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Abstract Background Both body mass index (BMI) and genetic factors independently contribute to cardiovascular disease (CVD). However, it is unclear whether genetic risk modifies the association between BMI and the risk of incident CVD. This study aimed to investigate whether BMI categories and genetic risk jointly and interactively contribute to incident CVD events, including hypertension (HTN), atrial fibrillation (AF), coronary heart disease (CHD), stroke, and heart failure (HF). Methods A total of 496,851 participants from the UK Biobank with one or more new-onset CVD events were included in the analyses. BMI was categorized as normal weight (

Abstract Background Both ambient air pollution and lifestyle factors contribute to the incidence of non-alcoholic fatty liver disease (NAFLD), but previous studies usually focused on single-factor associations. We aimed to assess the joint associations of ambient air pollution and lifestyle with the NAFLD risk and investigate whether lifestyle modifies the association of air pollution with NAFLD risk. Methods A total of 417,025 participants from the UK Biobank were included in this study. Annual average concentrations of NO2, NOx, PM2.5, PM10, and PM2.5−10 were estimated. A composite lifestyle score was determined based on physical activity, alcohol intake, smoking status, dietary patterns, sedentary time, and sleep duration. Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs), as well as the population attributable fraction (PAF). Potential additive interactions of air pollution with lifestyle were also examined by the relative excess risk due to the interaction (RERI) and the attributable proportion due to the interaction (AP). Results 4752 (1.14%) incident NAFLD events were recorded. Long-term exposure to air pollutants and an unhealthy lifestyle were significantly associated with the increased risk of incident NAFLD. Lifestyle was the primary factor of incident NAFLD, with a PAF of 37.18% (95% CI: 29.67%, 44.69%). In addition, a significant additive interaction between air pollution and lifestyle for NAFLD risk was observed (RERI: 0.36, 95% CI: 0.09–0.63). Conclusions Long-term exposure to ambient air pollutants and poor lifestyle were jointly associated with a higher risk of NAFLD.

Abstract The double carbon goal is a wide and profound economic and social systematic change. It is also crucial to China's sustainable development. How to promote emission reduction, the National High-Tech Industrial Development Zones(NHTDZs) policy is the key to addressing this problem. Based on urban data from 2003 to 2019 from China, this paper uses the multi-time point asymptotic difference method to explore the impact of the NHTDZs establishment on carbon emissions. The establishment of NHTDZs reduces CO2 emissions, which remains valid through robustness tests. The mechanism analysis demonstrated that the construction of NHTDZs reduces CO2 emissions by increasing innovation levels, increasing research expenditures and emphasizing human capital. Further analysis shown that geographic location, initial resource endowment, population size, and level of green finance development are difference in different cities. This provides guidance promoting the development of NHTDZs and future layout.

Abstract Background ccRCC is the prevailing form of RCC, accounting for the majority of cases. The formation of cancer and the body's ability to fight against tumors are strongly connected to Gamma delta (γδ) T cells. Methods We examined and analyzed the gene expression patterns of 535 individuals diagnosed with ccRCC and 72 individuals serving as controls, all sourced from the TCGA-KIRC dataset, which were subsequently validated through molecular biology experiments. Results In ccRCC, we discovered 304 module genes (DEGRGs) that were ex-pressed differentially and linked to γδ T cells. A risk model for ccRCC was constructed using 13 differentially DEGRGs identified through univariate Cox and LASSO regression analyses, which were found to be associated with prognosis. The risk model exhibited outstanding performance in both the training and validation datasets. The comparison of immune checkpoint inhibitors and the tumor immune microenvironment between the high- and low-risk groups indicates that immunotherapy could lead to positive results for low-risk patients. Moreover, the inhibition of ccRCC cell proliferation, migration, and invasion was observed in cell culture upon knocking down TMSB10, a gene associated with different types of cancers. Conclusions In summary, we have created a precise predictive biomarker using a risk model centered on γδ T cells, which can anticipate clinical results and provide direction for the advancement of innovative targeted therapies.

