Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes.

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Prophylactic Inferior Vena Cava Filters for Venous Thromboembolism in Adults With Trauma: An Updated Systematic Review and Meta-Analysis.

Background: Trauma is an independent risk factor for venous thromboembolism (VTE). Due to contraindications or delay in starting pharmacological prophylaxis among trauma patients with a high risk of bleeding, the inferior vena cava (IVC) filter has been utilized as alternative prevention for pulmonary embolism (PE). Albeit, its clinical efficacy has remained uncertain. Therefore, we performed an updated systematic review and meta-analysis on the effectiveness and safety of prophylactic IVC filters in severely injured patients. Methods: Three databases (MEDLINE, EMBASE, and Cochrane) were searched from August 1, 2012, to October 27, 2021. Independent reviewers performed data extraction and quality assessment. Relative risk (RR) at 95% confidence interval (CI) pooled in a randomized meta-analysis. A parallel clinical practice guideline committee assessed the certainty of evidence using the GRADE approach. The outcomes of interest included VTE, PE, deep venous thrombosis, mortality, and IVC filter complications. Results: We included 10 controlled studies (47 140 patients), of which 3 studies (310 patients) were randomized controlled trials (RCTs) and 7 were observational studies (46 830 patients). IVC filters demonstrated no significant reduction in PE and fatal PE (RR, 0.27; 95% CI, 0.06-1.28 and RR, 0.32; 95% CI, 0.01-7.84, respectively) by pooling RCTs with low certainty. However, it demonstrated a significant reduction in the risk of PE and fatal PE (RR, 0.25; 95% CI, 0.12-0.55 and RR, 0.09; 95% CI, 0.011-0.81, respectively) by pooling observational studies with very low certainty. IVC filter did not improve mortality in both RCTs and observational studies (RR, 1.44; 95% CI, 0.86-2.43 and RR, 0.63; 95% CI, 0.3-1.31, respectively). Conclusion: In trauma patients, moderate risk reduction of PE and fatal PE was demonstrated among observational data but not RCTs. The desirable effect is not robust to outweigh the undesirable effects associated with IVC filter complications. Current evidence suggests against routinely using prophylactic IVC filters.

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

Coarse-Grained Molecular Simulations and Ensemble-Based Mutational Profiling of Protein Stability in the Different Functional Forms of the SARS-CoV-2 Spike Trimers: Balancing Stability and Adaptability in BA.1, BA.2 and BA.2.75 Variants.

Evolutionary and functional studies have suggested that the emergence of Omicron variants can be determined by multiple fitness tradeoffs including immune escape, binding affinity, conformational plasticity, protein stability, and allosteric modulation. In this study, we embarked on a systematic comparative analysis of the conformational dynamics, electrostatics, protein stability, and allostery in the different functional states of spike trimers for BA.1, BA.2, and BA.2.75 variants. Using efficient and accurate coarse-grained simulations and atomistic reconstruction of the ensembles, we examined the conformational dynamics of the spike trimers that agree with the recent functional studies, suggesting that BA.2.75 trimers are the most stable among these variants. A systematic mutational scanning of the inter-protomer interfaces in the spike trimers revealed a group of conserved structural stability hotspots that play a key role in the modulation of functional dynamics and are also involved in the inter-protomer couplings through local contacts and interaction networks with the Omicron mutational sites. The results of mutational scanning provided evidence that BA.2.75 trimers are more stable than BA.2 and comparable in stability to the BA.1 variant. Using dynamic network modeling of the S Omicron BA.1, BA.2, and BA.2.75 trimers, we showed that the key network mediators of allosteric interactions are associated with the major stability hotspots that are interconnected along potential communication pathways. The network analysis of the BA.1, BA.2, and BA.2.75 trimers suggested that the increased thermodynamic stability of the BA.2.75 variant may be linked with the organization and modularity of the residue interaction network that allows for allosteric communications between structural stability hotspots and Omicron mutational sites. This study provided a plausible rationale for a mechanism in which Omicron mutations may evolve by targeting vulnerable sites of conformational adaptability to elicit immune escape while maintaining their control on balancing protein stability and functional fitness through robust allosteric communications with the stability hotspots.

