The role of biomarkers in diagnosis of COVID-19 - A systematic review. Life Sci. 2020;254:117788. [PMID: 32475810 PMCID: PMC7219356 DOI: 10.1016/j.lfs.2020.117788]

Occurrence and clinical correlates of SARS-CoV-2 viremia in two German patient cohorts

Viremia defined as detectable SARS-CoV-2 RNA in the blood is a potential marker of disease severity and prognosis in COVID-19 patients. Here, we determined the frequency of viremia in serum of two independent COVID-19 patient cohorts within the German National Pandemic Cohort Network (German: Nationales Pandemie Kohorten Netzwerk, NAPKON) with diagnostic RT-PCR against SARS-CoV-2. A cross-sectional cohort with 1122 COVID-19 patients (German: Sektorenuebergreifende Platform, SUEP) and 299 patients recruited in a high-resolution platform with patients at high risk to develop severe courses (German: Hochaufloesende Plattform, HAP) were tested for viremia. Our study also involved a comprehensive analysis and association of serological, diagnostic, and clinical parameters of the NAPKON medical dataset. Prevalence of viremia at the recruitment visit was 12.8% (SUEP) and 13% (HAP), respectively. Serological analysis revealed that viremic patients had lower levels of SARS-CoV-2 specific antibodies as well as lower neutralizing antibodies compared to aviremic patients. Viremia was associated with severity (<0.0001 SUEP; 0.002 HAP) and mortality of COVID-19 (both cohorts <0.0001) compared to aviremic patients. While rare, viremia was also detected in patients with mild disease (0.7%). In patients of the SUEP cohort with acute kidney disease (p = 0.0099) and hematooncological conditions (p = 0.0091), viremia was detected more frequently. Compared to the aviremic group, treatment with immunomodulating drugs as well as elevated levels of inflammatory markers in the blood was more frequent in the viremic group. In conclusion, our analysis revealed that detectable viremia correlates with hyperinflammatory conditions and higher risk for severe COVID-19 disease.

Self-reported health, neuropsychological tests and biomarkers in fully recovered COVID-19 patients vs patients with post-COVID cognitive symptoms: A pilot study

Substantial numbers of individuals who contract COVID-19 experience long-lasting cognitive symptoms such as brain fog. Yet research to date has not compared these patients with healthy controls with a history of laboratory-confirmed COVID-19 infection, making it difficult to understand why certain COVID patients develop post-COVID cognitive symptoms while others do not. The objective of this pilot study was to compare two groups of laboratory-confirmed post-COVID patients, with and without cognitive symptoms, on measures of cognitive and psychological functioning, self-reported perceptions of functional status and quality of life, and biomarkers of stress, inflammation, and neuroplasticity. Using a case-control design, 17 participants were recruited from a healthcare system in western Michigan, USA in 2022-2024. All participants were aged 25-65 and had a positive polymerase chain reaction (PCR) test confirming previous COVID-19 infection. Ten participants reported cognitive symptoms (long COVID group) while seven were fully recovered with no residual symptoms (controls). All participants underwent an interview on their self-rated health and quality of life, a battery of neurocognitive tests, and blood draw for biomarker analysis. No group differences were detected for neuropsychological test measures except for letter fluency where the long COVID group scored significantly lower (p < .05). The long COVID group had significantly lower ratings than controls on quality of life, physical health, emotional functioning, and psychological well-being. Serum levels of nerve growth factor (NGF), a biomarker of brain plasticity, were significantly lower in the long COVID group, which was significantly more likely than controls to have serum levels of inflammatory marker (interleukin (IL)-10) values greater than or equal to the median (p = 0.015). Biomarker analyses suggest possible prolonged inflammatory processes in long COVID patients compared to fully recovered patients. Results of decreased neuroplastic functioning give credence to patients' reports of post-COVID changes in brain function.

Recent advances in the diagnosis and treatment of DIHS/DRESS in 2025

Drug-induced hypersensitivity syndrome (DIHS) or drug reaction with eosinophilia and systemic symptoms (DRESS) is a severe drug reaction characterized by a range of clinical manifestations. These range from mild cases resolving upon cessation of the causative drug to severe cases involving complex disease progression and potential fatality. A hallmark of DIHS/DRESS is the sequential reactivation of herpesviruses, particularly human herpesvirus 6 (HHV-6), during the disease course, contributing to recurrent symptoms. Viral reactivation can lead to critical complications, including infectious DIHS/DRESS-associated complications (iDACs) and autoimmune sequelae (aDACs). Managing DIHS/DRESS remains challenging due to its complexity, requiring precise prediction and tailored treatment strategies. Recent studies suggest that early-stage classification using the DIHS/DRESS Severity (DDS) score may help identify refractory cases, including DACs. Furthermore, early intervention with anti-cytomegalovirus (anti-CMV) therapy can mitigate iDACs caused by CMV reactivation, preventing progression to severe CMV-related diseases. Long-term follow-up is crucial, as aDACs can manifest even 3 years postonset. Serial monitoring is recommended, particularly in patients treated with intravenous immunoglobulin or corticosteroid pulse therapy, which are recognized risk factors for aDAC development. This review highlights DIHS/DRESS management strategies, focusing on its clinical features, the role of viral reactivation, and therapeutic interventions.

Can the Early Warning Score (ANDC) Predict Tocilizumab Efficacy in Patients with COVID-19 Cytokine Storm?

Background

The aim of this study is to assess the effectiveness of the Early Warning Score [ANDC (age (A), neutrophil-to-lymphocyte ratio (NLR (N)), D-dimer (D), and CRP (C)] in predicting the treatment response in patients receiving tocilizumab for Coronavirus Disease 2019 (COVID-19)-related cytokine storm.

Methods

A retrospective review of medical records was conducted for patients treated with tocili- zumab for a cytokine storm related to COVID-19 between April 1, 2020, and April 1, 2021. Patient demographics, clinical characteristics, and laboratory parameters within 24 hours before tocilizumab were recorded. 1.14 × (age - 20) (years) + 1.63 × NLR + 5.00 × D-dimer (mg/L) + 0.14 × C-reactive protein (CRP) (mg/L) was used as the formula for the ANDC score. The study population was divided into 2 groups: those who died within 28 days of receiving tocilizumab and those who recovered. A comparative analysis was conducted.

Results

Within 28 days of tocilizumab treatment, 59 (35.32%) patients died. In comparison with living patients, deceased patients exhibited considerably higher levels of interleukin (IL)-6, lactate dehydro- genase (LDH), ANDC score, and CRP (P < .05). Lactate dehydrogenase was an independent predictor of response to tocilizumab treatment (P < .001) in a multivariate logistic regression analysis. In patients who did not receive steroid therapy before tocilizumab treatment, the ANDC score had the highest area under the curve (AUC). The optimal cut-off value was determined to be 92.56, with a sensitivity of 91.67% and a specificity of 60.61% (P < .001). In patients receiving steroids before tocilizumab, LDH had the highest AUC. The optimal cut-off value was 484.5 U/L (P < .001).

Conclusion

Lactate dehydrogenase was identified as an independent predictor of response to tocili- zumab treatment. The ANDC score showed the highest AUC value in steroid-naïve patients before tocilizumab, whereas LDH showed the highest AUC value in patients receiving steroids before tocili- zumab. Both the ANDC score and LDH levels show potential as valuable tools to guide treatment decisions.

Blood Transcriptome Profiling Highlights the Role of Intestinal Bacterial Translocation in Severe COVID-19

COVID-19 has caused millions of deaths globally; however, the characterization of molecular biomarkers of severe disease remains of great scientific importance. The aim of this study was to capture the transcriptional differences of the whole blood gene expression between COVID-19 patients with mild and severe disease, using Next Generation Sequencing technologies, on admission and after 7 days. The genes which were differentially expressed in severe compared to mild patients were used for Gene Ontology (GO) enrichment analysis. Gene expression data were used to estimate the cell abundance of 22 immune cell types via digital cytometry. GO terms related to the response to molecules of bacterial origin, such as intestine-derived lipopolysaccharide (LPS), were enriched, among other dysregulated pathways, which are well described as paramount mechanisms of severe manifestations of COVID-19. The neutrophil population increased in patients with severe disease, whereas the monocyte, CD8+ T cell, and activated Natural Killer (NK) cell populations were depleted. These cell population dynamics are also indicative of severe COVID-19 and intestinal bacterial translocation. This study elucidates the molecular basis of severe COVID-19 and highlights intestinal bacterial translocation as a potential driver of severe disease.

Methylation patterns of the nasal epigenome of hospitalized SARS-CoV-2 positive patients reveal insights into molecular mechanisms of COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has varied presentations from asymptomatic to death. Efforts to identify factors responsible for differential COVID-19 severity include but are not limited to genome wide association studies (GWAS) and transcriptomic analysis. More recently, variability in host epigenomic profiles have garnered attention, providing links to disease severity. However, whole epigenome analysis of the respiratory tract, the target tissue of SARS-CoV-2, remains ill-defined.

RESULTS

We interrogated the nasal methylome to identify pathophysiologic drivers in COVID-19 severity through whole genome bisulfite sequencing (WGBS) of nasal samples from COVID-19 positive individuals with severe and mild presentation of disease. We noted differential DNA methylation in intergenic regions and low methylated regions (LMRs), demonstrating the importance of distal regulatory elements in gene regulation in COVID-19 illness. Additionally, we demonstrated differential methylation of pathways implicated in immune cell recruitment and function, and the inflammatory response. We found significant hypermethylation of the FUT4 promoter implicating impaired neutrophil adhesion in severe disease. We also identified hypermethylation of ELF5 binding sites suggesting downregulation of ELF5 targets in the nasal cavity as a factor in COVID-19 phenotypic variability.

