Serum CK-MB, COVID-19 severity and mortality: An updated systematic review and meta-analysis with meta-regression

Advances in heart failure monitoring: Biosensors targeting molecular markers in peripheral bio-fluids

Cardiovascular diseases (CVDs), especially chronic heart failure, threaten many patients' lives worldwide. Because of its slow course and complex causes, its clinical screening, diagnosis, and prognosis are essential challenges. Clinical biomarkers and biosensor technologies can rapidly screen and diagnose. Multiple types of biomarkers are employed for screening purposes, precise diagnosis, and treatment follow-up. This article provides an up-to-date overview of the biomarkers associated with the six main heart failure etiology pathways. Plasma natriuretic peptides (BNP and NT-proBNP) and cardiac troponins (cTnT, cTnl) are still analyzed as gold-standard markers for heart failure. Other complementary biomarkers include growth differentiation factor 15 (GDF-15), circulating Galactose Lectin 3 (Gal-3), soluble interleukin (sST2), C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α). For these biomarkers, the electrochemical biosensors have exhibited sufficient sensitivity, detection limit, and specificity. This review systematically summarizes the latest molecular biomarkers and sensors for heart failure, which will provide comprehensive and cutting-edge authoritative scientific information for biomedical and electronic-sensing researchers in the field of heart failure, as well as patients. In addition, our proposed future outlook may provide new research ideas for researchers.

The association between coronary artery calcification and vectorcardiography in mechanically ventilated COVID-19 patients: the Maastricht Intensive Care COVID cohort

BACKGROUND

Coronary artery calcification (CAC) is associated with poor outcome in critically ill patients. A deterioration in cardiac conduction and loss of myocardial tissue could be an underlying cause. Vectorcardiography (VCG) and cardiac biomarkers provide insight into these underlying causes. The aim of this study was to investigate whether a high degree of CAC is associated with VCG-derived variables and biomarkers, including high-sensitivity troponin-T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP).

METHODS

Mechanically ventilated coronavirus-19 (COVID-19) patients with an available chest computed tomography (CT) and 12-lead electrocardiogram (ECG) were studied. CAC scores were determined using chest CT scans. Patients were categorized into 3 sex-specific tertiles: low, intermediate, and high CAC. Daily 12 leads-ECGs were converted to VCGs. Daily hs-cTnT and NT-proBNP levels were determined. Linear mixed-effects regression models examined the associations between CAC tertiles and VCG variables, and between CAC tertiles and hs-cTnT or NT-proBNP levels.

RESULTS

In this study, 205 patients (73.2% men, median age 65 years [IQR 57.0; 71.0]) were included. Compared to the lowest CAC tertile, the highest CAC tertile had a larger QRS area at baseline (6.65 µVs larger [1.50; 11.81], p = 0.012), which decreased during admission (- 0.27 µVs per day [- 0.43; - 0.11], p = 0.001). Patients with the highest CAC tertile also had a longer QRS duration (12.02 ms longer [4.74; 19.30], p = 0.001), higher levels of log hs-cTnT (0.79 ng/L higher [0.40; 1.19], p < 0.001) and log NT-proBNP (0.83 pmol/L higher [0.30; 1.37], p = 0.002).

CONCLUSION

Patients with a high degree of CAC had the largest QRS area and higher QRS amplitude, which decreased more over time when compared to patients with a low degree of CAC. These results suggest that CAC might contribute to loss of myocardial tissue during critical illness. These insights could improve risk stratification and prognostication of patients with critical illness.

Associations between antibiotic exposure and abnormal cardiac enzyme profiles in older Chinese adults

