Nonparametric Statistical Methods Research Articles

Rainfall Trend Analysis in Mysuru and Bengaluru Districts of Karnataka, India

Climate change is one of the most pressing challenges facing humanity today, with temperature, precipitation, and runoff being key indicators closely linked to its impact. Understanding the spatial variability and temporal trends of rainfall is crucial for effective water resource management and agriculture. This study focuses on analyzing rainfall data from the Mysuru and Bengaluru districts, located in the Southern and Eastern dry zones of Karnataka, respectively. These regions play a pivotal role in the state's agricultural development and overall economic growth. We utilized daily precipitation records from two meteorological stations, spanning the period from 1983 to 2018, to analyze rainfall trends on monthly, seasonal, and annual scales. To assess these trends, we applied both parametric and non-parametric statistical methods, including the simple regression method, Mann-Kendall test, and Sen’s slope estimator. The Mann-Kendall test was used to detect significant trends in the precipitation time series, while Sen’s slope estimator quantified the magnitude of these trends. Bengaluru had higher average annual rainfall (928.81 mm) compared to Mysuru (681.65 mm), with excess rainfall in 2005 and a positive Pre-Monsoon trend (+3.543 mm/year), while Mysuru experienced maximum rainfall deficit in 2016 and a negative Monsoon trend (-3.238 mm/year). Neither district showed a significant trend in annual rainfall. The findings of this trend analysis are essential for informed water resource planning and management in the region.

A Dunnett-Type Test and Its Sample Size Calculation for Comparing K ROC Curves with a Control.

Diagnostic biomarkers are key components of diagnostics. In this paper, we consider diagnostic biomarkers taking continuous values that are associated with a dichotomous disease status, called malignant or benign. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. We assume that, together with the disease status, one control and multiple experimental biomarkers are collected from each subject to test if any of the experimental biomarkers have a larger AUC than the control. In this case, each experimental biomarker will be compared with the control so that a multiple testing issue is involved in the comparisons. In this paper, we propose a simple non-parametric statistical testing procedure to compare K(≥2) experimental biomarkers with a control, adjusting for the multiplicity and its sample size calculation method. Our sample size formula requires the specification of the AUC values (or the standardized effect size of each biomarker between the benign and malignant groups) together with the correlation coefficients between the biomarkers, the prevalence of the malignant group in the study population, the type I error rate, and the power. Through simulations, we show that the statistical test controls the overall type I error rate accurately and the proposed sample size closely maintains the specified statistical power.

Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information

Early identification of the likelihood of autism spectrum disorder (ASD) using minimal information is crucial for early diagnosis and intervention, which can affect developmental outcomes. To develop and validate a machine learning (ML) model for predicting ASD using a minimal set of features from background and medical information and to evaluate the predictors and the utility of the ML model. For this diagnostic study, a retrospective analysis of the Simons Foundation Powering Autism Research for Knowledge (SPARK) database, version 8 (released June 6, 2022), was conducted, including data from 30 660 participants after adjustments for missing values and class imbalances (15 330 with ASD and 15 330 without ASD). The SPARK database contains participants recruited from 31 university-affiliated research clinicals and online in 26 states in the US. All individuals with a professional ASD diagnosis and their families were eligible to participate. The model performance was validated on independent datasets from SPARK, version 10 (released July 21, 2023), and the Simons Simplex Collection (SSC), consisting of 14 790 participants, followed by phenotypic associations. Twenty-eight basic medical screening and background history items present before 24 months of age. Generalizable ML prediction models were developed for detecting ASD using 4 algorithms (logistic regression, decision tree, random forest, and eXtreme Gradient Boosting [XGBoost]). Performance metrics included accuracy, area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and F1 score, offering a comprehensive assessment of the predictive accuracy of the model. Explainable AI methods were applied to determine the effect of individual features in predicting ASD as secondary outcomes, enhancing the interpretability of the best-performing model. The secondary outcome analyses were further complemented by examining differences in various phenotypic measures using nonparametric statistical methods, providing insights into the ability of the model to differentiate between different presentations of ASD. The study included 19 477 (63.5%) male and 11 183 (36.5%) female participants (mean [SD] age, 106 [62] months). The mean (SD) age was 113 (68) months for the ASD group and 100 (55) months for the non-ASD group. The XGBoost (termed AutMedAI) model demonstrated strong performance with an AUROC score of 0.895, sensitivity of 0.805, specificity of 0.829, and PPV of 0.897. Developmental milestones and eating behavior were the most important predictors. Validation on independent cohorts showed an AUROC of 0.790, indicating good generalizability. In this diagnostic study of ML prediction of ASD, robust model performance was observed to identify autistic individuals with more symptoms and lower cognitive levels. The robustness and ML model generalizability results are promising for further validation and use in clinical and population settings.

