Targeting the PHF8/YY1 axis suppresses cancer cell growth through modulation of ROS.

High levels of mitochondrial reactive oxygen species (mROS) are linked to cancer development, which is tightly controlled by the electron transport chain (ETC). However, the epigenetic mechanisms governing ETC gene transcription to drive mROS production and cancer cell growth remain to be fully characterized. Here, we report that protein demethylase PHF8 is overexpressed in many types of cancers, including colon and lung cancer, and is negatively correlated with ETC gene expression. While it is well known to demethylate histones to activate transcription, PHF8 demethylates transcription factor YY1, functioning as a co-repressor for a large set of nuclear-coded ETC genes to drive mROS production and cancer development. In addition to genetically ablating PHF8, pharmacologically targeting PHF8 with a specific chemical inhibitor, iPHF8, is potent in regulating YY1 methylation, ETC gene transcription, mROS production, and cell growth in colon and lung cancer cells. iPHF8 exhibits potency and safety in suppressing tumor growth in cell-line- and patient-derived xenografts in vivo. Our data uncover a key epigenetic mechanism underlying ETC gene transcriptional regulation, demonstrating that targeting the PHF8/YY1 axis has great potential to treat cancers.

Investigating the shared genetic architecture of uterine leiomyoma and breast cancer: A genome-wide cross-trait analysis.

Little is known regarding the shared genetic architecture or causality underlying the phenotypic association observed for uterine leiomyoma (UL) and breast cancer (BC). Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) conducted in each trait, we investigated the genetic overlap and causal associations of UL with BC overall, as well as with its subtypes defined by the status of estrogen receptor (ER). We observed a positive genetic correlation between UL and BC overall (rg = 0.09, p = 6.00 × 10-3), which was consistent in ER+ subtype (rg = 0.06, p = 0.01) but not in ER- subtype (rg = 0.06, p = 0.08). Partitioning the whole genome into 1,703 independent regions, local genetic correlation was identified at 22q13.1 for UL with BC overall and with ER+ subtype. Significant genetic correlation was further discovered in 9 out of 14 functional categories, with the highest estimates observed in coding, H3K9ac, and repressed regions. Cross-trait meta-analysis identified 9 novel loci shared between UL and BC. Mendelian randomization demonstrated a significantly increased risk of BC overall (OR = 1.09, 95% CI = 1.01-1.18) and ER+ subtype (OR = 1.09, 95% CI = 1.01-1.17) for genetic liability to UL. No reverse causality was found. Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis, pleiotropic loci, as well as a putative causal relationship between UL and BC, highlighting an intrinsic link underlying these two complex female diseases.

From virus to immune system: Harnessing membrane-derived vesicles to fight COVID-19 by interacting with biological molecules.

Emerging and re-emerging viral pandemics have emerged as a major public health concern. Highly pathogenic coronaviruses, which cause severe respiratory disease, threaten human health and socioeconomic development. Great efforts are being devoted to the development of safe and efficacious therapeutic agents and preventive vaccines to combat them. Nevertheless, the highly mutated virus poses a challenge to drug development and vaccine efficacy, and the use of common immunomodulatory agents lacks specificity. Benefiting from the burgeoning intersection of biological engineering and biotechnology, membrane-derived vesicles have shown superior potential as therapeutics due to their biocompatibility, design flexibility, remarkable bionics, and inherent interaction with phagocytes. The interactions between membrane-derived vesicles, viruses, and the immune system have emerged as a new and promising topic. This review provides insight into considerations for developing innovative antiviral strategies and vaccines against SARS-CoV-2. First, membrane-derived vesicles may provide potential biomimetic decoys with a high affinity for viruses to block virus-receptor interactions for early interruption of infection. Second, membrane-derived vesicles could help achieve a balanced interplay between the virus and the host's innate immunity. Finally, membrane-derived vesicles have revealed numerous possibilities for their employment as vaccines.

Hydrogen Spillover Phenomenon at the Interface of Metal-Supported Electrocatalysts for Hydrogen Evolution.

