A mobile serious game about diabetes self-management: Design and evaluation.

Type 2 Diabetes Mellitus (T2DM) is a chronic condition that requires ongoing self-management and education. In recent years, there has been a growing interest in utilizing mobile serious games as a tool for patient education and engagement. This article presents the development of DiaPo, a mobile serious game designed to improve self-management education for patients with T2DM. DiaPo integrates gamification techniques to increase patient engagement and motivation while providing essential information about disease management. The development of DiaPo followed a structured design process, utilizing the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) educational system. This systematic approach allowed for the integration of best practices in educational game design and diabetes care. The development team consisted of experts in medical informatics, game design, and diabetes care, ensuring a multidisciplinary approach to the game's creation. The game's narrative focuses on a T2DM patient who earns positive points for making healthy lifestyle choices and negative points for poor ones. This gamified approach aims to reinforce positive behaviors and provide immediate feedback on negative ones. Interactive animations confirm or deny options selected by the player, further enhancing the learning experience. DiaPo offers a flexible and adaptable platform suitable for diverse audiences, promoting inclusiveness and accessibility in T2DM education. DiaPo represents a novel approach to self-management education for patients with T2DM, utilizing gamification techniques and a multidisciplinary design process to create an engaging and informative mobile serious game. By promoting inclusiveness and accessibility, DiaPo has the potential to empower patients with T2DM to take an active role in their disease management. As the field of mobile serious games continues to evolve, DiaPo stands as a promising tool for improving T2DM education and patient outcomes.

Anthropometric indices growth references (length, Weight and Head Circumferences) of children aged 0-24 months in North-East of Iran by GAMLSS.

BACKGROUND: Growth references play a crucial role in the screening, evaluation, and surveillance of children, aiding in the early identification of the requirement for diverse growth-promoting interventions. Variations in human growth across distinct ethnic cohorts arise from genetic disparities, lifestyle variances, nutritional diversity, and diverse social and environmental contexts. Consequently, the selection of growth references markedly influences the prevalence of developmental disorders and nutritional imbalances in children. The study aims to assess the growth percentile of children in the north-east of Iran and establish population-specific reference charts for length, weight, and head circumference spanning from birth to 24 months. METHODS: This cross-sectional population-based research conducted in the north-east of Iran, from 2016 to 2023. The Data extracted from the electronic health records of Mashhad University of Medical Sciences. All apparently healthy children aged from birth to 24 months who were measured at least once by health staff at the ages of birth,1,2,4,6,7,9,12,15,18,24 months were included. The target population of the study were 479,089 children (96.21%), encompassing 233,565 girls (48.75%) and 245,524 boys (51.25%). Gender-specific percentile curves for length, weight, and head circumference concerning age, as well as weight concerning length, were derived using the GAMLSS approach. RESULTS: From the anthropometric information of 479,089 children (245,524 boys and 233,565 girls), growth charts were constructed. In comparison to the standard WHO chart, Iranian neonates displayed lower weight across all percentiles during the first month after birth, exhibited decreased head circumference at the 3rd percentile, and boys showed reduced length across all percentiles. After this age, Iranian children demonstrated increased weight, length, and head circumference. CONCLUSIONS: This research introduces the inaugural large-scale endeavor for indigenous reference charts. Through the noted distinctions from the international reference, the utilization of this novel resource offers the potential to enhance the surveillance of children's growth within the area. Moreover, by accurately assessing growth anomalies such as underweight, stunting, and wasting, it expands the domain of impactful policies in this sphere. Simultaneously, it enables the exploration of the secular trend of children's growth in the forthcoming years.

An open-access dataset of emergency department admissions at a large teaching hospital in Iran.

In recent years, the use of electronic health records (EHRs) has become increasingly prevalent in healthcare settings, including emergency departments (EDs). EHRs offer numerous advantages, such as improved documentation, streamlined communication, and enhanced patient care. Additionally, EHRs contain vital information about patient care and treatment outcomes, which opens up exciting research opportunities. The objective of this study was to present a database comprising information regarding patients admitted to the emergency department of a large hospital. In this study, we are introducing an open-access database sourced from the electronic health records of a general university hospital in Isfahan, Iran. The data were collected from patients admitted to the emergency department between March 2017 and March 2022, resulting in a database containing 143,582 ED stays. The database includes triage information, ED admission patients, and services. To ensure patient privacy, all patient-specific information has been removed from the records.

Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review.

