Abstract:Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the virus that causes COVID-19, continues to evolve resistance to vaccines and existing antiviral therapies at an alarming rate, increasing the need for new direct-acting antiviral drugs. Despite significant advances in our fundamental understanding of the kinetics and mechanism of viral RNA replication, there are still open questions regarding how the proofreading exonuclease (NSP10/NSP14 complex) contributes to replication fidelity and resistance … Show more
“…1 Furthermore, remdesivir's efficacy against various variants, including sublineages BQ.1.1 and XBB, has sustained its usage for over two-anda-half years. [2][3][4] Additionally, a meta-analysis revealed an exceptionally low pooled COVID-19 mortality rate associated with its use, and another study demonstrated its potential to reduce mortality among COVID-19 patients requiring supplemental oxygen therapy. 5,6 Despite its clinical benefits, the administration of remdesivir has been associated with the development of hyperglycemia, a condition characterized by elevated blood glucose levels both during and after therapy.…”
Section: Introductionmentioning
confidence: 99%
“… 1 Furthermore, remdesivir's efficacy against various variants, including sublineages BQ.1.1 and XBB, has sustained its usage for over two‐and‐a‐half years. 2 , 3 , 4 Additionally, a meta‐analysis revealed an exceptionally low pooled COVID‐19 mortality rate associated with its use, and another study demonstrated its potential to reduce mortality among COVID‐19 patients requiring supplemental oxygen therapy. 5 , 6 …”
The primary objective of this study was to investigate the factors contributing to hyperglycemic adverse events associated with the administration of remdesivir in hospitalized patients diagnosed with coronavirus disease 2019 (COVID‐19). Furthermore, the study aimed to develop a risk score model employing various machine learning approaches. A total of 1262 patients were enrolled in this investigation. The relationship between covariates and hyperglycemic adverse events was assessed through logistic regression analysis. Diverse machine learning algorithms were employed for the purpose of forecasting hyperglycemia‐related complications. After adjusting for covariates, individuals with a body mass index ≥ 23 kg/m2, those using proton pump inhibitors, cholinergic medications, or individuals with cardiovascular diseases exhibited approximately 2.41‐, 2.73‐, 2.65‐, and 1.97‐fold higher risks of experiencing hyperglycemic adverse events (95% confidence intervals (CIs) 1.271‐4.577, 1.223‐6.081, 1.168‐5.989, and 1.119‐3.472, respectively). Multivariate logistic regression, elastic net, and random forest models displayed area under the receiver operating characteristic curve values of 0.65, 0.66, and 0.60, respectively (95% CI 0.572 ‐ 0.719, 0.640 ‐ 0.671, and 0.583 ‐ 0.611, respectively). This study comprehensively explored factors associated with hyperglycemic complications arising from remdesivir administration and, concurrently, leveraged a range of machine learning methodologies to construct a risk scoring model, thereby facilitating the tailoring of individualized remdesivir treatment regimens for patients with COVID‐19.
“…1 Furthermore, remdesivir's efficacy against various variants, including sublineages BQ.1.1 and XBB, has sustained its usage for over two-anda-half years. [2][3][4] Additionally, a meta-analysis revealed an exceptionally low pooled COVID-19 mortality rate associated with its use, and another study demonstrated its potential to reduce mortality among COVID-19 patients requiring supplemental oxygen therapy. 5,6 Despite its clinical benefits, the administration of remdesivir has been associated with the development of hyperglycemia, a condition characterized by elevated blood glucose levels both during and after therapy.…”
Section: Introductionmentioning
confidence: 99%
“… 1 Furthermore, remdesivir's efficacy against various variants, including sublineages BQ.1.1 and XBB, has sustained its usage for over two‐and‐a‐half years. 2 , 3 , 4 Additionally, a meta‐analysis revealed an exceptionally low pooled COVID‐19 mortality rate associated with its use, and another study demonstrated its potential to reduce mortality among COVID‐19 patients requiring supplemental oxygen therapy. 5 , 6 …”
The primary objective of this study was to investigate the factors contributing to hyperglycemic adverse events associated with the administration of remdesivir in hospitalized patients diagnosed with coronavirus disease 2019 (COVID‐19). Furthermore, the study aimed to develop a risk score model employing various machine learning approaches. A total of 1262 patients were enrolled in this investigation. The relationship between covariates and hyperglycemic adverse events was assessed through logistic regression analysis. Diverse machine learning algorithms were employed for the purpose of forecasting hyperglycemia‐related complications. After adjusting for covariates, individuals with a body mass index ≥ 23 kg/m2, those using proton pump inhibitors, cholinergic medications, or individuals with cardiovascular diseases exhibited approximately 2.41‐, 2.73‐, 2.65‐, and 1.97‐fold higher risks of experiencing hyperglycemic adverse events (95% confidence intervals (CIs) 1.271‐4.577, 1.223‐6.081, 1.168‐5.989, and 1.119‐3.472, respectively). Multivariate logistic regression, elastic net, and random forest models displayed area under the receiver operating characteristic curve values of 0.65, 0.66, and 0.60, respectively (95% CI 0.572 ‐ 0.719, 0.640 ‐ 0.671, and 0.583 ‐ 0.611, respectively). This study comprehensively explored factors associated with hyperglycemic complications arising from remdesivir administration and, concurrently, leveraged a range of machine learning methodologies to construct a risk scoring model, thereby facilitating the tailoring of individualized remdesivir treatment regimens for patients with COVID‐19.
SARS-CoV-2, the causative virus of the COVID-19 pandemic, follows SARS and MERS as recent zoonotic coronaviruses causing severe respiratory illness and death in humans. The recurrent impact of zoonotic coronaviruses demands a better understanding of their fundamental molecular biochemistry. Nucleoside modifications, which modulate many steps of the RNA lifecycle, have been found in SARS-CoV-2 RNA, although whether they confer a pro- or anti-viral effect is unknown. Regardless, the viral RNA-dependent RNA polymerase will encounter these modifications as it transcribes through the viral genomic RNA. We investigated the functional consequences of nucleoside modification on the pre-steady state kinetics of SARS-CoV-2 RNA-dependent RNA transcription using an in vitro reconstituted transcription system with modified RNA templates. Our findings show that N6-methyladenosine and 2’O-methyladenosine modifications slow the rate of viral transcription at magnitudes specific to each modification, which has the potential to impact SARS-CoV-2 genome maintenance.
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