ConspectusHydrogen spillover, as a well-known phenomenon for thermal hydrogenation, generally involves the migration of active hydrogen on the surface of metal-supported catalysts. For thermocatalytic hydrogenation, hydrogen spillover generally takes place from metals with superiority for dissociating hydrogen molecules to supports with strong hydrogen adsorption under a H2 environment with high pressures. The former can bring high hydrogen chemical potential to largely reduce the kinetic barrier of the migration of active hydrogen species from metals to supports. At the same time, the latter can make H* migration thermodynamically spontaneous. For these reasons, hydrogen spillover is a common interfacial phenomenon occurring on metal-supported catalysts during thermocatalysis. Recently, this phenomenon has been observed for the exceptionally enhanced electrocatalytic performance for hydrogen evolution and other electrocatalytic organic synthesis. Different from hydrogen spillover for thermocatalysis under high H2 pressure, hydrogen spillover for electrocatalysis involves the migration of active hydrogen species (H*) from metals with strong hydrogen adsorption to supports with weak hydrogen adsorption, thereby suffering from a thermodynamically unfavorable process accompanied by a high kinetic barrier. Thus, the occurrence of hydrogen spillover at the electrocatalytic interface is not easy, and successful cases are rare. Understanding the underlying nature of hydrogen spillover at the electrocatalytic interface of metal-supported catalysts is critical to the rational design of advanced electrocatalysts.In this Account, we provide in-depth insights into recent advances in hydrogen spillover at the electrocatalytic interface for a significantly enhanced hydrogen evolution performance. Electron accumulation at the metal-support interface induces severe interfacial H* trapping and is recognized as the main factor in the failed hydrogen spillover. Given this, we developed two novel strategies to promote the occurrence of hydrogen spillover at the electrocatalytic interface. These strategies include (i) the introduction of ligand environments to enrich the local hydrogen coverage on metals and lower the barrier for interfacial hydrogen spillover and (ii) the minimization of work function difference between metals and supports (ΔΦ) to relieve electron accumulation and lower the kinetic barrier for hydrogen spillover. Also, we summarize the previously reported strategy of shortening the metal-support interface distance to lower the kinetic barrier for interfacial hydrogen spillover. Afterward, some criteria and methodologies are proposed to identify the hydrogen spillover phenomenon at the electrocatalytic interface. Finally, the remaining challenges and future perspectives are also discussed. Based on this Account, we aim to provide new insights into electrocatalysis, particularly the targeted control of hydrogen spillover at the electrocatalytic interface, and then to offer guidelines for the rational design of advanced electrocatalysts.

Site-specific allylic C-H bond functionalization with a copper-bound N-centred radical.

Methods for selective C-H bond functionalization have provided chemists with versatile and powerful toolboxes for synthesis, such as the late-stage modification of a lead compound without the need for lengthy de novo synthesis1-5. Cleavage of an sp3 C-H bond via hydrogen atom transfer (HAT) is particularly useful, given the large number of available HAT acceptors and the diversity of reaction pathways available to the resulting radical intermediate6-17. Site-selectivity, however, remains a formidable challenge, especially among sp3 C-H bonds with comparable properties. If the intermediate radical could be further trapped enantioselectively, this should enable highly site- and enantioselective functionalization of C-H bonds. Here we report a copper (Cu)-catalysed site- and enantioselective allylic C-H cyanation of complex alkenes, in which a Cu(II)-bound nitrogen (N)-centred radical plays the key role in achieving precise site-specific HAT. This method is shown to be effective for a diverse collection of alkene-containing molecules, including sterically demanding structures and complex natural products and pharmaceuticals.

Sex hormones in COVID-19 severity: The quest for evidence and influence mechanisms.

Studies have reported variable effects of sex hormones on serious diseases. Severe disease and mortality rates in COVID-19 show marked gender differences that may be related to sex hormones. Sex hormones regulate the expression of the viral receptors ACE2 and TMPRSS2, which affect the extent of viral infection and consequently cause variable outcomes. In addition, sex hormones have complex regulatory mechanisms that affect the immune response to viruses. These hormones also affect metabolism, leading to visceral obesity and severe disease can result from complications such as thrombosis. This review presents the latest researches on the regulatory functions of hormones in viral receptors, immune responses, complications as well as their role in COVID-19 progression. It also discusses the therapeutic possibilities of these hormones by reviewing the recent findings of clinical and assay studies.

Risk factors for prostate cancer: An umbrella review of prospective observational studies and mendelian randomization analyses.

