Government-enterprise green collaborative governance and urban carbon emission reduction: Empirical evidence from green PPP programs.

The reliance solely on the government or enterprises to promote climate governance is contingent upon the vested interests of economic entities and the regulatory bodies' efficiency in governance. Can the model of government-enterprise green collaborative governance evolve into a long-term mechanism for addressing the climate crisis and achieving the goals of sustainable development? By crawling data on public-private partnerships (PPP), employing ChatGPT to identify green PPP projects, and building a generalized difference-in-differences framework based on the Guidance on Building a Green Financial System issued in 2016, this present study investigates whether the involvement of private capital in government-led environmental and climate governance can effectively facilitate government-enterprise green collaborative governance, thereby mitigating urban carbon emissions. The study finds government-enterprise green collaborative governance can significantly reduce urban carbon emissions. The conclusion remains valid even after several rounds of robustness tests, including removing the influence of pertinent climate policies, adjusting the settings of independent and dependent variables, and removing self-selection issues. Heterogeneity tests show, on the first hand, the carbon emission reduction effect of government-enterprise green collaborative governance differs due to the differences in the characteristics of green PPP(Pubic-private partnership) projects such as project return mechanism, project investment volume, and project cooperation term; on the other hand, the carbon emission reduction effect also shows heterogeneity with various urban characteristics such as geographical location, city type and city size. Mechanism tests indicate government-enterprise green collaborative governance affects urban carbon emissions mainly through structural effects, technological effects and co-investment effects. This paper offers a valuable framework for effectively promoting environmental and climate co-governance between governmental bodies and enterprises, while enhancing the market's role in resource benefit allocation within climate governance to mitigate the risks associated with climate change.

Up-regulation of BRD4 contributes to gestational diabetes mellitus-induced cardiac hypertrophy in offspring by promoting mitochondria dysfunction in sex-independent manner.

Gestational diabetes mellitus (GDM) is associated with cardiovascular disease in postnatal life. The current study tested the hypothesis that GDM caused the cardiac hypertrophy in fetal (ED18.5), postnatal day 7 (PD7), postnatal day 21 (PD21) and postnatal day 90 (PD90) offspring by upregulation of BRD4 and mitochondrial dysfunction. Pregnant mice were divided into control and GDM groups. Hearts were isolated from ED18.5, PD7, PD21 and PD90. GDM increased the body weight (BW) and heart weight (HW) in ED18.5 and PD7, but not PD21 and PD90 offspring. However, HW/BW ratio was increased in all ages of GDM offspring compared to control group. Electron microscopy showed disorganized myofibrils, mitochondrial swelling, vacuolization, and cristae disorder in GDM offspring. GDM resulted in myocardial hypertrophy in offspring, which persisted from fetus to adult in a sex-independent manner. Echocardiography analysis revealed that GDM caused diastolic dysfunction, but had no effect on systolic function. Meanwhile, myocardial BRD4 was significantly upregulated in GDM offspring and BRD4 inhibition by JQ1 alleviated GDM-induced myocardial hypertrophy in offspring. Co-immunoprecipitation showed that BRD4 interacted with DRP1 and there was an increase of BRD4 and DRP1 interaction in GDM offspring. Furthermore, GDM caused the accumulation of damaged mitochondria in hearts from all ages of offspring, including mitochondrial fusion fission imbalance (upregulation of DRP1, and downregulation of MFN1, MFN2 and OPA1) and myocardial mitochondrial ROS accumulation, which was reversed by JQ1. These results suggested that the upregulation of BRD4 is involved in GDM-induced myocardial hypertrophy in the offspring through promoting mitochondrial damage in a gender-independent manner.

Pathogenic Autoimmunity in Atherosclerosis Evolves from HSP60-Reactive CD4 + T Cells.

