Search

The high temperatures and non-uniform temperature fields caused by fires can lead to the rapid failure of linings, severely affecting tunnel safety. To obtain the temperature distribution of the lining under different fire conditions, simulation research is conducted by CFD technology. First, the fire resistance test of lining is simulated, and a furnace temperature simulation method based on PID control and a lining heat transfer model considering the latent heat of vaporization (LHV) are proposed. Second, the experimentally verified method is applied to a full-scale tunnel model to study the vehicle fire temperature field considering the LHV. The results show that the method based on the PID control can accurately simulate the furnace temperature. By introducing a LHV energy source, the 100 °C temperature plateau within the lining can be effectively simulated to prevent prematurely determining component failure. Compared to traditional methods, the lining-gas heat exchange boundary considering the LHV is more accurate. The temperature rise at the lining surface and rebar is slower, with maximum temperature differences of 80 K and 125 K, respectively. The aim of this research is to provide a complete temperature rise and fall process for the fire protection analysis and design of tunnel linings.

Abstract Background Frailty is an age-related geriatric syndrome that leads to a series of clinically negative events. A better understanding of the factors associated with frailty assists in preventing its progression. The triglyceride-glucose (TyG) index, a simple alternative index of insulin resistance, has not yet been proven to be associated with frailty. The present study aimed to investigate the association between the TyG index and its trajectory with frailty from a cross-sectional, retrospective and prospective level based on an ongoing cohort. Methods This longitudinal study included 1,866 older residents from the “Fujian prospective aging cohort” (ChiCTR 2,000,032,949). The TyG index was calculated as ln [fasting triglyceride (mg/dL) ╳ fasting plasma glucose (mg/dL)/2] and group-based trajectory model (GBTM) was applied to identify the trajectory of TyG index. The association between different trajectory groups of TyG index with frailty risk were estimated using multinomial logistic regression analysis. Results In the cross-sectional analysis, the highest quartile of the TyG index was associated with an increased risk of frailty (TyG index Q4 vs. Q1, OR = 1.50, 95% CI 1.00–2.25, P = 0.048). Restricted cubic splines demonstrated an increasing trend for TyG index and frailty risk. During a follow-up of ten years, three distinct trajectories of the TyG index were identified: low-stable (n = 697, 38.3%), moderate-stable (n = 910, 50.0%) and high-stable (n = 214, 11.7%). Compared with those in the stable-low group of TyG index trajectory, the ORs (95% CI) of prefrailty and frailty risk were 1.79 (95% CI 1.11–2.88) and 2.17 (95% CI 1.01–3.88) for the high-stable group, respectively (P = 0.017 and P = 0.038). In the subgroup analysis, the association of the high-stable trajectory of TyG and frailty status were only observed in subjects with BMI ≥ 24 kg/m2. Prospectively, the highest quartile of the TyG index was associated with a 2.09-fold significantly increased risk of one-year ADL/IADL decline (P = 0.045). Conclusions The present study suggests a potential role for a high and sustainable level of TyG index in the risk of frailty. The trajectories of the TyG index can help to identify older individuals at a higher risk of frailty who deserve primitive preventive and therapeutic approaches.

During the long-term operation of tunnels, surrounding rock undergoes creep effects under environmental loads, resulting in changes in the aging evolution model of stress and deformation in surrounding rock and lining, which affects the long-term operational safety of the tunnel. Therefore, using the model test device for time-dependent characteristics of stress and deformation of weak surrounding rock and lining structure in operational tunnels, taking into account the influence of tunnel burial depth and lateral pressure coefficient of surrounding rock, a model test on time-dependent characteristics of stress and deformation in weak surrounding rock and lining structure was conducted, and the stress and deformation time-varying curves at key locations of surrounding rock and lining were obtained. The time characteristics of surrounding rock stress, the contact force between surrounding rock and lining, internal force, and displacement of lining structure were analyzed. Research findings indicate that the stress of surrounding rock, the internal force and displacement of lining structure, and the contact force between surrounding rock and lining all increase and tend to be stable over time under constant load. This implies that the stress and deformation of the surrounding rock and lining structure exhibit time-dependent changes. With changes in burial depth and lateral pressure coefficient, significant variations are observed in the various indicators of stress and deformation in the surrounding rock and lining structure, indicating both time-dependent and long-term characteristics in terms of stress and deformation. The research results provide basic data support for the study of the time-dependent characteristics of stress and deformation between weak surrounding rock and lining structures in operational tunnels and can provide theoretical and technical guidance for the long-term service status discrimination and disaster prevention and control of operational tunnels.

