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Background: COVID-19 has led to the demand for finding effective antiviral agents. Preliminary experiments showed Umifenovir inhibit replication in vivo. There is limited data on the clinical efficacy of COVID-19-infected pneumonia. Therefore, we aimed to evaluate this medication based on clinical findings. Methods: The present study was designed to investigate the advantages and disadvantages of Umifenovir and compared to empirical treatments. For this purpose, multi-stage sampling was considered. 56 people who had mild-to-moderate symptoms without signs of pneumonia, were selected by accidental non-random sampling method and divided into two groups [(group A with Hydroxychloroquine (HCQ) and group B in combination with Umifenovir] by randomized block sampling (1:1). During the study, three patients left the case group. Their clinical signs and symptoms were evaluated on 3rd, 7th, and 14th day after taking these medicines in the disease course. The SPSS software was used for data analysis and the significance level was considered to be p

The banking industry is one of the most complex and sensitive industries that undergoes significant changes daily. Most financial institutions and banks strive to diversify and innovate payment processes, making them more challenging and secure so that they can improve their digital skills .Understanding customer behavior has become a key factor in market success, which is why the Internet of Things (IoT) can be the best way to solve the problem of collecting and sharing data over the Internet, among different objects. Based on these concepts, the purpose of this paper is to show the current perspective of the e-banking industry with big data and the IoT, as well as the impact of big data on various financial and banking sectors, especially its impact on financial markets and banking, financial institutions, Internet credit services companies, risk analysis and so on. Since big data in the field of electronic banking and finance is a completely new concept, future research opportunities will be mentioned at the end of this study.

Epigenetic mechanisms may contribute to idiopathic scoliosis (IS). We identified 8 monozygotic twin pairs with IS, 6 discordant (Cobb angle difference > 10°) and 2 concordant (Cobb angle difference ≤ 2°). Genome-wide methylation in blood was measured with the Infinium HumanMethylation EPIC Beadchip. We tested for differences in methylation and methylation variability between discordant twins and tested the association between methylation and curve severity in all twins. Differentially methylated region (DMR) analyses identified gene promoter regions. Methylation at cg12959265 (chr. 7 DPY19L1) was less variable in cases (false discovery rate (FDR) = 0.0791). We identified four probes (false discovery rate, FDR < 0.10); cg02477677 (chr. 17, RARA gene), cg12922161 (chr. 2 LOC150622 gene), cg08826461 (chr. 2), and cg16382077 (chr. 7) associated with curve severity. We identified 57 DMRs where hyper- or hypo-methylation was consistent across the region and 28 DMRs with a consistent association with curve severity. Among DMRs, 21 were correlated with bone methylation. Prioritization of regions based on methylation concordance in bone identified promoter regions for WNT10A (WNT signaling), NPY (regulator of bone and energy homeostasis), and others predicted to be relevant for bone formation/remodeling. These regions may aid in understanding the complex interplay between genetics, environment, and IS.

Background The clinical course of COVID-19 may vary significantly. The presence of comorbidities prolongs the recovery time. The recovery in patients with mild-to-moderate symptoms might take 10 days, while in those with a critical illness or immunocompromised status could take 15 days. Considering the lack of data about predictors that could affect the recovery time, we conducted this study to identify them. Methods This cross-sectional study was implemented in the COVID-19 clinic of a teaching and referral university hospital in Tehran. Patients with the highly suggestive symptoms who had computed tomography (CT) imaging results with typical findings of COVID-19 or positive results of reverse transcriptase-polymerase chain reaction (RT-PCR) were enrolled in the study. Inpatient and outpatient COVID-19 participants were followed up by regular visits or phone calls, and the recovery period was recorded. Results A total of 478 patients were enrolled. The mean age of patients was 54.11 ± 5.65 years, and 44.2% were female. The median time to recovery was 13.5 days (IQR: 9). Although in the bivariate analysis, multiple factors, including hypertension, fever, diabetes mellitus, gender, and admission location, significantly contributed to prolonging the recovery period, in multivariate analysis, only dyspnea had a significant association with this variable (p = 0.02, the adjusted OR of 2.05; 95% CI 1.12–3.75). Conclusion This study supports that dyspnea is a predictor of recovery time. It seems like optimal management of the comorbidities plays the most crucial role in recovery from COVID-19.

