The effect of the COVID-19 pandemic period on the prevalence of metabolic syndrome in patients with schizophrenia and bipolar disorder: Metabolic syndrome and COVID-19 Pandemic Period

Aim: This study aimed at investigating the impact of COVID-19 lockdown on metabolic profile in bipolar disorder and schizophrenia patients at the time of COVID-19. Material(s) and Method(s): This study was completed with 190 individuals diagnosed with schizophrenia (116) and bipolar disorder (74). Waist circumference and blood pressure measurements were taken on the same date as triglyceride, glucose and HDL data in the electronic recording system of the participants. Initial assessment data are measurements within 3 months prior to the publication of the first case in Turkey. The second evaluation data, on the other hand, is the data obtained within 3 months after the date of removal or relaxation of the restrictions, which is defined as the normalization period. Result(s): A significant increase was observed in the incidence of metabolic syndrome in the participants after the COVID-19 lockdown. In addition, a significant increase in glucose, triglyceride and diastolic blood pressure values, and a significant decrease in HDL values were detected. There was no significant change in systolic blood pressure and waist circumference values. Discussion(s): It has been shown that the COVID-19 lockdown increases the incidence of metabolic syndrome in patients with schizophrenia and bipolar disorder and causes irregularities in metabolic syndrome parameters.Copyright © 2022, Derman Medical Publishing. All rights reserved.

Simulation modeling assessment and improvement of a COVID-19 mass vaccination center operations

The development of safe and effective vaccines against COVID-19 has been a turning point in the international effort to control this disease. However, vaccine development is only the first phase of the COVID-19 vaccination process. Correct planning of mass vaccination is important for any policy to immunize the population. For this purpose, it is necessary to set up and properly manage mass vaccination centers. This paper presents a discrete event simulation model of a real COVID-19 mass vaccination center located in Sfax, Tunisia. This model was used to evaluate the management of this center through different performance measures. Three person’s arrival scenarios were considered and simulated to verify the response of this real vaccination center to arrival variability. A second model was proposed and simulated to improve the performances of the vaccination center. Like the first model, this one underwent the same evaluation process through the three arrivals scenarios. The simulation results show that both models respond well to the arrival’s variability. Indeed, most of the arriving persons are vaccinated on time for all the studied scenarios. In addition, both models present moderate average vaccination and waiting times. However, the average utilization rates of operators are modest and need to be improved. Furthermore, both simulation models show a high average number of persons present in the vaccination center, which goes against the respect of the social distancing condition. Comparison between the two simulation models shows that the proposed model is more efficient than the actual one. © The Author(s) 2022.

COMMUNICATING COVID-19 IN MULTIPLE LANGUAGES: A MATURITY MODEL ASSESSMENT OF IRELAND'S CRISIS COMMUNICATION PRACTICE

Timely, accurate and clear communication is essential in crisis response. Given the multilingual and multicultural nature of many parts of today's populated world, it should be evident that translation is key to enabling crisis communication. Although receiving little attention previously, the COVID-19 pandemic has highlighted the important role of translation in responding to crises. Nevertheless, how prepared are jurisdictions for crisis translation? One way of measuring this is to use a maturity model assessment. In this article, we apply the Organisational Maturity for Disaster Preparedness (OMDP) model Mohamed & Qu (2018) to the Republic of Ireland, assessing the level of response through documentary and interview-based evidence. All considered, we place the response between June and November 2020 at Level 2 on the OMDP. Recommendations for moving up in the maturity model are provided and could be applied to many more jurisdictions.

Vicious cycle between severity of childhood obesity and pandemic: Potential impact of metformin.

