COVID-related dysphonia and persistent long-COVID voice sequelae: A systematic review and meta-analysis.

PURPOSE: Dysphonia is a common symptom due to the coronavirus disease of the 2019 (COVID-19) infection. Nonetheless, it is often underestimated for its impact on human's health. We conducted this first study to investigate the global prevalence of COVID-related dysphonia as well as related clinical factors during acute COVID-19 infection, and after a mid- to long-term follow-up following the recovery. METHODS: Five electronic databases including PubMed, Embase, ScienceDirect, the Cochrane Library, and Web of Science were systematically searched for relevant articles until Dec, 2022, and the reference of the enrolled studies were also reviewed. Dysphonia prevalence during and after COVID-19 infection, and voice-related clinical factors were analyzed; the random-effects model was adopted for meta-analysis. The one-study-removal method was used for sensitivity analysis. Publication bias was determined with funnel plots and Egger's tests. RESULTS: Twenty-one articles comprising 13,948 patients were identified. The weighted prevalence of COVID-related dysphonia during infection was 25.1 % (95 % CI: 14.9 to 39.0 %), and male was significantly associated with lower dysphonia prevalence (coefficients: -0.116, 95 % CI: -0.196 to -0.036; P = .004) during this period. Besides, after recovery, the weighted prevalence of COVID-related dysphonia declined to 17.1 % (95 % CI: 11.0 to 25.8 %). 20.1 % (95 % CI: 8.6 to 40.2 %) of the total patients experienced long-COVID dysphonia. CONCLUSIONS: A quarter of the COVID-19 patients, especially female, suffered from voice impairment during infection, and approximately 70 % of these dysphonic patients kept experiencing long-lasting voice sequelae, which should be noticed by global physicians.

The lived experiences of health care professionals regarding visiting restrictions in the emergency department during the COVID-19 pandemic: A multi-perspective qualitative study.

AIMS: To explore the lived experiences of emergency department (ED) healthcare professionals regarding visiting restrictions during the COVID-19 pandemic. DESIGN: A qualitative phenomenology study. METHODS: Semi-structured interviews were undertaken. Participants consisted of 10 physicians, 20 clinical nurses, and three managers, who were purposefully selected from two EDs in China between April and July 2021. Colaizzi's approach guided data analysis. RESULTS: Four themes arose: (i) burden moral injury, (ii) higher workload to provide and support patient- and family-centered care, (iii) dissatisfied and unsafe healthcare service for patients and families, and (iv) tailoring strategies to provide family-centered care. PATIENT OR PUBLIC CONTRIBUTION: This study explored the lived experiences of ED health care professionals regarding visiting restrictions during the COVID-19 pandemic. Noted challenges included communicating with families and ethical decision making. Strategies that support ED clinician welfare, and communication with families are warranted if visiting policy restrictions are continued or re-introduced.

Emulating a Target Trial of Interventions Initiated During Pregnancy with Healthcare Databases: The Example of COVID-19 Vaccination.

BACKGROUND: Observational studies are often the only option to estimate effects of interventions during pregnancy. Causal inference from observational data can be conceptualized as an attempt to emulate a hypothetical pragmatic randomized trial: the target trial. OBJECTIVE: To provide a step-by-step description of how to use healthcare databases to estimate the effects of interventions initiated during pregnancy. As an example, we describe how to specify and emulate a target trial of COVID-19 vaccination during pregnancy, but the framework can be generally applied to point and sustained strategies involving both pharmacologic and non-pharmacologic interventions. METHODS: First, we specify the protocol of a target trial to evaluate the safety and effectiveness of vaccination during pregnancy. Second, we describe how to use observational data to emulate each component of the protocol of the target trial. We propose different target trials for different gestational periods because the outcomes of interest vary by gestational age at exposure. We identify challenges that affect (i) the target trial and thus its observational emulation (censoring and competing events), and (ii) mostly the observational emulation (confounding, immortal time, and measurement biases). CONCLUSION: Some biases may be unavoidable in observational emulations, but others are avoidable. For instance, immortal time bias can be avoided by aligning the start of follow-up with the gestational age at the time of the intervention, as we would do in the target trial. Explicitly emulating target trials at different gestational ages can help reduce bias and improve the interpretability of effect estimates for interventions during pregnancy.

Temporal dynamics for areal unit-based co-occurrence COVID-19 trajectories.

