Background Many of the conditions of the COVID-19 pandemic were consistent with factors shown to be predictive of parental stress and burnout. The purpose of the current study was to use a retrospective pretest method to gain an understanding of the effects of the COVID-19 pandemic on levels of parental burnout and on parenting practices. Method A brief survey was conducted using a retrospective pretest method to examine parental burnout (The Parental Burnout Assessment , Roskam et al, 2018) and parenting practices (The Alabama Parenting Questionnaire, Frick, 1991). The survey asked parent participants to answer questions about their experiences before and during the pandemic. Results Findings indicated that the pandemic had a significant impact on parents, increasing overall levels of parental burnout and impacting parenting practices by reducing use of positive parenting strategies and increasing use of inconsistent discipline and corporal punishment. These changes in parenting practices were even more pronounced for parents whose levels of parental burnout moved from “normal” levels before the pandemic to clinical levels during the pandemic. Conclusion The findings of the current study suggest that the COVID-19 pandemic has had a negative impact on levels of parental burnout and parenting practices. Although additional research is needed, the results suggest that there is a need for clinicians to understand the effects that the pandemic may have had on parents and families with an understanding that families may be at ongoing risk despite a relaxation of COVID-19 restrictions.

The human body’s reaction to various therapeutic medications is critical to comprehend since it aids in the appropriate construction of automated decision support systems for healthcare. Healthcare Internet of Things (IoT) solutions are becoming more accessible and trusted, necessitating more testing before they are standardized for commercial usage. We have developed an activity diagram based on the Unified Modeling Language (UML) to represent acceptability testing in IoT systems. The activity flow graph is used to extract all of the necessary information by traversing the activity flow diagram from start to finish, displaying all its properties. In this paper, a test case is generated to compute the type of diabetes using blood sugar test results, estimate the kind of diabetes, and the probability that a person would get diabetes in the future. We have demonstrated how these test cases can function using a telehealth care case study. First, we offer a high-level overview of the topic as well as a design model working diagram. The test case creation method is then outlined using the activity diagram as a guide.

Objective: This study explores whether nominal demographics, symptoms, signs and laboratory predictors can determine a perforated appendix in pediatric population at an urban community hospital.Methods: A retrospective analysis was performed including variables such as demographics, clinical signs, symptoms and laboratory predictors. The patient population consisted of all children between the ages of 0 and 18 years who were treated for acute appendicitis between January 2014-December 2014. Patients with perforated appendicitis were identified using coding data.Results: During the 1-year period, 74 patients were treated for appendicitis. Using correlation and logistic regression, nineteen variables were assessed for any relationship to a ruptured appendix in these patients. No substantial correlations were detected. Conclusions:There have been reports of racial and socioeconomic disparities with regards to perforated appendicitis in children. However, there is not enough evidence in the literature that supports the prediction of perforated appendix based on clinical and laboratory values. This study, at Saint Anthony Hospital, found no correlation between socioeconomic status, clinical presentation, laboratory values and ruptured appendix.

Regression testing is a crucial process that ensures that changes made to a system do not affect existing functionalities. However, there is currently no adequate technique for selecting test cases that consider changes in Unified Modeling Language (UML) activity flow graphs. This paper proposes a novel approach to regression testing of UML diagrams, focusing on healthcare management systems. We provide a formal definition of sequence and activity diagrams and their relationship and construct corresponding activity flow graphs, which are used to develop a regression testing algorithm. The proposed algorithm categorizes test cases into reusable, retestable, obsolete, and newly generated categories by comparing old and new versions of UML activity flow graphs. The methodology is evaluated using a custom-designed hospital management system website as the test case, and the results demonstrate a significant reduction in time and resources required for regression testing. Our study provides valuable insights into the application of UML diagrams and activity flow graphs in regression testing, making it an important contribution to software testing research.

With changing lifestyle and increasing obesity, prevalence of type -II Diabetes Mellitus is increasing in geriatric individuals, who are generally prone to tooth loss. As a result the demand for implants in these patients is also increasing and planning implants has been a challenge to present day dentists. Diabetes Mellitus causes impaired metabolism in general, especially bone metabolism resulting in impaired Osseointegration and poor wound healing. Growing demand of implants in Type II DM patients has initiated research towards implants survival rates. Extensive research till now states - poorly controlled diabetic patients have higher implant failure rates, where as Diabetic patients with controlled blood glucose levels respond to implants in similar way as healthy patients. The present article is one such trail to confirm that type II Diabetes Mellitus is no more contra-indication for Implants till Blood glucose levels are controlled to normal levels. Key words: Type-II Diabetes Mellitus; Two-piece implant; Delayed loading;