Biomonitoring methods can be used to measure exposure to antibiotics in the general population; however, epidemiological data on the associations between urinary antibiotic levels and the cardiac profiles of enzymes lactate dehydrogenase, creatine kinase, and creatine kinase isoenzyme in older adults remain sparse. We investigated these associations in 990 individuals from the Cohort of Elderly Health and Environment Controllable Factors. Antibiotic residues in urine samples were analyzed using high-performance liquid chromatography-tandem mass spectrometry. Urinary levels of 34 antibiotics were measured. The participants' cardiac enzyme profiles were influenced by sex, age, marital status, education level, cohabitation status, physical activity, dietary structure, body mass index, depression presence and salt, sugar, and oil consumption (P < 0.05). Oxytetracycline, tetracycline, doxycycline, sulfaclozine, and, florfenicol concentrations were negatively associated with the risk of having an abnormal cardiac enzyme profile. Older adults exposed to higher concentrations of norfloxacin had a higher risk of LDH anomalies. After antibiotics were classified, we identified associations between exposure to chloramphenicols, sulfonamides, or veterinary antibiotics and a lower risk of having an abnormal cardiac enzyme profile. Obtaining an accurate epidemiological profile of antibiotic exposure is indispensable for the prevention and detection of cardiac enzyme profile abnormalities in older adults.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

The COVID-19 Pandemic and Coronary Heart Disease: the Next Surge

PURPOSE OF REVIEW

In this narrative review, we highlight different ways in which the COVID-19 pandemic has impacted coronary heart disease (CHD) burden and how a surge in morbidity and mortality may be expected in the near future. We also discuss potential solutions, and the direction subsequent research and corrective actions should take.

RECENT FINDINGS

COVID-19 has been implicated in the development and worsening of CHD via acute and chronic mechanisms in the form of plaque rupture, destabilization, and sustenance of a chronic inflammatory state leading to long COVID syndrome and increased rates of myocardial infarction. However, indirectly the pandemic is likely to further escalate the CHD burden through poor health behaviors such as tobacco consumption, reduced physical activity, economic devastation and its associated sequelae, and regular cardiac care interruptions and delays. COVID-19 has increased the total CHD burden and will require extensive resource allocation and multifaceted strategies to curb future rise.

The In Vitro, In Vivo, and PBPK Evaluation of a Novel Lung-Targeted Cardiac-Safe Hydroxychloroquine Inhalation Aerogel

Hydroxychloroquine (HCQ) was repurposed for COVID-19 treatment. Subtherapeutic HCQ lung levels and cardiac toxicity of oral HCQ were overcome by intratracheal (IT) administration of lower HCQ doses. The crosslinker-free supercritical fluid technology (SFT) produces aerogels and impregnates them with drugs in their amorphous form with efficient controlled release. Mechanistic physiologically based pharmacokinetic (PBPK) modeling can predict the lung's epithelial lining fluid (ELF) drug levels. This study aimed to develop a novel HCQ SFT formulation for IT administration to achieve maximal ELF levels and minimal cardiac toxicity. HCQ SFT formulation was prepared and evaluated for physicochemical, in vitro release, pharmacokinetics, and cardiac toxicity. Finally, the rat HCQ ELF concentrations were predicted using PBPK modeling. HCQ was amorphous after loading into the chitosan-alginate nanoporous microparticles (22.7±7.6 μm). The formulation showed a zero-order release, with only 40% released over 30 min compared to 94% for raw HCQ. The formulation had a tapped density of 0.28 g/cm3 and a loading efficiency of 35.3±1.3%. The IT administration of SFT HCQ at 1 mg/kg resulted in 23.7-fold higher bioavailability, fourfold longer MRT, and eightfold faster absorption but lower CK-MB and LDH levels than oral raw HCQ at 4 mg/kg. The PBPK model predicted 6 h of therapeutic ELF levels for IT SFT HCQ and a 100-fold higher ELF-to-heart concentration ratio than oral HCQ. Our findings support the feasibility of lung-targeted and more effective SFT HCQ IT administration for COVID-19 compared to oral HCQ with less cardiac toxicity. Graphical abstract.

Association between dietary antioxidant quality score and severity of coronavirus infection: a case-control study

