COVID-19 Patient Samples Research Articles

Clinical effectiveness of guided breathing exercises in reducing anxiety, stress, and depression in COVID-19 patients

The COVID-19 pandemic has led to an increase in the prevalence of anxiety, stress and depression among affected people. This study was conducted with the aim of investigating the clinical effectiveness of guided breathing exercises in reducing anxiety, stress and depression in patients with COVID-19. A quasi-experimental study design was used, involving a sample of COVID-19 patients who underwent guided breathing exercises as a complementary therapy. After simple sampling, eligible subjects were randomly divided into two groups: intervention (30 patients) and control (30 patients) using random block method. The Depression Anxiety Stress Scale-21 (DASS-21) questionnaire was administered before and after the intervention to evaluate changes in anxiety, stress, and depression levels. The results of this study demonstrated that clinically guided breathing exercises had a significant effect on reducing anxiety and stress in COVID-19 patients. The intervention significantly reduced anxiety and stress scores (p < 0.001). However, there was no significant reduction in depression scores among patients who participated in guided breathing exercises (p = 0.946). Guided breathing exercises are an effective complementary technique in reducing the level of anxiety and stress in COVID-19 patients. Moreover, the exercises may provide a worthy non-pharmacological approach to managing psychological distress in COVID-19 patients.

LITERATURE REVIEW: THE OMICS STUDY FOR DETERMINING BIOMARKERS IN HUMAN SERUM AND PLASMA WITH DIFFERENT COVID-19 SEVERITY

The severity of COVID-19 provides information on various stages of changes in the body's normal state in various parameters called biological markers (biomarkers) as the initial identification that facilitates management, selection, and total outcomes in therapy. These biomarkers were selected from the selection of samples that are often used in the advanced diagnosis of COVID-19, serum and plasma. This study aims to determine what biomarkers are measured in serum and plasma samples of COVID-19 patients. This literature review is classified as non-experimental, qualitative, and descriptive research. The inclusion criteria are the full-text journals published within the last two years regarding biomarkers in the serum and plasma of COVID-19 patients. Based on these criteria, 49 relevant articles were obtained. The results show that changes occur in the protein, lipid, and metabolite in serum and plasma by the omics approach. These alterations can be in the form of increasing or decreasing levels of each parameter determined through various analytical methods. The biomarkers profile correlates with the severity of COVID-19 as well as with host cells.

A novel metric-based approach of scoring early host immune response from oro-nasopharyngeal swabs predicts COVID-19 outcome

Unpredictable fatal outcome of COVID-19 is attributed to dysregulated inflammation. Impaired early adaptive immune response leads to late-stage inflammatory outcome. The purpose of this study was to develop biomarkers for early detection of host immune impairment at first diagnosis from leftover RNA samples, which may in turn identify high risk patients. Leftover RNA samples of COVID-19 patients at first diagnosis were stored. Following prospective follow-up, the samples were shorted and categorized into outcome groups. Impaired adaptive T cell response (severity score) and Impaired IL-10 response (undetectable IL-10 in the presence of high expression of a representative interferon response gene) were determined by RT-PCR based assay. We demonstrate that a T cell response based ‘severity score’ comprising rational combination of Ct values of a target genes’ signature can predict high risk noncomorbid potentially critical COVID-19 patients with a sensitivity of 91% (95% CI 58.7–99.8) and specificity of 92.6% (95% CI 75.7–99) (AUC:0.88). Although inclusion of comorbid patients reduced sensitivity to 77% (95% CI 54.6–92.2), the specificity was still 94% (95% CI 79.8–99.3) (AUC:0.82). The same for ‘impaired IL-10 response’ were little lower to predict high risk noncomorbid patients 64.2% (95% CI 35.1–87.2) and 82% (95% CI 65.5–93.2) respectively. Inclusion of comorbid patients drastically reduce sensitivity and specificity51.6% (95% CI 33.1–69.8) and 80.5% (95% CI 64.0–91.8) respectively. As best of our knowledge this is the first demonstration of a metric-based approach showing the ‘severity score’ as an indicator of early adoptive immune response, could be used as predictor of severe COVID-19 outcome at the time of first diagnosis using the same leftover swab RNA. The work flow could reduce expenditure and reporting time of the prognostic test for an earliest clinical decision ensuring possibility of early rational management.

