A comprehensive analysis of diabetic patient data before and during the COVID-19 pandemic - Lessons from the MÉRY diabetes database (MDD).

AIMS: Our study examined changes in average blood glucose levels (ABG), measurement frequency (MF), and data uploading (DU) before and during the COVID-19 pandemic in 882-day spans, which were divided into further 20-week intervals to highlight the pandemic's impact. METHODS: T-Tests assessed the statistical significance of blood glucose data from 26,655/20,936 patients and 19.5/16.6 million records during pre-COVID/COVID. RESULTS: During COVID, patients had significantly lower ABG levels (9.1/8.9 mmol/L, p < 0.001). Weekly DU decreased (155,945/128,445, p < 0.05), while daily MF increased (0.83/0.87, p < 0.001). Comparing the last 20 weeks pre-COVID to the first 20 weeks during COVID, ABG levels were lower (9.0 /8.9, p < 0.01), MF increased (0.83 /0.99, p < 0.001), and DU decreased (153,133/145,381, p < 0.05). In the initial 20 weeks of COVID compared to the second 20 weeks of COVID, ABG increased (8.9/9.1, p < 0.01), MF decreased (0.99/0.95, p < 0.001), and DU decreased (145,381/140,166, p < 0.05). Our most striking observation was the temporary dramatic fall in glucose uploads during the first few weeks of COVID. The changes of ABG and MF values were statistically significant, but were not deemed clinically relevant. CONCLUSIONS: Despite COVID's prolonged impact, diabetic patients showed improved attitudes. A significant drop in data uploads occurred during the first 20 weeks of COVID; home office and lockdowns apparently disrupted patient routines.

Psychometric Properties of the Knowledge of Hydration among Foreign Students of Óbuda University, Hungary.

It is known that the quantity, makeup, and distribution of bodily fluids have a significant impact on the cognitive health, physiological health, and cell activity of human beings. This narrative could be influenced by the level of knowledge about hydration, dehydration, and the practice of Adequate Water Intake (AWI) of an individual based on the recommended daily Total Water Intake (TWI) by either the World Health Organization (WHO) or the European Food Safety Authority (EFSA). In this study, we have developed and validated a scale to adequately measure knowledge of the recommended daily Total Water Intake (TWI) practices among foreign students at Óbuda University, Hungary. Hence, we implemented an 11-item scale to measure the Knowledge of Hydration (KH-11) and evaluate its psychometric properties among students. This study is an online cross-sectional study assessing water intake knowledge with the use of the KH-11 tool among 323 students with ages ranging from 18 to 35 years, who have enrolled for at least two semesters at the University. The statistical analysis performed was reliability (using Cronbach alpha ≥ 70%) and factor analysis. Knowledge levels were categorized as poor (<50%), intermediate (50-70%), or adequate (71-100%). The intraclass correlation, chi-square, and rotated component matrix were also estimated and reported. Data were analyzed using SPSS version 25. Cronbach's alpha analysis revealed that the KH-11 had an overall good reliability with a value of 0.80, where the survey items had an acceptable level of consistency ranging from 0.75 to 0.81 and demonstrated sufficient independence from each other as Pearson's R within factors was positive and ranged from 0.02 to 0.74. In evaluating the participants' knowledge of hydration, the total possible score for the scale is 72, while the mean score for the KH-11 was 55.2 ± 11.61 SD, and the factor analysis model yielded an acceptable fit (χ2 = 3259.4, p = 0.000). We recorded a high-level positive concordance of 0.770 with an average intraclass correlation of 0.80 at a 95% CI, where p-value = 0.000. Our findings show that the majority (66.3%) of the students have a good knowledge of hydration. However, the skewed distribution of the knowledge scores suggests that some may have lower levels of knowledge, which may warrant further study to improve knowledge in those students.

Factors Associated with HPV Genital Warts: A Self-Reported Cross-Sectional Study among Students and Staff of a Northern University in Nigeria.

The menace of human papillomavirus (HPV) infections among low- and middle-income countries with no access to a free HPV vaccine is a public health concern. HPV is one of the most common sexually transmitted infections (STIs) in Nigeria, while the most known types of HPV genotypes being transmitted are the high-risk HPV-16 and 18 genotypes. In this study, we explored the predictors of self-reported HPV infections and HPV genital warts infection among a population of students, non-academic staff, and academic staff of Ibrahim Badamasi Babangida (IBB) University located in Lapai, Nigeria. We also assessed their knowledge about HPV infections and genotypes, and sexual behaviors. An online cross-sectional study was conducted by setting up a structured questionnaire on Google Forms and it was distributed to the university community via Facebook and other social media platforms of the university. The form captured questions on HPV infection, and knowledge about HPV infection and genotypes, as well as the sexual health of the participants. All variables were described using frequencies and percentage distribution; chi-squared test statistics were used to explore the association between HPV infection (medical records of HPV infection) and the participants' profile, and a logistic regression analysis was performed to examine the factors associated with HPV genital warts infection among the population. This study reveals those participants between the ages of 26-40 years (81.3%) and those currently not in a sexually active relationship-single/divorced (26.4%)-who have self-reported having the HPV-16 and -18 genotypes. Moreover, participants between 26-40 years of age (OR: 0.45, 95%CI: 0.22-0.89) reported themselves to be carriers of HPV genital warts. Therefore, this study reveals the factors associated with HPV infection and genital warts peculiar to IBB university students and staff. Hence, we suggest the need for HPV awareness programs and free HPV vaccine availability at IBB university.

Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey.

BACKGROUND: The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences. METHODS: A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist's and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents' electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions. RESULTS: Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (p = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task (p = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen. CONCLUSIONS: Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.

Assessment of HPV Knowledge and Awareness among Students and Staff at IBB University, Niger State, Nigeria: Implications for Health Education and Prevention.

In Nigeria, statistics reveal that there is a high rate of cervical cancer among women and a significant lack of awareness surrounding Human Papillomavirus (HPV), which poses a substantial risk of HPV infection. This cross-sectional survey, conducted at Ibrahim Badamasi Babangida (IBB) University, focuses on adapting and exploring the factors that influence a 20-item scale to measure HPV knowledge, evaluating knowledge-associated patterns and HPV-associated risk factors. We examined HPV vaccination rates, infection awareness, vaccine awareness, and the impact of ethnicity on HPV knowledge. Various validated forms were adapted to measure HPV awareness and knowledge. Non-parametric tests addressed non-normality. Data were presented using median and IQR and categorical data were frequency-based. Bivariate tests (Mann-Witney, Kruskal Wallis) explored knowledge-associated factors, while quantile regression (75th percentile) examined HPV knowledge factors. Variables were considered statistically significant at p < 0.05. The adapted 20-item knowledge scale revealed strong reliability (Cronbach's alpha = 0.913), ensuring internal consistency. The median knowledge score was 0, with an interquartile range (IQR) of 0-5. Our findings revealed a significant lack of awareness and knowledge about HPV; only 34.8% of the population were aware of HPV infection and 25.0% were familiar with HPV vaccination. Furthermore, ethnicity was found to be significantly associated with knowledge of HPV. This study emphasizes the necessity for targeted interventions to enhance HPV awareness, especially within specific ethnic groups. Despite a robust knowledge scale, educational initiatives such as seminars/conferences about HPV and cervical cancer remain crucial in addressing this gap, ultimately reducing HPV infection and cervical cancer risks in Nigeria.

Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems.

As the field of routine pathology transitions into the digital realm, there is a surging demand for the full automation of microscope scanners, aiming to expedite the process of digitizing tissue samples, and consequently, enhancing the efficiency of case diagnoses. The key to achieving seamless automatic imaging lies in the precise detection and segmentation of tissue sample regions on the glass slides. State-of-the-art approaches for this task lean heavily on deep learning techniques, particularly U-Net convolutional neural networks. However, since samples can be highly diverse and prepared in various ways, it is almost impossible to be fully prepared for and cover every scenario with training data. We propose a data augmentation step that allows artificially modifying the training data by extending some artifact features of the available data to the rest of the dataset. This procedure can be used to generate images that can be considered synthetic. These artifacts could include felt pen markings, speckles of dirt, residual bubbles in covering glue, or stains. The proposed approach achieved a 1-6% improvement for these samples according to the F1 Score metric.

How to evaluate over 60 million blood glucose data - The design of the MÉRY Diabetes Database.

AIMS: The aim of the article is to describe the method for creating a close to ideal diabetes database. The MÉRY Diabetes Database (MDD) consists of a large quantity of reliable, well-maintained, precise and up-to-date data suited for clinical research with the intention to improve diabetes care in terms of maintaining targeted blood glucose levels, avoiding hypoglycemic episodes and complications and improving patient compliance and quality of life. METHODS: Based on the analysis of the databases found in the literature and the experience of our research team, nine important characteristics were identified as critical to an ideal diabetes database. The data for our database is collected using MÉRYkék glucometers, a device that meets all requirements of international regulations and measures blood glucose levels within the normal range with appropriate precision (10 %). RESULTS: Using the key characteristics defined, we were able to create a database suitable for the analysis of a large amount of data regarding diabetes care and outcomes. CONCLUSIONS: The MDD is a reliable and ever growing database which provides stable and expansive foundation for extensive clinical investigations that hold the potential to significantly influence the trajectory of diabetes care and enhance patient outcomes.

Epidemiology and patients' self-reported knowledge of implantable medical devices: Results of a cross-sectional survey in Hungary.