The purpose of this study was to examine the association between the dietary antioxidant quality score (DAQS) and the severity of Coronavirus disease 2019 (COVID-19). The present case-control study was carried out on 295 patients diagnosed with COVID-19 (≥18 years old), including 104 critical patients (Intensive care unit [ICU] admission) and 191 COVID-19 patients without severe complications (Non-intensive care unit [Non-ICU] patients) as cases. Dietary intake was assessed by a 147-item, semi-quantitative food frequency questionnaire (FFQ). Logistic regression was performed to calculate the odds ratio (OR) and 95% confidence interval (CI) for the considered risk factors. Our outcomes (after multivariate adjustment) suggested that higher adherence to DAQS was significantly associated with a decreased risk of COVID-19 infection severity (OR = 0.12; 95% CI: 0.04-0.29, p < 0.001). Similar results were seen when analyzed by sex [men (OR = 0.02; 95% CI: 0.002-0.15, p < 0.001) and women (OR = 0.21; 95% CI: 0.06-0.68, p = 0.012)]. A significant association between vitamin D3 intake and decreased risk of COVID-19 severity (OR = 0.91; 95% CI: 0.89-0.94, p < 0.001) was also observed. Moreover, multivariate results revealed that there were no significant associations between vitamin C (OR = 1.00; 95% CI: 0.99-1.00, p = 0.067), vitamin E (OR = 0.98; 95% CI: 0.86-1.11, p = 0.798), zinc (OR = 1.02; 95% CI: 0.86-1.20, p = 0.805), and selenium (OR = 0.99; 95% CI: 0.99-1.00, p = 0.077) intakes with the risk of COVID-19 severity. However, subgroup analyses by sex suggested a significant association between vitamin C intake and the risk of COVID-19 infection severity in women (OR = 1.00; 95% CI: 1.00-1.00, p = 0.028). Our findings showed a negative association between DAQS adherence and the risk of COVID-19 infection severity. Our results may be used to develop potential dietary therapies to decrease COVID-19 severity.

Integrating UPLC-Q-Exactive Orbitrap/MS, network pharmacology and experimental validation to reveal the potential mechanism of Tibetan medicine Rhodiola granules in improving myocardial ischemia-reperfusion injury

ETHNOPHARMACOLOGY RELEVANCE

Rhodiola granules (RG) is a traditional Tibetan medicine prescription that can be used to improve the symptoms of ischemia and hypoxia in cardiovascular and cerebrovascular diseases. However, there is no report on its use to improve myocardial ischemia/reperfusion (I/R) injury, and its potential active ingredients and mechanism against myocardial ischemia/reperfusion (I/R) injury remain unclear.

AIM OF THE STUDY

This study aimed to reveal the potential bioactive components and underlying pharmacological mechanisms of RG in improving myocardial I/R injury through a comprehensive strategy.

MATERIALS AND METHODS

UPLC-Q-Exactive Orbitrap/MS technology was used to analyze the chemical components of RG, the potential bioactive components and targets were tracked and predicted by the SwissADME and SwissTargetPrediction databases, and the core targets were predicted through the PPI network, as well the functions and pathways were determined by GO and KEGG analysis. In addition, the molecular docking and ligation of the anterior descending coronary artery-induced rat I/R models were experimentally validated.

RESULTS

A total of 37 ingredients were detected from RG, including nine flavones, ten flavonoid glycosides, one glycoside, eight organic acids, four amides, two nucleosides, one amino acid, and two other components. Among them, 15 chemical components, such as salidroside, morin, diosmetin, and gallic acid were identified as key active compounds. Ten core targets, including AKT1, VEGF, PTGS2, and STAT3, were discovered through the analysis of the PPI network constructed from 124 common potential targets. These possible targets were involved in the regulation of oxidative stress and HIF-1/VEGF/PI3K-Akt signaling pathways. Furthermore, molecular docking confirmed that the potential bioactive compounds in RG have good potential binding abilities to AKT1, VEGFA, PTGS2, STAT3, and HIF-1α proteins. Then, the animal experiments showed that RG could significantly improve the cardiac function of I/R rats, reduce the size of myocardial infarction, improve the myocardial structure, and reduce the degree of myocardial fibrosis, inflammatory cell infiltration, and myocardial cell apoptosis rate in I/R rats. In addition, we also found that RG could decrease the concentration of AGE, Ox-LDL, MDA, MPO, XOD, SDH, Ca²⁺, and ROS, and increase the concentration of Trx, TrxR1, SOD, T-AOC, NO, ATP, Na⁺k⁺-ATPase, Ca²⁺-ATPase, and CCO. Moreover, RG could significantly down-regulate the expressions of Bax, Cleaved-caspase3, HIF-1α, and PTGS2, as well up-regulate the expressions of Bcl-2, VEGFA, p-AKT1, and p-STAT3.