Introduction: COVID-19 is currently a global pandemic, and initial reports of identified COVID-19 lockdown and limitations can adversely affect childhood obesity and metabolic health. Studies conducted in recent years have shown that the rate of obesity in childhood increases with the changing lifestyle with the pandemic. However, there is insufficient data on how the situation changes and how metabolism is affected in those, who are already obese. The aim of this paper was to determine how the pandemic affects the current status, severity, and metabolic parameters of obese children. We also attempted to show potential effects of metformin therapy. Methods: The study was conducted with the participation of 101 patients with obesity (The mean age was 13.6 ± 2.2). The patients were evaluated using pre- and post-lockdown data with an interval of 6 months. The new classification system was used to determine the severity of obesity. All anthropometrics, metabolic parameters (Blood glucose, insulin, HbA1C, lipid profile), lifestyle, and comorbidities were evaluated by dividing the participants into various subgroups according to their obesity and metformin usage status. Results: Our data shows that weight, height, BMI, BMI-SD, and BMI percentiles all increased significantly, after the pandemic started. The severity of obesity increased statistically (overweight decreases and class 2 obesity increases, p = 0.001). No change was observed in metabolic parameters. Surprisingly, a significant increase was observed in insulin and HOMA-IR values in the group with-metformin. Discussion: Most studies about childhood obesity have only focused on obesity increases and pandemic relation. Our study showed that although there was no significant change in metabolic status at the end of a lockdown period, there was a serious increase in the severity of obesity. Metformin use had no effect on either obesity or metabolic parameters, and even an increase in insulin resistance indicators was observed.

Evaluation of functional training along with online nutritional education on autistic children's metabolic status during Covid-19 pandemic-A randomized clinical trial

Physical inactivity and poor dietary pattern are considered as health related challenges in ASD (ASD) which seems to be affected by Covid-19 pandemic. The purpose of this clinical trial was to investigate the effect of functional training along with online nutritional education on metabolic related biomarkers in children with ASD. 80 verified children with ASD (age=9.73 ± 1.29, weight=49.94 ± 2.08 kg, stature=146.08 ± 40 cm, BMI percentile= 64.88 ± 2.89, FM percentage+24.71 ± 1.48) were randomly divided into four groups including: (1) functional training, (2) online nutritional education, 3) training+ education and 4) control group. Pre-test was taken for metabolic related biomarkers and each experimental group received their interventions for 8 weeks. Post-test was taken at the end of 8 weeks. The results from this study, did not show significant changes for WHR (sig=0.06). Significant changes was indicated for FM (sig<0.001), TC(sig<0.001), TG (sig=0.006), HDL (sig<0.001), LDL (sig=0.001), HOMA (sig=0.04). In conclusion, functional training and online nutritional education can be considered as beneficial interventions for metabolic related biomarkers improvement in children with ASD during Covid-19 pandemic.

Herbal medications and natural products for patients with covid-19 and diabetes mellitus: Potentials and challenges.

Background: The presence of diabetes mellitus (DM) among COVID-19 patients is associated with increased hospitalization, morbidity, and mortality. Evidence has shown that hyperglycemia potentiates SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection and plays a central role in severe COVID-19 and diabetes comorbidity. In this review, we explore the therapeutic potentials of herbal medications and natural products in the management of COVID-19 and DM comorbidity and the challenges associated with the preexisting or concurrent use of these substances. Methods: Research papers that were published from January 2016 to December 2021 were retrieved from PubMed, ScienceDirect, and Google Scholar databases. Papers reporting clinical evidence of antidiabetic activities and any available evidence of the anti-COVID-19 potential of ten selected natural products were retrieved and analyzed for discussion in this review. Results: A total of 548 papers (73 clinical trials on the antidiabetic activities of the selected natural products and 475 research and review articles on their anti-COVID-19 potential) were retrieved from the literature search for further analysis. A total of 517 articles (reviews and less relevant research papers) were excluded. A cumulative sum of thirty-one (31) research papers (20 clinical trials and 10 others) met the criteria and have been discussed in this review. Conclusion: The findings of this review suggest that phenolic compounds are the most promising phytochemicals in the management of COVID-19 and DM comorbidity. Curcumin and propolis have shown substantial evidence against COVID-19 and DM in humans and are thus, considered the best potential therapeutic options.

