Quantifiable levels of 3-chloropropane-1,2-diol (3-MCPD) and 1,3-dichloro-2-propanol (1,3-DCP) were found in domestically manufactured soy-based sauces. Selected commercial foods in the Malaysian market (n = 43) were analyzed for their 3-MCPD and 1,3-DCP contents using a validated gas chromatography-mass spectrometry technique. The 3-MCPD and 1,3-DCP contents of the analyzed food samples varied from not detectable levels to 0.1223 ± 0.0419 mg kg-1 and not detectable levels to 0.025 ± 0.0041 mg kg-1, respectively. High concentrations of 3-MCPD, exceeding Malaysia's maximum tolerable limit of 0.02 mg kg-1, were found in chicken seasoning cubes (mean = 0.0898 ± 0.0378 mg kg-1). Monte Carlo simulation-based health risk assessment revealed that 3-MCPD and 1,3-DCP intakes in the 50th, 95th, and 99th percentiles were lower than 4 µg kg-1 bw day-1, the limit recommended by JECFA in 2016. Hence, it was concluded that the exposure of Malaysian citizens to chloropropanols through soy sauce consumption does not present a health risk.
Human health is threatened by significant emissions of heavy metals into the urban environment due to various activities. Various studies describing health risk analyses on soil and dust have been conducted previously. However, there are limited studies that have been carried out regarding the potential health risk assessment of heavy metals in urban road dust of < 63-μm diameter, via incidental ingestion, dermal contact, and inhalation exposure routes by children and adults in developing countries. Therefore, this study evaluated the health risks of heavy metal exposure via ingestion, dermal contact, and inhalation of urban dust particles in Petaling Jaya, Malaysia. Heavy metals such as lead (Pb), chromium (Cr), zinc (Zn), copper (Cu), and manganese (Mn) were measured using dust samples obtained from industrial, high-traffic, commercial, and residential areas by using inductively coupled plasma mass spectrometry (ICP-MS). The principal component and hierarchical cluster analysis showed the dominance of these metal concentrations at sites associated with anthropogenic activities. This was suggestive of industrial, traffic emissions, atmospheric depositions, and wind as the significant contributors towards urban dust contamination in the study sites. Further exploratory analysis underlined Cr, Pb, Cu, and Zn as the most representative metals in the dust samples. In accommodating the uncertainties associated with health risk calculations and simulating the reasonable maximum exposure of these metals, the related health risks were estimated at the 75th and 95th percentiles. Furthermore, assessing the exposure to carcinogenic and non-carcinogenic metals in the dust revealed that ingestion was the primary route of consumption. Children who ingested dust particles in Petaling Jaya could be more vulnerable to carcinogenic and non-carcinogenic risks, but the exposure for both children and adults showed no potential health effects. Therefore, this study serves as an important premise for a review and reformation of the existing environmental quality standards for human health safety.
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.
SERS detects single molecules with exceptional sensitivity. To counter the issue of selectivity faced by point-of-care, herein, an externally applied electric field that allows electrical modulation and electromigrates unbound SERS tags without multiple washing steps is successfully developed and demonstrated to improve the biosensor's selectivity and sensitivity in multiplexed detection of cTnI, HDL, and LDL in human serum at a low LoD. Ultra-sensitive detectors can detect signals from non-specifically absorbed species, and these species can cover up overlapping analyte peaks, amplifying the effect of non-specific binding. Even though antifouling molecules can prevent non-specific adsorption at the sensor interface, this approach does not completely eliminate it. Our significant findings show that an electrically regulated device can electromigrate non-specifically bound species without cross-reacting with endogenous albumin proteins. Stability, repeatability, and reproducibility were good, with an RSD of 10%. Artificial intelligence was employed to interpret and analyze high-dimensional fingerprint SERS spectra using feature selection and dimensionality reduction for accurate acute myocardial infarction diagnosis and prognosis. These machine learning methods allow quantification of cTnI, HDL, and LDL biomarkers with low RMSE. Machine learning classifiers showed strong AUROC values of 0.950 ± 0.111 and 0.884 ± 0.139 for early and recurrent AMI detection, respectively. A high negative predictive value (NPV) of ≥99% indicates an effective early AMI rule-out. In short, this work demonstrated that a simple, low-cost, electrophoretic modulated biosensor with machine learning can diagnose, rule out, and predict recurring AMI.
