A label-free graphene-based impedimetric biosensor for real-time tracing of the cytokine storm in blood serum; suitable for screening COVID-19 patients

An early HMGB1 rise 12 hours before creatinine predicts acute kidney injury and multiple organ failure in a smoke inhalation and burn swine model

Background

Acute kidney injury (AKI) and multiple organ failure (MOF) are leading causes of mortality in trauma injuries. Early diagnosis of AKI and MOF is vital to improve outcomes, but current diagnostic criteria rely on laboratory markers that are delayed or unreliable. In this study, we investigated whether damage associated molecular patterns such as high-mobility group box 1 (HMGB1), syndecan-1 (SDC-1) and C3a correlate with the development of trauma-induced AKI and MOF.

Methods

Thirty-nine swine underwent smoke inhalation and severe burns, then received critical care for 72 hours or until death. AKI was defined by the KDIGO (Kidney Disease: Improving Global Outcomes) criteria, which labels AKI when a 1.5-fold increase in blood creatinine levels from baseline or a urine output < 0.5 mL/kg/h for 6 hours or more occurs. MOF was defined by the presence of both AKI and acute respiratory distress syndrome (PaO2/FiO2<300 for 4 hours).

Results

Eight of 39 pigs developed AKI and seven of those developed MOF. Pathological analysis revealed that polytrauma induces significantly higher kidney injury scores compared to sham controls. The average time from injury to KDIGO AKI was 24 hours (interquartile range: 22.50-32.25). Twelve hours after injury, HMGB1 levels were significantly increased in animals that went on to develop AKI compared to those that did not (73.07 ± 18.66 ng/mL vs. 31.64 ± 4.15 ng/mL, p<0.01), as well as in animals that developed MOF compared to those that did not (81.52±19.68 ng/mL vs. 31.19 ± 3.972 ng/mL, p<0.05). SDC-1 and C3a levels were not significantly different at any time point between groups. ROC analysis revealed that HMGB1 levels at 12 hours post-injury were predictive of both AKI and MOF development (AKI: AUROC=0.81, cut-off value=36.41 ng/mL; MOF: AUROC=0.89, cut-off value=36.41 ng/mL). Spearman's correlation revealed that HMGB1 levels at 12 hours correlated with multiple parameters of AKI, including blood urea nitrogen, blood creatinine, and blood myoglobin.

Conclusion

Twelve-hour post-injury HMGB1 levels predict AKI and MOF in a smoke inhalation and burn swine model. Further research is needed to validate this result in other polytrauma models and in critical combat causalities.

Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs)

In pulmonary inflammation diseases, like COVID-19, lung involvement and inflammation determine the treatment regime. Respiratory inflammation is typically arisen due to the cytokine storm and the leakage of the vessels for immune cells recruitment. Currently, such a situation is detected by the clinical judgment of a specialist or precisely by a chest CT scan. However, the lack of accessibility to the CT machines in many poor medical centers as well as its expensive service, demands more accessible methods for fast and cheap detection of lung inflammation. Here, we have introduced a novel method for tracing the inflammation and lung involvement in patients with pulmonary inflammation, such as COVID-19, by a simple electrolyte detection in their sputum samples. The presence of the electrolyte in the sputum sample results in the fern-like structures after air-drying. These fern patterns are different in the CT positive and negative cases that are detected by an AI application on a smartphone and using a low-cost and portable mini-microscope. Evaluating 160 patient-derived sputum sample images, this method demonstrated an interesting accuracy of 95%, as confirmed by CT-scan results. This finding suggests that the method has the potential to serve as a promising and reliable approach for recognizing lung inflammatory diseases, such as COVID-19.

Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates

OBJECTIVES

Raman spectroscopy as a diagnostic tool for biofluid applications is limited by low inelastic scattering contributions compared to the fluorescence background from biomolecules. Surface-enhanced Raman spectroscopy (SERS) can increase Raman scattering signals, thereby offering the potential to reduce imaging times. We aimed to evaluate the enhancement related to the plasmonic effect and quantify the improvements in terms of spectral quality associated with SERS measurements in human saliva.

METHODS

Dried human saliva was characterized using spontaneous Raman spectroscopy and SERS. A fabrication protocol was implemented leading to the production of silver (Ag) nanopillar substrates by glancing angle deposition. Two different imaging systems were used to interrogate saliva from 161 healthy donors: a custom single-point macroscopic system and a Raman micro-spectroscopy instrument. Quantitative metrics were established to compare spontaneous RS and SERS measurements: the Raman spectroscopy quality factor (QF), the photonic count rate (PR), the signal-to-background ratio (SBR).

RESULTS

SERS measurements acquired with an excitation energy four times smaller than with spontaneous RS resulted in improved QF, PR values an order of magnitude larger and a SBR twice as large. The SERS enhancement reached 100×, depending on which Raman bands were considered.