The role of сhemokine CX3CL1/Fractalkine in chronic viral hepatitis B

Hepatitis B is an infectious disease resulting from infection with the hepatitis B virus. Chronic hepatitis B (CHB) is characterized by prolonged inflammation in the liver, the development of fibrosis, liver cirrhosis and hepatocellular carcinoma. Factors of the immune system play a critical role in the pathogenesis of CHB. Thanks to chemokines, immune cells migrate to the site of inflammation to implement their effector functions. The CX3CL1/Fractalkine chemokine is the only member of the CX3C family of chemokines with unique structural and functional properties. Its receptor CX3CR1 is expressed mainly on the surface of cytotoxic effector lymphocytes such as NK cells, TNK and cytotoxic T lymphocytes. The purpose of our study was to analyze the content of CX3CL1/Fractalkine in the blood plasma of patients with CHB and the analysis of this chemokine with liver fibrosis. The concentration of CX3CL1/Fractalkine was determined in the blood plasma of patients with CHB using a multiplex assay based on xMAP technology. Blood plasma from patients with chronic viral hepatitis C (CHC) and autoimmune liver diseases (AILD) was used as a comparison group. The control group consisted of healthy individuals. For statistical analysis of data, nonparametric statistics methods were used: Kruskal–Wallis test, Spearman correlation coefficient ROC-analysis. It was shown a reduced level of CX3CL1/Fractalkine in patients with CHB compared with the control group (p = 0.0003) and with the comparison groups of CHC (p 0.0001) and AILD (p = 0.0005). A reduced concentration of CX3CL1/Fractalkine was shown in the blood plasma of CHB patients with initial fibrosis (p = 0.0092) and severe fibrosis/cirrhosis (p = 0.0009), while in patients with severe fibrosis/cirrhosis, a significantly reduced level of this chemokine was established compared with the initial degree of liver fibrosis (p = 0.0081). Correlation analysis revealed a highly significant inverse relationship between the severity of liver fibrosis and the content of CX3CL1/Fractalkine in the blood plasma of patients with CHB (Spearman r = –0.33; p = 0.02). Thus, the chemokine CX3CL1/Fractalkine is included in the immunopathogenesis of CHB; its reduced content is characteristic only of CHB and does not change in other chronic liver diseases. It is involved in the processes of liver fibrosis during infection with the hepatitis B virus. The concentration of the chemokine CX3CL1/Fractalkine depends on the stage of liver fibrosis in CHB, and a decrease in the level of CX3CL1/Fractalkine in the blood plasma can serve as a negative factor in the development of CHB.

Personality traits in patients with amyotrophic lateral sclerosis

BACKGROUND: The results of several studies indicate that patients with amyotrophic lateral sclerosis (ALS) exhibit certain personality traits. However, it is currently unclear which specific traits are most typical for those patients, and whether these traits are universal across different ethno-cultural backgrounds. AIM: To analyse the personality traits of patients with ALS. MATERIALS AND METHODS: The study included patients with various forms of ALS according to the Revised El Escorial Criteria. The following data were collected: demographic information, the form of the disease, and the stage according to the King's College system. A short five-factor personality questionnaire (TIPI-RU) was used to assess personal characteristics. The statistical analysis employed non-parametric statistical methods. RESULTS: Patients with ALS had significantly higher levels of agreebleness (p=0.037). Analysis of personality traits within the group of patients with ALS showed that in patients with bulbar onset, had a significantly higher level of agreebleness — 5 (4.5–7) than in patients with spinal onset 4.5 (4–5), p=0.006. The correlation analysis revealed a direct weak relationship between the degree of extraversion and age (r=0.3; p=0.013). CONCLUSION: The analysis of the personality traits of the ALS patients showed a higher friendliness (agreeableness) compared to the control group. Future research should elucidate whether this parameter could be considered as one of the markers of increased risk of ALS development.