Background: The outbreak of COVID-19has led to a high demand for finding effective antiviral agents. Preliminary experiments showed that Umifenovir could inhibit coronavirus replication in vivo. There is limited data on the clinical efficacy of COVID-19-infected pneumonia. Therefore, we aimed to evaluate this medication based on clinical findings.Methods: The present study was designed as a clinical trial to investigate the advantages and disadvantages of this medicine compared to empirical treatment. For this purpose, multi-stage sampling was considered. 56 people who have mild to moderate symptoms without signs of sever pneumonia , were selected by accidental non-random sampling method. This sample size was subsequently divided into two groups (group A threated with hydroxycloroquine and group B who threated with combination hydroxycloroquine and Umifenovir) by randomized block sampling (1:1). During the study, three patients left the case group. Their clinical sighns and symptoms were evaluated on 3 th and 7 th and 14 th days after oncet illness while taking these medicines in the disease course. The SPSS software was used for data analysis and the significance level was considered to be PResults: On the seventh days after visit patients , there were statistically significant differences in recuperation dry cough(p = 0.001), weakness(p = 0.004), gastrointestinal symptoms(p = 0.043) and shortness of breath (p = 0.001) between the two groups, so that in patients who under orescription combination HCQ and Umifenovir group had a faster recovery.Generally during illness , there were statistically significantly faster melioratation myalgia(p = 0.03), gastrointestinal symptoms(p = 0.047), and weakness(p = 0.007)in patients who threated with both HCQ and Umifenovir .Conclusion: Evaluation clinical findings in mild to moderate COVID-19 patients symptoms and sighns recuperation faster in group who under orescription combination HCQ and Umifenovir .regimen. In other words, adding Umifenovir to the empirical treatment accelerated the recovery process of patients’ clinical symptoms.Registration: IR.TUMS.VCR.REC.1399.204, 04.13.2020 , http://ethics.research.ac.ir/IR.TUMS.VCR.REC.1399.204

Difficulty in obtaining bone tissue is an obstacle to studying epigenetics to understand gene–environment interactions, and their role in disease pathogenesis. Blood is an obvious alternative and in this proof of principle study, our aim was to systematically investigate whether blood is a viable surrogate for bone. We measured epigenome-wide DNA methylation at 850 K CpG sites in matched trabecular bone and peripheral blood collected from the same patients at the same time-point (n = 12 women; 66–85y), to investigate the between-tissue correspondence. What constituted a CpG site with corresponding methylation in both tissues was stringently defined. Only sites highly correlated (r2 > 0.74; FDR q-value

Lipoprotein profiling of human blood by 1H nuclear magnetic resonance (NMR) spectroscopy is a rapid and promising approach to monitor health and disease states in medicine and nutrition. However, lack of standardization of measurement protocols has prevented the use of NMR-based lipoprotein profiling in metastudies. In this study, a standardized NMR measurement protocol was applied in a ring test performed across three different laboratories in Europe on plasma and serum samples from 28 individuals. Data was evaluated in terms of (i) spectral differences, (ii) differences in LPD predictions obtained using an existing prediction model, and (iii) agreement of predictions with cholesterol concentrations in high- and low-density lipoproteins (HDL and LDL) particles measured by standardized clinical assays. ANOVA-simultaneous component analysis (ASCA) of the ring test spectral ensemble that contains methylene and methyl peaks (1.4-0.6 ppm) showed that 97.99% of the variance in the data is related to subject, 1.62% to sample type (serum or plasma), and 0.39% to laboratory. This interlaboratory variation is in fact smaller than the maximum acceptable intralaboratory variation on quality control samples. It is also shown that the reproducibility between laboratories is good enough for the LPD predictions to be exchangeable when the standardized NMR measurement protocol is followed. With the successful implementation of this protocol, which results in reproducible prediction of lipoprotein distributions across laboratories, a step is taken toward bringing NMR more into scope of prognostic and diagnostic biomarkers, reducing the need for less efficient methods such as ultracentrifugation or high-performance liquid chromatography (HPLC).

