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Despite many recent studies on non-alcoholic fatty liver disease (NAFLD) therapeutics, the optimal treatment has yet to be determined. In this unfinished project, we combined secondary metabolites (SMs) from the gut microbiota (GM) and Hordeum vulgare (HV) to investigate their combinatorial effects via network pharmacology (NP). Additionally, we analyzed GM or barley – signalling pathways – targets – metabolites (GBSTMs) in combinatorial perspectives (HV, and GM). A total of 31 key targets were analysed via a protein-protein interaction (PPI) network, and JUN was identified as the uppermost target in NAFLD. On a bubble plot, we revealed that apelin signalling pathway, which had the lowest enrichment factor antagonize NAFLD. Holistically, we scrutinized GBSTM to identify key components (GM, signalling pathways, targets, and metabolites) associated with the Apelin signalling pathway. Consequently, we found that the primary GMs (Eubacterium limosum, Eggerthella sp. SDG-2, Alistipes indistinctus YIT 12060, Odoribacter laneus YIT 12061, Paraprevotella clara YIT 11840, Paraprevotella xylaniphila YIT 11841) to ameliorate NAFLD. The molecular docking test (MDT) suggested that tryptanthrin-JUN is an agonist, conversely, dihydroglycitein-HDAC5, 1,3-diphenylpropan-2-ol-NOS1, and (10[(Acetyloxy)methyl]-9-anthryl)methyl acetate-NOS2, which are antagonistic conformers in the apelin signalling pathway. Overall, these results suggest that combination therapy could be an effective strategy for treating NAFLD.

AbstractWe comprised metabolites of gut microbiota (GM; endogenous species) and dietary plant-derived natural flavonoids (DPDNFs; exogenous species) were known as potent effectors against non-alcoholic fatty liver disease (NAFLD) via network pharmacology (NP). The crucial targets against NAFLD were identified via GM and DPDNFs. The protein interaction (PPI), bubble chart and networks of GM or natural products- metabolites-targets-key signalling (GNMTK) pathway were described via R Package. Furthermore, the molecular docking test (MDT) to verify the affinity was performed between metabolite(s) and target(s) on a key signalling pathway. On the networks of GNMTK, Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1 as key microbiota; flavonoid-rich products as key natural resources; luteolin and myricetin as key metabolites (or dietary flavonoids); AKT Serine/Threonine Kinase 1 (AKT1), CF Transmembrane conductance Regulator (CFTR) and PhosphoInositide-3-Kinase, Regulatory subunit 1 (PIK3R1) as key targets are promising components to treat NAFLD, by suppressing cyclic Adenosine MonoPhosphate (cAMP) signalling pathway. This study shows that components (microbiota, metabolites, targets and a key signalling pathway) and DPDNFs can exert combinatorial pharmacological effects against NAFLD. Overall, the integrated pharmacological approach sheds light on the relationships between GM and DPDNFs.

AbstractWe intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein–protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.

This study investigates the implementation of a cryogenic chiller utilizing a mixed-refrigerant cascade refrigeration cycle (MRCRC). In this setup, R-404A is employed in the high-temperature circuit (HTC), while a mixture of refrigerants is utilized in the low-temperature circuit (LTC). Unlike a conventional MRCRC that operates without a receiver to maintain the composition ratio, this research explores the impact of receiver installation on system performance. Experiments were conducted with and without a receiver to assess performance improvements and device behavior. With a fixed refrigerant charge of 4 kg, the suction and discharge pressures of the LTC compressor remained low and stable after the receiver’s installation. The addition of a receiver significantly reduced the cooling time, with further reductions observed as the refrigerant charge increased. The system achieved evaporative heat capacities of 0.59, 1.76, and 2 kW for refrigerant charges of 4, 7, and 9 kg, respectively. Notably, at the maximum refrigerant charge of 11 kg, the evaporative heat capacity peaked at 3.3 kW. These findings indicate that incorporating a receiver is crucial for enhancing the cooling performance of cryogenic coolers using mixed refrigerants and stabilizing device operation. This contrasts with previous studies that omitted receivers due to concerns over potential alterations in the composition ratio of the mixed refrigerant.

