Data Entry Research Articles

Predicting High-Strength Concrete's Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology.

Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict the compressive strength of high-strength concrete (HSC) using different methods. To achieve this purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), and response surface methodology (RSM) were used as ensemble methods. Using an ANN and ANFIS, high-strength concrete (HSC) output was modeled and optimized as a function of five independent variables. The RSM was designed with three input variables: cement, and fine and coarse aggregate. To facilitate data entry into Design Expert, the RSM model was divided into six groups, with p-values of responses 1 to 6 of 0.027, 0.010, 0.003, 0.023, 0.002, and 0.026. The following metrics were used to evaluate model compressive strength projection: R, R2, and MSE for ANN and ANFIS modeling; R2, Adj. R2, and Pred. R2 for RSM modeling. Based on the data, it can be concluded that the ANN model (R = 0.999, R2 = 0.998, and MSE = 0.417), RSM model (R = 0.981 and R2 = 0.963), and ANFIS model (R = 0.962, R2 = 0.926, and MSE = 0.655) have a good chance of accurately predicting the compressive strength of high-strength concrete (HSC). Furthermore, there is a strong correlation between the ANN, RSM, and ANFIS models and the experimental data. Nevertheless, the artificial neural network model demonstrates exceptional accuracy. The sensitivity analysis of the ANN model shows that cement and fine aggregate have the most significant effect on predicting compressive strength (45.29% and 35.87%, respectively), while superplasticizer has the least effect (0.227%). RSME values for cement and fine aggregate in the ANFIS model were 0.313 and 0.453 during the test process and 0.733 and 0.563 during the training process. Thus, it was found that both ANN and RSM models presented better results with higher accuracy and can be used for predicting the compressive strength of construction materials.

Electronic health record system use and documentation burden of acute and critical care nurse clinicians: a mixed-methods study.

Examine electronic health record (EHR) use and factors contributing to documentation burden in acute and critical care nurses. A mixed-methods design was used guided by Unified Theory of Acceptance and Use of Technology. Key EHR components included, Flowsheets, Medication Administration Records (MAR), Care Plan, Notes, and Navigators. We first identified 5 units with the highest documentation burden in 1 university hospital through EHR log file analyses. Four nurses per unit were recruited and engaged in interviews and surveys designed to examine their perceptions of ease of use and usefulness of the 5 EHR components. A combination of inductive/deductive coding was used for qualitative data analysis. Nurses acknowledged the importance of documentation for patient care, yet perceived the required documentation as burdensome with levels varying across the 5 components. Factors contributing to burden included non-EHR issues (patient-to-nurse staffing ratios; patient acuity; suboptimal time management) and EHR usability issues related to design/features. Flowsheets, Care Plan, and Navigators were found to be below acceptable usability and contributed to more burden compared to MAR and Notes. The most troublesome EHR usability issues were data redundancy, poor workflow navigation, and cumbersome data entry based on unit type. Overall, we used quantitative and qualitative data to highlight challenges with current nursing documentation features in the EHR that contribute to documentation burden. Differences in perceived usability across the EHR documentation components were driven by multiple factors, such as non-alignment with workflows and amount of duplication of prior data entries. Nurses offered several recommendations for improving the EHR, including minimizing redundant or excessive data entry requirements, providing visual cues (eg, clear error messages, highlighting areas where missing or incorrect information are), and integrating decision support. Our study generated evidence for nurse EHR use and specific documentation usability issues contributing to burden. Findings can inform the development of solutions for enhancing multi-component EHR usability that accommodates the unique workflow of nurses. Documentation strategies designed to improve nurse working conditions should include non-EHR factors as they also contribute to documentation burden.