CONCLUSIONS

Single-point measurement of dried saliva with silver nanopillars substrates led to reproducible SERS measurements, paving the way to real-time tools of diagnosis in human biofluids.

Surface chemistry applications and development of immunosensors using electrochemical impedance spectroscopy: A comprehensive review

Immunosensors are promising alternatives as detection platforms for the current gold standards methods. Electrochemical immunosensors have already proven their capability for the sensitive, selective, detection of target biomarkers specific to COVID-19, varying cancers or Alzheimer's disease, etc. Among the electrochemical techniques, electrochemical impedance spectroscopy (EIS) is a highly sensitive technique which examines the impedance of an electrochemical cell over a range of frequencies. There are several important critical requirements for the construction of successful impedimetric immunosensor. The applied surface chemistry and immobilisation protocol have impact on the electroanalytical performance of the developed immunosensors. In this Review, we summarise the building blocks of immunosensors based on EIS, including self-assembly monolayers, nanomaterials, polymers, immobilisation protocols and antibody orientation.

Detection of COVID-19-related biomarkers by electrochemical biosensors and potential for diagnosis, prognosis, and prediction of the course of the disease in the context of personalized medicine

As a more efficient and effective way to address disease diagnosis and intervention, cutting-edge technologies, devices, therapeutic approaches, and practices have emerged within the personalized medicine concept depending on the particular patient's biology and the molecular basis of the disease. Personalized medicine is expected to play a pivotal role in assessing disease risk or predicting response to treatment, understanding a person's health status, and, therefore, health care decision-making. This work discusses electrochemical biosensors for monitoring multiparametric biomarkers at different molecular levels and their potential to elucidate the health status of an individual in a personalized manner. In particular, and as an illustration, we discuss several aspects of the infection produced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a current health care concern worldwide. This includes SARS-CoV-2 structure, mechanism of infection, biomarkers, and electrochemical biosensors most commonly explored for diagnostics, prognostics, and potentially assessing the risk of complications in patients in the context of personalized medicine. Finally, some concluding remarks and perspectives hint at the use of electrochemical biosensors in the frame of other cutting-edge converging/emerging technologies toward the inauguration of a new paradigm of personalized medicine.

Healing Field: Using Alternating Electric Fields to Prevent Cytokine Storm by Suppressing Clonal Expansion of the Activated Lymphocytes in the Blood Sample of the COVID-19 Patients

In the case of the COVID-19 early diagnosis, numerous tech innovations have been introduced, and many are currently employed worldwide. But, all of the medical procedures for the treatment of this disease, up to now, are just limited to chemical drugs. All of the scientists believe that the major challenge toward the mortality of the COVID-19 patients is the out-of-control immune system activation and the subsequent cytokine production. During this process, the adaptive immune system is highly activated, and many of the lymphocytes start to clonally expand; hence many cytokines are also released. So, any attempt to harness this cytokine storm and calm down the immune outrage is appreciated. While the battleground for the immune hyperactivation is the lung ambient of the infected patients, the only medical treatment for suppressing the hypercytokinemia is based on the immunosuppressor drugs that systemically dampen the immunity with many unavoidable side effects. Here, we applied the alternating electric field to suppress the expansion of the highly activated lymphocytes, and by reducing the number of the renewed cells, the produced cytokines were also decreased. Applying this method to the blood of the COVID-19 patients in vitro showed ∼33% reduction in the average concentration of the three main cytokines after 4 days of stimulation. This method could carefully be utilized to locally suppress the hyperactivated immune cells in the lung of the COVID-19 patients without any need for systemic suppression of the immune system by the chemical drugs.

Graphene-Based Biosensors for Molecular Chronic Inflammatory Disease Biomarker Detection

Chronic inflammatory diseases, such as cancer, diabetes mellitus, stroke, ischemic heart diseases, neurodegenerative conditions, and COVID-19 have had a high number of deaths worldwide in recent years. The accurate detection of the biomarkers for chronic inflammatory diseases can significantly improve diagnosis, as well as therapy and clinical care in patients. Graphene derivative materials (GDMs), such as pristine graphene (G), graphene oxide (GO), and reduced graphene oxide (rGO), have shown tremendous benefits for biosensing and in the development of novel biosensor devices. GDMs exhibit excellent chemical, electrical and mechanical properties, good biocompatibility, and the facility of surface modification for biomolecular recognition, opening new opportunities for simple, accurate, and sensitive detection of biomarkers. This review shows the recent advances, properties, and potentialities of GDMs for developing robust biosensors. We show the main electrochemical and optical-sensing methods based on GDMs, as well as their design and manufacture in order to integrate them into robust, wearable, remote, and smart biosensors devices. We also describe the current application of such methods and technologies for the biosensing of chronic disease biomarkers. We also describe the current application of such methods and technologies for the biosensing of chronic disease biomarkers with improved sensitivity, reaching limits of detection from the nano to atto range concentration.