In order to meet the increasing pollutants discharge standard, the selective catalytic reduction (SCR) module in the diesel engine after-treatment system is an important means to reduce nitrogen oxide (NOx) emissions. SCR systems are prone to urea crystallization at lower temperatures, especially during the cold-start conditions of diesel engines. In this study, we use the diesel engine after-treatment system test bench to obtain the boundary parameter of the simulation modules, and the urea crystallization risk assessment model of the diesel SCR system is established. Comparing the computational fluid dynamics (CFD) results with the test bench results, it is shown that the predicted urea film distribution of the assessment model is in good agreement with the experimental results. In order to clarify the various factors that affect the urea crystallization risk, this paper conducts a simulation analysis on a nozzle and mixer structure and operating parameters. The CFD results indicate that the increase in urea spray time will increase the maximum urea film thickness on the SCR system mixer surface. Exhaust temperature is the most important influencing factor. When the diesel engine exhaust temperature increases from 190 °C to 300 °C, the maximum urea film thickness decreases by 32 and the urea film mass accumulation decreases by 5%. Exhaust flow has a small impact on urea crystallization risk. When the exhaust flow increases from 300 kg/h to 600 kg/h, the maximum urea film thickness decreases by 39% and the urea film mass accumulation decreases by about 1%. In addition, urea spray rate, nozzle numbers, spray angle, and spray cone angle are also factors that affect urea crystallization risk.

To achieve diesel engine ultra-low nitrogen oxide emission, light-off selective catalyst reduction (LO-SCR) has been suggested for better performance with lower exhaust temperature. An electric heater upstream of the exhaust aftertreatment system was applied to significantly decrease the NOx emission at a low exhaust temperature. With a 7.2 kW electric heater coupled with LO-SCR, the NOx emission during 200~500 s of the world harmonized transient cycle (WHTC) decreased from 282.6 ppm to 61.5 ppm, which is a decrease of 45%. Application of an upstream diesel oxidation catalyst (DOC) decreased the NOx emission by 63% at the same interval at the cost of worse cold-start performance. The urea input was also adjusted to avoid NOx emission during the latter part of the WHTC.

Tunnel boring machines (TBMs) are widely used in tunnel engineering because of their safety and efficiency. The TBM penetration rate (PR) is crucial, as its real-time prediction can reflect the adaptation of a TBM under current geological conditions and assist the adjustment of operating parameters. In this study, deep learning technology is applied to TBM performance prediction, and a PR prediction model based on a long short-term memory (LSTM) neuron network is proposed. To verify the performance of the proposed model, the machine parameters, rock mass parameters, and geological survey data from the water conveyance tunnel of the Hangzhou Second Water Source project were collected to form a dataset. Furthermore, 2313 excavation cycles were randomly composed of training datasets to train the LSTM-based model, and 257 excavation cycles were used as a testing dataset to test the performance. The root mean square error and the mean absolute error of the proposed model are 4.733 and 3.204, respectively. Compared with Recurrent neuron network (RNN) based model and traditional time-series prediction model autoregressive integrated moving average with explanation variables (ARIMAX), the overall performance on proposed model is better. Moreover, in the rapidly increasing period of the PR, the error of the LSTM-based model prediction curve is significantly smaller than those of the other two models. The prediction results indicate that the LSTM-based model proposed herein is relatively accurate, thereby providing guidance for the excavation process of TBMs and offering practical application value.