Linking community-acquired images to national data in the absence of interoperable systems and unique identifiers

Objective & ApproachRoutine optometry examinations, which may include retinal images, have been free to everyone in Scotland since 2006. Images and patient data are stored locally on non-interoperable systems, usually without unique identifiers. The Scottish Collaborative Optometry-Ophthalmology Network for e-research (SCONe) aimed to create a secure, longitudinal repository collating community-acquired images linked to national, routinely-collected healthcare data. We developed bulk export processes to retrieve images and patient data from current and archived systems and a series of deterministic and probabilistic algorithms to match images to patients. ResultsWe successfully exported images and patient data from several commonly used systems. Exact matching of available data between image and clinical systems within practices linked 78% of images (IQR 71.2-84.1). Our algorithms increased match rate to 90% (IQR 83.5-98.6) of images, efficiently reducing risk of systemic bias in the cohort. Match method is included in the repository metadata to facilitate quality assurance checks after national linkage and pseudonymisation. To date over 600k images have been processed and delivered to the National Safe Haven for linkage to healthcare data. Conclusions & ImplicationsBreakthrough research into early detection and pre-symptomatic disease can be facilitated by harnessing community-acquired retinal images which capture normal ageing and disease progression. Rather than await interoperability and widespread use of unique identifiers, this has been achieved through innovative solutions to real-world issues around time-pressured clinical data entry and non-standardised data storage. The SCONe repository continues to grow. It is currently supporting ocular disease prediction research and offers the potential for much more.

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems. This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits. A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention. This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions.

Insights from ARCOS-V’s Transition to Remote Data Collection during the COVID-19 Pandemic: A Descriptive Study

Introduction: The ARCOS-V study, an epidemiological study on stroke and transient ischaemic attack (TIA), faced the challenge of continuing data collection amidst the COVID-19 pandemic. This study aimed to describe the methodological changes and challenges encountered during the transition from paper-based methods to digital data collection for the ARCOS-V study and to provide insights into the potential of using digital tools to transform epidemiological research. Methods: The study adapted to remote data collection using REDCap and Zoom, involving daily health record reviews, direct data entry by trained researchers, and remote follow-up assessments. The process was secured with encryption and role-based access controls. The transition period was analysed to evaluate the effectiveness and challenges of the new approach. Results: The digital transition allowed for uninterrupted monitoring of stroke and TIA cases during lockdowns. Using REDCap and Zoom improved data reach, accuracy, and security. However, it also revealed issues such as the potential for systematic data entry errors and the need for robust security measures to protect sensitive health information. Conclusion: The ARCOS-V study’s digital transformation exemplifies the resilience of epidemiological research in the face of a global crisis. The successful adaptation to digital data collection methods highlights the potential benefits of such tools, particularly as we enter a new age of artificial intelligence (AI).

Disabled Individuals’ Negative Perceptions of Mobile Health Applications: Design Guidance for HFE Practitioners

Disabled individuals experience significant inequities in healthcare access, healthcare quality, social determinants of health, and health outcomes compared to non-disabled individuals. By including people with disabilities in the co-design of mobile health (MHealth) applications, we gain valuable insights to improve alignment between users’ needs and MHealth technologies. Through a secondary analysis of mixed-methods data, this paper investigates negative perceptions of disabled individuals regarding MHealth applications to inform MHealth application design guidance for human factors practitioners. Fifty participants with physical, cognitive, sensory, and/or mental health-related disabilities interacted with three MHealth applications during task analysis sessions, revealing concerns about data security, data privacy, and unnecessary patient work. Recommendations include clear articulation of security measures, clarifications that patient profiles are not publicly available, and simplified data entry processes. Implementing these recommendations may enhance the accessibility and usefulness of MHealth applications, promoting healthcare access for a broader range of individuals.