BACKGROUND AND OBJECTIVES: Comprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed. This comprehension allows them to suggest practical solutions for handling missing data, in a clinical data source. Accurate handling of missing values is critical for producing precise estimates and making informed decisions, especially in crucial areas like clinical research. With data's increasing diversity and complexity, numerous scholars have developed a range of imputation techniques. To address this, we conducted a systematic review to introduce various imputation techniques based on tabular dataset characteristics, including the mechanism, pattern, and ratio of missingness, to identify the most appropriate imputation methods in the healthcare field. MATERIALS AND METHODS: We searched four information databases namely PubMed, Web of Science, Scopus, and IEEE Xplore, for articles published up to September 20, 2023, that discussed imputation methods for addressing missing values in a clinically structured dataset. Our investigation of selected articles focused on four key aspects: the mechanism, pattern, ratio of missingness, and various imputation strategies. By synthesizing insights from these perspectives, we constructed an evidence map to recommend suitable imputation methods for handling missing values in a tabular dataset. RESULTS: Out of 2955 articles, 58 were included in the analysis. The findings from the development of the evidence map, based on the structure of the missing values and the types of imputation methods used in the extracted items from these studies, revealed that 45% of the studies employed conventional statistical methods, 31% utilized machine learning and deep learning methods, and 24% applied hybrid imputation techniques for handling missing values. CONCLUSION: Considering the structure and characteristics of missing values in a clinical dataset is essential for choosing the most appropriate data imputation technique, especially within conventional statistical methods. Accurately estimating missing values to reflect reality enhances the likelihood of obtaining high-quality and reusable data, contributing significantly to precise medical decision-making processes. Performing this review study creates a guideline for choosing the most appropriate imputation methods in data preprocessing stages to perform analytical processes on structured clinical datasets.

Remote monitoring system to support positive airway pressure therapy in patients with obstructive sleep apnea: a multi-center randomized controlled trial.

PURPOSE: To develop and evaluate an intervention tailored to patients' needs to increase the rate of positive airway pressure (PAP) adherence in patients afflicted with obstructive sleep apnea (OSA), who undergo PAP therapy. METHODS: A multi-center, 3 parallel-arm, randomized, controlled trial was conducted. Participants with OSA who undergo a PAP therapy were randomized to one of three groups: control arm (usual care), educational booklet arm, and mobile-based application arm. PAP usage, the percentage of days using the device for more than 4 h, change in knowledge, risk perception, outcome expectancy, self-efficacy, and ESS were assessed before and one month after interventions in the three groups. Also, the application usage data were analyzed. RESULTS: The result showed the change in average PAP usage, knowledge, risk perception, and self-efficacy in the application group was significantly higher than the control and booklet groups. Also, the change in use for more than 4 h in the application group was significantly higher than the control group. Comparing the actual and patients' self-report PAP use indicated patients' self-report about the use of the device is about 50 min (0.8 h) more than the actual amount of use. CONCLUSION: The study results indicated that the improvement of primary and secondary outcomes in adherence to PAP was significantly higher in the application group than in other study groups. Given the increasingly penetrating influence of smartphone-based technologies, it seems that mobile-based applications could potentially be adopted in the population of patients with OSA. CLINICAL TRIALS REGISTRATION: IRCT2017092236314N1; https://en.irct.ir/trial/27185.

The effect of using the hip exoskeleton assistive (HEXA) robot compared to conventional physiotherapy on clinical functional outcomes in stroke patients with hemiplegia: a pilot randomized controlled trial.

TRIAL DESIGN: This study is a pilot randomized clinical trial aimed to investigate the effect of using Hip Exoskeleton Assistive (HEXA) robot compared to conventional physiotherapy on the quality of walking, disability, and quality of life of stroke patients with hemiplegia. METHODS: In this study, 24 patients were randomly assigned to the intervention group (robotic physiotherapy with HEXA robot), or control group (conventional physiotherapy). In each session, both groups received 30 min of conventional physiotherapy including electrotherapy and conventional exercises, and then the intervention group did gait training for 30 min with the HEXA robot and the control group for 30 min without the HEXA robot. The treatment program was 12 sessions, 3 times a week. Before the 1st and after the 12th sessions, both groups were evaluated for walking quality, disability, and quality of life. RESULTS: The results showed that the main effect of time was significant (P < 0.05) in all outcomes and patients in both groups achieved significant improvement in all outcomes after the intervention. The main effect of the group was also significant in the outcomes of 6MWT (P < 0.05) and TUG (P < 0.05), and the intervention group patients experienced more distance and speed in these two tests. This study was approved by the ethics committee of Mashhad University of Medical Sciences (IR.MUMS.FHMPM.REC.1400.079 dated 28th Jan 2022). The trial was registered with the clinical trials site of www.IRCT.ir (IRCT20210730052024N1) on January 28th 2022. CONCLUSION: It seems that the HEXA robot can effectively improve walking capacity and speed.

