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Background/Objectives: Recently, antimicrobial-resistant pathogens and cancers have emerged as serious global health problems, highlighting the immediate need for novel therapeutics. Consequently, we aimed to isolate and characterize endophytic Streptomyces strains from the rhizospheres of the Himalayan region of Nepal and identify specialized metabolites with antibacterial, antifungal, and cytotoxic potential. Methods: To isolate Streptomyces sp., we collected two soil samples and cultured them on an ISP4 medium after pretreatment. We isolated and identified the strains PY108 and PY109 using a combination of morphological observations and 16S rRNA gene sequencing. Results: The BLAST results showed that PY108 and PY109 resembled Streptomyces hundungensis PSB170 and Streptomyces sp. Ed-065 with 99.28% and 99.36% nucleotide similarity, respectively. Antibacterial assays of ethyl acetate (EA) extracts from both isolates PY108 and PY109 in a tryptic soy broth (TSB) medium were conducted against four pathogenic bacteria. They showed significant antibacterial potential against Staphylococcus aureus and Klebsiella pneumoniae. Similarly, these extracts exhibited moderate antifungal activities against Saccharomyces cerevisiae and Aspergillus niger. Cytotoxicity assays on cervical cancer cells (HeLa) and breast cancer cells (MCF-7) revealed significant potential for both extracts. LC-MS/MS profiling of the EA extracts identified 27 specialized metabolites, including diketopiperazine derivatives, aureolic acid derivatives such as chromomycin A, and lipopeptide derivatives. In comparison, GC-MS analysis detected 34 metabolites, including actinomycin D and γ-sitosterol. Furthermore, a global natural product social molecular networking (GNPS)-based molecular networking analysis dereplicated 24 metabolites in both extracts. Conclusions: These findings underscore the potential of endophytic Streptomyces sp. PY108 and PY109 to develop new therapeutics in the future.

Ginkgo biloba L. stands as one of the oldest living tree species, exhibiting a diverse range of biological activities, including antioxidant, neuroprotective, anti-inflammatory, and cardiovascular activities. As part of our ongoing discovery of novel bioactive components from natural sources, we directed our focus toward the investigation of potential bioactive compounds from G. biloba fruit. The profiles of its chemical compounds were examined using a Global Natural Products Social (GNPS)-based molecular networking analysis. Guided by this, we successfully isolated and characterized 11 compounds from G. biloba fruit, including (E)-coniferin (1), syringin (2), 4-hydroxybenzoic acid 4-O-β-D-glucopyranoside (3), vanillic acid 4-O-β-D-glucopyranoside (4), syringic acid 4-O-β-D-glucopyranoside (5), (E)-ferulic acid 4-O-β-D-glucoside (6), (E)-sinapic acid 4-O-β-D-glucopyranoside (7), (1′R,2′S,5′R,8′S,2′Z,4′E)-dihydrophaseic acid 3′-O-β-D-glucopyranoside (8), eucomic acid (9), rutin (10), and laricitrin 3-rutinoside (11). The structural identification was validated through a comprehensive analysis involving nuclear magnetic resonance (NMR) spectroscopic data and LC/MS analyses. All isolated compounds were evaluated using an E-screen assay for their estrogen-like effects in MCF-7 cells. As a result, compounds 2, 3, 4, 8, and 9 promoted cell proliferation in MCF-7 cells, and these effects were mitigated by the ER antagonist, ICI 182,780. In particular, cell proliferation increased most significantly to 140.9 ± 6.5% after treatment with 100 µM of compound 2. The mechanism underlying the estrogen-like effect of syringin (2) was evaluated using a Western blot analysis to determine the expression of estrogen receptor α (ERα). We found that syringin (2) induced an increase in the phosphorylation of ERα. Overall, these experimental results suggest that syringin (2) can potentially aid the control of estrogenic activity during menopause.