Clinical evidence suggests anti-Hsp60 antibodies could contribute to atherosclerosis (AS) development, with unclear mechanisms. This study aims to explore the role of anti-HSP60-mediated autoimmunity in AS progression. HSP60-MHC tetramers were used to characterize HSP60-specific CD4 + T cells and assess TCR responses in mice. These cells were transplanted into AS mice to examine immune cell differentiation and infiltration in plaques and blood. Mice were injected with recombinant HSP60 or anti-HSP60 sera to evaluate effects on plaque progression and macrophage activity. Experiments with muMT-/-Apoe-/- mice examined humoral immunity's role in this autoimmunity. HSP60-reactive CD4 + T cells in AS mice differentiated into follicular helper cells, not Th1/Th17. Anti-HSP60 treatments increased macrophage infiltration and M1 polarization, indicating an anti-HSP60-driven inflammatory progression, dependent on humoral immunity. Anti-HSP60 influences macrophage infiltration, polarization, and plaque formation via humoral immunity, shedding light on its potential role in AS progression.

Transcription factor DIVARICATA1 positively modulates seed germination in response to salinity stress.

Seed germination is a critical checkpoint for plant growth under unfavorable environmental conditions. In Arabidopsis (Arabidopsis thaliana), the abscisic acid (ABA) and gibberellic acid (GA) signaling pathways play important roles in modulating seed germination. However, the molecular links between salinity stress and ABA/GA signaling are not well understood. Herein, we showed that the expression of DIVARICATA1 (DIV1), which encodes a MYB-like transcription factor, was induced by GA and repressed by ABA, salinity, and osmotic stress in germinating seeds. DIV1 positively regulated seed germination in response to salinity stress by directly regulating the expression of DELAY OF GERMINATION 1-LIKE 3 (DOGL3) and GA-STIMULATED ARABIDOPSIS 4 (GASA4) and indirectly regulating the expression of several germination-associated genes. Moreover, NUCLEAR FACTOR-YC9 (NF-YC9) directly repressed the expression of DIV1 in germinating seeds in response to salinity stress. These results help reveal the function of the NF-YC9-DIV1 module and provide insights into the regulation of ABA and GA signaling in response to salinity stress during seed germination in Arabidopsis.

An all-optical multidirectional mechano-sensor inspired by biologically mechano-sensitive hair sensilla.

Mechano-sensitive hair-like sensilla (MSHS) have an ingenious and compact three-dimensional structure and have evolved widely in living organisms to perceive multidirectional mechanical signals. Nearly all MSHS are iontronic or electronic, including their biomimetic counterparts. Here, an all-optical mechano-sensor mimicking MSHS is prototyped and integrated based on a thin-walled glass microbubble as a flexible whispering-gallery-mode resonator. The minimalist integrated device has a good directionality of 32.31 dB in the radial plane of the micro-hair and can detect multidirectional displacements and forces as small as 70 nm and 0.9 µN, respectively. The device can also detect displacements and forces in the axial direction of the micro-hair as small as 2.29 nm and 3.65 µN, respectively, and perceive different vibrations. This mechano-sensor works well as a real-time, directional mechano-sensory whisker in a quadruped cat-type robot, showing its potential for innovative mechano-transduction, artificial perception, and robotics applications.

One-photon three-dimensional printed fused silica glass with sub-micron features.

The applications of silica-based glass have evolved alongside human civilization for thousands of years. High-precision manufacturing of three-dimensional (3D) fused silica glass objects is required in various industries, ranging from everyday life to cutting-edge fields. Advanced 3D printing technologies have emerged as a potent tool for fabricating arbitrary glass objects with ultimate freedom and precision. Stereolithography and femtosecond laser direct writing respectively achieved their resolutions of ~50 µm and ~100 nm. However, fabricating glass structures with centimeter dimensions and sub-micron features remains challenging. Presented here, our study effectively bridges the gap through engineering suitable materials and utilizing one-photon micro-stereolithography (OµSL)-based 3D printing, which flexibly creates transparent and high-performance fused silica glass components with complex, 3D sub-micron architectures. Comprehensive characterizations confirm that the final material is stoichiometrically pure silica with high quality, defect-free morphology, and excellent optical properties. Homogeneous volumetric shrinkage further facilitates the smallest voxel, reducing the size from 2.0 × 2.0 × 1.0 µm3 to 0.8 × 0.8 × 0.5 µm3. This approach can be used to produce fused silica glass components with various 3D geometries featuring sub-micron details and millimetric dimensions. This showcases promising prospects in diverse fields, including micro-optics, microfluidics, mechanical metamaterials, and engineered surfaces.

