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Theory predicts that biological processes of aging may contribute to poor mental health in late life. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. At baseline, participants who were biologically older more often experienced depression/anxiety. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation [SD] of KDM-BA acceleration, 95% confidence intervals [CI]: 3.3%–8.5%; 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%–13.0%). Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention. Theory indicates that biological aging may contribute to poor mental health in late life. Here, authors show advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention.

Carbon-fiber reinforced silicon carbide matrix (C/SiC) composites are typical difficult-to-cut materials due to high hardness and brittleness. Aiming at the problem of the serious tool wear in conventional milling (CM) C/SiC composite process, ultrasonic vibration–assisted milling (UVAM) and conventional milling tests with a diamond-coated milling cutter were conducted. Theoretical and experimental research on the cutting force during the ultrasonic vibration milling process of C/SiC composites is carried out. Based on the kinematics analysis of tool path during ultrasonic vibration milling process, the cutting force model of ultrasonic vibration milling is established, and the influence mechanism of ultrasonic vibration on the cutting force is revealed. Based on the analysis of the evolution law of tool wear profile and wear curve during the traditional milling and ultrasonic vibration milling of C/SiC, the tool wear forms and mechanism of diamond-coated milling cutters in two processing modes and the influence mechanism of ultrasonic vibration on tool wear are revealed. It is found that the main wear mechanism of the diamond-coated milling cutter is abrasive wear, and the main wear form is the coating peeling. Compared with the traditional milling, the tool wear can be reduced by the ultrasonic vibration milling in machining process. In the range of test parameters, the tool wear decreases first and then increases with the increase of ultrasonic amplitude.

Major depressive disorder (MDD) is clinically documented to co-occur with multiple gastrointestinal disorders (GID), but the potential causal relationship between them remains unclear. We aimed to evaluate the potential causal relationship of MDD with 4 GID [gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), peptic ulcer disease (PUD), and non-alcoholic fatty liver disease (NAFLD)] using a two-sample Mendelian randomization (MR) design. We obtained genome-wide association data for MDD from a meta-analysis ( = 480 359), and for GID from the UK Biobank ( ranges: 332 601-486 601) and FinnGen ( ranges: 187 028-218 792) among individuals of European ancestry. Our primary method was inverse-variance weighted (IVW) MR, with a series of sensitivity analyses to test the hypothesis of MR. Individual study estimates were pooled using fixed-effect meta-analysis. Meta-analyses IVW MR found evidence that genetically predicted MDD may increase the risk of GERD, IBS, PUD and NAFLD. Additionally, reverse MR found evidence of genetically predicted GERD or IBS may increase the risk of MDD. Genetically predicted MDD may increase the risk of GERD, IBS, PUD and NAFLD. Genetically predicted GERD or IBS may increase the risk of MDD. The findings may help elucidate the mechanisms underlying the co-morbidity of MDD and GID. Focusing on GID symptoms in patients with MDD and emotional problems in patients with GID is important for the clinical management.

Esophageal squamous dysplasia is believed to be the precursor lesion of esophageal squamous cell carcinoma (ESCC); however, the genetic evolution from dysplasia to ESCC remains poorly understood. Here, we applied multi-region whole-exome sequencing to samples from two cohorts, 45 ESCC patients with matched dysplasia and carcinoma samples, and 13 tumor-free patients with only dysplasia samples. Our analysis reveals that dysplasia is heavily mutated and harbors most of the driver events reported in ESCC. Moreover, dysplasia is polyclonal, and remarkable heterogeneity is often observed between tumors and their neighboring dysplasia samples. Notably, copy number alterations are prevalent in dysplasia and persist during the ESCC progression, which is distinct from the development of esophageal adenocarcinoma. The sharp contrast in the prevalence of the ‘two-hit’ event on TP53 between the two cohorts suggests that the complete inactivation of TP53 is essential in promoting the development of ESCC. The pathogenesis of oesophageal squamous cell carcinoma is a multi-step process but the genetic determinants behind this progression are unknown. Here the authors use multi-region exome sequencing to comprehensively investigate the genetic evolution of precursor dysplastic lesions and untransformed oesophagus.

This discussion is a reply to the comments made by Dr. Jasper Vrugt on the Metropolis‐Hastings (M‐H) algorithm with multiple independent Markov chains proposed by Huang and Merwade (2023a), https://doi.org/10.1029/2023wr034947 concerning the validity of the methodology in estimating Bayesian model averaging (BMA) parameters (weights and variances) of the framework proposed by Raftery et al. (2005), https://doi.org/10.1175/mwr2906.1. In this reply, we address his concerns by emphasizing the motivation of applying the proposed M‐H algorithm to BMA analysis and the applicability of the effective sample size that accounts for the autocorrelation across samples in evaluating the efficiency of Markov chain Monte Carlo sampling. Moreover, the details of sampling procedure for BMA prediction distribution are clarified. On the other hand, we present a fair comparison of the default Expectation‐Maximization, M‐H, and differential evolution adaptive Metropolis (DREAM) algorithms in estimating BMA parameters based on a numerical experiment. Results reinforce the findings obtained from Huang and Merwade (2023a) https://doi.org/10.1029/2023wr034947 and further indicate that the proposed M‐H algorithm is better than the DREAM algorithm in terms of sampling efficiency and prediction accuracy. Accordingly, we raise concerns on the use of DREAM algorithm in BMA analysis and suggest conducting peer reviews on the MODELAVG toolbox. Key Points Metropolis‐Hastings (M‐H) algorithm with multiple independent chains is valid and effective in estimating Bayesian model averaging (BMA) parameters M‐H algorithm is better than the other algorithms in terms of sampling effectiveness and prediction accuracy The uncertainty of BMA parameters is not negligible in multi‐model analysis

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS cannot only alleviate the influence of quantum noise and barren plateaus but also outperforms VQAs with pre-selected ansatze.

Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Background Several biomarkers have been proposed to predict the occurrence of acute kidney injury (AKI); however, their efficacy varies between different trials. The aim of this study was to compare the predictive performance of different candidate biomarkers for AKI. Methods In this systematic review, we searched PubMed, Medline, Embase, and the Cochrane Library for papers published up to August 15, 2022. We selected all studies of adults (> 18 years) that reported the predictive performance of damage biomarkers (neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP)), inflammatory biomarker (interleukin-18 (IL-18)), and stress biomarker (tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein-7 (TIMP-2 × IGFBP-7)) for the occurrence of AKI. We performed pairwise meta-analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) individually. Hierarchical summary receiver operating characteristic curves (HSROCs) were used to summarize the pooled test performance, and the Grading of Recommendations, Assessment, Development and Evaluations criteria were used to appraise the quality of evidence. Results We identified 242 published relevant studies from 1,803 screened abstracts, of which 110 studies with 38,725 patients were included in this meta-analysis. Urinary NGAL/creatinine (diagnostic odds ratio [DOR] 16.2, 95% CI 10.1–25.9), urinary NGAL (DOR 13.8, 95% CI 10.2–18.8), and serum NGAL (DOR 12.6, 95% CI 9.3–17.3) had the best diagnostic accuracy for the risk of AKI. In subgroup analyses, urinary NGAL, urinary NGAL/creatinine, and serum NGAL had better diagnostic accuracy for AKI than urinary IL-18 in non-critically ill patients. However, all of the biomarkers had similar diagnostic accuracy in critically ill patients. In the setting of medical and non-sepsis patients, urinary NGAL had better predictive performance than urinary IL-18, urinary L-FABP, and urinary TIMP-2 × IGFBP-7: 0.3. In the surgical patients, urinary NGAL/creatinine and urinary KIM-1 had the best diagnostic accuracy. The HSROC values of urinary NGAL/creatinine, urinary NGAL, and serum NGAL were 91.4%, 85.2%, and 84.7%, respectively. Conclusions Biomarkers containing NGAL had the best predictive accuracy for the occurrence of AKI, regardless of whether or not the values were adjusted by urinary creatinine, and especially in medically treated patients. However, the predictive performance of urinary NGAL was limited in surgical patients, and urinary NGAL/creatinine seemed to be the most accurate biomarkers in these patients. All of the biomarkers had similar predictive performance in critically ill patients. Trial registration CRD42020207883 , October 06, 2020.

N 6 -methyladenosine (m 6 A) messenger RNA methylation is a gene regulatory mechanism affecting cell differentiation and proliferation in development and cancer. To study the roles of m 6 A mRNA methylation in cell proliferation and tumorigenicity, we investigated human endometrial cancer in which a hotspot R298P mutation is present in a key component of the methyltransferase complex (METTL14). We found that about 70% of endometrial tumours exhibit reductions in m 6 A methylation that are probably due to either this METTL14 mutation or reduced expression of METTL3, another component of the methyltransferase complex. These changes lead to increased proliferation and tumorigenicity of endometrial cancer cells, likely through activation of the AKT pathway. Reductions in m 6 A methylation lead to decreased expression of the negative AKT regulator PHLPP2 and increased expression of the positive AKT regulator mTORC2. Together, these results reveal reduced m 6 A mRNA methylation as an oncogenic mechanism in endometrial cancer and identify m 6 A methylation as a regulator of AKT signalling. Liu et al. show that reduced m 6 A mRNA methylation in endometrial cancer is oncogenic. Mechanistically, the AKT pathway is activated in these tumours due to altered expression of AKT regulators carrying m 6 A on their transcripts.

Background Growing evidence has shown that alterations in the gut microbiota composition were associated with a variety of neuropsychiatric conditions. However, whether such associations reflect causality remains unknown. We aimed to reveal the causal relationships among gut microbiota, metabolites, and neuropsychiatric disorders including Alzheimer’s disease (AD), major depressive disorder (MDD), and schizophrenia (SCZ). Methods A two-sample bi-directional Mendelian randomization analysis was performed by using genetic variants from genome-wide association studies as instrumental variables for gut microbiota, metabolites, AD, MDD, and SCZ, respectively. Results We found suggestive associations of host-genetic-driven increase in Blautia (OR, 0.88; 95%CI, 0.79–0.99; P = 0.028) and elevated γ-aminobutyric acid (GABA) (0.96; 0.92–1.00; P = 0.034), a downstream product of Blautia -dependent arginine metabolism, with a lower risk of AD. Genetically increased Enterobacteriaceae family and Enterobacteriales order were potentially associated with a higher risk of SCZ (1.09; 1.00–1.18; P = 0.048), while Gammaproteobacteria class (0.90; 0.83–0.98; P = 0.011) was related to a lower risk for SCZ. Gut production of serotonin was potentially associated with an increased risk of SCZ (1.07; 1.00–1.15; P = 0.047). Furthermore, genetically increased Bacilli class was related to a higher risk of MDD (1.07; 1.02–1.12; P = 0.010). In the other direction, neuropsychiatric disorders altered gut microbiota composition. Conclusions These data for the first time provide evidence of potential causal links between gut microbiome and AD, MDD, and SCZ. GABA and serotonin may play an important role in gut microbiota-host crosstalk in AD and SCZ, respectively. Further investigations in understanding the underlying mechanisms of associations between gut microbiota and AD, MDD, and SCZ are required.