Protein-protein interaction plays an essential role in almost all cellular processes and biological functions. Coupling molecular dynamics (MD) simulations and nanoparticle tracking analysis (NTA) assay offered a simple, rapid, and direct approach in monitoring the protein-protein binding process and predicting the binding affinity. Our case study of designed ankyrin repeats proteins (DARPins)-AnkGAG1D4 and the single point mutated AnkGAG1D4-Y56A for HIV-1 capsid protein (CA) were investigated. As reported, AnkGAG1D4 bound with CA for inhibitory activity; however, it lost its inhibitory strength when tyrosine at residue 56 AnkGAG1D4, the most key residue was replaced by alanine (AnkGAG1D4-Y56A). Through NTA, the binding of DARPins and CA was measured by monitoring the increment of the hydrodynamic radius of the AnkGAG1D4-gold conjugated nanoparticles (AnkGAG1D4-GNP) and AnkGAG1D4-Y56A-GNP upon interaction with CA in buffer solution. The size of the AnkGAG1D4-GNP increased when it interacted with CA but not AnkGAG1D4-Y56A-GNP. In addition, a much higher binding free energy (∆GB) of AnkGAG1D4-Y56A (-31 kcal/mol) obtained from MD further suggested affinity for CA completely reduced compared to AnkGAG1D4 (-60 kcal/mol). The possible mechanism of the protein-protein binding was explored in detail by decomposing the binding free energy for crucial residues identification and hydrogen bond analysis.
The onset of Covid-19 pandemic has resulted in the exponential growth of alcohol-based hand rub (ABHR)/hand sanitizer use. Reports have emerged of ABHR products containing methanol, a highly toxic compound to humans, exposing users to acute and chronic medical illnesses. While gas chromatography-mass spectrometry (GC-MS) remains the gold-standard method for the detection and identification of impurities in ABHRs, there exist limitations at widespread volume testing. This paper demonstrates the capability of an inexpensive portable pyroelectric linear array infrared spectrometer to rapidly test ABHR and compare the performance with a benchtop Fourier transform infrared spectrometer and HS-GC-MS. Multicomponent partial least square quantification models were built with performance found to be comparable between the two spectrometers and with the HS-GC-MS. Furthermore, the portable spectrometer was field-tested with real-world samples in Malaysia on both retail products (Group A) and freely deployed public dispensers (Group B) between May and November 2020. A total of 386 samples were tested. Only 75.2% of Group A met the criteria of safe and effective ABHR [no detectable methanol and alcohol concentration above 60% (v/v)], while <50% of Group B did. In addition, 7.4 and 18.8% of Group A and Group B, respectively, were found to contain methanol above permissible limits. The high percentage of sub-standard and methanol-containing samples combined with the frequent use of ABHR by the public highlights the need for and importance of a portable and rapid testing device for widespread screening of ABHR against falsified products and protects the general public.
Increasing public awareness of food quality and safety has prompted a rapid increase in food authentication of halal food, which covers the production method, technical processing, identification of undeclared components, and species substitution in halal food products. This urges for extensive research into analytical methods to obtain accurate and reliable results for monitoring and controlling the authenticity of halal food. Nonetheless, authentication of halal food is often challenging because of the complex nature of food and the increasing number of food adulterants that cause detection difficulties. This review provides a comprehensive and impartial overview of recent studies on the analytical techniques used in the analysis of halal food authenticity (from 1980 to the present, but there has been no significant trend in the choice of techniques for authentication of halal food during this period). Additionally, this review highlights the classification of different methodologies based on validity measures that provide valuable information for future developments in advanced technology. In addition, methodological developments, and novel emerging techniques as well as their implementations have been explored in the evaluation of halal food authentication. This includes food categories that require halal authentication, illustrating the advantages and disadvantages as well as shortcomings during the use of all approaches in the halal food industry.