CONCLUSIONS

This study demonstrated DNA methylation as a marker of the immune response to SARS-CoV-2 infection, with enhancer-like elements playing significant roles. It is difficult to discern whether this differential methylation is a predisposing factor to severe COVID-19, or if methylation differences occur in response to disease severity. These differences in the nasal methylome may contribute to disease severity, or conversely, the nasal immune system may respond to severe infection through differential immune cell recruitment and immune function, and through differential regulation of the inflammatory response.

Deep vein thrombosis in severe community-acquired pneumonia patients undergoing thromboprophylaxis: Prevalence, risk factors, and outcome

BACKGROUND

Even with adherence to thromboprophylaxis recommended by guidelines, the incidence of deep vein thrombosis (DVT) remains high among patients with severe community-acquired pneumonia (SCAP). There is an urgent need to identify the risk factors for DVT in these patients to optimize preventive strategies.

STUDY DESIGN AND METHODS

We retrospectively enrolled 309 adults with SCAP admitted to Beijing Chao-Yang Hospital between 1 January 2015 and 30 June 2023. All patients received guideline-recommended thromboprophylaxis and lower extremity venous compression ultrasound scanning. Clinical characteristics, including demographic information, clinical history, vital signs, laboratory findings, treatments, complications, and outcomes, were analyzed for patients with and without DVT in these two cohorts.

RESULTS

Of the 309 patients, 110 (35.6%) developed 1ower extremity DVT. There was no significant difference in the incidence of DVT among the different prophylactic measures (P = 0.393). Multivariate logistic regression analysis showed an association between a history of VTE (OR, 13.388, 95% CI: 2.179 ~ 82.257; P = 0.005), bedridden time > 3 days (OR, 17.672, 95% CI: 5.686 ~ 54.929; P < 0.001), D-dimer levels ≥ 1.0 µg/mL (OR, 2.109, 95% CI: 1.018 ~ 4.372; P = 0.045), LDH levels ≥ 400 U/L (OR, 2.548, 95% CI: 1.308 ~ 4.965; P = 0.006), IMV (OR, 2.479, 95% CI: 1.233 ~ 4.986; P = 0.011) and the occurrence of DVT. A new prediction model, including history of VTE, bedridden time, D-dimer levels, LDH levels and IMV, showed a better performance in predicting DVT (AUC = 0.856; 95% CI: 0.766 ~ 0.921; sensitivity: 80.6%; specificity: 81.4%) than Padua prediction score (AUC = 0.666) and Caprini prediction score (AUC = 0.688) for patients with SCAP. The 30-day mortality and in-hospital mortality in the DVT group were significantly higher than those in the non-DVT group.

CONCLUSIONS

Even received guideline-recommended thromboprophylaxis, the prevalence of DVT among patients with SCAP remains unexpectedly high which is also associated with a poor prognosis. It is necessary to identify people at high risk of DVT early and refine the preventive strategies accordingly to improve patient outcomes.

An analysis of novel endothelial biomarkers to ascertain their role as prognostic indicators in severe COVID-19 illness

Background & objectives COVID-19 is a complex disease that affects multiple systems and causes inflammation due to the SARS-CoV-2 virus's attraction to ACE-2 receptors. The severity of the disease ranges from mild upper respiratory tract infections to multi-organ failure and disseminated intravascular coagulation. Critical clinical features, such as respiratory failure, myocarditis, acute kidney injury, and a hypercoagulable state, are common in severe COVID-19 illness. Identifying patients at higher risk of rapid disease progression leading to severe complications and death is crucial. Available biomarkers lack specificity and accuracy in identifying the severity of COVID-19 infections. Therefore, there is a need to identify specific endothelial biomarkers that can predict early organ damage, facilitate effective management of patients, and categorise multisystem involvement during COVID-19 infection. Methods The participants constituted laboratory-confirmed COVID-19-positive patients with influenza-like illness (ILI) features under home isolation or moderate to severe acute respiratory infection (SARI) manifestations requiring admission. The chosen markers were analysed using the Human Luminex multiplex bead-based assay system. Results Our study revealed that certain markers, such as intercellular adhesion molecule (ICAM) and Galectin-3 were elevated in all COVID-19-positive patients, while others, like vascular cell adhesion molecule (VCAM), Angiopoietin-2, Thrombomodulin, and tumour necrosis factor receptor 1 (TNFR1) were specifically elevated in patients requiring admission. Such findings indicated the potential of some biomarkers in disease prognostication. Interpretation & conclusions Effective management of COVID-19 requires proper risk stratification. The findings of our investigation underscore the necessity for additional research to confirm the clinical utility and importance of these biomarkers and to fully leverage their capabilities in informing the management of severe COVID-19 disease.

Happy Hypoxia With SARS-CoV-2 Pneumonitis: A Life-Threatening Case of a Medical Doctor

The second wave of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) had devastating consequences in developing countries like Nepal and India. SARS-CoV-2 affected everyone, encompassing all professions, age brackets, and socioeconomic levels. Symptoms, along with the SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) results, were the primary factors used to determine whether patients should be instructed to quarantine at home or be admitted to hospitals immediately. Happy hypoxia refers to a situation where a patient shows no respiratory symptoms yet experiences a decline in oxygen saturation levels. This drop can go unnoticed, potentially resulting in a sudden deterioration of the patient's condition, with reports of fatalities occurring. We present a case involving a physician who experienced a sore throat and fever as symptoms but had no respiratory distress, along with a decrease in oxygen saturation to 87% at home and 82% at the hospital, resulting in a near-fatal situation.

Wireless power-up and readout from a label-free biosensor

Wearable and implantable biosensors have rapidly entered the fields of health and biomedicine to diagnose diseases and physiological monitoring. The use of wired medical devices causes surgical complications, which can occur when wires break, become infected, generate electrical noise, and are incompatible with implantable applications. In contrast, wireless power transfer is ideal for biosensing applications since it does not necessitate direct connections between measurement tools and sensing systems, enabling remote use of the biosensors. In addition, wireless sensors eliminate the need for a battery or energy harvester, reducing the size of the sensor. As far as we are aware, this is the first report ever describing a new method for wireless readout of a label-free electronic biosensor for detecting protein biomarkers. Our results reveal that we are able to successfully detect target protein and corresponding antibodies within this wireless setup. We are able to distinguish target protein in purified samples from a blank PBS sample as a negative control by tracking gradual changes in impedance at the input of the transmitter (P-value = 0.00788). We also demonstrate real-time wireless quantification of cytokines within rheumatoid arthritis patient serum samples (P-value = 0.00891). A Fine Gaussian Support Vector Machine is also used to differentiate protein from negative controls with the highest accuracy from a dataset of 54 experiments.

Profession and role-based analysis of occupational exposure for COVID-19 among frontline healthcare workers in the pandemic: a risk assessment study

Healthcare workers are exposed to a high risk of COVID-19 infection due to close contact with infected patients in healthcare centers. This study aimed to investigate the level of exposure and risk of COVID-19 virus infection among healthcare workers working in primary healthcare centers in Khuzestan province, Iran. This cross-sectional study was conducted among 599 healthcare workers working in primary healthcare centers in the northern region of Khuzestan province, Iran, in 2022. Participants were recruited using a multistage and proportional stratified random sampling method. The WHO COVID-19 risk assessment tool was used to collect data. Data were analyzed using STATA V14.2. The prevalence of occupational and community exposure was 95.7% (93.7 to 97.1) and 89.6% (87.0 to 92.1), respectively. Healthcare workers with occupational exposure had a high risk of exposure (92.7%; 95% CI 90.1 to 94.6). There was no significant association between the type of profession, the role of primary health care workers, and the level of occupational exposure risk (P value > 0.05). The strength of the association was very weak (PR = 1.00; 95% CI 0.94 to 1.07). Also, a significant association was observed between the history of contact with biological materials and adherence to infection prevention and control measures with the level of occupational exposure to the virus (P value < 0.001). The strength of the association between contact with biological materials and exposure risk was weak (aPR = 1.20; 95% CI 1.12 to 1.29), but the strength of the association between adherence to infection prevention and control protocols and exposure risk was strong (aPR = 3.85; 95% CI 2.60 to 5.71). Furthermore, infection prevention and control was identified as a strong confounder in this study. The results showed that the prevalence of occupational exposure was high among healthcare workers, regardless of their profession and roles, with the majority of exposures being of high risk. Primary healthcare managers can play a major role in reducing exposure among high-risk healthcare workers by providing continuous personnel training, investing in the supply chain, prioritizing regular testing and vaccination of HCWs, and ensuring dedicated supervision while accurately monitoring compliance with health protocols during pandemics.

Elevated serum mtDNA in COVID-19 patients is linked to SARS-CoV-2 envelope protein targeting mitochondrial VDAC1, inducing apoptosis and mtDNA release

Mitochondria dysfunction is implicated in cell death, inflammation, and autoimmunity. During viral infections, some viruses employ different strategies to disrupt mitochondria-dependent apoptosis, while others, including SARS-CoV-2, induce host cell apoptosis to facilitate replication and immune system modulation. Given mitochondrial DNAs (mtDNA) role as a pro-inflammatory damage-associated molecular pattern in inflammatory diseases, we examined its levels in the serum of COVID-19 patients and found it to be high relative to levels in healthy donors. Furthermore, comparison of serum protein profiles between healthy individuals and SARS-CoV-2-infected patients revealed unique bands in the COVID-19 patients. Using mass spectroscopy, we identified over 15 proteins, whose levels in the serum of COVID-19 patients were 4- to 780-fold higher. As mtDNA release from the mitochondria is mediated by the oligomeric form of the mitochondrial-gatekeeper-the voltage-dependent anion-selective channel 1 (VDAC1)-we investigated whether SARS-CoV-2 protein alters VDAC1 expression. Among the three selected SARS-CoV-2 proteins, small envelope (E), nucleocapsid (N), and accessory 3b proteins, the E-protein induced VDAC1 overexpression, VDAC1 oligomerization, cell death, and mtDNA release. Additionally, this protein led to mitochondrial dysfunction, as evidenced by increased mitochondrial ROS production and cytosolic Ca2+ levels. These findings suggest that SARS-CoV-2 E-protein induces mitochondrial dysfunction, apoptosis, and mtDNA release via VDAC1 modulation. mtDNA that accumulates in the blood activates the cGAS-STING pathway, triggering inflammatory cytokine and chemokine expression that contribute to the cytokine storm and tissue damage seen in cases of severe COVID-19.

