The clinical significance of serum HMGB1 in patients with lower extremity arteriosclerosis obliterans after interventional vascular restenosis

ObjectiveThis study explored the correlation between serum HMGB1 levels and postoperative vascular restenosis in patients with lower extremity arteriosclerosis obliterans (LEASO).MethodsA total of 362 patients LEASO who received vascular intervention were recruited in this study. Serum HMGB1 levels were measured by enzyme-linked immunosorbent assay. Logistic regression analysis was used to identify the influencing factors associated with vascular restenosis. The R procedure was used to create nomogram model. Receiver operating characteristic (ROC) analysis was used to determine the predictive value of serum HMGB1 and nomogram model for vascular restenosis.ResultsOf the 362 LEASO patients included, 103 (28.45%) developed restenosis within 6 months of postoperative follow-up. Postoperative HMGB1 levels were significantly higher in patients with restenosis compared to those with non-restenosis. Postoperative HMGB1 levels were significantly and positively correlated with the severity of postoperative restenosis (r = 0.819). The AUC of postoperative HMGB1 for the diagnosis of postoperative restenosis was 0.758 (95% CI: 0.703–0.812), with a sensitivity and specificity of 56.31% and 82.24%, respectively. Multivariate logistic regression analysis showed that diabetes, smoking, regular postoperative medication, increased fibrinogen, decreased red blood cells, increased hs-CRP, and increased postoperative HMGB1 were independently associated with postoperative restenosis in patients with LEASO. The C-index of the nomogram prediction model constructed based on the seven influencing factors mentioned above was 0.918. The nomogram model was significantly more predictive of postoperative restenosis in LEASO patients compared with a single postoperative HMGB1 (AUC: 0.918, 95% CI: 0.757–0.934).ConclusionPostoperative serum HMGB1 is an independent risk factor associated with postoperative vascular restenosis in patients with LEASO, and a novel nomogram model based on postoperative serum HMGB1 combined with clinical characteristics may help to accurately predict the risk of postoperative restenosis in patients with LEASO

Quality and inspection of machining operations: Review of condition monitoring and CMM inspection techniques 2000 to present

In order to consistently produce quality parts, many aspects of the manufacturing process must be carefully monitored, controlled, and measured. The methods and techniques by which to accomplish these tasks has been the focus of numerous studies in recent years. With the rapid advances in computing technology, the complexity and overhead that can be feasibly incorporated in any developed technique has dramatically improved. Thus, techniques that would have been impractical for implementation just a few years ago can now be realistically applied. This rapid growth has resulted in a wealth of new capabilities for improving part and process quality and reliability. In this paper, overviews of recent advances that apply to machining are presented. Moreover, due to the relative significance of two particular machining aspects, this review focuses specifically on research publications pertaining to using tool condition monitoring and coordinate measurement machines to improve the machining process. Tool condition has a direct effect on part quality and is discussed first. The application of tool condition monitoring as it applies to turning, drilling, milling, and grinding is presented. The subsequent section provides recommendations for future research opportunities. The ensuing section focuses on the use of coordinate measuring machines in conjunction with machining and is subdivided with respect to integration with machining tools, inspection planning and efficiency, advanced controller feedback, machine error compensation, and on-line tool calibration, in that specific order and concludes with recommendations regarding where future needs remain

A Method for Obtaining Optical Properties of Two-Layer Tissue such as Thin-Skinned Fruits by Using Spatial Frequency Domain Imaging

As a new imaging inspection method with characteristics of a wide view field and non-contact, spatial frequency domain imaging (SFDI) is very suitable to evaluate the optical properties of agricultural products to ensure the sustainable development of agriculture. However, due to the unique forward scattering characteristics of fruit skin, only a few photons can return to the skin surface after interacting with the flesh, thus affecting the detection accuracy of the flesh layer. This study aims to propose a more accurate and wider applicable method to extract the optical properties of two-layer tissue from SFDI measurements. Firstly, a two-layer model was proposed by optimizing the reflectivity of the flesh layer through the optical properties and thickness of the skin layer. Secondly, the influence of the optical properties and thickness of different skin layers on the reflectivity optimization of the flesh layer was investigated by a Monte Carlo simulation, and then, the accuracy and effectiveness of the proposed model was evaluated for practical inspection by phantom experiments. Finally, this model was used to obtain the optical properties, layer by layer, of four thin-skinned fruits (pear, apple, peach and muskmelon) to verify its universality. The results showed that, for the skin layer, the average errors of the absorption coefficient (μa1) and the reduced scattering coefficient (μ′s1) were 10.87% and 7.91%, respectively, and for the flesh layer, the average errors of the absorption coefficient (μa2) and the reduced scattering coefficient (μ′s2) were 16.76% and 8.64%, respectively. This study provides the basis for the SFDI detection of optical properties of two-layer tissue such as thin-skinned fruits, which can be further used for nondestructive fruit quality evaluations

