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Background: The application of nanomaterials (NMs) in the treatment of periodontitis and peri-implantitis has shown multifunctional benefits, such as antibacterial properties, immune regulation, and promotion of osteogenesis. However, a comprehensive bibliometric analysis to evaluate global scientific production in this field has not yet been conducted. Method: We searched for publications related to nanomaterials in periodontitis and peri-implantitis using the WOSCC database. The contributions from institutions, journals, countries, and authors were assessed using VOSviewer, the bibliometrix R package, and Microsoft Excel 2019. Results: We identified 2275 publications from 66 countries/regions focusing on nanomaterials in periodontitis and peri-implantitis, published between 1993 and 2023. China and the USA were the top contributors in this field, with 653 and 221 publications, respectively. Key topics include antibacterial properties, delivery systems, nanoparticles, and regeneration. The research focus has evolved from traditional treatments to advanced applications of multifunctional nanomaterials. Conclusion: Significant progress has been made in the application of NMs in periodontitis and peri-implantitis from 1993 to 2023. Future research hotspots will likely focus on multifunctional nanomaterials and those adhering to good manufacturing practices (GMP).

The Naoli River Basin (NRB), a pivotal agricultural production area in China, is poised to undergo substantial impacts on water resources due to projected climate and land use/cover (LULC) changes. Despite its significance in the context of China's expanding farmland construction in the NRB, there exists limited research on the potential repercussions of future shifts in runoff, soil water content (SWC), and evapotranspiration (ET) on crop productivity and water availability (both in terms of quantity and timing). This study employs future LULC maps and an ensemble of ten CMIP6 Global Climate Models (GCMs) across three scenarios to drive the well-calibrated distributed hydrological model, ESSI-3. The objective of present study is aimed on projecting hydrological consequences under climate and land use/land cover changes in near-term (2026–2050), middle-term (2051–2075), and far-term (2076–2100) future in comparison to the baseline period of 1990–2014. Results consistently indicate an increase trend in annual average ET, runoff, and SWC in the NRB across all three future periods under the three SSP scenarios. LULC changes emerge as the primary driver influencing regional hydrological processes in the near future. Notably, under high-emission scenarios, monthly runoff and SWC are projected to significantly increase in March but decrease in April during the middle and far future periods compared to the baseline. This shift is attributed to the anticipated warming of winter and spring, leading to a transition in peak snowmelt from April to March. Concurrently, the expansion of cropland intensifies crop evapotranspiration demand, potentially exacerbating water stress during the early stages of crop growth in April. The findings underscore the importance of addressing the substantial impacts of climate change and land use planning on regional water cycling processes. Early planning to mitigate water shortages during the initial stage of future crop growth is crucial for ensuring food security and managing water-related challenges in the NRB and neighboring mid-high latitude regions.

Background: The immune response associated with periprosthetic joint infection (PJI) is an emerging but relatively unexplored topic. The aim of this study was to investigate immune cell infiltration in periprosthetic tissues and identify potential immune-related biomarkers. Methods: The GSE7103 dataset from the GEO database was selected as the data source. Differentially expressed genes (DEGs) and significant modular genes in weighted correlation network analysis (WGCNA) were identified. Functional enrichment analysis and transcription factor prediction were performed on the overlapping genes. Next, immune-related genes from the ImmPort database were matched. The protein-protein interaction (PPI) analysis was performed to identify hub genes. CIBERSORTx was used to evaluate the immune cell infiltration pattern. Spearman correlation analysis was used to evaluate the relationship between hub genes and immune cells. Results: A total of 667 DEGs were identified between PJI and control samples, and 1847 PJI-related module genes were obtained in WGCNA. Enrichment analysis revealed that the common genes were mainly enriched in immune and host defense-related terms. TFEC, SPI1, and TWIST2 were the top three transcription factors. Three hub genes, SDC1, MMP9, and IGF1, were identified in the immune-related PPI network. Higher levels of plasma cells, CD4+ memory resting T cells, follicular helper T cells, resting mast cells, and neutrophils were found in the PJI group, while levels of M0 macrophages were lower. Notably, the expression of all three hub genes correlated with the infiltration levels of seven types of immune cells. Conclusion: The present study revealed immune infiltration signatures in the periprosthetic tissues of PJI patients. SDC1, MMP9, and IGF1 were potential immune-related biomarkers for PJI.