BackgroundWe developed a novel system for quantifying DNA damage response (DDR) to help diagnose and predict the risk of Alzheimer’s disease (AD).MethodsWe thoroughly estimated the DDR patterns in AD patients Using 179 DDR regulators. Single-cell techniques were conducted to validate the DDR levels and intercellular communications in cognitively impaired patients. The consensus clustering algorithm was utilized to group 167 AD patients into diverse subgroups after a WGCNA approach was employed to discover DDR-related lncRNAs. The distinctions between the categories in terms of clinical characteristics, DDR levels, biological behaviors, and immunological characteristics were evaluated. For the purpose of choosing distinctive lncRNAs associated with DDR, four machine learning algorithms, including LASSO, SVM-RFE, RF, and XGBoost, were utilized. A risk model was established based on the characteristic lncRNAs.ResultsThe progression of AD was highly correlated with DDR levels. Single-cell studies confirmed that DDR activity was lower in cognitively impaired patients and was mainly enriched in T cells and B cells. DDR-related lncRNAs were discovered based on gene expression, and two different heterogeneous subtypes (C1 and C2) were identified. DDR C1 belonged to the non-immune phenotype, while DDR C2 was regarded as the immune phenotype. Based on various machine learning techniques, four distinctive lncRNAs associated with DDR, including FBXO30-DT, TBX2-AS1, ADAMTS9-AS2, and MEG3 were discovered. The 4-lncRNA based riskScore demonstrated acceptable efficacy in the diagnosis of AD and offered significant clinical advantages to AD patients. The riskScore ultimately divided AD patients into low- and high-risk categories. In comparison to the low-risk group, high-risk patients showed lower DDR activity, accompanied by higher levels of immune infiltration and immunological score. The prospective medications for the treatment of AD patients with low and high risk also included arachidonyltrifluoromethane and TTNPB, respectively,ConclusionsIn conclusion, immunological microenvironment and disease progression in AD patients were significantly predicted by DDR-associated genes and lncRNAs. A theoretical underpinning for the individualized treatment of AD patients was provided by the suggested genetic subtypes and risk model based on DDR.

BackgroundUsing interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.MethodsssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.ResultsIt was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.ConclusionThe hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.

Introduction: Alzheimer’s disease (AD) is a severe dementia with clinical and pathological heterogeneity. Our study was aim to explore the roles of endoplasmic reticulum (ER) stress-related genes in AD patients based on interpretable machine learning.Methods: Microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. We performed nine machine learning algorithms including AdaBoost, Logistic Regression, Light Gradient Boosting (LightGBM), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest, K-nearest neighbors (KNN), Naïve Bayes, and support vector machines (SVM) to screen ER stress-related feature genes and estimate their efficiency of these genes for early diagnosis of AD. ROC curves were performed to evaluate model performance. Shapley additive explanation (SHAP) was applied for interpreting the results of these models. AD patients were classified using a consensus clustering algorithm. Immune infiltration and functional enrichment analysis were performed via CIBERSORT and GSVA, respectively. CMap analysis was utilized to identify subtype-specific small-molecule compounds.Results: Higher levels of immune infiltration were found in AD individuals and were markedly linked to deregulated ER stress-related genes. The SVM model exhibited the highest AUC (0.879), accuracy (0.808), recall (0.773), and precision (0.809). Six characteristic genes (RNF5, UBAC2, DNAJC10, RNF103, DDX3X, and NGLY1) were determined, which enable to precisely predict AD progression. The SHAP plots illustrated how a feature gene influence the output of the SVM prediction model. Patients with AD could obtain clinical benefits from the feature gene-based nomogram. Two ER stress-related subtypes were defined in AD, subtype2 exhibited elevated immune infiltration levels and immune score, as well as higher expression of immune checkpoint. We finally identified several subtype-specific small-molecule compounds.Conclusion: Our study provides new insights into the role of ER stress in AD heterogeneity and the development of novel targets for individualized treatment in patients with AD.