An Insight into Advances in Developing Nanotechnology Based Therapeutics, Drug Delivery, Diagnostics and Vaccines: Multidimensional Applications in Tuberculosis Disease Management.

Tuberculosis (TB), one of the deadliest contagious diseases, is a major concern worldwide. Long-term treatment, a high pill burden, limited compliance, and strict administration schedules are all variables that contribute to the development of MDR and XDR tuberculosis patients. The rise of multidrug-resistant strains and a scarcity of anti-TB medications pose a threat to TB control in the future. As a result, a strong and effective system is required to overcome technological limitations and improve the efficacy of therapeutic medications, which is still a huge problem for pharmacological technology. Nanotechnology offers an interesting opportunity for accurate identification of mycobacterial strains and improved medication treatment possibilities for tuberculosis. Nano medicine in tuberculosis is an emerging research field that provides the possibility of efficient medication delivery using nanoparticles and a decrease in drug dosages and adverse effects to boost patient compliance with therapy and recovery. Due to their fascinating characteristics, this strategy is useful in overcoming the abnormalities associated with traditional therapy and leads to some optimization of the therapeutic impact. It also decreases the dosing frequency and eliminates the problem of low compliance. To develop modern diagnosis techniques, upgraded treatment, and possible prevention of tuberculosis, the nanoparticle-based tests have demonstrated considerable advances. The literature search was conducted using Scopus, PubMed, Google Scholar, and Elsevier databases only. This article examines the possibility of employing nanotechnology for TB diagnosis, nanotechnology-based medicine delivery systems, and prevention for the successful elimination of TB illnesses.

Tocilizumab Outcomes in Critically Ill COVID-19 Patients Admitted to the ICU and the Role of Non-Tocilizumab COVID-19-Specific Medical Therapeutics.

BACKGROUND: Tocilizumab is a monoclonal antibody proposed to manage cytokine release syndrome (CRS) associated with severe COVID-19. Previously published reports have shown that tocilizumab may improve the clinical outcomes of critically ill patients admitted to the ICU. However, no precise data about the role of other medical therapeutics concurrently used for COVID-19 on this outcome have been published. OBJECTIVES: We aimed to compare the overall outcome of critically ill COVID-19 patients admitted to the ICU who received tocilizumab with the outcome of matched patients who did not receive tocilizumab while controlling for other confounders, including medical therapeutics for critically ill patients admitted to ICUs. METHODS: A prospective, observational, multicenter cohort study was conducted among critically ill COVID-19 patients admitted to the ICU of 14 hospitals in Saudi Arabia between 1 March 2020, and October 31, 2020. Propensity-score matching was utilized to compare patients who received tocilizumab to patients who did not. In addition, the log-rank test was used to compare the 28 day hospital survival of patients who received tocilizumab with those who did not. Then, a multivariate logistic regression analysis of the matched groups was performed to evaluate the impact of the remaining concurrent medical therapeutics that could not be excluded via matching 28 day hospital survival rates. The primary outcome measure was patients' overall 28 day hospital survival, and the secondary outcomes were ICU length of stay and ICU survival to hospital discharge. RESULTS: A total of 1470 unmatched patients were included, of whom 426 received tocilizumab. The total number of propensity-matched patients was 1278. Overall, 28 day hospital survival revealed a significant difference between the unmatched non-tocilizumab group (586; 56.1%) and the tocilizumab group (269; 63.1%) (p-value = 0.016), and this difference increased even more in the propensity-matched analysis between the non-tocilizumab group (466.7; 54.6%) and the tocilizumab group (269; 63.1%) (p-value = 0.005). The matching model successfully matched the two groups' common medical therapeutics used to treat COVID-19. Two medical therapeutics remained significantly different, favoring the tocilizumab group. A multivariate logistic regression was performed for the 28 day hospital survival in the propensity-matched patients. It showed that neither steroids (OR: 1.07 (95% CI: 0.75-1.53)) (p = 0.697) nor favipiravir (OR: 1.08 (95% CI: 0.61-1.9)) (p = 0.799) remained as a predictor for an increase in 28 day survival. CONCLUSION: The tocilizumab treatment in critically ill COVID-19 patients admitted to the ICU improved the overall 28 day hospital survival, which might not be influenced by the concurrent use of other COVID-19 medical therapeutics, although further research is needed to confirm this.