The ecological conservation red line (ECRL) framework plays an increasingly important role in biodiversity conservation practices in China. Following the implementation of ECRL, it is essential to systematically assess the effectiveness of this national policy in mitigating the negative impacts of human disturbances on habitat quality at the landscape and regional levels. Combined with remote sensing and ground survey data, the Integrated Valuation of Ecosystem Services and Trade-off (InVEST) habitat quality model was used to evaluate the impact of six major anthropogenic activities, including built-up land, roads, mining land, crop land, water pollution, and grazing, on habitat degradation in two grassland river corridors (Kherlen River corridor and Wuerxun River corridor) in Northeastern China. Then, the effectiveness of different management scenarios was assessed based on the ECRL framework. The results indicated that over 80% of the habitat degradation was attributed to grazing activities, followed by water pollution. Built-up land, roads, mining land, and crop land played minor roles in causing habitat degradation. Comparing the implementation of ECRL management scenario with no ECRL management scenario, habitat degradation in the Kherlen River corridor reduced by 17.86%, and habitat quality improved by 11.11%. Similarly, in the Wuerxun River corridor, compared with the priority ECRL management scenario, habitat degradation reduced by 69.23%, and habitat quality improved by 2.38% in the implementation of ECRL management scenario. Moreover, an optimized ECRL management scenario, including grazing prohibition, grassland enclosure, and ecological restoration of river habitats, further improved the habitat quality of both river corridors, resulting in a 12.70% improvement for the Kherlen River corridor and a 3.57% improvement for the Wuerxun River corridor. Our work provides a quantitative framework for optimizing conservation and restoration measures for river corridors based on the ECRL.

Hp0521 is the number of cag pathogenicity island (cagPAI) family in Helicobacter pylori (H. pylori, Hp), which encoded Cag2 protein. The aim of this study was to investigate the role of hp0521 on the H. pylori 26,695 strain. We constructed the recombinant prokaryotic expression plasmid pET-32a-hp0521 and pET-32a-hpc0521. Then, we co-cultured the H. pylori wild strain 26,695 and Δhp0521 strain with GES-1 cells to detect CagA protein transport and IL-8 secretion. We found that Δhp0521 mutation increased the expression of cagA, rpoB and promoted the transportation of CagA protein in GES-1 cells. In addition, we also observed that Δhp0521 mutation had no effect on other cagPAI protein stability and the expression of IL-8. Our findings suggested that hp0521 may down-regulated the expression of cagA, rpoB and inhibited the transportation of CagA protein in GES-1 cells and had no effect on growth.

Due to the expression of “reduced activation”, the severe radioactivity of tantalum (Ta) after neutron irradiation in early decay years is easily neglected. In the presented study, after an irradiation of only 0.3 dpa by fission neutron and followed by a cooling stage of 305 days, the RAFM steel specimen with a weight of 0.8 g possessed drastic radioactivity of 4 mSv/hour at a measuring distance of 20 cm. It was ascertained that the high radioactivity was generated by 182Ta. The severe radioactivity seriously increased the danger of material experiments, even though the original Ta addition of the steel was only 0.13 wt%. Similar deteriorative radioactivity of Ta can be generated in both fission and fusion neutron irradiations. We appeal to seriously deliberate the radioactivity of Ta and control the Ta content in the RAFM steel.