Abstract Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urogenital tract. Given that ccRCC is often resistant to radiotherapy and traditional chemotherapy, the clinical treatment of patients with ccRCC remains a challenge. The present study found that ATAD2 was significantly upregulated in ccRCC tissues. In vitro and in vivo experiments showed that the inhibition of ATAD2 expression mitigated the aggressive phenotype of ccRCC. ATAD2 was also associated with glycolysis in ccRCC. Interestingly, we found that ATAD2 could physically interact with c-Myc and promote the expression of its downstream target gene, thereby enhancing the Warburg effect of ccRCC. Overall, our study emphasizes the role of ATAD2 in ccRCC. The targeted expression or functional regulation of ATAD2 could be a promising method to reduce the proliferation and progression of ccRCC.

Abstract The survival of tumor cells in the bloodstream, and vasculature adhesion at metastatic sites are crucial for tumor metastasis. Perivascular invasion aids tumor cell self-renewal, survival, and formation of metastases by facilitating readily available oxygen, nutrients, and endothelial-derived paracrine factors. Renal cell carcinoma (RCC) is among the most prevalent tumors of the urinary system, and the formation of venous tumor thrombus (VTT) is a characteristic feature of RCC. We observed high expression of L1CAM in the VTT with vessel wall invasion. L1CAM promotes the adhesion, migration, and invasion ability of RCC and enhances metastasis by interacting with ITGA5, which elicits activation of signaling downstream of integrin α5β1. L1CAM promotes ADAM17 transcription to facilitate transmembrane ectodomain cleavage and release of soluble L1CAM. In response to soluble L1CAM, vascular endothelial cells release several cytokines and chemokines. Endothelial-derived CXCL5 and its receptor CXCR2 promote the migration and intravasation of RCC toward endothelial cells suggesting that crosstalk between endothelial cells and tumor cells has a direct guiding role in driving the metastatic spread of RCC. LICAM plays a crucial role in the invasive ability of RCC, and regulation of L1CAM expression may contribute therapeutically to preventing RCC progression.

Introduction: The global challenge of sustainable development is encapsulated in the 17 United Nations Sustainable Development Goals (SDGs), to which China is committed. As outlined in the UNESCO World Water Assessment Program (WWAP) report, water fundamentally impacts on sustainable development, making the achievement of SDG 6 (water and sanitation) crucial. China, as a leading manufacturing hub with extensive agriculture, grapples with challenges in monitoring SDG 6 indicators, especially concerning water quality.Methods: In light of these challenges, this study explores the utilisation of non-traditional data sources, specifically citizen science (CS), to address aspects of SDG 6 monitoring pertaining to water quality monitoring in China. Specifically, our study employs a combination of research techniques–including a literature review, semi-structured interviews, and participant observation–to critically evaluate 13 existing CS projects focused on water quality monitoring. A modified CS assessment framework has been utilised, offering a comprehensive, structured approach to evaluation.Results: In total, this analysis identified four key findings: 1) CS projects related to water monitoring in China predominantly focus on freshwater and drinking water; 2) Greater attention and support are needed for freshwater monitoring initiatives to align with China’s “ecological civilisation” policy; 3) Replacing “citizen science” with “public participation” may promote broader acceptance of these initiatives; 4) Training requires enhancement, particularly for participants from third- and fourth-tier cities as well as remote rural areas.Discussion: This research delineates feasible strategies for the future expansion and integration of CS projects throughout the country. It advocates for a more participatory data-driven approach to sustainable water management in China, given the highlighted challenges and the potential solutions unearthed through the use of citizen science and public participation.

Abstract Background Prostate cancer (PCa) is a common malignant tumor of the genitourinary system. Clinical intervention in advanced PCa remains challenging. Tropomyosins 2 (TPM2) are actin-binding proteins and have been found as a biomarker candidate for certain cancers. However, no studies have explored the role of TPM2 in PCa and its regulatory mechanism. Methods TPM2 expression was assessed in Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) PCa patient dataset. The effect of TPM2 on PCa progression was assessed in vitro and in vivo by quantifying proliferation, migration, invasion and tumor growth assays, and the mechanism of TPM2 in PCa progression was gradually revealed by Western blotting, immunoprecipitation, and immunofluorescence staining arrays. Results TPM2 was found to be severely downregulated in tumor tissues of PCa patients compared with tumor-adjacent normal tissues. In vitro experiments revealed that TPM2 overexpression inhibited PCa cell proliferation, invasion and androgen-independent proliferation. Moreover, TPM2 overexpression inhibited the growth of subcutaneous xenograft tumors in vivo. Mechanistically, this effect was noted to be dependent on PDZ-binding motif of TPM2. TPM2 competed with YAP1 for binding to PDLIM7 through the PDZ-binding motif. The binding of TPM2 to PDLIM7 subsequently inhibited the nuclear transport function of PDLIM7 for YAP1. YAP1 sequestered in the cytoplasm phosphorylated at S127, resulting in its inactivation or degradation which in turn inhibited the expression of YAP1 downstream target genes. Conclusions This study investigated the role of TPM2, PDLIM7, and YAP1 in PCa progression and castration resistance. TPM2 attenuates progression of PCa by blocking PDLIM7-mediated nuclear translocation of YAP1. Accordingly, targeting the expression or functional modulation of TPM2, PDLIM7, or YAP1 has the potential to be an effective therapeutic approach to reduce PCa proliferation and prevent the progression of castration-resistant prostate cancer (CRPC).