The dynamic mechanism of the COVID-19 pandemic has been studied for disease prevention and health protection through areal unit-based log-linear Poisson processes to understand the outbreak of the virus with confirmed daily empirical cases. The predictor of the evolution is structured as a function of a short-term dependence and a long-term trend to identify the pattern of exponential growth in the main epicenters of the virus. The study provides insight into the possible pandemic path of each areal unit and a guide to drive policymaking on preventive measures that can be applied or relaxed to mitigate the spread of the virus. It is significant that knowing the trend of the virus is very helpful for institutions and organizations in terms of instituting resources and measures to help provide a safe working environment and support for all workers/staff/students.

Inhibition of SARS-CoV-2 Viral Channel Activity Using FDA-Approved Channel Modulators Independent of Variants.

BACKGROUND: SARS-CoV-2 has undergone mutations, yielding clinically relevant variants. HYPOTHESIS: We hypothesized that in SARS-CoV-2, two highly conserved Orf3a and E channels directly related to the virus replication were a target for the detection and inhibition of the viral replication, independent of the variant, using FDA-approved ion channel modulators. METHODS: A combination of a fluorescence potassium ion assay with channel modulators was developed to detect SARS-CoV-2 Orf3a/E channel activity. Two FDA-approved drugs, amantadine (an antiviral) and amitriptyline (an antidepressant), which are ion channel blockers, were tested as to whether they inhibited Orf3a/E channel activity in isolated virus variants and in nasal swab samples from COVID-19 patients. The variants were confirmed by PCR sequencing. RESULTS: In isolated SARS-CoV-2 Alpha, Beta, and Delta variants, the channel activity of Orf3a/E was detected and inhibited by emodin and gliclazide (IC50 = 0.42 mM). In the Delta swab samples, amitriptyline and amantadine inhibited the channel activity of viral proteins, with IC50 values of 0.73 mM and 1.11 mM, respectively. In the Omicron swab samples, amitriptyline inhibited the channel activity, with an IC50 of 0.76 mM. CONCLUSIONS: We developed an efficient method to screen FDA-approved ion channel modulators that could be repurposed to detect and inhibit SARS-CoV-2 viral replication, independent of variants.

Impact of COVID-19 Pandemic on Emergency Department Volume and Acuity in Low Incidence Area: Taiwan's Experience in Three Hospitals.

Background: The decrease in emergency department (ED) patient visits during the COVID-19 pandemic was reported by various studies. Our study aimed to investigate whether a similar trend can be observed in a country with a low incidence of COVID-19 as well as the impact caused by the pandemic on ED patients in different triage levels and categories. Methods: This multicenter retrospective study collected data from three regional hospitals between March 2019 and December 2020. We evaluated the differences between patient volume, disease severity, and patient composition in ED before and after the COVID-19 pandemic among these hospitals. Results: There was a 23% reduction in ED patient volume in the urban hospital (hospital A) as well as a 16% reduction in suburban hospitals (hospitals B and C) during the pandemic period, respectively. The regression analysis showed a high correlation in the change in monthly patient volume among these hospitals. In terms of severity, there was a 24% reduction in ED visits with high severity levels (Taiwan Triage and Acuity Scale [TTAS] I, II) in hospital A, as well as 16% and 12% in hospitals B and C during the pandemic period, respectively. Similarly, there was a 23% reduction in ED visits with low severity levels (TTAS III, IV, V) in hospital A, as well as 20% and 16% in hospitals B and C during the pandemic period, respectively. In terms of patient types, there was a significant decline in non-traumatic adult patients (19%, 17%, and 10%), and pediatric patients (49%, 50%, and 46%) in hospitals A, B, and C, respectively. Conclusions: Despite the low incidence of COVID-19 in Taiwan, a decrease in total ED visits was still found during the pandemic, especially in non-trauma adult visits and pediatric visits. In addition, ED visits in both high and low severity levels decreased in these regional hospitals.

Incorporating field-based research into remote learning: An assessment of soil lead pollution in different land-use types in Los Angeles.

A research-based course was developed to investigate the legacy of soil lead (Pb) pollution in Los Angeles, California. During the course, undergraduate and graduate students collected a total of 270 soil samples for analyses of metal (loid) concentrations in different land-use types (residential, park, and school). Residential soils had significantly higher Pb concentrations than other land uses (p < 0.01) exceeding the California recommended safety level for soil Pb (80 mg/kg) at the highest frequency (64% of samples), followed by schools (42%) and parks (6.0%). Soil Pb from all 87 census block groups was correlated with battery recycling plant and railroad proximity as geospatial indicators of childhood Pb exposure risk. Meanwhile, census block groups with higher Pb levels were correlated with higher percentages of the following population: those without health insurance, without college degrees, with a lower median household income and income below the poverty line, and ethnic and racial minorities (r = -0.46 to 0.59, p < 0.05). Principal component regression models significantly improved soil Pb estimation over correlation analysis by incorporating sociodemographic, economic, and geospatial risk factors for Pb exposure (R2 = 0.58, p < 0.05). This work provides new insights into how topsoil Pb prevails in various land-use types and their co-occurring sociodemographic, economic, and geospatial risk factors, indicating the need for multi-scalar assessment across urban land uses.