Abstract Persea americana fruit (PAF) is a favorable nutraceutical resource that comprises diverse unsaturated fatty acids (UFAs). UFAs are significant dietary supplementation, as they relieve metabolic disorders, including obesity (OB). In another aspect, this study was focused on the anti‐OB efficacy of the non‐fatty acids (NFAs) in PAF through network pharmacology (NP). Natural product activity & species source (NPASS), SwissADME, similarity ensemble approach (SEA), Swiss target prediction (STP), DisGeNET, and online Mendelian inheritance in man (OMIM) were utilized to gather significant molecules and its targets. The crucial targets were adopted to construct certain networks: protein–protein interaction (PPI), PAF‐signaling pathways‐targets‐compounds (PSTC) networks, a bubble chart, molecular docking assay (MDA), and density function theory (DFT). Finally, the toxicities of the key compounds were validated by ADMETlab 2.0 platform. All 41 compounds in PAF conformed to Lipinski's rule, and the key 31 targets were identified between OB and PAF. On the bubble chart, PPAR signaling pathway had the highest rich factor, suggesting that the pathway might be an agonism for anti‐OB. Conversely, estrogen signaling pathway had the lowest rich factor, indicating that the mechanism might be antagonism against OB. Likewise, the PSTC network represented that AKT1 had the greatest degree value. The MDA results showed that AKT1‐gamma‐tocopherol, PPARA‐fucosterol, PPARD‐stigmasterol, (PPARG)‐fucosterol, (NR1H3)‐campesterol, and ILK‐alpha‐tocopherol formed the most stable conformers. The DFT represented that the five molecules might be promising agents via multicomponent targeting. Overall, this study suggests that the NFAs in PAF might play important roles against OB.

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is a serious public health issue globally, currently, the treatment of NAFLD lies still in the labyrinth. In the inchoate stage, the combinatorial application of food regimen and favorable gut microbiota (GM) are considered as an alternative therapeutic. Accordingly, we integrated secondary metabolites (SMs) from GM and Avena sativa (AS) known as potent dietary grain to identify the combinatorial efficacy through network pharmacology. Methods We browsed the SMs of AS via Natural Product Activity & Species Source (NPASS) database and SMs of GM were retrieved by gutMGene database. Then, specific intersecting targets were identified from targets related to SMs of AS and GM. The final targets were selected on NAFLD-related targets, which was considered as crucial targets. The protein–protein interaction (PPI) networks and bubble chart analysis to identify a hub target and a key signaling pathway were conducted, respectively. In parallel, we analyzed the relationship of GM or AS─a key signaling pathway─targets─SMs (GASTM) by merging the five components via RPackage. We identified key SMs on a key signaling pathway via molecular docking assay (MDA). Finally, the identified key SMs were verified the physicochemical properties and toxicity in silico platform. Results The final 16 targets were regarded as critical proteins against NAFLD, and Vascular Endothelial Growth Factor A (VEGFA) was a key target in PPI network analysis. The PI3K-Akt signaling pathway was the uppermost mechanism associated with VEGFA as an antagonistic mode. GASTM networks represented 122 nodes (60 GM, AS, PI3K-Akt signaling pathway, 4 targets, and 56 SMs) and 154 edges. The VEGFA-myricetin, or quercetin, GSK3B-myricetin, IL2-diosgenin complexes formed the most stable conformation, the three ligands were derived from GM. Conversely, NR4A1-vestitol formed stable conformation with the highest affinity, and the vestitol was obtained from AS. The given four SMs were no hurdles to develop into drugs devoid of its toxicity. Conclusion In conclusion, we show that combinatorial application of AS and GM might be exerted to the potent synergistic effects against NAFLD, dampening PI3K-Akt signaling pathway. This work provides the importance of dietary strategy and beneficial GM on NAFLD, a data mining basis for further explicating the SMs and pharmacological mechanisms of combinatorial application (AS and GM) against NAFLD.