Circulating inflammatory cytokines predict severity disease in hospitalized COVID-19 patients: A prospective multicenter study of the European DRAGON consortium

BACKGROUND

COVID-19 has put a huge strain on the healthcare systems worldwide, requiring unprecedented intensive care resources. There is still an unmet clinical need for easily available biomarkers capable of predicting the risk for severe disease. The main goal of this prospective multicenter study was to identify biomarkers that could predict ICU admission and in-hospital mortality.

METHODS

We prospectively recruited COVID-19 PCR positive patients in two hospitals, in Belgium and Italy. Blood samples were collected at hospital admission and 20 potential biomarkers were measured with the Luminex technology. Logistic regression models were performed to identify the biomarkers that, alone or together, were associated with patient disease severity.

RESULTS

Our study demonstrates that elevated levels of circulating inflammatory cytokines were associated with disease severity in COVID-19 hospitalized patients. CXCL10, IL-4, IL-6 and MCP-1 values were predictive of ICU admission. Elevated levels of IL-6 and MCP-1 were also associated with in hospital death in COVID-19 hospitalized patients.

CONCLUSION

Altogether, elevated and correlated inflammatory cytokines in the blood of COVID-19 patients at hospital admission are predictive of disease severity and suggest a dysregulated inflammation induced by SARS-CoV-2 infection.

Predicting survival in patients with SARS-CoV-2 based on cytokines and soluble immune checkpoint regulators

Background

Coronavirus disease 2019 (COVID-19) has been widespread for over four years and has progressed to an endemic stage. Accordingly, the evaluation of host immunity in infected patients and the development of markers for prognostic prediction in the early stages have been emphasized. Soluble immune checkpoints (sICs), which regulate T cell activity, have been reported as promising biomarkers of viral infections.

Methods

In this study, quantitative values of 17 sICs and 16 cytokines (CKs) were measured using the Luminex multiplex assay. A total of 148 serum samples from 100 patients with COVID-19 were collected and the levels were compared between survivors vs. non-survivors and pneumonic vs. non-pneumonic conditions groups. The impact of these markers on overall survival were analyzed using a machine learning algorithm.

Results

sICs, including sCD27, sCD40, herpes virus entry mediator (sHVEM), T-cell immunoglobulin and mucin-domain containing-3 (sTIM-3), and Toll-like receptor 2 (sTLR-2) and CKs, including chemokine CC motif ligand 2 (CCL2), interleukin-6 (IL-6), IL-8, IL-10, IL-13, granulocyte-macrophage colony-stimulating factor (GM-CSF), and tumor necrosis factor-α (TNF- α), were statistically significantly increased in the non-survivors compared to those of in the survivors. IL-6 showed the highest area under the receiver-operating curve (0.844, 95% CI = 0.751-0.913) to discriminate non-survival, with a sensitivity of 78.9% and specificity of 82.4%. In Kaplan-Meier analysis, patients with procalcitonin over 0.25 ng/mL, C-reactive protein (CRP) over 41.0 mg/dL, neutrophil-to-lymphocyte ratio over 18.97, sCD27 over 3828.8 pg/mL, sCD40 over 1283.6 pg/mL, and IL-6 over 21.6 pg/mL showed poor survival (log-rank test). In the decision tree analysis, IL-6, sTIM-3, and sCD40 levels had a strong impact on survival. Moreover, IL-6, CD40, and CRP levels were important to predict the probability of 90-d mortality using the SHapley Additive exPlanations method.

Conclusion

sICs and CKs, especially IL-6, sCD27, sCD40, and sTIM-3 are expected to be useful in predicting patient outcomes when used in combination with existing markers.

Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19: a retrospective study

OBJECTIVES

The COVID-19 pandemic has exposed a number of key challenges that need to be urgently addressed. Mass spectrometric studies of blood plasma proteomics provide a deep understanding of the relationship between the severe course of infection and activation of specific pathophysiological pathways. Analysis of plasma proteins in whole blood may also be relevant for the pandemic as it requires minimal sample preparation.

METHODS

The frozen whole blood samples were used to analyze 203 plasma proteins using multiple reaction monitoring (MRM) mass spectrometry and stable isotope-labeled peptide standards (SIS). A total of 131 samples (FRCC, Russia) from patients with mild (n=41), moderate (n=39) and severe (n=19) COVID-19 infection and healthy controls (n=32) were analyzed.

RESULTS

Levels of 94 proteins were quantified and compared. Significant differences between all of the groups were revealed for 44 proteins. Changes in the levels of 61 reproducible COVID-19 markers (SERPINA3, SERPING1, ORM1, HRG, LBP, APOA1, AHSG, AFM, ITIH2, etc.) were consistent with studies performed with serum/plasma samples. The best-performing classifier built with 10 proteins achieved the best combination of ROC-AUC (0.97-0.98) and accuracy (0.90-0.93) metrics and distinguished patients from controls, as well as patients by severity.

CONCLUSIONS

Here, for the first time, frozen whole blood samples were used for proteomic analysis and assessment of the status of patients with COVID-19. The results obtained with frozen whole blood samples are consistent with those from plasma and serum.

Cytokine Storm-Induced Thyroid Dysfunction in COVID-19: Insights into Pathogenesis and Therapeutic Approaches

Angiotensin-converting enzyme 2 receptors (ACE2R) are requisite to enter the host cells for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). ACE2R is constitutive and functions as a type I transmembrane metallo-carboxypeptidase in the renin-angiotensin system (RAS). On thyroid follicular cells, ACE2R allows SARS-CoV-2 to invade the thyroid gland, impose cytopathic effects and produce endocrine abnormalities, including stiff back, neck pain, muscle ache, lethargy, and enlarged, inflamed thyroid gland in COVID-19 patients. Further damage is perpetuated by the sudden bursts of pro-inflammatory cytokines, which is suggestive of a life-threatening syndrome known as a "cytokine storm". IL-1β, IL-6, IFN-γ, and TNF-α are identified as the key orchestrators of the cytokine storm. These inflammatory mediators upregulate transcriptional turnover of nuclear factor-kappa B (NF-κB), Janus kinase/signal transducer and activator of transcription (JAK/STAT), and mitogen-activated protein kinase (MAPK), paving the pathway for cytokine storm-induced thyroid dysfunctions including euthyroid sick syndrome, autoimmune thyroid diseases, and thyrotoxicosis in COVID-19 patients. Targeted therapies with corticosteroids (dexamethasone), JAK inhibitor (baricitinib), nucleotide analogue (remdesivir) and N-acetyl-cysteine have demonstrated effectiveness in terms of attenuating the severity and frequency of cytokine storm-induced thyroid dysfunctions, morbidity and mortality in severe COVID-19 patients. Here, we review the pathogenesis of cytokine storms and the mechanisms and pathways that establish the connection between thyroid disorder and COVID-19. Moreover, cross-talk interactions of signalling pathways and therapeutic strategies to address COVID-19-associated thyroid diseases are also discussed herein.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

Galectin-3 as a biomarker for periodontal disease severity in postcoronavirus disease 2019 patients: A clinical and biochemical investigation

Background

The relationship between coronavirus disease 2019 (COVID-19) and periodontitis has been interesting. Understanding this connection is crucial for comprehensive patient care and highlights the importance of monitoring and addressing oral health issues in an individual's post-COVID-19 recovery. This study compared and evaluated the serum Galectin-3 (Gal-3) levels and clinical parameters in healthy individuals with chronic periodontitis with no history of COVID-19 and post-COVID-19 patients with periodontitis.

Materials and Methods

Group I consists of 25 post-COVID-19 patients with Stage II Grade B periodontitis, 25 patients with Stage II Grade B periodontitis without any history of COVID-19 (Group II), and 25 healthy controls (Group III) were recruited for the study. Gal-3 levels in serum were assessed using an enzyme-linked immunosorbent assay (ELISA) kit. Clinical periodontal variables were measured and recorded, including clinical attachment level (CAL), plaque index (PI), and probing pocket depth (PPD). Data analyses were done using the one-way analysis of variance (ANOVA) test and Tukey's honest significant difference post hoc tests.

Results

Group I patients had a higher serum Gal-3 concentrations of 14.757 ng/ml compared to 11.127 ng/ml and 8.673 ng/ml in Group II and Group III, respectively, and the mean difference in Gal-3 levels was statistically significant with P = 0.000. The mean PPD, CAL, and PI were significantly high in Groups I and II compared to Groups II and III with P = 0.000.

Conclusion

The current study's findings demonstrated that periodontitis patients and individuals with post-COVID-19 and periodontitis had increased Gal-3 levels in the serum compared to healthy participants. This study highlights the relevance of treating periodontitis during the post-COVID-19/long-COVID era and the need to maintain oral hygiene by identifying possible biomarkers and understanding specific underlying processes.

