Search

Abstract We previously identified that serum EFNA1 and MMP13 were potential biomarker for early detection of esophageal squamous cell carcinoma. In this study, our aim is to explore the diagnostic value of serum EFNA1 and MMP13 for gastric cancer. We used enzyme-linked immunosorbent assay (ELISA) to detect the expression levels of serum EFNA1 and MMP13 in 210 GCs and 223 normal controls. The diagnostic value of EFNA1 and MMP13 was evaluated in an independent cohorts of GC patients and normal controls (n = 238 and 195, respectively). Receiver operating characteristics were used to calculate diagnostic accuracy. In training and validation cohorts, serum EFNA1 and MMP13 levels in the GC groups were significantly higher than those in the normal controls (P

Purpose: Capillary Refill Time (CRT) measurement has gained increasing attention in the field of sepsis and septic shock. Recognizing pressure as a fundamental determinant in CRT measurement is crucial for establishing a standardized CRT measurement procedure. In this preliminary study, we elucidated the optimal pressing strength for CRT measurement by analyzing the CRTs measured under varying pressures. Method: Seventeen healthy individuals were enlisted to undergo CRT tests on their fingertips at various pressure levels. The applied force was initiated at 0.5N and incrementally increased by 0.5N until it reached 10.5N. An integrated Photoplethysmography (PPG) device was employed to capture fluctuations in light intensity. The CRT was automatically derived from the PPG signals via a specialized algorithm. The study included correlation assessment and reliability evaluation. Box plot and Bland-Altman plot were used to visualize the impact of pressure levels on CRTs. Results: A dataset of 1414 CRTs across 21 pressures showed significant differences (Kruskal-Wallis test, p

To address rotor imbalance and misalignment in oil transfer pumps, an innovative diagnostic framework using Residual Network (ResNet) is proposed. The model incorporates advanced signal processing algorithms and strategic sensor placement to enhance diagnostic efficacy. A fault simulation test rig captured vibration signals from eight key measurement points on the pump. One-dimensional and multi-dimensional signal processing techniques generated comprehensive datasets for training and validating the model. Sensor placement optimization, focusing on the bearing seat's axial direction, inlet flange's vertical direction, and outlet flange's axial direction, increased rotor fault sensitivity. Time-frequency data processed via Short-Time Fourier Transform (STFT) achieved the highest diagnostic accuracy, surpassing 98 %. This study highlights the importance of optimal signal processing and precise sensor placement in improving the accuracy of diagnosing rotor faults in oil transfer pumps, thus enhancing the operational reliability and efficiency of energy transportation systems.

Background: Early detection of cancer remains an unmet need in clinical practice, and high diagnostic sensitivity and specificity biomarkers are urgently required. Here, we attempted to identify secreted proteins encoded by super-enhancer (SE)-driven genes as diagnostic biomarkers for esophageal squamous cell carcinoma (ESCC). Methods: We conducted an integrative analysis of multiple data sets including ChIP-seq data, secretome data, CCLE data and GEO data to screen secreted proteins encoded by SE-driven genes. Using ELISA, we further identified up-regulated secreted proteins through a small size of clinical samples and verified in a multi-centre validation stage (345 in test cohort and 231 in validation cohort). Receiver operating characteristic curves were used to calculate diagnostic accuracy. Artificial intelligence (AI) method named gradient boosting machine (GBM) were applied for model construction to enhance diagnostic accuracy. Results: Serum EFNA1 and MMP13 were identified, and showed significantly higher levels in ESCC patients compared to normal controls. An integrated Five-Biomarker Panel (iFBPanel) established by combining EFNA1, MMP13, carcino-embryonic antigen, Cyfra21-1 and squmaous cell carcinoma antigen had AUCs of 0.881 and 0.880 for ESCC in test and validation cohorts, respectively. Importantly, the iFBPanel also exhibited good performance in detecting early-stage ESCC patients (0.872 and 0.864). Furthermore, the iFBPanel was further empowered by AI technology which showed excellent diagnostic performance in early-stage ESCC (0.927 and 0.907). Conclusions: Our study suggested that serum EFNA1 and MMP13 could potentially assist ESCC detection, and provided an easy-to-use detection model that might help the diagnosis of early-stage ESCC.

