Folic acid - importance for human health and its role in COVID-19 therapy

Folic acid (folacin, B9) is a vitamin that performs many very important functions in the human body, and its inadequate level - deficiency as well as excess, may contribute to an increased risk of developing many disease processes. The aim of this study was to analyze the available scientific literature on folic acid and its impact on human health. A systematic review of the studies, published until November 2022, was made on the basis of searching bibliographic databases such as: PubMed, Elsevier and Google Scholar. The following keywords and combinations were used: folic acid, folate, folic acid supplementation, folate deficiency. Folic acid, thanks to its high biological activity, has a direct and indirect effect on the metabolism of the human body cells. It plays a very important role, among others in the prevention of neural tube defects and megaloblastic anemia, the proper functioning of the nervous system, as well as reducing the risk of developing certain cancers. Currently, the important role of folic acid in maintaining the proper functioning of the immune system is also emphasized, which is of particular importance both in the prevention and in the situation of SARS-CoV-2 (COVID-19) infection. The effects of deficiency and excess of vitamin B9 may turn out to be dangerous to health and even life. There is a need for nutritional and health education of the society regarding the importance of folic acid for human health, due to the presence of large deficiencies in the population, which is particularly important for some social groups, such as, for example, women of procreation age, pregnant or breastfeeding, people with a nutrient malabsorption, and people who smoke or abuse alcohol

Exploration of Rashomon Set Assists Explanations for Medical Data

The machine learning modeling process conventionally culminates in selecting a single model that maximizes a selected performance metric. However, this approach leads to abandoning a more profound analysis of slightly inferior models. Particularly in medical and healthcare studies, where the objective extends beyond predictions to valuable insight generation, relying solely on performance metrics can result in misleading or incomplete conclusions. This problem is particularly pertinent when dealing with a set of models with performance close to maximum one, known as $\textit{Rashomon set}$. Such a set can be numerous and may contain models describing the data in a different way, which calls for comprehensive analysis. This paper introduces a novel process to explore Rashomon set models, extending the conventional modeling approach. The cornerstone is the identification of the most different models within the Rashomon set, facilitated by the introduced \texttt{Rashomon_DETECT} algorithm. This algorithm compares profiles illustrating prediction dependencies on variable values generated by eXplainable Artificial Intelligence (XAI) techniques. To quantify differences in variable effects among models, we introduce the Profile Disparity Index (PDI) based on measures from functional data analysis. To illustrate the effectiveness of our approach, we showcase its application in predicting survival among hemophagocytic lymphohistiocytosis (HLH) patients - a foundational case study. Additionally, we benchmark our approach on other medical data sets, demonstrating its versatility and utility in various contexts.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Satysfakcja obywateli i innych interesariuszy a instrumenty zarządzania w JST

Publikacja recenzowanaProblematyka zarządzania w jednostkach samorządu terytorialnego (JST) stanowi jeden z najważniejszych obszarów badań dotyczących funkcjonowania władzy lokalnej. Z tego powodu stanowi ona przedmiot zainteresowania teoretyków i praktyków, którzy swoje rozważania kierują w stronę samorządu terytorialnego. Jest to szczególnie istotne w sytuacji, gdy rysuje się presja na poprawę ekonomicznych i społecznych wyników działania tych organizacji. Osiąganie korzystnych rezultatów nie jest możliwe bez nowoczesnego zarządzania, a jednocześnie różnorodność procesów i zjawisk składających się na ten system powoduje znaczne trudności i perturbacje w osiąganiu celów bieżących i rozwojowych. Przeciwdziałanie tym negatywnym zjawiskom wymaga zatem podejmowania już na etapie planowania decyzji, które stanowić będą wyznacznik dla skuteczności, wydajności i oszczędności w procesie świadczenia usług publicznych na szczeblu lokalnym

The Use of Indicator Kriging for Analyzing Prices in the Real Estate Market

The patterns and relations between real estate prices and the factors which shape them can be presented, among others, in the form of traditional statistical models, as well as by means of geostatistical methods. In the case of research involving the diagnosis and prediction of transaction prices, the key role is played by the spatial aspect, hence the particular significance of geostatistical methods using spatial information

Beetroot Juice - Legal Doping for Athletes?