Multifocal character of lesions in gunshot open globe injury type B in experiment

BACKGROUND: An increase was noted in the number of gunshot eyeball injuries, which are accompanied by low functional outcomes. Reproduction and experimental study of this type of eye injury would help improving the functional and cosmetic treatment results in patients. AIM: The aim of the study is to investigate gunshot open globe injury type B (penetrating wound without intraocular foreign body) on a standardized experimental model. MATERIALS AND METHODS: A complete investigation of the standardized model of gunshot open globe injury type B (penetrating wound without intraocular foreign body) simulated on the ballistic test facility was carried out. The experiment was accomplished at the ophthalmology chair on 36 rabbits (71 eyes). The injury was inflicted in the projection of the ciliary body — zone II (Birmingham Eye Trauma Terminology). The examination in the control period included ophthalmologic (ophthalmoscopy, full field electroretinography, optical coherence tomography), biochemical (testing of vitreous fibronectin level), histological and radiological (magnetic resonance imaging, ultrasound examination) methods. Statistical non-parametric methods of data analysis were used. RESULTS: The analysis of gunshot open globe injury type B model demonstrated the rate and multiple foci of abnormalities practically of all eyeball structures. CONCLUSIONS: For the first time ever, the characteristics of gunshot open globe injury type B model were studied using a new complex of methods, their high reproducibility (91.5–100%) was demonstrated. Based on recorded abnormalities in all ocular structures, including proliferative vitreoretinopathy, the multifocal character of damage in this type of injury is validated.

A novel non-parametric statistical method in reliability theory: Mathematical characterization and analysis of asymmetric data in the fields of biological sciences and engineering

In recent decades, strategies devised to address challenges in life testing have noticeably veered away from those designed for related yet more intricate issues. The increasing complexity of data generated daily across practical fields has given rise to a novel realm in reliability that includes life classes and specific probability distributions. This study serves as an illustration of the effectiveness of integrating the goodness-of-fit technique into life testing quandaries, resulting in more efficient processes comparable to or surpassing traditional methods. Furthermore, these techniques exhibit potential for enhanced performance, particularly when dealing with smaller sample sizes. The comparison between the Exponential Better than Used in Increasing Convex in Laplace Transform Order (EBUCL) test statistic and a distinct goodness-of-fit test statistic upholds an approach leaning towards normalcy. Analysis encompassing powers, Pitman asymptotic effectiveness, and critical points, as well as the exploration of methodologies for handling suppressed data, has been undertaken. Additionally, a simulation study has been conducted to examine the study's motivations across various sample sizes. The results derived from our experiments hold practical implications for both bioscience and engineering sustainability data analyses.

VALIDATION OF THE DIABETES SCREENING QUESTIONNAIRE USE ON THE INITIAL APPOINTMENT DAY, AFTER 6 AND 12 MONTHS

Background. Psoriasis is an immune-mediated inflammatory disease affecting the skin, characterized by keratinocyte hyperproliferation and T-lymphocyte inflammation, with a worldwide prevalence of 2–4% [1]. Proinflammatory molecules released during chronic inflammation can lead to one or more comorbidities such as atherosclerosis, insulin resistance, hypertension, obesity, dyslipidemia, metabolic syndrome, and type 2 diabetes mellitus [2–4]. Given the close comorbid relationship between psoriasis and type 2 diabetes mellitus, we recommend screening patients using a previously developed diabetes screening questionnaire. Purpose. To determine the results of validation of the diabetes screening test from October 2021 to October 2022 and to conduct a single-center cohort retrospective continuous study of 100 people with psoriasis. Materials and Methods. Data collection, its subsequent correction, systematization of initial information and visualization of the results obtained were carried out in Microsoft Office Excel (2016) spreadsheets. Statistical processing of the results was carried out using the Python language (Python 3.8.). For calculations, built-in functions from the Statsmodels.api and Scipy modules were used. Quantitative indicators were assessed for compliance with normal distribution, using the Shapiro-Wilk test. Results. A test for normal distribution showed that the data in the study were not normally distributed. Therefore, further calculations were made using nonparametric statistics methods. The median was calculated as the center of distribution, and quartiles were calculated as indicators of variation (Me [Q1; Q3]). Mann-Whitney U test was used to compare unrelated samples. Conclusion. The validation stage showed that the use of screening questionnaires developed by the authors is a simple and low- cost method that is effective in identifying the pathology of type 2 diabetes, which makes it possible to recommend them for testing psoriasis patients at annual medical examinations.

