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Purpose: With new hybrid working models in place post COVID-19, it is requisite that knowledge workers (KWs) stay agile. Knowledge-oriented leadership (KOL) can help employees with essential knowledge acquisition (KA) facilitating the journey toward hybrid work agility (HWA). This study, thus, aims to explore the impact of KOL and KA on HWA and reveal whether this effect stems uniformly from a single homogenous population or if there is unobserved heterogeneity leading to identifiable segments of agile KWs. Design/methodology/approach: Data was collected through stratified sampling from 416 employees from 20 information technology enabled services companies involved in knowledge-intensive tasks. Partial least squares (PLS) structural equation modeling approach, using SMART PLS 4.0, has been applied to examine the effect of KOL and KA on HWA. Finite mixture PLS, PLS prediction-oriented segmentation and multigroup analysis have been used to identify segments, test segment-specific path models and analyze the significance of the differences in the path coefficients for unobserved heterogeneity. Predictive relevance of the model has been determined using PLS Predict. Findings: Results indicate that KOL contributes to employees' KA and HWA. A significant positive relationship is also reported between KA and HWA. The model has medium predictive relevance. A two-segment solution has been delineated, wherein independent agile KWs (who value autonomy and personal agency over leadership for KA) and dependent agile KWs (who depend on leaders for relational and structural support for KA) have been identified. Thus, KOL and KA play a differential role in determining HWA. Research limitations/implications: The authors' major contribution to the knowledge body constitutes the determination of antecedents of HWA and a typology of agile KWs. Future researchers may conduct segment-wise qualitative analysis to delineate other variables that contribute to HWA. Practical implications: Technological advances necessitate that knowledge-intensive industries foster agility in employees for strategic agility of the organization. For effecting agile adaption of an organization to the knowledge economy conditions, it is pertinent that the full potential of this human resource be used. By profiling HWA of KWs on the basis of dimensions of KOL and the level of their KA, organizations will be able to help employees adapt better to rapidly changing work conditions. Originality/value: HWA is a novel concept and very germane in a hybrid working environment. To the best of the authors' knowledge, this is the first study to examine the effects of the dimensions of KOL and KA in relation to HWA, along with an empirical examination of unobserved heterogeneity in the aforementioned relationship. © 2023, Emerald Publishing Limited.
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INTRODUCTION: The current CDC guidelines recommend COVID-19 vaccine boosters for all eligible individuals to enhance protection. Resources have been allocated to research done regarding the COVID-19 vaccine, and we speculate that there is a correlation between COVID booster rates and number of COVID patients in the ICU. We hypothesize that the states with a higher percentage of the population that received the booster shot will have decreased COVID ICU bed utilization and vice versa. METHOD(S): The percentage of people who received the COVID-19 booster vaccine and the number of ICU beds occupied by patients with COVID-19 per 10,000 population, both stratified by states, were reviewed to determine the pattern of correlation. The data for both the variables was sourced from Becker's Healthcare as it used information from the CDC's data tracker to rank states by their booster rates. The rankings were last updated based on data from July 20th, 2022. The state of Idaho was excluded because the data was not available. Limitations of the study included reporting lags between the states and CDC, the emergence of numerous variants of the virus, and a lack of a standardized timeline across the states. RESULT(S): Pearson Correlation Coefficient was used to determine the pattern of correlation between COVID booster rates and the number of COVID patients in the ICU for all US states. Booster rates was set as x and ICU patients was set as y. The data was analyzed while using the formula r = SIGMA((X - My)(Y - Mx)) / ((SSx)(SSy)). X Values were calculated with SIGMA = 2407.7, Mean = 48.154 and SIGMA(X - Mx)2 = SSx = 2308.544. Y Values were calculated with SIGMA = 5112, Mean = 102.24 and SIGMA(Y - My)2 = SSy = 835103.12. The coefficient of determination, R2, was 0.0611. Our obtained R was -0.25 which means no strong correlation was found. The data was analyzed independently by two statisticians and the same results were obtained. The results failed to confirm our hypothesis and suggested that there was no correlation between COVID booster rates and the number of COVID patients in the ICU. CONCLUSION(S): Based on our results, no correlation was found between the states' COVID booster rates and ICU bed occupancy. Further studies are needed to quantify this association if any as highly virulent COVID strains pose a threat to humanity.
