Notes

Perspective: electronic paediatrics-so close up yet thus far apart.
To examine the impact of the lockdown caused by the COVID-19 pandemic on both the glycemic control and the daily habits of a group of patients with type 1 diabetes mellitus (T1DM) using flash continuous glucose monitoring devices (flash CGMs).

Retrospective analysis based on all the information gathered in virtual consultations from a cohort of 50 adult patients with T1DM with follow-up at our site. We compared their CGM metrics during lockdown with their own previous data before the pandemic occurred, as well as the potential psychological and therapeutic changes.

We observed a reduction of average glucose values 160.26 ± 22.55 mg/dL vs 150 ± 20.96 mg/dL,
= .0009; estimated glycosylated hemoglobin 7.21 ± 0.78% vs 6.83 ± 0.71%,
= .0005; glucose management indicator 7.15 ± 0.57% vs 6.88 ± 0.49%;
= .0003, and glycemic variability 40.74 ± 6.66 vs 36.43 ± 6.09
< .0001. BAY 2402234 cell line Time in range showed an improvement 57.46 ± 11.85% vs a 65.76 ± 12.09%,
< .0001, without an increase in percentage of time in hypoglycemia.

COVID-19 lockdown was associated with an improvement in glycemic control in patients with T1DM using CGMs.
COVID-19 lockdown was associated with an improvement in glycemic control in patients with T1DM using CGMs.The outbreak of coronavirus disease 2019 showed various transmission rate (R t ) across different regions. The determination of the factors affecting transmission rate is urgent and crucial to combat COVID-19. Here we explored variation of R t between 277 regions across the globe and the associated potential socioeconomic, demographic and environmental factors. At global scale, the R t started to decrease approximately two weeks after policy interventions initiated. This lag from the date of policy interventions initiation to the date when R t started to decrease ranges from 9 to 19 days, largest in Europe and North America. We find that proportion of elderly people or life expectancy can explain 50% of variation in transmission rate across the 277 regions. The transmission rate at the point of inflection (R I ) increases by 29.4% (25.2-34.0%) for 1% uptick in the proportion of people aged above 65, indicating elderly people face ~2.5 times higher infection risk than younger people. Air temperature is negatively correlated with transmission rate, which is mainly attributed to collinearities between air temperature and demographic factors. Our model predicted that temperature sensitivity of R I is only -2.7% (-5.2 - 0%) per °C after excluding collinearities between air temperature and demographic factors. This low temperature sensitivity of R I suggests that a warm summer is unlikely to impede the spread of COVID-19 naturally.
There are limited data pertaining to comparative outcomes of remaining on dialysis versus kidney transplantation as the threat of coronavirus disease 2019 (COVID-19) remains. In this study we delineate the differential risks involved using serologic methods to help define exposure rates.

From a cohort of 1433 patients with end-stage kidney disease (ESKD), we analyzed COVID-19 infection rates and outcomes in 299 waitlist patients compared with 237 transplant recipients within their first year post-transplant. Patients were followed over a 68-day period from the time our transplant program closed due to COVID-19.

The overall mortality rates in waitlist and transplant populations were equivalent (
= 0.69). However, COVID-19 infection was more commonly diagnosed in the waitlist patients (
= 0.001), who were more likely to be tested by reverse transcriptase polymerase chain reaction (
= 0.0004). Once infection was confirmed, mortality risk was higher in the transplant patients (
= 0.015). The seroprevalence in dialysis and transplant patients with undetected infection was 18.3% and 4.6%, respectively (
= 0.0001). After adjusting for potential screening bias, the relative risk of death after a diagnosis of COVID-19 remained higher in transplant recipients (hazard ratio= 3.36 [95% confidence interval = 1.19-9.50],
= 0.022).