A test of meta-heuristic algorithms for parameter extraction of next-generation solar cells with S-shaped current–voltage curves

Identifying parameters of photovoltaic (PV) models based on measured current–voltage (IV) characteristic curves is critical for simulating, evaluating, and controlling PV systems. IV characteristics of the latest-generation solar cells (SCs) often display an S-shaped deformation. In this paper, we explore the potential of meta-heuristic algorithms to address the parameter estimation problems associated with PV cells that exhibit S-shaped IV characteristics. This estimation is performed within the framework of the opposed two-diode model. We implemented a total of 14 algorithms from various classes to extract the SC parameters from synthetic IV curves, which were generated using a range of parameter values. The results were compared by using nonparametric statistical methods. These methods include the Wilcoxon signed-rank test for pairwise comparisons, and the Friedman, Friedman Aligned, and Quade tests for multiple comparisons. Comprehensive results and analyses show that the STLBO (Simplified teaching–learning based optimization algorithm) and ADELI (Adaptive differential evolution with the Lagrange interpolation argument) algorithms demonstrate highly competitive performance in terms of accuracy and reliability. This paper underscores the efficacy of advanced meta-heuristic algorithms in solving complex non-linear optimization problems in the domain of photovoltaic research, particularly concerning the unique challenges posed by S-shaped IV characteristics of new-generation solar cells.

Urbanization signature on hourly rainfall extremes of Kuala Lumpur

This study uses high-resolution satellite data to investigate the impact of urbanization on the local climate of Kuala Lumpur, Malaysia. Nonparametric statistical methods, including trend test, spatial correlation, and quantile regression, were employed to quantify interrelationships between satellite nighttime light (NTL), Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and Global Satellite Mapping of Precipitation (GSMaP) extreme rainfall events. The results revealed that urbanization increased Kuala Lumpur's LST by 3.8 to 4.1 °C and the 99th percentile rainfall by 10 to 43 % compared to surrounding areas. The correlation analysis showed an association (r = 0.62) between the trends in LST and rainfall extremes, with a significance level of p < 0.05. The NTL trend analysis showed a rapid expansion of the city, particularly in the west, and thus, an expansion of high LST areas and extreme rainfall amounts. Quantile regression results confirmed the influence of increased LST on rainfall extremes. The trends for hourly rainfall revealed increases mainly between 2pm and 9pm in the city, which is attributed to the intensification of convective rainfall caused by increased LST. The study highlights the importance of mitigating urban LST to reduce climatic extremes and promote sustainable urban development.

Healthcare Worker's Satisfaction Assessment for a Healthcare Adverse Event Reporting Framework and the Management Approach for Such Reporting in the Emergency Department of Rural Government Hospitals.

The healthcare adverse event (HAE) reporting framework is more than just a tool. It is a crucial pillar in our pursuit of patient safety, quality improvement, trust maintenance, regulatory compliance, and ethical standards in healthcare delivery. To assess healthcare workers' satisfaction with the HAE reporting framework and the management approach towards such reporting in the emergency department of rural government hospitals by conducting a satisfaction survey. This prospective observational research was conducted in the Department of Emergency Medicine of the Uttar Pradesh University of Medical Sciences, Saifai, and adjoining district hospitals from November 2023 to January 2024. The study involved 320 healthcare professionals working in the emergency department. The quantitative survey research used a questionnaire and a quality Likert scale response.The data were analyzed on an ordinal measurement scale using nonparametric statistical methods. The sample data were analyzed using frequency tables, percentage pie charts, and comparison bar graphs. In nonparametric statistical tests, the one-sample Wilcoxon signed rank test was used to infer the population's central tendency, and the Kruskal-Wallis testwas used to make inferences about the population categories. The satisfaction of healthcare professionals with the HAE reporting framework and the management approach was diverse. When surveyed about the HAE reporting framework in the emergency department, out of the 320 healthcare professionals,50% (161) expressed dissatisfaction,47% (149) were satisfied, and 3% (10) did not comment. Paramedics were most dissatisfied (61% of 133). When asked about the management approach while dealing with such reporting, 50% (159) were satisfied, 46% (147) were unsatisfied, and 4% (14) did not comment. On comparingprofessions, 43% (29) of 33 doctorsand 62% (83) of 133 paramedics were unsatisfied, giving a poor response. Additionally, 61% (72) of the 119 nursing staff were satisfied. The non-parametric inferential tests yielded a p-value of less than 0.001 for both questions, indicating a notable difference in the population's response to the HAE reporting framework and management approach. On pairwise comparison, there was a significant difference in perception (p<0.001) between the occupation groups, except for doctors and paramedics (p = 0.638) in the HAE reporting framework. By encouraging reporting, standardizing processes, analyzing incidents thoroughly, and using data-driven insights to inform improvement efforts, healthcare organizations can enhance patient safety, improve quality of care, and prevent future adverse events. The management approach to HAE reporting involves fostering a culture of safety and transparency, implementing standardized reporting systems, providing education and training to healthcare staff, establishing feedback mechanisms, conducting robust analysis of reported events, promoting continuous improvement, and ensuring transparency and accountability.