On the Optimal Combination of Elliptically Distributed Biomarkers to Improve Diagnostic Accuracy

Diagnostic biomarkers play a critical role in biomedical research, particularly for the diagnosis and prediction of diseases, etc. To enhance diagnostic accuracy, extensive research about combining multiple biomarkers has been developed based on the multivariate normality, which is often not true in practice, as most biomarkers follow distributions that deviate from normality. While the likelihood ratio combination is recognized to be the optimal approach, it is complicated to calculate. To achieve a more accurate and effective combination of biomarkers, especially when these biomarkers deviate from normality, we propose using a receiver operating characteristic (ROC) curve methodology based on the optimal combination of elliptically distributed biomarkers. In this paper, we derive the ROC curve function for the elliptical likelihood ratio combination. Further, proceeding from the derived best combinations of biomarkers, we propose an efficient technique via nonparametric maximum likelihood estimate (NPMLE) to build empirical estimation. Simulation results show that the proposed elliptical combination method consistently provided better performance, demonstrating its robustness in handling various distribution types of biomarkers. We apply the proposed method to two real datasets: Autism/autism spectrum disorder (ASD) and neural tube defects (NTD). In both applications, the elliptical likelihood ratio combination improves the AUC value compared to the multivariate normal likelihood ratio combination and the best linear combination.

Neopterin production in relation to COVID-19 in the Haut-Ogooué Province, Gabon

BACKGROUND

In sub-Saharan Africa, understanding of the immune process associated with the COVID-19 pandemic remains scarce. This study aimed to investigate the relationship between plasma neopterin concentrations and COVID-19 infection, focusing on changes over time and age-related changes in immune response.

METHODS

A retrospective case study was conducted during the first wave of COVID-19 from March to August 2020. Whole blood and associated symptoms and comorbidities were collected from patients of all ages and sexes. Concentrations of plasma neopterin were measured using a commercial competitive neopterin ELISA (Neopterin ELISA, IBL International GmbH, Germany).

RESULTS

We analyzed data for 325 patients: 38% (n = 124) with COVID-19, and 62% (n = 201) without COVID-19, as a control group. We found that plasma neopterin concentrations were significantly higher in the COVID-19 group (mean value 45.1 nmol/L (SD 19)) than in the control group (mean value 33.8 nmol/L (SD 13)) (p = 0.004). In addition, neopterin levels decreased gradually over time in patients with COVID-19 (p < 0.001). Moreover, ROC analysis found that the best cut-off value for diagnosing COVID-19 patients based on plasma neopterin levels was 38.85 nmol/L with 70% sensitivity and 82% specificity (AUC, 0.74 [0.69-0.82], p < 0.05). We also found an increase in neopterin production with increasing age (p < 0.001).

CONCLUSION

Our findings contribute to our growing understanding of neopterin levels as a promising biomarker for the detection of COVID-19 cases in sub-Saharan Africa.

An Increase in Aspartate Aminotransferase Can Predict Worsening Disease Severity in Japanese Patients with COVID-19

BACKGROUND

The prognostic significance of liver dysfunction in COVID-19 patients remains unclear. In this study, we investigated the association between liver function test results and severe disease progression in COVID-19 patients.

METHODS

This retrospective study included consecutive Japanese COVID-19 patients admitted between February 2020 and July 2021. Predictive variables for severe disease progression were identified by multivariate logistic regression analysis. Severe disease-free survival was estimated with the Kaplan-Meier method and Cox regression analysis. Aspartate aminotransferase (AST) was divided into three grades: grade 1, AST < 30 U/L; grade 2, 30 U/L ≤ AST < 60 U/L; and grade 3, AST > 60 U/L.

RESULTS

Among 604 symptomatic patients, 141 (23.3%) developed severe disease at a median of 2 days postadmission. The median hospital stay was 10 days, and 43 patients (7.1%) died during hospitalization. Multivariate regression analysis revealed that hypertension, decreased lymphocyte count, and elevated LDH, CRP, and AST levels (grade 2 and grade 3 relative to grade 1) were the significant predictive variables. Severe disease-free survival time was significantly different between the different AST grades (hazard ratio (HR): grade 2 vs. grade 1, 4.07 (95% confidential interval (CI): 2.06-8.03); grade 3 vs. grade 1, 7.66 (95% CI: 3.89-15.1)).

CONCLUSIONS

The AST level at admission was an independent risk factor for severe disease in hospitalized Japanese patients with COVID-19.

Can Biomarkers Correctly Predict Ventilator-associated Pneumonia in Patients Treated With Targeted Temperature Management After Cardiac Arrest? An Exploratory Study of the Multicenter Randomized Antibiotic (ANTHARTIC) Study

IMPORTANCE

Ventilator-associated pneumonia (VAP) frequently occurs in patients with cardiac arrest. Diagnosis of VAP after cardiac arrest remains challenging, while the use of current biomarkers such as C-reactive protein (CRP) or procalcitonin (PCT) is debated.

OBJECTIVES

To evaluate biomarkers' impact in helping VAP diagnosis after cardiac arrest.

DESIGN SETTING AND PARTICIPANTS

This is a prospective ancillary study of the randomized, multicenter, double-blind placebo-controlled ANtibiotherapy during Therapeutic HypothermiA to pRevenT Infectious Complications (ANTHARTIC) trial evaluating the impact of antibiotic prophylaxis to prevent VAP in out-of-hospital patients with cardiac arrest secondary to shockable rhythm and treated with therapeutic hypothermia. An adjudication committee blindly evaluated VAP according to predefined clinical, radiologic, and microbiological criteria. All patients with available biomarker(s), sample(s), and consent approval were included.

MAIN OUTCOMES AND MEASURES

The main endpoint was to evaluate the ability of biomarkers to correctly diagnose and predict VAP within 48 hours after sampling. The secondary endpoint was to study the combination of two biomarkers in discriminating VAP. Blood samples were collected at baseline on day 3. Routine and exploratory panel of inflammatory biomarkers measurements were blindly performed. Analyses were adjusted on the randomization group.

RESULTS

Among 161 patients of the ANTHARTIC trial with available biological sample(s), patients with VAP (n = 33) had higher body mass index and Acute Physiology and Chronic Health Evaluation II score, more unwitnessed cardiac arrest, more catecholamines, and experienced more prolonged therapeutic hypothermia duration than patients without VAP (n = 121). In univariate analyses, biomarkers significantly associated with VAP and showing an area under the curve (AUC) greater than 0.70 were CRP (AUC = 0.76), interleukin (IL) 17A and 17C (IL17C) (0.74), macrophage colony-stimulating factor 1 (0.73), PCT (0.72), and vascular endothelial growth factor A (VEGF-A) (0.71). Multivariate analysis combining novel biomarkers revealed several pairs with p value of less than 0.001 and odds ratio greater than 1: VEGF-A + IL12 subunit beta (IL12B), Fms-related tyrosine kinase 3 ligands (Flt3L) + C-C chemokine 20 (CCL20), Flt3L + IL17A, Flt3L + IL6, STAM-binding protein (STAMBP) + CCL20, STAMBP + IL6, CCL20 + 4EBP1, CCL20 + caspase-8 (CASP8), IL6 + 4EBP1, and IL6 + CASP8. Best AUCs were observed for CRP + IL6 (0.79), CRP + CCL20 (0.78), CRP + IL17A, and CRP + IL17C.

CONCLUSIONS AND RELEVANCE

Our exploratory study shows that specific biomarkers, especially CRP combined with IL6, could help to better diagnose or predict early VAP occurrence in cardiac arrest patients.

A brief review on the lessons learned from COVID-19 on drug discovery and research

COVID-19 pandemic has taught many lessons regarding drug discovery and development. This review covers these aspects of drug discovery and research for COVID-19 which might be used as a tool for future. It summarizes the positives such as progresses in antiviral drug discovery, drug repurposing, adaptations of clinical trial and its regulations, as well as the negative points such as the need to develop more collaboration among stakeholders and future directions. It also discusses the benefits and limitations of finding new indications for existing drugs, and the lessons learned regarding rigorous and robust clinical trials, pharmacokinetic/pharmacodynamic modelling, as well as combination therapy. The pandemic has also revealed some gaps regarding global collaboration and coordination, data sharing and transparency and equitable distribution. Finally, the review enumerates the future directions and implications of drug discovery and research for COVID-19 and other infectious diseases such as preparedness and resilience, interdisciplinary and integrative approaches, diversity and inclusion, and personalized and precision medicine.

Biomarkers associated with vaccine-associated enhanced respiratory disease following influenza A virus infection in swine

Influenza A virus (IAV) is a major pathogen in the swine industry. Whole-inactivated virus (WIV) vaccines in swine are highly effective against homologous viruses but provide limited protection to antigenically divergent viruses and may lead to vaccine-associated enhanced respiratory disease (VAERD) after heterologous infection. Although VAERD is reproducible in laboratory studies, clinical diagnosis is challenging, as it would require both knowledge of prior vaccine history and evidence of severe disease by assessment of pathologic lesions at necropsy following infection with a heterologous virus. The objective of this study was to identify potential biomarkers for VAERD for antemortem clinical diagnosis. Naïve pigs were split into two groups, and one group was vaccinated with IAV WIV vaccine. All pigs were then challenged with a heterologous virus to induce VAERD in the vaccinated group and necropsied at 5 days post infection (dpi). Blood was collected on 0, 1, 3, and 5 dpi, and assessed by hematology, plasma chemistry, acute phase proteins, and citrullinated H3 histone (CitH3) assays. Additionally, cytokine and CitH3 levels were assessed in bronchoalveolar lavage fluid (BALF) collected at necropsy. Compared to nonvaccinated challenged pigs, blood collected from vaccinated and challenged (V/C) pigs with VAERD had elevated white blood cells and neutrophils, elevated C-reactive protein and haptoglobin acute phase proteins, and elevated CitH3. In BALF, the proinflammatory cytokine IL-8 and CitH3 were elevated in V/C pigs. In conclusion, a profile of elevated white blood cells and neutrophils, elevated C-reactive protein and haptoglobin, and elevated CitH3 may be relevant for a clinical antemortem IAV VAERD diagnosis.

Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning

The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients' care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This classification is based on the analysis of clinical parameters and routine blood tests, which are not standardized across the globe. Some laboratory test alterations have been associated to COVID-19 severity, although these data are conflicting partly due to the different methodologies used across different studies. This study aimed to construct and validate a disease severity prediction model using machine learning (ML). Seventy-two patients admitted to a Brazilian hospital and diagnosed with COVID-19 through RT-PCR and/or ELISA, and with varying degrees of disease severity, were included in the study. Their electronic medical records and the results from daily blood tests were used to develop a ML model to predict disease severity. Using the above data set, a combination of five laboratorial biomarkers was identified as accurate predictors of COVID-19 severe disease with a ROC-AUC of 0.80 ​±​ 0.13. Those biomarkers included prothrombin activity, ferritin, serum iron, ATTP and monocytes. The application of the devised ML model may help rationalize clinical decision and care.

Assessing the impact of long-term inhaled corticosteroid therapy on patients with COVID-19 and coexisting chronic lung disease: A multicenter retrospective cohort study

Background

Patients with chronic lung disease (CLD), such as asthma or chronic obstructive pulmonary disease, were expected to have an increased risk of clinical manifestations and severity of COVID-19. However, these comorbidities have been reported less frequently than expected. Chronic treatment with inhaled corticosteroids (ICS) may impact the clinical course of COVID-19. The main objective of this study is to know the influence of chronic treatment with ICS on the prognosis of COVID-19 hospitalized patients with CLD.

Methods

A multicenter retrospective cohort study was designed, including patients hospitalized with COVID-19. Epidemiological and clinical data were collected at admission and at seven days, and clinical outcomes were collected. Patients with CLD with and without chronic treatment with ICS were compared.

Results

Two thousand five hundred ninety-eight patients were included, of which 1,171 patients had a diagnosis of asthma and 1,427 of COPD (53.37% and 41.41% with ICS, respectively). No differences were found in mortality, transfer to ICU, or development of moderate-severe ARDS. Patients with chronic ICS had a longer hospital stay in both asthma and COPD patients (9 vs. 8 days, p = 0.031 in asthma patients), (11 vs. 9 days, p = 0.018 in COPD patients); although they also had more comorbidity burden.

Conclusions

Patients with chronic inhaled corticosteroids had longer hospital stays and more chronic comorbidities, measured by the Charlson comorbidity index, but they did not have more severe disease at admission, evaluated with qSOFA and PSI scores. Chronic treatment with inhaled corticosteroids had no influence on the prognosis of patients with chronic lung disease and COVID-19.

Evaluation of the relationship of treatment and vaccination with prognosis in patients with a diagnosis of COVID-19

PURPOSE

Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide and caused mortality. Many factors have been reported to affect the prognosis of COVID-19. In this study, we aimed to investigate the effects of drug therapy and vaccination on prognosis in patients hospitalized with a COVID-19 diagnosis.

METHODS

In this single-center, cross-sectional study, data were retrospectively collected from patients receiving inpatient treatment at a university hospital with a diagnosis of COVID-19 between January 1, 2020, and April 30, 2022. The patients' demographic and clinical characteristics were recorded. The Chi-square, Cox and logistic regression was performed, P < 0.05 was considered statistically significant.

RESULTS

Total 1723 patients (50.1% were men, mean age: 60.6 ± 16.90) who had not been vaccinated rate was 27.0% (> 3 doses: 45.7%). Mortality rate was 17.0%. Increasing age, male, a high Charlson Comorbidity Index (CCI), and no vaccination significantly increased mortality (P < 0.05). The mortality rate was significantly lower in the chloroquine treatment group than in the other treatment groups. Increasing age, male, and a high CCI were determined to be factors that significantly increased the length of hospital stay (LOHS). LOHS found to be significantly lower in the favipiravir or chloroquine groups compared to the remaining treatment groups (P < 0.001). Both mortality and the LOHS significantly differed according to AST, d-dimer, ferritin, and GFR.

CONCLUSION

This study primarily investigated the effect of treatment and vaccination on the prognosis of COVID-19. This was determined to be prepared for another potential pandemic that may arise due to COVID-19.

Clinical Characteristics and Comparative Proteomics Analysis of COVID-19-Related Atrioventricular Block

Background

Atrioventricular block (AVB) is thought to be a rare cardiovascular complication of the coronavirus disease 2019 (COVID-19), though limited data are available beyond case reports. We aim to describe the baseline characteristics, proteomics profile, and outcomes for patients with COVID-19-related AVB.

Methods

We prospectively recruited patients diagnosed with COVID-19-related AVB between November 2022 and March, 2023. Inclusion criteria were hospitalization for COVID-19 with the diagnosis of AVB. A total of 24 patients diagnosed with COVID-19 without AVB were recruited for control. We analyzed patient characteristics and outcomes and performed a comparative proteomics analysis on plasma samples of those patients and controls.

Results

A total of 17 patients diagnosed with COVID-19-related AVB and 24 individuals diagnosed with COVID-19 infection without AVB were included. Among patients with COVID-19-related AVB, the proportion of concurrent pneumonia was significantly higher than controls (7/17 versus 2/24, p < 0.05). All 17 AVB patients (9 of permanent AVB, 8 of paroxysmal AVB) received permanent pacemaker implantation. No procedural-related complication occurred. In laboratory tests, the level of biomarkers indicating myocardial damage were substantially higher than controls, including high-sensitivity cardiac troponin-I (median 0.005 versus 0.002 ng/mL, p < 0.05), myoglobulin (median 39.0 versus 27.6 ng/mL, p < 0.05), and MB isoenzyme of creatine kinase (median 1.2 versus 0.8 U/L, p < 0.05). The level of N-terminal pro-b-type natriuretic peptide (median 241.0 versus 33.5 pg/mL, p < 0.05), C-reactive protein (median 4.8 versus 2.0 mg/L, p < 0.05), D-dimer (median 1.2 versus 0.2 µg/mL, p < 0.05), left ventricular end-diastolic diameter (median 49.3 versus 45.7 mm, p < 0.05) in patients with COVID-19-related AVB were significantly higher than controls. The level of albumin (median 41.9 versus 44.5 g/L, p < 0.05) was significantly lower than controls. In comparative proteomics analysis, we identified 397 human proteins. Several significantly altered plasma proteins related to inflammatory response (Serum amyloid A protein, C-reactive protein, Protein Adenosine 5'-monophosphate-activated protein kinase (AMPK), Alpha-2-macroglobulin), complement and coagulation cascades (Tetranectin, haptoglobin), and immune response (Neutrophil defensin 3, Fibrinogen beta chain) may contribute to the pathogenesis of COVID-19-related AVB.

Conclusions

Patients with COVID-19-related AVB are more prone to have myocardial damage and concurrent pneumonia. Through laboratory tests and comparative proteomics analysis, we identified several differential expressed proteins (Serum amyloid A protein, Tetranectin, Neutrophil defensin 3) releated to the inflammatory response, complement and coagulation cascades, and immune response, which provides evidence of potential biomarkers and sheds light on the pathogenesis of COVID-19-related AVB.

Effects of toll-like receptor agonists and SARS-CoV-2 antigens on interferon (IFN) expression by peripheral blood CD3+ T cells from COVID-19 patients

BACKGROUND

Signaling by toll-like receptors (TLRs) initiates important immune responses against viral infection. The role of TLRs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is not well elucidated. Thus, we investigated the interaction of TLRs agonists and SARS-COV-2 antigens with immune cells in vitro.

MATERIAL & METHODS

30 coronavirus disease 2019 (COVID-19) patients (15 severe and 15 moderate) and 10 age and sex-matched healthy control (HC) were enrolled. Peripheral blood mononuclear cells (PBMCs) were isolated and activated with TLR3, 7, 8, and 9 agonists, the spike protein (SP) of SARS-CoV-2, and the receptor binding domain (RBD) of SP. Frequencies of CD3+IFN-β+ T cells, and CD3+IFN-γ+ T cells were evaluated by flow cytometry. Interferon (IFN)-β gene expression was assessed by qRT-PCR.

RESULTS

The frequency of CD3+IFN-β+ T cells was higher in PBMCs from moderate (p < 0.0001) and severe (p = 0.009) patients at baseline in comparison with HCs. The highest increase in the frequency of CD3+IFN-β+ T cells in cell from moderate patients was induced by TLR8 agonist and SP (p < 0.0001 for both) when compared to HC, while, the highest increase of the frequency of CD3+IFN-β+ T cells in sample of severe patients was seen with TLR8 and TLR7 agonists (both p = 0.002). The frequency of CD3+IFN-γ+ T cells was significantly increased upon stimulation with TLR agonists in cell from patients with moderate and severe COVID-19, compared with HC (all p < 0.01), except with TLR7 and TLR8 agonists. The TLR8 agonist did not significantly increase the frequency of CD3+IFN-γ+ T cells in PBMCs of severe patients, but did so in cells from patients with moderate disease (p = 0.01). Moreover, IFN-β gene expression was significantly upregulated in CD3+T cells from moderate (p < 0.0001) and severe (p = 0.002) COVID-19 patients, compared to HC after stimulation with the TLR8 agonist, while, stimulation of T cells with SP, significantly up-regulated IFN-β mRNA expression in cells from patients with moderate (p = 0.0003), but not severe disease.

CONCLUSION

Stimulation of PBMCs from COVID-19 patients, especially patients with moderate disease, with TLR8 agonist and SP increased the frequency of IFN-β-producing T cells and IFN-β gene expression.