FLOWERING LOCUS T1 and TERMINAL FLOWER1 regulatory networks mediate flowering initiation in apple.

Flower bud formation is a critical process that directly determines yield and fruit quality in fruit crops. Floral induction is modulated by the balance between 2 flowering-related proteins, FLOWERING LOCUS T (FT) and TERMINAL FLOWER1 (TFL1); however, the mechanisms underlying the establishment and maintenance of this dynamic balance remain largely elusive. Here, we showed that in apple (Malus × domestica Borkh.), MdFT1 is predominantly expressed in spur buds and exhibits an increase in expression coinciding with flower induction; in contrast, MdTFL1 exhibited downregulation in apices during flower induction, suggesting that MdTFL1 has a role in floral repression. Interestingly, both the MdFT1 and MdTFL1 transcripts are directly regulated by transcription factor basic HELIX-LOOP-HELIX48 (MdbHLH48), and overexpression of MdbHLH48 in Arabidopsis (Arabidopsis thaliana) and tomato (Solanum lycopersicum) results in accelerated flowering. Binding and activation analyses revealed that MdbHLH48 functions as a positive regulator of MdFT1 and a negative regulator of MdTFL1. Further studies established that both MdFT1 and MdTFL1 interact competitively with MdWRKY6 protein to facilitate and inhibit, respectively, MdWRKY6-mediated transcriptional activation of target gene APPLE FLORICAULA/LFY (AFL1, an apple LEAFY-like gene), ultimately regulating apple flower bud formation. These findings illustrate the fine-tuned regulation of flowering by the MdbHLH48-MdFT1/MdTFL1-MdWRKY6 module and provide insights into flower bud formation in apples.

Machine learning-based extrachromosomal DNA identification in large-scale cohorts reveals its clinical implications in cancer.

The clinical implications of extrachromosomal DNA (ecDNA) in cancer therapy remain largely elusive. Here, we present a comprehensive analysis of ecDNA amplification spectra and their association with clinical and molecular features in multiple cohorts comprising over 13,000 pan-cancer patients. Using our developed computational framework, GCAP, and validating it with multifaceted approaches, we reveal a consistent pan-cancer pattern of mutual exclusivity between ecDNA amplification and microsatellite instability (MSI). In addition, we establish the role of ecDNA amplification as a risk factor and refine genomic subtypes in a cohort from 1015 colorectal cancer patients. Importantly, our investigation incorporates data from four clinical trials focused on anti-PD-1 immunotherapy, demonstrating the pivotal role of ecDNA amplification as a biomarker for guiding checkpoint blockade immunotherapy in gastrointestinal cancer. This finding represents clinical evidence linking ecDNA amplification to the effectiveness of immunotherapeutic interventions. Overall, our study provides a proof-of-concept of identifying ecDNA amplification from cancer whole-exome sequencing (WES) data, highlighting the potential of ecDNA amplification as a valuable biomarker for facilitating personalized cancer treatment.

TCCIA: a comprehensive resource for exploring CircRNA in cancer immunotherapy.

