HbA1c measurement is currently routinely used to predict long term outcome of diabetes, thus playing a fundamental role in the management of diabetes. The relationship between HbA1c value and long term diabetic complications has been established by a randomised control Diabetes Control and Complications Trial (DCCT) which used high performance liquid chromatography (HPLC) as a reference method for HbA1c assay. To ensure that HbA1c results from a variety HbA1c assay methods are similar to the DCCT values, the American Diabetes Association (ADA) recommended that all laboratories should use methods certified by the National Glycohemoglobin Standardization Programme (NGSP) with interassay coefficient variation (CV) of < 5% (ideally < 3%). The International Federation of Clinical Chemistry (IFCC) working group on HbA1c standardisation has set a CV < 2.5% as a criteria for its reference laboratories.
The early detection of acute myocardial infarction (AMI) upon the onset of chest pain symptoms is crucial for patient survival. However, this detection is challenging, particularly without a persistent elevation of ST-segment reflected in an electrocardiogram or in blood tests. A majority of the available point-of-care testing devices allow accurate and rapid diagnosis of AMI. However, AMI diagnosis is reliable only at intermediate and later stages, with myocardial injury (> 6 h) and MI, based on the expression of specific cardiac biomarkers including troponin I or T (cTnI or cTnT), creatine kinase-MB (CK-MB), and myoglobin. Diagnosis at the early myocardial ischemia stage is not possible. To overcome this limitation, a sensitive and rapid microfluidic paper-based device (µPAD) was developed for the simultaneous detection of multiple cardiac biomarkers for the early and late diagnosis of AMI. The glycogen phosphorylase isoenzyme BB (GPBB) was detected during early (within first 4 h) ischemic myocardial injury. On the same µPAD platform, detection of prolonged elevation of levels of cTnT and CK-MB, which are only produced 6 h after the onset of chest pain in human serum, was possible. Sandwich immunoassay performed on the µPAD achieved reproducibility (RSD approximately 10% and intra-and inter-day precision (CV 10-20%, 99th percentile), as well as consistently stable test results for 28 days, with strong correlation (r2= 0.962), using the standard Siemens Centaur XPT Immunoassay system. The present findings indicate the potential of the µPAD platform as a point-of-care device for the early diagnosis and prognosis of AMI.
Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limitations of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative approaches are proposed.
We describe a gold nanoparticle-based sandwich immunoassay for the dual detection and measurement of hemoglobin A1c (HbA1c) and total hemoglobin in the whole blood (without pretreatment) in a single step for personalized medicine. The optimized antibody-functionalized gold nanoparticles immunoreact simultaneously with HbA1c and total hemoglobin to form a sandwich at distinctive test lines to transduce visible signals. The applicability of this method as a personal management tool was demonstrated by establishing a calibration curve to relate % HbA1c, a useful value for type 2 diabetes management, to the signal ratio of captured HbA1c to all other forms of hemoglobin. The platform showed excellent selectivity (100%) toward HbA1c at distinctive test lines when challenged with HbA0, glycated HbA0 and HbA2. The reproducibility of the measurement was good (6.02%) owing to the dual measurement of HbA1c and total hemoglobin. A blood sample stability test revealed that the quantitative measurement of % HbA1c was consistent and no false-positive results were detected. Also, this method distinguished the blood sample with elevated HbF from the normal samples and the variants. The findings of this study highlight the potential of a lateral flow immunosensor as a simple, inexpensive, consistent, and convenient strategy for the dual measurement of HbA1c and total Hb to provide useful % HbA1c values for better on-site diabetes care.
Cardiovascular disease is a major global health issue. In particular, acute myocardial infarction (AMI) requires urgent attention and early diagnosis. The use of point-of-care diagnostics has resulted in the improved management of cardiovascular disease, but a major drawback is that the performance of POC devices does not rival that of central laboratory tests. Recently, many studies and advances have been made in the field of surface-enhanced Raman scattering (SERS), including the development of POC biosensors that utilize this detection method. Here, we present a review of the strengths and limitations of these emerging SERS-based biosensors for AMI diagnosis. The ability of SERS to multiplex sensing against existing POC detection methods are compared and discussed. Furthermore, SERS calibration-free methods that have recently been explored to minimize the inconvenience and eliminate the limitations caused by the limited linear range and interassay differences found in the calibration curves are outlined. In addition, the incorporation of artificial intelligence (AI) in SERS techniques to promote multivariate analysis and enhance diagnostic accuracy are discussed. The future prospects for SERS-based POC devices that include wearable POC SERS devices toward predictive, personalized medicine following the Fourth Industrial Revolution are proposed.