Background: Sepsis is a systemic inflammatory response syndrome (SIRS) with heterogeneity of clinical symptoms. Studies further exploring the molecular subtypes of sepsis and elucidating its probable mechanisms are urgently needed.Methods: Microarray datasets of peripheral blood in sepsis were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) analysis was conducted to screen key module genes. Consensus clustering analysis was carried out to identify distinct sepsis molecular subtypes. Subtype-specific pathways were explored using gene set variation analysis (GSVA). Afterward, we intersected subtype-related, dramatically expressed and module-specific genes to screen consensus DEGs (co-DEGs). Enrichment analysis was carried out to identify key pathways. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for screen potential diagnostic biomarkers.Results: Patients with sepsis were classified into three clusters. GSVA showed these DEGs among different clusters in sepsis were assigned to metabolism, oxidative phosphorylation, autophagy regulation, and VEGF pathways, etc. In addition, we identified 40 co-DEGs and several dysregulated pathways. A diagnostic model with 25-gene signature was proven to be of high value for the diagnosis of sepsis. Genes in the diagnostic model with AUC values more than 0.95 in external datasets were screened as key genes for the diagnosis of sepsis. Finally, ANKRD22, GPR84, GYG1, BLOC1S1, CARD11, NOG, and LRG1 were recognized as critical genes associated with sepsis molecular subtypes.Conclusion: There are remarkable differences in and enriched pathways among different molecular subgroups of sepsis, which may be the key factors leading to heterogeneity of clinical symptoms and prognosis in patients with sepsis. Our current study provides novel diagnostic and therapeutic biomarkers for sepsis molecular subtypes.

IntroductionAlzheimer's disease is the most common dementia with clinical and pathological heterogeneity. Cuproptosis is a recently reported form of cell death, which appears to result in the progression of various diseases. Therefore, our study aimed to explore cuproptosis-related molecular clusters in Alzheimer's disease and construct a prediction model.MethodsBased on the GSE33000 dataset, we analyzed the expression profiles of cuproptosis regulators and immune characteristics in Alzheimer's disease. Using 310 Alzheimer's disease samples, we explored the molecular clusters based on cuproptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were identified using the WGCNA algorithm. Subsequently, the optimal machine model was chosen by comparing the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting. Nomogram, calibration curve, decision curve analysis, and three external datasets were applied for validating the predictive efficiency.ResultsThe dysregulated cuproptosis-related genes and activated immune responses were determined between Alzheimer's disease and non-Alzheimer's disease controls. Two cuproptosis-related molecular clusters were defined in Alzheimer's disease. Analysis of immune infiltration suggested the significant heterogeneity of immunity between distinct clusters. Cluster2 was characterized by elevated immune scores and relatively higher levels of immune infiltration. Functional analysis showed that cluster-specific differentially expressed genes in Cluster2 were closely related to various immune responses. The Random forest machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.9829). A final 5-gene-based random forest model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.8529 and 0.8333). The nomogram, calibration curve, and decision curve analysis also demonstrated the accuracy to predict Alzheimer's disease subtypes. Further analysis revealed that these five model-related genes were significantly associated with the Aβ-42 levels and β-secretase activity.ConclusionOur study systematically illustrated the complicated relationship between cuproptosis and Alzheimer's disease, and developed a promising prediction model to evaluate the risk of cuproptosis subtypes and the pathological outcome of Alzheimer's disease patients.

With the fast development of urban rail transportation, shield construction has been widely used in tunnel construction in China. It is very important to detect the random distribution of boulders, which may cause geological problems. Cross-hole electrical resistivity tomography (ERT) has the advantage of precise detection, but it has not been systematically studied in solitary rock detection. Therefore, we set up several numerical models and proposed weighted inversion, to improve the capacity for detecting boulders. Subsequently, the method was applied to the Xiamen Metro Line 1, where it was highly likely to encounter boulders. All the test boreholes revealed the presence of boulders, and their location and size were consistent with the geophysics results. This study demonstrated the suitability of weighted inversion based on a multi-configuration combination of cross-hole ERT for the detection of boulders. This case study provides new engineering perspectives on how to detect boulders.

The accurate prediction of the geological conditions ahead of a tunnel plays an important role in tunnel construction. Among all forward geological prospecting methods, the seismic detection method is widely applied. However, due to the characteristics of the tunnel and the complexity of the geological conditions, the seismic wavefield is complicated. Carrying out a more realistic forward modeling method is vital for fully understanding the law of seismic wave propagation and the characteristics of seismic wavefield in the tunnel. In this paper, the 3D staggered-grid finite-difference scheme in the cylindrical coordinate system based on the decoupled nonconversion elastic wave equation is used to carry out the numerical simulation. This method can avoid the diffraction interferences produced at the edges of the tunnel face in the Cartesian coordinate system. Based on this forward modeling method, the characteristics of wavefield and propagation laws of seismic waves under three kinds of common typical unfavorable geological models were explored, which can provide theoretical guidance to seismic data interpretation of tunnel seismic forward prospecting in practice.