Evaluation of no-show rate in outpatient clinics with open access scheduling system: A systematic review.

Background: Patients' missed appointments can cause interference in the functions of the clinics and the visit of other patients. One of the most effective strategies to solve the problem of no-show rate is the use of an open access scheduling system (OA). This systematic review was conducted with the aim of investigating the impact of OA on the rate of no-show of patients in outpatient clinics. Methods: Relevant articles in English were investigated based on the keywords in title and abstract using PubMed, Scopus, and Web of Science databases and Google Scholar search engine (July 23, 2023). The articles using OA and reporting the no-show rate were included. Exclusion criteria were as follows: (1) review articles, opinion, and letters, (2) inpatient scheduling system articles, and (3) modeling or simulating OA articles. Data were extracted from the selected articles about such issues as study design, outcome measures, interventions, results, and quality score. Findings: From a total of 23,403 studies, 16 articles were selected. The specialized fields included family medicine (62.5%, 10), pediatrics (25%, four), ophthalmology, podiatric, geriatrics, internal medicine, and primary care (6.25%, one). Of 16 articles, 10 papers (62.5%) showed a significant decrease in the no-show rate. In four articles (25%), the no-show rate was not significantly reduced. In two papers (12.5%), there were no significant changes. Conclusions: According to this study results, it seems that in most outpatient clinics, the use of OA by considering some conditions such as conducting needs assessment and system design based on the patients' and providers' actual needs, and cooperating of all system stakeholders through consistent training caused a significant decrease in the no-show rate.

An inversion-based clustering approach for complex clusters.

BACKGROUND: The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. METHODS: In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. RESULTS: The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. CONCLUSIONS: The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

Effect of a gamified mobile-based self-management application on disease activity index, quality of life, and mental health in adults with inflammatory bowel disease: A protocol of a randomized controlled trial study.

Background and Aims: Inflammatory bowel disease (IBD) is a chronic inflammatory gastrointestinal tract disease subdivided into Crohn's disease (CD) and ulcerative colitis (UC). There is currently no cure for IBD, and individuals with IBD frequently experience a lower health-related quality of life (HRQOL) than the general population. Gamification has become an increasingly popular topic in recent years. Adapting game design concepts to nongaming contexts represents a novel and potential approach to changing user engagement. This study will be conducted with the aim of evaluating the effect of a gamified mobile-based self-management application on disease activity index, quality of life, and mental health in adults with IBD. Methods: A multicenter, parallel, two-arm, exploratory randomized controlled trial with a 6-month follow-up per patient will be designed to compare the impact of the gamified mobile-based tele-management system on primary and secondary health outcomes and outpatient visits in 210 patients with all types of IBD which are divided equally into a control group with standard care and an intervention group which will use the developed mobile application named MY IBD BUDDY. All patients will attend study visits at baseline, 12 and 24 weeks, and routine IBD clinic visits or telephone consultations based on randomization group assignment. Disease activity or disease activity index, mental health (anxiety and depression) symptoms, quality of life, self-efficacy, and IBD-specific knowledge will be measured at baseline with two follow-ups at 12 and 24 weeks. Conclusions: In sum, the outcomes of our trial will demonstrate the impact of the gamified mobile-based self-management system on disease activity, quality of life, and anxiety and depression by means of interactive care and patient empowerment. Trial Registration: IRCT: IRCT20200613047757N1. Registered November 16, 2021. Prospectively registered and visible at OSF (https://doi.org/10.17605/OSF.IO/AWFY9).

Inflammatory bowel disease patients' perspectives of non-medical needs.