Alcoholic liver disease (ALD) is linked to a broad spectrum of diseases, including diabetes, hypertension, atherosclerosis, and even liver carcinoma. The ALD spectrum includes alcoholic fatty liver disease (AFLD), alcoholic hepatitis, and cirrhosis. Most recently, some reports demonstrated that the pathogenesis of ALD is strongly associated with metabolites of human microbiota. AFLD was the onset of disease among ALDs, the initial cause of which is alcohol consumption. Thus, we analyzed the significant metabolites of microbiota against AFLD via the network pharmacology concept. The metabolites from microbiota were retrieved by the gutMGene database; sequentially, AFLD targets were identified by public databases (DisGeNET, OMIM). The final targets were utilized for protein–protein interaction (PPI) networks and signaling pathway analyses. Then, we performed a molecular docking test (MDT) to verify the affinity between metabolite(s) and target(s) utilizing the Autodock 1.5.6 tool. From a holistic viewpoint, we integrated the relationships of microbiota-signaling pathways-targets-metabolites (MSTM) using the R Package. We identified the uppermost six key targets (TLR4, RELA, IL6, PPARG, COX-2, and CYP1A2) against AFLD. The PPI network analysis revealed that TLR4, RELA, IL6, PPARG, and COX-2 had equivalent degrees of value (4); however, CYP1A2 had no associations with the other targets. The bubble chart showed that the PI3K-Akt signaling pathway in nine signaling pathways might be the most significant mechanism with antagonistic functions in the treatment of AFLD. The MDT confirmed that Icaritin is a promising agent to bind stably to RELA (known as NF-Κb). In parallel, Bacterium MRG-PMF-1, the PI3K-Akt signaling pathway, RELA, and Icaritin were the most significant components against AFLD in MSTM networks. In conclusion, we showed that the Icaritin–RELA complex on the PI3K-Akt signaling pathway by bacterial MRG-PMF-1 might have promising therapeutic effects against AFLD, providing crucial evidence for further research.

The metabolites produced by the gut microbiota have been reported as crucial agents against obesity; however, their key targets have not been revealed completely in complex microbiome systems. Hence, the aim of this study was to decipher promising prebiotics, probiotics, postbiotics, and more importantly, key target(s) via a network pharmacology approach. First, we retrieved the metabolites related to gut microbes from the gutMGene database. Then, we performed a meta-analysis to identify metabolite-related targets via the similarity ensemble approach (SEA) and SwissTargetPrediction (STP), and obesity-related targets were identified by DisGeNET and OMIM databases. After selecting the overlapping targets, we adopted topological analysis to identify core targets against obesity. Furthermore, we employed the integrated networks to microbiota–substrate–metabolite–target (MSMT) via R Package. Finally, we performed a molecular docking test (MDT) to verify the binding affinity between metabolite(s) and target(s) with the Autodock 1.5.6 tool. Based on holistic viewpoints, we performed a filtering step to discover the core targets through topological analysis. Then, we implemented protein–protein interaction (PPI) networks with 342 overlapping target, another subnetwork was constructed with the top 30% degree centrality (DC), and the final core networks were obtained after screening the top 30% betweenness centrality (BC). The final core targets were IL6, AKT1, and ALB. We showed that the three core targets interacted with three other components via the MSMT network in alleviating obesity, i.e., four microbiota, two substrates, and six metabolites. The MDT confirmed that equol (postbiotics) converted from isoflavone (prebiotics) via Lactobacillus paracasei JS1 (probiotics) can bind the most stably on IL6 (target) compared with the other four metabolites (3-indolepropionic acid, trimethylamine oxide, butyrate, and acetate). In this study, we demonstrated that the promising substate (prebiotics), microbe (probiotics), metabolite (postbiotics), and target are suitable for obsesity treatment, providing a microbiome basis for further research.

Non-alcoholic fatty liver disease (NAFLD) is considered to be a significant health threat globally, and has attracted growing concern in the research field of liver diseases. NAFLD comprises multifarious fatty degenerative disorders in the liver, including simple steatosis, steatohepatitis and fibrosis. The fundamental pathophysiology of NAFLD is complex and multifactor-driven. In addition to viruses, metabolic syndrome and alcohol, evidence has recently indicated that the microbiome is related to the development and progression of NAFLD. In this review, we summarize the possible microbiota-based therapeutic approaches and highlight the importance of establishing the diagnosis of NAFLD through the different spectra of the disease via the gut–liver axis.