A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

Objectives: Consumer data privacy on e-commerce platforms is increasingly crucial. This study aims to investigate privacy protection mechanisms, particularly focusing on personal and corporate secrets. It seeks to understand individual perspectives on privacy and preferences for data disclosure. The primary objective is to explore methods for safeguarding personal information while maintaining data integrity. Methods/Analysis: We employ non-parametric statistical techniques to analyze consumer behavior and preferences on e-commerce platforms. This involves examining patterns of data disclosure and identifying sensitive information shared by users. By studying communication dynamics and recording practices, we assess the efficacy of current privacy protection measures. Novelty/Improvement: This study contributes to the understanding of consumer privacy protection by emphasizing the importance of non-parametric statistical methods in e-commerce research. Our findings underscore the need for enhanced privacy measures. We advocate for further research and development of innovative privacy-enhancing technologies to address evolving privacy challenges in online commerce. Findings:Our research highlights the significance of personal privacy concerns in e-commerce settings. We identify a spectrum of privacy attitudes among users, ranging from strict confidentiality to selective disclosure. Furthermore, our analysis reveals potential vulnerabilities in current privacy safeguards, particularly regarding the collection and storage of sensitive data on e-commerce platforms. Doi: 10.28991/HIJ-2024-05-02-013 Full Text: PDF

Comparative evaluation of the effectiveness and safety of acne treatment with systemic isotretinoin in monotherapy and together with combined oral contraceptives in women

Objective. To give a comparative evaluation of the effectiveness and safety of acne treatment with systemic isotretinoin in monotherapy and together with combined oral contraceptives in women. Materials and methods. 65 women with severe papulo-pustular acne took part in the study. They were divided into two groups: group I consisted of 35 women who used systemic isotretinoin for acne in monotherapy; group II included 30 women who took systemic isotretinoin together with combined oral contraceptives. A comprehensive clinical and laboratory study was conducted. The study included a biochemical blood test to determine the lipid profile, liver transaminases, and hormonal homeostasis. The obtained results were analyzed using the methods of parametric and nonparametric statistics, the standard error (m) was used in the calculations, the level of statistical significance was noted at p 0.05. Results. In women treated for acne with systemic isotretinoin in monotherapy relief occurs in 4 weeks, and complete clinical recovery in 24 weeks after the beginning of therapy; when using systemic isotretinoin together with combined oral contraceptives, relief is determined in 2 weeks, and complete clinical recovery in 16 weeks of treatment. The main side effects of systemic isotretinoin were cheilitis, skin xerosis, retinoic dermatitis and telogen alopecia. Cheilitis was revealed in the majority of patients in both groups equally. Xerosis of the skin, retinoic dermatitis and telogen alopecia were more often observed in the 1st group. In women of the 1st group, the level of progesterone was significantly higher and the level of estradiol was lower than in women of the 2nd group. Conclusions. Systemic isotretinoin has shown high efficacy and safety in treatment for acne in women. Combined oral contraceptives normalize hormonal homeostasis, which helps to eliminate acne. When using systemic isotretinoin in combination with combined oral contraceptives, the main clinical effect occurs earlier, and adverse reactions are less frequent and less severe than in monotherapy. The combined use of systemic isotretinoin and combined oral contraceptives is the optimal method of acne treatment in women.