BACKGROUND: Inflammatory bowel disease (IBD) imposes a huge burden on the healthcare systems and greatly declines the patient's quality of life. However, there is a paucity of detailed data regarding information and supportive needs as well as sources and methods of obtaining information to control different aspects of the disease from the perspectives of the patients themselves. This study aimed to establish the IBD patients' preferences of informational and supportive needs through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). METHODS: IBD patients were recruited from different centers. Considering inclusion and exclusion criteria, 521 participants were filled a predefined questionnaire. This questionnaire was prepared through literature review of the recent well-known guidelines on the needs of IBD patients, which was further approved by the experts of IBD area in three rounds of Delphi consensus. It includes 56 items in four sections of informational needs (25), supportive needs (15), sources of information (7), and methods of obtaining information (9). RESULTS: In particular, EFA was used to apply data reduction and structure detection. Given that this study tries to identify patterns, structures as well as inter-relationships and classification of the variables, EFA was utilized to simplify presentation of the variables in a way that large amounts of observations transform into fewer ones. Accordingly, the EFA identified five factors out of 25 items in the information needs section, three factors out of 15 items in the supportive needs section, two factors out of 7 items in the information sources section, and two factors out of 9 items in the information presentation methods. Through the CFA, all 4 models were supported by Root Mean Squared Error of Approximation (RMSEA); Incremental Fit Index (IFI); Comparative Fit Index (CFI); Tucker-Lewis Index (TLI); and SRMR. These values were within acceptable ranges, indicating that the twelve factors achieved from EFA were validated. CONCLUSIONS: This study introduced a reliable 12-factor model as an efficient tool to comprehensively identify preferences of IBD patients in informational and supportive needs along with sources and methods of obtaining information. An in-depth understanding of the needs of IBD patients facilitates informing and supporting health service provision. It also assists patients in a fundamental way to improve adaptation and increase the quality of life. We suggest that health care providers consider the use of this tool in clinical settings in order to precisely assess its efficacy.

A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests.

BACKGROUND: The pharmaceutical industry is continually striving to innovate drug development and formulation processes. Orally disintegrating tablets (ODTs) have gained popularity due to their quick release and patient-friendly characteristics. The choice of excipients in tablet formulations plays a critical role in ensuring product quality, highlighting its importance in tablet creation. The traditional trial-and-error approach to this process is both expensive and time-intensive. To tackle these obstacles, we introduce a fresh approach leveraging machine learning and deep learning methods to automate and enhance pre-formulation drug design. METHODS: We collected a comprehensive dataset of 1983 formulations, including excipient names, quantities, active ingredient details, and various physicochemical attributes. Our study focused on predicting two critical control test parameters: tablet disintegration time and hardness. We compared a range of models like deep learning, artificial neural networks, support vector machines, decision trees, multiple linear regression, and random forests. RESULTS: A 12-layer deep neural network, as a form of deep learning, surpassed alternative techniques by achieving 73% accuracy for disintegration time and 99% for tablet hardness. This success underscores its efficacy in predicting complex pharmaceutical factors. Such an approach streamlines the drug formulation process, reducing iterations and material consumption. CONCLUSIONS: Our findings highlight the deep learning potential in pharmaceutical formulations, particularly for tablet hardness prediction. Future work should focus on enlarging the dataset to improve model effectiveness and extend its application in pharmaceutical product development and assessment.

Modifying the false discovery rate procedure based on the information theory under arbitrary correlation structure and its performance in high-dimensional genomic data.

BACKGROUND: Controlling the False Discovery Rate (FDR) in Multiple Comparison Procedures (MCPs) has widespread applications in many scientific fields. Previous studies show that the correlation structure between test statistics increases the variance and bias of FDR. The objective of this study is to modify the effect of correlation in MCPs based on the information theory. We proposed three modified procedures (M1, M2, and M3) under strong, moderate, and mild assumptions based on the conditional Fisher Information of the consecutive sorted test statistics for controlling the false discovery rate under arbitrary correlation structure. The performance of the proposed procedures was compared with the Benjamini-Hochberg (BH) and Benjamini-Yekutieli (BY) procedures in simulation study and real high-dimensional data of colorectal cancer gene expressions. In the simulation study, we generated 1000 differential multivariate Gaussian features with different levels of the correlation structure and screened the significance features by the FDR controlling procedures, with strong control on the Family Wise Error Rates. RESULTS: When there was no correlation between 1000 simulated features, the performance of the BH procedure was similar to the three proposed procedures. In low to medium correlation structures the BY procedure is too conservative. The BH procedure is too liberal, and the mean number of screened features was constant at the different levels of the correlation between features. The mean number of screened features by proposed procedures was between BY and BH procedures and reduced when the correlations increased. Where the features are highly correlated the number of screened features by proposed procedures reached the Bonferroni (BF) procedure, as expected. In real data analysis the BY, BH, M1, M2, and M3 procedures were done to screen gene expressions of colorectal cancer. To fit a predictive model based on the screened features the Efficient Bayesian Logistic Regression (EBLR) model was used. The fitted EBLR models based on the screened features by M1 and M2 procedures have minimum entropies and are more efficient than BY and BH procedures. CONCLUSION: The modified proposed procedures based on information theory, are much more flexible than BH and BY procedures for the amount of correlation between test statistics. The modified procedures avoided screening the non-informative features and so the number of screened features reduced with the increase in the level of correlation.