Long-term outcomes of patients with COVID-19 treated with helmet noninvasive ventilation or usual respiratory support: follow-up study of the Helmet-COVID randomized clinical trial.

PURPOSE: To evaluate whether helmet noninvasive ventilation compared to usual respiratory support reduces 180-day mortality and improves health-related quality of life (HRQoL) in patients with acute hypoxemic respiratory failure due to COVID-19 pneumonia. METHODS: This is a pre-planned follow-up study of the Helmet-COVID trial. In this multicenter, randomized clinical trial, adults with acute hypoxemic respiratory failure (n = 320) due to coronavirus disease 2019 (COVID-19) were randomized to receive helmet noninvasive ventilation or usual respiratory support. The modified intention-to-treat population consisted of all enrolled patients except three who were lost at follow-up. The study outcomes were 180-day mortality, EuroQoL (EQ)-5D-5L index values, and EQ-visual analog scale (EQ-VAS). In the modified intention-to-treat analysis, non-survivors were assigned a value of 0 for EQ-5D-5L and EQ-VAS. RESULTS: Within 180 days, 63/159 patients (39.6%) died in the helmet noninvasive ventilation group compared to 65/158 patients (41.1%) in the usual respiratory support group (risk difference - 1.5% (95% confidence interval [CI] - 12.3, 9.3, p = 0.78). In the modified intention-to-treat analysis, patients in the helmet noninvasive ventilation and the usual respiratory support groups did not differ in EQ-5D-5L index values (median 0.68 [IQR 0.00, 1.00], compared to 0.67 [IQR 0.00, 1.00], median difference 0.00 [95% CI - 0.32, 0.32; p = 0.91]) or EQ-VAS scores (median 70 [IQR 0, 93], compared to 70 [IQR 0, 90], median difference 0.00 (95% CI - 31.92, 31.92; p = 0.55). CONCLUSIONS: Helmet noninvasive ventilation did not reduce 180-day mortality or improve HRQoL compared to usual respiratory support among patients with acute hypoxemic respiratory failure due to COVID-19 pneumonia.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

Convalescent Plasma Therapy against COVID-19: An Update on the Changing Facets of the ongoing Pandemic.

The severe respiratory infections in the current pandemic coronavirus disease-19 (COVID-19) have influenced more or less every human life. The first person to get infected with this virus was reported in the capital of Hubei province (Wuhan), China, in late December 2019. Since the disease has been declared a pandemic, research scholars and experts have been manufacturing new vaccines or targeted therapies to curb the spread of SARS-CoV-2. However, only limited options have emerged so far, which yet require complete scientific validation by long-term data collection regarding safety and efficacy. In the wake of the recent emerging wave of the pandemic viz omicron variant, changing facets of the viral genome and dearth of preventative and therapeutic possibilities for the management of COVID-19, the usage of Convalescent Plasma Therapy (CPT) may be looked at as a potentially viable option of treatment in the existing situation. Earlier, immune plasma has been used with success in the management of H1N1 influenza virus, MERS-CoV, and SARS-CoV-1 epidemics. In the present unpredictable situation created by the COVID-19 pandemic, the CPT is used with a positive outcome amongst many infected individuals in different parts of the world with acceptable efficacy. This article aimed to present an up-to-date evaluation of existing literature on the efficacy of convalescent plasma as a potential therapy, its safety and effectiveness and the challenges in treating COVID-19.

Efficacy of awake prone positioning in patients with covid-19 related hypoxemic respiratory failure: systematic review and meta-analysis of randomized trials.

