Salomonsenberntsen6977

Fever is one of the most common reasons for pediatric consultation in Africa. Malaria incidence has now dropped considerably, yet etiologies of non-malarial febrile diseases are poorly documented. This pilot study aimed to 1) identify pathogens potentially associated with non-malarial fever in children younger than 10 years in the suburbs of Dakar and 2) describe the epidemiological characteristics of these patients. During the study period, all eligible children ( less then 10 years of age, body temperature ≥ 38°C, negative result for the malaria rapid diagnostic test, living in Guediawaye/Pikine for the previous four calendar months, not receiving any anti-infectious treatment since the onset of fever, and with parent's consent to participate) presenting to the health post in Medina Gounass located in Guediawaye on Mondays and Fridays were included. In total, 106 children participated in the study, and PCR from nasopharyngeal swabs, hemoculture, C-reactive protein, blood cell counts, and quantitative buffy coat from blood samples and coproculture from stool samples were performed. In 70 (66%) children, at least one pathogen was isolated. Viruses were identified in 55 children, most commonly enteroviruses, rhinoviruses, and adenoviruses, and dengue virus was identified in three children. Only five children had bacterial infections, and 10 had bacterial and viral coinfections. Ninety-seven children (92%) received prescription for antibiotics. Many strains of bacteria were found to be resistant to several antibiotics. Despite limitations, this pilot study showed that pathogens potentially associated with non-malarial fever in children younger than 10 years near Dakar were predominantly viruses, most commonly upper respiratory infections, although bacteria accounted for a small proportion.Ultrasensitive PCR used in low-transmission malaria-endemic settings has revealed a much higher burden of asymptomatic infections than that detected by rapid diagnostic tests (RDTs) or standard PCR, but there is limited evidence as to whether this is the case in higher transmission settings. Using dried blood spots (DBS) collected among 319 schoolchildren in Bagamoyo, Tanzania, we found good correlation (Pearson's R = 0.995) between Plasmodium falciparum parasite densities detected by a DNA-based 18s rRNA real-time PCR (qPCR) and an RNA-based ultrasensitive RT-PCR (usPCR) for the same target. Whereas prevalence by usPCR was higher than that found by qPCR (37% versus 32%), the proportion of additionally detected low-density infections (median parasite density less then 0.050 parasites/µL) represented an incremental increase. It remains unclear to what extent these low-density infections may contribute to the infectious reservoir in different malaria transmission settings.

Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people's preferences for AI clinicians and traditional clinicians are worth exploring.

We aimed to quantify and compare people's preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people's preferences were affected by the pressure of pandemic.

We used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017 n=1520; 2020 n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people's preferences for different diagnosis methods.

In total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3%demic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.

The COVID-19 pandemic has negatively affected medical education. However, little data are available about medical students' distress during the pandemic.

This study aimed to provide details on how medical students have been affected by the pandemic.

A cross-sectional study was conducted. A total of 717 medical students participated in the web-based survey. TED-347 The survey included questions about how the participants' mental status had changed from before to after the Japanese nationwide state of emergency (SOE).

Out of 717 medical students, 473 (66.0%) participated in the study. In total, 29.8% (141/473) of the students reported concerns about the shift toward online education, mostly because they thought online education would be ineffective compared with in-person learning. The participants' subjective mental health status significantly worsened after the SOE was lifted (P<.001). Those who had concerns about a shift toward online education had higher odds of having generalized anxiety and being depressed (odds ratio [OR] 1.97, 95% CI 1.19-3.28) as did those who said they would request food aid (OR 1.99, 95% CI 1.16-3.44) and mental health care resources (OR 3.56, 95% CI 2.07-6.15).

Given our findings, the sudden shift to online education might have overwhelmed medical students. Thus, we recommend that educators inform learners that online learning is not inferior to in-person learning, which could attenuate potential depression and anxiety.

Given our findings, the sudden shift to online education might have overwhelmed medical students. Thus, we recommend that educators inform learners that online learning is not inferior to in-person learning, which could attenuate potential depression and anxiety.

