Effectiveness of Mesenchymal Stem Cell Therapy for COVID-19 Patients in the Intensive Care Unit: A Case-Control Study

Objective: Many methods are used in the treatment of coronavirus disease 2019 (COVID-19), which causes acute respiratory distress syndrome (ARDS), and there are conflicting reports in the literature regarding the results of mesenchymal stem cell (MSC) therapy, which is one of those methods. The aim of our study is to evaluate the effect of MSC treatment applied together with standard treatments on survival. Materials and Methods: This retrospective case-control study evaluates the survival effect of MSC treatment administered to patients treated in intensive care after the development of ARDS due to COVID-19 between March 2020 and March 2021. The age, gender, comorbid disease status, APACHE II score, and overall and comorbidity-based survival rates were compared between patients who received standard medical treatment (SMT) and patients who received MSC treatment together with SMT. Results: There were 62 patients in the group receiving only SMT and 81 patients in the group receiving SMT and MSC. No difference was observed between the groups in terms of age, gender, presence of comorbid diseases, or APACHE II scores. There were also no differences according to Kaplan-Maier analysis for the survival statuses of the groups. There was no serious adverse effect due to MSC treatment among these patients. Conclusion: Our study presents the largest case series in the literature, and it was observed that MSC treatment may not significantly affect overall survival or comorbid disease-based survival, in contrast to many other studies in the literature.

Changes in Hepatitis C Awareness in Different Disciplines During COVID-19.

BACKGROUND: We aimed to determine the awareness of referring hepatitis C virus patients to the relevant departments and the effect of the pandemic period on this subject. METHODS: A total of 65 743 patients with anti-hepatitis C virus requests before and during the COVID-19 pandemic were retrospectively screened. Anti-hepatitis C virus-positive patients were divided into 5 groups according to age distribution. The distribution of patients with anti-hepatitis C virus positivity was compared according to age groups, before and during COVID-19. Anti-hepatitis C virus-pos- itive patients who were not requested hepatitis C virus RNA were evaluated individually according to the departments, and hepatitis C virus awareness was compared before and during COVID-19. RESULTS: Anti-hepatitis C virus positivity rate was 1.54% before COVID-19; this rate was 2.15% during COVID-19. When the anti-hep- atitis C virus positivity rate was compared in terms of age distribution according to before and during COVID-19, it was observed that there was a statistically significant decrease in the >65 age group in the COVID-19 period (P = .004). It was found that 216 (32%) of the patients who had anti-hepatitis C virus (+) before COVID-19 and 231 (48.1%) of the patients during COVID-19 were not requested hepatitis C virus RNA test (P < .0001). The departments with the highest awareness of hepatitis C virus were gastroenterology, infec- tious diseases, hematology, gynecology and obstetrics, and oncology, while the departments with the lowest hepatitis C virus awareness were ophthalmology, psychiatry, and general surgery. It was found that chronic hepatitis C virus awareness decreased in all departments during COVID-19. CONCLUSION: Hepatitis C virus awareness has decreased in all medical departments despite the physician alert system during COVID-19 and also the rate of anti-hepatitis C virus (+) patients decreased in the group aged >65 years during the pandemic.

Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.

OBJECTIVES: The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. METHODS: We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. RESULTS: The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). CONCLUSIONS: ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.

Caring for the Most Vulnerable: A Model for Managing Maladaptive Behavior in Children with Mental Special Needs During the COVID-19 Pandemic.

Background: This article presents the results of a comprehensive national model developed for managing maladaptive behaviors (MBs) in children with mental special needs (CMSNs) during the coronavirus disease 2019 (COVID-19) pandemic that combines telehealth-based Applied Behavioral Analyses, psychiatric interventions, and support from local psychosocial teams. This study aims to determine the effectiveness of a system that combined telehealth applications with local psychosocial support teams, allowing services from video calls to emergency interventions. Materials and Methods: The system combines the telehealth applications with the services from local psychosocial intervention teams. In addition to system records covering sociodemographic variables and initial complaints, a telephone survey questioning the effectiveness and satisfaction of the system was used as the main outcome. Results: In total, 347 individuals used the system with mothers constituting the majority of applicants (88.7%, n = 332). The overall satisfaction of the system was 8.8/10. In terms of effectiveness, 63.3% (n = 237) of caregivers reported an improvement in the reason of application. Counselors decided on a need for follow-up visits for 36.6% (n = 137) of applications. A referral to a psychiatrist was asked for 40 patients (10.6%). Discussion: To our best knowledge, this is the first study presenting a model for managing MBs of CMSNs during the COVID-19 outbreak. In general, therefore, it seems that there is a need for unique systems to handle behavioral problems of CMSNs. Conclusions: The findings of this study suggest that it is possible to establish an integrative multistep multidisciplinary telehealth-based approach in a short while.