Long non-coding RNA NRAV in the 12q24.31 risk locus drives gastric cancer development through glucose metabolism reprogramming.

Long non-coding RNAs (lncRNAs) serve as vital candidates to mediate cancer risk. Here, we aimed to identify the risk single-nucleotide polymorphisms (SNPs)-induced lncRNAs and to investigate their roles in gastric cancer (GC) development. Through integrating the differential expression analysis of lncRNAs in GC tissues and expression quantitative trait loci analysis in normal stomach tissues and GC tissues, as well as genetic association analysis based on GC genome-wide association studies and an independent validation study, we identified four lncRNA-related SNPs consistently associated with GC risk, including SNHG7 [odds ratio (OR) = 1.16, 95% confidence interval (CI): 1.09-1.23], NRAV (OR = 1.11, 95% CI: 1.05-1.17), LINC01082 (OR = 1.16, 95% CI: 1.08-1.22) and FENDRR (OR = 1.16, 95% CI: 1.07-1.25). We further found that a functional SNP rs6489786 at 12q24.31 increases binding of MEOX1 or MEOX2 at a distal enhancer and results in up-regulation of NRAV. The functional assays revealed that NRAV accelerates GC cell proliferation while inhibits GC cell apoptosis. Mechanistically, NRAV decreases the expression of key subunit genes through the electron transport chain, thereby driving the glucose metabolism reprogramming from aerobic respiration to glycolysis. These findings suggest that regulating lncRNA expression is a crucial mechanism for risk-associated variants in promoting GC development.

"Sweat-Driven" MXene Composites with Energy-Storage and Thermal-Management Multifunctions: A Platform for Versatile Electronic Skins.

Most exogenous electronic skins (e-skins) currently face challenges of complex structure and poor compatibility with the human body. Utilizing human secretions (e.g., sweat) to develop e-skins is an effective solution strategy. Here, a new kind of "sweat-driven" e-skin is proposed, which realizes energy-storage and thermal-management multifunctions. Through the layer-by-layer assembly of MXene-carbon nanotube (CNT) composite with paper, lightweight and versatile e-skins based on supercapacitors and actuators are fabricated. Long CNTs wrap and entangle MXene nanosheets, enhancing their long-distance conductivity. Furthermore, the CNT network overcomes the structural collapse of MXene in sweat, improving the energy-storage performance of e-skin. The "sweat-driven" all-in-one supercapacitor with a trilayer structure is patternable, which absorbs sweat as electrolyte and harnesses the ions therein to store energy, exhibiting an areal capacitance of 282.3 mF cm-2 and a high power density (2117.8 µW cm-2 ). The "sweat-driven" actuator with a bilayer structure can be driven by moisture (bending curvature of 0.9 cm-1 ) and sweat for personal thermal management. Therefore, the paper serves as a separator, actuating layer, patternable layer, sweat extractor, and reservoir. The "sweat-driven" MXene-CNT composite provides a platform for versatile e-skins, which achieve the interaction with humans and offer insights into the development of multifunctional wearable electronics.

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana.

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 µg/m3, followed by PurpleAir PA-II (4.54 µg/m3) and Clarity Node-S (13.68 µg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 µg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 µg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 µg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Serosurvey in SARS-CoV-2 inactivated vaccine-elicited neutralizing antibodies against authentic SARS-CoV-2 and its viral variants.

Various variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been emerging and circulating in different parts of the world. Millions of vaccine doses have been administered globally, which reduces the morbidity and mortality of coronavirus disease-2019 efficiently. Here, we assess the immune responses of individuals after two shots of BBIBP-CorV or CoronaVac inactivated vaccine. We measured neutralizing antibody responses after the second vaccination by using authentic SARS-CoV-2 and its viral variants. All the serum samples efficiently neutralized SARS-CoV-2 wild-type lineage, in contrast, a part of serum samples failed to neutralize Alpha, Beta, Gamma, Delta, or Eta lineages, and only several serum samples were able to neutralize Omicron lineage virus strains (BA.1 and BA.2) with low neutralization titer. As compared with the neutralization of SARS-CoV-2 wild-type lineage, the neutralization of all other SARS-CoV-2 variant lineages was significantly lower. Considering that all the SARS-CoV-2 mutation viruses challenged the antibody neutralization induced by BBIBP-CorV and CoronaVac, it is necessary to carry out a third booster vaccination to increase the humoral immune response against the SARS-CoV-2 mutation viruses.