BACKGROUND: Immunotherapies targeting immune checkpoints have gained increasing attention in cancer treatment, emphasizing the need for predictive biomarkers. Circular RNAs (circRNAs) have emerged as critical regulators of tumor immunity, particularly in the PD-1/PD-L1 pathway, and have shown potential in predicting immunotherapy efficacy. Yet, the detailed roles of circRNAs in cancer immunotherapy are not fully understood. While existing databases focus on either circRNA profiles or immunotherapy cohorts, there is currently no platform that enables the exploration of the intricate interplay between circRNAs and anti-tumor immunotherapy. A comprehensive resource combining circRNA profiles, immunotherapy responses, and clinical outcomes is essential to advance our understanding of circRNA-mediated tumor-immune interactions and to develop effective biomarkers. METHODS: To address these gaps, we constructed The Cancer CircRNA Immunome Atlas (TCCIA), the first database that combines circRNA profiles, immunotherapy response data, and clinical outcomes across multicancer types. The construction of TCCIA involved applying standardized preprocessing to the raw sequencing FASTQ files, characterizing circRNA profiles using an ensemble approach based on four established circRNA detection tools, analyzing tumor immunophenotypes, and compiling immunotherapy response data from diverse cohorts treated with immune checkpoint blockades (ICBs). RESULTS: TCCIA encompasses over 4,000 clinical samples obtained from 25 cohorts treated with ICBs along with other treatment modalities. The database provides researchers and clinicians with a cloud-based platform that enables interactive exploration of circRNA data in the context of ICB. The platform offers a range of analytical tools, including browse of identified circRNAs, visualization of circRNA abundance and correlation, association analysis between circRNAs and clinical variables, assessment of the tumor immune microenvironment, exploration of tumor molecular signatures, evaluation of treatment response or prognosis, and identification of altered circRNAs in immunotherapy-sensitive and resistant tumors. To illustrate the utility of TCCIA, we showcase two examples, including circTMTC3 and circMGA, by employing analysis of large-scale melanoma and bladder cancer cohorts, which unveil distinct impacts and clinical implications of different circRNA expression in cancer immunotherapy. CONCLUSIONS: TCCIA represents a significant advancement over existing resources, providing a comprehensive platform to investigate the role of circRNAs in immuno-oncology.

Antagonistic MADS-box transcription factors SEEDSTICK and SEPALLATA3 form a transcriptional regulatory network that regulates seed oil accumulation.

Transcriptional regulation is essential for balancing multiple metabolic pathways that influence oil accumulation in seeds. Thus far, the transcriptional regulatory mechanisms that govern seed oil accumulation remain largely unknown. Here, we identified the transcriptional regulatory network composed of MADS-box transcription factors SEEDSTICK (STK) and SEPALLATA3 (SEP3), which bridges several key genes to regulate oil accumulation in seeds. We found that STK, highly expressed in the developing embryo, positively regulates seed oil accumulation in Arabidopsis (Arabidopsis thaliana). Furthermore, we discovered that SEP3 physically interacts with STK in vivo and in vitro. Seed oil content is increased by the SEP3 mutation, while it is decreased by SEP3 overexpression. The chromatin immunoprecipitation, electrophoretic mobility shift assay, and transient dual-luciferase reporter assays showed that STK positively regulates seed oil accumulation by directly repressing the expression of MYB5, SEP3, and SEED FATTY ACID REDUCER 4 (SFAR4). Moreover, genetic and molecular analyses demonstrated that STK and SEP3 antagonistically regulate seed oil production and that SEP3 weakens the binding ability of STK to MYB5, SEP3, and SFAR4. Additionally, we demonstrated that TRANSPARENT TESTA 8 (TT8) and ACYL-ACYL CARRIER PROTEIN DESATURASE 3 (AAD3) are direct targets of MYB5 during seed oil accumulation in Arabidopsis. Together, our findings provide the transcriptional regulatory network antagonistically orchestrated by STK and SEP3, which fine tunes oil accumulation in seeds.

Elucidating Multiple Electron-Transfer Pathways for Metavanadate Bioreduction by Actinomycetic Streptomyces microflavus.