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Background. Multisystem inflammatory syndrome in children (MIS-C) is a severe post-infectious complication occurring weeks after SARS-CoV-2 infection. The exact mechanisms leading to immune dysregulation and organ damage remain incompletely understood. Progress in understanding the immunopathology underlying MIS-C has been halted by limited availability of pre-treatment patient samples and confounding effects of immunomodulatory treatment on previously studied specimens. Methods. In this study, we have restricted enrollment to treatment-naive patients with MIS-C and used a systems biology approach combining CyTOF, single cell transcriptomics, serum cytokine profiling and T cell receptor sequencing to dissect how immune responses in children with MIS-C differ from children with mild SARS-CoV2 infection, adults with severe COVID-19 and healthy individuals. We also integrated single cell transcriptomics datasets from post-treatment MIS-C samples to study how immune responses change along disease course. Results. We identified increased activation markers and antigen presentation across multiple immune cell types in MIS-C patients from both CyTOF and single cell transcriptomics data. Importantly, in PBMCs of MIS-C patients, we identified a distinct subset of proinflammatory monocytes, with increased expression of interferon gamma response genes combined with a signature of enhanced complement expression, antigen processing and presentation, which was not observed in post-treatment MIS-C samples. Interestingly, this monocyte population bears resemblance to a subset of monocytes that emerges after the BNT162b2 mRNA vaccine booster. In addition, in PBMCs of MIS-C patients, we identify increased proportion of proliferating T/NK cells, suggesting distinct T cell expansions in MIS-C. T and NK cells in MIS-C samples also showed increased cell cytotoxicity markers. Conclusion. Taken together, treatment-naive MIS-C samples display distinct monocyte clusters, activated antigen presentation and complement expression, and increased T and NK cell cytotoxicity, which may account for the clinical presentation of MIS-C.
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Introduction: Host immune response has been repeatedly shown to diagnose the presence and type of infection. Recently, we described a 42-gene blood-based signature, conserved across several viruses, including influenza, Ebola, SARS-CoV-2, chikungunya, that is associated with and predicts the severity of viral infection, irrespective of age, sex, and host or pathogen genetics. Importantly, we showed the 42-gene signature is composed of 4 modules (2 protective, 2 detrimental). We investigated whether these modules, individually or collectively, are also associated with severity in patients with a bacterial infection. Methods: We analyzed 29 publicly available datasets comprised of blood transcriptome profiles from 1,806 patients (637 healthy patients, 1169 patients with bacterial infection) from 10 countries. We co-normalized these datasets using COCONUT. We also included 3,183 blood samples across an additional 29 datasets from 18 countries from patients with viral infection (1,663 healthy patients, 1,520 patients with viral infection) from our previous study. Severity of disease was stratified into healthy controls, asymptomatic infection, mild, moderate, serious, critical and fatal illness. We assessed the performance of our previously described four module scores and a composite severe-or-mild “SoM” score in these samples. We then applied our previously described 7 gene signature (7GS) that distinguishes viral from bacterial infections to both the bacterial and viral samples. Results: Similar to viral infections, the two detrimental module scores were positively correlated with severity of bacterial infections (module 1: r=0.64, module 2: r=0.53), and one of two protective modules was inversely correlated (module 4: r=-0.59). Module 3, initially protective in viral infections, was minimally positively correlated with severity of bacterial illness (module 3: r=0.20). The SoM score, which integrates the four module scores, was positively correlated with severity (r=0.63) and distinguish mild/moderate bacterial infections from severe (serious/critical/fatal) bacterial infection with 71% sensitivity and 73% specificity (Figure 1A, AUROC=0.77, 95% CI:0.73-0.80). Interestingly, the SoM score was not different between patients with severe bacterial or viral infection, but was significantly higher in mild/moderate bacterial infections compared to mild/moderate viral infections. Conclusion: The SoM score can accurately distinguish patients with severe infection, irrespective of bacterial or viral infection. When used in conjunction with our previously described 7-gene signature, it may help decide whether a patient should be (1) treated with an antibiotic and (2) discharged or admitted to hospital upon presentation to an emergency department.