OBJECTIVE: To determine the efficacy and safety of awake prone positioning versus usual care in non-intubated adults with hypoxemic respiratory failure due to covid-19. DESIGN: Systematic review with frequentist and bayesian meta-analyses. STUDY ELIGIBILITY: Randomized trials comparing awake prone positioning versus usual care in adults with covid-19 related hypoxemic respiratory failure. Information sources were Medline, Embase, and the Cochrane Central Register of Controlled Trials from inception to 4 March 2022. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data and assessed risk of bias. Random effects meta-analyses were performed for the primary and secondary outcomes. Bayesian meta-analyses were performed for endotracheal intubation and mortality outcomes. GRADE certainty of evidence was assessed for outcomes. MAIN OUTCOME MEASURES: The primary outcome was endotracheal intubation. Secondary outcomes were mortality, ventilator-free days, intensive care unit (ICU) and hospital length of stay, escalation of oxygen modality, change in oxygenation and respiratory rate, and adverse events. RESULTS: 17 trials (2931 patients) met the eligibility criteria. 12 trials were at low risk of bias, three had some concerns, and two were at high risk. Awake prone positioning reduced the risk of endotracheal intubation compared with usual care (crude average 24.2% v 29.8%, relative risk 0.83, 95% confidence interval 0.73 to 0.94; high certainty). This translates to 55 fewer intubations per 1000 patients (95% confidence interval 87 to 19 fewer intubations). Awake prone positioning did not significantly affect secondary outcomes, including mortality (15.6% v 17.2%, relative risk 0.90, 0.76 to 1.07; high certainty), ventilator-free days (mean difference 0.97 days, 95% confidence interval -0.5 to 3.4; low certainty), ICU length of stay (-2.1 days, -4.5 to 0.4; low certainty), hospital length of stay (-0.09 days, -0.69 to 0.51; moderate certainty), and escalation of oxygen modality (21.4% v 23.0%, relative risk 1.04, 0.74 to 1.44; low certainty). Adverse events related to awake prone positioning were uncommon. Bayesian meta-analysis showed a high probability of benefit with awake prone positioning for endotracheal intubation (non-informative prior, mean relative risk 0.83, 95% credible interval 0.70 to 0.97; posterior probability for relative risk <0.95=96%) but lower probability for mortality (0.90, 0.73 to 1.13; <0.95=68%). CONCLUSIONS: Awake prone positioning compared with usual care reduces the risk of endotracheal intubation in adults with hypoxemic respiratory failure due to covid-19 but probably has little to no effect on mortality or other outcomes. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022314856.

Helmet noninvasive ventilation for COVID-19 patients (Helmet-COVID): statistical analysis plan for a randomized controlled trial.

BACKGROUND: Noninvasive respiratory support is frequently needed for patients with acute hypoxemic respiratory failure due to coronavirus disease 19 (COVID-19). Helmet noninvasive ventilation has multiple advantages over other oxygen support modalities but data about effectiveness are limited. METHODS: In this multicenter randomized trial of helmet noninvasive ventilation for COVID-19 patients, 320 adult ICU patients (aged ≥14 years or as per local standards) with suspected or confirmed COVID-19 and acute hypoxemic respiratory failure (ratio of arterial oxygen partial pressure to fraction of inspired oxygen < 200 despite supplemental oxygen with a partial/non-rebreathing mask at a flow rate of 10 L/min or higher) will be randomized to helmet noninvasive ventilation with usual care or usual care alone, which may include mask noninvasive ventilation, high-flow nasal oxygen, or standard oxygen therapy. The primary outcome is death from any cause within 28 days after randomization. The trial has 80% power to detect a 15% absolute risk reduction in 28-day mortality from 40 to 25%. The primary outcome will be compared between the helmet and usual care group in the intention-to-treat using the chi-square test. Results will be reported as relative risk  and 95% confidence interval. The first patient was enrolled on February 8, 2021. As of August 1, 2021, 252 patients have been enrolled from 7 centers in Saudi Arabia and Kuwait. DISCUSSION: We developed a detailed statistical analysis plan to guide the analysis of the Helmet-COVID trial, which is expected to conclude enrollment in November 2021. TRIAL REGISTRATION: ClinicalTrials.gov NCT04477668 . Registered on July 20, 2020.