CONCLUSION

In summary, we revealed for the first time the potential active ingredients and mechanisms of RG for myocardial I/R injury therapy through a comprehensive research strategy. RG may synergistically improve myocardial I/R injury through anti-inflammatory, regulating energy metabolism, and oxidative stress, improving I/R-induced myocardial apoptosis, which may be related to the HIF-1/VEGF/PI3K-Akt signaling pathway. Our study provides new insights into the clinical application of RG and also provides a reference for the development and mechanism research of other Tibetan medicine compound preparations.

COVID19 biomarkers: What did we learn from systematic reviews?

The coronavirus disease 2019 (COVID19) pandemic continues to represent a substantial public health concern. It can rapidly progress to severe disease, with poor prognosis and a high mortality risk. An early diagnosis and specific prognostic tools can help healthcare providers to start interventions promptly, understand the likely prognosis and to identify and treat timely individuals likely to develop severe disease with enhanced mortality risk. Here we focused on an impressive set of systematic reviews and meta-analyses that were performed since the start of the COVID19 pandemic and summarized their results related to the levels of hematologic, inflammatory, immunologic biomarkers as well as markers of cardiac, respiratory, hepatic, gastrointestinal and renal systems and their association with the disease progression, severity and mortality. The evidence outlines the significance of specific biomarkers, including inflammatory and immunological parameters (C-reactive protein, procalcitonin, interleukin-6), hematological (lymphocytes count, neutrophil-to-lymphocyte ratio, D-dimer, ferritin, red blood cell distribution width), cardiac (troponin, CK-MB, myoglobin), liver (AST, ALT, total bilirubin, albumin) and lung injury (Krebs von den Lungen-6) that can be used as prognostic biomarkers to aid the identification of high-risk patients and the prediction of serious outcomes, including mortality, in COVID19. Thus, these parameters should be used as essential tools for an early risk stratification and adequate intervention in improving disease outcomes in COVID19 patients.

Biosensors based detection of novel biomarkers associated with COVID-19: Current progress and future promise

The pandemic situation of COVID-19 has caused global alarm in health care, devastating loss of lives, strangled economy, and paralysis of normal livelihood. The high inter-individual transmission rate created havoc in the global community. Although tremendous efforts are pitching in from across the globe to understand this disease, the clinical features seemed to have a wide range including fever, cough, and fatigue are the prominent features. Congestion, rhinorrhea, sore throat, and diarrhea are other less common features observed. The challenge of this disease lies in the difficulty in maneuvering the clinical course causing severe complications. One of the major causative factors for multi-organ failure in patients with severe COVID-19 complications is systemic vasculitis and cytokine-mediated coagulation disorders. Hence, effective markers trailing the disease severity and disease prognosis are urgently required for prompt medical treatment. In this review article, we have emphasized currently identified inflammatory, hematological, immunological, and biochemical biomarkers of COVID-19. We also discussed currently available biosensors for the detection of COVID-19-associated biomarkers & risk factors and the detection methods as well as their performances. These could be effective tools for rapid and more promising diagnoses in the current pandemic situation. Effective biomarkers and their rapid, scalable, & sensitive detection might be beneficial for the prevention of serious complications and the clinical management of the disease.

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

Correlations between Kidney and Heart Function Bioindicators and the Expressions of Toll-Like, ACE2, and NRP-1 Receptors in COVID-19

BACKGROUND

COVID-19 impacts the cardiovascular system resulting in myocardial damage, and also affects the kidneys leading to renal dysfunction. This effect is mostly through the binding with angiotensin-converting enzyme 2 (ACE2) and Neuropilin-1 (NRP-l) receptors. Toll-Like Receptors (TLRs) typically combine with microbial pathogens and provoke an inflammatory response.

AIM

This work aims to compare the changes in kidney and heart function bioindicators and expressions of TLRs (TLR2 and TLR2) as well as ACE2 and NRP-l receptors in moderate and severe COVID-19 patients. The correlations between kidney and heart function bioindicators and expressions of these receptors are also studied.

PATIENTS AND METHODS

In this study, 50 healthy control and 100 COVID-19 patients (55 males and 45 females) were enrolled. According to WHO guidelines, these participants were divided into severe (50 cases) and moderate (50 cases). Serum creatinine, blood urea, CK-MB, LDH, and Troponin I were estimated. We measured the gene expression for Toll-Like Receptors (TLR2 and TLR4), ACE2, and NRP-1 in the blood samples using quantitative real-time PCR (qRT-PCR).