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Aerosols emitted in densely populated and industrialized Indo-Gangetic Plain, one of the most polluted regions in the world, modulate regional climate, monsoon, and Himalayan glacier retreat. Thus, this region is important for understanding aerosol perturbations and their resulting impacts on atmospheric changes during COVID-19 lockdown period, a natural experimental condition created by the pandemic. By analyzing 5 years (2016-2020) data of aerosols and performing a radiative transfer calculation, we found that columnar and near-surface aerosol loadings decreased, leading to reductions in radiative cooling at the surface and top of the atmosphere and atmospheric warming during lockdown period. Further, satellite data analyses showed increases in cloud optical thickness and cloud-particle effective radius and decrease in lower tropospheric air temperature during lockdown period. These results indicate critical influences of COVID-19 lockdown on regional climate and water cycle over Indo-Gangetic Plain, emphasizing need for further studies from modeling perspectives.
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Background and Aims: The effect of the COVID pandemic on stroke networks performance are unclear, particularly with consideration of drip & ship versus mothership models. We systematically reviewed and metaanalyzed variations in stroke admissions, rate and timing of reperfusion treatments during the COVID pandemic versus the prepandemic timeframe. Methods: The systematic review followed registered protocol (PROSPERO-CRD42020211535), PRISMA and MOOSE guidelines. We searched MEDLINE, EMBASE and Cochrane CENTRAL until 9/10/ 2020, for studies reporting variations in ischemic stroke admissions, treatment rates and timing in COVID vs control-period. Primary outcome was the weekly admission incidence rate ratio (IRR=admissions during COVID-period/admissions during control-period). Secondary outcomes were (i) changes in rate of patients undergoing reperfusion treatment and (ii) time metrics for pre-and in-hospital phase. Results: Twenty-nine studies were included in qualitative synthesis, with 212960 patients observed for 532 cumulative weeks (325 control-period, 207 COVID-period). COVID-period was associated with a significant reduction in stroke admission rates (IRR=0.69, 95%CI, 0.61-0.79) and a higher relative presentation with large vessel occlusion stroke (RR=1.62, 95%CI, 1.24-2.12). Proportions of patients treated with intravenous thrombolysis remained unchanged, while endovascular treatment increased (RR=1.14, 95%CI, 1.02-1.28). Onset-to-door time was longer for drip&ship compared to mothership model (+32 minutes vs-12 minutes, pmeta-regression =.03). Conclusions: Despite a 35% drop in stroke admissions during the pandemic, proportions of patients receiving reperfusion and time-metrics were not inferior to control-period, justifying allocation of resources to keep stroke networks up and running.
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Lockdown measures have been adopted in many countries worldwide due to the onset of the COVID-19 pandemic, including in Thailand. Air quality improvements with regard to restrictions of daily movement among Bangkok people have been reported. This study explores the impact of the COVID-19 outbreak and long-range pollution on air quality in Bangkok Metropolitan, Thailand by using ground-based and satellite measurements such as MODIS and TROPOMI data. Moreover, the results project some possible future trends of air quality in Bangkok Metropolitan. The 24-hr average concentrations of PM2.5, O-3, NO2, CO and SO2 were compared between the periods of Normal, Lockdown and New Normal. PM2.5 concentrations increased by 20.56% during the Normal period and decreased by -15.79% and -23.34% during the Lockdown and New Normal periods, respectively, compared to the same periods in 2017-2019. There were also significant decreasing trends in O-3: -7.13% and 4.72%;and CO: -8.01% and 23.59% during the Lockdown and New Normal periods, respectively, while NO2 and SO2 concentrations showed increasing trends during the three periods. The MODIS and TROPOMI data analyses indicate the COVID-19 outbreak has had significant positive impact on surface pollution, but no impact on upper atmospheric pollution due to added pollution from long-range transport. The results also demonstrate that surface air pollution had a combination effect from biomass burning, traffic, industrial and household sources during the Lockdown period, except for SO2 concentrations, which were attributed to long-range transport pollution loading. In some cases, a negative impact of the COVID-19 lockdown on air pollution can be observed due to certain activities increasing within Bangkok Metropolitan. Additionally, the results also show that changing the lifestyle into a "new normal" for people in Bangkok after the Lockdown period has had a positive effect on air pollution.