Comparison of microbiological and molecular diagnosis for identification of respiratory secondary infections in COVID-19 patients and their antimicrobial resistance patterns

We report the use of a new multiplex Real-Time PCR platform to simultaneously identify 24 pathogens and 3 antimicrobial-resistance genes directly from respiratory samples of COVID-19 patients. Results were compared to culture-based diagnosis.Secondary infections were detected in 60% of COVID-19 patients by molecular analysis and 73% by microbiological assays, with no significant differences in accuracy, indicating Gram-negative bacteria as the predominant species. Among fungal superinfections, Aspergillus spp. were detected by both methods in more than 7% of COVID-19 patients. Oxacillin-resistant S. aureus and carbapenem-resistant K. pneumoniae were highlighted by both methods.Secondary microbial infections in SARS-CoV-2 patients are associated with poor outcomes and an increased risk of death. Since PCR-based tests significantly reduce the turnaround time to 4 hours and 30 minutes (compared to 48 hours for microbial culture), we strongly support the routine use of molecular techniques, in conjunction with microbiological analysis, to identify co/secondary infections.

Diagnostic potential of blood plasma longitudinal viscosity measured using Brillouin light scattering

Blood plasma viscosity (PV) is an established biomarker for numerous diseases. Measurement of the shear PV using conventional rheological techniques is, however, time consuming and requires significant plasma volumes. Here, we show that Brillouin light scattering (BLS) and angle-resolved spectroscopy measurements of the longitudinal PV from microliter-sized plasma volumes can serve as a proxy for the shear PV measured using conventional viscometers. This is not trivial given the distinct frequency regime probed and the longitudinal viscosity, a combination of the shear and bulk viscosity, representing a unique material property on account of the latter. We demonstrate this for plasma from healthy persons and patients suffering from different severities of COVID-19 (CoV), which has been associated with an increased shear PV. We further show that the additional information contained in the BLS-measured effective longitudinal PV and its temperature scaling can provide unique insight into the chemical constituents and physical properties of plasma that can be of diagnostic value. In particular, we find that changes in the effective longitudinal viscosity are consistent with an increased suspension concentration in CoV patient samples at elevated temperatures that is correlated with disease severity and progression. This is supported by results from rapid BLS spatial-mapping, angle-resolved BLS measurements, changes in the elastic scattering, and anomalies in the temperature scaling of the shear viscosity. Finally, we introduce a compact BLS probe to rapidly perform measurements in plastic transport tubes. Our results open a broad avenue for PV diagnostics based on the high-frequency effective longitudinal PV and show that BLS can provide a means for its implementation.

Sample-to-Answer Detection of SARS-CoV-2 Viremia Using Thermally Responsive Alkane Partitions.

The diagnosis of bloodborne viral infections (viremia) is currently relegated to central laboratories because of the complex procedures required to detect viruses in blood samples. The development of point-of-care diagnostics for viremia would enable patients to receive a diagnosis and begin treatment immediately instead of waiting days for results. Point-of-care systems for viremia have been limited by the challenges of integrating multiple precise steps into a fully automated (i.e., sample-to-answer), compact, low-cost system. We recently reported the development of thermally responsive alkane partitions (TRAPs), which enable the complete automation of diagnostic assays with complex samples. Here we report the use of TRAPs for the sample-to-answer detection of viruses in blood using a low-cost portable device and easily manufacturable cassettes. Specifically, we demonstrate the detection of SARS-CoV-2 in spiked blood samples, and we show that our system detects viremia in COVID-19 patient samples with good agreement to conventional RT-qPCR. We anticipate that our sample-to-answer system can be used to rapidly diagnose SARS-CoV-2 viremia at the point of care, leading to better health outcomes for patients with severe COVID-19 disease, and that our system can be applied to the diagnosis of other life-threatening bloodborne viral diseases, including Hepatitis C and HIV.

Unbiased plasma profiling using pre-selected RNA aptamer pools predicts mortality in COVID-19 and identifies protein risk factors

