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The COVID-19 pandemic has had a globally devastating impact. This highly contagious virus has significantly overburdened and undermined medical systems. While most infected patients experience only mild symptoms, those who are severely affect require urgent medical interventions and some develop acute respiratory failure and require mechanical ventilation. The broad and potentially deadly impact of infection underscores the critical need for early recognition, especially for those at risk for respiratory failure. Those who are severely impacted and at high risk for respiratory failure have been found to present high levels of serum cytokines, such as interleukin-6 (IL-6). Timely diagnosis and management of those at risk for respiratory failure is crucial. Measurement of IL-6 may provide a means for distinguishing such patients. Currently, most serum IL-6 detection relies on the use of laboratory-based conventional enzyme-linked immunosorbent assays. Although some rapid assays have been developed recently, they need to be conducted by specific technicians in central laboratory settings with advanced and expensive equipment. In this study, we propose an IL-6 test strip combined with a spectrum-based optical reader for early recognition of COVID-19-infected patients at imminent risk of acute respiratory failure requiring mechanical ventilator support. For our analyses, clinical demographic data and sera samples were obtained from three medical centers, and test strip specificity and detection performance were analyzed. This would help healthcare personnel stratify the risk of respiratory failure and provide prompt, and suitable management.

Influenza virus infection is a major worldwide public health problem. Influenza virus infections are associated with a high hospitalization rate in children between the ages of 5 and 14. The predominant reason for poor influenza prognosis is the lack of any effective means for early diagnosis. Early diagnosis of severe illness is critical to improving patient outcome, and could be especially useful in areas with limited medical resources. Accurate, inexpensive, and easy-to-use diagnostic tools could improve early diagnosis and patient outcome, and reduce overall healthcare costs. We developed an interleukin-6 paper-based test strip that used colloidal gold-conjugated antibodies to detect human interleukin-6 protein. These complexes were captured on a paper-based test strip patterned with perpendicular T lines that were pre-coated with anti-human interleukin-6 antibodies. Applied serum samples interacted with these antibodies and presented as colored bands that could be read using a spectrum-based optical reader. The full-spectrum of the reflected light interleukin-6 protein signal could be obtained from the spectral optics module, and the standard could be used to quantitatively analyze interleukin 6 level in serum. We retrospectively evaluated 10 children (23 serum samples) with severe influenza virus infections, 26 children (26 serum samples) with mild influenza virus infections, and 10 healthy children (10 serum samples). Our system, the combined use of a paper-based test strip and a spectrum-based optical reader, provided both qualitative and quantitative information. When used with the optical reader, the detection limit was improved from a qualitative, naked-eye level of 400 pg/ml to a quantitative, optical reader level of 76.85 pg/ml. After monitoring serum interleukin-6 level via our system, we found a high correlation between our system results and those obtainable using a conventional sandwich enzyme-linked immunosorbent assay method (Rho = 0.706, p < 0.001). The sensitivity and specificity for differentiating between severe and mild influenza using our combined method (test strip coupled with optical reader) were 78.3 and 50.0%, respectively. When interleukin-6 was combined with serum C-reaction protein, the sensitivity and specificity were 85.7 and 95.5%, and the receiver operating characteristic area-under-the-curve was quite high (AUC = 0.911, p < 0.001). The potential advantages of our system, i.e., a paper-based test strip coupled with a spectrum-based optical reader, are as follows: 1) simple user operation; 2) rapid turnaround times–within 20 min; 3) high detection performance; and, 4) low-cost fabrication.

The early stage of wound infection is always non-specific. Prompt intervention may help to prevent the wound from worsening. We developed a new protocol, based on previous research, that employs a paper-based IL-6 test strip used in combination with a spectrum-based optical reader to detect IL-6 in normal tissue (n = 19), acute wounds (n = 31), and chronic wounds (n = 32). Our data indicated the presence of significantly higher levels of IL-6 in acute wound tissues, but no significant difference in serum CRP. Receiver operating characteristics were used to determine clinical sensitivity and specificity of tissue IL-6 and systemic CRP. The area under the curve values were 0.87 and 0.63, respectively. The cut-off value of 30 pg/mL for IL-6 provided good sensitivity (75.0%) and superior specificity (88.9%). We found a high correlation between the IL-6 test strip and conventional ELISA results (R2 = 0.85, p < 0.001), and good agreement was also observed according to Bland-Altman analysis. We showed a promising role of tissue IL-6 to help early diagnosis of wound infection when clinical symptoms were non-specific. The advantages of this wound detection protocol included minimal invasiveness, small sample requirements, speed, sample preparation ease, and user-friendliness. This methodology could help care providers quickly clarify wound infection status and implement timely, optimal management.

As the COVID-19 (Coronavirus disease 19) pandemic spreads worldwide, the massive numbers of COVID-19 patients have created a considerable healthcare burden for every country. The clinical spectrum of SARS-CoV-2 infection is broad, ranging from asymptomatic to mild, moderate, severe, and critical. Most COVID-19 patients present with no or mild symptoms, but nearly one-fifth of all patients develop severe or life-threatening complications. In addition to localized respiratory manifestations, severe COVID-19 cases also show extra-pulmonary complications or induce multiorgan failure. Identifying, triaging, and treating patients at risk early is essential and urgent. This article reviews the potential prognostic value of various biomarkers at different clinical spectrum stages of COVID-19 infection and includes information on fundamental prognostic mechanisms as well as potential clinical implications. Biomarkers are measurable biochemical substances used to recognize and indicate disease severity or response to therapeutic interventions. The information they provide is objective and suitable for delivering healthcare providers with a means of stratifying disease state in COVID-19 patients. This, in turn, can be used to help select and guide intervention efforts as well as gauge the efficacy of therapeutic approaches. Here, we review a number of potential biomarkers that may be used to guide treatment, monitor treatment efficacy, and form individualized therapeutic guidance based on patient response. Implementation of the COVID-19 biomarkers discussed here may lead to significantly improved quality of care and patient outcomes for those infected with SARS-CoV-2 worldwide.

With the current worldwide outbreak of COVID-19, developing rapid, effective, and convenient detection tools has become imperative [...]

Aromatic amines are prevalently used in a wide variety of industries and are ubiquitous in foods and environment. Many of this class of compounds are potentially mutagenic or even carcinogenic, and the assessment and prediction of their mutagenicity are of practical importance because mutagenicity and carcinogenicity are toxicological end points that play major roles in the genesis of cancer and tumor. Quantitative structure-activity relationship of a homogeneous set of mutagenicity data (TA98 + S9), which was comprehensively compiled from literature, was developed by four machine learning methods, namely hierarchical support vector regression (HSVR), support vector machine, radial basis function neural networks, and genetic function algorithm. The predictions by these models are in good agreement with the experimental observations for those molecules in the training set (n = 97, r2 = 0.78–0.93, q2 = 0.64–0.93, root mean square error [RMSE] = 0.51–0.90, SD = 0.34–0.56) and the test set (n = 25, r2 = 0.73–0.85, RMSE = 0.65–0.85, SD = 0.33–0.51). In addition, several validation criteria were adopted to verify those generated models, and a set of outliers was deliberately selected to examine the robustness of these four predictive models (n = 14, r2 = 0.35–0.84, RMSE = 0.55–1.21, SD = 0.25–0.72). Finally, various cross-comparison schemes, namely forward comparisons, backward comparisons, and most common molecule comparisons, with assorted published predictive models were carried out. Our results indicate that the HSVR model is the most accurate, robust, and consistent and can be employed as a tool for predicting mutagenicity of aromatic amines. [ABSTRACT FROM PUBLISHER]