Cultivation of Positive Psychological Quality of College Students' English Learning Under the Online and Offline Teaching Mode During the Epidemic.

During the COVID-19 pandemic, long-term isolation and loneliness will cause college students' psychological fluctuations. Especially in online teaching, the lack of communication for a long time has led to a greatly reduced learning enthusiasm of college students. Therefore, this paper aims to explore the cultivation methods of the positive psychological quality of college students under the epidemic situation through the research on the positive psychology of college students' English learning. Aiming at the psychological status of college students, this paper focuses on analyzing the relationship between social support, psychological capital, and psychological health to explore more targeted ways of cultivating positive psychology. Because of the online and offline teaching mode, this paper focuses on analyzing the support environment of the online teaching mode, and analyses the current forms of English teaching. Experiments show that the direct path from psychological capital to mental health is not significant. However, the mediating path of psychological capital to mental health through social support was significant (p < 0.001). It shows that social support plays a complete mediating role, and the effect size of the mediation model reaches 49.70%. It shows that the current college students' English learning positive psychological quality is not high. In response to this, it is necessary to strengthen the tendency and ability to use social support and use the family environment to communicate more to achieve the cultivation of positive psychological quality.

Arterial Occlusion and Acute Deep Vein Thrombosis-Induced Acute Limb Ischemia in a COVID-19 Patient.

Coronavirus disease 2019 (COVID-19) is a viral illness known to elicit a hypercoagulable state leading to a myriad of vascular pathologies. Over the course of the COVID-19 pandemic, widespread insults to the venous system have been well documented, with an increasing number of arterial events being reported. Despite the rising incidence of both pathological manifestations, these events are rare, but when present, serve as significant life threats to the patient in question. We report and discuss a case of a 69-year-old female with no thromboembolic risk factors or systemic signs of illness who presented with signs and symptoms consistent with acute limb ischemia (ALI). The patient was ultimately found to have occlusion of multiple arterial and venous vessels. She tested positive for COVID-19 despite being otherwise asymptomatic from a viral syndrome standpoint. To our knowledge, there are no reports in the medical literature of ALI - in the setting of arterial occlusion and concomitant deep vein thrombosis (DVT) - as the sole clinical manifestation in an asymptomatic patient without thrombotic risk factors who was only incidentally found to be COVID-19-positive. This case underscores the atypical manifestations and deleterious effects associated with COVID-19 and the need to have a high index of suspicion for a multitude of pathologies when facing this viral illness.

Who is pregnant? defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C) (preprint)

Objective To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS) and applied it to EHR data in the N3C from 1 January 2018 to 7 April 2022. HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational aging-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated three types of pregnancy cohorts based on the level of precision for gestational aging and pregnancy outcomes for comparison of COVID-19 and other characteristics. Results We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, spontaneous abortions), and 23.3% had unknown outcomes. We were able to estimate start dates within one week of precision for 431,173 (52.8%) episodes. 66,019 (8.1%) episodes had incident COVID-19 during pregnancy. Across varying COVID-19 cohorts, patient characteristics were generally similar though pregnancy outcomes differed. Discussion HIPPS provides support for pregnancy-related variables based on EHR data for researchers to define pregnancy cohorts. Our approach performed well based on clinician validation. Conclusion We have developed a novel and robust approach for inferring pregnancy episodes and gestational aging that addresses data inconsistency and missingness in EHR data.

Inpatient Administration of Alpha-1-Adrenergic Receptor Blocking Agents Reduces Mortality in Male COVID-19 Patients.