Prevalence of obsessive-compulsive symptoms and their psychosocial correlates among medical students during COVID-19 pandemic.

This study aimed to assess the prevalence of obsessive-compulsive symptoms (OCS) among medical students during COVID-19 pandemic and to evaluate their association with related sociodemographic features and other psychological symptoms. In this cross-sectional study, students from Mashhad University of Medical Sciences with no major exam in the preceding or following month were surveyed during April to August 2021 through stratified available sampling. Data were collected by a structured online questionnaire distributed through social media platforms. OCS were assessed using Obsessive-Compulsive Inventory-Revised (OCI-R) and COVID-related stress was evaluated using COVID Stress Scale (CSS). Overall, 347 students with a mean age of 22.67 ±â€…2.56 years were included in this study, of whom 30.3% had probable obsessive-compulsive disorder (OCD; OCI-R score ≥21). Mean CSS scores in students with and without probable OCD were 38.64 ±â€…19.82 and 26.72 ±â€…16.63, respectively ( P  < 0.005). Total CSS score was significantly correlated with OCI-R score ( r  = 0.38, P  = 0.001). Around one-third of the medical students reported significant OCS during COVID-19 pandemic, which was associated with higher COVID-19-related stress. Further research provides insight into management of OCD and related disorders during the COVID-19 pandemic.

Development and Validation of an Educational Book on Self-Management in Inflammatory Bowel Disease Based on Patient Preferences and Expert Opinions: A Methodological Study.

BACKGROUND: Self-management education resources for inflammatory bowel disease (IBD) using concepts remain infrequent. We aim to describe the development and evaluation process of educational material for self-management in IBD based on patient preferences and expert opinions. RESEARCH DESIGN AND METHODS: The method of this study includes two main phases of development and validation in five steps in the following order: (1) identification of information needs for patients with IBD; (2) content development with a comprehensive literature review and scientific texts related to IBD; (3) measuring the face validity of the content based on the expert opinions in the field of IBD; (4) validation of the content with the experts in the field of IBD; and (5) validation by target audiences. RESULTS: The expert panel comprises ten gastroenterologists, nutritionists, psychologists, gynecologists, and nurses. The total suitability score is 79.5%. The final draft version of the educational self-management material was presented to 30 IBD patients who were satisfied (n = 24; 80%) with the material. CONCLUSIONS: This study shows the development process and is validated for face and content validity by the academic multidisciplinary expert panel and target group. Patients and their caregivers can use this content to cope with their disease.

Expert opinions on informational and supportive needs and sources of obtaining information in patients with inflammatory bowel disease: a Delphi consensus study.

Background: The present study introduces informational and supportive needs and sources of obtaining information in patients with inflammatory bowel disease (IBD) through a three-round Expert Delphi Consensus Opinions method. Methods: According to our previous scoping review, important items in the area of informational and supportive needs and sources of obtaining information were elucidated. After omitting duplicates, 56 items in informational needs, 36 items in supportive needs, and 36 items in sources of obtaining information were retrieved. Both open- and close-ended questions were designed for each category in the form of three questionnaires. The questionnaires were sent to selected experts from different specialties. Experts responded to the questions in the first round. Based on the feedback, questions were modified and sent back to the experts in the second round. This procedure was repeated up to the third round. Results: In the first round, five items from informational needs, one item from supportive needs, and seven items from sources of obtaining information were identified as unimportant and omitted. Moreover, two extra items were proposed by the experts, which were added to the informational needs category. In the second round, seven, three, and seven items from informational needs, supportive needs, and sources of obtaining information were omitted due to the items being unimportant. In the third round, all the included items gained scores equal to or greater than the average and were identified as important. Kendall coordination coefficient W was calculated to be 0.344 for information needs, 0.330 for supportive needs, and 0.325 for sources of obtaining information, indicating a fair level of agreement between experts. Conclusions: Out of 128 items in the first round, the omission of 30 items and the addition of two items generated a 100-item questionnaire for three sections of informational needs, supportive needs, and sources of obtaining information with a high level of convergence between experts' viewpoints.