Humoral immune response to authentic circulating severe acute respiratory syndrome coronavirus 2 variants elicited by booster vaccination with distinct receptor-binding domain subunits in mice.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants could induce immune escape by mutations of the spike protein which are threatening to weaken vaccine efficacy. A booster vaccination is expected to increase the humoral immune response against SARS-CoV-2 variants in the population. We showed that immunization with two doses of wild type receptor-binding domain (RBD) protein, and booster vaccination with wild type or variant RBD protein all significantly increased binding and neutralizing antibody titers against wild type SARS-CoV-2 and its variants in mice. Only the booster immunization by Omicron (BA.1)RBD induced a strong antibody titer against the omicron virus strain and comparable antibody titers against all the other virus strains. These findings might shed the light on coronavirus disease 2019 booster immunogens.

Assessment of antibody responses against SARS-CoV-2 in unvaccinated individuals and vaccinees from Omicron-BA.2 infection in Zhaoqing, Guangdong Province, China.

Currently, the majority of the global population has been vaccinated with the COVID-19 vaccine, and characterization studies of antibodies in vivo from Omicron breakthrough infection and naive infection populations are urgently needed to provide pivotal clues about accurate diagnosis, treatment, and next-generation vaccine design against SARS-CoV-2 infection. We showed that after infection with Omicron-BA.2, the antibody levels of specific IgM against the Wuhan strain and specific IgG against Omicron were not significantly elevated within 27 days of onset. Interestingly, in this study, the levels of humoral immunity against Omicron-specific IgM were significantly increased after breakthrough infection, suggesting that the detection of Omicron-specific IgM antibodies can be used as a test criterion of Omicron breakthrough infection. In addition, we observed that serums from unvaccinated individuals and the majority of vaccinated infections possessed only low or no neutralizing activity against Omicron at the onset of Omicron breakthrough infections, and at the later stage of Omicron-BA.2 breakthrough infection, levels of neutralization antibody against the Wuhan and Omicron strains were elevated in infected individuals. The findings of this study provide important clues for the diagnosis of Omicron breakthrough infections, antibody characterization studies and vaccine design against COVID-19.

Research progress of natural products interfering with cell signaling pathway in liver fibrosis.

Liver cirrhosis and hepatocellular carcinoma are the late stage of liver fibrosis. How to early use drugs to intervene in liver fibrosis is a prerequisite for the reversal of liver fibrosis. This paper mainly introduces a cell signaling transduction pathway in liver fibrosis and the intervention of natural products in order to provide theoretical basis for the treatment of liver fibrosis.

LEPR hypomethylation is significantly associated with gastric cancer in males.

BACKGROUND: Previous study has shown LEPR is a candidate gene of prediction and treatment of gastric cancer (GC). The purpose of this study was to test whether LEPR methylation could predict the risk of GC. MATERIALS AND METHODS: Tumor tissues and 5-cm adjacent non-tumor tissues from 117 newly diagnosed and untreated GC patients were collected for the current methylation study. LEPR methylation levels were determined by quantitative methylation specific PCR (qMSP), and the methylation level of LEPR was described by the percentage of methylated reference (PMR). RESULTS: Our results showed that LEPR methylation levels were significantly lower in tumor tissues than those in adjacent non-tumor tissues (median PMR: 36.64% vs. 50.29%, P = 1E-4). In addition, LEPR methylation levels were found to be significantly associated with platelet (r = -0.198, P = .037). Further subgroup analysis showed that the association of LEPR promoter hypomethylation with GC was specific to males (males: P = 7E-5; females: P = .500). Notably, significant hypomethylation of LEPR promoter was found only in GC patients without recurrence (P = .002) but not in GC patients with recurrence (P = .146). The AUC of LEPR hypomethylation for identification of GC risk was 0.649 with a sensitivity of 67.5% and a specificity of 63.2%. In addition, the AUC of LEPR hypomethylation in males was 0.685 with a sensitivity of 68.4% and a specificity of 69.6%. CONCLUSION: LEPR hypomethylation can be used to predict the risk of GC in males. And it might also have the potential to predict the recurrence in GC patients.

Predicting particulate matter, nitrogen dioxide, and ozone across Great Britain with high spatiotemporal resolution based on random forest models.

