Post-coronavirus disease condition (PCC) continues to affect many people globally, yet there remains a lack of diagnostic biomarkers to distinguish PCC from those recovered from acute COVID-19. This study compared biomarkers between two age- and gender-matched groups: PCC individuals and those recovered within three months of acute COVID-19 in 2020 ( = 85 each). Biomarkers were assessed 12-24 months after initial diagnosis, examining biochemical profiles, blood cell counts, coagulation status, antibody serology, lymphocyte populations, and cytokine levels. PCC individuals exhibited significant alterations in 49 of 167 markers, including K+ levels, αGAD antibodies, antithrombin III, insulin-like growth factor-binding protein 3 (IGFBP3), and interleukin-10 (IL-10). A panel of αGAD, IL-10, potassium levels, and CD16CD56 cell presence distinguished PCC individuals from recovered patients with >88% accuracy and <92% precision.
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http://dx.doi.org/10.1016/j.isci.2024.110839 | DOI Listing |
BMC Infect Dis
January 2025
Department of Pulmonology, Semmelweis University, Budapest, Hungary.
Background: Post-COVID condition (PCC) is characterized by persisting symptoms after the resolution of acute COVID-19. Remdesivir (RDV), a broad-spectrum antiviral drug, has been widely used in patients hospitalized with COVID-19 requiring oxygen therapy. We aimed to evaluate the effects of RDV on PCC by assessing patient-reported and functional outcomes.
View Article and Find Full Text PDFBMJ Open
January 2025
Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada.
Objective: Many individuals exposed to SARS-CoV-2 experience long-term symptoms as part of a syndrome called post-COVID condition (PCC). Research on PCC is still emerging but is urgently needed to support diagnosis, clinical treatment guidelines and health system resource allocation. In this study, we developed a method to identify PCC cases using administrative health data and report PCC prevalence and predictive factors in Manitoba, Canada.
View Article and Find Full Text PDFJ Surg (Lisle)
November 2024
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Purpose: Appropriate opioid management is crucial to reduce opioid overdose risk for ICU surgical patients, which can lead to severe complications. Accurately predicting postoperative opioid needs and understanding the associated factors can effectively guide appropriate opioid use, significantly enhancing patient safety and recovery outcomes. Although machine learning models can accurately predict postoperative opioid needs, lacking interpretability hinders their adoption in clinical practice.
View Article and Find Full Text PDFNurs Crit Care
January 2025
Paediatric Critical Care, Birmingham Children's Hospital, Birmingham, UK.
Background: Research has demonstrated that staff working in Paediatric Critical Care (PCC) experience high levels of burnout, post-traumatic stress and moral distress. There is very little evidence of how this problem could be addressed.
Aim: To develop evidence-based, psychologically informed interventions designed to improve PCC staff well-being that can be feasibility tested on a large scale.
BMC Med Imaging
January 2025
Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Purpose: We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs).
Methods: 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%).
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