BACKGROUND: The incidence of prostate cancer is increasing in older males globally. Age, ethnicity, and family history are identified as the well-known risk factors for prostate cancer, but few modifiable factors have been firmly established. The objective of this study was to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and mendelian randomization (MR) analyses. METHODS AND FINDINGS: We searched PubMed, Embase, and Web of Science from the inception to January 10, 2022, updated on September 9, 2023, to identify meta-analyses and MR studies on prostate cancer. Eligibility criteria for meta-analyses were (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further data synthesis. Similar criteria were applied to MR studies. Meta-analysis was repeated using the random-effects inverse-variance model with DerSimonian-Laird method. Quality assessment was then conducted for included meta-analyses using AMSTAR-2 tool and for MR studies using STROBE-MR and assumption evaluation. Subsequent evidence grading criteria for significant associations in meta-analyses contained sample size, P values and 95% confidence intervals, 95% prediction intervals, heterogeneity, and publication bias, assigning 4 evidence grades (convincing, highly suggestive, suggestive, or weak). Significant associations in MR studies were graded as robust, probable, suggestive, or insufficient considering P values and concordance of effect directions. Finally, 92 selected from 411 meta-analyses and 64 selected from 118 MR studies were included after excluding the overlapping and outdated studies which were published earlier and contained fewer participants or fewer instrument variables for the same exposure. In total, 123 observational associations (45 significant and 78 null) and 145 causal associations (55 significant and 90 null) were categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and environmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Twenty-six overlapping factors between meta-analyses and MR studies were identified, with consistent significant effects found for physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94; accelerator-measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72), height (meta: OR = 1.09, 95% CI: 1.06, 1.12; MR: OR = 1.07, 95% CI: 1.01, 1.15, for aggressive prostate cancer), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97). Methodological limitation is that the evidence grading criteria could be expanded by considering more indices. CONCLUSIONS: In this large-scale study, we summarized the associations of various factors with prostate cancer risk and provided comparisons between observational associations by meta-analysis and genetically estimated causality by MR analyses. In the absence of convincing overlapping evidence based on the existing literature, no robust associations were identified, but some effects were observed for height, physical activity, and smoking.

Phenotypic and genetic effect of carotid intima-media thickness on the risk of stroke.

While carotid intima-media thickness (cIMT) as a noninvasive surrogate measure of atherosclerosis is widely considered a risk factor for stroke, the intrinsic link underlying cIMT and stroke has not been fully understood. We aimed to evaluate the clinical value of cIMT in stroke through the investigation of phenotypic and genetic relationships between cIMT and stroke. We evaluated phenotypic associations using observational data from UK Biobank (N = 21,526). We then investigated genetic relationships leveraging genomic data conducted in predominantly European ancestry for cIMT (N = 45,185) and any stroke (AS, Ncase/Ncontrol=40,585/406,111). Observational analyses suggested an increased hazard of stroke per one standard deviation increase in cIMT (cIMTmax-AS: hazard ratio (HR) = 1.39, 95%CI = 1.09-1.79; cIMTmean-AS: HR = 1.39, 95%CI = 1.09-1.78; cIMTmin-AS: HR = 1.32, 95%CI = 1.04-1.68). A positive global genetic correlation was observed (cIMTmax-AS: [Formula: see text]=0.23, P=9.44 × 10-5; cIMTmean-AS: [Formula: see text]=0.21, P=3.00 × 10-4; cIMTmin-AS: [Formula: see text]=0.16, P=6.30 × 10-3). This was further substantiated by five shared independent loci and 15 shared expression-trait associations. Mendelian randomization analyses suggested no causal effect of cIMT on stroke (cIMTmax-AS: odds ratio (OR)=1.12, 95%CI=0.97-1.28; cIMTmean-AS: OR=1.09, 95%CI=0.93-1.26; cIMTmin-AS: OR=1.03, 95%CI = 0.90-1.17). A putative association was observed for genetically predicted stroke on cIMT (AS-cIMTmax: beta=0.07, 95%CI = 0.01-0.13; AS-cIMTmean: beta=0.08, 95%CI = 0.01-0.15; AS-cIMTmin: beta = 0.08, 95%CI = 0.01-0.16) in the reverse direction MR, which attenuated to non-significant in sensitivity analysis. Our work does not find evidence supporting causal associations between cIMT and stroke. The pronounced cIMT-stroke association is intrinsic, and mostly attributed to shared genetic components. The clinical value of cIMT as a surrogate marker for stroke risk in the general population is likely limited.