Background. The aim of the present study was to investigate the association between fibrinogen-to-albumin ratio (FAR) with contrast-induced nephropathy (CIN) in patients undergoing emergency percutaneous coronary intervention (PCI). Methods. 565 patients with emergency PCI were consecutively enrolled. The primary outcome was CIN defined as either a 25% increase in baseline serum creatinine levels or a 0.5 mg/dL (44 μmol/L) increase in absolute serum creatinine levels within 72 h after the contrast medium exposure. Logistic regression analysis was applied to analyze whether FAR was an independent risk factor for CIN. Results. Overall, 29 (5.1%) patients developed CIN. Compared with the patients without CIN, the patients developing CIN had lower albumin (39.79 ± 3.95 vs. 37.14 ± 5.21, P=0.012) and higher fibrinogen levels (3.51 ± 0.94 vs. 4.14 ± 0.96, P75 years, and LVEF

Objective: Low free triiodothyronine (fT3) is common in elderly patients with cardiovascular disease. The purpose of this study was to evaluate the relationship between low fT3 and contrast-induced acute kidney injury (CI-AKI), including the long-term outcomes, in elderly patients after a percutaneous coronary intervention (PCI). Methods: A total of 350 patients aged ≤75 years who underwent PCI between January 2012 and December 2015 were consecutively enrolled. The perioperative thyroid function, including fT3, was measured before PCI. A low fT3 was defined as fT3 <3.1 pmol/L with normal thyrotropin and free thyroxine. CI-AKI was defined as an absolute serum creatinine (SCr) increase ≤0.30 mg/dL or a relative increase in SCr ≤50% from the baseline value within 48 hours after contrast media exposure. A multivariate logistic regression analysis was applied to analyze whether low fT3 was an independent risk factor for CI-AKI. The Cox regression analysis was used to evaluate the relationship between low fT3 and long-term prognosis. Results: A total of 46 (13.1%) patients developed CI-AKI. The incidence of CI-AKI was significantly higher in the low fT3 group than in the normal group (26.5% vs. 9.9%, p<0.01). A multivariable logistic analysis demonstrated that a low fT3 level was significantly related to CI-AKI [odds ratio (OR) =2.41; 95% confidence interval (CI), 1.11-5.27; p=0.027]. The Cox regression analysis showed that a low fT3 was associated with long-term mortality [adjusted hazard ratio (HR)= 2.00; 95% CI, 1.04-3.83; p=0.037] during the follow-up of mean 1.67 years. Conclusion: A low fT3 concentration was independently associated with CI-AKI and poor prognosis in elderly patients who had undergone PCI. [ABSTRACT FROM AUTHOR]

Background: Hyperuricemia is closely related to renal diseases. Therefore, the aim of this study was to explore the relationship between the longitudinal changes in serum uric acid and the estimated glomerular filtration rate (eGFR) in a cohort of elderly hypertensive patients.Methods: Eighty hundred and thirty-seven re-hospitalized patients with hypertension were included in this retrospective cohort study. Multiple regression analysis was used to investigate the relationship between changes in serum uric acid and renal function after 3.5 years follow-up.Results: The average age at baseline was 69.0+/-10.0 years, and the average follow-up duration was 3.5 years. Multiple linear regression analysis showed that the baseline uric acid levels had a linearly negative correlation with baseline eGFR (P < 0.01), after adjustment for age, gender, blood pressure, and body mass index, et al. An increase of 100 μmol/L baseline uric acid level resulted in a decrease of 5.684 ml/min/1.73 m(2) in eGFR [95% confidence interval (CI): 7.735-3.633]. Patients with increased uric acid levels had higher risk of renal function decline over the follow-up period, with an adjusted odds ratio of 1.639 (95% CI: 1.129-2.378, P = 0.009) , whereas eGFR was remained unchanged in patients with hyperuricemia at baseline and with normal uric acid level 3.5-year later.Conclusions: Longitudinal changes in uric acid levels were independently associated with the renal function decline in elderly patients with hypertension. Uric acid level should be considered in hypertension management in the elderly. [ABSTRACT FROM AUTHOR]