Stellar abundances for a large number of stars provide key information for the study of Galactic formation history. Large spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and LAMOST take median-to-low-resolution ( R ≲ 5000) spectra in the full optical wavelength range for millions of stars. However, the line-blending effect in these spectra causes great challenges for elemental abundance determination. Here we employ DD-Payne , a data-driven method regularized by differential spectra from stellar physical models, to the DESI early data release spectra for stellar abundance determination. Our implementation delivers 15 labels, including effective temperature T _eff , surface gravity $\mathrm{log}\,g$ , microturbulence velocity v _mic , and the abundances for 12 individual elements, namely C, N, O, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, and Ni. Given a spectral signal-to-noise ratio of 100 per pixel, the internal precisions of the label estimates are about 20 K for T _eff , 0.05 dex for $\mathrm{log}\,g$ , and 0.05 dex for most elemental abundances. These results agree with the theoretical limits from the Crámer–Rao bound calculation within a factor of 2. The majority of the accreted halo stars contributed by the Gaia–Enceladus–Sausage are discernible from the disk and in situ halo populations in the resultant [Mg/Fe]–[Fe/H] and [Al/Fe]–[Fe/H] abundance spaces. We also provide distance and orbital parameters for the sample stars, which spread over a distance out to ∼100 kpc. The DESI sample has a significantly higher fraction of distant (or metal-poor) stars than the other existing spectroscopic surveys, making it a powerful data set for studying the Galactic outskirts. The catalog is publicly available.

【Objective】The large-scale cultivation of white sapote in China is in its initial stage, and the adaptability of white sapote in Guangdong is not clear yet. Clarifying the phenology of white sapote in Guangdong as well as its flowering and fruiting habits and summarizing cultivation techniques can provide guidance for the large-scale cultivation of white sapote.【Method】A white sapote cultivar 'W4' was used as the research material to observe its botanical characteristics, phenology, fruit growth law, fruit setting and fruit quality. The key points of its high-quality cultivation techniques were expounded from aspects of garden construction, fertilizer and water management, shaping and pruning, pest control and fruit harvesting.【Result】The flowering period of white sapote on was from late January to early February, and there were three growth peaks of shoot growth in April, June and September, respectively. The slow growth period was from November to next February. During fruit growth and development, there were three fruit-dropping peaks, which were in mid-March, mid-April and May to June before harvest. The average fruit weight is 148.93 g, and the shape index is 0.83. The fruit melts in the mouth like ice cream, the edible rate is 79.7%, the soluble solid content is 20.36%, and the solid acid ratio is 45.83. White sapote can withstand a low temperature of-2--4℃, which is higher than the average temperature of the coldest month(January) in Guangdong. Therefore, it can be cultivated in most areas of Guangdong.【Conclusion】This variety has good growth potential, strong adaptability, large single fruit quality, good flavor and excellent quality, and has high economic benefit in the market. It is a new fruit variety with planting potential. Attention should be paid to preventing anthracnose and sooty blotch in cultivation.White sapote is suitable for planting on the slope with loose soil and good drainage and irrigation in Guangdong. Dwarfing treatment should be done and tree shape be controlled for young trees.