Study region: The Sanjiang Plain (SJP), located at the confluence reaches of the Heilong, Songhua, and Wusuli Rivers in Northeast China. Study focus: This study aimed to quantify the effects of varying climate and land-use/land-cover (LULC) dynamics on green water (GW) over the SJP during two distinctive periods (i.e., pre-2000 and post-2000), when synergetic effects of increased precipitation and temperature and rapid development of agriculture occurred. This assessment used the distributed eco-hydrological model ESSI-3. Multivariable and multi-objective calibration approaches (i.e., discharge, evapotranspiration, and terrestrial water storage anomaly) were used to ensure the high accuracies of the model outputs. New hydrological insights for the region: This research concluded that GW flow and GW storage in the SJP evidently increased after 2000 compared with before. Across the SJP, GW flow and GW storage responded differently to climate changes and LULC dynamics during pre-2000 and post-2000 period. Our results demonstrated that GW storage changes were predominately affected by climatic changes, especially variations in precipitation, whose contribution accounted for more than 56% after 2000. However, GW flow changes were mainly governed by LULC changes, especially the influence of cropland transformation, whose contribution increased over 20% in the post-2000 period. This study posed a deep insight regarding impacts of regional climatic and LULC changes on GW dynamics, which may benefit decision-making for planting structure optimization and adjustment over the region.

Study region: Amu Darya River Basin (ADRB), Central Asia. Study focus: Reference-evapotranspiration (ETo) is the most prominent variable for agriculture water-resource management. The Penman-Monteith (PM) equation has been accepted as universal-standard for estimating ETo but it requires extensive climatic-variables. The PM alternative Hargreaves-Samani (HS) equation inadequately estimates ETo with an average error of RMSE 1.84 mm/day, NSE 0.27, and MAE 0.61 mm/day, respectively, in the ADRB. New hydrological insight for the region: The present study proposes the calibration of original HS-equation based on actual vapor pressure and linear regression to estimate daily ETo at 114 meteorological stations in the ADRB. The locally-calibrated HS-equation coefficient (C) was found variated-widely across the different climatic-conditions and months, ranging from 0.0025 to 0.0186, due to hyper-aridity conditions in the study area. Compared with proposed methods by other studies, the modified-HS equation and empirical-coefficient using newly-proposed variable leads to a significant reduction of the average root means square error and mean absolute error by about 55 % and 52 %, respectively, demonstrates their feasibility and accuracy in the ADRB, Central Asia. In-addition, the modified-HS-equation based on climatic zones were found to be more accurate and comprehensive than geographical localizations. The modified-HS equation and empirical-coefficient using newly-proposed variable and approach is suitable to estimate ETo in a data scarce region (e.g., ADRB), when only temperatures, solar radiation and vapor pressure data are available.

Lead-based reactors have high core outlet temperature and have the potential to achieve high performance of Power Generation System (PGS). The increase in the thermal performance of the PGS is really a key challenge for the lead-based reactor. In this paper, a reference scheme with two reheater has been chosen for lead-based reactor with 400 MWth to improve the thermal performance of the system based on the optimization of inlet parameters to overcome and meet the energy demands. The influence of turbine pressure, feed water temperature and condenser back pressure have been analyzed to improve the performance of lead-based power reactor. The analysis results show that as turbine pressure is increased from 18 MPa to 22 MPa at steam inlet temperature of 465 °C, the thermal efficiency of system is increased from 44.30% to 46.35%. As PGS is operated at 22 MPa and the temperature of the feed water is increased from 335 °C to 356 °C, the thermal efficiency of system can be increased up to 49.45% and it decreased with the increase of condenser back pressure from 0.004 MPa to 0.015 MPa. The maximum thermal efficiency of the system was 45.28% at 0.004 MPa condenser back pressure. This work can be used for the analysis and optimization of a nuclear power plant based on the heavy metal cooled reactor concept.