While microbial reduction has gained widespread recognition for efficiently remediating environments polluted by toxic metavanadate [V(V)], the pool of identified V(V)-reducing strains remains rather limited, with the vast majority belonging to bacteria and fungi. This study is among the first to confirm the V(V) reduction capability of Streptomyces microflavus, a representative member of ubiquitous actinomycetes in environment. A V(V) removal efficiency of 91.0 ± 4.35% was achieved during 12 days of operation, with a maximum specific growth rate of 0.073 d-1. V(V) was bioreduced to insoluble V(IV) precipitates. V(V) reduction took place both intracellularly and extracellularly. Electron transfer was enhanced during V(V) bioreduction with increased electron transporters. The electron-transfer pathways were revealed through transcriptomic, proteomic, and metabolomic analyses. Electrons might flow either through the respiratory chain to reduce intracellular V(V) or to cytochrome c on the outer membrane for extracellular V(V) reduction. Soluble riboflavin and quinone also possibly mediated extracellular V(V) reduction. Glutathione might deliver electrons for intracellular V(V) reduction. Bioaugmentation of the aquifer sediment with S. microflavus accelerated V(V) reduction. The strain could successfully colonize the sediment and foster positive correlations with indigenous microorganisms. This study offers new microbial resources for V(V) bioremediation and improve the understanding of the involved molecular mechanisms.

Single-cell and transcriptome analysis reveals TAL cells in diabetic nephropathy.

Diabetic nephropathy is a global public health concern with multifaceted pathogenesis, primarily involving hypertension. Excessive activation of AT1R has been strongly associated with hypertension onset and progression in diabetic nephropathy. This study aimed to conduct thick ascending limb cell single-cell and transcriptomic analysis in diabetic nephropathy, including screening for biological markers, cellular communication, and immune infiltration, to identify potential biomarkers and effective means for prevention and treatment. By using high-dimensional weighted gene co-expression network analysis, least absolute shrinkage and selection operator, machine learning, neural deconvolution, quasi-chronological analysis, non-negative matrix factorization clustering, and monocyte chemotactic protein-induced counter, we identified 7 potential thick ascending limb cell biomarkers for diabetic nephropathy and elucidated the bone morphogenetic protein pathway's regulation of thick ascending limb cells through podocyte epithelial cells and podocyte cells. The study also highlighted the role of COBL, PPARGC1A, and THSD7A in non-negative matrix factorization clustering and their relationship with thick ascending limb cell immunity in diabetic nephropathy. Our findings provide new insights and avenues for managing diabetic nephropathy, ultimately alleviating the burden on patients and society.

Unveiling the interplay between mutational signatures and tumor microenvironment: a pan-cancer analysis.

Background: While recent studies have separately explored mutational signatures and the tumor microenvironment (TME), there is limited research on the associations of both factors in a pan-cancer context. Materials and methods: We performed a pan-cancer analysis of over 8,000 tumor samples from The Cancer Genome Atlas (TCGA) project. Machine learning methods were employed to systematically explore the relationship between mutational signatures and TME and develop a risk score based on TME-associated mutational signatures to predict patient survival outcomes. We also constructed an interaction model to explore how mutational signatures and TME interact and influence cancer prognosis. Results: Our analysis revealed a varied association between mutational signatures and TME, with the Clock-like signature showing the most widespread influence. Risk scores based on mutational signatures mainly induced by Clock-like and AID/APOBEC activity exhibited strong pan-cancer survival stratification ability. We also propose a novel approach to predict transcriptome decomposed infiltration levels using genome-derived mutational signatures as an alternative approach for exploring TME cell types when transcriptome data are unavailable. Our comprehensive analysis revealed that certain mutational signatures and their interaction with immune cells significantly impact clinical outcomes in particular cancer types. For instance, T cell infiltration levels only served as a prognostic biomarker in melanoma patients with high ultraviolet radiation exposure, breast cancer patients with high homologous recombination deficiency signature, and lung adenocarcinoma patients with high tobacco-associated mutational signature. Conclusion: Our study comprehensively explains the complex interplay between mutational signatures and immune infiltration in cancer. The results highlight the importance of considering both mutational signatures and immune phenotypes in cancer research and their significant implications for developing personalized cancer treatments and more effective immunotherapy.