Time to death and its determinant factors of stroke patients at Gambella General Hospital, Gambella, Ethiopia

Background and purposeA stroke or a cerebrovascular accident is a common cause of death and a leading cause of long-term, severe disability in both developed and developing countries. The most recent global burden of disease report states that there were 11.9 million new cases of stroke worldwide; stroke accounts for nearly 1 in 8 deaths globally (12%, 6.5 million deaths) and claims a life every 5 s, making it the second most common cause of death worldwide. The goal of the study was to identify the most important factors influencing stroke patients' time to death at Gambella General Hospital.MethodsData was gathered from patient files in a hospital using a retrospective study methodology, spanning the period from September 2018 to September 2020. R 3.4.0 statistical software and STATA version 14.2 were used for data entry and analysis. The survival time was compared using the log-rank tests and the Kaplan–Meier survival curve. The fitness of the Cox proportional hazard model was examined.ResultsThe final model that was fitted was the log-logistic AFT model. A statistically significant correlation was defined as having a p value of less than 0.05 and the accelerated factor (γ) with its 95% confidence interval was employed. Eight days was the total median death time (95% CI 6–10). Significant predictors for shortened mortality time were age (γ = 0.94; 95% CI (0.0.920–0.980), hypertension (γ = 0.63; 95% CI (0.605–0.660), and baseline complications (γ = 0.24; 95% CI (0.223–0.256).ConclusionsThe shortened timing of death was significantly predicted by age, hypertension, and baseline complications. In light of the study's findings, health administrators and caregivers should work to improve society's overall health.

Bangla Optical Character Recognition for Mobile Platforms: A Comprehensive Cross-Platform Approach

The development of Optical Character Recognition (OCR) systems for Bangla script has been an area of active research since the 1980s. This study presents a comprehensive analysis and development of a cross-platform mobile application for Bangla OCR, leveraging the Tesseract OCR engine. The primary objective is to enhance the recognition accuracy of Bangla characters, achieving rates between 90% and 99%. The application is designed to facilitate the automatic extraction of text from images selected from the device's photo library, promoting the preservation and accessibility of Bangla language materials. This paper discusses the methodology, including the preparation of training datasets, preprocessing steps, and the integration of the Tesseract OCR engine within a Dart programming environment for cross-platform functionality. This integration provides that the application could be introduced on mobile platforms without substantial alterations. The results demonstrate significant improvements in recognition accuracy, making this application a valuable tool for various practical applications such as data entry for printed Bengali documents, automatic recognition of Bangla number plates, and the digital archiving of vintage Bangla books. These improvements are crucial to further enhance the usability and reliability of Bangla OCR on mobile devices. Our cross-platform method for Bangla OCR on mobile devices provides a strong solution with exceptional identification accuracy, which helps in preserving and making Bangla language information accessible in digital format. This study has significant implications for future research and advancement in the field of optical character recognition (OCR) for intricate writing systems, especially in mobile settings.

Home literacy environment mediated socioeconomic status’ impact on handwriting skill of children with intellectual disabilities

Objectives Mastering Chinese handwriting is a fundamental skill, yet it poses a challenging task for children with intellectual disability (ID). Socioeconomic status (SES) and home literacy environment (HLE) are two important family factors known to influence children’s academic achievement. This study aimed to explore the HLE’s role in SES’ influence on Chinese handwriting skill of children with ID. Methods Chinese handwriting skill was tested directly on 192 children with ID (7-12 years old), while SES and HLE questionnaires were completed by their parents. Data entry, analysis and structural equation modeling were performed using the Statistical Package for Social Sciences software. Results SES was shown not directly correlated with any of the three handwriting skills (β = 0.02, p > 0.05). Instead, the former influenced the latter indirectly via HLE; informal HLE played a complete intermediary role in this relationship (SES-informal HLE: β = 0.42, p < 0.001; informal HLE-handwriting skill: β = 0.41, p < 0.001), while formal HLE had no effect (SES-formal HLE: β = 0.34, p < 0.001; formal HLE-handwriting skill: β=-0.10, p > 0.05). Conclusions SES influences Chinese handwriting of children with ID via mediation by informal HLE. These findings emphasize the importance of maintaining high SES and creating rich HLE in the development of Chinese handwriting skill of children with ID.