Abstract Background Body mass index (BMI) has been found to be associated with a decreased risk of non-small cell lung cancer (NSCLC); however, the effect of BMI trajectories and potential interactions with genetic variants on NSCLC risk remain unknown. Methods Cox proportional hazards regression model was applied to assess the association between BMI trajectory and NSCLC risk in a cohort of 138,110 participants from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. One-sample Mendelian randomization (MR) analysis was further used to access the causality between BMI trajectories and NSCLC risk. Additionally, polygenic risk score (PRS) and genome-wide interaction analysis (GWIA) were used to evaluate the multiplicative interaction between BMI trajectories and genetic variants in NSCLC risk. Results Compared with individuals maintaining a stable normal BMI (n = 47,982, 34.74%), BMI trajectories from normal to overweight (n = 64,498, 46.70%), from normal to obese (n = 21,259, 15.39%), and from overweight to obese (n = 4,371, 3.16%) were associated with a decreased risk of NSCLC (hazard ratio [HR] for trend = 0.78, P < 2×10−16). An MR study using BMI trajectory associated with genetic variants revealed no significant association between BMI trajectories and NSCLC risk. Further analysis of PRS showed that a higher GWAS-identified PRS (PRSGWAS) was associated with an increased risk of NSCLC, while the interaction between BMI trajectories and PRSGWAS with the NSCLC risk was not significant (P sPRS= 0.863 and P wPRS= 0.704). In GWIA analysis, four independent susceptibility loci (P < 1×10−6) were found to be associated with BMI trajectories on NSCLC risk, including rs79297227 (12q14.1, located in SLC16A7, P interaction = 1.01×10−7), rs2336652 (3p22.3, near CLASP2, P interaction = 3.92×10−7), rs16018 (19p13.2, in CACNA1A, P interaction = 3.92×10−7), and rs4726760 (7q34, near BRAF, P interaction = 9.19×10−7). Functional annotation demonstrated that these loci may be involved in the development of NSCLC by regulating cell growth, differentiation, and inflammation. Conclusions Our study has shown an association between BMI trajectories, genetic factors, and NSCLC risk. Interestingly, four novel genetic loci were identified to interact with BMI trajectories on NSCLC risk, providing more support for the aetiology research of NSCLC. Trial registration http://www.clinicaltrials.gov , NCT01696968 .

Abstract Background Acute kidney injury (AKI) is a major complication following cardiac surgery that substantially increases morbidity and mortality. Current diagnostic guidelines based on elevated serum creatinine and/or the presence of oliguria potentially delay its diagnosis. We presented a series of models for predicting AKI after cardiac surgery based on electronic health record data. Methods We enrolled 1457 adult patients who underwent cardiac surgery at Nanjing First Hospital from January 2017 to June 2019. 193 clinical features, including demographic characteristics, comorbidities and hospital evaluation, laboratory test, medication, and surgical information, were available for each patient. The number of important variables was determined using the sliding windows sequential forward feature selection technique (SWSFS). The following model development methods were introduced: extreme gradient boosting (XGBoost), random forest (RF), deep forest (DF), and logistic regression. Model performance was accessed using the area under the receiver operating characteristic curve (AUROC). We additionally applied SHapley Additive exPlanation (SHAP) values to explain the RF model. AKI was defined according to Kidney Disease Improving Global Outcomes guidelines. Results In the discovery set, SWSFS identified 16 important variables. The top 5 variables in the RF importance matrix plot were central venous pressure, intraoperative urine output, hemoglobin, serum potassium, and lactic dehydrogenase. In the validation set, the DF model exhibited the highest AUROC (0.881, 95% confidence interval [CI] 0.831–0.930), followed by RF (0.872, 95% CI 0.820–0.923) and XGBoost (0.857, 95% CI 0.802–0.912). A nomogram model was constructed based on intraoperative longitudinal features, achieving an AUROC of 0.824 (95% CI 0.763–0.885) in the validation set. The SHAP values successfully illustrated the positive or negative contribution of the 16 variables attributed to the output of the RF model and the individual variable’s effect on model prediction. Conclusions Our study identified 16 important predictors and provided a series of prediction models to enhance risk stratification of AKI after cardiac surgery. These novel predictors might aid in choosing proper preventive and therapeutic strategies in the perioperative management of AKI patients.

