The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments

PNPLA3 downregulation exacerbates the fibrotic response in human hepatic stellate cells

Non-alcoholic steatohepatitis (NASH) results, in part, from the interaction of metabolic derangements with predisposing genetic variants, leading to liver-related complications and mortality. The strongest genetic determinant is a highly prevalent missense variant in patatin-like phospholipase domain-containing protein 3 (PNPLA3 p.I148M). In human liver hepatocytes PNPLA3 localizes to the surface of lipid droplets where the mutant form is believed to enhance lipid accumulation and release of pro-inflammatory cytokines. Less is known about the role of PNPLA3 in hepatic stellate cells (HSCs). Here we characterized HSC obtained from patients carrying the wild type (n = 8 C/C) and the heterozygous (n = 6, C/G) or homozygous (n = 6, G/G) PNPLA3 I148M and investigated the effect of genotype and PNPLA3 downregulation on baseline and TGF-β-stimulated fibrotic gene expression. HSCs from all genotypes showed comparable baseline levels of PNPLA3 and expression of the fibrotic genes α-SMA, COL1A1, TIMP1 and SMAD7. Treatment with TGF-β increased PNPLA3 expression in all 3 genotypes (~2-fold) and resulted in similar stimulation of the expression of several fibrogenic genes. In primary human HSCs carrying wild-type (WT) PNPLA3, siRNA treatment reduced PNPLA3 mRNA by 79% resulting in increased expression of α-SMA, Col1a1, TIMP1, and SMAD7 in cells stimulated with TGF-β. Similarly, knock-down of PNPLA3 in HSCs carrying either C/G or G/G genotypes resulted in potentiation of TGF-β induced expression of fibrotic genes. Knockdown of PNPLA3 did not impact fibrotic gene expression in the absence of TGF-β treatment. Together, these data indicate that the presence of the I148M PNPLA3 mutation in HSC has no effect on baseline activation and that downregulation of PNPLA3 exacerbates the fibrotic response irrespective of the genotype

Functional characterization of missense mutations in severe methylenetetrahydrofolate reductase deficiency using a human expression system

5,10-Methylenetetrahydrofolate reductase (MTHFR) catalyzes the NADPH-dependent reduction of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate using FAD as the cofactor. Severe MTHFR deficiency is the most common inborn error of folate metabolism, resulting in hyperhomocysteinemia and homocystinuria. Approximately 70 missense mutations have been described that cause severe MTHFR deficiency, however, in most cases their mechanism of dysfunction remains unclear. Few studies have investigated mutational specific defects; most of these assessing only activity levels from a handful of mutations using heterologous expression. Here, we report the in vitro expression of 22 severe MTHFR missense mutations and two known single nucleotide polymorphisms (p.Ala222Val, p.Thr653Met) in human fibroblasts. Significant reduction of MTHFR activity (<20 % of wild-type) was observed for five mutant proteins that also had highly reduced protein levels on Western blot analysis. The remaining mutations produced a spectrum of enzyme activity levels ranging from 22-122 % of wild-type, while the SNPs retained wild-type-like activity levels. We found increased thermolability for p.Ala222Val and seven disease-causing mutations all located in the catalytic domain, three of which also showed FAD responsiveness in vitro. By contrast, six regulatory domain mutations and two mutations clustering around the linker region showed increased thermostability compared to wild-type protein. Finally, we confirmed decreased affinity for NADPH in individual mutant enzymes, a result previously described in primary patient fibroblasts. Our expression study allows determination of significance of missense mutations in causing deleterious loss of MTHFR protein and activity, and is valuable in detection of aberrant kinetic parameters, but should not replace investigations in native material