The impact of the COVID-19 pandemic demands effective prognostic tools for precise risk evaluation and timely intervention. This study utilised the APTASHAPE technology to profile plasma proteins in COVID-19 patient samples. Employing a highly diverse 2’-fluoro-protected RNA aptamer pool enriched toward proteins in the plasma samples from COVID-19 patients, we performed a single round of parallel selection on the derivation cohort and identified 93 discriminatory aptamers capable of distinguishing COVID-19 and healthy plasma samples. A subset of these aptamers were then used to predict 30-day mortality with high sensitivity and specificity in a validation cohort of 165 patients. We predicted 30-day mortality with an Area Under Curve (AUC) of 0.91 in females and 0.68 in males. Affinity purification coupled with mass spectrometry analysis of the aptamer-targeted proteins identified potential biomarkers associated with disease severity, including complement system components. The study demonstrates the APTASHAPE technology as an unbiased approach that not only aids in predicting disease outcomes but also offers insights into gender-specific differences, shedding light on the nuanced aspects of COVID-19 pathophysiology. In conclusion, the findings highlight the promise of APTASHAPE as a valuable tool for estimating risk factors in COVID-19 patients and enabling stratification for personalised treatment management.

Whole Blood as a Sample Matrix in Homogeneous Time-Resolved Assay-Förster Resonance Energy Transfer-Based Antibody Detection.

The protein-L-utilizing Förster resonance energy transfer (LFRET) assay enables mix-and-read antibody detection, as demonstrated for sera from patients with, e.g., severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Zika virus, and orthohantavirus infections. In this study, we compared paired serum and whole blood (WB) samples of COVID-19 patients and SARS-CoV-2 vaccine recipients. We found that LFRET also detects specific antibodies in WB samples. In 44 serum-WB pairs from patients with laboratory-confirmed COVID-19, LFRET showed a strong correlation between the sample materials. By analyzing 89 additional WB samples, totaling 133 WB samples, we found that LFRET results were moderately correlated with enzyme-linked immunosorbent assay results for samples collected 2 to 14 months after receiving COVID-19 diagnosis. However, the correlation decreased for samples >14 months after receiving a diagnosis. When comparing the WB LFRET results to neutralizing antibody titers, a strong correlation emerged for samples collected 1 to 14 months after receiving a diagnosis. This study also highlights the versatility of LFRET in detecting antibodies directly from WB samples and suggests that it could be employed for rapidly assessing antibody responses to infectious agents or vaccines.

A map of signaling responses in the human airway epithelium

Receptor-mediated signaling plays a central role in tissue regeneration, and it is dysregulated in disease. Here, we build a signaling-response map for a model regenerative human tissue: the airway epithelium. We analyzed the effect of 17 receptor-mediated signaling pathways on organotypic cultures to determine changes in abundance and phenotype of epithelial cell types. This map recapitulates the gamut of known airway epithelial signaling responses to these pathways. It defines convergent states induced by multiple ligands and diverse, ligand-specific responses in basal cell and secretory cell metaplasia. We show that loss of canonical differentiation induced by multiple pathways is associated with cell-cycle arrest, but that arrest is not sufficient to block differentiation. Using the signaling-response map, we show that a TGFB1-mediated response underlies specific aberrant cells found in multiple lung diseases and identify interferon responses in COVID-19 patient samples. Thus, we offer a framework enabling systematic evaluation of tissue signaling responses. A record of this paper's transparent peer review process is included in the supplemental information.

Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis

BackgroundThe prevalence of obstructive sleep apnea (OSA) was found to be higher in individuals following COVID-19 infection. However, the intricate mechanisms that underscore this concomitance remain partially elucidated. The aim of this study was to delve deeper into the molecular mechanisms that underpin this comorbidity.MethodsWe acquired gene expression profiles for COVID-19 (GSE157103) and OSA (GSE75097) from the Gene Expression Omnibus (GEO) database. Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein–protein interaction networks, module construction, and identification of pivotal genes. Furthermore, we established regulatory networks encompassing transcription factor (TF)-gene and TF-miRNA interactions, and searched for promising drug targets. Subsequently, the expression levels of pivotal genes were validated through proteomics data from COVID-19 cases.ResultsFourteen feature genes shared between OSA and COVID-19 were selected for further investigation. Through functional annotation, it was indicated that metabolic pathways play a role in the pathogenesis of both disorders. Subsequently, employing the cytoHubba plugin, ten hub genes were recognized, namely TP53, CCND1, MDM2, RB1, HIF1A, EP300, STAT3, CDK2, HSP90AA1, and PPARG. The finding of proteomics unveiled a substantial augmentation in the expression level of HSP90AA1 in COVID-19 patient samples, especially in severe conditions.ConclusionsOur investigation illuminate a mutual pathogenic mechanism that underlies both OSA and COVID-19, which may provide novel perspectives for future investigations into the underlying mechanisms.

