slovak University for testing the new process. The proposed process offers the methods of analysis of the association between two categorical variables based on pseudo-population estimated from the census data with a high level of nonresponse. We recommend using the process in the surveys in which the costs of survey execution by the census are practically not different from sample survey costs, and the connections to all units of the population are available.

The present study thoroughly evaluates the most common blocking challenges faced by the federated learning (FL) ecosystem and analyzes existing state-of-the-art solutions. A system adaptation pipeline is designed to enable the integration of different AI-based tools in the FL system, while FL training is conducted under realistic conditions using a distributed hardware infrastructure. The suggested pipeline and FL system’s robustness are tested against challenges related to tool deployment, data heterogeneity, and privacy attacks for multiple tasks and data types. A representative set of AI-based tools and related datasets have been selected to cover several validation cases and distributed to each edge device to closely reflect real-world scenarios. The study presents significant outcomes of the experiments and analyzes the models’ performance under different realistic FL conditions, while highlighting potential limitations and issues that occurred during the FL process.

The Si elegans platform targets the complete virtualization of the nematode Caenorhabditis elegans, and its environment. This paper presents a suite of unified web-based Graphical User Interfaces (GUIs) as the main user interaction point, and discusses their underlying technologies and methods. The user-friendly features of this tool suite enable users to graphically create neuron and network models, and behavioral experiments, without requiring knowledge of domain-specific computer-science tools. The framework furthermore allows the graphical visualization of all simulation results using a worm locomotion and neural activity viewer. Models, experiment definitions and results can be exported in a machine-readable format, thereby facilitating reproducible and cross-platform execution of in silico C. elegans experiments in other simulation environments. This is made possible by a novel XML-based behavioral experiment definition encoding format, a NeuroML XML-based model generation and network configuration description language, and their associated GUIs. User survey data confirms the platform usability and functionality, and provides insights into future directions for web-based simulation GUIs of C. elegans and other living organisms. The tool suite is available online to the scientific community and its source code has been made available.

This paper focusses on the simulation of the neural network of the Caenorhabditis elegans living organism, and more specifically in the modeling of the stimuli applied within behavioral experiments and the stimuli that is generated in the interaction of the C. elegans with the environment. To the best of our knowledge, all efforts regarding stimuli modeling for the C. elegansare focused on a single type of stimulus, which is usually tested with a limited subnetwork of the C. elegansneural system. In this paper, we follow a different approach where we model a wide-range of different stimuli, with more flexible neural network configurations and simulations in mind. Moreover, we focus on the stimuli sensation by different types of sensory organs or various sensory principles of the neurons. As part of this work, most common stimuli involved in behavioral assays have been modeled. It includes models for mechanical, thermal, chemical, electrical and light stimuli, and for proprioception-related self-sensed information exchange with the neural network. The developed models have been implemented and tested with the hardware-based Si elegans simulation platform.