Incidence of Bacterial and Fungal Secondary Infections in COVID-19 Patients Admitted to the ICU

Purpose Secondary infections have been observed among coronavirus disease 2019 (COVID-19) patients, especially in the intensive care unit (ICU) setting, which is associated with worse clinical outcomes. The current study aimed to investigate the incidence, common pathogens, and outcome of bacterial and fungal secondary infections among ICU patients with COVID-19. Methods A retrospective chart review of all patients admitted to the ICU at King Fahd Hospital of the University in Saudi Arabia. All adult patients aged ≥18 admitted in the ICU for ≥48 hours with positive COVID-19 reverse transcription-polymerase chain reaction test during the period between March 2020 till September 2021 were included. Results Out of 314 critically ill patients, 133 (42.4%) developed secondary infections. The incidence of secondary bacterial infection was 32.5% with Pseudomonas aeruginosa (n = 34), Acinetobacter baumannii (n = 33), and Klebsiella pneumoniae (n = 17) being the predominant pathogens, while secondary fungal infection was 25.2% mainly caused by Candida albicans (n = 43). Invasive mechanical ventilation was significantly associated with the development of secondary bacterial infections (odds ratio [OR] = 17.702, 95% confidence interval [CI] 7.842–39.961, p < 0.001) and secondary fungal infections (OR = 12.914, 95% CI 5.406–30.849, p < 0.001). Mortality among patients with secondary infections was 69.2% (n = 92). Secondary infections were associated with longer hospital and ICU stays with a median of 25 days (interquartile range [IQR] 17–42) and 19 days (IQR 13–32), respectively. Conclusion Bacterial and fungal secondary infections are common among COVID-19 patients admitted to the ICU with a predominance of gram-negative bacteria and Candida species. The development of secondary infections was significantly associated with invasive mechanical ventilation. Poor clinical outcomes have been observed, demonstrated with a prolonged hospital and ICU stays and higher mortality.

Prevalence, Causes, and Adverse Clinical Impact of Delayed Presentation of Non-COVID-19-Related Emergencies during the COVID-19 Pandemic: Findings from a Multicenter Observational Study.

OBJECTIVE: The coronavirus disease (COVID-19) pandemic has disrupted healthcare systems worldwide, resulting in decreased and delayed hospital visits of patients with non-COVID-19-related acute emergencies. We evaluated the impact of the COVID-19 pandemic on the presentation and outcomes of patients with non-COVID-19-related medical and surgical emergencies. METHOD: All non-COVID-19-related patients hospitalized through emergency departments in three tertiary care hospitals in Saudi Arabia and Bahrain in June and July 2020 were enrolled and categorized into delayed and non-delayed groups (presentation ≥/=24 or <24 h after onset of symptom). Primary outcome was the prevalence and cause of delayed presentation; secondary outcomes included comparative 28-day clinical outcomes (i.e., 28-day mortality, intensive care unit (ICU) admission, invasive mechanical ventilation, and acute surgical interventions). Mean, median, and IQR were used to calculate the primary outcomes and inferential statistics including chi-square/Fisher exact test, t-test where appropriate were used for comparisons. Stepwise multivariate regression analysis was performed to identify the factors associated with delay in seeking medical attention. RESULTS: In total, 24,129 patients visited emergency departments during the study period, compared to 48,734 patients in the year 2019. Of the 256 hospitalized patients with non-COVID-19-related diagnoses, 134 (52%) had delayed presentation. Fear of COVID-19 and curfew-related restrictions represented 46 (34%) and 25 (19%) of the reasons for delay. The 28-day mortality rates were significantly higher among delayed patients vs. non-delayed patients (n = 14, 10.4% vs. n = 3, 2.5%, OR: 4.628 (CI: 1.296-16.520), p = 0.038). CONCLUSION: More than half of hospitalized patients with non-COVID-19-related diagnoses had delayed presentation to the ED where mortality was found to be significantly higher in this group. Fear of COVID-19 and curfew restrictions were the main reasons for delaying hospital visit.

