Changes in fecal metabolic and lipidomic features by anti-TNF treatment and prediction of clinical remission in patients with ulcerative colitis.

Background: Therapeutic targets for ulcerative colitis (UC) and prediction models of antitumor necrosis factor (TNF) therapy outcomes have not been fully reported. Objective: Investigate the characteristic metabolite and lipid profiles of fecal samples of UC patients before and after adalimumab treatment and develop a prediction model of clinical remission following adalimumab treatment. Design: Prospective, observational, multicenter study was conducted on moderate-to-severe UC patients (n = 116). Methods: Fecal samples were collected from UC patients at 8 and 56 weeks of adalimumab treatment and from healthy controls (HC, n = 37). Clinical remission was assessed using the Mayo score. Metabolomic and lipidomic analyses were performed using gas chromatography mass spectrometry and nano electrospray ionization mass spectrometry, respectively. Orthogonal partial least squares discriminant analysis was performed to establish a remission prediction model. Results: Fecal metabolites in UC patients markedly differed from those in HC at baseline and were changed similarly to those in HC during treatment; however, lipid profiles did not show these patterns. After treatment, the fecal characteristics of remitters (RM) were closer to those of HC than to those of non-remitters (NRM). At 8 and 56 weeks, amino acid levels in RM were lower than those in NRM and similar to those in HC. After 56 weeks, levels of 3-hydroxybutyrate, lysine, and phenethylamine decreased, and dodecanoate level increased in RM similarly to those in HC. The prediction model of long-term remission in male patients based on lipid biomarkers showed a higher performance than clinical markers. Conclusion: Fecal metabolites in UC patients markedly differ from those in HC, and the levels in RM are changed similarly to those in HC after anti-TNF therapy. Moreover, 3-hydroxybutyrate, lysine, phenethylamine, and dodecanoate are suggested as potential therapeutic targets for UC. A prediction model of long-term remission based on lipid biomarkers may help implement personalized treatment.

Prediction of Clinical Remission with Adalimumab Therapy in Patients with Ulcerative Colitis by Fourier Transform-Infrared Spectroscopy Coupled with Machine Learning Algorithms.

We aimed to develop prediction models for clinical remission associated with adalimumab treatment in patients with ulcerative colitis (UC) using Fourier transform-infrared (FT-IR) spectroscopy coupled with machine learning (ML) algorithms. This prospective, observational, multicenter study enrolled 62 UC patients and 30 healthy controls. The patients were treated with adalimumab for 56 weeks, and clinical remission was evaluated using the Mayo score. Baseline fecal samples were collected and analyzed using FT-IR spectroscopy. Various data preprocessing methods were applied, and prediction models were established by 10-fold cross-validation using various ML methods. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation of healthy controls and UC patients, applying area normalization and Pareto scaling. OPLS-DA models predicting short- and long-term remission (8 and 56 weeks) yielded area-under-the-curve values of 0.76 and 0.75, respectively. Logistic regression and a nonlinear support vector machine were selected as the best prediction models for short- and long-term remission, respectively (accuracy of 0.99). In external validation, prediction models for short-term (logistic regression) and long-term (decision tree) remission performed well, with accuracy values of 0.73 and 0.82, respectively. This was the first study to develop prediction models for clinical remission associated with adalimumab treatment in UC patients by fecal analysis using FT-IR spectroscopy coupled with ML algorithms. Logistic regression, nonlinear support vector machines, and decision tree were suggested as the optimal prediction models for remission, and these were noninvasive, simple, inexpensive, and fast analyses that could be applied to personalized treatments.

Differentiation of Geographical Origin of White and Brown Rice Samples Using NMR Spectroscopy Coupled with Machine Learning Techniques.

Rice (Oryza sativa L.) is a widely consumed food source, and its geographical origin has long been a subject of discussion. In our study, we collected 44 and 20 rice samples from different regions of the Republic of Korea and China, respectively, of which 35 and 29 samples were of white and brown rice, respectively. These samples were analyzed using nuclear magnetic resonance (NMR) spectroscopy, followed by analyses with various data normalization and scaling methods. Then, leave-one-out cross-validation (LOOCV) and external validation were employed to evaluate various machine learning algorithms. Total area normalization, with unit variance and Pareto scaling for white and brown rice samples, respectively, was determined as the best pre-processing method in orthogonal partial least squares-discriminant analysis. Among the various tested algorithms, support vector machine (SVM) was the best algorithm for predicting the geographical origin of white and brown rice, with an accuracy of 0.99 and 0.96, respectively. In external validation, the SVM-based prediction model for white and brown rice showed good performance, with an accuracy of 1.0. The results of this study suggest the potential application of machine learning techniques based on NMR data for the differentiation and prediction of diverse geographical origins of white and brown rice.

Development and metabolic profiling of a postbiotic complex exhibiting antibacterial activity against skin microorganisms and anti-inflammatory effect on human keratinocytes.

Beyond probiotics, the interest in the application of postbiotics to various fields has been growing. We aimed to develop a novel postbiotic complex (PC) with antibacterial and anti-inflammatory properties. Through antibacterial activity testing against Staphylococcus aureus or Cutibacterium acnes, a PC [a mixture of cell-free supernatants (postbiotics) from probiotic Lactobacillus helveticus (HY7801) and Lactococcus lactis (HY449)] was developed. Anti-inflammatory activity of the PC was investigated using HaCaT keratinocytes treated with S. aureus or C. acnes. PC significantly decreased IL-8 levels and increased hyaluronic acid levels in HaCaT cells cultured with S. aureus or C. acnes. GC-MS based metabolic profiling suggested 2-hydroxyisocaproic acid, hypoxanthine, succinic acid, ornithine, and γ-aminobutyric acid as potential contributing metabolites for the antibacterial and anti-inflammatory effects of PC. The PC developed in this study could be utilized in food, cosmetics, and pharmaceutical products as an alternative or complementary resources of probiotics. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-022-01123-x.

Right Unilateral Versus Bilateral Electroconvulsive Therapy in Patients With Clinical Depression.

Introduction Electroconvulsive therapy (ECT) is a functional treatment for a significant mental illness that involves a momentary application of electrical stimulation to induce generalized seizures. The use of right unilateral (RUL) and bilateral (BL) ECT has been controversial. Thus, the study aimed at comparing the effectiveness of RUL ECT and BL ECT in treating depression. Methodology A longitudinal study was conducted between September 2016 and January 2021 at a tertiary care hospital in Sindh, Pakistan. All patients over the age of 18 with clinically diagnosed depression in the last month were included in the study. Baseline depression scores and post-treatment scores were determined using Hamilton Depression Rating Scale (HDRS). All patients were assigned to each treatment group. Group A was administered right unilateral electroconvulsive therapy, while group B was administered bilateral electroconvulsive therapy. Adverse effects were documented right after treatment, at four hours, and then one day after therapy. Depression severity was determined after each ECT session using the HDRS scale. Electroconvulsive therapy was discontinued when an HDRS score of 10 was achieved.  Results  The mean HDRS score at baseline in the bilateral ECT group was 24.99 ± 3.938, which lowered to 17.56 ± 2.65 by the 3rd session, 12.45 ± 3.76 by the 6th session, and to 11.86 ± 2.3 by the end of treatment (p<0.0001). Similarly, the right unilateral ECT was equally effective in improving the depressive symptoms (p<0.0001). There was no significant difference between the efficacy of bilateral and unilateral placements of electrodes in electroconvulsive therapy (p=0.116).

Duckweeds: their utilization, metabolites and cultivation.

Duckweeds are floating plants of the family Lemnaceae, comprising 5 genera and 36 species. They typically live in ponds or lakes and are found worldwide, except the polar regions. There are two duckweed subfamilies-namely Lemnoidea and Wolffioideae, with 15 and 21 species, respectively. Additionally, they have characteristic reproduction methods. Several metabolites have also been reported in various duckweeds. Duckweeds have a wide range of adaptive capabilities and are particularly suitable for experiments requiring high productivity because of their speedy growth and reproduction rates. Duckweeds have been studied for their use as food/feed resources and pharmaceuticals, as well as for phytoremediation and industrial applications. Because there are numerous duckweed species, culture conditions should be optimized for industrial applications. Here, we review and summarize studies on duckweed species and their utilization, metabolites, and cultivation methods to support the extended application of duckweeds in future.

Genome-wide methylation profiling of HCV pathogenesis to develop diabetes and diabetic complications.

HCV is key pathological factor for inducting insulin resistance. Such HCV-induced insulin resistance is linked with metabolic syndrome, type 2 diabetes mellitus, extrahepatic manifestations, hepatic fibrosis progression and development of hepatocellular carcinoma. DNA methylation alterations can cause developmental abnormalities, tumours and other diseases. In our study, PBMCs were isolated and genomic DNA was extracted. DNA fragmentation was achieved by sonication to 200-400 bp; subsequently, end repair and adenylation was performed. Manufacturer's guidelines were followed to ligate Cytosine-methylated barcodes to sonicated DNA. EZ DNA Methylation-GoldTM Kit was then employed to treat these DNA segments twice with bisulphite. A Library was maintained, sequenced on an Illumina platform and 150/125 bp paired-end reads generated. GO seq R package was used to perform Gene Ontology (GO) enrichment analysis for genes linked to DMRs and DMPs; gene length bias was corrected. We identified 12 945 significant hypermethylated DMRs among all samples that were screened as those with at least 0.1 methylation level differences and P-value less than 0.05. Fisher's exact test with FDR multiple test correction is used for identification of DMPs and DMRs. High throughput bisulphite sequencing (Illumina) was carried out, and bioinformatics analysis was performed to analyse methylation status. Gene ontology (GO) and KEGG pathway enrichment analysis showed differentially methylated regions enriched in various pathways that include PI3K-AKT/IRS1 signalling pathway, metabolic pathway, oxidative phosphorylation, Renin-angiotensin system that are all involved in developing type-2 diabetes (T2D). Our study provides supporting evidence for significant involvement of HCV infection in development of epigenetic modifications in regulation of metabolic disorders like T2D and its complications.