Application of distributed lag models and spatial analysis for comparing the performance of the COVID-19 control decisions in European countries.

Over the past three years, the COVID-19 outbreak has become a major worldwide problem, affecting the health systems and economies of countries. The mean delays, the expected time to observe the average effect of the number of new cases on the number of deaths, are gold times for decision-making regarding disease control and treatment facilities to reduce the fatality rate. The interest of the present study is estimating the mean delays and adjusted fatality rates of COVID-19 with the new application of Distributed Lag Models (DLM) and their spatial distributions. The daily cases and deaths data of COVID-19 for 39 European countries was obtained from two sources; the "European Centre for Disease Prevention and Control" and the "Our World in Data" database. The mean delay and the Adjusted Fatality Rate (AFR) for each country at three-time intervals; the first and subsequent peaks before and after vaccination were estimated by the Distributed Lag Models. The spatial analysis was applied to find the spatial correlation of the mean delays and adjusted fatality rates among European countries. In the three-time intervals, the first and the subsequent peaks before vaccination, and after vaccination, the median and interquartile range of the mean delays; and AFRs were: 1.1 (0.4, 3.2); 0.024 (0.016, 0.044), 9.2 (6.2, 12.40); 0.013 (0.005, 0.020) and 7.3 (4.4, 11.0); 0.001 (0.001, 0.005), respectively. In the subsequent peaks before vaccination, the mean delays considerably increased, and the AFRs decreased for most European countries. After vaccination, the AFRs decreased considerably. Except for the first peak, the spatial correlations of AFRs were not significant among neighboring countries. Consecutive outcomes will occur with delays in outbreaks of infectious disease. Also, the fatality rates for these outcomes should be adjusted on delays. Estimating the mean delays and adjusted fatality rates by Distributed lag Models and the spatial distributions of theme in outbreaks showed that prevention and medical policies after the first peak as well as vaccination were effective to reduce the fatality rate of COVID-19, but these effects were different between countries. These results recommended policymakers and governments assign prevention and medical resources more effectively.

Breast cancer prediction using different machine learning methods applying multi factors.

OBJECTIVE: Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors. METHODS: The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated. RESULT: Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54). CONCLUSION: Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.

Comparing morphologic features and complications of main clear corneal incision between junior and senior residents observed using anterior segment optical coherence tomography.

Background: Wound construction is a critical step in phacoemulsification. Using anterior segment optical coherence tomography (AS-OCT), we compared the morphological features and complications of main incisions made by junior or senior residents during phacoemulsification. Methods: This cross-sectional comparative study included eyes with senile cataracts that underwent uneventful phacoemulsification with a clear corneal incision made by seven senior and eight junior ophthalmology residents. All eyes underwent postoperative image acquisition using AS-OCT on day one and at three months, examining for morphological features and potential complications of the main incision. Results: We included 50 eyes of 50 patients with a male-to-female ratio of 22 (44%) to 28 (56%); 26 (52%) were operated on by junior residents and 24 (48%) by seniors. The mean geometric features of the main incisions and the frequency of early and late wound complications were comparable between the two groups (all P > 0.05). A significant correlation was found between the incision length and angle with the superior (r = + 0.80; P < 0.001 and r = - 0.63; P < 0.001, respectively) and inferior (r = + 0.84; P < 0.001 and r = - 0.68; P < 0.001, respectively) areas of the incision, as well as between the length and angle of incision (r = - 0.74; P < 0.001). The number of planes in the wound architecture was not significantly different according to senior or junior resident status (P > 0.05). Although the number of eyes with stromal hydration was significantly greater for junior residents than for seniors (P < 0.001), the corneal thickness at the entrance to the cornea or the anterior chamber, presence of endothelial wound gaping, and Descemet's membrane detachment were comparable between eyes with and without stromal hydration (all P > 0.05). At three months, 29 (58%) patients returned for examination, in whom seven (24%) had late wound complications. Conclusions: This study found no significant differences in the performances of junior and senior residents in terms of wound construction or its associated complications. However, considering the overall rate of some observed wound-related complications, we recommended revision of the resident educational curriculum concerning the structure and complications of the main incision.