Nitric oxide (NO) is a physiologically important signaling molecule that promotes the expansion of blood vessels and thus facilitates the transport of oxygen (O2) and energy substrates to the muscles. Research shows that nitric oxide (NO) also improves the effectiveness of mitochondrial respiration, which is manifested by reduced oxygen consumption during exercise. Until recently, it was thought that nitric oxide (NO) could only be formed as a result of the endogenous pathway of oxidative transformations L-arginine. Recent research results indicate, however, that an alternative to the endogenous pathway of nitric oxide (NO) formation may be the exogenous supply of inorganic nitrates (NO3-) with food. The aim of the study was to review the current literature on the properties of beetroot juice as an important source of nitrates (NO3-) and its effectiveness in improving the exercise capacity of physically active people. A systematic review of the research, published from 2005 to January 31, 2021, was made on the basis of searching bibliographic databases such as: PubMed, Elsevier and Web of Science. The following keywords were used: “beetroot”, “beetroot juice”, “nitrates”, “nitrites”, “nitric oxide”, “supplementation”, “ergogenic substances”, “sports nutrition”. Although there are conflicting data, it appears that beetroot juice supply may be a cheap, natural, and promising nutritional strategy for improving sports performance in both endurance and intermittent high intensity (start-stop) exercise. More detailed studies are analyzing the effect of dietary nitrate (NO3-) supply in anaerobic exercise - especially in high-volume resistance training - are needed. It is also emphasized that further research is needed to elucidate the effects of specific factors on the variability of ergogenic effects after beetroot juice consumption, which may be of the greatest importance in terms of the effectiveness of this nutritional intervention

Application of Hierarchical Spatial Autoregressive Models to Develop Land Value Maps in Urbanized Areas

This article aims at testing the possibilities of applying hierarchical spatial autoregressive models to create land value maps in urbanized areas. The use of HSAR (Hierarchical Spatial Autoregressive) models for spatial differentiation of prices in the property market supports the multilevel diagnosis of the structure of this phenomenon, taking into account the effect of spatial interactions. The article applies a two-level hierarchical spatial autoregressive model, which will permit the evaluation of interactions and control spatial heterogeneity at two levels of spatial aggregation (general and detailed). The results of the research include both the evaluation of the impact of location on prices (taking into account non-spatial factors) and the creation of the average land price map, taking into consideration the spatial structure of the city. In empirical studies, the HSAR model was compared with classic LM (Linear Model), HLM (Hierarchical Linear Model), and SAR (Spatial Autoregressive) models to perform comparative analyses of the results

Using Kernel density estimation for modelling and simulating transaction location

Simulation modelling performs a prognostic function through model research and the shaping of the future. Thorough insight into the analysed system and exploring its characteristics for the selection of optimal tools of analysis is an extremely significant process that precedes the stage of the simulation itself. For modelling and transaction simulation, the problem concerning the optimal range of the kernel function used for exploring the spatial activity of a property market should be addressed first. A probability function is the basis for the subsequent phase of research, which allows one to answer the question of whether the transaction density in a given year can be reflected in the transactions of the following year and subsequent years, and whether transaction distribution is correlated, in any way, with the transaction density in the previous year. The final results of the work are maps of the dynamics of transactions on the market and of the simulated transaction density

Explainable Machine Learning for Lung Cancer Screening Models

Modern medicine is supported by increasingly sophisticated algorithms. In diagnostics or screening, statistical models are commonly used to assess the risk of disease development, the severity of its course, and expected treatment outcome. The growing availability of very detailed data and increased interest in personalized medicine are leading to the development of effective but complex machine learning models. For these models to be trusted, their predictions must be understandable to both the physician and the patient, hence the growing interest in the area of Explainable Artificial Intelligence (XAI). In this paper, we present selected methods from the XAI field in the example of models applied to assess lung cancer risk in lung cancer screening through low-dose computed tomography. The use of these techniques provides a better understanding of the similarities and differences between three commonly used models in lung cancer screening, i.e., BACH, PLCOm2012, and LCART. For the presentation of the results, we used data from the Domestic Lung Cancer Database. The XAI techniques help to better understand (1) which variables are most important in which model, (2) how they are transformed into model predictions, and facilitate (3) the explanation of model predictions for a particular screenee

Selected aspects of child nutrition and the risk of obesity

Introduction. Food is the basic source of energy for humans. Properly matched for the age and needs of the body, a balanced diet is a condition for proper growth and development during childhood. The aim of the study is to review the current state of knowledge regarding the impact of the nutrition model, diet composition and behavioral aspects related to food intake on the occurrence of obesity in children and adolescents