In Great Britain, limited studies have employed machine learning methods to predict air pollution especially ozone (O3) with high spatiotemporal resolution. This study aimed to address this gap by developing random forest models for four key pollutants (fine and inhalable particulate matter [PM2.5 and PM10], nitrogen dioxide [NO2] and O3) by integrating multiple-source predictors at a daily level and 1-km resolution. The out-of-bag R2 (root mean squared error, RMSE) between predictions from models and measurements from monitoring stations in 2006-2013 was 0.85 (3.63 µg/m3) for PM2.5, 0.77 (6.00 µg/m3) for PM10, 0.85 (9.71 µg/m3) for NO2, and 0.85 (9.39 µg/m3) for maximum daily 8-h average (MDA8) O3 at daily level, and the predicting accuracy was higher at monthly and annual level. The high-resolution predictions captured characterized spatiotemporal patterns of the four pollutants. Higher concentrations of PM2.5, PM10, and NO2 were distributed in densely populated southern regions of Great Britain while O3 showed an inverse spatial pattern in general, which could not be fully depicted by monitoring stations. Therefore, predictions produced in this study could improve exposure assessment with less exposure misclassification and flexible exposure windows for future epidemiological studies to investigate the impact of air pollution across Great Britain.

Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions.

Incomplete combustion of fossil fuels and biomass burning emit large amounts of soot particles into the troposphere. The condensation process is considered to influence the size (Dp) and mixing state of soot particles, which affects their solar absorption efficiency and lifetimes. However, quantifying aging evolution of soot remains hampered in the real world because of complicated sources and observation technologies. In the Himalayas, we isolated soot sourced from transboundary transport of biomass burning and revealed soot aging mechanisms through microscopic observations. Most of coated soot particles stabilized one soot core under Dp < 400 nm, but 34.8% of them contained multi-soot cores (nsoot ≥ 2) and nsoot increased 3-9 times with increasing Dp. We established the soot mixing models to quantify transformation from condensation- to coagulation-dominant regime at Dp ≈ 400 nm. Studies provide essential references for adopting mixing rules and quantifying the optical absorption of soot in atmospheric models.

Discrete Dynamic Modeling of Learner Behavior Analysis in Physical Education Teaching.

With the advent of the big data era, the combination of information technology and education has become an important way for the development of the industry. The large-scale realization of teaching tasks under the background of information data requires the prediction and analysis of learners' characteristics, behavior, and development trend. Based on the above situation, this paper uses discrete dynamic modeling technology in big data environment to study the learners' behavior in physical education teaching. By quantifying the learning process data, the feature points of each learner are extracted to realize the personalized construction of dynamic learning data. Due to the rapid development of network technology, we mainly analyze the online education platform and explore the influencing factors of learners' behavior characteristics from many aspects. Finally, it carries out dynamic modeling and prediction for physical education learners from the aspect of achievement change, uses the grey model to build the achievement change system, and combines the dynamic modeling technology to reflect the development trend of achievement. The results show that the main factor affecting learners' behavior change in physical education is video learning. Most students are passive and lack initiative in the learning process. Discrete dynamic modeling technology can improve the accuracy of predicting student achievement changes and provide effective data for the research content.

MicroRNA-122-3p plays as the target of long non-coding RNA LINC00665 in repressing the progress of arthritis.

MicroRNAs (miRNAs) play important roles in many diseases, including rheumatoid arthritis (RA). However, the mechanisms underlying the effects of miR-122-3p-3p on RA are not distinct and require further investigation. Patients with RA and healthy controls were recruited to analyze the miR-122-3p levels. The MH7A cells were stimulated with interleukin (IL)-1ß to mimic the local inflammation of RA. Cell Counting Kit-8 (CCK-8) and flow cytometry were performed to measure the viability and apoptosis of MH7A cells. Diana tools and TargetScan were used to predict the target relationships. Luciferase reporter assay was used to validate the target relationship. miR-122-3p is downregulated in RA patients and IL-1ß-stimulated MH7A cells. miR-122-3p suppresses MH7A cell viability and promotes MH7A cell apoptosis. miR-122-3p targets LINC00665. LINC00665 eliminates the inhibitory effect of miR-122-3p on IL-1ß-stimulated MH7A cells. Eukaryotic translation initiation factor 2 alpha kinase 1 (EIF2AK1) targets miR-122-3p. In addition, EIF2AK1 is highly expressed in patients with RA. In addition, EIF2AK1 activates the mTOR signaling pathway. miR-122-3p represses RA progression by reducing cell viability and increasing synoviocyte apoptosis.

PRKCDBP Methylation is a Potential and Promising Candidate Biomarker for Non-small Cell Lung Cancer.

