Determination of Selected Amino Acids in Serum of Patients with Liver Disease

Dried blood spot as an alternative sample for screening of fatty acids, amino acids, and keto acids metabolism in humans

The dried blood spot is a simple and non-invasive sample collection technique allowing self-collection at home. It can be used as an alternative sample for the screening of metabolism in humans since changes in the levels of some fatty acids, amino acids, and keto acids can be associated with metabolic disorders (for example diabetes mellitus). In this study we optimized three different methods that are sensitive enough for the determination of above-mentioned analytes from a small volume of a biological material in dried blood spot. In total 20 amino acids, 5 keto acids, and 24 fatty acids were determined. This sample technique was applied to prepare samples from 60 individuals by a finger prick. Samples were analysed with chromatographic methods and acquired data were statistically evaluated. Even though most analytes were higher in men, only 5 amino acids, 3 keto acids and 8 fatty acids showed significant gender-dependency (α = 0.05). Asparagine, serine, α- and γ-linolenic acids showed significant age-dependency (α = 0.05). The most of statistically significant correlations were positive and were found within one category. This work shows that because of many benefits, the dried blood spot sample could be a good alternative to whole blood sample collection for the screening of metabolism in humans in general or in individualised medicine. The chromatographic methods can be used in the next research, for example to set reference range or plasma-correction factors (various aspects as age or gender should be considered).

Dynamic metabolic profiles for HBeAg seroconversion in chronic hepatitis B (CHB) patients by gas chromatography-mass spectrometry (GC-MS)

Chronic hepatitis B (CHB) remains a major public health problem globally. HBeAg seroconversion is a vital hallmark for the improvement of CHB. The plasma metabolic profile has not been clear in CHB patients and searching metabolic candidates to represent HBeAg seroconversion is also difficult currently. In this study, CHB patients were recruited, followed and divided into the HBeAg-positive (HBeAg-pos.) group (n = 29) and the HBeAg-negative (HBeAg-neg.) group (n = 29) based on HBeAg seroconversion or not. The plasma metabolic profiles were measured by gas chromatography-mass spectrometry (GC-MS) at 0 week (0w), 24 weeks (24w) and 48 weeks (48w) after administration. The acquired data was analyzed using orthogonal partial least squares discriminate analysis (OPLS-DA) and the differential metabolites were further assessed by self and group comparison. No differences of age, gender and serological characteristics were observed between two groups at 0w and 48w separately. The OPLS-DA score plots depending on administration time displayed robust metabolic differences no matter HBeAg turned to be negative or not. According to VIP> 1.0, a total of 15 differential metabolites were same in the two groups, 7 differential metabolites (glycolic acid, D-talose, L-proline, L-(-)-arabitol, ethyl-alpha-D-glucopyranoside, L-leucine and dihydroxybutanoic acid) were derived from one group alone and considered as metabolic candidates. At 0w versus (vs.) 24w, only 3 of 7 candidates (L-proline, L-(-)-arabitol, dihydroxybutanoic acid) showed nonuniform in the two groups, while at 0w vs. 48w, all of them varied inconsistently. Conclusively the dynamic metabolic profiles assayed by GC-MS were different between CHB patients with and without HBeAg seroconversion. The 7 metabolic candidates probably had the ability to reflect the CHB progression for HBeAg seroconversion and 3 of them showed strong relationship with HbeAg seroconversion early.

Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network

This study presents a rapid method to screen hepatitis B patients using serum Raman spectroscopy combined with long short-term memory neural network (LSTM). The serum samples taken from 435 hepatitis B patients and 699 non-hepatitis B people were measured in this experiment. Specific biomolecular changes in three groups of serum samples could be seen in the tentative assignment of Raman peaks. First, principal component analysis (PCA) was used for extracting key features of spectral data, which reduces the dimension of the multidimensional spectrum. Then, LSTM is used to train the spectral data. Finally, the full connection layer completes the classification of HBV. The diagnostic accuracy of the first LSTM model is 97.32%, and the value of AUC is 0.995. The results from the study demonstrate that the combination of serum Raman spectroscopy technique and LSTM provides an effective technical approach to the screening of hepatitis B.