Specific molecular peak analysis by ion mobility spectrometry of volatile organic compounds in urine of COVID-19 patients: A novel diagnostic approach

IntroductionSARS-CoV-2 is usually diagnosed from naso-/oropharyngeal swabs which are uncomfortable and prone to false results. This study investigated a novel diagnostic approach to Covid-19 measuring volatile organic compounds (VOC) from patients’ urine. MethodsBetween June 2020 and February 2021, 84 patients with positive RT-PCR for SARS-CoV-2 were recruited as well as 54 symptomatic individuals with negative RT-PCR. Midstream urine samples were obtained for VOC analysis using ion mobility spectrometry (IMS) which detects individual molecular components of a gas sample based on their size, configuration, and charge after ionization. ResultsPeak analysis of the 84 Covid and 54 control samples showed good group separation. In total, 37 individual specific peaks were identified, 5 of which (P134, 198, 135, 75, 136) accounted for significant differences between groups, resulting in sensitivities of 89–94% and specificities of 82–94%. A decision tree was generated from the relevant peaks, leading to a combined sensitivity and specificity of 98% each. DiscussionVOC-based diagnosis can establish a reliable separation between urine samples of Covid-19 patients and negative controls. Molecular peaks which apparently are disease-specific were identified. IMS is an additional non-invasive and cheap device for the diagnosis of this ongoing endemic infection. Further studies are needed to validate sensitivity and specificity.

Bacterial co-infections and secondary infections and their antimicrobial resistance in Covid-19 patients during the second pandemic wave.

COVID-19 pneumonia with an unusual outbreak is considered a new, global public health threat. Microbiological characterization of co-infections in patients with COVID-19 is important, and antimicrobial use is high. We aimed to describe microbiologically confirmed co-infections and the antimicrobial resistance of the causative pathogens. From January to December 2020, we tested 1,301 patients who were COVID-19 positive. We received clinical samples (blood, respiratory and sterile body fluids) of COVID-19 patients who were suspected to have bacterial co-infections. Samples were processed and antimicrobial susceptibility testing was performed based on the CLSI recommendation. Demographic, clinical, laboratory and outcome data of those with positive cultures were collected. A total of 1301 COVID-19 patients (568 from the COVID ward and 733 from ICU) were admitted to the Covid care ward of a tertiary care hospital. 363 samples were sent for culturing and testing antibiotic susceptibility, of which 131 (36%) were found to be culture-positive (90 from ICUs, 41 from wards). Out of the 143 total isolates thus obtained from 131 samples, the majority (62.2%) were Gram-negative bacteria, and most of them were (70.8%) multidrug resistant. Bacterial co-infection in patients with COVID-19 is more commonly reported in the severely ill hospitalized individuals (58%), particularly in the ICU (73.3%) setting. In terms of mortality, almost half of co-infected patients died (51.1%). In most of them, the cause of death was found to be sepsis with post-COVID ARDS (58%). Co-infection in COVID-19 patients may affect the outcome in terms of increasing the hospital stay.

Prevalence of SARS-CoV-2 virus in saliva, stool, and urine samples of COVID-19 patients in Bihar, India.

Introduction. The coronavirus illness caused by SARS-CoV-2 can cause multiple organ involvement, with varying degrees of severity. Besides inhalation as a route for transmission, feco-oral has also been proposed. Its transmission to sewage systems is a growing public health issue. Objective. To detect SARS-CoV-2 RNA in non-respiratory samples (saliva, urine, and stool) collected from COVID-19 cases, in Bihar. Methods. This cross-sectional observational study was conducted from January 2021 to March 2022 on human non-respiratory samples. A total of 345 samples including saliva (116), stool (97), and urine (132) were collected from 143 COVID-19 cases. Samples were analysed for SARS-CoV-2 by multiplex RT-PCR targeted against E, ORF 1ab, and RdRp genes. Results. In this study, out of 143 cases, a total of 107 (74.8 %) were positive for SARS-CoV-2 RNA in at least one of the non-respiratory samples. Conclusion. There is a high prevalence of SARS-CoV-2 virus in non-respiratory samples.

Yeast species in the respiratory samples of COVID-19 patients; molecular tracking of Candida auris.

