Efficacy and safety of awake prone positioning in the treatment of non-intubated spontaneously breathing patients with COVID-19-related acute respiratory failure: A systematic review and meta-analysis.

Background: Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, prone positioning has been widely applied for non-intubated, spontaneously breathing patients. However, the efficacy and safety of prone positioning in non-intubated patients with COVID-19-related acute hypoxemic respiratory failure remain unclear. We aimed to systematically analyze the outcomes associated with awake prone positioning (APP). Methods: We conducted a systematic literature search of PubMed/MEDLINE, Cochrane Library, Embase, and Web of Science from January 1, 2020, to June 3, 2022. This study included adult patients with acute respiratory failure caused by COVID-19. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study quality was assessed using the Cochrane risk-of-bias tool. The primary outcome was the reported cumulative intubation risk across randomized controlled trials (RCTs), and the effect estimates were calculated as risk ratios (RRs; 95% confidence interval [CI]). Results: A total of 495 studies were identified, of which 10 fulfilled the selection criteria, and 2294 patients were included. In comparison to supine positioning, APP significantly reduced the need for intubation in the overall population (RR=0.84, 95% CI: 0.74-0.95). The two groups showed no significant differences in the incidence of adverse events (RR=1.16, 95% CI: 0.48-2.76). The meta-analysis revealed no difference in mortality between the groups (RR=0.93, 95% CI: 0.77-1.11). Conclusions: APP was safe and reduced the need for intubation in patients with respiratory failure associated with COVID-19. However, it did not significantly reduce mortality in comparison to usual care without prone positioning.

Efficacy and safety of awake prone positioning in the treatment of non-intubated spontaneously breathing patients with COVID-19-related acute respiratory failure: A systematic review and meta-analysis

Background Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, prone positioning has been widely applied for non-intubated, spontaneously breathing patients. However, the efficacy and safety of prone positioning in non-intubated patients with COVID-19-related acute hypoxemic respiratory failure remain unclear. We aimed to systematically analyze the outcomes associated with awake prone positioning (APP). Methods We conducted a systematic literature search of PubMed/MEDLINE, Cochrane Library, Embase, and Web of Science from January 1, 2020, to June 3, 2022. This study included adult patients with acute respiratory failure caused by COVID-19. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study quality was assessed using the Cochrane risk-of-bias tool. The primary outcome was the reported cumulative intubation risk across randomized controlled trials (RCTs), and the effect estimates were calculated as risk ratios (RRs;95% confidence interval [CI]). Results A total of 495 studies were identified, of which 10 fulfilled the selection criteria, and 2294 patients were included. In comparison to supine positioning, APP significantly reduced the need for intubation in the overall population (RR=0.84, 95% CI: 0.74–0.95). The two groups showed no significant differences in the incidence of adverse events (RR=1.16, 95% CI: 0.48–2.76). The meta-analysis revealed no difference in mortality between the groups (RR=0.93, 95% CI: 0.77–1.11). Conclusions APP was safe and reduced the need for intubation in patients with respiratory failure associated with COVID-19. However, it did not significantly reduce mortality in comparison to usual care without prone positioning.

An ultrasensitive fluorescence aptasensor for SARS-CoV-2 antigen based on hyperbranched rolling circle amplification.

Sensitive and accurate diagnosis of SARS-CoV-2 infection at early stages can help to attenuate the effects of the COVID-19. Compared to RNA and antibodies detection, direct detection of viral antigens could reflect infectivity more appropriately. However, it is still a great challenge to construct a convenient, accurate and sensitive biosensor with a suitable molecular recognition element for SARS-CoV-2 antigens. Herein, we report a HRCA-based aptasensor for simple, ultrasensitive and quantitative detection of SARS-CoV-2 S1 protein and pseudovirus. The aptamer sequence used here is selected from several published aptamers by enzyme-linked oligonucleotide assay and molecular docking simulation. The sensor forms an antibody-target-aptamer sandwich complex on the surface of microplates and elicits HRCA for fluorescent detection. Without complicated operations or special instruments and reagents, the aptasensor can detect S1 protein with a LOD of 89.7 fg/mL in the linear range of 100 fg/mL to 1 µg/mL. And it can also detect SARS-CoV-2 spike pseudovirus in artificial saliva with a LOD of 51 TU/µL. Therefore, this simple and ultrasensitive aptasensor has the potential to detect SARS-CoV-2 infection at early stages. It may improve the timeliness and accuracy of SARS-CoV-2 diagnosis and demonstrate a strategy to conduct aptasensors for other targets.

Pleural Disease in Acute COVID-19 Pneumonia: A Single Center Retrospective Cohort Study.

