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Background Evidence links gamma‐glutamyl transferase (GGT) to mortality in the general population. However, the relationship of GGT with all‐cause and cause‐specific mortality risk has been little explored in type 2 diabetes mellitus (T2DM) patients. Methods We recruited 20 340 community‐dwelling T2DM patients between 2013 and 2014 in Jiangsu, China. Cox regression models were used to assess associations of GGT with all‐cause and specific‐cause mortality. Restricted cubic splines were used to analyze dose–response relationships between GGT and mortality. Stratified analysis was conducted to examine potential interaction effects by age, sex, smoking status, body mass index (BMI), diabetes duration, and dyslipidemia. Results During a median follow‐up period of 7.04 years (interquartile range: 6.98–7.08), 2728 deaths occurred, including 902 (33.09%) due to cardiovascular disease (CVD), and 754 (27.58%) due to cancer. GGT concentrations were positively associated with all‐cause, CVD, and cancer mortality. Multivariable hazard ratios (HRs) for the highest (Q5) vs. the lowest quintile (Q1) were 1.63 (95% confidence intervals [CI]: 1.44–1.84) for all‐cause mortality, 1.87 (95% CI: 1.49–2.35) for CVD mortality, and 1.43 (95% CI: 1.13–1.81) for cancer mortality. Effect modification by BMI and dyslipidemia was observed for all‐cause mortality (both p for interaction <.05), and HRs were stronger in the BMI <25 kg/m2 group and those without dyslipidemia. Conclusions Our findings suggest that, in Chinese T2DM patients, elevated serum GGT concentrations were associated with mortality for all‐cause, CVD, and cancer, and further research is needed to elucidate the role of obesity, nonalcoholic fatty liver disease, and lipids in this association.

ObjectiveTo investigate the associations of circulating liver function marker levels with the risk of chronic obstructive pulmonary disease (COPD).MethodsWe leveraged the data of 372,056 participants from the UK Biobank between 2006 and 2010. The assessed circulating liver function markers included alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), total bilirubin (TBIL), albumin (ALB), and total protein (TP). Incident COPD was identified through linkage to the National Health Service registries. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).ResultsDuring a median follow-up period of 12.3 (interquartile range:11.4-13.2) years, we documented 10,001 newly diagnosed COPD cases. Lower levels of ALT, TBIL, ALB, and TP and higher levels of GGT and ALP were nonlinearly associated with elevated COPD risk. The HR (95% CI) for decile 10 vs. 1 was 0.92 (0.84-1.01) for ALT, 0.82 (0.75-0.89) for TBIL, 0.74 (0.67-0.81) for ALB, 0.96 (0.88-1.04) for TP, 1.45 (1.31-1.62) for GGT, and 1.31 (1.19-1.45) for ALP. Restricted cubic spline analyses suggested a U-shaped relationship between AST levels and COPD risk (P for nonlinearity &lt;0.05).ConclusionWe observed that all seven circulating liver function markers were nonlinearly associated with the risk of COPD, indicating the importance of liver function in COPD.

Many numerical methods, such as tensor network approaches including density matrix renormalization group calculations, have been developed to calculate the extreme/ground states of quantum many-body systems. However, little attention has been paid to the central states, which are exponentially close to each other in terms of system size. We propose a delta-Davidson (DELDAV) method to efficiently find such interior (including the central) states in many-spin systems. The DELDAV method utilizes a delta filter in Chebyshev polynomial expansion combined with subspace diagonalization to overcome the nearly degenerate problem. Numerical experiments on Ising spin chain and spin glass shards show the correctness, efficiency, and robustness of the proposed method in finding the interior states as well as the ground states. The sought interior states may be employed to identify many-body localization phase, quantum chaos, and extremely long-time dynamical structure.

A quantum computer is not necessarily alone, e.g., thousands and millions of quantum computers are simultaneously working together for adiabatic quantum computers based on nuclear spins. Long-range interaction is inevitable between these nuclear spin qubits. Here we investigate the effect of long-range dipolar interaction between different adiabatic quantum computers. Our analytical and numerical results show that the dipolar interaction can enhance the final fidelity in adiabatic quantum computation for solving the factorization problem, when the overall interaction is negative. The enhancement will become more prominent if a single quantum computer encounters an extremely small energy gap which occurs more likely for larger-size systems.

IntroductionGastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.MethodsHP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.Results and discussionIn this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies.

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Computation of a large group of interior eigenvalues at the middle spectrum is an important problem for quantum many-body systems, where the level statistics provides characteristic signatures of quantum chaos. We propose an exact numerical method, dual applications of Chebyshev polynomials (DACP), to simultaneously find thousands of central eigenvalues, where the level space decreases exponentially with the system size. To disentangle the near-degenerate problem, we employ twice the Chebyshev polynomials, to construct an exponential semicircle filter as a preconditioning step and to generate a large set of proper basis states in the desired subspace. Numerical calculations on Ising spin chain and spin glass shards confirm the correctness and efficiency of DACP. As numerical results demonstrate, DACP is 30 times faster than the state-of-the-art shift-invert method for the Ising spin chain while 8 times faster for the spin glass shards. In contrast to the shift-invert method, the computation time of DACP is only weakly influenced by the required number of eigenvalues, which renders it a powerful tool for large scale eigenvalues computations. Moreover, the consumed memory also remains a small constant (5.6 GB) for spin-1/2 systems consisting of up to 20 spins, making it desirable for parallel computing.