Although COVID-19 infection was more common in the waitlist patients, a higher COVID-19‒associated mortality rate was seen in the transplant recipients, resulting in comparable overall mortality rates.
Although COVID-19 infection was more common in the waitlist patients, a higher COVID-19‒associated mortality rate was seen in the transplant recipients, resulting in comparable overall mortality rates.SARS-CoV-2 is a virus belonging to the betacoronavirus family, causing fatal respiratory disease in humans, which became pandemic in 2020. Italy is one of the most affected countries by COVID-19, particularly in the northern regions. Several studies consider COVID-19 a zoonotic disease and, since Italy is the repository of a high biodiversity, SARS-CoV-2 infection in animals can be considered as a reservoir of the virus or favor the spreading between animals and humans. In this work, we analyzed the amino acid sequences of ACE2 protein of the most common domestic and wild animals present in Italy. Among the latter, we focused on ACE2 of the Chiroptera species present in Italy to identify the primary reservoir in this region. First, we reproduced in silico the Chiroptera ACE2/viral spike (S) protein interactions on the human ACE2/SARS-CoV-2 S complex model and identified the critical residues for the binding. In silico molecular docking of ACE2 belonging to Chiroptera vs SARS-CoV-2 S protein pointed to Rhinolophus ferrumequinum as a bat living in Italy, that may be a potential primary reservoir of the virus. On the other hand, a sequence similarity search on ACE2 of domestic and wild animals living in Italy pointed to domestic (horses, cats, cattle and sheep) and wild (European rabbits and grizzly bears) animal species as potential SARS-CoV-2 secondary reservoirs. Molecular docking of ACE2 belonging to these species vs S protein of Bat coronavirus (Bt-CoV/Rp3/2004) suggests that the primary reservoir Rhinolophus ferrumequinum may infect the secondary reservoirs, domestic and worldwide animals living in Italy, determining a specific risk of SARS-CoV-2 infection.As a public health measure during the COVID-19 pandemic, governments around the world instituted a variety of interventions to 'flatten the curve'. The government of Maryland instituted similar measures. We observed a striking decline in paediatric intensive care unit (PICU) admissions during that period, mostly due to a decease in respiratory infections. We believe this decline is multifactorial less person-to-person contact, better air quality and perhaps 'fear' of going to a hospital during the pandemic. We report an analysis of our PICU admissions during the lockdown period and compared them with the same time period during the four previous years.Aging in place (AIP) is a term that is commonly used and defined in a plethora of ways. Multiple disciplines take a different stance on the definition of AIP, and its definition has evolved over time. Such diverse ways to define AIP could be a barrier to reach a shared expectation among multiple stakeholders when formulating research studies, making policy decisions, developing care plans, or designing technology tools to support older adults. We conducted a scoping review for the term AIP to understand specifically how it has been defined across time and disciplines. We collected exemplary definitions of AIP from 7 databases that represent different fields of study; namely, AgeLine, Anthropology Plus, Art and Architecture Source, CINAHL, PsycINFO, PubMed, and SocINDEX. We conducted a thematic analysis to identify the common concepts that emerged across the definitions identified in the scoping review. We developed 3 main categories from the themes space, person, and time to illustrate the root of meaning across the definitions. Intersectionality across the categories yielded a comprehensive understanding of AIP, which does not constrain its definition to a place-related phenomenon. We propose that AIP be defined as "One's journey to maintain independence in one's place of residence as well as to participate in one's community." With this shared understanding of the term AIP, policymakers, researchers, technology designers, and caregivers can better support those who aim to age in the place of their choice.The direct relation between the overweight/obesity, MAFLD and the severity SARS-CoV-2 infection. increase number of cases of obesity and MAFLD is an important risk factor for high mortality of COVID-19 patients.In response to the COVID-19 public health emergency, the Tulsa Health Department created local models. This was an iterative process, with the focus predicting all infections (including asymptomatic and mild cases that would not meet testing criteria,) and deaths for the Tulsa area. SEIR-type models were utilized. Developing infectious disease models is challenging due to data issues related to validity, and complex interrelated assumptions, and this was exacerbated with the COVID-19 crisis. Directly related to these data challenges were challenges with communicating without spreading misinformation, and being clear about the model limitations.Query logs include valuable information for understanding user intent and behavior in Web search. In this article, we investigate COVID-19-related query logs by dividing search sessions into different intent and analyzing the user behavior of groups and individuals. We believe it important to learn about the epidemic's influence on users' search behavior and refine search engine to confront similar epidemic outbreaks in the future.In this poster, we report the preliminary results of an inventory of 149 publicly accessible active COVID-19 tracking systems. Key findings include the frequency distribution of the systems' web domain names, the countries where the systems were created, the languages they support, the visual display format, the map platforms, and the data sources. These findings help to advance the knowledge of the data characteristics and design of pandemic surveillance/tracking systems.The COVID-19 outbreak has posed significant threats to international health and the economy. In the absence of treatment for this virus, public health officials asked the public to practice social distancing to reduce the number of physical contacts. However, quantifying social distancing is a challenging task and current methods are based on human movements. We propose a time and cost-effective approach to measure how people practice social distancing. This study proposes a new method based on utilizing the frequency of hashtags supporting and encouraging social distancing for measuring social distancing. We have identified 18 related hashtags and tracked their trends between Jan and May 2020. Our evaluation results show that there is a strong correlation (p  less then  .05) between our findings and the Google social distancing report.COVID-19 has now become a global pandemic. During the widespread of COVID-19, Twitter, as an online social media platform, has been a preferred channel for interaction and communication. As a result, it provides huge amount of information from which latent signals such as sentiments can be mined for a better understanding of COVID-19 transmission patterns. As a preliminary attempt, we reveal a strongly positive zero-order correlation between sentiments of tweets and COVID-19 confirmed cases in U.S. Considering the unique hierarchical structure of the U.S. government, state governments exert their own power to issue public health policies. Indeed, there are different patterns of correlations between sentiments and COVID-19 confirmed cases, affirming that country-level characteristics suppress that of state-level. Diving deeper into the textual content of COVID-19 related tweets, there manifests a diverse set of topics which in turn lead to dispersed sentiments. Our preliminary investigation paves the way for a finer-grained analysis of the COVID-19 transmission and social media activities by considering varying situations across states and topics.
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