Background: Regular-fat cheese does not seem to increase low density lipoprotein cholesterol (LDL-C) concentrations compared to reduced-fat cheese. However, plasma LDL-C concentrations do not reflect levels and size of LDL particles, which might be a better predictor of cardiovascular risk.Methods: The aim was to compare the effects of regular-fat cheese vs reduced-fat cheese and carbohydrate-rich foods on LDL particle size distribution in adults with ≥ 2 metabolic syndrome (MetS) risk factors. The study was part of a 12 weeks’ randomized controlled trial in which subjects had been randomly allocated to 1 of 3 intervention groups; regular-fat cheese (REG), reduced-fat cheese (RED) or a no-cheese/carbohydrate (CHO) group. Subjects in the REG and RED groups consumed 80 g cheese/d per 10 MJ, whereas subjects in the CHO consumed bread and jam corresponding to 90 g/d and 25 g/d per 10 MJ, respectively. Fasting blood samples at wk. 0 (baseline) and wk. 12 were analyzed for LDL particle size distribution and cholesterol content using nuclear magnetic resonance (NMR) spectroscopy.Results: A total of 85 subjects [mean ± SD age: 54.0 ± 12.8 y; BMI: 28.7 ± 3.6 kg/m2] completed the study. Overall, regular-fat cheese did not impact lipoprotein particle number and size differently than reduced-fat cheese. In men (n = 23), the REG diet decreased total LDL particle number (LDL-P, − 223.2 ± 91.1 nmol/l, P = 0.01) compared with the RED diet. The reduction was primarily in the medium-sized LDL fraction (− 128.5 ± 51.8 nmol/l, P = 0.01). In women (n = 62), the REG diet increased the concentration of cholesterol in the small high density lipoprotein (HDL) particles compared with the CHO diet (2.9 ± 1.0 mg/dl, P = 0.006).Conclusion: Overall, regular-fat cheese did not alter LDL particle size distribution compared to reduced-fat cheese after a 12 wk. intervention in subjects with ≥2 MetS risk factors. However, our results suggest that lipoprotein response to cheese intake is gender-specific. This warrants further investigation.

NMR is one of the most powerful analytical techniques of our time. It allows detailed investigation of qualitative and quantitative characteristics of complex chemical and biological samples. The resulting NMR data provides a wealth of information about the samples, but the NMR data analysis has been and still is suffering from oversimplified approaches making it difficult to extract all the information efficiently. For instance, univariate methods that just use one or a few selected variables for the analysis from a whole spectrum lead to a huge loss of information. Such a simplifying approach reduces the chance of discovering new findings and truly learning about complex aspects of the samples investigated. Multivariate data analysis techniques allows for truly exploratory and comprehensive analysis of NMR data. This is particularly advantageous in the investigation of complex biological samples. Chemometrics can be helpful here by providing tools for unsupervised and supervised data exploration, multivariate calibration, classification and discrimination. This chapter presents some important steps in the pre-processing of NMR data as well as some of the most common chemometric techniques for data exploration and analysis. An example of NMR spectra of apple juice samples is given, to illustrate the power of the combination of NMR data and chemometrics. (Less)

NMR spectroscopy provides valuable data for metabolomics, but the information sought can be partly obscured by errors from hardware imperfection, causing frequency, phase, and spectral lineshape to change significantly between measurements. Clearly, this is a highly undesirable source of variation in multivariate quantitative studies such as metabolomics. Fortunately, many hardware imperfections affect all resonances in the same way. They can therefore be corrected for by comparing an experimental reference peak with the known correct peak shape, in a procedure known as reference deconvolution. This post-measurement processing method can correct many systematic errors in data. The aim of this study is to investigate how reference deconvolution can improve the results obtained by multivariate analysis of NMR data. For this purpose, 1 H NMR data were recorded for a set of 136 mixture samples. Spectra were then produced with and without reference deconvolution and analyzed by principal component analysis and partial least squares methods. The results showed that reference deconvolution resulted in simpler and improved models, requiring fewer latent variables to explain the same or higher percentage of the variance. It was also evident that the recovery of the design concentrations was significantly enhanced. This confirms that reference deconvolution can significantly improve multivariate data analysis and should be considered as a standard tool in high throughput quantitative NMR spectroscopy.

Author Summary: Lactic acid bacteria (LAB) are widely used in the food industry as starter cultures to manufacture fermented food, and more recently there has been an increasing interest in using LAB for biopreservation of food products. The study of the metabolism of these bacteria is therefore of great interest, and NMR is the only omics analytical technology that has the potential to measure their metabolome non-destructively. In vivo NMR can provide valuable information about the dynamic metabolism of microorganisms, for example for studying the adaptive responses of microorganism to different environmental and stress factors such as substrate, cohabitation, deleterious metabolites, as well as temperature and pH. This paper describes a protocol for in vitro NMR measurement of the fermentation of lactic acid bacteria. We call it microPAT (microbial process analytical technology) [1] as it monitors in real time the microbial processes of the fermentation. Some of the experimental issues that were encountered during the process of developing the protocol as well as the selected approach to overcome them are presented.