Human skin comprises the epidermis and dermis, which perform interactive functional activities with each other in order to maintain the skin’s tensile strength. In particular, the dermal layer is crucial for skin protection. However, skin aging destroys collagen and elastin fibers, causing wrinkles, pigments, and sagging. Skin aging-related factors, such as tumor necrosis factor-α (TNF-α), promote the generation of intercellular reactive oxygen species (ROS). These are known to stimulate the hypersecretion of matrix metalloproteinase-1 (MMP-1), which degrades collagen and inhibits collagen synthesis. In this study, as part of our ongoing discovery of natural products, we investigated potential natural products derived from ginkgo fruit (Ginkgo biloba fruit) with protective effects against TNF-α-induced skin aging. Phytochemical investigation of the MeOH extract of G. biloba fruits, aided by liquid chromatography–mass spectrometry, led to the isolation of 14 compounds (1–14) from the n-butanol-soluble fraction. These were structurally determined to be: (E)-coniferin (1), syringin (2), 4-hydroxybenzoic acid 4-O-β-D-glucopyranoside (3), vanillic acid 4-O-β-D-glucopyranoside (4), glucosyringic acid (5), (E)-ferulic acid 4-O-β-D-glucoside (6), (E)-sinapic acid 4-O-β-D-glucopyranoside (7), ginkgotoxin-5-glucoside (8), ginkgopanoside (9), (Z)-4-coumaric acid 4-O-β-D-glucopyranoside (10), (1′R,2′S,5′R,8′S,2′Z,4′E)-dihydrophaseic acid 3’-O-β-D-glucopyranoside (11), eucomic acid (12), rutin (13), and laricitrin 3-rutinoside (L3R) (14). Biological evaluation of the isolated compounds for their effects on intracellular ROS generation showed that, of these 14 compounds, L3R (14) inhibited TNF-α-stimulated ROS generation (p < 0.001 at 100 μM). Inhibition of ROS generation by L3R led to the suppression of MMP-1 secretion and protection against collagen degradation. The inhibitory effect of L3R was mediated by the inhibition of extracellular signal regulated kinase (ERK) phosphorylation. Furthermore, L3R diminished the secretion of pro-inflammatory cytokines interleukin 6 (IL-6) and interleukin 8 (IL-8). Based on these experimental results, L3R is a potential bioactive natural product that can be used to protect against skin damage, including aging, in cosmetics and pharmaceuticals.

Kaempferia parviflora Wall. ex Baker (Zingiberaceae), commonly known as Thai ginseng or black ginger, is a tropical medicinal plant in many regions. It has been traditionally used to treat various ailments, including ulcers, dysentery, gout, allergies, abscesses, and osteoarthritis. As part of our ongoing phytochemical study aimed at discovering bioactive natural products, we investigated potential bioactive methoxyflavones from K. parviflora rhizomes. Phytochemical analysis aided by liquid chromatography–mass spectrometry (LC-MS) led to the isolation of six methoxyflavones (1–6) from the n-hexane fraction of the methanolic extract of K. parviflora rhizomes. The isolated compounds were structurally determined to be 3,7-dimethoxy-5-hydroxyflavone (1), 5-hydroxy-7-methoxyflavone (2), 7,4′-dimethylapigenin (3), 3,5,7-trimethoxyflavone (4), 3,7,4′-trimethylkaempferol (5), and 5-hydroxy-3,7,3′,4′-tetramethoxyflavone (6), based on NMR data and LC-MS analysis. All of the isolated compounds were evaluated for their anti-melanogenic activities. In the activity assay, 7,4′-dimethylapigenin (3) and 3,5,7-trimethoxyflavone (4) significantly inhibited tyrosinase activity and melanin content in IBMX-stimulated B16F10 cells. In addition, structure–activity relationship analysis revealed that the methoxy group at C-5 in methoxyflavones is key to their anti-melanogenic activity. This study experimentally demonstrated that K. parviflora rhizomes are rich in methoxyflavones and can be a valuable natural resource for anti-melanogenic compounds.

Background: Genomic instability of N6-methyladenosine (m6A)–related long noncoding RNAs (lncRNAs) plays a pivotal role in the tumorigenesis of lung adenocarcinoma (LUAD). Our study identified a signature of genomic instability of m6A-associated lncRNA signature and revealed its prognostic role in LUAD.Methods: We downloaded RNA-sequencing data and somatic mutation data for LUAD from The Cancer Genome Atlas (TCGA) and the GSE102287 dataset from the Gene Expression Omnibus (GEO) database. The “Limma” R package was used to identify a network of regulatory m6A-related lncRNAs. We used the Wilcoxon test method to identify genomic-instability–derived m6A-related lncRNAs. A competing endogenous RNA (ceRNA) network was constructed to identify the mechanism of the genomic instability of m6A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct a prognostic model for internal testing and validation of the prognostic m6A-related lncRNAs using the GEO dataset. Performance analysis was conducted to compare our prognostic model with the previously published lncRNA models. The CIBERSORT algorithm was used to explore the relationship of m6A-related lncRNAs and the immune microenvironment. Prognostic m6A-related lncRNAs in prognosis, the tumor microenvironment, stemness scores, and anticancer drug sensitivity were analyzed to explore the role of prognostic m6A-related lncRNAs in LUAD.Results: A total of 42 genomic instability–derived m6A-related lncRNAs were differentially expressed between the GS (genomic stable) and GU (genomic unstable) groups of LUAD patients. Four differentially expressed lncRNAs, 17 differentially expressed microRNAs, and 75 differentially expressed mRNAs were involved in the genomic-instability–derived m6A-related lncRNA-mediated ceRNA network. A prediction model based on 17 prognostic m6A-associated lncRNAs was constructed based on three TCGA datasets (all, training, and testing) and validated in the GSE102287 dataset. Performance comparison analysis showed that our prediction model (area under the curve [AUC] = 0.746) could better predict the survival of LUAD patients than the previously published lncRNA models (AUC = 0.577, AUC = 0.681). Prognostic m6A-related-lncRNAs have pivotal roles in the tumor microenvironment, stemness scores, and anticancer drug sensitivity of LUAD.Conclusion: A signature of genomic instability of m6A-associated lncRNAs to predict the survival of LUAD patients was validated. The prognostic, immune microenvironment and anticancer drug sensitivity analysis shed new light on the potential novel therapeutic targets in LUAD.