In response to the problem that existing impact hazard assessment methods cannot accurately reflect the features of stress concentration changes under the influence of impact hazard factors, an impact hazard area classification method base on multi factor coupled quantitative characterization model is proposed. Firstly, based on the geological conditions of the underground coal seam, the distribution of roadways, and the scope of mining, the main impact influencing factors are analyzed. Secondly, referring to the multi factor superposition method and stress analysis method respectively, the method determines the influence range and relative stress concentration coefficient of various impact influencing factors. Thirdly, based on the distribution function of micro element strength inside the coal rock mass, a multi factor coupled quantitative characterization model for impact hazard is constructed. Finally, the impact range and relative stress concentration coefficient of the impact influencing factors are input into the quantitative characterization model to obtain the stress distribution results of the coal seam. Based on the stress distribution results, the impact hazard level is classified, and the distribution of impact hazard areas is obtained. Taking the No.3 coal seam of Shandong Xinjulong Energy Co., Ltd. as an example, by analyzing the stress concentration caused by the main impact risk factors such as superimposed faults, large roadways, and goaf, a reasonable impact hazard level classification standard is formulated. The results of the impact hazard area classification of the No.3 coal seam are obtained and verified on site. According to the mining seismic events that occurred before and after the completion of the evaluation work, it can be seen that the impact source is mainly concentrated in the strong impact hazard area. It is consistent with the regional division results, thus verifying that this method can effectively quantitatively divide the coal seam impact hazard area.

Abstract Our previous study showed that levels of circulating insulin-like growth factor binding protein-1 (IGFBP-1) has potential diagnostic value for early-stage upper gastrointestinal cancers. This study aimed to assess whether serum IGFBP-1 is a potential diagnostic and prognostic biomarker for CRC patients. IGFBP-1 mRNA expression profile data of peripheral blood in colorectal cancer (CRC) patients were downloaded and analyzed from Gene Expression Omnibus database. We detected serum IGFBP-1 in 138 CRC patients and 190 normal controls using enzyme-linked immunosorbent assay. Blood IGFBP-1 mRNA levels were higher in CRC patients than those in normal controls (P = 0.027). In addition, serum IGFBP-1 protein levels in the CRC group were significantly higher than those in normal control group (P

In urban metro construction, shield tunneling often needs to pass through building and bridge pile foundations, potentially affecting the stability of existing structures. Therefore, accurately assessing the impact of shield tunneling on bridges and buildings is crucial. This study presents a comprehensive prediction method combining numerical simulation and empirical formulas, taking the underpass project of the Shijiazhuang–Wuhan High-Speed Railway Bridge by Zhengzhou Metro Line 5 as a case study. Three-dimensional numerical model calculations were performed using finite element software to analyze the displacement and stress changes of buildings and tunnel structures at different construction stages, revealing the deformation patterns of buildings adjacent to the tunnel during shield tunneling. In particular, the ground settlement caused by twin-tunnel excavation was compared with Peck’s empirical formula to verify the reliability of the numerical simulation. The results show that twin-tunnel excavation exacerbates the horizontal displacement, uplift, and settlement of the ground, with maximum deformation rates increasing by 7.10%, 20%, and 11.4%, respectively. Comparing the ground deformation results of Peck’s empirical formulas with numerical calculations revealed similar trends in the settlement curves, with a maximum deviation of 6.67%. It can be concluded that using Peck’s empirical formula to calculate ground deformation characteristics complements the limitations of numerical simulations, making the assessment results more reliable. The findings of this study demonstrate that integrating numerical simulation with empirical formulas significantly enhances the reliability of deformation predictions in complex tunneling scenarios. This research not only offers a comprehensive safety assessment method for shield tunneling construction but also provides valuable guidance for the design and construction of similar projects, serving as a theoretical reference for future engineering endeavors.

Urbanization and population concentration in China’s major cities drive high land utilization demands, affecting nearby bridges during underground construction. Foundation pit construction alters the internal forces, deformation, and displacement of bridge piles. To understand these impacts and assess excavation support rationality, a case study was conducted on an ultra-deep foundation pit near an elevated ring road bridge in Wuhan. Considering the engineering geological conditions of the project site, construction sequence, and traffic load on the bridge, a three-dimensional finite element model was established to simulate the impact of foundation pit excavation on the elevated structure. And through an analysis of tracked monitoring data from the construction site, comparisons were also made with the simulation results. The findings of this case study indicated that throughout the entire construction phase of the foundation pit, the maximum horizontal and vertical displacements of the bridge foundation structure caused by the construction are 2.98 mm and −1.75 mm, respectively; the maximum change rate of the bending moment in the bridge structure due to the foundation pit construction is 6.3%, while the change in the axial forces is small, and completely within the safety control standards for bridge structure displacement. By analyzing the monitoring data over three stages within one year after the completion of the foundation pit, it is shown that the bridge structure gradually tends to stabilize. Additionally, due to soil consolidation, its displacement shows uniform rebound and tends to stabilize. The research findings provide valuable reference points for the design and construction of similar deep foundation pit projects.