Nano-treatment of HEPA filters in COVID-19 isolation rooms in an academic medical center in Saudi Arabia.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has spread globally. The major reservoir for SARS-CoV-2 transmission remains controversial, with the airborne route remaining a possible transmission vehicle for carrying the virus within indoor environments. This study aimed to detect contamination of SARS-CoV-2 in high-efficiency particulate air (HEPA) filters within hospital isolation rooms of confirmed COVID-19 patients, exploring the role of nano-treatment of these filters with silver and titanium dioxide nanoparticles (Ag/TiO2 NPs). MATERIALS AND METHODS: We investigated the effectiveness of Ag-NPs/TiO2-treated HEPA filters in the air of rooms occupied by patients with confirmed COVID-19 in a university teaching hospital in the Eastern province of Saudi Arabia during the first wave of the pandemic. Ag/TiO2 NPs were designed and coated on HEPA filters to examine the filtration efficiency and antiviral ability in the presence of aerosolized virus particles. A total of 20 viral swab samples were collected from five patients' rooms before and after treatment with nanoparticle-prepared solutions into the sterile virus-transporting media. Samples were evaluated for SARS-CoV-2 with a reverse transcription-polymerase chain reaction. RESULTS: Two samples taken from the HEPA filter air exhaust outlets prior to nano-treatment tested positive for SARS-CoV-2 RNA in the intensive care unit, which has stringent aerosolization control procedures, suggesting that small virus-laden droplets may be displaced by airflow. All air samples collected from the HEPA filters from the rooms of patients with confirmed COVID-19 following nano-treatment were negative. CONCLUSION: We recommend further experimental exploration using a larger number of HEPA filters in areas with aerosol-generating procedures, along with viability studies on the HEPA filters to facilitate decision-making in high-risk facilities regarding the replacement, storage, and disposal of HEPA filters in wards occupied by cases diagnosed with a highly transmissible disease.

Statistical Analysis of COVID-19 Data for Three Different Regions in the Kingdom of Saudi Arabia: Using a New Two-Parameter Statistical Model.

Since December 2019, the COVID-19 outbreak has touched every area of everyday life and caused immense destruction to the planet. More than 150 nations have been affected by the coronavirus outbreak. Many academics have attempted to create a statistical model that may be used to interpret the COVID-19 data. This article extends to probability theory by developing a unique two-parameter statistical distribution called the half-logistic inverse moment exponential (HLIMExp). Advanced mathematical characterizations of the suggested distribution have explicit formulations. The maximum likelihood estimation approach is used to provide estimates for unknown model parameters. A complete simulation study is carried out to evaluate the performance of these estimations. Three separate sets of COVID-19 data from Al Bahah, Al Madinah Al Munawarah, and Riyadh are utilized to test the HLIMExp model's applicability. The HLIMExp model is compared to several other well-known distributions. Using several analytical criteria, the results show that the HLIMExp distribution produces promising outcomes in terms of flexibility.

Novel Galactopyranoside Esters: Synthesis, Mechanism, In Vitro Antimicrobial Evaluation and Molecular Docking Studies.