Background: In many solid tumors, CD44 has been identified as a cancer stem cell marker as well as an important molecular in cancer progression and metastasis, making it attractive for potential therapeutic applications. However, our knowledge of the biological function and mechanism of CD44 in clear cell renal cell carcinoma (ccRCC) is limited. Methods: In this study, the expression, prognostic values and functional enrichment analysis of CD44 in ccRCC were analyzed using public databases. Quantitative real-time PCR (qRT-PCR), Western blotting, and immunohistochemical (IHC) assays were taken to detect CD44 expression in ccRCC tissues. The effects of CD44 on the proliferation, migration and invasion of ccRCC cells were investigated by gain-of-function and loss-of-function experiments. Subcutaneous models further confirmed the role of CD44 in tumor growth. The relationship between CD44, HAS1 and MMP9 was investigated to uncover the regulatory mechanism of CD44 in ccRCC. Results: CD44 was significantly upregulated in ccRCC and associated with poor overall survival (OS). Based on the functional enrichment analysis and PPI network, we found that CD44 had associations with ECM interaction and focal adhesion pathway. Clinical ccRCC sample validation revealed that CD44 mRNA and protein expression were significantly increased in ccRCC tissues, and strong CD44 staining was observed in four metastatic ccRCC cases. In vitro experiments showed that CD44 overexpression promoted cell proliferation, migration and invasion. In vivo experiments also demonstrated that CD44 overexpression accelerated tumor formation in mice. Finally, we found that CD44 regulates the expression of HAS1 in ccRCC, which is essential for the secretion of MMP9 and cell migratory ability. Conclusion: The upregulation of CD44 mRNA and protein expressions in ccRCC is indicative of unfavorable clinical prognoses. The CD44/HAS1/MMP9 axis is believed to exert a significant influence on the regulation of ECM degradation and ccRCC metastasis.

About 3% of adult cancers are caused by renal cell carcinoma (RCC) and its pathogenesis remains elusive. Among RCC, clear cell renal cell carcinoma (ccRCC) is the predominant histological subtype. Resistance to conventional treatments leaves few treatment options for advanced ccRCC. Although the transcriptome profile of primary ccRCC has been comprehensively summarized, the transcriptome profile of metastatic ccRCC is still lacking. In this study we identified a list of metastasis-related genes and constructing a metastasis-associated prognostic gene signature. By analyzing data from GSE85258 and GSE105288 datasets, 74 genes were identified as metastasis-related genes. To construct prognostic features, we downloaded the expression data of ccRCC from the Cancer Genome Atlas (TCGA). Metastasis-associated genes were initially selected through the LASSO Cox regression analysis and 12 metastasis-related were included to construct prognostic model. Transcriptome profile, patient prognosis, and immune cell infiltration characteristics differed between low- and high-risk groups after grouping according to median risk score. Through explored the functions of differentially expressed genes (DEGs) between the two groups. Kinesin family member 23 (KIF23) was identified as a prognostic marker in ccRCC patients. Furthermore, inhibition of KIF23 expression reduced the proliferation, migration and invasion of ccRCC cells. We further demonstrated that KIF23 promote nuclear translocation of β-catenin in ccRCC cells, which provides novel insight into the functions and molecular machinery of KIF23 in ccRCC.

BackgroundFew studies have answered the guiding significance of individual components of the Framingham risk score (FRS) to the risk of cardiovascular disease (CVD) after antihypertensive treatment. This study on the systolic blood pressure intervention trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD-BP) aimed to reveal previously undetected association patterns between individual components of the FRS and heterogeneity of treatment effects (HTEs) of intensive blood pressure control.MethodsA self-organizing map (SOM) methodology was applied to identify CVD-risk-specific subgroups in the SPRINT (n = 8,773), and the trained SOM was utilized directly in 4,495 patients from the ACCORD. The primary endpoints were myocardial infarction (MI), non-myocardial infarction acute coronary syndrome (non-MI ACS), stroke, heart failure (HF), death from CVD causes, and a primary composite cardiovascular outcome. Cox proportional hazards models were then used to explore the potential heterogeneous response to intensive SBP control.ResultsWe identified four SOM-based subgroups with distinct individual components of FRS profiles and the CVD risk. For individuals with type 2 diabetes mellitus (T2DM) in the ACCORD or without diabetes in the SPRINT, subgroup I characterized by male with the lowest concentrations for total cholesterol (TC) and high-density lipoprotein (HDL) cholesterol measures, experienced the highest risk for major CVD. Conversely, subgroup III characterized by a female with the highest values for these measures represented as the lowest CVD risk. Furthermore, subgroup II, with the highest systolic blood pressure (SBP) and no antihypertensive agent use at baseline, had a significantly greater frequency of non-MI ACS under intensive BP control, the number needed to harm (NNH) was 84.24 to cause 1 non-MI ACS [absolute risk reduction (ARR) = −1.19%; 95% CI: −2.08, −0.29%] in the SPRINT [hazard ratio (HR) = 3.62; 95% CI: 1.33, 9.81; P = 0.012], and the NNH of was 43.19 to cause 1 non-MI ACS (ARR = −2.32%; 95% CI: −4.63, 0.00%) in the ACCORD (HR = 1.81; 95% CI: 1.01–3.25; P = 0.046). Finally, subgroup IV characterized by mostly younger patients with antihypertensive medication use and smoking history represented the lowest risk for stroke, HF, and relatively low risk for death from CVD causes and primary composite CVD outcome in SPRINT, however, except stroke, a low risk for others were not observed in ACCORD.ConclusionSimilar findings in patients with hypertensive with T2DM or without diabetes by multivariate subgrouping suggested that the individual components of the FRS could enrich or improve CVD risk assessment. Further research was required to clarify the potential mechanism.