Logistic regression analysis of the value of biomarkers, clinical symptoms, and imaging examinations in COVID-19 for SARS-CoV-2 nucleic acid detection

The detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) nucleic acid detection provides a direct basis for diagnosing Coronavirus Disease 2019. However, nucleic acid test false-negative results are common in practice and may lead to missed diagnosis. Certain biomarkers, clinical symptoms, and imaging examinations are related to SARS-CoV-2 nucleic acid detection and potential predictors. We examined nucleic acid test results, biomarkers, clinical symptoms, and imaging examination data for 116 confirmed cases and asymptomatic infections in Zhuhai, China. Patients were divided into nucleic acid-positive and -false-negative groups. Predictive values of biomarkers, symptoms, and imaging for the nucleic acid-positive rate were calculated by Least Absolute Shrinkage and Selection Operators regression analysis and binary logistic regression analysis, and areas under the curve of these indicators were calculated. Hemoglobin (OR = 1.018, 95% CI: 1.006-1.030; P = .004) was higher in the respiratory tract-positive group than the nucleic acid-negative group, but platelets (OR = 0.996, 95% CI: 0.993-0.999; P = .021) and eosinophils (OR = 0.013, 95% CI: 0.001-0.253; P = .004) were lower; areas under the curve were 0.563, 0.614, and 0.642, respectively. Some biomarkers can predict SARS-CoV-2 viral nucleic acid detection rates in Coronavirus Disease 2019 and are potential auxiliary diagnostic tests.

Associated Risk Factors and Clinical Outcomes of Bloodstream Infections among COVID-19 Intensive Care Unit Patients in a Tertiary Care Hospital

Introduction

The COVID-19 infection is an ongoing public health crisis causing millions of deaths worldwide. COVID-19 patients admitted to the intensive care unit (ICU) are more vulnerable to acquire secondary bloodstream infections (sBSIs) which cause a significant morbidity and mortality. Thus, we aim to assess the risk factors of sBSIs and outcomes in COVID-19 ICU patients.

Methods

One hundred blood culture samples with growth (cases) and other 100 blood culture with no growth(controls) were collected.. All the demographic data, laboratory data and antimicrobial resistance pattern were analysed . Blood culture bottle received in the Microbiology laboratory were loaded into Automated blood culture system. Flagged bottles were processed for final identification by MALDI TOF and automated antibiotic susceptibility testing. Flagged bottles were processed for final identification by MALDI TOF and automated antibiotic susceptibility testing.

Results

Raised C-reactive protein (CRP) (P = 0.0035), interleukin-6 (P = 0.0404), mechanical ventilation (MV) (P = 0.024), prior antimicrobial exposure (P = 0.002), longer ICU stay with median 11 days (P = 0.022), and higher mortality rate (P = 0.001) were significantly associated with the BSI. A significant proportion of BSIs were Gram-negative bacteria (n = 115) such as Acinetobacter baumannii 38 (33%) and Klebsiella pneumoniae 30 (26%). Monomicrobial organisms in blood yielded a higher proportion in our study 72 (72%). The highest resistance for Acinetobacter species (50) was observed with ceftazidime 29 (96.6%) amikacin 48 (96%), meropenem 48 (96%), cefotaxime 47 (94%), ciprofloxacin 46 (92%), and netilmicin 46 (92%). K. pneumoniae was highly resistant to cefotaxime 29 (96.6%), ceftazidime 29 (96.6%), ciprofloxacin 22 (73.3%), and cefuroxime 21 (70%). Among Gram-positive organisms, Enterococcus species showed that a resistance for high-level gentamicin and penicillin was 66.6%.

Conclusions

Raised CRP, need of MV, prior antimicrobial exposure, and longer ICU stay should alarm clinicians for BSI. Hence, our study highlights the associated risk factors for BSI and emphasizes adherence to hospital infection control policies and antibiotic stewardship program.

NEW PREDICTIVE BIOMARKERS FOR SCREENING COVID-19 PATIENTS WITH RHABDOMYOLYSIS IN COMBINATION WITH CYSTATIN C

ABSTRACT

Purpose: Cystatin C (CysC) has been linked to the prognosis of corona virus disease 2019 (COVID-19). The study aims to investigate a predictor correlated with CysC screening for poor prognosis in COVID-19 patients combined with skeletal muscle (SKM) impairment and rhabdomyolysis (RM). Methods: A single-center retrospective cohort analysis was carried out. Demographic information, clinical data, laboratory test results, and clinical outcome data were gathered and analyzed. Results: According to the inclusion and exclusion criteria, 382 patients were included in this study. The subjects were divided into three groups based on CysC tertiles. Multivariate analysis revealed that SaO 2 (hazard ratio [HR], 0.946; 95% confidence interval [CI], 0.906-0.987; P = 0.011), CysC (HR, 2.124; 95% CI, 1.223-3.689; P = 0.008), aspartate aminotransferase (AST) (HR, 1.009; 95% CI, 1.000-1.018; P = 0.041), and hypersensitive C-reactive protein (HR, 1.005; 95% CI, 1.000-1.010; P = 0.045) were significantly associated with survivals. The area under curve (AUC) in the model characterized by RM incidence was 0.819 (0.698-0.941), as shown by CysC receiver operating characteristic curves. LDH*CysC and AST*CysC had better predictive values than CysC and the best prediction for RM, with an AUC of 0.880 (0.796,0.964) for LDH*CysC ( P < 0.05, vs CysC) and 0.925 (0.878,0.972) for AST*CysC ( P < 0.05, vs CysC). Conclusion: CysC is an essential evaluation indicator for COVID-19 patients' prognosis. AST*CysC and LDH*CysC have superior predictive value to CysC for SKM, RM, and death, and optimal classification for RM.

Role of C-reactive protein, IL-6, and D-dimers in prediction of severity of coronavirus disease 2019: A pilot study

Background

The global outbreak of coronavirus disease 2019 (COVID-19) presents numerous obstacles for healthcare professionals. The present study aimed to evaluate and compare the role of serum biomarkers like- C-reactive protein (CRP), interleukin-6 (IL-6), and D-dimers in the severity of COVID-19 infection.

Methodology

A cross-sectional, observational retrospective pilot study was conducted in Udaipur, Rajasthan, wherein data was collected from 250 subjects, out of which, data of 100 subjects were included as per the inclusion criteria. The data was recorded retrospectively among the health professionals via Google Forms in Udaipur, Rajasthan.

Results

There were 1 (1%), 3 (3%), 31 (31%) and 65 (65%) participants with minor elevation (0.3-1.0), moderate elevation (1-10), marked elevation (10-50) and severe elevation (>50) of CRP respectively. The difference between the groups was statistically highly significant with a significantly higher number of study participants with a severe elevation of CRP levels (χ2 = 107.84, P < 0.001). The results showed that there was a significant difference between the groups with IL6 in 0-7 range while 96 (96%) study participants had >7 IL6, and the difference was statistically highly significant (2 = 84.640, P 0.001).

Conclusion

In conclusion, the existing body of research indicates a discernible correlation between COVID-19 infection and the fluctuation of biomarker levels. This supplement has the potential to be utilised in clinical practice as a means of informing treatment decisions and determining the necessity of admission to the intensive care unit (ICU).

Calprotectin serum levels on admission and during follow-up predict severity and outcome of patients with COVID-19: A prospective study

BACKGROUND & AIMS

Calprotectin reflects neutrophil activation and is increased in various inflammatory conditions including severe COVID-19. However, serial serum calprotectin measurements in COVID-19 patients are limited. We assessed prospectively, calprotectin levels as biomarker of severity/outcome of the disease and a COVID-19 monitoring parameter in a large cohort of consecutive COVID-19 patients.

METHODS

Calprotectin serum levels were measured in 736 patients (58.2 % males; median age 63-years; moderate disease, n = 292; severe, n = 444, intubated and/or died, n = 50). Patients were treated with combined immunotherapies according to our published local algorithm. The endpoint was the composite event of intubation due to severe respiratory failure (SRF)/COVID-19-related mortality.

RESULTS

Median (interquartile range) calprotectin levels were significantly higher in patients with severe disease [7(8.2) vs. 6.1(8.1)μg/mL, p = 0.015]. Calprotectin on admission was the only independent risk factor for intubation/death (HR=1.473, 95 %CI=1.003-2.165, p = 0.048) even after adjustment for age, sex, body mass index, comorbidities, neutrophils, lymphocytes, neutrophil to lymphocytes ratio, ferritin, and CRP. The area under the curve (AUC, 95 %CI) of calprotectin for prediction of intubation/death was 0.619 (0.531-0.708), with an optimal cut-off at 13 μg/mL (sensitivity: 44 %, specificity: 79 %, positive and negative predictive values: 13 % and 95 %, respectively). For intubated/died patients, paired comparisons from baseline to middle of hospitalization and subsequently to intubation/death showed significant increase of calprotectin (p = 0.009 and p < 0.001, respectively). Calprotectin alteration had the higher predictive ability for intubation/death [AUC (95 %CI):0.803 (0.664-0.943), p < 0.001].

CONCLUSIONS

Calprotectin levels on admission and their subsequent dynamic alterations could serve as indicator of COVID-19 severity and predict the occurrence of SRF and mortality.

Proteomic Analysis of Salivary Extracellular Vesicles from COVID-19 Patients Reveals a Specific Anti-COVID-19 Response Protein Signature

Despite the understanding of the coronavirus disease-19 (COVID-19), the role of salivary extracellular vesicles (sEVs) in COVID-19 remains unclear. Exploring the proteomic cargo of sEVs could prove valuable for diagnostic and prognostic purposes in assessing COVID-19. The proteomic cargo of sEVs from COVID-19(+) subjects and their healthy close contacts (HCC) was explored. sEVs were isolated by ultracentrifugation from unstimulated saliva samples, and subsequently characterized through nanoparticle tracking, transmission electron microscopy, and Western blot analyses. The proteomic cargo of sEVs was processed by LC-MS/MS. sEVs were morphologically compatible with EVs, with the presence of Syntenin-1 and CD81 EV markers. The sEV pellet showed 1417 proteins: 1288 in COVID-19(+) cases and 1382 in HCC. In total, 124 proteins were differentially expressed in sEVs from COVID-19(+) subjects. "Coronavirus-disease response", "complement and coagulation cascades", and "PMN extracellular trap formation" were the most enriched KEGG pathways in COVID-19(+) cases. The most represented biological processes were "Hemoglobin and haptoglobin binding" and "oxygen carrier activity", and the best-denoted molecular functions were "regulated exocytosis and secretion" and "leucocyte and PMN mediated immunity". sEV proteomic cargo in COVID-19(+) suggests activity related to immune response processes, oxygen transport, and antioxidant mechanisms. In contrast, in HCC, sEV signature profiles are mainly associated with epithelial homeostasis.