Apha-1-adrenergic receptor antagonists (α1-blockers) can suppress pro-inflammatory cytokines, thereby potentially improving outcomes among patients with COVID-19. Accordingly, we evaluated the association between α1-blocker exposure (before or during hospitalization) and COVID-19 in-hospital mortality. We identified 2,627 men aged 45 or older who were admitted to Mount Sinai hospitals with COVID-19 between February 24 and May 31, 2020, in New York. Men exposed to α1-blockers (N = 436) were older (median age 73 vs. 64 years, P < 0.001) and more likely to have comorbidities than unexposed men (N = 2,191). Overall, 777 (29.6%) patients died in hospital, and 1,850 (70.4%) were discharged. Notably, we found that α1-blocker exposure was independently associated with improved in-hospital mortality in a multivariable logistic analysis (OR 0.699; 95% CI, 0.498-0.982; P = 0.039) after adjusting for patient demographics, comorbidities, and baseline vitals and labs. The protective effect of α1-blockers was stronger among patients with documented inpatient exposure to α1-blockers (OR 0.624; 95% CI 0.431-0.903; P = 0.012). Finally, age-stratified analyses suggested variable benefit from inpatient α1-blocker across age groups: Age 45-65 OR 0.483, 95% CI 0.216-1.081 (P = 0.077); Age 55-75 OR 0.535, 95% CI 0.323-0.885 (P = 0.015); Age 65-89 OR 0.727, 95% CI 0.484-1.092 (P = 0.124). Taken together, clinical trials to assess the therapeutic value of α1-blockers for COVID-19 complications are warranted.

Application of Telemedicine in COVID-19: A Bibliometric Analysis.

Background: Telemedicine as a tool that can reduce potential disease spread and fill a gap in healthcare has been increasingly applied during the COVID-19 pandemic. Many studies have summarized telemedicine's technologies or the diseases' applications. However, these studies were reviewed separately. There is a lack of a comprehensive overview of the telemedicine technologies, application areas, and medical service types. Objective: We aimed to investigate the research direction of telemedicine at COVID-19 and to clarify what kind of telemedicine technology is used in what diseases, and what medical services are provided by telemedicine. Methods: Publications addressing telemedicine in COVID-19 were retrieved from the PubMed database. To extract bibliographic information and do a bi-clustering analysis, we used Bicomb and gCLUTO. The co-occurrence networks of diseases, technology, and healthcare services were then constructed and shown using R-studio and the Gephi tool. Results: We retrieved 5,224 research papers on telemedicine at COVID-19 distributed among 1460 journals. Most articles were published in the Journal of Medical Internet Research (166/5,224, 3.18%). The United States published the most articles on telemedicine. The research clusters comprised 6 clusters, which refer to mental health, mhealth, cross-infection control, and self-management of diseases. The network analysis revealed a triple relation with diseases, technologies, and health care services with 303 nodes and 5,664 edges. The entity "delivery of health care" was the node with the highest betweenness centrality at 6,787.79, followed by "remote consultation" (4,395.76) and "infection control" (3,700.50). Conclusions: The results of this study highlight widely use of telemedicine during COVID-19. Most studies relate to the delivery of health care and mental health services. Technologies were primarily via mobile devices to deliver health care, remote consultation, control infection, and contact tracing. The study assists researchers in comprehending the knowledge structure in this sector, enabling them to discover critical topics and choose the best match for their survey work.

A potential controlling approach on surface ozone pollution based upon power big data

Surface ozone pollution has attracted extensive attention with the decreasing of haze pollution, especially in China. However, it is still difficult to efficiently control the pollution in time despite numbers of reports on mechanism of ozone pollution. Here we report a method for implementing effective control of ozone pollution through power big data. Combining the observation of surface ozone, NO2, meteorological parameters together with hourly electricity consumption data from volatile organic compounds (VOCs) emitting companies, a generalized additive model (GAM) is established for quantifying the influencing factors on the temporal and spatial distribution of surface ozone pollution from 2020 to 2021 in Anhui province, central China. The average R2 value for the modelling results of 16 cities is 0.82, indicating that the GAM model effectively captures the characteristics of ozone. The model quantifies the contribution of input variables to ozone, with both NO2 and industrial VOCs being the main contributors to ozone, contributing 33.72% and 21.12% to ozone formation respectively. Further analysis suggested the negative correlation between ozone and NO2, revealing VOCs primarily control the increase in ozone. Under scenarios controlling for a 10% and 20% reduction in electricity use in VOC-electricity sensitive industries that can be identified by power big data, ozone concentrations decreased by 9.7% and 19.1% during the pollution period. This study suggests a huge potential for controlling ozone pollution through power big data and offers specific control pathways. Supplementary Information The online version contains supplementary material available at 10.1007/s42452-022-05045-5. Article Highlights Surface ozone pollution in central China was investigated during the prevalence of the COVID-19 (2020.1–2021.5) NO2 and industrial VOCs contributing 33.72% and 21.12% to ozone formation Potential controlling pathway was proposed Supplementary Information The online version contains supplementary material available at 10.1007/s42452-022-05045-5.