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Background: The University of Cincinnati Stroke Team provides acute stroke care to the southwest Ohio, northern Kentucky, and southeast Indiana catchment area of ∼2 million people and 30 healthcare facilities. We previously published a significant decline in stroke activations and reperfusion treatment (IV thrombolysis and EVT) rates following state announcements of COVID-19 mitigation measures. Here, we update these trends after state reopening guidelines. Methods: We compared Stroke Team activations and reperfusion treatments logged in a prospectively collected database, comparing the same period in 2020 versus 2019. Kentucky and Ohio announced school and restaurant closures on March 12 and 13, respectively, followed by Indiana. A stepwise reopening of our tristate area started on May 1, 2020. We also compared trends in activations and treatment rates before (Weeks 1-10), during (Weeks 11-17), and after (Weeks 18- 26) the lifting of COVID-19 mitigation efforts using the Poisson test, and graphically with segmented regression analysis. Results: Compared to 2019, stroke team activations declined by 12% in 2020 (95% CI 7 - 16%;p<0.01). During 2020, an initial decline in stroke activations following COVID-19 mitigation announcements was followed by a 28% increase in activations after reopening (Weeks 18-26: 95% CI 15 - 42%;p<0.01). In contrast, compared to 2019, treatment rates were unchanged (0%, 95% CI -15 - 18%;p=1.00), including specifically IV thrombolysis and thrombectomy rates. Similarly, an initial decline in reperfusion treatments was followed by a 24% nonsignificant increase after reopening (95% CI -10 - 71%;p=0.19) in 2020. Conclusion: The initial decline in stroke team activations during COVID-19 mitigation efforts was followed by an increase in activations after reopening. Hospital capacity and 911 services remained fully intact, suggesting that the reduction in activations were related to reduced presentation by patients for emergent stroke care.(Figure Presented).
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Background: COVID-19 is a pandemic caused by the SARS-CoV-2 virus that shares and differs in clinical characteristics of known viral infections. Methods: We obtained RNAseq profiles of 62 prospectively enrolled COVID-19 patients and 24 healthy controls (HC). We collected 23 independent studies profiling 1,855 blood samples from patients covering six viruses (influenza, RSV, HRV, Ebola, Dengue and SARS-CoV-1). We studied host whole-blood transcriptomic responses in COVID-19 compared to non-COVID-19 viral infections to understand similarities and differences in host response. Gene signature threshold was absolute effect size ≥1, FDR ≤ 0.05%. Results: Differential gene expression of COVID-19 vs HC are highly correlated with non-COVID-19 vs HC (r=0.74, p< 0.001). We discovered two gene signatures: COVID-19 vs HC (2002 genes) (COVIDsig) and non-COVID-19 vs HC (635 genes) (nonCOVIDsig). Pathway analysis of over-expressed signature genes in COVIDsig or nonCOVIDsig identified similar pathways including neutrophil activation, innate immune response, immune response to viral infection and cytokine production. Conversely, for under-expressed genes, pathways indicated repression of lymphocyte differentiation and activation (Fig1). Intersecting the two gene signatures found two genes significantly oppositely regulated (ACO1, ATL3). We derived a third gene signature using COCONUT to compare COVID-19 to non-COVID-19 viral infections (416 genes) (Fig2). Pathway analysis did not result in significant enrichment, suggesting identification of novel biology (Fig1). Statistical deconvolution of bulk transcriptomic data found M1 macrophages, plasmacytoid dendritic cells, CD14+ monocytes, CD4+ T cells and total B cells changed in the same direction across COVID-19 and non-COVID-19 infections. Cell types that increased in COVID-19 relative to non-COVID-19 were CD56bright NK cells, M2 macrophages and total NK cells. Those that decreased in non- COVID-19 relative to COVID-19 were CD56dim NK cells & memory B cells and eosinophils (Fig3). Conclusion: The concordant and discordant responses mapped here provide a window to explore the pathophysiology of COVID-19 vs other viral infections and show clear differences in signaling pathways and cellularity as part of the host response to SARS-CoV-2.