Background: Autophagy plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the autophagy-related gene (ARG) expression pattern and to identify promising autophagy-related biomarkers to improve the prognosis of LUAD.Methods: The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. The Human Autophagy Database (HADb) was used to extract ARGs. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the differentially expressed ARGs (DEARGs). Then, consensus clustering revealed autophagy-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, the univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic ARGs. After overlapping DEGs and the independent prognostic ARGs, the predictive risk model was established and validated. Correlation analyses between ARGs and clinicopathological variables were also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels and the risk score as well as clinicopathological variables in the predictive risk model.Results: A total of 222 genes from the HADb were identified as ARGs, and 28 of the 222 genes were pooled as DEARGs. The most significant GO term was autophagy (p = 3.05E-07), and KEGG analysis results indicated that 28 DEARGs were significantly enriched in the ErbB signaling pathway (p < 0.001). Then, consensus clustering analysis divided the LUAD into two clusters, and a total of 168 DEGs were identified according to cluster subtypes. Then univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators. After overlapping 168 DEGs and 12 genes, 10 genes (ATG4A, BAK1, CAPNS1, CCR2, CTSD, EIF2AK3, ITGB1, MBTPS2, SPHK1, ST13) were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model identified the prognosis (p = 4.379E-10). Combined with the correlation analysis results between ARGs and clinicopathological variables, five ARGs were screened as prognostic genes. Among them, SPHK1 expression levels were positively correlated with CD4+ T cells and dendritic cell infiltration levels.Conclusions: In this study, we constructed a predictive risk model and identified a five autophagy subtype-related gene expression pattern to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful to accurately characterize the LUAD and develop personalized treatment.

Increasing studies have proved that malignant tumors are associated with energy metabolism. This study was aimed to explore biological variables that impact the prognosis of patients in the glycolysis-related subgroups of lung adenocarcinoma (LUAD). The mRNA expression profiling and mutation data in large LUAD samples were collected from the Cancer Genome Atlas (TCGA) database. Then, we identified the expression level and prognostic value of glycolysis-related genes, as well as the fractions of 22 immune cells in the tumor microenvironment. The differences between glycolysis activity, mutation, and immune infiltrates were discussed in these groups, respectively. Two hundred fifty-five glycolysis-related genes were identified from gene set enrichment analysis (GSEA), of which 43 genes had prognostic values (p < 0.05). Next, we constructed a glycolysis-related competing endogenous RNA (ceRNA) network which related to the survival of LUAD. Then, two subgroups of LUAD (clusters 1 and 2) were identified by applying unsupervised consensus clustering to 43 glycolysis-related genes. The survival analysis showed that the cluster 1 patients had a worse prognosis (p < 0.001), and upregulated differentially expressed genes (DEGs) are interestingly enriched in malignancy-related biological processes. The differences between the two subgroups are SPTA1, KEAP1, USH2A, and KRAS among top 10 mutated signatures, which may be the underlying mechanism of grouping. Combined high tumor mutational burden (TMB) with tumor subgroups preferably predicts the prognosis of LUAD patients. The CIBERSORT algorithm results revealed that low TMB samples were concerned with increased infiltration level of memory resting CD4+ T cell (p = 0.03), resting mast cells (p = 0.044), and neutrophils (p = 0.002) in cluster 1 and high TMB samples were concerned with increased infiltration level of memory B cells, plasma cells, CD4 memory-activated T cells, macrophages M1, and activated mast cells in cluster 2, while reduced infiltration of monocytes, resting dendritic cells, and resting mast cells was captured in cluster 2. In conclusion, significant different gene expression characteristics were pooled according to the two subgroups of LUAD. The combination of subgroups, TMB and tumor-infiltrating immune cell signature, might be a novel prognostic biomarker in LUAD.