Aromatic compounds, including many polycyclic aromatic hydrocarbons (PAHs), are suspected carcinogens and may originate from different sources. To investigate the impact of anthropogenic emission reductions on unknown aromatic compounds in particulate matter, we collected samples during the pre-COVID period in 2020, the COVID-19 lockdown period in 2020, and the same period as the lockdown in 2019. Besides the 16 PAHs, other aromatic compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. Four main compound classes were identified: CH, CHO, CHNO, and CHOS. Hierarchical cluster analysis showed the aromatic compounds varied during the different periods. Compared with before the pandemic, the relative abundances of aromatic compounds with low degrees of unsaturation and long alkyl chains (e.g., alkylbenzenes) increased. These compounds probably mainly arose from fossil fuel combustion and petrochemical industry emissions. The CHO compounds, which were dominated by those with high degrees of oxidation, might originate from secondary organic aerosols. Aromatic aldehydes (e.g., cyclamen aldehyde) and benzoates (e.g., 2-ethylhexyl benzoate) probably with high toxicity deserve more attention. During lockdown, nitro derivatives of condensed PAHs were the main CHNO compounds, and the numbers of homologs decreased perhaps because of significant reductions in NOx and PAHs. CHOS compounds with long carbon chains and low degrees of unsaturation were predominant and the numbers of homologs increased. Five compounds (e.g. 1,3-dimethyl pyrene) were predicted to possibly exhibit persistent and bio-accumulated by EPI Suite model, which need further research. The results provide insight on aromatic compounds and their source appointment in atmospheric particulate matter.

This paper first estimates the energy efficiency of 13 cities and 30 key industries in the Beijing-Tianjin-Hebei (BTH) region between 2008 and 2017 using the super-efficiency slacks-based measure (SBM) model. Structural deviation, Moore index, and coupling coordination degree quantify the cities' industrial restructuring, upgrading, and coordination. Afterward, the extended STIRPAT model reveals the relationship between energy efficiency and industrial structure. Finally, a coupling coordination degree model measures the coupling between energy efficiency and industrial advanced development coefficients in key industries. The results show a wide variation in energy efficiency between cities, with most cities experiencing a decline in energy efficiency. Industrial structure adjustment and upgrading are on the rise, and coordination is on the wane, with the most significant changes occurring in the cities around Beijing. In most cities, industrial structure adjustment, upgrading, and coordination on energy efficiency are negative, positive, and negative. The coupling imbalance of energy efficiency and industrial advanced development coefficients is the most apparent feature of the key sectors, and this feature is not improving.

Summary: Direct myocardial and vascular injuries due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-driven inflammation is the leading cause of acute cardiac injury associated with coronavirus disease 2019 (COVID-19). However, in-depth knowledge of the injury characteristics of the heart affected by inflammation is lacking. In this study, using a quantitative spatial proteomics strategy that combines comparative anatomy, laser-capture microdissection, and histological examination, we establish a region-resolved proteome map of the myocardia and microvessels with obvious inflammatory cells from hearts of patients with COVID-19. A series of molecular dysfunctions of myocardia and microvessels is observed in different cardiac regions. The myocardia and microvessels of the left atrial are the most susceptible to virus infection and inflammatory storm, suggesting more attention should be paid to the lesion and treatment of these two parts. These results can guide in improving clinical treatments for cardiovascular diseases associated with COVID-19.

In this study we present new mineral chemistry, whole-rock geochemical and zircon U–Pb geochronological data for 12 metamafic dykes in the mantle sequence of the Sangsang ophiolite in South Tibet (China). Modal analyses of these dykes gave averages of ~40%–65% plagioclase and ~35%–60% amphibole and small amounts of (igneous) clinopyroxene, epidote and opaque minerals. This mineral assemblage resembles that of typical orthoamphibolites. Nevertheless, due to the absence of foliation the investigated rocks are described as metamafic lithologies. These rocks have primitive mantle (PM)-normalized multi-element patterns with negative Nb and Ta anomalies as well as weak, negative Ti anomalies. In addition, they have initial 87Sr/86Sr ratios [(87Sr/86Sr)i] of 0.702844–0.703581, initial 143Nd/144Nd ratios [(143Nd/144Nd)i] of 0.512891–0.512959 and high εNd(t) values (+7.9 to +9.3). Uranium-Pb ages of magmatic zircons separated from the investigated metamafic dykes indicate that the parental melts of their protoliths intruded the Sangsang mantle at ~119.0–118.5 ​Ma.The metamorphic mineral assemblages recognized in the investigated dykes are suggestive of a retrograde metamorphic process, from (epidote-)amphibolite facies (~470–610 ​°C, ~1.9–4.3 ​kbar) and to prehnite-pumpellyite facies (≤280 ​°C, ≤ 3 ​kbar), active within a rift-produced oceanic lithosphere. Microtextural and geochemical data suggest that the protoliths of the dykes were most likely massive gabbros. Compositional data show that the parental magmas of the gabbroic protoliths were generated by melting of a depleted mantle (DM) source that had been weakly modified by fluids emanating from a subducted oceanic lithospheric slab. The age of the gabbroic protoliths is slightly younger than the existing ages for ophiolites from the central Yarlung-Zangbo Suture Zone (YZSZ) in the literature (~129–123 ​Ma). We, therefore, suggest that the gabbroic protoliths of the Sangsang metamafic dykes were formed in an incipient forearc setting during Neo-Tethyan subduction re-initiation (Aptian). Our tectonomagmatic model provides insights into the igneous accretion and post-solidification evolution of the oceanic lithosphere in South Tibet.