BACKGROUND: Pleural diseases encompass pleural effusion and pneumothorax (PTX), both of which were uncommon in coronavirus disease of 2019 (COVID-19). We aimed to describe the frequency, characteristics, and main outcomes of these conditions in patients with COVID-19 pneumonia. METHODS: We performed a retrospective cohort analysis of inpatients with COVID-19 pneumonia between January 1, 2020 and January 1, 2022, at Beth Israel Deaconess Medical Center in Boston, Massachusetts. RESULTS: Among 4419 inpatients with COVID-19 pneumonia, 109 (2.5%) had concurrent pleural disease. Ninety-four (2.1%) had pleural effusion (50% seen on admission) and 15 (0.3%) had PTX, both with higher rates of underlying conditions such as heart failure, liver disease, kidney disease, and malignancy. A total of 28 (30%) pleural effusions were drained resulting in 32% being exudative, 43% pseudoexudative, and 25% transudative. Regarding PTX, 5 (33%) were spontaneous and 10 (67%) were due to barotrauma while on mechanical ventilation. We found that the presence of underlying lung disease was not associated with an increased risk of developing PTX. In addition, patients with pleural disease had a higher incidence of severe or critical illness as represented by intensive care unit admission and intubation, longer hospital and intensive care unit stay, and a higher mortality rate as compared with patients without the pleural disease. CONCLUSION: Pleural effusions and pneumothoraces are infrequent findings in patients admitted due to COVID-19 pneumonia, worsened outcomes in these patients likely reflect an interplay between the severity of inflammation and parenchymal injury due to COVID-19 disease and underlying comorbidities.

Applications of Artificial Intelligence in Myopia: Current and Future Directions.

With the continuous development of computer technology, big data acquisition and imaging methods, the application of artificial intelligence (AI) in medical fields is expanding. The use of machine learning and deep learning in the diagnosis and treatment of ophthalmic diseases is becoming more widespread. As one of the main causes of visual impairment, myopia has a high global prevalence. Early screening or diagnosis of myopia, combined with other effective therapeutic interventions, is very important to maintain a patient's visual function and quality of life. Through the training of fundus photography, optical coherence tomography, and slit lamp images and through platforms provided by telemedicine, AI shows great application potential in the detection, diagnosis, progression prediction and treatment of myopia. In addition, AI models and wearable devices based on other forms of data also perform well in the behavioral intervention of myopia patients. Admittedly, there are still some challenges in the practical application of AI in myopia, such as the standardization of datasets; acceptance attitudes of users; and ethical, legal and regulatory issues. This paper reviews the clinical application status, potential challenges and future directions of AI in myopia and proposes that the establishment of an AI-integrated telemedicine platform will be a new direction for myopia management in the post-COVID-19 period.

Analysis of Epidemic Characteristics and Related Pathogenic Factors of 2726 Cases of Pityriasis Alba.

PURPOSE: To understand the distribution characteristics of onset time, onset age and gender of pityriasis alba (PA) patients in the dermatology clinic of our hospital and to further explore the pathogenesis of the disease to provide a scientific basis for the prevention and treatment of this disease. PATIENTS AND METHODS: The clinical data of 2726 outpatients with PA diagnosed for the first time from January 2016 to December 2020 were collected and descriptively analyzed. RESULTS: The number of patients with PA was less from January to March. The peak was reached in July and August. The number of cases affected by the COVID-19 epidemic in 2020 was significantly lower than that in previous years. Furthermore, the onset age of the patients ranged from 0 to 64 years old, and the median age of the total population was 7 (3, 13) years old, including 1566 males (57.45%) and 1160 females (42.55%). The ratio of male to female was 1.35:1. The number of male patients before 18 years old was higher than that of female patients, especially in the high paroxysmal age group. CONCLUSION: PA can be seen all the year round, and the onset peak of the disease is from July to August every year. It occurs frequently at the age of 1 to 14 before puberty. In the season of high incidence of PA, the protection from sun and moisture retention should be strengthened for infants and adolescents.

Machine Learning Models of COVID-19 Cases in the United States: A Study of Initial Lockdown and Reopen Regimes

The purpose of this paper is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Our novel contribution is that we have obtained highly accurate models focused on two different regimes, lockdown and reopen, modeling each regime separately. The predictor variables include aggregated individual movement as well as state population density, health rank, climate temperature, and political color. We apply a variety of machine learning methods to each regime: Multiple Regression, Ridge Regression, Elastic Net Regression, Generalized Additive Model, Gradient Boosted Machine, Regression Tree, Neural Network, and Random Forest. We discover that Gradient Boosted Machines are the most accurate in both regimes. The best models achieve a variance explained of 95.2% in the lockdown regime and 99.2% in the reopen regime. We describe the influence of the predictor variables as they change from regime to regime. Notably, we identify individual person movement, as tracked by GPS data, to be an important predictor variable. We conclude that government lockdowns are an extremely important de-densification strategy. Implications and questions for future research are discussed.