One-step direct unimolar valeroylation of methyl α-D-galactopyranoside (MDG) mainly furnished the corresponding 6-O-valeroate. However, DMAP catalyzed a similar reaction that produced 2,6-di-O-valeroate and 6-O-valeroate, with the reactivity sequence as 6-OH > 2-OH > 3-OH,4-OH. To obtain novel antimicrobial agents, 6-O- and 2,6-di-O-valeroate were converted into several 2,3,4-tri-O- and 3,4-di-O-acyl esters, respectively, with other acylating agents in good yields. The PASS activity spectra along with in vitro antimicrobial evaluation clearly indicated that these MDG esters had better antifungal activities than antibacterial agents. To rationalize higher antifungal potentiality, molecular docking was conducted with sterol 14α-demethylase (PDB ID: 4UYL, Aspergillus fumigatus), which clearly supported the in vitro antifungal results. In particular, MDG ester 7-12 showed higher binding energy than the antifungal drug, fluconazole. Additionally, these compounds were found to have more promising binding energy with the SARS-CoV-2 main protease (6LU7) than tetracycline, fluconazole, and native inhibitor N3. Detailed investigation of Ki values, absorption, distribution, metabolism, excretion, and toxicity (ADMET), and the drug-likeness profile indicated that most of these compounds satisfy the drug-likeness evaluation, bioavailability, and safety tests, and hence, these synthetic novel MDG esters could be new antifungal and antiviral drugs.

Effect of Awake Prone Positioning on Endotracheal Intubation in Patients With COVID-19 and Acute Respiratory Failure: A Randomized Clinical Trial.

Importance: The efficacy and safety of prone positioning is unclear in nonintubated patients with acute hypoxemia and COVID-19. Objective: To evaluate the efficacy and adverse events of prone positioning in nonintubated adult patients with acute hypoxemia and COVID-19. Design, Setting, and Participants: Pragmatic, unblinded randomized clinical trial conducted at 21 hospitals in Canada, Kuwait, Saudi Arabia, and the US. Eligible adult patients with COVID-19 were not intubated and required oxygen (≥40%) or noninvasive ventilation. A total of 400 patients were enrolled between May 19, 2020, and May 18, 2021, and final follow-up was completed in July 2021. Intervention: Patients were randomized to awake prone positioning (n = 205) or usual care without prone positioning (control; n = 195). Main Outcomes and Measures: The primary outcome was endotracheal intubation within 30 days of randomization. The secondary outcomes included mortality at 60 days, days free from invasive mechanical ventilation or noninvasive ventilation at 30 days, days free from the intensive care unit or hospital at 60 days, adverse events, and serious adverse events. Results: Among the 400 patients who were randomized (mean age, 57.6 years [SD, 12.83 years]; 117 [29.3%] were women), all (100%) completed the trial. In the first 4 days after randomization, the median duration of prone positioning was 4.8 h/d (IQR, 1.8 to 8.0 h/d) in the awake prone positioning group vs 0 h/d (IQR, 0 to 0 h/d) in the control group. By day 30, 70 of 205 patients (34.1%) in the prone positioning group were intubated vs 79 of 195 patients (40.5%) in the control group (hazard ratio, 0.81 [95% CI, 0.59 to 1.12], P = .20; absolute difference, -6.37% [95% CI, -15.83% to 3.10%]). Prone positioning did not significantly reduce mortality at 60 days (hazard ratio, 0.93 [95% CI, 0.62 to 1.40], P = .54; absolute difference, -1.15% [95% CI, -9.40% to 7.10%]) and had no significant effect on days free from invasive mechanical ventilation or noninvasive ventilation at 30 days or on days free from the intensive care unit or hospital at 60 days. There were no serious adverse events in either group. In the awake prone positioning group, 21 patients (10%) experienced adverse events and the most frequently reported were musculoskeletal pain or discomfort from prone positioning (13 of 205 patients [6.34%]) and desaturation (2 of 205 patients [0.98%]). There were no reported adverse events in the control group. Conclusions and Relevance: In patients with acute hypoxemic respiratory failure from COVID-19, prone positioning, compared with usual care without prone positioning, did not significantly reduce endotracheal intubation at 30 days. However, the effect size for the primary study outcome was imprecise and does not exclude a clinically important benefit. Trial Registration: ClinicalTrials.gov Identifier: NCT04350723.

Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU.