Systematical targeted multicomponent characterization and comparison of Arnebiae Radix and its three confusing species by offline two-dimensional liquid chromatography/LTQ-Orbitrap mass spectrometry.

Arnebiae Radix, commonly known as "Zicao," can be easily confused with other compounding species, posing challenges for its clinical use. Here, we developed a comprehensive strategy to systematically characterize the diverse components across Arnebiae Radix and its three confusing species. First, an offline two-dimensional liquid chromatography (2D-LC) system integrating hydrophilic interaction chromatography (HILIC) and reverse phase (RP) separations was established, enabling effective separation and detection of more trace constituents. Second, a polygonal mass defect filtering (MDF) workflow was implemented to screen target ions and generate a precursor ion list (PIL) to guide multistage mass (MSn) data acquisition. Third, a three-step characterization strategy utilizing diagnostic ions and neutral losses was developed for rapid determination of molecular formulas, structure classes, and compound identification. This approach enabled systematic characterization of Arnebiae Radix and its three confusing species, with 437 components characterized including 112 shikonins, 22 shikonfurans, 144 phenolic acids, 131 glycosides, 18 flavonoids, and 10 other compounds. Additionally, 361, 230, 340, and 328 components were identified from RZC, YZC, DZC, and ZZC, respectively, with 142 common components and 30 characteristic components that may serve as potential markers for distinguishing the four species. In summary, this is the first comprehensive characterization and comparison of the phytochemical profiles of Arnebiae Radix and its three confusing species, advancing our understanding of this herbal medicine for quality control.

Diastereomers of Steroidal Alkaloids with Cytotoxic Activities against Non-Small-Cell Lung Cancer from the Bulbs of Fritillaria sinica.

Eleven new steroidal alkaloids, along with nine known related compounds, were isolated from the bulbs of Fritillaria sinica. Seven pairs of diastereomers were identified, including six and four 20-deoxy cevanine-type steroidal alkaloid diastereomers with molecular weights of 413 and 415, respectively. Structures were elucidated based on spectroscopic data analysis, chemical derivatization, and single-crystal X-ray diffraction analysis. Compounds 5, 9, 11, 12, 16, and 20 exhibited significant in vitro cytotoxic activity against non-small-cell lung cancer with CC50 values from 6.8 ± 3.9 to 12 ± 5 µM.

Computational determination of the S1(Ã1A″) absorption spectra of HONO and DONO using full-dimensional neural network potential energy surfaces.

The Ã1A″ ← X̃1A' absorption spectra of HONO and DONO were simulated by a full six-dimensional quantum mechanical method based on the newly constructed potential energy surfaces for the ground and excited electronic states, which were represented by the neural network method utilizing over 36 000 ab initio energy points calculated at the multireference configuration interaction level with Davidson correction. The absorption spectrum of HONO/DONO comprises a superposition of the spectra from two isomers, namely, trans- and cis-HONO/DONO, due to their coexistence in the ground X̃1A' state. Our calculated spectra of both HONO and DONO were found to be in fairly good agreement with the experiment, including the energy positions and widths of the peaks. The dominant progression was assigned to the N=O stretch mode (20n) associated with trans-HONO/DONO, which can be attributed to the promotion of an electron to the π* orbital of N=O. Specifically, the resonances with higher vibrational quanta were found to be in the domain of the Feshbach-type resonances. The assignments of the spectra and mode specificity therein are discussed.

Social support and cognitive activity and their associations with incident cognitive impairment in cognitively normal older adults.