BACKGROUND: Implantable medical devices (IMDs) are medical instruments embedded inside the body. Well-informed and empowered patients living with IMDs are key players of improving IMD-related patient safety and health outcomes. However, little is known about IMD patients' epidemiology, characteristics, and current awareness levels. Our primary aim was to investigate the point and lifetime prevalence of patients living with IMDs. Patients' IMD-related knowledge and determinants of IMDs' impact on their life were also explored. METHODS: An online cross-sectional survey was conducted. Respondents' IMD history, whether they received instructions for use and IMD's overall impact on life were recorded by self-reports. Patients' knowledge about living with IMDs was assessed on visual analogue scales (VAS, 0-10). Shared decision-making was analyzed by the 9-item Shared Decision Making Questionnaire (SDM-Q-9). Descriptive statistics and subgroup comparisons between IMD wearers were performed for statistical differences. Significant determinants of IMD's overall impact on life were examined in linear regression analysis. RESULTS: In the total sample (N = 1400, mean age 58.1 ±11.1; female 53.7%), nearly one third of respondents were living with IMD (30.9%; 433/1400). Among them, the most frequent IMDs were tooth implants (30.9%) and intraocular lens (26.8%). Mean knowledge VAS scores were similar (range: 5.5 ±3.8-6.5 ±3.2) but differences by IMD types were observed. Patients who received instructions for use or reported better impact on life indicated higher self-reported knowledge. Regression confirmed that patients' knowledge was significant predictor of IMD's impact on life, but this effect was overwritten by the SDM-Q-9. CONCLUSIONS: This first comprehensive epidemiological study on IMDs provides basic data for public health strategy planning alongside the implementation of MDR. Improved self-perceived outcomes were associated with higher knowledge hence education of patients receiving IMD deserves consideration. We suggest to investigate further the role of shared decision-making on IMD's overall impact on patients' life in future prospective studies.

Calibration-Aimed Comparison of Image-Cytometry- and Flow-Cytometry-Based Approaches of Ploidy Analysis.

Ploidy analysis is the fundamental method of measuring DNA content. For decades, the principal way of conducting ploidy analysis was through flow cytometry. A flow cytometer is a specialized tool for analyzing cells in a solution. This is convenient in laboratory environments, but prohibits measurement reproducibility and the complete detachment of sample preparation from data acquisition and analysis, which seems to have become paramount with the constant decrease in the number of pathologists per capita all over the globe. As more open computer-aided systems emerge in medicine, the demand for overcoming these shortcomings, and opening access to even more (and more flexible) options, has also emerged. Image-based analysis systems can provide an alternative to these types of workloads, placing the abovementioned problems in a different light. Flow cytometry data can be used as a reference for calibrating an image-based system. This article aims to show an approach to constructing an image-based solution for ploidy analysis, take measurements for a basic comparison of the data produced by the two methods, and produce a workflow with the ultimate goal of calibrating the image-based system.

Regression Based Iterative Illumination Compensation Method for Multi-Focal Whole Slide Imaging System.

Image quality, resolution and scanning time are critical in digital pathology. In order to create a high-resolution digital image, the scanner systems execute stitching algorithms to the digitized images. Due to the heterogeneity of the tissue sample, complex optical path, non-acceptable sample quality or rapid stage movement, the intensities on pictures can be uneven. The evincible and visible intensity distortions can have negative effect on diagnosis and quantitative analysis. Utilizing the common areas of the neighboring field-of-views, we can estimate compensations to eliminate the inhomogeneities. We implemented and validated five different approaches for compensating output images created with an area scanner system. The proposed methods are based on traditional methods such as adaptive histogram matching, regression-based corrections and state-of-the art methods like the background and shading correction (BaSiC) method. The proposed compensation methods are suitable for both brightfield and fluorescent images, and robust enough against dust, bubbles, and optical aberrations. The proposed methods are able to correct not only the fixed-pattern artefacts but the stochastic uneven illumination along the neighboring or above field-of-views utilizing iterative approaches and multi-focal compensations.

Increase of Short-Term Heart Rate Variability Induced by Blood Pressure Measurements during Ambulatory Blood Pressure Monitoring.

Objective. The possible effect of blood pressure measurements per se on heart rate variability (HRV) was studied in the setting of concomitant ambulatory blood pressure monitoring (ABPM) and Holter ECG monitoring (HM). Methods. In 25 hypertensive patients (14 women and 11 men, mean age: 58.1 years), 24-hour combined ABPM and HM were performed. For every blood pressure measurement, 2-minute ECG segments (before, during, and after measurement) were analyzed to obtain time domain parameters of HRV: SDNN and rMSSD. Mean of normal RR intervals (MNN), SDNN/MNN, and rMSSD/MNN were calculated, too. Parameter variations related to blood pressure measurements were analyzed using one-way ANOVA with multiple comparisons. Results. 2281 measurements (1518 during the day and 763 during the night) were included in the analysis. Both SDNN and SDNN/MNN had a constant (the same for 24-hour, daytime, and nighttime values) and significant change related to blood pressure measurements: an increase during measurements and a decrease after them (p < 0.01 for any variation). Conclusion. In the setting of combined ABPM and HM, the blood pressure measurement itself produces an increase in short-term heart rate variability. Clarifying the physiological basis and the possible clinical value of this phenomenon needs further studies.

[Big Data- challenges and risks]. / Big Data - kihívások és kockázatok.

The term "Big Data" is commonly used to describe the growing mass of information being created recently. New conclusions can be drawn and new services can be developed by the connection, processing and analysis of these information. This affects all aspects of life, including health and medicine. The authors review the application areas of Big Data, and present examples from health and other areas. However, there are several preconditions of the effective use of the opportunities: proper infrastructure, well defined regulatory environment with particular emphasis on data protection and privacy. These issues and the current actions for solution are also presented.