Comparative effectiveness of dexamethasone in treatment of hospitalized COVID-19 patients in the United States during the first year of the pandemic: Findings from the National COVID Cohort Collaborative (N3C) data repository

BACKGROUND

Dexamethasone was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial, but evidence is still needed to support its real-world effectiveness in heterogeneous populations of patients with a wide range of comorbidities.

METHODS

COVID-19 inpatients represented within the National COVID Cohort Collaborative (N3C) Data Enclave, prior to vaccine availability, were studied. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome defined by use of ECMO or mechanical ventilation. Missing data were imputed with single imputation. Dexamethasone-treated patients were propensity score (PS) matched to non-dexamethasone-treated controls, stratified by remdesivir treatment and based on demographics, baseline laboratory values, comorbidities, and amount of missing data before imputation. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS.

RESULTS

Dexamethasone treatment was associated with reduced risk of in-hospital mortality for n = 1,263 treated, matched 1:3 to untreated, patients not receiving remdesivir (OR = 0.77, 95% CI: 0.62 to 0.95, p = 0.017), and for n = 804 treated, matched 1:1 to untreated, patients receiving remdesivir (OR = 0.74, 95% CI: 0.53 to 1.02, p = 0.054). Treatment showed secondary outcome benefit. In sensitivity analyses, treatment effect generally remained similar with some heterogeneity of benefit across quartiles of PS, possibly reflecting concentration of benefit among the more severely affected.

CONCLUSIONS

We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability.

Elevated lactate dehydrogenase predicts pneumonia in spontaneous intracerebral hemorrhage

Background

Although a variety of risk factors for pneumonia after spontaneous intracerebral hemorrhage have been established, an objective and easily obtainable predictor is still needed. Lactate dehydrogenase is a nonspecific inflammatory biomarker. In this study, we aimed to assess the association between lactate dehydrogenase and pneumonia in spontaneous intracerebral hemorrhage patients.

Methods

Our study was a retrospective, multicenter cohort study, undertaken in 7562 patients diagnosed with spontaneous intracerebral hemorrhage from 3 hospitals. All serum Lactate dehydrogenase was collected within 7 days from admission and divided into four groups as quartile(Q). We conducted a multivariable logistic regression analysis to assess the association of Lactate dehydrogenase with pneumonia.

Results

Among a total of 7562 patients, 2971 (39.3%) patients were diagnosed with pneumonia. All grades of elevated lactate dehydrogenase were associated with increased raw and risk-adjusted risk of pneumonia. Multiple logistic regression analysis showed odds ratios for Q2-Q4 compared with Q1 were 1.21 (95% CI, 1.04-1.42), 1.64(95% CI, 1.41-1.92), and 1.92 (95% CI, 1.63-2.25) respectively. The odds ratio after adjustment was 4.42 (95% CI, 2.94-6.64) when lactate dehydrogenase was a continuous variable after log-transformed.

Conclusions

Elevated lactate dehydrogenase is significantly associated with an increase in the odds of pneumonia and has a predictive value for severe pneumonia in patients with pneumonia. Lactate dehydrogenase may be used to predict pneumonia events in spontaneous intracerebral hemorrhage patients as a laboratory marker.

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

BACKGROUND

From 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.

METHODS

This 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.

RESULTS

The 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.

CONCLUSION

The 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.

Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review

INTRODUCTION

Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes.

METHODS

We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance.

RESULTS

Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators.

CONCLUSION

Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.

First COVID-19 wave in the province of Bergamo, Italy: epidemiological and clinical characteristics, outcome and management of the first hospitalized patients

BACKGROUND

Northern Italy was the first European country affected by the spread of the SARS-CoV-2, with the epicenter in the province of Bergamo.

AIM

This study aims to analyze the characteristics of patients who experienced more severe symptoms during the first wave of COVID-19 pandemic.

MATERIALS AND METHODS

We retrospectively collected epidemiological and clinical data on patients with laboratory-confirmed wild-type SARS-CoV-2 infection who were admitted to the "ASST Bergamo Ovest" hospital between February 21 and May 31, 2020.

RESULTS

A total of seven hundred twenty-three inpatients met the eligible criteria and were included in the study cohort. Among the inpatients who survived, the average hospital length of stay was more than two weeks, with some lasting up to three months. Among the 281 non-survivors, death occurred in 50% within five days. Survivors were those whose first aid operators recorded higher oxygen saturation levels at home. The request for first aid assistance came more than one week after symptom onset, within three days in 10% of cases.

CONCLUSION

In similar future scenarios, based on our data, if we aim to enhance the survival rate, we need to improve the territorial healthcare assistance and admit to hospitals only those patients who are at risk of severe illness requiring specialized and urgent interventions within two, three, or, at most, five days from the onset of symptoms. This implies that the crucial factor is, has been, and will be the ability of a healthcare system to react promptly in its entirety within a few days.

Low-Cost Biosensor Technologies for Rapid Detection of COVID-19 and Future Pandemics

Many systems have been designed for the detection of SARS-CoV-2, which is the virus that causes COVID-19. SARS-CoV-2 is readily transmitted, resulting in the rapid spread of disease in human populations. Frequent testing at the point of care (POC) is a key aspect for controlling outbreaks caused by SARS-CoV-2 and other emerging pathogens, as the early identification of infected individuals can then be followed by appropriate measures of isolation or treatment, maximizing the chances of recovery and preventing infectious spread. Diagnostic tools used for high-frequency testing should be inexpensive, provide a rapid diagnostic response without sophisticated equipment, and be amenable to manufacturing on a large scale. The application of these devices should enable large-scale data collection, help control viral transmission, and prevent disease propagation. Here we review functional nanomaterial-based optical and electrochemical biosensors for accessible POC testing for COVID-19. These biosensors incorporate nanomaterials coupled with paper-based analytical devices and other inexpensive substrates, traditional lateral flow technology (antigen and antibody immunoassays), and innovative biosensing methods. We critically discuss the advantages and disadvantages of nanobiosensor-based approaches compared to widely used technologies such as PCR, ELISA, and LAMP. Moreover, we delineate the main technological, (bio)chemical, translational, and regulatory challenges associated with developing functional and reliable biosensors, which have prevented their translation into the clinic. Finally, we highlight how nanobiosensors, given their unique advantages over existing diagnostic tests, may help in future pandemics.

A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g., in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g., means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone.

Predictions based on inflammatory cytokine profiling of Egyptian COVID-19 with 2 potential therapeutic effects of certain marine-derived compounds

BACKGROUNDS

A worldwide coronavirus pandemic has affected many healthcare systems in 2019 (COVID-19). Following viral activation, cytokines and chemokines are released, causing inflammation and tissue death, particularly in the lungs, resulting in severe COVID-19 symptoms such as pneumonia and ARDS. COVID-19 induces the release of several chemokines and cytokines in different organs, such as the cardiovascular system and lungs.

RESEARCH IDEA

COVID-19 and its more severe effects, such as an elevated risk of death, are more common in patients with metabolic syndrome and the elderly. Cytokine storm and COVID-19 severity may be mitigated by immunomodulation targeting NF-κB activation in conjunction with TNF- α -inhibition. In severe cases of COVID-19, inhibiting the NF-κB/TNF- α, the pathway may be employed as a therapeutic option.

MATERIAL AND METHODS

The study will elaborate on the Egyptian pattern for COVID-19 patients in the first part of our study. An Egyptian patient with COVID-19 inflammatory profiling will be discussed in the second part of this article using approved marine drugs selected to inhabit the significant inflammatory signals. A biomarker profiling study is currently being performed on Egyptian patients with SARS-COV-2. According to the severity of the infection, participants were divided into four groups. The First Group was non-infected with SARS-CoV-2 (Control, n = 16), the Second Group was non-intensive care patients (non-ICU, n = 16), the Third Group was intensive care patients (ICU, n = 16), and the Fourth Group was ICU with endotracheal intubation (ICU + EI, n = 16). To investigate COVID-19 inflammatory biomarkers for Egyptian patients, several inflammatory, oxidative, antioxidant, and anti-inflammatory biomarkers were measured. The following are examples of blood tests: CRP, Ferritin, D-dimer, TNF-α, IL-8, IL-6., IL-Ib, CD8, NF-κB, MDA, and total antioxidants.

RESULTS AND DISCUSSION

The results of the current study revealed many logical findings, such as the elevation of CRP, Ferritin, D-dimer, TNF- α, CD8, IL-6, IL-, NF-κB, and MDA. Where a significant increase showed in ICU group results (23.05 ± 0.30, 2.35 ± 0.86, 433.4 ± 159.3, 26.67 ± 3.51, 7.52 ± 1.48, 7.49 ± 1.04, 5.76 ± 1.31, 7.41 ± 0.73) respectively, and also ICU group results (54.75 ± 3.44, 0.65 ± 0.13, 460.2 ± 121.42, 27.43 ± 2.52, 8.63 ± 2.68, 10.65 ± 2.75, 5.93 ± 1.4, 10.64 ± 0.86) respectively, as well as ICU + EI group results (117.63 ± 11.89, 1.22 ± 0.65, 918.8 ± 159.27, 26.68 ± 2.00, 6.68 ± 1.08, 11.68 ± 6.16, 6.23 ± 0.07, 22.41 ± 1.39),respectively.The elevation in laboratory biomarkers of cytokines storm in three infected groups with remarkable increases in the ICU + EI group was due to the elevation of oxidative stress and inflammatory storm molecules, which lead to highly inflammatory responses, specifically in severe patients of COVID-19. Another approach to be used in the current study is investigating new computational drug compounds for SARS-COV-2 protective agents from the marine environment. The results revealed that (Imatinib and Indinavir) had the highest affinity toward Inflammatory molecules and COVID-19 proteins (PDB ID: -7CZ4 and 7KJR), which may be used in the future as possible COVID-19 drug candidates.