The Mediating Effects of Work-Life Balance (WLB) and Ease of Using WLB Programs in the Relationship between WLB Organizational Culture and Turnover Intention.

Work-life balance (WLB) is an important concern for all workers irrespective of their age, sex, education level, family structure, or occupation. This study analyzes WLB's mediating effects and the ease of using WLB programs in the relationship between WLB organizational culture of hotels and turnover intention of its culinary staff. We conducted a survey featuring 320 culinary staff members at hotels in Incheon from 10 to 30 August 2020 and performed statistical analysis using 290 responses. We find that the company's willingness for WLB, empathetic communication with colleagues, material support of colleagues for WLB, and the ease of using WLB programs in organizational culture had a positive impact on WLB. The company's willingness for WLB, boss's consideration for WLB, empathetic communication with colleagues, and material support of colleagues for WLB in organizational culture had a negative impact on turnover intention. The ease of using WLB programs had no indirect effect on the relationship between organizational culture and turnover intention. However, WLB had an indirect effect on the relationship between the four components except for the boss's consideration for WLB and turnover intention. Hotel management should create an organizational culture that supports the WLB of culinary staff.

Investigating the Immunomodulatory Potential of Dental Pulp Stem Cell Cultured on Decellularized Bladder Hydrogel towards Macrophage Response In Vitro.

Mesenchymal stem cells (MSCs) possess immunomodulatory properties and capacity for endogenous regeneration. Therefore, MSC therapy is a promising treatment strategy for COVID-19. However, the cells cannot stay in the lung long enough to exert their function. The extracellular matrix from porcine bladders (B-ECM) has been shown not only to regulate cellular activities but also to possess immunoregulatory characteristics. Therefore, it can be hypothesized that B-ECM hydrogel could be an excellent scaffold for MSCs to grow and could anchor MSCs long enough in the lung so that they can exhibit their immunomodulatory functions. In this study, ECM degradation products and a co-culture system of MSCs and macrophages were developed to study the immunomodulatory properties of ECM and MSCs under septic conditions. The results showed that B-ECM degradation products could decrease pro-inflammatory and increase anti-inflammatory cytokines from macrophages. In an in vivo mimicking co-culture system, MSCs cultured on B-ECM hydrogel exhibited immunomodulatory properties at both gene and protein levels. Both B-ECM degradation products and MSC conditioned medium supported the wound healing of alveolar epithelial cells. The results from the study could offer a basis for investigation of immunomodulation by ECM and MSCs before conducting in vivo experiments, which could later be applied in regenerative medicine.

Credit Risk Measurement of Credit Bonds of Chinese Listed Companies Based on KMV Model

The sudden attack of COVID-19 has brought more unstable factors to the economic development of China and even the world. The risk fluctuation in the market has attracted the attention of all parties in the risk game.In this paper, the KMV model is used to measure credit risk. Taking the listed companies that issued Ashares and issued credit bonds in Shanghai and Shenzhen Stock Exchange as samples from 2016 to 2020, the validity of the model is verified at first.It is found that the average DD of the companies listed in Shanghai Stock Exchange is slightly higher than that of the companies listed in Shenzhen Stock Exchange from 2016 to 2020.In 2017, the overall default risk of the sample increased year by year after the minimum;Through the fluctuation of DD mean value in recent 3 years, it is found that the credit risk course of the whole industry is rising, which is related to the impact of the epidemic in 2020 on the industry economy.

Hospital sewage treatment facilities witness the fighting against the COVID-19 pandemic.

Real-time evaluation of the fighting activities during a sudden unknown disaster like the COVID-19 pandemic is a critical challenge for control. This study demonstrates that the temporal variations of effluents from hospital sewage treatment facilities can be used as an effective indicator for such evaluation. Taking a typical infection-suffering city in China as an example, we found that there was an obvious decrease in effluent ammonia and COD concentrations in line with the start of city lockdown, and its temporal variations well indicated the major events happened during the pandemic control. Notably, the lagging period between the change point of effluent residual chlorine and the change points of COD and ammonia concentration coincided with a period in which there was a deficiency in local medical resources. In addition, the diurnal behavior of effluents from designated hospitals has varied significantly at different stages of the pandemic development. The effluent ammonia peaks shifted from daytime to nighttime after the outbreak of the COVID-19 pandemic, suggesting a high workload of the designated hospitals in fighting the rapidly emerging pandemic. This work well demonstrates the necessary for data integration at the wastewater-medical service nexus and highlights an unusual role of the effluents from hospital sewage treatment facilities in revealing the status of fighting the pandemic, which helps to control the pandemic.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.