OBJECTIVES: To explore the associations of social support, and cognitive activity with cognitive impairment incidence, and further examine the mediation effect of cognitive activity on the association between social support and cognitive impairment incidence based on a nationwide elderly Chinese cohort. METHODS: We collected the participants from an ongoing cohort of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 9394 older adults aged 65 or more years and free of cognitive impairment who participated in the CLHLS between 2008 and 2018 were included. The information on social support and cognitive activity was collected through a questionnaire. The incident cognitive impairment cases were identified through the Mini-Mental State Examination scale (MMSE). Cox proportional hazard regression models were conducted to calculate the hazard ratios (HRs) and 95% confidence interval (CI) of social support and cognitive activity associated with cognitive impairment. We used casual mediation models to assess the indirect association of cognitive activities underlying the association between social support and cognitive impairment. RESULTS: The adjusted HRs (95% CI) of incident cognitive impairment were 0.956 (0.932 to 0.980), and 0.895 (0.859 to 0.933) associated with per 1 score increase in social support and cognitive activity score, respectively. Better adherence to social support was associated with a higher cognitive activity score (adjusted ß = 0.046, 95% CI[0.032-0.060]). The baseline cognitive activity, as well as the mean cognitive activity at baseline and during the first follow-up wave, mediate the association between social support and the incidence of cognitive impairment, accounting for 11.4% and 12.6% of the total association, respectively. The participants who were aged 80 years or older, or those with mild daily functional limitations gained more benefits in the development of cognitive activity related to social support, leading to a reduction in the risks of cognitive impairment. CONCLUSION: The results of this nationwide cohort provide consistent evidence linking social support, and cognitive activity to reduced risk of subsequent cognitive impairment incidence. These findings provide additional evidence to inform the social strategies to prevent cognitive impairment incidence in elderly people.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction. METHOD: We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models. RESULT: Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis. CONCLUSION: The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

[Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service]. / åŸºäºŽå›½å®¶åŸºæœ¬å ¬å ±å«ç”ŸæœåŠ¡ä½“æ£€çš„ä¸­è€å¹´äºº2åž‹ç³–å°¿ç— é£Žé™©é¢„æµ‹æ¨¡åž‹æž„å»º.

Objective: To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2 (T2DM) in the middle-aged and elderly populations based on the results of a Meta-analysis, and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service. Methods: Cohort studies evaluating T2DM risks were identified in Chinese and English databases. The logistic model utilized Meta-combined effect values such as the odds ratio (OR) to derive ß, the partial regression coefficient, of the logistic model. The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model. Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service. The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7602 individuals who did not have T2DM at their baseline medical checkups done at the community health center. This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people. Results: A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM, including age, central obesity, smoking, physical inactivity, impaired fasting glucose, a reduced level of high-density lipoprotein cholesterol (HDL-C), hypertension, body mass index (BMI), triglyceride glucose (TYG) index, and a family history of diabetes, with the OR values and 95% confidence interval (CI) being 1.04 (1.03, 1.05), 1.55 (1.29, 1.88), 1.36 (1.11, 1.66), 1.26 (1.07, 1.49), 3.93 (2.94, 5.24), 1.14 (1.06, 1.23), 1.47 (1.34, 1.61), 1.11 (1.05, 1.18), 2.15 (1.75, 2.62), and 1.66 (1.55, 1.78), respectively, and the combined ß values being 0.039, 0.438, 0.307, 0.231, 1.369, 0.131, 0.385, 0.104, 0.765, and 0.507, respectively. A total of 37 studies reported the incidence rate, with the combined incidence being 0.08 (0.07, 0.09) and the parameter α being -2.442 for the logistic model. The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7602 individuals who had medical checkups and were followed up for at least once. External validation results showed that the predictive model had an area under curve (AUC) of 0.794 (0.771, 0.816), accuracy of 74.5%, sensitivity of 71.0%, and specificity of 74.7% in the 7602 individuals. Conclusion: The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.

Hydrogen Spillover Mechanism at the Metal-Metal Interface in Electrocatalytic Hydrogenation.

Hydrogen spillover in metal-supported catalysts can largely enhance electrocatalytic hydrogenation performance and reduce energy consumption. However, its fundamental mechanism, especially at the metal-metal interface, remains further explored, impeding relevant catalyst design. Here, we theoretically profile that a large free energy difference in hydrogen adsorption on two different metals (|ΔGH-metal(i) - ΔGH-metal(ii)|) induces a high kinetic barrier to hydrogen spillover between the metals. Minimizing the difference in their d-band centers (Δεd) should reduce |ΔGH-metal(i) - ΔGH-metal(ii)|, lowering the kinetic barrier to hydrogen spillover for improved electrocatalytic hydrogenation. We demonstrated this concept using copper-supported ruthenium-platinum alloys with the smallest Δεd, which delivered record high electrocatalytic nitrate hydrogenation performance, with ammonia production rate of 3.45±0.12 mmol h-1 cm-2 and Faraday efficiency of 99.8±0.2 %, at low energy consumption of 21.4 kWh kgamm-1. Using these catalysts, we further achieve continuous ammonia and formic acid production with a record high-profit space.