Data quality assessment of interventional trials in public trial databases

ObjectiveHigh-quality data entry in clinical trial databases is crucial to the usefulness, validity, and replicability of research findings, as it influences evidence-based medical practice and future research. Our aim is to assess the quality of self-reported data in trial registries and present practical and systematic methods for identifying and evaluating data quality. Study Design and SettingWe searched ClinicalTrials.Gov (CTG) for interventional total knee arthroplasty (TKA) trials between 2000 and 2015. We extracted required and optional trial information elements and used the CTG's variables' definitions. We performed a literature review on data quality reporting on frameworks, checklists, and overviews of irregularities in healthcare databases. We identified and assessed data quality attributes as follows: consistency, accuracy, completeness, and timeliness. ResultsWe included 816 interventional TKA trials. Data irregularities varied widely: 0%–100%. Inconsistency ranged from 0% to 36%, and most often nonrandomized labeled allocation was combined with a “single-group” assignment trial design. Inaccuracy ranged from 0% to 100%. Incompleteness ranged from 0% to 61%; 61% of finished TKA trials did not report their outcome. With regard to irregularities in timeliness, 49% of the trials were registered more than 3 months after the start date. ConclusionWe found significant variations in the data quality of registered clinical TKA trials. Trial sponsors should be committed to ensuring that the information they provide is reliable, consistent, up-to-date, transparent, and accurate. CTG's users need to be critical when drawing conclusions based on the registered data. We believe this awareness will increase well-informed decisions about published articles and treatment protocols, including replicating and improving trial designs.

Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records.

In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection in medical applications remains underdeveloped. We address this deficiency by introducing a novel tool for identifying and correcting anomalies specifically in dense functional EMR data. Our approach utilizes studentized residuals from a mean-shift model, and therefore assumes that the data adheres to a smooth functional trajectory. Additionally, our method is tailored to be conservative, focusing on anomalies that signify actual errors in the data collection process while controlling for false discovery rates and type II errors. To support widespread implementation, we provide a comprehensive R package, ensuring that our methods can be applied in diverse settings. Our methodology's efficacy has been validated through rigorous simulation studies and real-world applications, confirming its ability to accurately identify and correct errors, thus enhancing the reliability and quality of medical data analysis.

Sistem Manajemen Kepegawaian dan Pencatatan Pekerja Migran Indonesia di BP3MI Riau dengan Metode Prototipe

Balai Pelayanan Pelindungan Pekerja Migran Indonesia (BP3MI) Riau, a government agency dedicated to protecting Indonesian migrant workers. The agency faces challenges with its unstructured and inefficient data management system. The use of Microsoft Excel for documenting the data of Indonesian Migrant Workers (PMI) and employees at BP3MI Riau is prone to data entry errors, time inefficiencies, unstructured data, potential damage, limited access, restricted analysis, difficulties in tracking changes, and a high risk of data loss. To address these issues, research was conducted to develop a more effective employee management and PMI data recording system using the prototype method. This method emphasizes user collaboration in understanding system requirements, resulting in practical solutions. The prototype method stages include user requirements gathering, designing input, process, and output needs, followed by rapid UML model design, program code implementation, and finally, testing. The system development results show that users have a very positive impression from the usability testing, achieving a score of 97.76%. This indicates the effectiveness of the prototype. The system significantly improves manual methods, enhancing the efficiency, accuracy, and accessibility of data at BP3MI Riau, and lays the foundation for future advancements with greater integration and automation prospects in BP3MI Riau's operations.

Prevalence of Asthma and Its Associated Risk Factors Among Children Aged 5-10 Years in a Rural Area of Coimbatore: A Community-Based Cross-Sectional Study.