CONCLUSION

The investigated inflammatory biomarkers in Egyptian COVID-19 patients showed a strong correlation between IL6, TNF-α, NF-κB, CRB, DHL, and ferritin as COVID-19 biomarkers and determined the severity of the infection. Also, the oxidative /antioxidant showed good biomarkers for infection recovery and progression of the patients.

The Role of Sequentially Monitored Laboratory Values and Inflammatory Biomarkers in Assessing the Severity of COVID-19

With the onset of the pandemic in 2020, COVID-19 pneumonia has become a common cause for hospitalization and is associated with high mortality rates. Inflammatory biomarkers play a crucial role in understanding and monitoring the progression of various diseases, including COVID-19. The objective of this study was to assess the significance of sequentially monitored standard laboratory tests, including complete blood cell count, D-dimer, fibrinogen, ferritin, albumin, C-reactive protein (CRP), as well as newly calculated inflammatory biomarkers in predicting the severity and prognosis of COVID-19 pneumonia. This single-center retrospective study included 194 patients hospitalized due to COVID-19 pneumonia. Patients were grouped based on the severity of their clinical symptoms, with 134 categorized as severe disease and 60 as mild-moderate disease. The patients' demographic data and laboratory values at hospital admission and on the third day of hospitalization were comparatively evaluated. In the severe illness group, there were more complaints about shortness of breath and a significant drop in the SPO2 value was observed at the time of application (p =0.005 and p<0.001, respectively). The overall mortality rate in all patients was 9% (18/194), and all deaths occurred within the severe disease group. All laboratory parameters, with the exception of platelet count and ferritin levels, were significantly associated and correlated with the severity of the disease during the hospitalization period. Among the biomarkers, there was no significant difference in neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) on the first day, a significant increase was observed on the third day of hospitalization in the severe disease group (p=0.050 vs. 0.003 and p=0.073 vs. 0.020, respectively). No significant difference was observed only in the PNR (platelet/neutrophil ratio) value among the inflammatory biomarkers (p=0.090 vs. p=0.354). In conclusion, the SPO2 level of COVID-19 patients at admission and the subsequent laboratory parameters examined show a significant relationship with the severity of the disease. In addition, simple inflammation biomarkers derived from laboratory values have shown a very significant relationship and correlation in the diagnosis and follow-up of the disease. In both admission and follow-up evaluation, a more significant association was observed with CRP-related biomarkers such as CRP/albumin ratio and CRP/lymphocyte ratio rather than NLR and PLR, which are widely used in the literature, in showing the severity of COVID-19. In patients with pneumonia, the laboratory assessment made on the third day of hospitalization reflects the severity of the disease more clearly than on the first day.

Innovation and Patenting Activities During COVID-19 and Advancement of Biochemical and Molecular Diagnosis in the Post- COVID-19 Era

The COVID-19 pandemic is to escalate globally and acquire new mutations quickly, so accurate diagnostic technologies play a vital role in controlling and understanding the epidemiology of the disease. A plethora of technologies acquires diagnosis of individuals and informs clinical management of COVID. Some important biochemical parameters for COVID diagnosis are the elevation of liver enzymes, creatinine, and nonspecific inflammatory markers such as C-reactive protein (CRP) and Interleukin 6 (IL-6). The main progression predictors are lymphopenia, elevated D-dimer, and hyperferritinemia, although it is also necessary to consider LDH, CPK, and troponin in the marker panel of diagnosis. Owing to the greater sensitivity and accuracy, molecular technologies such as conventional polymerase chain reaction (PCR), reverse transcription (RT)-PCR, nested PCR, loop-mediated isothermal amplification (LAMP), and xMAP technology have been extensively used for COVID diagnosis for some time now. To make so many diagnostics accessible to general people, many techniques may be exploited, including point of care (POC), also called bedside testing, which is developing as a portable promising tool in pathogen identification. Some other lateral flow assay (LFA)-centered techniques like SHERLOCK, CRISPR-Cas12a (AIOD-CRISPR), and FNCAS9 editor limited uniform detection assay (FELUDA), etc. have shown auspicious results in the rapid detection of pathogens. More recently, low-cost sequencing and advancements in big data management have resulted in a slow but steady rise of next-generation sequencing (NGS)-based approaches for diagnosis that have potential relevance for clinical purposes and may pave the way toward a better future. Due to the COVID-19 pandemic, various institutions provided free, specialized websites and tools to promote research and access to critically needed advanced solutions by alleviating research and analysis of data within a substantial body of scientific and patent literature regarding biochemical and molecular diagnosis published since January 2020. This circumstance is unquestionably unique and difficult for anyone using patent information to find pertinent disclosures at a specific date in a trustworthy manner.

Drug Repositioning: A Monetary Stratagem to Discover a New Application of Drugs

Drug repurposing, also referred to as drug repositioning or drug reprofiling, is a scientific approach to the detection of any new application for an already approved or investigational drug. It is a useful policy for the invention and development of new pharmacological or therapeutic applications of different drugs. The strategy has been known to offer numerous advantages over developing a completely novel drug for certain problems. Drug repurposing has numerous methodologies that can be categorized as target-oriented, drug-oriented, and problem-oriented. The choice of the methodology of drug repurposing relies on the accessible information about the drug molecule and like pharmacokinetic, pharmacological, physicochemical, and toxicological profile of the drug. In addition, molecular docking studies and other computer-aided methods have been known to show application in drug repurposing. The variation in dosage for original target diseases and novel diseases presents a challenge for researchers of drug repurposing in present times. The present review critically discusses the drugs repurposed for cancer, covid-19, Alzheimer's, and other diseases, strategies, and challenges of drug repurposing. Moreover, regulatory perspectives related to different countries like the United States (US), Europe, and India have been delineated in the present review.

Comparative study of the severity of Covid-19 infection between female and male patients

Background

Different studies have identified the prognostic factors of COVID-19 infection. These studies have revealed that COVID-19 infection is more severe in males than in females. The aim of our study was to compare the severity of COVID-19 infection between males and females in terms of clinical, biological, radiological, and evolutionary aspects.

Methodology

This is a cross-sectional observational study conducted in patients hospitalized with COVID-19 infection over a 6-month period from 1 August 2021 to 1 February 2022.

Results

The comparison of clinical, biological, radiological, and evolutionary severity factors of covid-19 infection between the two sexes revealed that this infection was more severe in males. Statistically significant differences were noted for the rate of high dimers (p =0.01) and for lung involvement greater than 25% on chest CT (Computed tomography) (p =0.008).

Conclusion

The severity of covid-19 infection in men is due to biological differences between men and women in the renin-angiotensin system, the immune system, genetics, and sex hormones. Further research into the pathophysiological mechanisms behind this finding is needed.

Amino acid catabolite markers for early prognostication of pneumonia in patients with COVID-19

Effective early-stage markers for predicting which patients are at risk of developing SARS-CoV-2 infection have not been fully investigated. Here, we performed comprehensive serum metabolome analysis of a total of 83 patients from two cohorts to determine that the acceleration of amino acid catabolism within 5 days from disease onset correlated with future disease severity. Increased levels of de-aminated amino acid catabolites involved in the de novo nucleotide synthesis pathway were identified as early prognostic markers that correlated with the initial viral load. We further employed mice models of SARS-CoV2-MA10 and influenza infection to demonstrate that such de-amination of amino acids and de novo synthesis of nucleotides were associated with the abnormal proliferation of airway and vascular tissue cells in the lungs during the early stages of infection. Consequently, it can be concluded that lung parenchymal tissue remodeling in the early stages of respiratory viral infections induces systemic metabolic remodeling and that the associated key amino acid catabolites are valid predictors for excessive inflammatory response in later disease stages.

Epidemiological, Clinical, and Laboratory Findings of 235 Hospitalized COVID-19 Adult Patients (Survivors and Non-Survivors) at Sohar Hospital in Oman

OBJECTIVES

The aim of this study was to describe the epidemiological and clinical characteristics and laboratory findings of coronavirus disease 2019 (COVID-19) patients at the Sohar Hospital, Sohar, Oman.

METHODS

This retrospective study of admitted COVID-19 patients at Sohar Hospital in Oman was carried out from March to October 2020. Demographics and laboratory data of 19 tests for 235 COVID-19 patients, of which 202 were survivors and 33 were non-survivors, were collected from the hospital information system after ethics approval.

RESULTS

Thirteen factors were significantly correlated with in-hospital mortality, including older age, having chronic disease, high neutrophil count, high troponin T, high creatinine, low albumin (p < 0.0001), high white blood cell (WBC) count, low hemoglobin, high D-dimer (p < 0.001), high C-reactive protein (CRP) (p < 0.002), low lymphocyte count (p < 0.003), high alkaline phosphatase (ALP) enzyme (p < 0.007) and high ferritin (p < 0.045). The most common laboratory blood test abnormalities that were highly correlated with mortality were increased values of CRP (100% of non-survivors), D-dimer (94.1% of non-survivors), ferritin (88.2% of non-survivors), and troponin T (85% of non-survivors) and reduced lymphocyte count (73.9% of non-survivors).

CONCLUSION

These findings could help in categorizing COVID-19 patients for risk-based assessment and early identification of patients with poor prognosis.