Tandem Electrocatalytic Alkyne Semihydrogenation over Bicomponent Catalysts through Hydrogen Spillover.

Electrocatalytic alkyne semihydrogenation under mild conditions is a more attractive approach for alkene production than industrial routes but suffers from either low production efficiency or high energy consumption. Here, we describe a tandem catalytic concept that overcomes these challenges. Component (i), which can trap hydrogen effectively, is partnered with component (ii), which can readily release hydrogen for hydrogenation, to enable efficient generation of active hydrogen on component (i) at low overpotentials and timely (i)-to-(ii) hydrogen spillover and facile desorptive hydrogenation on component (ii). We examine this concept over bicomponent palladium-copper catalysts for the production of representative 2-methyl-3-butene-2-ol (MBE) from 2-methyl-3-butyne-2-ol (MBY) and achieve a record high MBE production rate of 1.44 mmol h-1 cm-2 and a Faraday efficiency of ~88.8 % at a low energy consumption of 1.26 kWh kgMBE -1. With these catalysts, we further achieve 60 h continuous production of MBE with record high profit space.

Epidemiological and genetic evidence for the relationship between ABO blood group and human cancer.

To comprehensively evaluate the etiological role of ABO blood group in human cancer, we conducted a large-scale meta-analysis of 127 publications totaling 20 million participants including 231 737 patients of 20 cancers, supplemented by genetic evidence. Effects of A, AB and B groups on cancer risk were investigated by respectively comparing with O group and their combined counterparts, and subgroup analysis by ethnicity was conducted for O-referent models. For cancer categories, A group increased risk of cancers of oral cavity and nasopharynx, digestive and female genital organs, while both AB and B groups showed associations with cancers of digestive and female genital organs. For individual cancers, A group significantly increased the risk of nine cancers including oral cavity (OR = 1.17, P = .013), stomach (OR = 1.19, P = 3.90 × 10-15 ), pancreas (OR = 1.33, P = 9.89 × 10-33 ), colorectum (OR = 1.09, P = .001), liver (OR = 1.23, P = .011), ovary (OR = 1.13, P = .001), cervix (OR = 1.17, P = .025), bladder (OR = 1.12, P = .025) and breast (OR = 1.06, P = .043). AB group showed associations with only three cancers: stomach (OR = 1.10, P = .007), pancreas (OR = 1.21, P = .001) and ovary (OR = 1.28, P = .006). B group, except for shared associations with A group on pancreas (OR = 1.20, P = 2.27 × 10-5 ) and cervix cancers (OR = 1.13, P = .011), had two distinct associations with esophagus (OR = 1.17, P = .002) and nonmelanoma skin cancers (OR = 0.96, P = .017). Ethnicity-specific analyses revealed the notable effects of non-O groups on pancreatic cancer both in Caucasians and Asians. In genetic analysis, four SNPs were associated with the risk of pancreatic cancer, with rs505922 corresponding to O group showing the strongest protective effect (P = 1.16 × 10-23 ). Our study provided comprehensive evidence of ABO blood group associated with cancers and highlighted its carcinogenic role.

Migraine, chronic kidney disease and kidney function: observational and genetic analyses.

Epidemiological studies demonstrate an association between migraine and chronic kidney disease (CKD), while the genetic basis underlying the phenotypic association has not been investigated. We aimed to help avoid unnecessary interventions in individuals with migraine through the investigation of phenotypic and genetic relationships underlying migraine, CKD, and kidney function. We first evaluated phenotypic associations using observational data from UK Biobank (N = 255,896). We then investigated genetic relationships leveraging genomic data in European ancestry for migraine (Ncase/Ncontrol = 48,975/540,381), CKD (Ncase/Ncontrol = 41,395/439,303), and two traits of kidney function (estimated glomerular filtration rate [eGFR, N = 567,460] and urinary albumin-to-creatinine ratio [UACR, N = 547,361]). Observational analyses suggested no significant association of migraine with the risk of CKD (HR = 1.13, 95% CI = 0.85-1.50). While we did not find any global genetic correlation in general, we identified four specific genomic regions showing significant for migraine with eGFR. Cross-trait meta-analysis identified one candidate causal variant (rs1047891) underlying migraine, CKD, and kidney function. Transcriptome-wide association study detected 28 shared expression-trait associations between migraine and kidney function. Mendelian randomization analysis suggested no causal effect of migraine on CKD (OR = 1.03, 95% CI = 0.98-1.09; P = 0.28). Despite a putative causal effect of migraine on an increased level of UACR (log-scale-beta = 0.02, 95% CI = 0.01-0.04; P = 1.92 × 10-3), it attenuated to null when accounting for both correlated and uncorrelated pleiotropy. Our work does not find evidence supporting a causal association between migraine and CKD. However, our study highlights significant biological pleiotropy between migraine and kidney function. The value of a migraine prophylactic treatment for reducing future CKD in people with migraine is likely limited.