Introduction Asthma is one of the serious health problems among children, accounting for a major proportion of lost school days due to absenteeism, decreased academic performances, and lesser social interactions. It can impair a child's activities like playing sports and also their sleeping pattern with frequent school absenteeism. In South India, only a few school-based studies to determine the prevalence of asthma have been carried out, and even fewer studies are done in community settings in rural areas of Tamil Nadu. Hence, this community-based study was done to find out the prevalence of asthma among children aged 5-10 years in rural areas of Coimbatore, Tamil Nadu. Aim & objective To estimate the prevalence of asthma among children aged 5-10 years in rural areas of Coimbatore, Tamil Nadu. Materials and methods A community-based cross-sectional study was conducted in rural areas of Coimbatore, Tamil Nadu. A sample of 726 children from the randomly selected seven villages were studied. The sample size was calculated for a finite population. The standardized International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire and calibrated instruments were used as data collection tools. Ethical approval was obtained. Data entry was done in Microsoft Excel and analyzed using the SPSS 19.0 version. The "t" test was used to compare the mean difference between the quantitative variables and the chi-square test was used to test the association between categorical variables. P-value <0.05 was considered as statistically significant. Results The overall prevalence of self-reported asthma was 5.1%. Boys had 1.7 times higher prevalence compared to girls. The prevalence of possible or suspected cases of asthma using the ISAAC screening questionnaire was 20.9%. In univariate analysis, the risk of having asthma among children is significantly higher among those with a positive family history of asthma. Conclusion The study found that the prevalence of self-reported asthma in rural settings is low (5.1%). However, our study also identifies a large number of children (20.9%) with symptoms suggestive of asthma using the ISAAC questionnaire that need the attention of a specialist for early diagnosis and care for reducing morbidity and episode severity.

Application for Tridimensional Renal Visualization and Operative Planning

Background: Partial nephrectomy is the preferred surgical approach for renal neoplasms smaller than 7 cm, without compromising disease-free or cancer-specific survival. It is increasingly being performed in patients with complex renal tumors. Three-dimensional models of preoperative computed tomography (CT) or magnetic resonance imaging (MRI) scans are the future for accurately identifying the tumor's location and planning the surgical strategy. Objectives: This study aims to develop an application for use by medical professionals, designed to view three-dimensional (3D) renal images with radius, extension, nearness, anterior/posterior, and lines scores from preoperative CT or MRI exams. Methods: This descriptive study focuses on developing a mobile application, which was entirely created by a urologist on a no-code platform without using programming languages. The renal 3D images were generated from the patient's specific CT or MRI scans. A dedicated database was also utilized for the application. Results: The development of the NeoRenal application resulted in ten complexly designed screens created within a no-code platform, each serving a specific purpose in a coherent sequence to ensure an optimal user experience. The uniform flow and design of these screens aim to provide intuitive and comprehensive user acceptance, facilitating registration, data entry, visualization, and editing functionalities. Conclusions: The application was successfully developed and is ready to be published on various operating systems.

Analysis of excessive corticosteroid use as an indicator of poor quality of care in patients with inflammatory bowel disease

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Aim &amp;lt;/strong&amp;gt;To evaluate the clinical impact of corticosteroids (CS) overuse in inflammatory bowel disease (IBD) patients. Excessive use of CS could delay more efficacious treatment and may indicate poor quality of care.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Method &amp;lt;/strong&amp;gt;This is a two-phase study that used Steroid Assessment Tool (SAT) to measure corticosteroid exposure in IBD patients. In the first phase data from 211 consecutive ambulatory patients with IBD (91 with ulcerative colitis, 115 with Crohn&amp;#039;s disease, and five with unclassified inflammatory bowel disease) were analyzed by SAT. In the second phase, one year after data entry, clinical outcome of patients with cortico-steroids overuse was analysed.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Results &amp;lt;/strong&amp;gt;Of the 211 IBD patients, 132 (62%) were not on corticosteroids, 45 (22%) were cortico-steroid-dependent and 34 (16%) used corticosteroids appropriately, according to the European Crohn&amp;#039;s and Colitis Organization guidelines. In the group of patients with ulcerative colitis, 57 (63%) were not on cortico-steroids, 18 (20%) were corticosteroid-dependent, and 16 (16%) used cortico-steroids appropriate-ly; in the group of patients with Crohn&amp;#039;s disease 70 (61%), 27 (23%) and 18 (16%), respectively. Overall, 24 (out of 45; 53%) patients with IBD could avoid the overuse of cortico-steroids if they had a timely change of the treatment, surgery or entered a clinical trial.&amp;lt;br /&amp;gt;&amp;lt;strong&amp;gt;Conclusion &amp;lt;/strong&amp;gt;An excessive corticosteroid use can be recognized on time using the SAT. We have proven that excessive corticosteroid use could be avoided in almost half of cases and thus the overuse of CS may indicate poor quality of care in those patients.&amp;lt;/p&amp;gt;