Although the existence of Candida species in the respiratory tract is often considered commensal, it is crucial to recognize the significance of Candida colonization in immunocompromised or COVID-19 patients. The emergence of Candida auris as an emerging pathogen further emphasizes the importance of monitoring yeast infection/colonization, particularly in COVID-19 patients. In this study, respiratory samples mainly from COVID-19 patients, primarily those suspected of having a fungal infection, were cultured on Sabouraud dextrose agar plates and the yeast colonies were identified using a two-step multiplex PCR method. The samples suspected of C. auris underwent specific nested PCR followed by sequence analysis. A total of 199 respiratory samples were collected from 73 women and 126 men, ranging in age from 1.6 to 88 years. Among the patients, 141 had COVID-19, 32 had cancer, 5 were hospitalized in ICU, 2 had chronic obstructive pulmonary disease)COPD(, and others were patients with combination diseases. From these samples, a total of 334 yeast strains were identified. C. albicans (n=132, 39.52%) was the most common species, followed by C. tropicalis (n=67, 20%), C. glabrata (n=56, 16.76%), C. krusei (n=18, 5.4%), C. parapsilosis (n=17, 5.08%), Saccharomyces cerevisiae (n=10, 3%), C. kefyr (n=9, 2.6%), C. dubliniensis (n=7, 2.1%), C. lusitaniae (n=5, 1.5%), C. auris (n=3, 0.9%), C. guilliermondii (n=2, 0.6%), C. rugosa (n=1, 0.3%), C. intermedia (n=1, 0.3%), and Trichosporon spp. (n=1, 0.3%). C. auris was detected in a patient in ICU and two COVID-19 patients. While its presence was confirmed through sequence analysis, our extensive efforts to isolate C. auris were unsuccessful. While C. albicans colonization remains prevalent, our study found no evidence of Candida lung infection. Since the role of Candida colonization in airway secretions remains ambiguous due to limited research, further studies are imperative to shed light on this matter.

Impact of IL-6 rs1800795 and IL-17A rs2275913 gene polymorphisms on the COVID-19 prognosis and susceptibility in a sample of Iranian patients

BackgroundFrom asymptomatic to acute and life-threatening pulmonary infection, the clinical manifestations of COVID-19 are highly variable. Interleukin (IL)-6 and IL-17A are key drivers of hyper inflammation status in COVID-19, and their elevated levels are hallmarks of the infection progression. To explore whether prognosis and susceptibility to COVID-19 are linked to IL-6 rs1800795 and IL-17A rs2275913, these single-nucleotide polymorphisms (SNPs) were assessed in a sample of Iranian COVID-19 patients. MethodsThis study enrolled two hundred and eighty COVID-19 patients (140 non-severe and 140 severe). Genotyping for IL-6 rs1800795 and IL-17A rs2275913 was performed using tetra primer-amplification refractory mutation system-polymerase chain reaction (tetra-ARMS-PCR). IL-6 and IL-17A circulating levels were measured using enzyme-linked immunosorbent assay (ELISA). Also, mortality predictors of COVID-19 were investigated. ResultsThe rs1800795 GG genotype (78/140 (55.7 %)) and G allele (205/280 (73.2 %)) were significantly associated with a positive risk of COVID-19 severe infection (OR = 2.19, 95 %CI: 1.35–3.54, P =.006 and OR = 1.79, 95 %CI: 1.25–2.56, P <.001, respectively). Also, rs1800795 GG genotype was significantly linked to disease mortality (OR = 1.95, 95 %CI: 1.06–3.61, P =.04). The rs2275913 GA genotype was protective against severe COVID-19 (OR = 0.5, 95 %CI: 0.31––0.80, P =.012). However, the present study did not reveal any significant link between rs2275913 genotypes with disease mortality. INR ≥ 1.2 (OR = 2.19, 95 %CI: 1.61–3.78, P =.007), D-dimer ≥ 565.5 ng/mL (OR = 3.12, 95 %CI: 1.27–5.68, P =.019), respiratory rate ≥ 29 (OR = 1.19, 95 %CI: 1.12–1.28, P =.001), IL-6 serum concentration ≥ 28.5 pg/mL (OR = 1.97, 95 %CI: 1.942–2.06, P =.013), and IL-6 rs1800795 GG genotype (OR = 1.95, 95 %CI: 1.06–3.61, P =.04) were predictive of COVID-19 mortality. ConclusionThe rs1800795 GG genotype and G allele were associated with disease severity, and INR, D-dimer, respiratory rate, IL-6 serum concentration, and IL-6 rs1800795 GG genotype were predictive of COVID-19 mortality.