Objective: Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application. Methods: This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD. Conclusion: ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.

Using Gaia DR3 data and the wavelet transformation technique, we study the substructures of the Hercules moving group (HMG): Hercules 1 (H1) and Hercules 2 (H2). Spectroscopic survey data from LAMOST, APOGEE, and GALAH are used to obtain metallicities and ages of stars belonging to the HMG. Our analysis leads to several key findings as follows: (a) the HMG is on average richer in metallicity than the Galactic disk, with H2 being metal richer than H1; (b) the HMG likely has a radial metallicity gradient distinct from that of the disk; (c) the HMG is on average older than the disk, with H2 being older than H1; (d) the HMG likely has a radial age gradient distinct from that of the disk; and (e) the metallicity and age distributions of the HMG depend mainly on the Galactic radius but show no dependence on the azimuthal velocity. Taken all together, we conclude that the HMG is composed primarily of stars undergoing radial migration. We suggest that the HMG is associated with a higher-order dynamical resonance of the bar of the Galaxy.

Varying oxygen abundance could impact modeling-inferred ages. This work aims to estimate the ages of dwarfs considering observed oxygen abundance. To characterize 67,503 LAMOST and 4006 GALAH FGK-type dwarf stars, we construct a grid of stellar models, which take into account oxygen abundance as an independent model input. Compared with ages determined with commonly used α -enhanced models, we find a difference of ∼9% on average when the observed oxygen abundance is considered. The age differences between the two types of models are correlated to [Fe/H] and [O/ α ], and they are relatively significant on stars with [Fe/H] ≲ −0.6 dex. Generally, varying 0.2 dex in [O/ α ] will alter the age estimates of metal-rich (−0.2 < [Fe/H] < 0.2) stars by ∼10% and relatively metal-poor (−1 < [Fe/H] < −0.2) stars by ∼15%. Of the low-O stars with [Fe/H] < 0.1 dex and [O/ α ] ∼−0.2 dex, many have fractional age differences of ≥ 10% and even reach up to 27%. The fractional age difference of high-O stars with [O/ α ] ∼0.4 dex reaches up to −33% to −42% at [Fe/H] ≲ −0.6 dex. We also analyze the chemical properties of these stars. We find a decreasing trend of [Fe/H] with ages from 7.5–9 Gyr to 5–6.5 Gyr for the stars from the LAMOST and GALAH. The [O/Fe] of these stars increases with decreasing age from 7.5–9 Gyr to 3–4 Gyr, indicating that the younger population is more O rich.

(1) Background: The association between metabolic obesity phenotypes and incident lung cancer (LC) remains unclear. (2) Methods: Based on the combination of baseline BMI categories and metabolic health status, participants were categorized into eight groups: metabolically healthy underweight (MHUW), metabolically unhealthy underweight (MUUW), metabolically healthy normal (MHN), metabolically unhealthy normal (MUN), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). The Cox proportional hazards model and Mendelian randomization (MR) were applied to assess the association between metabolic obesity phenotypes with LC risk. (3) Results: During a median follow-up of 9.1 years, 3654 incident LC patients were confirmed among 450,482 individuals. Compared with participants with MHN, those with MUUW had higher rates of incident LC (hazard ratio (HR) = 3.24, 95% confidence interval (CI) = 1.33–7.87, p = 0.009). MHO and MHOW individuals had a 24% and 18% lower risk of developing LC, respectively (MHO: HR = 0.76, 95% CI = 0.61–0.95, p = 0.02; MHO: HR = 0.82, 95% CI = 0.70–0.96, p = 0.02). No genetic association of metabolic obesity phenotypes and LC risk was observed in MR analysis. (4) Conclusions: In this prospective cohort study, individuals with MHOW and MHO phenotypes were at a lower risk and MUUW were at a higher risk of LC. However, MR failed to reveal any evidence that metabolic obesity phenotypes would be associated with a higher risk of LC.