In April 2020, two independent clinical trials to assess SARS-CoV-2 prophylaxis strategies among health care workers were initiated at our hospital MeCOVID (melatonin vs placebo) and EPICOS (tenofovir disoproxil/emtricitabine vs hydroxychloroquine vs combination therapy vs placebo).

This study aimed to evaluate the reasons why health care workers chose to participate in the MeCOVID and EPICOS trials, as well as why they chose one over the other.

Both trials were offered to health care workers through an internal news bulletin. After an initial screening visit, all subjects were asked to respond to a web-based survey.

In the first month, 206 health care workers were screened and 160 were randomized. The survey participation was high at 73.3%. Health care workers cited "to contribute to scientific knowledge" (n=80, 53.0%), followed by "to avoid SARS-CoV-2 infection" (n=33, 21.9%) and "the interest to be tested for SARS-CoV-2" (n=28, 18.5%), as their primary reasons to participate in the trials. We observed significant differences in the expected personal benefits across physicians and nurses (P=.01). The vast majority of volunteers (n=202, 98.0%) selected the MeCOVID trial, their primary reason being their concern regarding adverse reactions to treatments in the EPICOS trial (n=102, 69.4%).

Health care workers' reasons to participate in prophylaxis trials in an acute pandemic context appear to be driven largely by their desire to contribute to science and to gain health benefits. Safety outweighed efficacy when choosing between the two clinical trials.

Health care workers' reasons to participate in prophylaxis trials in an acute pandemic context appear to be driven largely by their desire to contribute to science and to gain health benefits. Safety outweighed efficacy when choosing between the two clinical trials.COVID-19 cases are exponentially increasing worldwide; however, its clinical phenotype remains unclear. Natural language processing (NLP) and machine learning approaches may yield key methods to rapidly identify individuals at a high risk of COVID-19 and to understand key symptoms upon clinical manifestation and presentation. Data on such symptoms may not be accurately synthesized into patient records owing to the pressing need to treat patients in overburdened health care settings. In this scenario, clinicians may focus on documenting widely reported symptoms that indicate a confirmed diagnosis of COVID-19, albeit at the expense of infrequently reported symptoms. While NLP solutions can play a key role in generating clinical phenotypes of COVID-19, they are limited by the resulting limitations in data from electronic health records (EHRs). A comprehensive record of clinic visits is required-audio recordings may be the answer. A recording of clinic visits represents a more comprehensive record of patient-reported symptoms. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, thus rapidly generating a clinical phenotype of COVID-19. We propose the generation of a pipeline extending from audio or video recordings of clinic visits to establish a model that factors in clinical symptoms and predict COVID-19 incidence. With vast amounts of available data, we believe that a prediction model can be rapidly developed to promote the accurate screening of individuals at a high risk of COVID-19 and to identify patient characteristics that predict a greater risk of a more severe infection. If clinical encounters are recorded and our NLP model is adequately refined, benchtop virologic findings would be better informed. While clinic visit recordings are not the panacea for this pandemic, they are a low-cost option with many potential benefits, which have recently begun to be explored.

Telemedicine modalities, such as videoconferencing, are used by health care providers to remotely deliver health care to patients. Telemedicine use in pediatrics has increased in recent years. This has resulted in improved health care access, optimized disease management, progress in the monitoring of health conditions, and fewer exposures to patients with illnesses during pandemics (eg, the COVID-19 pandemic).

We aimed to systematically evaluate the most recent evidence on the feasibility and accessibility of telemedicine services, patients' and care providers' satisfaction with these services, and treatment outcomes related to telemedicine service use among pediatric populations with different health conditions.

Studies were obtained from the PubMed database on May 10, 2020. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. In this review, we included randomized controlled trials from the last 10 years that used a telemedicine approach as a study ire research should focus on improving patients' access to care, increasing the cost-effectiveness of telemedicine services, and eliminating barriers to telemedicine use.

Although more research is needed, the evidence from this review suggests that telemedicine services for the general public and pediatric care are comparable to or better than in-person services. Patients, health care professionals, and caregivers may benefit from using both telemedicine services and traditional, in-person health care services. To maximize the potential of telemedicine, future research should focus on improving patients' access to care, increasing the cost-effectiveness of telemedicine services, and eliminating barriers to telemedicine use.