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Lung cancer is the most commonly diagnosed cancer worldwide, and metastasis in lung cancer is the leading cause of cancer‐related deaths. Thus, understanding the mechanism of lung cancer metastasis will improve the diagnosis and treatment of lung cancer patients. Herein, we found that expression of cluster of differentiation 109 (CD109) was correlated with the invasive and metastatic capacities of lung adenocarcinoma cells. CD109 is upregulated in tumorous tissues, and CD109 overexpression was associated with tumor progression, distant metastasis, and a poor prognosis in patient with lung adenocarcinoma. Mechanistically, expression of CD109 regulates protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling via its association with the epidermal growth factor receptor (EGFR). Inhibition of CD109 decreases EGFR phosphorylation, diminishes EGF‐elicited activation of AKT/mTOR, and sensitizes tumor cells to an EGFR inhibitor. Taken together, our results show that CD109 is a potential diagnostic and therapeutic target in lung cancer patients. CD109 promotes lung cancer metastasis through promoting EGFR‐AKT‐mTOR signaling and CD109 is an independent prognostic marker for lung adenocarcinoma.

Introduction Microbial residents of the human oral cavity have long been a major focus of microbiology due to their influence on host health and intriguing patterns of site specificity amidst the lack of dispersal limitation. However, the determinants of niche partitioning in this habitat are yet to be fully understood, especially among taxa that belong to recently discovered branches of microbial life. Results Here, we assemble metagenomes from tongue and dental plaque samples from multiple individuals and reconstruct 790 non-redundant genomes, 43 of which resolve to TM7, a member of the Candidate Phyla Radiation, forming six monophyletic clades that distinctly associate with either plaque or tongue. Both pangenomic and phylogenomic analyses group tongue-specific clades with other host-associated TM7 genomes. In contrast, plaque-specific TM7 group with environmental TM7 genomes. Besides offering deeper insights into the ecology, evolution, and mobilome of cryptic members of the oral microbiome, our study reveals an intriguing resemblance between dental plaque and non-host environments indicated by the TM7 evolution, suggesting that plaque may have served as a stepping stone for environmental microbes to adapt to host environments for some clades of microbes. Additionally, we report that prophages are widespread among oral-associated TM7, while absent from environmental TM7, suggesting that prophages may have played a role in adaptation of TM7 to the host environment. Conclusions Our data illuminate niche partitioning of enigmatic members of the oral cavity, including TM7, SR1, and GN02, and provide genomes for poorly characterized yet prevalent members of this biome, such as uncultivated Flavobacteriaceae.

(1)Objective: In this study, a quantitative analysis of chemical groups (the triterpenoids, water-soluble polysaccharides, and acidic polysaccharides) and quantitative high liquid performance chromatography (HPLC) fingerprint of Poria cocos (Schw.) Wolf (PC) for quality control was developed. (2) Methodology: First, three main chemical groups, including triterpenoids, water-soluble polysaccharides, and acidic polysaccharides, in 16 batches of PC were evaluated by ultraviolet spectrophotometry. Afterward, the quantitative fingerprint of PC was established, and the alcohol extract of PC was further evaluated. The method involves establishing 16 batches of PC fingerprints by HPLC, evaluating the similarity of different batches of PC, and identifying eight bioactive components, including poricoic acid B (PAB), dehydrotumulosic acid (DTA), poricoic acid A (PAA), polyporenic acid C (PAC), 3-epidehydrotumulosic acid (EA), dehydropachymic acid (DPA), dehydrotrametenolic acid (DTA-1), and dehydroeburicoic acid (DEA), in PC by comparison with the reference substance. Combined with the quantitative analysis of multi-components by a single marker (QAMS), six bioactive ingredients, including PAB, DTA, PAC, EA, DPA, and DEA, in PC from different places were established. In addition, the multivariate statistical analyses, such as principal component analysis and heatmap hierarchical clustering analysis are more intuitive, and the visual analysis strategy was used to evaluate the content of bioactive components in 16 batches of PC. Finally, the analysis strategy of three main chemical groups in PC was combined with the quantitative fingerprint strategy, which reduced the error caused by the single method. (3) Results: The establishment of a method for the quantification of chemical groups and quantitative HPLC fingerprint of PC was achieved as demonstrated through the quantification of six triterpenes in PC by a single marker. (4) Conclusions: Through qualitative and quantitative chemical characterization, a multi-directional, simple and efficient routine evaluation method of PC quality was established. The results reveal that this strategy can provide an analytical method for the quality evaluation of PC and other Chinese medicinal materials.