Extracting Tissue Optical Properties and Detecting Bruised Tissue in Pears Quickly and Accurately Based on Spatial Frequency Domain Imaging and Machine Learning

Recently, Spatial Frequency Domain Imaging (SFDI) has gradually become an alternative method to extract tissue optical properties (OPs), as it provides a wide-field, no-contact acquisition. SFDI extracts OPs by least-square fitting (LSF) based on the diffuse approximation equation, but there are shortcomings in the speed and accuracy of extracting OPs. This study proposed a Long Short-term Memory Regressor (LSTMR) solution to extract tissue OPs. This method allows for fast and accurate extraction of tissue OPs. Firstly, the imaging system was developed, which is more compact and portable than conventional SFDI systems. Next, numerical simulation was performed using the Monte Carlo forward model to obtain the dataset, and then the mapping model was established using the dataset. Finally, the model was applied to detect the bruised tissue of ‘crown’ pears. The results show that the mean absolute errors of the absorption coefficient and the reduced scattering coefficient are no more than 0.32% and 0.21%, and the bruised tissue of ‘crown’ pears can be highlighted by the change of OPs. Compared with the LSF, the speed of extracting tissue OPs is improved by two orders of magnitude, and the accuracy is greatly improved. The study contributes to the rapid and accurate extraction of tissue OPs based on SFDI and has great potential in food safety assessment

Metal-Semiconductor-Metal Ultraviolet Photodetectors Based on TiO2 Films Deposited by Radio-Frequency Magnetron Sputtering

Metal-semiconductor-metal ultraviolet (UV) photodetectors (PDs) with Au electrodes, based on TiO2 thin films prepared by radio-frequency magnetron sputtering, are fabricated and characterized. The PDs exhibit a low dark current of 9.73 pA at 5-V bias and a high breakdown voltage of over 90 V, owing to the achievement of high-quality stoichiometric TiO2 films. Meanwhile, the high responsivity with a cutoff wavelength at around 380 nm and a large UV-to-visible rejection ratio (310 versus 400 nm) of more than three orders of magnitude are obtained, which suggest that the fabricated PDs are very promising in UV detection applications

Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves

Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400–1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400–1000 nm (visible range) and 900–1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way

Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit

An effective and rapid way to detect thiophanate-methyl residue on mulberry fruit is important for providing consumers with quality and safe of mulberry fruit. Chemical methods are complex, time-consuming, and costly, and can result in sample contamination. Rapid detection of thiophanate-methyl residue on mulberry fruit was studied using laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) techniques. Principal component analysis (PCA) and partial least square regression (PLSR) were used to qualitatively and quantitatively analyze the data obtained by using LIBS and HSI on mulberry fruit samples with different thiophanate-methyl residues. The competitive adaptive reweighted sampling algorithm was used to select optimal variables. The results of model calibration were compared. The best result was given by the PLSR model that used the optimal preprocessed LIBS–HSI variables, with a correlation coefficient of 0.921 for the prediction set. The results of this research confirmed the feasibility of using LIBS and HSI for the rapid detection of thiophanate-methyl residue on mulberry fruit

A Novel Low-Cost GNSS Solution for the Real-Time Deformation Monitoring of Cable Saddle Pushing: A Case Study of Guojiatuo Suspension Bridge

Extreme loadings, a hostile environment and dangerous operation lead to the unsafe state of bridges under construction, especially large-span bridges. Global Navigation Satellite Systems (GNSS) tend to be the best choice for real-time deformation monitoring due to the significant advantage of automation, continuation, all-weather operation and high precision. Unfortunately, the traditional geodetic GNSS instrument with its high price and large volume is limited in its applications. Hence, we design and develop low-cost GNSS equipment by simplifying the monitoring module. The performance of the proposed solution is evaluated through an experimental dynamic scenario, proving its ability to track abrupt deformation down to 3–5 mm. We take Chongqing Guojiatuo Suspension Bridge in China as a case study. We build a real-time low-cost GNSS monitoring cloud platform. The low-cost bridge GNSS monitoring stations are located at the top of the south and north towers, midspan upstream and downstream respectively and the reference station is located in the stable zone 400 m away from the bridge management buildings. We conducted a detailed experimental assessment of low-cost GNSS on 5 April and a real-time deformation detection experiment of the towers and main cables during the dynamic cable saddle pushing process on 26 February 2022. In the static experiment, the standard deviation of the residual using the multi-GNSS solution is 2 mm in the horizontal direction and 5 mm in the vertical direction. The multi-GNSS solution significantly outperforms the BDS/GPS single system. The dynamic experiment shows that, compared with the movement measured by the robotic total station, the horizontal error of the south tower and north tower measured by low-cost GNSS is below 0.005 m and 0.008 m respectively. This study highlights the potential of low-cost GNSS solutions for Structural Health Monitoring (SHM) applications