"Effects of floating aquatic vegetation and canal sediment on phosphorus in drainage discharges in agricultural canals: A case study in the everglades agricultural area, Florida"

Phosphorus (P) discharge from agricultural and urban drainage is known for causing downstream eutrophication worldwide. Agricultural best management practices that are designed to reduce P load out of farms target different P species from various sources such as fertilizers leaching and farm soil and canal sediment erosion, however, few studies have assessed the impact of floating aquatic vegetation (FAV) on canal sediment and farm drainage water quality. This study evaluated the impact of FAVs on canal sediment properties and P water quality in drainage canals in the Everglades Agricultural Area in south Florida, USA. Non-parametric statistical methods, correlation analysis, trend analysis and principal component analysis (PCA) were used to determine the relationship between FAV coverage with sediment properties and P water quality parameters. Results showed that FAV coverage was correlated with the highly recalcitrant and most stable form of P in the sediment layer (Residual P Pool). FAV coverage also correlated with the dissolved organic P (DOP) which was the smallest P pool (7 %) of total P concentration in drainage water, therefore FAV coverage had no correlation with farm P load. The trend analysis showed no trend in farm P loads, despite a decline in FAV coverage at farm canals over an 8-year period. Phosphorus content in the sediment surface layer was strongly associated with farm P load and had a significant correlation with particulate P (PP) and soluble reactive P (SRP) which constituted 47 % and 46 % of the total P concentration in the drainage water, respectively. Equilibrium P concentration assays also showed the potential to release SRP from the sediment layer. The P budget established for this study reveals that sediment stores the largest P mass (333 kg P), while FAVs store the smallest P mass (8 kg P) in a farm canal, highlighting the significant contribution of canal sediment to farm P discharges. Further research is required to evaluate the impact of sediment removal and canal maintenance practices that help reduce farm P discharges.

Analisis Penerimaan Aplikasi Naskah Dinas Elektronik (Nadine) Di Biro Umum Sekretariat Jenderal Kementerian Keuangan Menggunakan Technology Acceptance Model (TAM)

One of the impacts of the rapid development of information and technology (IT) in the archives sector is the presence of an electronic records management system (ERMS). Many ERMS has been developed, but not all of them have been successfully implemented. One important factor in its implementation is user acceptance. User acceptance influences user intentions when using an ERMS application. One of the ERMS is the Naskah Dinas Elektronik (Nadine) application atthe Ministry of Finance, which was implemented five years ago but has not yet been tested for user acceptance. This research was conducted to analyze the factors involved in accepting Nadine's application at the General Bureau as Centre Archives of the Ministry of Finance through the technology acceptance model (TAM). The research method used was quantitativedescriptive, with questionnaires distributed to 72 respondents using the Nadine application at the General Bureau. The data obtained was processed using a non-parametric statistical method, namelypartial least squares structural equation modelling (PLS-SEM). The research results show that system quality factors, user training factors, and facility conditions significantly influence user intentions when using the Nadine application. Perceived ease of use and perceived usefulness show a positive correlation with intention to use. Perceived usefulness has a significant direct influence on intention to use, while perceived ease of use has an indirect influence on intention to use through perceived usefulness. The implications of this research provide suggestions for policymakers to improve the quality of implementation of the Nadine application by improving system quality, providing training programs, and providing adequate facilities. This research can also be used to explore factors that influence users' intentions to accept ERMS, especially perceived usefulness and perceived ease of use.

Radiomics analysis of the tissue content of the esophageal hiatus of normal subjects without esophageal symptoms (establishing control values)

