Articles: sars-cov-2.
-
The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. ⋯ ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.
-
Background: SARS-CoV-2 infection causes immune response and produces protective antibodies, and these changes may persist after patients discharged from hospital. Methods: This study conducted a one-year follow-up study on patients with COVID-19 to observe the dynamic changes of circulating leukocyte subsets and virus-specific antibodies. Results: A total of 66 patients with COVID-19 and 213 healthy patients with inactivated SARS-CoV-2 vaccination were included. ⋯ The counts of CD4+ and CD8+ T, B and NK cells increased with the time of recovery, and remained basically stable from 9 to 12 months after discharge. After 12 months, the positivity of IgG antibody was 85.3% and IgM was 11.8%, while the virus-specific antibody changed dynamically in patients within one year after discharge. Conclusions: The SARS-CoV-2 specific antibody of recovered patients showed dynamic fluctuation after discharge, while the leukocyte subsets gradually increased and basically stabilized after 9 months.
-
Data on neonatal COVID-19 are limited to the immediate postnatal period, with a primary focus on vertical transmission in inborn infants. This study was aimed to assess the characteristics and outcome of COVID-19 in outborn neonates. ⋯ SARS-CoV-2 positivity rate among the outborn neonates reporting to the paediatric emergency and tested for COVID-19 was observed to be low. The selective testing policy had poor diagnostic accuracy in distinguishing COVID-19 from non-COVID illness.
-
Since the spread of Severe Acute Respiratory Syndrom Corona Virus 2 (SARS-CoV‑2) in Germany, intensive care beds have been kept free for patients suffering from Corona Virus Disease (COVID-19). Also, after the number of infections had declined, intensive care beds were kept free prophylactically; however, the percentage of beds reserved for COVID-19 differ in the individual federal states in Germany. The aim of this article is to define a necessary clearance quota of intensive beds for COVID-19 patients in Germany. An escalation and de-escalation scheme was created for rising and falling numbers of infected patients. ⋯ If the number of infections is low a general nationwide retention rate of more than 10% of intensive care beds for COVID-19 patients is not justified. Locally increasing numbers of infections require a local dynamic approach. If the number of infections increases, the free holding capacity should be increased according to a step by step concept in close coordination with the local health authorities and other internal hospital triggers. In order not to overwhelm hospital capacities in the event of local outbreaks, a corresponding relocation concept should be considered at an early stage.
-
The aim of the present study was to evaluate if neutralizing antibody responses induced by infection with the SARS-CoV-2 strain that was dominant at the beginning of the pandemic or by the Gamma variant was effective against the Omicron variant. ⋯ Neutralizing antibodies generated following mild or moderate infection with the SARS-CoV-2 ancestral strain or the Gamma variant are not protective against the Omicron variant.