Food Safety Knowledge, Attitudes, and Practices Among Food Handlers in Jeddah, Saudi Arabia

Background: Several outbreaks of foodborne illness have recently occurred in Saudi Arabia, posing a public health concern, especially considering the increasing trend of food consumption at restaurants. Objectives: This study evaluates the food safety knowledge, attitudes, and self-reported practices among food handlers in Jeddah, Saudi Arabia, and their association with the food handlers’ sociodemographic characteristics. Methods: A cross-sectional study was performed for a convenience sample of 80 food handlers (15–65 years old), followed by face-to-face interviews. A validated questionnaire with 65 questions across four sections (sociodemographic characteristics, knowledge, attitudes, and practice related to food safety) was utilized for data collection. Data entry and analysis were conducted using the SPSS version 26 software. Results: Most of the participants exhibited moderate levels of food safety knowledge and practices and a high level of food safety attitudes. Food safety knowledge was significantly greater among the oldest participants (&gt;45 years old), those holding valid health cards, and those who had attended training courses (p&lt;0.05). However, no significant differences in food safety attitudes and practices were found based on the sociodemographic characteristics of the participants (p&gt;0.05). Food safety knowledge was found to be significantly correlated with the food safety attitudes of the participants (r= 0.379, p= 0.001), and food safety attitudes were significantly correlated with practices (r= 0.264, p= 0.018). Conclusion: This study revealed that continuous implementation of food safety educational and training programs is necessary to improve the knowledge of food handlers and transform it into appropriate attitudes and practices.

Preliminary Results of Iran Premature Coronary Artery Disease (IPAD study) and Implementation of IPAD Biobank: A Case-Control Study.

Iran Premature Coronary Artery Disease (IPAD) is one of the first and largest studies of its kind in Asia that investigates different aspects of premature coronary artery disease (PCAD) in different ethnic groups in multiple cities. In this paper, we aim to describe the IPAD biobank establishment and present some preliminary results of the IPAD study. This case-control study was conducted on patients with documented angiography from different ethnicities in more than ten cities of Iran (males aged 60 years and below and females aged 70 years and below). Patients with coronary artery stenosis of more than 75% in at least one vessel (or left-main stenosis of more than 50%) were defined as the case group and patients with normal coronary angiography were considered as the control group. In addition to completing questionnaires and performing physical measurements, samples of serum, buffy coat, plasma, whole blood, saliva, urine, and feces were stored in the freezer at -80 °C. The mean age of patients was 51.1±8.2, of which 43% were women. There were 1221 people in the control group and 1702 in the case group. Our enrollment is completed and data entry and transferring biosamples from different cities is still ongoing. About 30000 biosamples of different ethnicities are saved in the IPAD biobank. This study aims to develop a high-quality biobank and facilitate research on different aspects of PCAD, especially gene-environment interaction regarding ethnicity.

New-onset diabetes mellitus in patients with COVID19 infection admitted to a tertiary care hospital: A single-center experience.