BACKGROUND: The occurrence and development of lung cancer are closely linked to epigenetic modification. Abnormal DNA methylation in the CpG island region of genes has been found in many cancers. Protein kinase C delta binding protein (PRKCDBP) is a potential tumor suppressor and its epigenetic changes are found in many human malignancies. This study investigated the possibility of PRKCDBP methylation as a potential biomarker for non-small cell lung cancer (NSCLC). METHODS: We measured the methylation levels of PRKCDBP in the three groups of NSCLC tissues. Promoter activity was measured by the dual luciferase assay, with 5'-aza-deoxycytidine to examine the effect of demethylation on the expression level of PRKCDBP. RESULTS: The methylation levels of PRKCDBP in tumor tissues and 3 cm para-tumor were higher than those of distant (>10 cm) non-tumor tissues. Receiver operating characteristic (ROC) curve analysis between tumor tissues and distant non-tumor tissues showed that the area under the line (AUC) was 0.717. Dual luciferase experiment confirmed that the promoter region was able to promote gene expression. Meanwhile, in vitro methylation of the fragment (PRKCDBP_Me) could significantly reduce the promoter activity of the fragment. Demethylation of 5'-aza-deoxycytidine in lung cancer cell lines A549 and H1299 showed a significant up-regulation of PRKCDBP mRNA levels. CONCLUSIONS: PRKCDBP methylation is a potential and promising candidate biomarker for non-small cell lung cancer.

EPAS1 Promoter Hypermethylation is a Diagnostic and Prognostic Biomarker for Non-Small Cell Lung Cancer.

Background: The importance of promoter methylation in non-small cell lung cancers (NSCLC) remains to be understood. Thus, we aimed to determine the diagnostic and prognostic value of the methylation of the endothelial PAS domain containing protein-1 (EPAS1) promoter in NSCLC. Methods: EPAS1 promoter methylation levels were quantitated by methylation-specific PCR. Further, we evaluated the expression, promoter methylation, prognostic value, and impact on immune cell infiltration of EPAS1 by analyzing the TCGA database using web-based bioinformatics tools including GEPIA, UALCAN and MethSurv. Results: Our results demonstrated that promoter methylation of EPAS1 downregulated its expression in NSCLC tissues. Additionally, an AUC value of 0.772 indicated that the methylation of the EPAS1 promoter is a potential diagnostic marker for NSCLC. A Kaplan-Meier analysis demonstrated that high methylation levels of CpG sites in the EPAS1 promoter were indicative of poorer overall survival. Further, EPAS1 expression levels were highly correlated with the infiltration of several types of immune cells, including γδ T cells, T follicular helper cells, CD8+ T cells, and CD4+ T-cells. Conclusion: Collectively, our findings suggest that methylation analyses of the EPAS1 promoter could be used as a prognostic biomarker for NSCLC and that EPAS1 potentially plays an important role in immune cell infiltration in NSCLC.

Viral infection and transmission in a large, well-traced outbreak caused by the SARS-CoV-2 Delta variant.

The SARS-CoV-2 Delta variant has spread rapidly worldwide. To provide data on its virological profile, we here report the first local transmission of Delta in mainland China. All 167 infections could be traced back to the first index case. Daily sequential PCR testing of quarantined individuals indicated that the viral loads of Delta infections, when they first become PCR-positive, were on average ~1000 times greater compared to lineage A/B infections during the first epidemic wave in China in early 2020, suggesting potentially faster viral replication and greater infectiousness of Delta during early infection. The estimated transmission bottleneck size of the Delta variant was generally narrow, with 1-3 virions in 29 donor-recipient transmission pairs. However, the transmission of minor iSNVs resulted in at least 3 of the 34 substitutions that were identified in the outbreak, highlighting the contribution of intra-host variants to population-level viral diversity during rapid spread.

Large model structural uncertainty in global projections of urban heat waves.

Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Here, we use an urban climate emulator combined with large ensemble global climate simulations to show that, at the urban scale a large proportion of the variability results from the model structural uncertainty in projecting UHWs in the coming decades under climate change. Omission of this uncertainty would considerably underestimate the risk of UHW. Results show that, for cities in four high-stake regions - the Great Lakes of North America, Southern Europe, Central India, and North China - a virtually unlikely (0.01% probability) UHW projected by single-model ensembles is estimated by our model with probabilities of 23.73%, 4.24%, 1.56%, and 14.76% respectively in 2061-2070 under a high-emission scenario. Our findings suggest that for urban-scale extremes, policymakers and stakeholders will have to plan for larger uncertainties than what a single model predicts if decisions are informed based on urban climate simulations.