Introduction: The opening in the diaphragm through which the esophagus enters from the thorax into the abdomen, i.e., esophageal hiatus is formed by the right crus of the diaphragm. During esophageal peristalsis, the lower end of the esophagus, i.e., lower esophageal sphincter (LES), slides from the abdomen into chest, which we suspect is due to the presence of fat in the hiatus that serves as a lubricant. A recent study from our laboratory found that in patients with achalasia esophagus, the LES and crus of diaphragm are firmly anchored together, which we suspect is due to the loss of fat from the hiatus. Gray scale intensity analysis of the radiological images can be used to determine the nature of tissue, i.e., fat, connective tissue, muscle etc. Goal: To establish gray scale values of the esophageal hiatus from the CT scan images in controls. Methods: 42 patients who had undergone an abdominal CT scan for symptoms unrelated to upper gastrointestinal pathology were selected for this analysis. These patients had no esophageal symptoms and were not taking any medications related to the esophagus and stomach, which included acid inhibition medications. The CT scan was visualized in the Visage (San Diego, CA), a software program that allows rotation of images in X, Y and Z planes to visualize the esophageal hiatus in a 2D image. A software (Segcrus) was built to perform the grayscale intensity analysis of the 3 regions of equal size in the image, 1) subcutaneous fat, 2) muscle, and 3) esophageal hiatus. The 3 regions of interest were demarcated manually in the 2D image. The inner margin of the hiatus was selected as the region of interest for the hiatus image. The software generated a statistical non-parametric method approximation using kernel density estimation (KDE) of the grey scale intensity distributions of the 3 regions of interest. The median values were computed to represent the central tendencies of the distributions, providing an insight into the typical greyscale values of the tissue types. Results: distinct patterns in the region intensity distributions for hiatal region, muscle, and fat were seen. For the hiatus, the values were (Median = 105), interquartile range (IQR = 80.3), and range (Min = 61.5, Max = 137.95), providing a comprehensive description of central tendency, variability, and data spread. For muscle, (Median = 139.92), interquartile range (IQR = 71.1), and range (Min = 119.67, Max = 157.17), and for fat, (Median = 48.24), interquartile range (IQR = 52.9), and range (Min = 23.99, Max = 67.79. Hiatus to fat tissue intensity overlap was not related to BMI (r=0.17, p=0.29), or Age (r=-0.04, p=0.82). There was a notable correlation between the overlap of Hiatal-Muscle tissue intensity and Age (r=0.38, p=0.012). However, such correlation was not observed with BMI (r=-0.3, p=0.06). Additionally, there was no significant correlation between the overlap of Hiatal-Fat tissue intensity and gender (r=-0.26, p=0.09), and similarly, the overlap of Hiatal-Muscle intensity showed no correlation with sex (r=0.09, p=0.58). The results highlight the variability and central tendencies within these tissue types across the normal subjects. Conclusion: These analysis in subjects with no esophageal symptoms may be used as control values to determine if there are difference in the tissue content of the esophageal hiatus between normal and patients with various esophageal motility disorders. R01 DK109376. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Studying the characteristics of mentoring university students with the main focus on supporting their professional development

Introduction. The problem of training and development of demanded, highly qualified professionals is explored. The purpose of this article is to identify the characteristic features of mentoring university students aimed at enhancing their professional development. Materials and Methods. The study was conducted using theoretical, empirical and auxiliary methods of psychological and educational research. General logical methods of scientific knowledge included analysis, synthesis and generalization of information. The analysis was based on questionnaires, as well as the method of collecting social information. Mathematical and statistical methods for interpreting the results were used as auxiliary methods. When assessing the reliability of differences between the data obtained from students of different years of study, a nonparametric statistical method was used (the Pearson chi-square test). Results. The authors identified features of mentoring university students. Four types of mentors have been identified: 1) mentors for learning; 2) mentors as psychologists; 3) research mentors; 4) field-based mentors. The functions of each type of mentors have been described and clarified. Furthermore, the authors proposed a system of interaction between mentors. Conclusions. The article concludes about the characteristic features of mentoring university students in order to facilitate their professional development as future professionals. The authors proposed a system of mentoring focusing on training, support and guidance of students which includes four types of mentors. A system of interaction between mentor and student is described.