Interaction effect of psoriasis and chronic kidney disease on the risk of all-cause mortality: a prospective cohort study of NHANES data.

BACKGROUND: The association between psoriasis, chronic kidney disease (CKD) and mortality remains unclear. This study aimed to examine the combined impact of psoriasis and CKD on mortality in a representative sample of US adults. METHODS: The data for this analysis came from 13 208 participants of the National Health and Nutrition Examination Survey conducted between 2003-06 and 2009-14. Psoriasis was determined through self-reported questionnaire data, while CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 or urinary albumin to creatinine ratio (UACR) ≥30 mg/g. A four-level variable was created using the information on psoriasis and CKD, and survival probability was estimated using the Kaplan-Meier method. The survival analysis was conducted using weighted Cox proportional hazards regression models. RESULTS: In a 9.83-year average follow-up period, 539 deaths occurred, with a prevalence of psoriasis in CKD at 2.94% and an all-cause mortality rate of 33.30%. In the multivariable analyses, individuals with both psoriasis and CKD had hazard ratios (HRs) of 5.38 (95% CI 2.43-11.91) for all-cause mortality compared with those with neither psoriasis nor CKD. Participants with both psoriasis and low eGFR had an HR of 6.40 (95% CI 2.01-20.42), while those with both psoriasis and albuminuria had an HR of 5.30 (95% CI 2.24-12.52). A significant interaction between psoriasis, CKD and all-cause mortality was found in the fully adjusted model (P = .026), and a significant synergistic effect between psoriasis and albuminuria was discovered (P = .002). However, the interaction effects between psoriasis, low eGFR and all-cause mortality were only observed in the unadjusted model (P = .036). CONCLUSIONS: Screening for psoriasis in individuals at risk for developing CKD may help in risk stratification for all-cause mortality related to psoriasis. The assessment of UACR may be useful in identifying psoriasis at increased risk for all-cause mortality.

Common gene signatures and molecular mechanisms of diabetic nephropathy and metabolic syndrome.

Background: Diabetic nephropathy (DN) is the leading cause of end-stage renal disease. Multiple metabolic toxicities, redox stress, and endothelial dysfunction contribute to the development of diabetic glomerulosclerosis and DN. Metabolic syndrome (MetS) is a pathological state in which the body's ability to process carbohydrates, fats, and proteins is compromised because of metabolic disorders, resulting in redox stress and renal remodeling. However, a causal relationship between MetS and DN has not been proven. This study aimed to provide valuable information for the clinical diagnosis and treatment of MetS with DN. Methods: Here, transcriptome data of DN and MetS patients were obtained from the Gene Expression Omnibus database, and seven potential biomarkers were screened using bioinformatics analysis. In addition, the relationship between these marker genes and metabolism and immune infiltration was explored. Among the identified marker genes, the relationship between PLEKHA1 and the cellular process, oxidative phosphorylation (OXPHOS), in DN was further investigated through single-cell analysis. Results: We found that PLEKHA1 may represent an important biomarker that perhaps initiates DN by activating B cells, proximal tubular cells, distal tubular cells, macrophages, and endothelial cells, thereby inducing OXPHOS in renal monocytes. Conclusion: Overall, our findings can aid in further investigation of the effects of drug treatment on single cells of patients with diabetes to validate PLEKHA1 as a therapeutic target and to inform the development of targeted therapies.

Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites.