A narrative review on environmental impacts of cannabis cultivation.

Interest in growing cannabis for medical and recreational purposes is increasing worldwide. This study reviews the environmental impacts of cannabis cultivation. Results show that both indoor and outdoor cannabis growing is water-intensive. The high water demand leads to water pollution and diversion, which could negatively affect the ecosystem. Studies found out that cannabis plants emit a significant amount of biogenic volatile organic compounds, which could cause indoor air quality issues. Indoor cannabis cultivation is energy-consuming, mainly due to heating, ventilation, air conditioning, and lighting. Energy consumption leads to greenhouse gas emissions. Cannabis cultivation could directly contribute to soil erosion. Meanwhile, cannabis plants have the ability to absorb and store heavy metals. It is envisioned that technologies such as precision irrigation could reduce water use, and application of tools such as life cycle analysis would advance understanding of the environmental impacts of cannabis cultivation.

An EAV-HP insertion in the promoter region of SLCO1B3 has pleiotropic effects on chicken liver metabolism based on the transcriptome and proteome analysis.

Solute carrier organic anion transporter 1B3 (SLCO1B3) is an important liver primarily highly expressed gene, its encoded protein (OATP1B3) involved in the transport of multi-specific endogenous and exogenous substances. We previously reported that an EAV-HP inserted mutation (IM+) in the 5' flanking region of SLCO1B3 was the causative mutation of chicken blue eggs, and a further research showed that IM+ significantly reduced the expression of SLCO1B3 in liver. Herein, we confirmed a cholate response element (IR-1) played an important role in activating SLCO1B3 and in vitro experiments showed that the activation of IR-1 can be significantly reduced by the EAV-HP IM+ . We performed transcriptome and proteomic analysis using the same set of IM+ and IM- liver tissues from Yimeng hens (a Chinese indigenous breed) to study the effect of SLCO1B3 and OATP1B3 expression reduction on chicken liver function. The results showed that common differential expression pathways were screened out from both transcriptome and proteome, in which fatty acid metabolism and drug metabolism-cytochrome P450 were significantly enriched in the KEGG analysis. The lipid-related metabolism was weakened in IM+ group, which was validated by serum biochemical assay. We unexpectedly found that EAV-HP fragment was highly expressed in the liver of the IM+ chickens. We cloned the EAV-HP full-length transcript and obtained the complete open reading frame. It is worth noting that there was some immune related differential expressed genes, such as NFKBIZ, NFKBIA, and IL1RL1, which were higher expressed in the IM+ group, which may due to the high expression of EAV-HP. Our study showed that EAV-HP IM+ reduced the expression of SLCO1B3 in liver, resulting in the decrease of fatty metabolism and exogenous substance transport capacity. The mutation itself also expressed in the liver and may be involved in the immune process. The mechanism needs further study.

Genomic Evolution and Variation of SARS-CoV-2 in the Early Phase of COVID-19 Pandemic in Guangdong Province, China.

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) with unknown origin spread rapidly to 222 countries, areas or territories. To investigate the genomic evolution and variation in the early phase of COVID-19 pandemic in Guangdong, 60 specimens of SARS-CoV-2 were used to perform whole genome sequencing, and genomics, amino acid variation and Spike protein structure modeling analyses. Phylogenetic analysis suggested that the early variation in the SARS-CoV-2 genome was still intra-species, with no evolution to other coronaviruses. There were one to seven nucleotide variations (SNVs) in each genome and all SNVs were distributed in various fragments of the genome. The Spike protein bound with human receptor, an amino acid salt bridge and a potential furin cleavage site were found in the SARS-CoV-2 using molecular modeling. Our study clarified the characteristics of SARS-CoV-2 genomic evolution, variation and Spike protein structure in the early phase of local cases in Guangdong, which provided reference for generating prevention and control strategies and tracing the source of new outbreaks.

The biological role of arachidonic acid 12-lipoxygenase (ALOX12) in various human diseases.

ALOX12 encodes arachidonic acid 12-lipoxygenase that acts on different polyunsaturated fatty acid substrates to produce biologically active lipid mediators including eicosanes and lipoxins. ALOX12 protein plays an important role in inflammation and oxidation, while abnormal DNA methylation and genetic variants of ALOX12 are associated with various human diseases and pathological phenotypes, such as cardiovascular disease, diabetes, neurodegenerative diseases, respiratory system disease, cancer, infection, etc. Here, this article reviews the mechanisms by which ALOX12 participates in related diseases, which will provide systematic knowledge for future ALOX12 related studies.