Early Detection of Allergy Symptoms in Children and Adolescents, Characteristics of Possible Pathogens, Pre-Treatment Prevention Measures (in Poland)

Background: The purpose of this study was to investigate the main allergens and signs of the onset of allergic diseases and explore methods of prevention that do not include medicines. Methods: Non-parametric statistical methods were used. A retrospective study was conducted, which included 270 case histories. The average age of the patients was 19 (12.5; 40.1) years, 127 (47%) were men and 143 (53%) were women. Clinical diagnoses, early symptoms, allergen spectrum, and treatment recommendations provided by doctors were analyzed. It was established that the early signs of allergic rhinitis and rhinoconjunctivitis are nasal congestion, runny nose, and lacrimation, and the onset of atopic dermatitis is -characterized by dry and itchy skin. Results: Allergy to triggers from one group was present in 136 (50.4%) patients, irritants from two groups – in 95 (35.2%) patients, and irritants from three groups – in 39 (14.4%) patients. Polish doctors advised patients to limit their exposure to allergens but did not give recommendations for concrete actions. Conclusion: The results of the study can be used to help in the identification of the most common symptoms of allergic diseases and allergens, which is vital for the early diagnosis of this pathology by clinicians.

Improved stochastic fractal search algorithm involving design operators for solving parameter extraction problems in real-world engineering optimization problems

The characteristic parameters of renewable energy sources (RESs) are changes over time under changing operating conditions. Therefore, the accuracy of the model is important in order to establish appropriate control and operating plans for the stable operation of such systems. Determining the parameters of RESs and creating an accurate model of them is an inevitable reality and plays an important role in the success of system modeling. Many methods have been used to determine these parameters in the literature. Furthermore, optimization algorithms are now considered to be among the most significant approaches for resolving problems pertaining to system parameter extraction in many scientific areas. In the study, this parameter extraction is done by a new optimization method called stochastic fractal search algorithm (FDB-NSM-SFS-OBLs), which includes natural survivor updating (NSM) and fitness distance balance (FDB) guiding mechanisms, and opposite based learning (OBL) methods. The performance of the proposed FDB-NSM-SFS-OBL algorithm has been tested in two different experimental studies. In the first experimental study, the proposed approach is tested on CEC2020 benchmark test functions and the algorithm structure containing the best OBL approach is determined using nonparametric Wilcoxon and Friedman statistical analysis methods. The second experimental study is carried out with the proposed optimization algorithm to estimate the parameters of the photovoltaic cell used in the modeling, which is the building block of photovoltaic panels used in renewable energy systems, proton-exchange membrane fuel cell (PEMFC) that is another renewable energy source, and Li-Ion battery used as a backup power unit. The FDB-NSM-SFS-OBL algorithm, which is used both in the CEC2020 benchmark test functions and in determining the parameters of photovoltaic (PV) cells, PEMFC and Li-Ion batteries, performed better in searching and finding the global solution point during the optimization process.

Exploring coagulation parameters as predictive biomarkers of Plasmodium infection: A comprehensive analysis of coagulation parameters.

Malaria affects the intravascular environment, leading to abnormal coagulation activation, prolonged prothrombin time, and activated partial thromboplastin time. Despite the high prevalence of malaria in the study area, there has been little published research on the effects of Plasmodium infection on coagulation parameters. The aim was to assess the effect of malaria on basic coagulation parameters among patients attending Dembia Primary Hospital and Makisegnit Health Center. A cross-sectional study was carried out from January to March 2020. The study involved 120 participants. Blood specimens were collected, which were analyzed using a Huma Clot Due Plus analyzer. The collected data were entered into EpiData and exported to SPSS version 21 for analysis. Non-parametric statistical methods were employed to analyze the data. The results were considered statistically significant if the p-value was less than 0.05. Individuals infected with Plasmodium exhibit coagulation disorders with elevated levels of PT (Prothrombin Time), APTT (Activated Partial Thromboplastin Time), and INR (International Normalization Ratio) in comparison to healthy controls. The median PT, APTT, and INR values for infected cases were measured at 20.5 [8.6], 39.5 [17.9], and 1.8 [0.9], respectively, while healthy controls had measurements of 15.1 [2.5], 28.8 [8.3], and 1.3 [0.2] (p ≤ 0.001). The severity of coagulation disorders increased with an increase in parasitemia levels. The type of Plasmodium species present had a significant impact on PT and INR values (p ≤ 0.001), whereas APTT did not show any significant impact across the Plasmodium species (p > 0.05). The results of this study found that malaria has a substantial impact on various blood clotting parameters, including PT, APTT, and INR. Parasitemia severity is significantly associated with extended PT and INR, implying that the higher the parasitemia, the longer it takes for blood to clot. Furthermore, the study discovered that the PT and INR levels differed based on the type of Plasmodium species responsible for the infection.