DNA methylation analysis has been applied to determine the primary site of cancer; however, robust and accurate prediction of cancer types with a minimum number of sites is still a significant scientific challenge. To build an accurate and robust cancer type prediction tool with a minimum number of DNA methylation sites, we internally benchmarked different DNA methylation site selection and ranking procedures, as well as different classification models. We used The Cancer Genome Atlas dataset (26 cancer types with 8296 samples) to train and test models and used an independent dataset (17 cancer types with 2738 samples) for model validation. A deep neural network model using a combined feature selection procedure (named MethyDeep) can predict 26 cancer types using 30 methylation sites with superior performance compared with the known methods for both primary and metastatic cancers in independent validation datasets. In conclusion, MethyDeep is an accurate and robust cancer type predictor with the minimum number of DNA methylation sites; it could help the cost-effective clarification of cancer of unknown primary patients and the liquid biopsy-based early screening of cancers.

Guanxining injection alleviates fibrosis in heart failure mice and regulates SLC7A11/GPX4 axis.

ETHNOPHARMACOLOGICAL RELEVANCE: Radix et Rhizoma Salviae Miltiorrhizae (Salvia miltiorrhiza Bge., Lamiaceae, Danshen in Chinese) and Chuanxiong Rhizoma (rhizomes of Ligusticum chuanxiong Hort., Apiaceae, Chuanxiong in Chinese) both are important traditional Chinese medicine (TCM) for activating blood and eliminating stasis. Danshen-chuanxiong herb pair has been used for more than 600 years in China. Guanxinning injection (GXN) is a Chinese clinical prescription refined from aqueous extract of Danshen and Chuanxiong at the ratio of 1:1 (w/w). GXN has been mainly used in the clinical therapy of angina, heart failure (HF) and chronic kidney disease in China for almost twenty years. AIM OF THE STUDY: This study aimed to explore the role of GXN on renal fibrosis in heart failure mice and the regulation of GXN on SLC7A11/GPX4 axis. MATARIALS AND METHODS: The transverse aortic constriction model was used to mimic HF accompanied by kidney fibrosis model. GXN was administrated by tail vein injection in dose of 12.0, 6.0, 3.0 mL/kg, respectively. Telmisartan (6.1 mg/kg, gavage) was used as a positive control drug. Cardiac ultrasound indexes of ejection fraction (EF), cardiac output (CO), left ventricle volume (LV Vol), HF biomarker of pro-B type natriuretic peptide (Pro-BNP), kidney function index of serum creatinine (Scr), kidney fibrosis index of collagen volume fraction (CVF) and connective tissue growth factor (CTGF) were evaluated and contrasted. Metabolomic method was employed to analyze the endogenous metabolites changes in kidneys. Besides, contents of catalase (CAT), xanthine oxidase (XOD), nitricoxidesynthase (NOS), glutathione peroxidase 4 (GPX4), the x(c)(-) cysteine/glutamate antiporter (SLC7A11) and ferritin heavy chain (FTH1) in kidney were quantitatively analyzed. In addition, ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to analyze the chemical composition of GXN and network pharmacology was used to predict possible mechanisms and the active ingredients of GXN. RESULTS: The cardiac function indexes of EF, CO and LV Vol, kidney functional indicators of Scr, the degree of kidney fibrosis indicators CVF and CTGF were all relieved to different extent for the model mice treated with GXN. 21 differential metabolites involved in redox regulation, energy metabolism, organic acid metabolism, nucleotide metabolism, etc were identified. Aspartic acid, homocysteine, glycine, and serine, methionine, purine, phenylalanine and tyrosine metabolism were found to be the core redox metabolic pathways regulated by GXN. Furthermore, GXN were found to increase CAT content, upregulate GPX4, SLC7A11 and FTH1 expression in kidney significantly. Not only that, GXN also showed good effect in down-regulating XOD and NOS contents in kidney. Besides, 35 chemical constituents were initially identified in GXN. Active ingredients of GXN-targets-related enzymes/transporters-metabolites network was established to find out that GPX4 was a core protein for GXN and the top 10 active ingredients with the most relevant to renal protective effects of GXN were rosmarinic acid, caffeic acid, ferulic acid, senkyunolide E, protocatechualdehyde, protocatechuic acid, danshensu, L-Ile, vanillic acid, salvianolic acid A. CONCLUSION: GXN could significantly maintain cardiac function and alleviate the progression of fibrosis in the kidney for HF mice, and the mechanisms of action were related to regulating redox metabolism of aspartate, glycine, serine, and cystine metabolism and SLC7A11/GPX4 axis in kidney. The cardio-renal protective effect of GXN may be attributed to multi-components like rosmarinic acid, caffeic acid, ferulic acid, senkyunolide E, protocatechualdehyde, protocatechuic acid, danshensu, L-Ile, vanillic acid, salvianolic acid A et al.