Nuclear magnetic resonance-determined lipoprotein profile and risk of breast cancer: a Mendelian randomization study.

PURPOSE: While crudely quantified lipoproteins have been reported to affect the risk of breast cancer, the effects of subclass lipoproteins characterized by particle size, particle number, and lipidomes remain unknown. METHODS: Utilizing nuclear magnetic resonance-based GWAS of 85 lipoprotein traits, we performed two-sample univariable Mendelian randomization (MR) to evaluate the causal relationship between each trait with breast cancer (Ncase/control = 133,384/113,789) and with its estrogen receptor (ER) subtypes. Then, we applied multivariable MR to investigate the independent effects considering both general and central obesity. RESULTS: In univariable MR, a heterogeneous effect of subclass high-density lipoproteins (HDL) was observed, in which small HDL traits (ORs ranged from 0.89 to 0.94) were associated with a decreased risk of breast cancer while non-small HDLs traits (OR ranged from 1.04 to 1.08) were associated with an increased risk of breast cancer. Very-low-density lipoproteins (VLDL) traits and serum total triglycerides (TG) were associated with a decreased risk of breast cancer (ORs ranged from 0.88 to 0.94). Similar association patterns were found for ER + subtype. In multivariable MR, only the protective effects of small HDL, VLDL and TG on ER + subtype remained significant. CONCLUSION: We identified a heterogeneous effect of subclass HDLs and a consistent protective effect of VLDL on breast cancer. Only the effects of small HDL and VLDL on ER + subtype remained robust after controlling for obesity. These findings provide new insight into the causal pathway underlying lipoproteins and breast cancer.

Increases in BMI contribute to worsening inflammatory biomarkers related to breast cancer risk in women: a longitudinal study.

BACKGROUND: Inflammatory adipokines and cytokines play a pivotal role in linking obesity and breast cancer (BC) risk in women. We investigated the longitudinal associations between BMI change and trajectories of inflammatory biomarkers related to BC risk. METHODS: A longitudinal study was conducted among 442 Chinese women with 3-year repeated measures from 2019 to 2021. Plasma circulating inflammatory biomarkers related to BC risk, including adiponectin (ADP), resistin (RETN), soluble leptin receptor (sOB-R), insulin-like growth factor-binding protein-3 (IGFBP-3), and C-reactive protein (CRP), were examined annually. Linear mixed-effect models (LMM) were applied to investigate associations of time-varying BMI with trajectories of biomarkers. We additionally examined the modification effect of baseline BMI groups, menopausal status, and metabolic syndrome. RESULTS: BMI was associated with increased levels of RETN, CRP, sOB-R, and decreased levels of ADP at baseline. An increasing BMI rate was significantly associated with an average 3-year increase in RETN (ß = 0.019, 95% CI 0.004 to 0.034) and sOB-R (ß = 0.022, 95% CI 0.009 to 0.035), as well as a decrease in ADP (ß = - 0.006, 95% CI - 0.012 to 0.001). These associations persisted across different baseline BMI groups. An increasing BMI rate was significantly associated with an average 3-year increase in CRP levels among normal weight (ß = 0.045, 95% CI 0.001 to 0.088) and overweight (ß = 0.060, 95% CI 0.014 to 0.107) women. As BMI increased over time, a more remarkable decrease in ADP was observed among women with metabolic syndrome (ß = - 0.016, 95% CI - 0.029 to - 0.004) than those without metabolic syndrome at baseline. CONCLUSIONS: A higher increase rate of BMI was associated with poorer trajectories of inflammatory biomarkers related to BC risk. Recommendations for BMI reduction may benefit BC prevention in women, particularly for those with metabolic syndrome.