Deep learning is very promising in solving problems in omics research, such as genomics, epigenomics, proteomics, and metabolics. The design of neural network architecture is very important in modeling omics data against different scientific problems. Residual fully-connected neural network (RFCN) was proposed to provide better neural network architectures for modeling omics data. The next challenge for omics research is how to integrate information from different omics data using deep learning, so that information from different molecular system levels could be combined to predict the target. In this paper, we present a novel multi-omics integration approach named AutoOmics that could efficiently integrate information from different omics data and achieve better accuracy than previous approaches. We evaluated our method on four different tasks: drug repositioning, target gene prediction, breast cancer subtyping and cancer type prediction, and all the four tasks achieved state of art performances.

Deep learning has achieved great successes in traditional fields like computer vision (CV), natural language processing (NLP), speech processing, and more. These advancements have greatly inspired researchers in genomics and made deep learning in genomics an exciting and popular topic. The convolutional neural network (CNN) and recurrent neural network (RNN) are frequently used to solve genomic sequencing and prediction problems, and multiple layer perception (MLP) and auto-encoders (AE) are frequently used for genomic profiling data like RNA expression data and gene mutation data. Here, we introduce a new neural network architecture-the residual fully-connected neural network (RFCN)-and describe its advantage in modeling genomic profiling data. We also incorporate AutoML algorithms and implement AutoGenome, an end-to-end, automated deep learning framework for genomic studies. By utilizing the proposed RFCN architecture, automatic hyper-parameter search, and neural architecture search algorithms, AutoGenome can automatically train high-performance deep learning models for various kinds of genomic profiling data. To help researchers better understand the trained models, AutoGenome can assess the importance of different features and export the most critical features for supervised learning tasks and the representative latent vectors for unsupervised learning tasks. We expect AutoGenome will become a popular tool in genomic studies.

Nanopore sequencing from Oxford Nanopore Technologies (ONT) is a promising third-generation sequencing (TGS) technology that generates relatively longer sequencing reads compared to the next-generation sequencing (NGS) technology. A basecaller is a piece of software that translates the original electrical current signals into nucleotide sequences. The accuracy of the basecaller is crucially important to downstream analysis. Bonito is a deep learning-based basecaller recently developed by ONT. Its neural network architecture is composed of a single convolutional layer followed by three stacked bidirectional gated recurrent unit (GRU) layers. Although Bonito has achieved state-of-the-art base calling accuracy, its speed is too slow to be used in production. We therefore developed Fast-Bonito, by using the neural architecture search (NAS) technique to search for a brand-new neural network backbone, and trained it from scratch using several advanced deep learning model training techniques. The new Fast-Bonito model balanced performance in terms of speed and accuracy. Fast-Bonito was 153.8% faster than the original Bonito on NVIDIA V100 GPU. When running on HUAWEI Ascend 910 NPU, Fast-Bonito was 565% faster than the original Bonito. The accuracy of Fast-Bonito was also slightly higher than that of Bonito. We have made Fast-Bonito open source, hoping it will boost the adoption of TGS in both academia and industry.