The COVID-19 pandemic might lead to more mental health problems. However, few studies have examined sleep problems, depression, and posttraumatic symptoms among the general adult population during the COVID-19 outbreak, and little is known about coping behaviors. This survey was conducted online in China from February 1st to February 10th, 2020. Quota sampling was used to recruit 2993 Chinese citizens aged ≥18 years old. Mental health problems were assessed with the Post-Traumatic Stress Disorders (PTSD) Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), the Center for Epidemiological Studies Depression inventory, and the Pittsburgh Sleep Quality Index. Exposure to COVID-19 was measured with questions about residence at outbreak, personal exposure, media exposure, and impact on livelihood. General coping style was measured by the brief Coping Style Questionnaire (SCSQ). Respondents were also asked 12 additional questions about COVID-19 specific coping behaviors. Direct exposure to COVID-19 instead of the specific location of (temporary) residence within or outside the epicenter (Wuhan) of the pandemic seems important (standardized beta: 0.05, 95% confidence interval (CI): 0.02–0.09). Less mental health problems were also associated with less intense exposure through the media (standardized beta: −0.07, 95% CI: −0.10–−0.03). Perceived negative impact of the pandemic on livelihood showed a large effect size in predicting mental health problems (standardized beta: 0.15, 95% CI: 0.10–0.19). More use of cognitive and prosocial coping behaviors were associated with less mental health problems (standardized beta: −0.30, 95% CI: −0.34–−0.27). Our study suggests that the mental health consequences of the lockdown impact on livelihood should not be underestimated. Building on cognitive coping behaviors reappraisal or cognitive behavioral treatments may be most promising.

Blood urea nitrogen (BUN) is a commonly used biomarker for assessing kidney function and neuroendocrine activity. Previous studies have indicated that elevated BUN levels are associated with increased mortality in various critically ill patient populations. The focus of this study was to investigate the relationship between BUN and 28-day mortality in intensive care patients. This was a multi-centre retrospective cohort study that made use of data from the eICU Collaborative Research Database. The primary exposure variable was BUN, and the outcome was 28-day mortality. The following variables were included as covariates: age, gender, BMI, white blood cell count, creatinine, GCS score, APACHE IV score, and diabetes. The statistical analyses included univariate and multivariate logistic regression, as well as generalized additive modelling, which was employed to assess the non-linear relationship between BUN and mortality. A total of 63,757 elderly patients were included in the study, with a 28-day mortality of 6.5%. The univariate analysis indicated that elevated BUN quartiles were associated with an increased risk of mortality. The results of the multivariate analysis further confirmed the non-linear relationship between BUN and mortality. When BUN was less than 32 mg/dL, there was a significant positive association, with an adjusted odds ratio of 1.230 (95% CI: 1.154-1.311, p<0.0001) for every 10 mg/dL increase in BUN. However, when BUN was greater than or equal to 32 mg/dL, BUN level had no significant effect on mortality. BUN showed a nonlinear, threshold correlation with 28-day mortality in critically ill patients. The higher the BUN, the greater the risk of death if the BUN is below the threshold.

Geothermal environments, such as hot springs and hydrothermal vents, are hotspots for carbon cycling and contain many poorly described microbial taxa. Here, we reconstructed 15 archaeal metagenome-assembled genomes (MAGs) from terrestrial hot spring sediments in China and deep-sea hydrothermal vent sediments in Guaymas Basin, Gulf of California. Phylogenetic analyses of these MAGs indicate that they form a distinct group within the TACK superphylum, and thus we propose their classification as a new phylum, ‘Brockarchaeota’, named after Thomas Brock for his seminal research in hot springs. Based on the MAG sequence information, we infer that some Brockarchaeota are uniquely capable of mediating non-methanogenic anaerobic methylotrophy, via the tetrahydrofolate methyl branch of the Wood-Ljungdahl pathway and reductive glycine pathway. The hydrothermal vent genotypes appear to be obligate fermenters of plant-derived polysaccharides that rely mostly on substrate-level phosphorylation, as they seem to lack most respiratory complexes. In contrast, hot spring lineages have alternate pathways to increase their ATP yield, including anaerobic methylotrophy of methanol and trimethylamine, and potentially use geothermally derived mercury, arsenic, or hydrogen. Their broad distribution and their apparent anaerobic metabolic versatility indicate that Brockarchaeota may occupy previously overlooked roles in anaerobic carbon cycling. Geothermal environments are hotspots for carbon cycling. Here, De Anda et al. reconstruct archaeal genomes from terrestrial and deep-sea geothermal sediments, and propose the classification of these microbes as a new phylum, ‘Brockarchaeota’, with unique metabolic capabilities including non-methanogenic anaerobic methylotrophy.