Recently, surface enhanced Raman scattering (SERS) has attracted much attention in medical diagnosis applications owing to better detection sensitivity and lower limit of detection (LOD) than colorimetric detection. In this paper, a novel calibration-free SERS-based μPAD with multi-reaction zones for simultaneous quantitative detection of multiple cardiac biomarkers - GPBB, CK-MB and cTnT for early diagnosis and prognosis of acute myocardial infarction (AMI) are presented. Three distinct Raman probes were synthesised, subsequently conjugated with respective detecting antibodies and used as SERS nanotags for cardiac biomarker detection. Using a conventional calibration curve, quantitative simultaneous measurement of multiple cardiac biomarkers on SERS-based μPAD was performed based on the characteristic Raman spectral features of each reporter used in different nanotags. However, a calibration free point-of-care testing device is required for fast screening to rule-in and rule-out AMI patients. Partial least squares predictive models were developed and incorporated into the immunosensing system, to accurately quantify the three unknown cardiac biomarkers levels in serum based on the previously obtained Raman spectral data. This method allows absolute quantitative measurement when conventional calibration curve fails to provide accurate estimation of cardiac biomarkers, especially at low and high concentration ranges. Under an optimised condition, the LOD of our SERS-based μPAD was identified at 8, 10, and 1 pg mL-1, for GPBB, CK-MB and cTnT, respectively, which is well below the clinical cutoff values. Therefore, this proof-of-concept technique shows significant potential for highly sensitive quantitative detection of multiplex cardiac biomarkers in human serum to expedite medical decisions for enhanced patient care.
An immunosensor that operates based on the principles of lateral flow was developed for direct detection of hemoglobin A1c (HbA1c) in whole blood. We utilized colloidal gold-functionalized antibodies to transduce the specific signal generated when sandwich immuno-complexes were formed on the strip in the presence of HbA1c. The number and intensity of the test lines on the strips indicate normal, under control, and elevated levels of HbA1c. In addition, a linear relationship between HbA1c levels and immunosensor signal intensity was confirmed, with a dynamic range of 4-14% (20-130 mmol mol(-1)) HbA1c. Using this linear relationship, we determined the HbA1c levels in blood as a function of the signal intensity on the strips. Measurements were validated using the Bio-Rad Variant II HPLC and DCA Vantage tests. Moreover, the immunosensor was verified to be highly selective for detection of HbA1c against HbA0, glycated species of HbA0, and HbA2. The limit of detection was found to be 42.5 μg mL(-1) (1.35 mmol mol(-1)) HbA1c, which is reasonably sensitive compared to the values reported for microarray immunoassays. The shelf life of the immunosensor was estimated to be 1.4 months when stored at ambient temperature, indicating that the immunoassay is stable. Thus, the lateral flow immunosensor developed here was shown to be capable of performing selective, accurate, rapid, and stable detection of HbA1c in human blood samples.
Acute myocardial infarction (AMI) or heart attack is a significant global health threat and one of the leading causes of death. The evolution of machine learning has greatly revamped the risk stratification and death prediction of AMI. In this study, an integrated feature selection and machine learning approach was used to identify potential biomarkers for early detection and treatment of AMI. First, feature selection was conducted and evaluated before all classification tasks with machine learning. Full classification models (using all 62 features) and reduced classification models (using various feature selection methods ranging from 5 to 30 features) were built and evaluated using six machine learning classification algorithms. The results showed that the reduced models performed generally better (mean AUPRC via random forest (RF) algorithm for recursive feature elimination (RFE) method ranges from 0.8048 to 0.8260, while for random forest importance (RFI) method, it ranges from 0.8301 to 0.8505) than the full models (mean AUPRC via RF: 0.8044). The most notable finding of this study was the identification of a five-feature model that included cardiac troponin I, HDL cholesterol, HbA1c, anion gap, and albumin, which had achieved comparable results (mean AUPRC via RF: 0.8462) as to the models that containing more features. These five features were proven by the previous studies as significant risk factors for AMI or cardiovascular disease and could be used as potential biomarkers to predict the prognosis of AMI patients. From the medical point of view, fewer features for diagnosis or prognosis could reduce the cost and time of a patient as lesser clinical and pathological tests are needed.