BACKGROUND: Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently a major cause of intensive care unit (ICU) admissions globally. The role of machine learning in the ICU is evolving but currently limited to diagnostic and prognostic values. A decision tree (DT) algorithm is a simple and intuitive machine learning method that provides sequential nonlinear analysis of variables. It is simple and might be a valuable tool for bedside physicians during COVID-19 to predict ICU outcomes and help in critical decision-making like end-of-life decisions and bed allocation in the event of limited ICU bed capacities. Herein, we utilized a machine learning DT algorithm to describe the association of a predefined set of variables and 28-day ICU outcome in adult COVID-19 patients admitted to the ICU. We highlight the value of utilizing a machine learning DT algorithm in the ICU at the time of a COVID-19 pandemic. METHODS: This was a prospective and multicenter cohort study involving 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The predictors of 28-day ICU mortality were identified using two predictive models: conventional logistic regression and DT analyses. RESULTS: There were 1468 critically ill COVID-19 patients included in the study. The 28-day ICU mortality was 540 (36.8 %), and the 90-day mortality was 600 (40.9 %). The DT algorithm identified five variables that were integrated into the algorithm to predict 28-day ICU outcomes: need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio. CONCLUSION: DT is a simple tool that might be utilized in the ICU to identify critically ill COVID-19 patients who are at high risk of 28-day ICU mortality. However, further studies and external validation are still required.

Sensitivity and Specificity of Rapid SARS-CoV-2 Antigen Detection Using Different Sampling Methods: A Clinical Unicentral Study.

Rapid antigen detection of SARS-CoV-2 has been widely used. However, there is no consensus on the best sampling method. This study aimed to determine the level of agreement between SARS-CoV-2 fluorescent detection and a real-time reverse-transcriptase polymerase chain reaction (rRT-PCR), using different swab methods. Fifty COVID-19 and twenty-six healthy patients were confirmed via rRT-PCR, and each patient was sampled via four swab methods: oropharyngeal (O), nasal (N), spit saliva (S), and combined O/N/S swabs. Each swab was analyzed using an immunofluorescent Quidel system. The combined O/N/S swab provided the highest sensitivity (86%; Kappa = 0.8), followed by nasal (76%; Kappa = 0.68), whereas the saliva revealed the lowest sensitivity (66%; kappa = 0.57). Further, when considering positive detection in any of the O, N, and S samples, excellent agreements with rRT-PCR were achieved (Kappa = 0.91 and 0.97, respectively). Finally, among multiple factors, only patient age revealed a significant negative association with antigenic detection in the saliva. It is concluded that immunofluorescent detection of SARS-CoV-2 antigen is a reliable method for rapid diagnosis under circumstances where at least two swabs, one nasal and one oropharyngeal, are analyzed. Alternatively, a single combined O/N/S swab would improve the sensitivity in contrast to each site swabbed alone.

Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia.

The COVID-19 virus has spread rapidally throughout the world. Managing resources is one of the biggest challenges that healthcare providers around the world face during the pandemic. Allocating the Intensive Care Unit (ICU) beds' capacity is important since COVID-19 is a respiratory disease and some patients need to be admitted to the hospital with an urgent need for oxygen support, ventilation, and/or intensive medical care. In the battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), to contain the pandemic and limit its hazardous effects. In this paper, Machine Learning models (ML) were developed to help in detecting the COVID-19 patients' need for the ICU and the estimated duration of their stay. Four ML algorithms were utilized: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models were trained and validated on a dataset of 895 COVID-19 patients admitted to King Fahad University hospital in the eastern province of Saudi Arabia. The conducted experiments show that the Length of Stay (LoS) in the ICU can be predicted with the highest accuracy by applying the RF model for prediction, as the achieved accuracy was 94.16%. In terms of the contributor factors to the length of stay in the ICU, correlation results showed that age, C-Reactive Protein (CRP), nasal oxygen support days are the top related factors. By searching the literature, there is no published work that used the Saudi Arabia dataset to predict the need for ICU with the number of days needed. This contribution is hoped to pave the path for hospitals and healthcare providers to manage their resources more efficiently and to help in saving lives.