Risk Factors Associated With Opioid/Benzodiazepine Iatrogenic Withdrawal Syndrome in COVID-19 Acute Respiratory Distress Syndrome.

BackgroundMechanically ventilated COVID-19 acute respiratory distress syndrome (ARDS) patients often receive deeper sedation and analgesia to maintain respiratory compliance and minimize staff exposure, which incurs greater risk of iatrogenic withdrawal syndrome (IWS) and has been associated with worse patient outcomes. Objective: To identify potential risk factors and differences in patient outcomes associated with the development of IWS in COVID-19 ARDS patients. Methods: Retrospective analysis of ventilated COVID-19 ARDS intensive care unit (ICU) patients who received continuous intravenous (IV) analgesia and sedation for ≥5 days from March 2020-May 2021. Patients were classified as IWS and non-IWS based on receipt of scheduled oral sedative/analgesic regimens after cessation of IV therapy. Risk factors were assessed in univariate analyses and multivariable modeling. Results: A total of 115 patients were included. The final multivariable model showed: (1) each additional day of IV opioid therapy was associated with an 8% increase in odds of IWS (95% CI, 1.02-1.14), (2) among sedatives, receipt of lorazepam was associated with 3 times higher odds of IWS (95% CI 1.12-8.15), and (3) each 1-point increase in Simplified Acute Physiology Score (SAPS) II was associated with a 4% reduction in odds of IWS (95% CI 0.93-0.999). Conclusion: Prolonged and high dose exposures to IV opioids and benzodiazepines should be limited when possible. Additional prospective studies are needed to identify modifiable risk factors to prevent IWS.

A Bioinformatics Approach to Investigate Structural and Non-Structural Proteins in Human Coronaviruses.

Recent studies confirmed that people unexposed to SARS-CoV-2 have preexisting reactivity, probably due to previous exposure to widely circulating common cold coronaviruses. Such preexistent reactivity against SARS-CoV-2 comes from memory T cells that can specifically recognize a SARS-CoV-2 epitope of structural and non-structural proteins and the homologous epitopes from common cold coronaviruses. Therefore, it is important to understand the SARS-CoV-2 cross-reactivity by investigating these protein sequence similarities with those of different circulating coronaviruses. In addition, the emerging SARS-CoV-2 variants lead to an intense interest in whether mutations in proteins (especially in the spike) could potentially compromise vaccine effectiveness. Since it is not clear that the differences in clinical outcomes are caused by common cold coronaviruses, a deeper investigation on cross-reactive T-cell immunity to SARS-CoV-2 is crucial to examine the differential COVID-19 symptoms and vaccine performance. Therefore, the present study can be a starting point for further research on cross-reactive T cell recognition between circulating common cold coronaviruses and SARS-CoV-2, including the most recent variants Delta and Omicron. In the end, a deep learning approach, based on Siamese networks, is proposed to accurately and efficiently calculate a BLAST-like similarity score between protein sequences.

A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation

Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method was evaluated on the segmentation of chest radiographic images, showing promising results. The multi-stage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach.

Clinical characteristics and predictors of survival in adults with coronavirus disease 2019 receiving tocilizumab.

Coronavirus disease 2019 (COVID-19) can progress to cytokine storm that is associated with organ dysfunction and death. The purpose of the present study is to determine clinical characteristics associated with 28 day in-hospital survival in patients with coronavirus disease 2019 (COVID-19) that received tocilizumab. This was a retrospective observational cohort study conducted at a five hospital health system in Michigan, United States. Adult patients with confirmed COVID-19 that were admitted to the hospital and received tocilizumab for cytokine storm from March 1, 2020 through April 3, 2020 were included. Patients were grouped into survivors and non-survivors based on 28 day in-hospital mortality. Study day 0 was defined as the day tocilizumab was administered. Factors independently associated with in-hospital survival at 28 days after tocilizumab administration were assessed. Epidemiologic, demographic, laboratory, prognostic scores, treatment, and outcome data were collected and analyzed. Clinical response was collected and defined as a decline of two levels on a six-point ordinal scale of clinical status or discharged alive from the hospital. Of the 81 patients included, the median age was 64 (58-71) years and 56 (69.1%) were male. The 28 day in-hospital mortality was 43.2%. There were 46 (56.8%) patients in the survivors and 35 (43.2%) in the non-survivors group. On study day 0 no differences were noted in demographics, clinical characteristics, severity of illness scores, or treatments received between survivors and non-survivors. C-reactive protein was significantly higher in the non-survivors compared to survivors. Compared to non-survivors, recipients of tocilizumab within 12 days of symptom onset was independently associated with survival (adjusted OR: 0.296, 95% CI: 0.098-0.889). SOFA score ≥8 on day 0 was independently associated with mortality (adjusted OR: 2.842, 95% CI: 1.042-7.753). Clinical response occurred more commonly in survivors than non-survivors (80.4% vs. 5.7%; p < 0.001). Improvements in the six-point ordinal scale and SOFA score were observed in survivors after tocilizumab. Early receipt of tocilizumab in patients with severe COVID-19 was an independent predictor for in-hospital survival at 28 days.

A possible strategy to fight COVID-19: Interfering with spike glycoprotein trimerization.

The recent release of COVID-19 spike glycoprotein allows detailed analysis of the structural features that are required for stabilizing the infective form of its quaternary assembly. Trying to disassemble the trimeric structure of COVID-19 spike glycoprotein, we analyzed single protomer surfaces searching for concave moieties that are located at the three protomer-protomer interfaces. The presence of some druggable pockets at these interfaces suggested that some of the available drugs in Drug Bank could destabilize the quaternary spike glycoprotein formation by binding to these pockets, therefore interfering with COVID-19 life cycle. The approach we propose here can be an additional strategy to fight against the deadly virus. Ligands of COVID-19 spike glycoprotein that we have predicted in the present computational investigation, might be the basis for new experimental studies in vitro and in vivo.