To determine the frequency of new-onset diabetes mellitus (NODM) in patients with COVID-19 in a tertiary care hospital. It was a retrospective descriptive study carried out in Lady Reading Hospital Peshawar, Khyber Pakhtunkhwa province of Pakistan from November 2021 to April 2022. All patients having new onset Diabetes Mellitus (NODM) were identified among a total of 300 patients admitted to the hospital with COVID-19 infection. Patients' data including relevant investigations were accessed through the hospital management information system (HMIS). SPSS version-23 was used for data entry and statistical analysis. Out of 300 COVID-19 patients included in the study, 163 (54.3%) were female and 137(45.7%) were male. The mean age of the patients was 56.80±13.72 (IQR 15) years. Frequency of the new onset diabetes was 44(14.7%); 19 (6.33%) male and 25(8.33%) female. Among the 44 NODM patients, the majority (57%) were female (p=0.720). Most (64%) of the patients with new-onset DM were in the middle age (p=0.018). A significant number of patients with COVID-19 infection are prone to develop new-onset diabetes during their admission to the hospital.

Mediating role of alexithymia in relationship between cyberbullying and psychotic experiences in adolescents

BackgroundToday, addressing issues related to the use of virtual space is of paramount importance due to its significant impact on mental well-being. This is especially crucial when the research community consists of teenagers who are cyber bullies or their victims who have higher vulnerability. The aim of the present study was to investigate the mediating role of alexithymia in the relationship between cyberbullying and psychotic experiences in adolescents.MethodsThe research method employed in this study was correlational, and the study population consisted of all male and female middle school students in Tehran during the 2022–2023 academic years. As for data collection, the Cyber-Bullying/Victimization Experiences questionnaire, Community Assessment of Psychic Experiences, and the Toronto Alexithymia scale were applied. A total of 602 samples were gathered by using multi-stage cluster sampling from Tehran in Iran. Four selection of the sample, the regions in Tehran were selected randomly according to the geographical directions of them and then some schools and classes were chosen randomly. Sample was included in the analysis after data entry into SPSS software and subsequent structural equation modeling using AMOS software.ResultsAccording to the findings, cyberbullying (β = 0.11,p < 0.05) and cyber victimization(β = 0.41, p < 0.001) were significant predictors of psychotic experiences. Alexithymia partially mediated the relationship between cyberbullying and psychotic experiences with the mediation effect of 0.28 and cyber victimization and psychotic experiences with the mediation effect of 0.18.ConclusionsThese findings underscore the importance of identifying cyber victims or cyberbullies in order to prevent alexithymia and psychotic experiences in future, in order to prevent more serious problems and becoming psychotic.Trial registrationThe goals and conditions of this research were investigated and approved by the Ethics Committee of Alzahra University in Tehran (code: ALZAHRA.REC.1402.055) on 13th September 2023.

Proportion and Associated Factors of Adverse Birth Outcomes Among Women Who Gave Birth at Debre Markos General Hospital, East Gojjam Zone, Ethiopia, 2022: A Cross-Sectional Study

Background: Adverse birth outcomes are a common public health problem in the developing world, including Ethiopia. There are several indices of adverse birth outcomes, such as stillbirth, preterm birth, low birth weight, small for gestational age and congenital anomalies. Objective: This study aimed to determine the proportion and associated factors of adverse birth outcomes among women who gave birth at Debre Markos General Hospital. Methods: A health facility-based cross-sectional study was conducted from July 1 to August 1/2022 on laboring women who attended Debre Markos General Hospital. A total of 337 study participants were selected using a systematic sampling method. Data were collected through interviews with a structured questionnaire. Epi Data version 3.1 was used for data entry and analyzed using Statical Package for Social Sciences software. Bivariable and multivariable logistic regression analyses were employed; a P value of less than 0.05 was considered a statistically significant factor. Result: The overall proportion of this study showed that 26.1% of women had adverse birth outcomes; 7.7% stillbirths and 9.2% each preterm and low birth weights. Rural residence, no antenatal care, antepartum hemorrhage, pregnancy-induced hypertension, illiteracy, post term pregnancy, birth interval less than two years, and age less than 20 years were independently associated with adverse birth outcomes. Conclusion: In this study, the overall proportion of adverse birth outcomes was low compared to previous studies. Residence, no antenatal care follow-up, antepartum hemorrhage, pregnancy-induced hypertension, post term pregnancy, birth interval less than two years, illiteracy, and age less than 20 years were associated with adverse birth outcomes.