Covid19 social distancing effect on body fat and insulin resistance in teens with overweight/obesity

Background: Teens gain more weight in summer than in school, with greatest gains among teens with overweight/obesity (OWOB). Virtual learning for COVID19 social distancing may increase this risk. This study assesses change in total body fat% (TBF%) and insulin resistance (IR) during COVID19 in teens with OWOB. Methods: Teens (N = 14, Mage = 15.3, 57% male, 71% black) with OWOB who completed at least 1 fasting blood draw and DXA pre-COVID19 and were in virtual learning returned to complete another fasting blood draw and DXA (measuring TBF%). HOMA-IR was calculated via fasting glucose and insulin. Change scores were calculated for 1) pre-and during-COVID19 TBF% and 2) 2 pre-COVID19 TBF% scores. Two norm TBF% change scores were calculated from NHANES population-based data matched for age, sex, BMI% and race based on time between: 1) pre-and during-COVID19 DXAs, and 2) 2 pre-COVID19 DXAs. HOMA-IR change scores were calculated for 1) pre-and during-COVID19 and 2) 2 pre-COVID19 scores. Norm HOMA-IR change was calculated based on time between blood draws, age and BMI%. Paired t-tests compared 1) TBF% change pre-to during-COVID19 with TBF% norm change and 2) TBF% change pre-to during-COVID19 with change in 2 pre-COVID19 TBF% scores. ANCOVAs compared 1) HOMA-IR change pre-to during-COVID19 with HOMA-IR change in 2 pre-COVID19 visits controlling for BMIz and difference in time between visits and 2) HOMA-IR change from pre-to during-COVID19 with norm change in HOMA-IR controlling for sex/race. Results: TBF% change between 2 pre-COVID19 DXAs (M = .2%) and norm change (M = -.2%) were similar, but TBF% increased by ~6% more from pre-to during-COVID19 (M = 6.45%) than norm change (M = .5%,p < .01). HOMA-IR increased by ~2.5 units more from pre-to during-COVID19 (M = 3.4) than pre-COVID19 blood draws (M = 0.8;p = .03) and by ~3.5 units more than norm change (M = -.03;p = 0.01). Conclusions: TBF% and IR increased exponentially during COVID19 in teens with OWOB compared to before COVID19 and norm changes, leaving an enduring effect on health.

Assessment of Updated Fuel‐Based Emissions Inventories Over the Contiguous United States Using TROPOMI NO2 Retrievals

Nitrogen oxides (NOx) are air pollutants critical to ozone and fine particle production in the troposphere. Here, we present fuel‐based emission inventories updated to 2018, including for mobile source engines using the Fuel‐based Inventory of Vehicle Emissions (FIVEs) and oil and gas production using the Fuel‐based Oil and Gas (FOG) inventory. The updated FIVE emissions are now consistent with the NEI17 estimates differing within 2% across the contiguous US (CONUS). Tropospheric NO2 columns modeled by the Weather Research and Forecasting with Chemistry model (WRF‐Chem) are compared with those observed by TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) during the summer of 2018. Modeled NO2 columns show strong temporal and spatial correlations with TROPOMI (OMI), identified with biases of −3% (−21%) over CONUS, and +8% (−6%) over point sources plus urban regions. Taking account of the negative bias (∼20%) in early version of TROPOMI over polluted regions, WRF‐Chem shows good performance with updated FIVE and FOG emissions. Our model tends to under‐predict the tropospheric NO2 columns over background and rural regions (bias of −21% to −3%). Through model sensitivity analyses, we demonstrate the important roles of emissions from soils (11.7% average over CONUS), oil and gas production (4.1%), wildfires (10.6%), and lightning (2.3%) with greater contributions at regional scales. This work provides a roadmap for satellite‐based evaluations for emission updates from various sources.Alternate :Plain Language SummarySatellite observations of tropospheric NO2 columns provide important constraints on air pollutants from space, which have been widely used to validate the performance of atmospheric models. To gain better knowledge of the accuracy of the recently updated fuel‐based emissions inventory, we conducted NO2 assessments between a regional chemical transport model (Weather Research and Forecasting with Chemistry model, WRF‐Chem), with the TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) over the contiguous United States. We find that model simulation results show strong spatial and temporal correlations with satellite observations across point sources, urban, oil and gas production, and rural regions. With updated emissions, our regional atmospheric model can reconcile with satellite retrievals differing from −3% (TROPOMI) to −21% (OMI) overall. Soils, oil and gas production, wildfires and lightning emissions can play key roles in regional air quality. This work provides an important baseline of a pre‐COVID year by which sharp changes in anthropogenic NOx emissions due to the pandemic can be assessed.