Omics data can be used to identify biological characteristics from genetic to phenotypic levels during the life span of a living being, while molecular interaction networks have a fundamental impact on life activities. Integrating omics data and molecular interaction networks will help researchers delve into comprehensive information hidden in the data. Here, we propose a new multimodal method — AutoGGN — to integrate multi-omics data with molecular interaction networks based on graph convolutional neural networks (GCNs). We evaluated AutoGGN using three classification tasks: single-cell embryonic developmental stage classification, pan-cancer type classification, and breast cancer subtyping. On all three tasks, AutoGGN showed better performance than other methods. This means AutoGGN has the potential to extract insights more effectively by means of integrating molecular interaction networks with multi-omics data. Additionally, in order to provide a better understanding of how our model makes predictions, we utilized the SHAP module and identified the key genes contributing to the classification, providing insight for the design of downstream biological experiments.

Increasing effort has been devoted to restoring coastal ecosystems to counteract their degradation globally. Restoration success of coastal ecosystems often relies on harnessing biotic interactions that shape the performance of foundation plant species. Crabs acting as essential ecosystem engineers and consumers are commonly present in coastal saltmarshes. Previous work has focused on the grazing effect of crabs on plants. In line with the idea of top-down control, plant performance (in terms of biomass or height) is often used as indicators of plant-animal interactions. However, crabs could also produce facilitative effects through non-consumptive behavior. It remains unclear if and to what extent the net effect of crabs on coastal plants is dependent on environmental context. Therefore, systematic assessments are needed to test if the reliability of plant performance as an indicator of plant-crab interactions is context-dependent. Here, we conducted field survey in two similar intertidal saltmarsh ecosystems along the Yellow Sea coast, eastern China, and examined the effects of crab burrowing, soil properties, and nutrient availability on plant (Suaeda salsa) performance using multiple regression and structural equation models. We found that crab burrow density was significantly correlated with plant height, which signals strong plant-crab interactions. Surprisingly, we observed opposite signs (positive vs. negative) of plant-crab relationships between the two study sites that have highly similar composition and structure of plant communities. A possible explanation is that soil compactness can mediate crab burrowing behavior, resulting in differential facilitative effects on the plants. Thus, the usefulness of plant height serving as an indicator of plant-crab interactions depends on environmental context to a substantial degree. More comprehensive indicators taking into account soil compactness may facilitate robust inference of plant-crab interactions. Our results highlight landscape-scale spatial heterogeneity and context-dependency of plant-animal interactions in coastal saltmarshes, and provide a useful implication to inferring nuanced biotic interactions using compressive biotic and abiotic indicators in coastal ecosystems.

Summary: The recent mass mortality event of more than 330 African elephants in Botswana has been attributed to biotoxins produced by cyanobacteria; however, scientific evidence for this is lacking. Here, by synthesizing multiple sources of data, we show that, during the past decades, the widespread hypertrophic waters in Southern Africa have entailed an extremely high risk and frequent exposure of cyanotoxins to the wildlife within this area, which functions as a hotspot of mammal species richness. The hot and dry climatic extremes have most likely acted as the primary trigger of the recent and perhaps also of prehistoric mass mortality events. As such climate extremes are projected to become more frequent in Southern Africa in the near future, there is a risk that similar tragedies may take place, rendering African megafauna species, especially those that are already endangered, in risk of extinction. Moreover, cyanotoxin poisoning amplified by climate change may have unexpected cascading effects on human societies. Seen within this perspective, the tragic mass death of the world's largest terrestrial mammal species serves as an alarming early warning signal of future environmental catastrophes in Southern Africa. We suggest that systematic, quantitative cyanotoxin risk assessments are made and precautionary actions to mitigate the risks are taken without hesitation to ensure the health and sustainability of the megafauna and human societies within the region. Public Summary: • Cyanotoxin is the most likely cause for the massive death of elephants in Botswana. • Mammal species hotspot in Africa is under increasing risk of cyanotoxin poisoning. • This tragic event serves as an alarming early warning signal of future catastrophes. • Systematic assessments on cyanotoxin risk are strongly suggested in Southern Africa.