Bacteriophages typically have small genomes 1 and depend on their bacterial hosts for replication 2 . Here we sequenced DNA from diverse ecosystems and found hundreds of phage genomes with lengths of more than 200 kilobases (kb), including a genome of 735 kb, which is—to our knowledge—the largest phage genome to be described to date. Thirty-five genomes were manually curated to completion (circular and no gaps). Expanded genetic repertoires include diverse and previously undescribed CRISPR–Cas systems, transfer RNAs (tRNAs), tRNA synthetases, tRNA-modification enzymes, translation-initiation and elongation factors, and ribosomal proteins. The CRISPR–Cas systems of phages have the capacity to silence host transcription factors and translational genes, potentially as part of a larger interaction network that intercepts translation to redirect biosynthesis to phage-encoded functions. In addition, some phages may repurpose bacterial CRISPR–Cas systems to eliminate competing phages. We phylogenetically define the major clades of huge phages from human and other animal microbiomes, as well as from oceans, lakes, sediments, soils and the built environment. We conclude that the large gene inventories of huge phages reflect a conserved biological strategy, and that the phages are distributed across a broad bacterial host range and across Earth’s ecosystems. Genomic analyses of major clades of huge phages sampled from across Earth’s ecosystems show that they have diverse genetic inventories, including a variety of CRISPR–Cas systems and translation-relevant genes.

Deep learning uses multilayered artificial neural networks to learn digitally from large datasets. It then performs advanced identification and classification tasks. To date, these multilayered neural networks have been implemented on a computer. Lin et al. demonstrate all-optical machine learning that uses passive optical components that can be patterned and fabricated with 3D-printing. Their hardware approach comprises stacked layers of diffractive optical elements analogous to an artificial neural network that can be trained to execute complex functions at the speed of light. Science , this issue p. 1004 All-optical deep learning can be implemented with 3D-printed passive optical components. Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D 2 NN) architecture that can implement various functions following the deep learning–based design of passive diffractive layers that work collectively. We created 3D-printed D 2 NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D 2 NNs.

Studies have shown that a series of molecular events caused by oxidative stress is associated with ferroptosis and oxidation after ischemic stroke (IS). Differential analysis was performed to identify differentially expressed mRNA (DEmRNAs) between IS and control groups. Critical module genes were identified using weighted gene co-expression network analysis (WGCNA). DEmRNAs, critical module genes, oxidative stress-related genes (ORGs), and ferroptosis-related genes (FRGs) were crossed to screen for intersection mRNAs. Candidate mRNAs were screened based on the protein–protein interaction (PPI) network and the MCODE plug-in. Biomarkers were identified based on two types of machine learning algorithms, and the intersection was obtained. Functional items and related pathways of the biomarkers were identified using gene set enrichment analysis (GSEA). Finally, single-sample GSEA (ssGSEA) and Wilcoxon tests were used to identify differential immune cells. An miRNA-mRNA-TF network was created. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the expression levels of biomarkers in the IS and control groups. There were 8287 DE mRNAs between the IS and control groups. The genes in the turquoise module were selected as critical module genes for IS. Thirty intersecting mRNAs were screened for overlaps. Seventeen candidate mRNAs were also identified. Four biomarkers (CDKN1A, GPX4, PRDX1, and PRDX6) were identified using two types of machine-learning algorithms. GSEA results indicated that the biomarkers were associated with steroid biosynthesis. Nine types of immune cells (activated B cells and neutrophils) were markedly different between the IS and control groups. We identified 3747 miRNA-mRNA-TF regulatory pairs in the miRNA-mRNA-TF regulatory network, including hsa-miR-4469-CDKN1A-BACH2 and hsa-miR-188-3p-GPX4-ATF2. CDKN1A, PRDX1, and PRDX6 were upregulated in IS samples compared with control samples. This study suggests that four biomarkers (CDKN1A, GPX4, PRDX1, and PRDX6) are significantly associated with IS. This study provides a new reference for the diagnosis and treatment of IS.

Background Melatonin is considered to be a polyfunctional master regulator in animals and higher plants. Exogenous melatonin inhibits plant infection by multiple diseases; however, the role of melatonin in Cucumber green mottle mosaic virus (CGMMV) infection remains unknown. Results In this study, we demonstrated that exogenous melatonin treatment can effectively control CGMMV infection. The greatest control effect was achieved by 3 days of root irrigation at a melatonin concentration of 50 μM. Exogenous melatonin showed preventive and therapeutic effects against CGMMV infection at early stage in tobacco and cucumber. We utilized RNA sequencing technology to compare the expression profiles of mock-inoculated, CGMMV-infected, and melatonin+CGMMV-infected tobacco leaves. Defense-related gene CRISP1 was specifically upregulated in response to melatonin, but not to salicylic acid (SA). Silencing CRISP1 enhanced the preventive effects of melatonin on CGMMV infection, but had no effect on CGMMV infection. We also found exogenous melatonin has preventive effects against another Tobamovirus , Pepper mild mottle virus (PMMoV) infection. Conclusions Together, these results indicate that exogenous melatonin controls two Tobamovirus infections and inhibition of CRISP1 enhanced melatonin control effects against CGMMV infection, which may lead to the development of a novel melatonin treatment for Tobamovirus control.