Investigating the Relationship between Components of Knowledge Management and Performance in Schools of Shahryar County by Using Path Analysis

Abstract In the present study, an attempt was made to investigate the relationship between processes of knowledge management and performance. In this study, components of creation, acquisition, organization, storage, dissemination and application have been used as the independent variables and it was assumed that it is related to the schools performance as the dependent variable. The main question is that how components of knowledge management affect the schools performance. This study deals with and answers this question by using the path analysis and closed questionnaire tools. The statistical population of this study is the teachers of schools i

A Comparative Analysis of Clinical Characteristics and Laboratory Findings of COVID-19 between Intensive Care Unit and Non-Intensive Care Unit Pediatric Patients: A Multicenter, Retrospective, Observational Study from Iranian Network for Research in Viral

Introduction: To date, little is known about the clinical features of pediatric COVID-19 patients admitted to intensive care units (ICUs). Objective: Herein, we aimed to describe the differences in demographic characteristics, laboratory findings, clinical presentations, and outcomes of Iranian pediatric COVID-19 patients admitted to ICU versus those in non-ICU settings. Methods: This multicenter investigation involved 15 general and pediatrics hospitals and included cases with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on positive real-time reverse transcription polymerase chain reaction (RT-PCR) admitted to these centers between March and May 2020, during the initial peak of the COVID-19 pandemic in Iran. Results: Overall, 166 patients were included, 61 (36.7%) of whom required ICU admission. The highest number of admitted cases to ICU were in the age group of 1–5 years old. Malignancy and heart diseases were the most frequent underlying conditions. Dyspnea was the major symptom for ICU-admitted patients. There were significant decreases in PH, HCO3 and base excess, as well as increases in creatinine, creatine phosphokinase (CPK), lactate dehydrogenase (LDH), and potassium levels between ICU-admitted and non-ICU patients. Acute respiratory distress syndrome (ARDS), shock, and acute cardiac injury were the most common features among ICU-admitted patients. The mortality rate in the ICU-admitted patients was substantially higher than non-ICU cases (45.9% vs. 1.9%, respectively; p<0.001). Conclusions: Underlying diseases were the major risk factors for the increased ICU admissions and mortality rates in pediatric COVID-19 patients. There were few paraclinical parameters that could differentiate between pediatrics in terms of prognosis and serious outcomes of COVID-19. Healthcare providers should consider children as a high-risk group, especially those with underlying medical conditions

Classifying Pediatric Central Nervous System Tumors through near Optimal Feature Selection and Mutual Information: A Single Center Cohort

Background: Labeling, gathering mutual information, clustering and classificationof central nervous system tumors may assist in predicting not only distinct diagnosesbased on tumor-specific features but also prognosis. This study evaluates the epidemi-ological features of central nervous system tumors in children who referred to Mahak’sPediatric Cancer Treatment and Research Center in Tehran, Iran.Methods: This cohort (convenience sample) study comprised 198 children (≤15years old) with central nervous system tumors who referred to Mahak's PediatricCancer Treatment and Research Center from 2007 to 2010. In addition to the descriptiveanalyses on epidemiological features and mutual information, we used the LeastSquares Support Vector Machines method in MATLAB software to propose apreliminary predictive model of pediatric central nervous system tumor feature-labelanalysis. Results:Of patients, there were 63.1% males and 36.9% females. Patients' mean±SDage was 6.11±3.65 years. Tumor location was as follows: supra-tentorial (30.3%), infra-tentorial (67.7%) and 2% (spinal). The most frequent tumors registered were: high-gradeglioma (supra-tentorial) in 36 (59.99%) patients and medulloblastoma (infra-tentorial)in 65 (48.51%) patients. The most prevalent clinical findings included vomiting,headache and impaired vision. Gender, age, ethnicity, tumor stage and the presence ofmetastasis were the features predictive of supra-tentorial tumor histology.Conclusion: Our data agreed with previous reports on the epidemiology of centralnervous system tumors. Our feature-label analysis has shown how presenting features maypartially predict diagnosis. Timely diagnosis and management of central nervous systemtumors can lead to decreased disease burden and improved survival. This may be furtherfacilitated through development of partitioning, risk prediction and prognostic models