The repertoire of copy number alteration signatures in human cancer.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

Ultra-precision manufacturing of microlens arrays using an optimum machining process chain.

There are still significant challenges in the accurate and uniform manufacturing of microlens arrays (MLAs) with advanced ultra-precision diamond cutting technologies due to increasingly stringent requirements and shape complexity. In this paper, an optimum machining process chain is proposed based on the integration of a micro-abrasive fluid jet polishing (MAFJP) process to improve the machining quality by single point diamond turning (SPDT). The MLAs were first machined and compensated by SPDT until the maximum possible surface quality was obtained. The MAFJP was used to correct the surface form error and reduce the nonuniformity for each lens. The polishing characterization was analyzed based on the computational fluid dynamics (CFD) method to enhance the polishing efficiency. To better polish the freeform surface, two-step tool path generation using a regional adaptive path and a raster and cross path was employed. Moreover, the compensation error map was also investigated by revealing the relationship between the material removal mechanism and the surface curvature and polishing parameters. A series of experiments were conducted to prove the reliability and capability of the proposed method. The results indicate that the two integrated machining processes are capable of improving the surface form accuracy with a decrease in PV value from 1.67 µm to 0.56 µm and also elimination of the nonuniform surface error for the lenses.

Higher Serum Magnesium Is a Survival Advantage in Maintenance Hemodialysis Patients.

INTRODUCTION: Elevated serum magnesium is common and associated with survival in maintenance hemodialysis (MHD) patients by observational studies. However, the results of these studies were underpowered and inconclusive. This work was designed to explore the predictive value of serum magnesium on the mortality of patients with MHD. METHODS: We retrospectively analyzed mortality rates in 267 patients with MHD. The collected parameters included anthropometrics and laboratory parameters. Serum magnesium included baseline serum magnesium (BS-Mg) and average serum magnesium (AS-Mg). Receiver operator characteristic (ROC) curves were drawn, and multivariate Cox proportional hazards models were applied to identify the predictive value of serum magnesium on patient mortality. RESULTS: During the 64-month follow-up period, 121 (45.3%) all-cause and 75 (28.1%) cardiovascular disease (CVD) deaths were recorded. The predictability of death of AS-Mg yielded results similar to those of serum albumin, secondary only to age, and superior to those of the high-sensitivity C-reactive protein (Hs-CRP), BS-Mg, by ROC curves. There were significant differences in all-cause and CVD mortality between the four groups (by quartile). Kaplan-Meier survival analyses revealed that the lowest 25th percentile had the poorest prognosis for both all-cause mortality (p < 0.001) and CVD mortality (p = 0.011). Finally, multivariate Cox proportional hazards models showed that increased age, increased Hs-CRP, decreased serum albumin, and AS-Mg were independent predictors of all-cause and CVD mortality. The hazard ratios of AS-Mg (per 0.01 mmol/L) were 0.925 (95% confidence interval, 0.884-0.968, p = 0.001) for all-cause mortality and 0.976 (95% confidence interval, 0.954-0.999, p = 0.040) for CVD mortality. CONCLUSION: AS-Mg was a good indicator for assessing all-cause and CVD mortality in patients with MHD in China. Higher serum magnesium had a survival advantage. Further studies with larger sample sizes should be needed to clarify the best reference value for maximizing the beneficial effects of magnesium.