Lactic acid bacteria (LAB) play an important role in the food industry as starter cultures to manufacture fermented food, and as probiotics. In recent years, there has been an increasing interest in using LAB cultures for biopreservation of food products. It is therefore of great interest to study the detailed metabolism of these bacteria. This study aimed at developing an efficient analytical protocol for real-time in vitro NMR measurements of LAB fermentations, from sample preparation, over data acquisition and preprocessing, to the extraction of the kinetic metabolic profiles. The developed analytical protocol is applied to an experimental design with two LAB strains (Lactobacillus rhamnosus DSM 20021 and Lactobacillus plantarum subsp. plantarum DSM 20174), two initial pH levels (pHi 6.5 and 5.5), two levels of glucose concentration (2.5 and 0.25 g/l), and two batch fermentation replicates. The design factors proved to be strongly significant and led to interesting biological information. The protocol allowed for detailed real-time kinetic analysis of 11 major metabolites involved in the glycolysis, pyruvate catabolism, amino acid catabolism and cell energy metabolism. New biological knowledge was obtained about the different patterns of glutamine and aspartic acid consumption by the two strains. It was observed that L. plantarum consumes more glutamine at low pH (pH 5.5) whereas the opposite applies to L. rhamnosus. Regarding aspartic acid, both of the strains consume it higher at low pH, and overall L. plantarum consumes it more. L. rhamnosus did not consume aspartic acid at pH 6.5. The developed analytical protocol for real-time in vitro NMR measurements of bacterial metabolism allows a relatively easy investigation of different fermentation factors such as new strains, new substrates, cohabitations, temperature, and pH and has a great potential in biopreservation studies to discover new efficient bioprotective cultures.

Lactic acid bacteria with antifungal properties are applied for biopreservation of food. In order to further our understanding of their antifungal mechanism, there is an ongoing search for bioactive molecules. With a focus on the metabolites formed, bioassay-guided fractionation and comprehensive screening have identified compounds as antifungal. Although these are active, the compounds have been found in concentrations that are too low to account for the observed antifungal effect. It has been hypothesized that the formation of metabolites and consumption of nutrients during bacterial fermentations form the basis for the antifungal effect, i.e., the composition of the exometabolome. To build a more comprehensive view of the chemical changes induced by bacterial fermentation and the effects on mold growth, a strategy for correlating the exometabolomic profiles with mold growth was applied. The antifungal properties were assessed by measuring mold growth of two Penicillium strains on cell-free ferments of three strains of Lactobacillus paracasei pre-fermented in a chemically defined medium. Exometabolomic profiling was performed by reversed-phase liquid chromatography in combination with mass spectrometry in electrospray positive and negative modes. By multivariate data analysis, the three strains of Lb. paracasei were readily distinguished by the relative difference of their exometabolomes. The relative differences correlated with the relative growth of the two Penicillium strains. Metabolic footprinting proved to be a supplement to bioassay-guided fractionation for investigation of antifungal properties of bacterial ferments. Additionally, three previously identified and three novel antifungal metabolites from Lb. paracasei and their potential precursors were detected and assigned using the strategy.

Lactic acid bacteria with antifungal properties can be used to control spoilage of food and feed. Previously, most of the identified metabolites have been isolated from cell-free fermentate of lactic acid bacteria with methods suboptimal for detecting possible contribution from volatiles to the antifungal activity. The role of volatile compounds in the antifungal activity of Lactobacillus paracasei DGCC 2132 in a chemically defined interaction medium (CDIM) and yogurt was therefore investigated with a sampling technique minimizing volatile loss. Diacetyl was identified as the major volatile produced by L. paracasei DGCC 2132 in CDIM. When the strain was added to a yogurt medium diacetyl as well as other volatiles also increased but the metabolome was more complex. Removal of L. paracasei DGCC 2132 cells from CDIM fermentate resulted in loss of both volatiles, including diacetyl, and the antifungal activity towards two strains of Penicillium spp. When adding diacetyl to CDIM or yogurt without L. paracasei DGCC 2132, marked inhibition was observed. Besides diacetyl, the antifungal properties of acetoin were examined, but no antifungal activity was observed. Overall, the results demonstrate the contribution of diacetyl in the antifungal effect of L. paracasei DGCC 2132 and indicate that the importance of volatiles may have been previously underestimated.