In this study, common multiwalled and carboxylated carbon nanotubes (CNTs) were added to the cemented lithium tailings backfill (CLTB). The effects of CNTs on the mechanical properties, hydration products, damage process, and microstructure of CLTB specimens were studied by uniaxial compression (UCS), infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM). The experimental results show that the addition of CNTs effectively increased the compressive strength compared with the blank control group. When the concentration was 0.05–0.20%, the compressive strength was proportional to the content, the optimal addition amount was 0.2%, and the enhancement effect was 75% and 95.31%, respectively. The FT-IR results indicate that the addition of CNTs increased the total amount of the hydration product but did not affect its type. The hydration of the three-dimensional reciprocal penetration network formed by moderate amounts of CNTs has a positive effect on the mechanical strength of CLTB specimens.

Abstract Background Previous studies have shown that ALDH2 and ADH1B genes may be associated with alcohol metabolism and the risk of esophageal squamous cell carcinoma (ESCC), with inconsistent results. This meta‐analysis aimed at comprehensively assessing the associations between ALDH2 and ADH1B polymorphisms and the risk of ESCC to synthesize and clarify the evidence. Methods We calculated summary estimates of the associations between four genetic variants (rs671 and rs674 in ALDH2, and rs1229984 and rs1042026 in ADH1B) and the ESCC risk across 23 publications in the additive model and allelic model. Venice criteria, Bayesian false discovery probability (BFDP), and false‐positive reporting probability (FPRP) were used to assess the strength of epidemiological evidence. Heterogeneity among studies was evaluated by using the Higgin's I2 statistic, and publication bias was assessed by using funnel plots and Begg's test. A Mendelian randomization (MR) analysis was performed to determine the causal association between alcohol intake and esophageal cancer risk. Data from the HaploReg v4.1 and PolyPhen‐2 were analyzed for functional annotations. Results Of the four genetic variants, rs671 of ALDH2 was associated with a significantly reduced risk of ESCC (OR: 0.60, 95% CI: 0.50–0.73), whereas rs1229984 of ADH1B was associated with a significantly increased risk (2.50, 95% CI: 1.70–3.69) in the additive model. In the allelic model, the variant rs1229984 of ADH1B also increased the risk of ESCC (OR: 1.50; 95% CI: 1.21–1.87). The result for the variant rs671 was considered as strong epidemiological evidence. Functional annotations identified that the four variants were related to the enhancer histone marks and motif changes. The other two variants were not associated with the ESCC risk (rs674 of ALDH2 OR: 1.22, 95% CI: 0.71–2.12; rs1042026 of ADH1B OR: 1.28, 95% CI: 0.52–3.14) in the additive model. The MR analysis did not find a causal effect of alcohol on the esophageal cancer risk. Conclusions The results showed that ADH1B rs1229984 was significantly associated with an increased the risk of ESCC.

Abstract In this study, we propose a piezoelectric ceramic sensor-based health monitoring method to monitor the stress state of diagonally braced H-shaped steel structures. Loading experiments were carried out on diagonally braced H-shaped steel under different working conditions. Subsequently, the amplitude and energy of piezoelectric signals under these two working conditions were compared and analyzed, and finite element analysis was performed using ABAQUS software to verify the results. The experimental results showed that with an increase in the web height or load, the time-domain waveform energy index of the H-shaped steel increased. Under different working conditions, such as a diagonally braced H-shaped steel member with a web height of 10 cm, when the pressure value was less than 10 N/mm2, the energy index increased by approximately 15.98% for every 1 N/mm2 increase in the pressure. When the pressure value was greater than 10 N/mm2 and less than 15 N/mm2, the energy index increased by approximately 1% for every 1 N/mm2 increase in pressure. Further, the energy index increased by approximately 8.4% for every 1 cm increase in the height of the web. Simultaneously, it can be seen from the results of the finite element analysis that the stress and strain at the induction position of the piezoelectric ceramic sensor increased with an increase in the external pressure. The study of structural health monitoring for diagonally braced H-shaped steel structures holds significant importance in ensuring the safety and reliability of these structures, achieving predictive maintenance, evaluating structural performance and energy efficiency, and optimizing structural maintenance.

