University of Rijeka

About: University of Rijeka is a education organization based out in Rijeka, Croatia. It is known for research contribution in the topics: Population & Tourism. The organization has 3471 authors who have published 7993 publications receiving 110386 citations. The organization is also known as: Rijeka University & Sveučilište u Rijeci.

Correlation between vascular endothelial growth factor, angiogenesis, and tumor-associated macrophages in invasive ductal breast carcinoma

Abstract: Angiogenic and anti-angiogenic factors, secreted by tumor, inflammatory, and stromal cells play an important role in regulation of neovascularization. Among the most important of these is vascular endothelial growth factor (VEGF), a specific mitogen for endothelium, which increases vascular permeability and induces proteolytic enzymes necessary for vascular remodeling. Tumor-associated macrophages (TAMs) can express complex functions related to tumor biology, including growth, proliferative rate, stroma formation and dissolution, and neovascularization. The aim of this study was to define, using immunohistochemical analysis, the microvessel density (MVD), VEGF expression, and TAMs level in 97 human invasive ductal breast carcinomas not otherwise specified (NOS), investigate a possible relationship between them and then correlate their values with tumor grade, mitotic activity index (MAI), tumor size and lymph-node status. Statistical analysis showed a strong positive relationship between MVD and VEGF expression (P<0.001). Furthermore, both MVD and VEGF expression were significantly correlated with tumor grade and lymph-node status, and TAMs infiltration with MAI. TAM level showed a significant positive connection with VEGF expression and MVD. These in situ observations suggest that VEGF stimulates angiogenesis in human invasive ductal breast carcinoma NOS and attracts macrophages to the tumor locus, which then may be involved in angiogenesis promotion. The expression of this angiogenic molecule, and MVD and TAM level, can provide additional prognostic significance and help in the identification of patients who need postoperative adjuvant therapy.

Migraine and Tension-Type Headache in Croatia: A Population-Based Survey of Precipitating Factors

Abstract: The careful monitoring of the trigger factors of headache could be an important step in treatment, because their avoidance may lessen the frequency and severity of attacks. Furthermore, they may provide a clue to the aetiology of headache. The aim of the present study was to estimate the prevalence of tension-type headache (TTH) and to establish the frequency of precipitating factors in subjects with migraine and TTH in the adult population of Bakar, County of the Coast and Gorski Kotar, Croatia. Another important purpose of the study was to examine the relationship of the precipitating factors with migraine and TTH, and with migraine subtypes: migraine with aura (MA) and migraine without aura (MO). We performed a population-based survey using a 'face-to-face door-to-door' interview method. The surveyed population consisted of 5173 residents aged between 15 and 65 years. The 3794 participants (73.3%) were screened for headache history according to the International Headache Society (IHS) criteria. Headache screen-positive responders, 2475 (65.2%), were interviewed by trained medical students with a structured detailed interview focused on the precipitating factors. The following precipitating factors in lifetime migraineurs and tension-type headachers have been assessed: stress, sleep disturbances, eating habits, menstrual cycle, oral contraceptives, food items, afferent stimulation, changes in weather conditions and temperature, frequent travelling and physical activity. A total of 720 lifetime migraineurs and 1319 tension-type headachers have been identified. The most common precipitants for both migraine and TTH were stress and frequent travelling. Stress (odds ratio (OR) 1.4, 95% confidence interval (CI) 1.17, 1.69) was associated with migraine, whereas physical activity (OR 0.72, 95% CI 0.59, 0.87) was related to TTH. Considering MA and MO, frequent travelling (OR 2.2, 95% CI 1.59, 2.99), food items (OR 2.2, 95% CI 1.35, 3.51) and changes in weather conditions and temperature (OR 1.75, 95% CI 1.27, 2.41) exhibited a significant positive association with MA. The present study demonstrated that precipitant-dependent attacks are frequent among both migraineurs and tension-type headachers. Lifetime migraineurs experienced headache attacks preceded by triggering factors more frequently than tension-type headachers. MA was more frequently associated with precipitating factors than MO. We suggest that some triggering factors may contribute to the higher occurrence of precipitant-dependent headache attacks in susceptible individuals.

COVID-19 infection in adult patients with hematological malignancies: a European Hematology Association Survey (EPICOVIDEHA).

Abstract: Patients with hematological malignancies (HM) are at high risk of mortality from SARS-CoV-2 disease 2019 (COVID-19). A better understanding of risk factors for adverse outcomes may improve clinical management in these patients. We therefore studied baseline characteristics of HM patients developing COVID-19 and analyzed predictors of mortality. The survey was supported by the Scientific Working Group Infection in Hematology of the European Hematology Association (EHA). Eligible for the analysis were adult patients with HM and laboratory-confirmed COVID-19 observed between March and December 2020. The study sample includes 3801 cases, represented by lymphoproliferative (mainly non-Hodgkin lymphoma n = 1084, myeloma n = 684 and chronic lymphoid leukemia n = 474) and myeloproliferative malignancies (mainly acute myeloid leukemia n = 497 and myelodysplastic syndromes n = 279). Severe/critical COVID-19 was observed in 63.8% of patients (n = 2425). Overall, 2778 (73.1%) of the patients were hospitalized, 689 (18.1%) of whom were admitted to intensive care units (ICUs). Overall, 1185 patients (31.2%) died. The primary cause of death was COVID-19 in 688 patients (58.1%), HM in 173 patients (14.6%), and a combination of both COVID-19 and progressing HM in 155 patients (13.1%). Highest mortality was observed in acute myeloid leukemia (199/497, 40%) and myelodysplastic syndromes (118/279, 42.3%). The mortality rate significantly decreased between the first COVID-19 wave (March–May 2020) and the second wave (October–December 2020) (581/1427, 40.7% vs. 439/1773, 24.8%, p value < 0.0001). In the multivariable analysis, age, active malignancy, chronic cardiac disease, liver disease, renal impairment, smoking history, and ICU stay correlated with mortality. Acute myeloid leukemia was a higher mortality risk than lymphoproliferative diseases. This survey confirms that COVID-19 patients with HM are at high risk of lethal complications. However, improved COVID-19 prevention has reduced mortality despite an increase in the number of reported cases.

Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron.

Abstract: Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can provide satisfactory models, it can also fail to comprehend the intricacies contained within the data. In this paper, authors use a publicly available dataset, containing information on infected, recovered, and deceased patients in 406 locations over 51 days (22nd January 2020 to 12th March 2020). This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN). The aim of training is to achieve a worldwide model of the maximal number of patients across all locations in each time unit. Hyperparameters of the MLP are varied using a grid search algorithm, with a total of 5376 hyperparameter combinations. Using those combinations, a total of 48384 ANNs are trained (16128 for each patient group-deceased, recovered, and infected), and each model is evaluated using the coefficient of determination (R2). Cross-validation is performed using K-fold algorithm with 5-folds. Best models achieved consists of 4 hidden layers with 4 neurons in each of those layers, and use a ReLU activation function, with R2 scores of 0.98599 for confirmed, 0.99429 for deceased, and 0.97941 for recovered patient models. When cross-validation is performed, these scores drop to 0.94 for confirmed, 0.781 for recovered, and 0.986 for deceased patient models, showing high robustness of the deceased patient model, good robustness for confirmed, and low robustness for recovered patient model.