Meteorin-like (METRNL) protein is a newly identified myokine that functions to modulate energy expenditure and inflammation in adipose tissue. Herein, we aim to investigate the potential role and molecular basis of METRNL in doxorubicin (DOX)-induced cardiotoxicity. METRNL was found to be abundantly expressed in cardiac muscle under physiological conditions that was decreased upon DOX exposure. Cardiac-specific overexpression of METRNL by adeno-associated virus serotype 9 markedly improved oxidative stress, apoptosis, cardiac dysfunction and survival status in DOX-treated mice. Conversely, knocking down endogenous METRNL by an intramyocardial injection of adenovirus exacerbated DOX-induced cardiotoxicity and death. Meanwhile, METRNL overexpression attenuated, while METRNL silence promoted oxidative damage and apoptosis in DOX-treated H9C2 cells. Systemic METRNL depletion by a neutralizing antibody aggravated DOX-related cardiac injury and dysfunction in vivo, which were notably alleviated by METRNL overexpression within the cardiomyocytes. Besides, we detected robust METRNL secretion from isolated rodent hearts and cardiomyocytes, but to a less extent in those with DOX treatment. And the beneficial effects of METRNL in H9C2 cells disappeared after the incubation with a METRNL neutralizing antibody. Mechanistically, METRNL activated SIRT1 via the cAMP/PKA pathway, and its antioxidant and antiapoptotic capacities were blocked by SIRT1 deficiency. More importantly, METRNL did not affect the tumor-killing action of DOX in 4T1 breast cancer cells and tumor-bearing mice. Collectively, cardiac-derived METRNL activates SIRT1 via cAMP/PKA signaling axis in an autocrine manner, which ultimately improves DOX-elicited oxidative stress, apoptosis and cardiac dysfunction. Targeting METRNL may provide a novel therapeutic strategy for the prevention of DOX-associated cardiotoxicity.

Purpose: The mitogen-activated protein kinases (MAPKs) and protein kinase B (AKT) pathways have emerged as essential intracellular signaling pathways in eukaryotic cells, particularly as regulators of cardiac hypertrophy. Previous studies indicated that arctiin, an active ingredient of biennial dried ripe burdock, could exhibit potent anti-inflammatory and anti-allergic activities via down-regulating the activation of MAPKs and AKT pathways. However, little is known about its effects on cardiac hypertrophy. Therefore, the present study aimed to explore whether arctiin could attenuate cardiac hypertrophy. General methods: Arctiin (80 mg/kg) was administered by oral gavage once daily for 3 weeks from 1 week after surgery. Then, the mice were subjected to either chronic pressure overload generated by aortic banding (AB) or sham surgery (control group). Cardiac function was assessed by echocardiography. Findings: The results indicated that arctiin attenuated cardiac hypertrophy induced by AB, and suppressed cardiac fibrosis and accumulation of collagen in vivo. Arctiin also inhibit the activation of MAPKs and AKT occurs in response to hypertrophic stimuli. Arctiin attenuated phenylephrine-induced hypertrophy of myocytes in vitro. Conclusions: In conclusion, arctiin can improve cardiac function and prevent the development of cardiac hypertrophy by blocking the MAPKs and AKT signaling pathways.

Mitogen-activated protein kinases (MAPKs) and AMP­activated protein kinase α (AMPKα) play critical roles in the process of cardiac hypertrophy. Previous studies have demonstrated that piperine activates AMPKα and reduces the phosphorylation of extracellular signal-regulated kinase (ERK). However, the effect of piperine on cardiac hypertrophy remains completely unknown. Here, we show that piperine-treated mice had similar hypertrophic responses as mice treated with vehicle but exhibited significantly attenuated cardiac fibrosis after pressure overload or isoprenaline (ISO) injection. Piperine inhibited the transformation of cardiac fibroblasts to myofibroblasts induced by transforming growth factor-β (TGF-β) or angiotensin II (Ang II) in vitro. This anti-fibrotic effect was independent of the AMPKα and MAPK pathway. Piperine blocked activation of protein kinase B (AKT) and, downstream, glycogen synthase kinase 3β (GSK3β). The overexpression of constitutively active AKT or the knockdown of GSK3β completely abolished the piperine-mediated protection of cardiac fibroblasts. The cardioprotective effects of piperine were blocked in mice with constitutively active AKT. Pretreatment with GW9662, a specific inhibitor of peroxisome proliferator activated receptor-γ (PPAR-γ), reversed the effect elicited by piperine in vitro. In conclusion, piperine attenuated cardiac fibrosis via the activation of PPAR-γ and the resultant inhibition of AKT/GSK3β.