Excavation in foundation pits can result in serious issues for nearby tunnel structures like deformation, differential settlement, and seepage damage, which profoundly impact project timelines and potentially endanger life and property safety. Therefore, it is imperative to investigate these impacts before and after construction and to facilitate timely adjustments of construction measures and reinforcement where possible. In this study, a foundation pit construction project near a rail transit line is employed as a case to comprehensive study the impact of on-site deep foundation pit excavation on adjacent rail transit structures by numerical simulation. A three-dimensional finite-element model of the foundation pit based on site geological characteristics and construction procedures is established to study the excavation and maintenance processes. Through analysis of key parameters including soil deformation, displacement, shear force, and bending moment of the tunnel structures, the designed protective structure is found to have effectively mitigated soil deformation, ensuring the stability of the foundation pit. As excavation progresses, lateral soil deformation and vertical uplift gradually increase but remain within specified control values. During various excavation stages, the maximum displacement of the tunnel structure gradually increases, with the increase rates of maximum settlement being 29.09%, 20.51%, and 6.45%, respectively. This indicates a gradual enhancement of the stability of the tunnel structure. Additionally, excavation of the foundation pit has a significant impact on the bending moment distribution of the tunnel structure but does not affect the axial force and shear force of the tunnel structure. The findings of this study offer crucial scientific insights for evaluating the safety and stability of construction near tunnel structures.

With diminishing natural aggregate resources and increasing environmental protection efforts, the use of recycled fine aggregate is a more sustainable approach, although challenges persist in achieving comparable mechanical properties. Exploration into the incorporation of steel fibers with recycled aggregate has led to the development of steel-fiber-reinforced recycled aggregate concrete. This study investigates the shrinkage performance and compressive constitutive relationship of steel fiber recycled concrete with different steel fibers and recycled aggregate dosages. Initially, based on different replacement rates of recycled coarse aggregate and different volume contents of steel fiber, experimental results demonstrate that as the replacement rate of recycled coarse aggregate increases, shrinkage also increases, while the addition of steel fiber can mitigate this effect. An empirical shrinkage model for steel fiber recycled concrete under natural curing conditions is also proposed. Subsequently, based on the uniaxial compression test, findings indicate that with an increasing replacement rate of recycled fine aggregate, the peak stress and elastic modulus of concrete decrease, accompanied by an increase in peak strain, and the addition of steel fiber limits concrete crack development and enhances its brittleness while the peak stress and strain of recycled fine aggregate concrete are enhanced. However, the steel fiber volume percentage has a negligible effect on the elastic modulus. A constitutive relationship for concrete considering the effects of recycled fine aggregate and steel fiber is also proposed. This finding provides foundational support for the influence patterns of steel fiber dosage and recycled aggregate ratio on the mechanical properties of steel fiber recycled concrete.

α-Dicarbonyl compounds (α-DCs) are commonly present in various foods. We conducted the investigation into concentration changes of α-DCs including 3-deoxyglucosone (3-DG), glyoxal (GO), and methylglyoxal (MGO) in fresh fruits and decapped commercial juices during storage at room temperature and 4 °C, as well as in homemade juices during storage at 4 °C. The studies indicate the presence of α-DCs in all samples. The initial contents of 3-DG in the commercial juices (6.74 to 65.61 μg/mL) are higher than those in the homemade ones (1.97 to 4.65 μg/mL) as well as fruits (1.58 to 3.33 μg/g). The initial concentrations of GO and MGO are normally less than 1 μg/mL in all samples. During storage, the α-DC levels in the fruits exhibit an initial increase followed by a subsequent decrease, whereas, in all juices, they tend to accumulate continuously over time. As expected, 4 °C storage reduces the increase rates of the α-DC concentrations in most samples. From the viewpoint of the α-DC contents, fruits and homemade juices should always be the first choice for daily intake of nutrients and commercial juices ought to be mostly avoided.