Cancer suppression through the inhibition of N-acetyltransferase 10 (NAT10) by its specific inhibitor Remodelin has been demonstrated in a variety of human cancers. Here, we report the inhibitory effects of Remodelin on prostate cancer (PCa) cells and the possible associated mechanisms. The prostate cancer cell lines VCaP, LNCaP, PC3, and DU145 were used. The in vitro proliferation, migration, and invasion of cells were measured by a cell proliferation assay, colony formation, wound healing, and Transwell assays, respectively. In vivo tumor growth was analyzed by transplantation into nude mice. The inhibition of NAT10 by Remodelin not only suppressed growth, migration, and invasion in vitro, but also the in vivo cancer growth of prostate cancer cells. The involvement of NAT10 in DNA replication was assessed by EdU labeling, DNA spreading, iPOND, and ChIP-PCR assays. The inhibition of NAT10 by Remodelin slowed DNA replication. NAT10 was detected in the prereplication complex, and it could also bind to DNA replication origins. Furthermore, the interaction between NAT10 and CDC6 was analyzed by Co-IP. The altered expression of NAT10 was measured by immunofluorescence staining and Western blotting. Remodelin markedly reduced the levels of CDC6 and AR. The expression of NAT10 could be altered under either castration or noncastration conditions, and Remodelin still suppressed the growth of in vitro-induced castration-resistant prostate cancers. The analysis of a TCGA database revealed that the overexpression of NAT10, CDC6, and MCM7 in prostate cancers were correlated with the Gleason score and node metastasis. Our data demonstrated that Remodelin, an inhibitor of NAT10, effectively inhibits the growth of prostate cancer cells under either no castration or castration conditions, likely by impairing DNA replication.

The cGAS (GMP-AMP synthase)-mediated senescence-associated secretory phenotype (SASP) and DNA-induced autophagy (DNA autophagy) have been extensively investigated in recent years. However, cGAS-mediated autophagy has not been elucidated in cancer cells. The described investigation revealed that active DNA autophagy but not SASP activity could be detected in the BT-549 breast cancer cell line with high micronucleus (MN) formation. DNA autophagy was identified as selective autophagy of free genomic DNA in the cytoplasm but not nucleophagy. The process of DNA autophagy in the cytosol could be initiate by cGAS and usually cooperates with SQSTM1-mediated autophagy of ubiquitinated histones. Cytoplasmic DNA, together with nuclear proteins such as histones, could be derived from DNA replication-induced nuclear damage and MN collapse. The inhibition of autophagy through chemical inhibitors as well as the genomic silencing of cGAS or SQSTM1 could suppress the growth and survival of cancer cells, and induced DNA damage could increase the sensitivity to these inhibitors. Furthermore, expanded observations of several other kinds of human cancer cells indicated that high relative DNA autophagy or enhancement of DNA damage could also increase or sensitize these cells to inhibition of DNA autophagy.

The formation of micronuclei (MN) is prevalent in human cancer cells and its role in activating the senescence-associated secretory phenotype (SASP) machinery has been identified recently. However, the role of MN in regulation of SASP signaling still needs to define in practical cancers. Here, we reported that in colorectal cancer cells the expression of NAT10 (N-acetyltransferase 10) could mediate MN formation through DNA replication and NAT10-positive MN could activate SASP by binding to cGAS. The chemical inhibition of NAT10 by Remodelin or genomic depletion could markedly reduce MN formation, SASP activation, and senescence in colorectal cancer cells. Cell stress such as oxidative or hypoxia could upregulate NAT10 and its associated MN formation senescence and expression of SASP factors. Statistical analysis of clinical specimens revealed correlations between NAT10 expression, MN formation, SASP signaling, and the clinicopathological features of colorectal cancer. Our data suggest that NAT10 increasing MN formation and SASP pathway activation, promoting colorectal cancer progression.