Summary: Although chronic inflammation increases many cancers’ risk, how inflammation facilitates cancer development is still not well studied. Recognizing whether and when inflamed tissues transition to cancerous tissues is of utmost importance. To unbiasedly infer molecular events, immune cell types, and secreted factors contributing to the inflammation-to-cancer (I2C) transition, we develop a computational package called “SwitchDetector” based on liver, gastric, and colon cancer I2C data. Using it, we identify angiogenesis associated with a common critical transition stage for multiple I2C events. Furthermore, we infer infiltrated immune cell type composition and their secreted or suppressed extracellular proteins to predict expression of important transition stage genes. This identifies extracellular proteins that may serve as early-detection biomarkers for pre-cancer and early-cancer stages. They alone or together with I2C hallmark angiogenesis genes are significantly related to cancer prognosis and can predict immune therapy response. The SwitchDetector and I2C database are publicly available at www.inflammation2cancer.org. : Chen et al. develop the SwitchDetector package for transcriptome module detection during inflammation-to-cancer (I2C) stage transitions. They show that angiogenesis is a common critical event for I2C in multiple cancers. The data also suggest that immune cells and secreted cytokines contribute to the I2C transition. Keywords: inflammation, cancer, network analysis, microenvironment, secreted factor, biomarker, cancer transition, immune therapy, cancer survival

Summary: Experimental large-scale screens for drug repositioning are limited by restriction to in vitro conditions and lack of applicability to real human conditions. Here, we developed an in silico screen in human in vivo conditions using a reference of single gene mutations’ non-tissue-specific “core transcriptome signatures” (CSs) of 8,476 genes generated from the TCGA database. We developed the core-signature drug-to-gene (csD2G) software to scan 3,546 drug treatment profiles against the reference signatures. csD2G significantly outperformed conventional cell line-based gene perturbation signatures and existing drug-repositioning methods in both coverage and specificity. We highlight this with 3 demonstrated applications: (1) repositioned category of psychiatric drugs to inhibit the TGF-β pathway; (2) antihypertensive calcium channel blockers predicted to activate AMPK and inhibit AKT pathways, and validated by clinical electronic medical records; and (3) 7 drugs predicted and validated to selectively target the AKT-FOXO and AMPK pathways and thus regulate worm lifespan. : Xu et al. generated core transcriptome signatures representing single-gene mutations for 8,476 human genes from the cancer transcriptomes in the TCGA database and developed a drug-repositioning method to predict high-specificity candidate target genes for 1,938 drugs. Applied to targeting aging pathways, 7 lifespan-extending drugs are found and functionally validated. Keywords: drug repositioning, drug target, cancer genomics, data integrative analysis

Objective: To investigate the effect of auricular stimulation in cavum conchae on the glycemic control for patients with type 2 diabetes mellitus., Methods: Seventy-one cases were treated with auricular electrical stimulator in the cavum choncha for 30 min, once daily for consecutive 3 months. The changes on the fasting plasma glucose (FBG), 2-h postprandial blood glucose after a 75 g oral glucose load (P2BG), glycated hemoglobin (HbA1c), blood urea nitrogen (BUN), serum creatinine (SCr), total cholesterol (TC) and aspartate transaminase (AST) were compared before and after the treatment., Results: The level of the HbA1c was significantly decreased (P<0.05), and there were also statistically significant decreases in BUN, SCr, TC and AST after the treatment (P<0.05). A few patients (n=7) reduced the dose of the hypoglycemic agents in response to repeated hypoglycemia during the treatment., Conclusion: The stimulation in the cavum conchae of patients with types 2 diabetes mellitus may help decrease HbA1c, BUN, SCr, TC and AST, and may be an effective treatment for type 2 diabetes mellitus., (Copyright © 2014 Elsevier Ltd. All rights reserved.)