BackgroundTumor microenvironment (TME) plays important roles in different cancers. Our study aimed to identify molecules with significant prognostic values and construct a relevant Nomogram, immune model, competing endogenous RNA (ceRNA) in lung adenocarcinoma (LUAD).Methods“GEO2R,” “limma” R packages were used to identify all differentially expressed mRNAs from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Genes with P-value 2 or

BackgroundRecent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD).MethodsRNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method.ResultsA total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance (P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis.ConclusionsWe have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

Emerging quantum hardware provides new possibilities for quantum simulation. While much of the research has focused on simulating closed quantum systems, the real-world quantum systems are mostly open. Therefore, it is essential to develop quantum algorithms that can effectively simulate open quantum systems. Here we present an adaptive variational quantum algorithm for simulating open quantum system dynamics described by the Lindblad equation. The algorithm is designed to build resource-efficient ansätze through the dynamical addition of operators by maintaining the simulation accuracy. We validate the effectiveness of our algorithm on both noiseless simulators and IBM Q quantum processors and observe good quantitative and qualitative agreement with the exact solution. We also investigate the scaling of the required resources with system size and accuracy and find polynomial behavior. Our results demonstrate that near-future quantum processors are capable of simulating open quantum systems.

A forward secure proxy signature scheme was proposed combining the concept of forward security with proxy signature. This means even if an adversary has gotten the current proxy signers’ key. It could not forge proxy signature pertaining to the past, that is, previous generated proxy signatures remain still valid. Furthermore, the scheme can control the proxy signature time of the proxy singer.

Quantum annealers require accurate control and optimized operation schemes to reduce noise levels, in order to eventually demonstrate a computational advantage over classical algorithms. We study a high coherence four-junction capacitively shunted flux qubit (CSFQ), using dispersive measurements to extract system parameters and model the device. Josephson junction asymmetry inherent to the device causes a deleterious nonlinear cross-talk when annealing the qubit. We implement a nonlinear annealing path to correct the asymmetry in situ, resulting in a substantial increase in the probability of the qubit being in the correct state given an applied flux bias. We also confirm the multi-level structure of our CSFQ circuit model by annealing it through small spectral gaps and observing quantum signatures of energy level crossings. Our results demonstrate an anneal-path correction scheme designed and implemented to improve control accuracy for high-coherence and high-control quantum annealers, which leads to an enhancement of success probability in annealing protocols.

Purpose: To assess the quality of clinical practice guidelines (CPGs) related to drug therapy for prevention and control of ventilator-associated pneumonia (VAP) and compare the differences and similarities between recommendations.Methods: Electronic databases (including PubMed, Cochrane library, Embase, Web of Science), guideline development organizations, and professional societies were searched to identify CPGs for VAP from 20 January 2012 to 20 January 2022. The Appraisal of Guidelines Research & Evaluation (AGREE) II instrument was used to evaluate the quality of the guidelines. The recommendations on drug therapy for prevention and treatment for each guideline were extracted, and then a descriptive synthesis was performed to analyze the scope/topic, and consistency of the recommendations.Results: Thirteen CPGs were included. The median score and interquartile range (IQR) in each domain are shown below: scope and purpose 72.22% (63.89%,83.33%); stakeholder involvement 44.44% (38.89%,52.78%); rigor of development 43.75% (31.25%,57.29%); clarity and presentation 94.44% (77.78%,94.44%); applicability 20.83 (8.34%,33.34%) and editorial independence 50% (33.33%,66.67%). We extracted 21 recommendations on drug therapy for prevention of VAP and 51 recommendations on drugs used for treatment. Some controversies remained among the included guidelines.Conclusion: There is considerable variability in the development processes and reporting of VAP guidelines. Despite many similarities, the recommendations still had some inconsistencies in the details. For the prevention and treatment of VAP, local microbial epidemiology and antibiotic sensitivity must be considered, and recommendations should be regularly revised as new evidence emerges.

Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking-more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.

Glycomacropeptide (GMP) is a bioactive peptide derived from whey protein, consisting of 64 amino acids. It is a phenylalanine-free peptide, making it a beneficial dietary option for individuals dealing with phenylketonuria (PKU). PKU is an inherited metabolic disorder characterized by high levels of phenylalanine in the bloodstream, resulting from a deficiency of phenylalanine dehydrogenase in affected individuals. Consequently, patients with PKU require lifelong adherence to a low-phenylalanine diet, wherein a significant portion of their protein intake is typically sourced from a phenylalanine-free amino acid formula. GMP has several nutritional values, numerous bioactivity properties, and therapeutic effects in various inflammatory disorders. Despite all these features, the purification of GMPis an imperative requirement; however, there are no unique methods for achieving this goal. Traditionally, several methods have been used for GMP purification, such as thermal or acid treatment, alcoholic precipitation, ultrafiltration (UF), gel filtration, andmembrane separation techniques. However, these methods have poor specificity, and the presence of large amounts of impurities can interfere with the analysis of GMP. More efficient and highly specific GMP purification methods need to be developed. In this review, we have highlighted and summarized the current research progress on the major biological features and purification methodologies associated with GMP, as well as providing an extensive overview of the recent developments in using charged UF membranes for GMP purification and the influential factors. [ABSTRACT FROM AUTHOR]