Investigation of the Frequency of Respiratory Viral Co-Infection in Respiratory Samples of Suspected COVID-19 Patients

Investigation of the Frequency of Respiratory Viral Co-Infection in Respiratory Samples of Suspected COVID-19 Patients

Abstract 18084: Coagulopathy and Hospital Outcome in Patients With Covid-19: An Insight From National Inpatient Sample

Introduction: SARS-CoV-2 infection has been linked to thrombosis and bleeding. In this study we aim to investigate coagulopathy rates, predictors and in-hospital outcomes in a nationally representative sample of COVID-19 patients. Methods: Using the 2020 National Inpatient Sample. We identified adult patients who were admitted with a principal diagnosis of COVID-19. Mortality difference, LOS, and COC were evaluated as indicators of healthcare resource usage. Regression analysis adjusted for baseline variables and comorbidities compared individuals who developed coagulopathy to those who did not. Logistic regression analysis was used to identify COVID-19 coagulopathy facrtors. p-value &lt;0.05 indicated statistical significance. Results: Among 1,050,045 COVID-19 admissions, 12% (126,225 patients) developed coagulopathy. Those with coagulopathy group had higher rates of in-hospital mortality (aOR 1.31, CI 1.23-1.39, p &lt;0.001), prolonged LOS (aMD 2.29 days, 95% CI 2.12-2.46, p&lt;0.001), and increased COC (aMD 142615$, 95% CI 32905-41299, p&lt;0.001) compared to those that did not develop coagulopathy. Factors associated with coagulopathy included male gender, black race, higher Charleston comorbidity index, and lowest income quartile Conclusion: Approximately 12% of patients developed coagulopathy, which was associated with increased in-hospital mortality, longer length of stay, and higher cost of care. These findings emphasize the importance of identifying and managing coagulation disorders in COVID-19 patients to improve outcomes and allocate healthcare resources effectively. Further research is needed to explore interventions and strategies for mitigating the impact of coagulopathy in this population

Severity Classification Using Dynamic Time Warping-Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study.

In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores. This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients' voices to distinguish moderate illness from mild illness at a significant level. We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy. Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants' attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/. The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care.

XCovNet: A wide deep learning model for CXR-based COVID-19 detection

The high death toll and economic impact of the COVID-19 pandemic emphasize the need for effective population screening technologies. The high cost, limited availability, and slow nature of CT scans and PCR-based tests renders them impractical for frequent use among the general public. As chest X-ray (CXR) imaging is fast and economical, a high accuracy CXR-based test would be well-suited for such screenings. Deep learning algorithms are widely used to aid medical image diagnoses. We use a collection of state-of-the-art pre-trained deep neural network models with additional layers to detect the COVID-19 cases from a sample of healthy, COVID-19, and pneumonia patients. We observed models trained with concatenated features of multiple pre-trained deep learning architectures outperform the individual and ensemble models. Our final model obtained a recall of greater than 98% and a precision of greater than 95% on two separate datasets. The wide architecture of xCovNet may contribute to its robust behavior, providing a systematic approach to construct a reliable deep learning model for emerging datasets.

Psychological factors associated with vaccination hesitancy: an observational study of patients hospitalized for COVID-19 in a later phase of the pandemic in Italy.

Vaccination against SARS-CoV-2 has been used to reduce the severity of COVID-19 disease and the incidence of new cases. However, a significant proportion of people have shown vaccination hesitancy. This study explored psychological factors related to vaccination hesitancy in a sample of Italian COVID-19 patients (N = 54), hospitalized during 2021, after vaccines had been made available and while the vaccination campaign was on-going. Consecutive patients, aged 18 or older, admitted to the hospital with a diagnosis of COVID-19 were assessed with a set of standardized measures. In our sample, 48.1% was not vaccinated and 7.4% died within 6months after hospitalization, with a preponderance of deaths among non-vaccinated patients. Non-vaccinated participants had higher resilience scores at the CD-RISC-10 scale than vaccinated ones (33.6 ± 5.50 vs 28.6 ± 6.61; t40.2=+ 2.94, p = 0.005). No statistically significant differences were found between the two groups for any other measures. Higher levels of resilience among non-vaccinated patients may reflect greater identity worth and self-esteem, in turn resulting in a decrease in vaccination likelihood. This finding may have important public health implications, as it indicates that specific psychological aspects, such as resilience, may result in vaccination hesitancy, with implications for hospitalization rates, and thus healthcare costs, as well as loss of lives.