RESULTS

In comparison with the healthy group, all patients exhibited a significant elevation in serum creatinine, urea, cardiac enzymes (CK-MB and LDH), and CRP. Serum Troponin I level was significantly increased in severe COVID-19 patients. Furthermore, all studied patients revealed a significant elevation in the expression levels of TLR2, TLR4, ACE2, and NRP-1 mRNA. In all patients, CK-MB, ACE2, and NRP-1 mRNA expression levels were positively correlated with both TLR2 and TLR4 expression levels. Moreover, serum creatinine and urea levels were positively correlated with both TLR2 and TLR 4 expression levels in the severe group only. In the moderate group, serum CK-MB activity and Troponin I level had a significant positive correlation with both NRP-1 and ACE2 expression levels, while serum urea level and LDH activity had a significant positive correlation with NRP-1 only. In severe patients, the increases in serum creatinine, urea, CK-MB, and LDH were significantly associated with the elevations in both ACE2 and NRP-1 expression levels, whereas serum Troponin I level had a positive direct relationship with NRP-1 only.

CONCLUSIONS

Our study concluded that expression levels for TLR2, TLR4, ACE2, and NRP-1 mRNA in both severe and moderate patients were positively correlated with renal biomarkers and cardiac enzymes. Innate immune markers can be important because they correlate with the severity of illness in COVID-19.

Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes a wide spectrum of clinical manifestations, with progression to multiorgan failure in the most severe cases. Several biomarkers can be altered in coronavirus disease 2019 (COVID-19), and they can be associated with diagnosis, prognosis, and outcomes. The most used biomarkers in COVID-19 include several proinflammatory cytokines, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), aspartate transaminase (AST), neutrophil count, neutrophils-to-lymphocytes ratio, troponins, creatine kinase (MB), myoglobin, D-dimer, brain natriuretic peptide (BNP), and its N-terminal pro-hormone (NT-proBNP). Some of these biomarkers can be readily used to predict disease severity, hospitalization, intensive care unit (ICU) admission, and mortality, while others, such as metabolomic and proteomic analysis, have not yet translated to clinical practice. This narrative review aims to identify laboratory biomarkers that have shown significant diagnostic and prognostic value for risk stratification in COVID-19 and discuss the possible clinical application of novel analytic strategies, like metabolomics and proteomics. Future research should focus on identifying a limited but essential number of laboratory biomarkers to easily predict prognosis and outcome in severe COVID-19.

Electroanalytical point-of-care detection of gold standard and emerging cardiac biomarkers for stratification and monitoring in intensive care medicine - a review

Determination of specific cardiac biomarkers (CBs) during the diagnosis and management of adverse cardiovascular events such as acute myocardial infarction (AMI) has become commonplace in emergency department (ED), cardiology and many other ward settings. Cardiac troponins (cTnT and cTnI) and natriuretic peptides (BNP and NT-pro-BNP) are the preferred biomarkers in clinical practice for the diagnostic workup of AMI, acute coronary syndrome (ACS) and other types of myocardial ischaemia and heart failure (HF), while the roles and possible clinical applications of several other potential biomarkers continue to be evaluated and are the subject of several comprehensive reviews. The requirement for rapid, repeated testing of a small number of CBs in ED and cardiology patients has led to the development of point-of-care (PoC) technology to circumvent the need for remote and lengthy testing procedures in the hospital pathology laboratories. Electroanalytical sensing platforms have the potential to meet these requirements. This review aims firstly to reflect on the potential benefits of rapid CB testing in critically ill patients, a very distinct cohort of patients with deranged baseline levels of CBs. We summarise their source and clinical relevance and are the first to report the required analytical ranges for such technology to be of value in this patient cohort. Secondly, we review the current electrochemical approaches, including its sub-variants such as photoelectrochemical and electrochemiluminescence, for the determination  of important CBs highlighting the various strategies used, namely the use of micro- and nanomaterials, to maximise the sensitivities and selectivities of such approaches. Finally, we consider the challenges that must be overcome to allow for the commercialisation of this technology and transition into intensive care medicine.