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factors. Therefore, the established histogram provides a visual tool for clinicians to evaluate the prognosis of adult HLH.

High-quality clinical research is dependent on adequate design, methodology, and data collection. The utilization of electronic data capture (EDC) systems is recommended to optimize research data through proper management. This paper's objective is to present the procedures of REDCap (Research Electronic Data Capture), which supports research development, and to promote the utilization of this software among the scientific community.

REDCap's web application version 10.4.1 released on 2021 (Vanderbilt University) is an EDC system suitable for clinical research development. This paper describes how to join the REDCap consortium and presents how to develop survey instruments and use them to collect and analyze data.

Since REDCap is a web application that stimulates knowledge-sharing among the scientific community, its development is not finished and it is constantly receiving updates to improve the system. REDCap's tools provide access control, audit trails, and data security to the research team.

REDCap is a web application that can facilitate clinical research development, mainly in health fields, and reduce the costs of conducting research. Its tools allow researchers to make the best use of EDC components, such as data storage.

REDCap is a web application that can facilitate clinical research development, mainly in health fields, and reduce the costs of conducting research. Its tools allow researchers to make the best use of EDC components, such as data storage.

The knowledge of anatomy is an integral part of dental and medical education that builds the foundations of pathology, physiology, and other related disciplines. Traditional three-dimensional (3D) models used to teach anatomy cannot represent dynamic physiological processes and lack structural detail in the oral regions relevant for dental education. We developed an interactive computer program to teach oral anatomy, pathology, and microbiology in an integrated manner to improve students' learning experiences.

The computer program, Jawnatomy, was developed as a 3D human head. Cognitive load theory guided the design of the tool, with the goal of reducing the heavy cognitive load of learning anatomy and integrating this knowledge with pathology and microbiology. Keller's attention, relevance, confidence, and satisfaction (ARCS) model of motivational design was used while creating the tool to improve learners' motivation and engagement. Blender was used to create the graphics, and Unity 3D was used to incorporate interactivity in the program. The 3D renderings of oral anatomy and progression of tooth decay were created with the input of content experts.

Jawnatomy will be launched in our institution's dentistry and dental hygiene program to support project- and team-based learning. This program will also be introduced to students as a self-directed learning tool to help them practice and strengthen their anatomical knowledge at their own pace.

Surveys and focus groups will be conducted to evaluate and further improve the computer program. We believe that Jawnatomy will become an invaluable teaching tool for dental education.

Surveys and focus groups will be conducted to evaluate and further improve the computer program. We believe that Jawnatomy will become an invaluable teaching tool for dental education.

Physical distancing is a control measure against coronavirus disease 2019 (COVID-19). Lockdowns are a strategy to enforce physical distancing in urban areas, but they are drastic measures. Therefore, we assessed the effectiveness of the lockdown measures taken in the world's second-most populous country, India, by exploring their relationship with community mobility patterns and the doubling time of COVID-19.

We conducted a retrospective analysis based on community mobility patterns, the stringency index of lockdown measures, and the doubling time of COVID-19 cases in India between February 15 and April 26, 2020. Pearson correlation coefficients were calculated between the stringency index, community mobility patterns, and the doubling time of COVID-19 cases. Multiple linear regression was applied to predict the doubling time of COVID-19.

Community mobility drastically fell after the lockdown was instituted. selleck chemicals The doubling time of COVID-19 cases was negatively correlated with population mobility patterns in outdoor areas (r = -0.45 to -0.58). The stringency index and outdoor mobility patterns were also negatively correlated (r = -0.89 to -0.95). Population mobility patterns (R2 = 0.67) were found to predict the doubling time of COVID-19, and the model's predictive power increased when the stringency index was also added (R2 = 0.73).

Lockdown measures could effectively ensure physical distancing and reduce short-term case spikes in India. Therefore, lockdown measures may be considered for tailored implementation on an intermittent basis, whenever COVID-19 cases are predicted to exceed the health care system's capacity to manage.

Lockdown measures could effectively ensure physical distancing and reduce short-term case spikes in India. Therefore, lockdown measures may be considered for tailored implementation on an intermittent basis, whenever COVID-19 cases are predicted to exceed the health care system's capacity to manage.

Health systems are shifting from traditional methods of healthcare delivery to delivery using digital applications. This change was introduced at a primary care centre in Chandigarh, India that served a marginalised population. After establishing the digital health system, we explored stakeholders' perceptions regarding its implementation.

Ethnographic methods were used to explore stakeholders' perceptions regarding the implementation of the Integrated Health Information System for Primary Health Care (IHIS4PHC), which was developed as a patient-centric digital health application. Data were collected using focus group discussions and in-depth interviews. Participatory observations were made of day-to-day activities including outpatient visits, outreach field visits, and methods of health practice. The collected information was analysed using thematic coding.

Healthcare workers highlighted that working with the digital health system was initially arduous, but they later realised its usefulness, as the di services, and apprehensions continued because of increased transparency, accountability, and dependence on computers and digital technicians.

Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalance and missing data, which are two common issues in medical data. The current study's main goal was to compare the performance of six machine learning (ML) methods for predicting hospital readmission in HF patients.

In this retrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in Farshchian Heart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM), least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predict hospital readmission. These methods' performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Two imputation methods were also used to deal with missing data.

Of the 1,856 HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracy in the range of 0.57-0.60, while RF performed the best, with the highest accuracy (range, 0.90-0.91). Other ML methods showed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance of the SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the median imputation method.

This study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.

This study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.

Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem.

This system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction.

Despite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%.

We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.

We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.

An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration.

We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies-Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68.

Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships.