Identification of differentially expressed mRNA/lncRNA modules in acutely regorafenib-treated sorafenib-resistant Huh7 hepatocellular carcinoma cells.

The multikinase inhibitor sorafenib is the standard first-line treatment for advanced hepatocellular carcinoma (HCC), but many patients become sorafenib-resistant (SR). This study investigated the efficacy of another kinase inhibitor, regorafenib (Rego), as a second-line treatment. We produced SR HCC cells, wherein the PI3K-Akt, TNF, cAMP, and TGF-beta signaling pathways were affected. Acute Rego treatment of these cells reversed the expression of genes involved in TGF-beta signaling but further increased the expression of genes involved in PI3K-Akt signaling. Additionally, Rego reversed the expression of genes involved in nucleosome assembly and epigenetic gene expression. Weighted gene co-expression network analysis (WGCNA) revealed four differentially expressed long non-coding RNA (DElncRNA) modules that were associated with the effectiveness of Rego on SR cells. Eleven putative DElncRNAs with distinct expression patterns were identified. We associated each module with DEmRNAs of the same pattern, thus obtaining DElncRNA/DEmRNA co-expression modules. We discuss the potential significance of each module. These findings provide insights and resources for further investigation into the potential mechanisms underlying the response of SR HCC cells to Rego.

Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES).

BACKGROUND: Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). RESULTS: First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen's Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. CONCLUSIONS: Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods.

Enhanced methane production with co-feeding spent coffee grounds using spare capacity of existing anaerobic food waste digesters.

With increasing coffee consumption worldwide, the efficient and sustainable management of spent coffee grounds (SCG) has become increasingly challenging. This study investigated the anaerobic co-digestion of small amounts of SCG with food waste (FW) at increasing co-feeding ratios of 1:100-1:10 (volatile solids basis) to assess the possibility of SCG treatment using the spare capacity of existing anaerobic digesters. Co-feeding SCG increased methane production compared to FW mono-digestion in the tested range of co-feeding ratios without compromising process stability. Methane yield did not further increase when the SCG/FW ratio increased above 4%, and process failure occurred at a 1:10 co-feeding ratio without trace element supplementation. The enhanced methanogenic performance was attributed to increased protein removal efficiency, which was potentially related to the promotion of peptide hydrolysis. The overall results suggest that co-feeding appropriate small amounts of SCG to FW digesters can be a realistic sustainable option for SCG management.

Comparative study of exoelectrogenic utilization preferences and hydrogen conversion among major fermentation products in microbial electrolysis cells.

This study comparatively investigated the exoelectrogenic utilization and hydrogen conversion of major dark fermentation products (acetate, propionate, butyrate, lactate, and ethanol) from organic wastes in dual-chamber microbial electrolysis cells (MECs) alongside their mixture as a simulated dark fermentation effluent (DFE). Acetate-fed MECs showed the highest hydrogen yield (1,465 mL/g chemical oxygen demand), near the theoretical maximum yield, with the highest coulombic efficiency (105%) and maximum current density (7.9 A/m2), followed by lactate-fed, propionate-fed, butyrate-fed, mixture-fed, and ethanol-fed MECs. Meanwhile, the highest hydrogen production rate (514 mL/L anolyte∙d) was observed in ethanol-fed MECs despite their lower coulombic efficiency. Butyrate was the least favored substrate, followed by propionate, leading to significantly delayed startup and reaction. The active anodic microbial community structure varied considerably among the MECs utilizing different substrates, particularly between Geobacter and Acetobacterium dominance. The results highlight the substantial effect of the DFE composition on its utilization and current-producing bioanode development.

A study of electron source preference and its impact on hydrogen production in microbial electrolysis cells fed with synthetic fermentation effluent.

Fermentation effluents from organic wastes contain simple organic acids and ethanol, which are good electron sources for exoelectrogenic bacteria, and hence are considered a promising substrate for hydrogen production in microbial electrolysis cells (MECs). These fermentation products have different mechanisms and thermodynamics for their anaerobic oxidation, and therefore the composition of fermentation effluent significantly influences MEC performance. This study examined the microbial electrolysis of a synthetic fermentation effluent (containing acetate, propionate, butyrate, lactate, and ethanol) in two-chamber MECs fitted with either a proton exchange membrane (PEM) or an anion exchange membrane (AEM), with a focus on the utilization preference between the electron sources present in the effluent. Throughout the eight cycles of repeated batch operation with an applied voltage of 0.8 V, the AEM-MECs consistently outperformed the PEM-MECs in terms of organic removal, current generation, and hydrogen production. The highest hydrogen yield achieved for AEM-MECs was 1.26 L/g chemical oxygen demand (COD) fed (approximately 90% of the theoretical maximum), which was nearly double the yield for PEM-MECs (0.68 L/g COD fed). The superior performance of AEM-MECs was attributed to the greater pH imbalance and more acidic anodic pH in PEM-MECs (5.5-6.0), disrupting anodic respiration. Although butyrate is more thermodynamically favorable than propionate for anaerobic oxidation, butyrate was the least favored electron source, followed by propionate, in both AEM- and PEM-MECs, while ethanol and lactate were completely consumed. Further research is needed to better comprehend the preferences for different electron sources in fermentation effluents and enhance their microbial electrolysis.

Gene-Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors.

Asthma is a complex heterogeneous disease caused by gene-environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene-environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel (SETDB1 and ZNF8) and five previously reported (DM4C, DOCK8, MMP20, MYL7, and ADCY9) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma.

Hydrogen sensing under ambient conditions using SnO2 nanowires: synergetic effect of Pd/Sn codeposition.

Semiconducting SnO2 nanowires deposited with Pd and Sn nanoparticles on their surface are shown to be a highly sensitive hydrogen sensor with fast response time at room temperature. Compared with the SnO2 nanowire deposited with Pd or Sn nanoparticles alone, the Pd/Sn-deposited SnO2 nanowire exhibits a significant improvement in the sensitivity and reversibility of sensing hydrogen gas in the air at room temperature. Our investigation indicates that two factors are responsible for the synergistic effect of Pd/Sn codeposition on SnO2 nanowires. One is that in the presence of Pd the oxidation of Sn nanoparticles on the surface of the SnO2 nanowire is incomplete leading only to suboxides SnOx (1 ≤ x < 2), and the other is that the surface of the Pd/Sn-deposited SnO2 nanowire is almost perfectly hydrophobic.