Abstract Oral tongue squamous cell carcinoma (OTSCC) is one of the most aggressive oral tumors. The aim of this study was to establish a nomogram to predict overall survival (OS) of TSCC patients after surgery. 169 TSCC patients who underwent surgical treatments in the Cancer Hospital of Shantou University Medical College were included. A nomogram based on Cox regression analysis results was established and internally validated using bootstrap resampling method. pTNM stage, age and total protein, immunoglobulin G, factor B and red blood cell count were identified as independent prognostic factors to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion of the nomogram were lower than those of pTNM stage, indicating a better goodness-of-fit of the nomogram for predicting OS. The bootstrap-corrected concordance index of nomogram was higher than that of pTNM stage (0.794 vs. 0.665, p = 0.0008). The nomogram also had a good calibration and improved overall net benefit. Based on the cutoff value obtained from the nomogram, the proposed high-risk group had poorer OS than low-risk group (p

AbstractPurpose Due to the poor and unpredictable prognosis of breast cancer (BC) patients with bone metastasis, it is necessary to find convenient and available prognostic predictors. This study aimed to recognize the clinical and prognostic factors related to clinical laboratory examination and to construct a prognostic nomogram for BC bone metastasis.Methods We retrospectively analyzed 32 candidate indicators from clinical features and laboratory examination data of 276 BC patients with bone metastasis. Univariate and multivariate regression analyses were performed to identify significant prognostic factors related to BC with bone metastasis. Nomogram was constructed and estimated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.Results Patients were randomly grouped into training (n = 197) and validation cohorts (n = 79). In training cohort, the multivariate regression analysis revealed that age, other organ metastasis sites, serum level of lactate dehydrogenase, globulin, white blood cell count, mean corpuscular volume, mean corpuscular hemoglobin, and monocyte ratio were independent prognostic factors for BC with bone metastasis. The prognostic nomogram in training cohort exhibited areas under the ROC curve (AUCs) of 0.797, 0.782, and 0.794, respectively, for predicting 1-, 3-, and 5-year overall survival. In validation cohort, the nomogram still showed acceptable discrimination ability (AUCs: 0.723, 0.742, and 0.704) and calibration.Conclusion This study constructed a novel prognostic nomogram for BC patients with bone metastasis. It could serve as a potential tool of survival assessment to help individual treatment decision-making for clinicians.

Our purpose was to devise a radiomics model using preoperative computed tomography angiography (CTA) images to differentiate new from old emboli of acute lower limb arterial embolism. 57 patients (95 regions of interest; training set: n = 57; internal validation set: n = 38) with femoral popliteal acute lower limb arterial embolism confirmed by pathology and with preoperative CTA images were retrospectively analyzed. We selected the best prediction model according to the model performance tested by area under the curve (AUC) analysis across 1,000 iterations of prediction from three most common machine learning methods: support vector machine, feed-forward neural network (FNN), and random forest, through several steps of feature selection. Then, the selected best model was also validated in an external validation dataset (n = 24). The established radiomics signature had good predictive efficacy. FNN exhibited the best model performance on the training and validation groups: its AUC value was 0.960 (95% CI, 0.899–1). The accuracy of this model was 89.5%, and its sensitivity and specificity were 0.938 and 0.864, respectively. The AUC of external validation dataset was 0.793. Our radiomics model based on preoperative CTA images is valuable. The radiomics approach of preoperative CTA to differentiate new emboli from old is feasible.

This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street metrics and GIS, the study analysed the urban form through the random forest method, which reveals the predictive features of urban patterns and enables a deeper understanding of the spatial structures of cities. The findings showed distinctive spatial structures, such as ring formations and urban cores, indicating stages of urban development and socioeconomic narratives. It also showed that the unit of analysis has a major impact on the identification and study of street patterns. Concluding that machine learning is a critical tool in urban morphology, the research suggests that future studies should expand this framework to include more cities and urban elements. This would enhance the predictive modelling of urban growth and inform sustainable, human-centric urban planning. The implications of this study are significant for policymakers and urban planners seeking to harness data-driven insights for the development of cities.

Pulmonary arterial hypertension (PAH) is a severe disease resulting from progressive increases in pulmonary vascular resistance and pulmonary vascular remodeling, ultimately leading to right ventricular failure and even death. Hypoxia, inflammation, immune reactions, and epigenetic modifications all play significant contributory roles in the mechanism of PAH. Increasingly, epigenetic changes and their modifying factors involved in reprogramming through regulation of methylation or the immune microenvironment have been identified. Among them, histone lactylation is a new post-translational modification (PTM), which provides a novel visual angle on the functional mechanism of lactate and provides a promising diagnosis and treatment method for PAH. This review detailed introduces the function of lactate as an important molecule in PAH, and the effects of lactylation on N6-methyladenosine (m6A) and immune cells. It provides a new perspective to further explore the development of lactate regulation of pulmonary hypertension through histone lactylation modification.