Prostaglandin (PG) A2, one of cyclopentenone PGs, is known to induce activation of apoptosis in various cancer cells. Although PGA2 has been reported to cause activation of apoptosis by altering the expression of apoptosis-related genes, the role of p53, one of the most critical pro-apoptotic genes, on PGA2-induced apoptosis has not been clarified yet. To address this issue, we compared the apoptosis in HCT116 p53 null cells (HCT116 p53-/-) to that in HCT116 cells containing the wild type p53 gene. Cell death induced by PGA2 was associated with phosphorylation of histone H2A variant H2AX (H2AX), activation of caspase-3 and cleavage of poly(ADP-ribose) polymerase 1 in HCT116 cells. Induction of apoptosis in PGA2-treated cells was almost completely prevented by pretreatment with a pan-caspase inhibitor, z-VAD-Fmk, or an inhibitor of protein synthesis, cycloheximide. While PGA2 induced apoptosis in HCT116 cells, phosphorylation of p53 and transcriptional induction of p53-target genes such as p21WAF1, PUMA, BAX, NOXA, and DR5 occurred. Besides, pretreatment of pifithrin-α (PFT-α), a chemical inhibitor of p53’s transcriptional activity, interfered with the induction of apoptosis in PGA2-treated HCT116 cells. Pretreatment of NU7441, a small molecule inhibitor of DNA-activated protein kinase (DNA-PK) suppressed PGA2-induced phosphorylation of p53 and apoptosis as well. Moreover, among target genes of p53, knockdown of DR5 expression by RNA interference, suppressed PGA2-induced apoptosis. In the meanwhile, in HCT116 p53-/- cells, PGA2 induced apoptosis in delayed time points and with less potency. Delayed apoptosis by PGA2 in HCT116 p53-/- cells was also associated with phosphorylation of H2AX but was not inhibited by either PFT-α or NU7441. Collectively, these results suggest the following. PGA2 may induce p53-dependent apoptosis in which DNA-PK activates p53, and DR5, a transcriptional target of p53, plays a pivotal role in HCT116 cells. In contrast to apoptosis in HCT116 cells, PGA2 may induce apoptosis in a fashion of less potency, which is independent of p53 and DNA-PK in HCT116 p53-/- cells

Purpose: To evaluate the usefulness of ridge augmentation using a customized titanium mesh (CTM) that was preformed by trimming and bending the commercial titanium mesh on a virtually reconstructed 3D acrylic resin model using clinical, radiologic, histologic, and histomorphometric analyses. Materials and Methods: This study was designed prospectively for patients who required vertical ridge augmentation using a staged approach before implant surgery. After installation of the CTM, grafting was performed using deproteinized porcine bone mineral covered with an absorbable membrane. Computed tomography was performed preoperatively and 6 months after simultaneous/staged guided bone regeneration to measure planned, reconstructed, and lacking bone volume, and the reconstruction rate was calculated based on these values. Clinical complications were also recorded, particularly the mesh exposure rate. At re-entry, the bone core was obtained using a trephine bur, and histologic and histomorphometric analyses were performed. Results: A total of 10 sites in eight patients were used for the study analysis. The mean planned bone volume was 1.15 cm3 (range: 0.78 to 1.56 cm3), mean lacking bone volume was 0.13 cm3 (range: 0 to 0.59 cm3), and mean reconstructed bone volume was 1.02 cm3 (range: 0.56 to 1.43 cm3). The exposure rate was 30% (3 out of 10 sites). The reconstruction rate was over 80%, except for one case that showed suppuration. From histomorphometric analysis, 27.52% ± 16.87% of new bone, 7.62% ± 5.19% of residual graft, and 64.86% ± 23.76% of connective tissue were observed. The core biopsy samples demonstrated different pseudoperiosteum layer appearances based on the healing stage of the augmented sites. In the premature bone, the inner osteogenic layer consisted of multiple layers of osteoblast cells with adjacent large blood vessels. However, in the mature augmented site, there was no specific inner osteogenic layer, and the outer fibrous layer was dominant. Conclusions: The fabrication of CTM based on the application of the 3D-printing technique makes vertical ridge augmentation easier and can reduce complications and achieve target bone acquisition. In addition, it is expected that quantitative analysis of the pseudoperiosteum layer will be facilitated using the CTM. [ABSTRACT FROM AUTHOR]