Significant association between high neutrophil-lymphocyte ratio and poor prognosis in patients with hepatocellular carcinoma: a systematic review and meta-analysis

ObjectiveWhether neutrophil-lymphocyte ratio (NLR) is an applicative predictor of poor prognosis in patients with hepatocellular carcinoma (HCC) remains controversial. In response to the current conflicting data, this meta-analysis was conducted to gain a comprehensive and systematic understanding of prognostic value of NLR in HCC.MethodsSeveral English databases, including PubMed, EMBASE, and the Cochrane Library, with an update date of February 25, 2023, were systematically searched. We set the inclusion criteria to include randomized controlled trial (RCT) studies that reported the prognostic value of serum NLR levels in patients with HCC receiving treatment. Both the combined ratio (OR) and the diagnosis ratio (DOR) were used to assess the prognostic performance of NLR. Additionally, we completed the risk of bias assessment by Cochrane Risk of Bias Assessment Tool.ResultsThis meta-analysis ultimately included 16 studies with a total of 4654 patients with HCC. The results showed that high baseline NLR was significantly associated with poor prognosis or recurrence of HCC. The sensitivity of 0.67 (95% confidence interval [CI]. 0.59-0.73); specificity of 0.723 (95% CI: 0.64-0.78) and DOR of 5.0 (95% CI: 4.0-7.0) were pooled estimated from patient-based analyses. Subsequently, the combined positive likelihood ratio (PLR) and negative likelihood ratio (NLHR) were calculated with the results of 2.4 (95% CI: 1.9-3.0) and 0.46 (95% CI: 0.39-0.56), respectively. In addition, area under the curve (AUC) of the summary receiver operating characteristic (SROC) reflecting prognostic accuracy was calculated to be 0.75 (95% CI: 0.71-0.78). The results of subgroup analysis suggested that high NLR was an effective predictive factor of poor prognosis in HCC in mainland China as well as in the northern region.ConclusionOur findings suggest that high baseline NLR is an excellent predictor of poor prognosis or relapse in patients with HCC, especially those from high-incidence East Asian populations.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/#recordDetails, identifier CRD42023440640

Rodent hole detection in a typical steppe ecosystem using UAS and deep learning

IntroductionRodent outbreak is the main biological disaster in grassland ecosystems. Traditional rodent damage monitoring approaches mainly depend on costly field surveys, e.g., rodent trapping or hole counting. Integrating an unmanned aircraft system (UAS) image acquisition platform and deep learning (DL) provides a great opportunity to realize efficient large-scale rodent damage monitoring and early-stage diagnosis. As the major rodent species in Inner Mongolia, Brandt’s voles (BV) (Lasiopodomys brandtii) have markedly small holes, which are difficult to identify regarding various seasonal noises in this typical steppe ecosystem.MethodsIn this study, we proposed a novel UAS-DL-based framework for BV hole detection in two representative seasons. We also established the first bi-seasonal UAS image datasets for rodent hole detection. Three two-stage (Faster R-CNN, R-FCN, and Cascade R-CNN) and three one-stage (SSD, RetinaNet, and YOLOv4) object detection DL models were investigated from three perspectives: accuracy, running speed, and generalizability.ResultsExperimental results revealed that: 1) Faster R-CNN and YOLOv4 are the most accurate models; 2) SSD and YOLOv4 are the fastest; 3) Faster R-CNN and YOLOv4 have the most consistent performance across two different seasons.DiscussionThe integration of UAS and DL techniques was demonstrated to utilize automatic, accurate, and efficient BV hole detection in a typical steppe ecosystem. The proposed method has a great potential for large-scale multi-seasonal rodent damage monitoring

Damage detection techniques for wind turbine blades : a review

Blades play a vital role in wind turbine system performances. However, they are susceptible to damage arising from complex and irregular loading or even cause catastrophic collapse, and they are expensive to maintain. Defects or damages on wind turbine blades (WTBs) not only reduce the lifespan and power generation efficiency of the wind turbine, but also increase monitoring errors, safety risks and maintenance costs. Therefore, damage detection for WTBs is of great importance for failure avoidance, maintenance planning, and operation sustainability of wind turbines. This paper provides a comprehensive review of state-of-the-art damage detection techniques for WTBs, including most of those updated methods based on strain measurement, acoustic emission, ultrasound, vibration, thermography and machine vision. Firstly, typical damages of WTBs are comprehensively introduced. Secondly, detection principles, development methods, pros and cons of the aforementioned techniques for blade inspection, and their fault indicators are reviewed. Finally, potential research directions of WTB damage detection techniques are addressed via a comparative analysis, and conclusions are drawn. It is expected that this review will provide guidelines for practical WTB inspections, as well as research prospects for damage detection techniques

A microfluidic device for three-dimensional wear debris imaging in online condition monitoring

Three-dimensional morphologies of wear particles are important information sources for machine condition assessment and fault diagnosis. However, existing three-dimensional image acquisition systems, such as laser scanning confocal microscopy and atomic force microscopy, cannot be directly applied in condition-based maintenance of machines. In order to automatically acquire three-dimensional information of wear debris for online condition monitoring, a microfluidic device consisting of an oil flow channel and a video imaging system is developed. This paper focuses on the control of particle motions. A microchannel is designed to ensure the continuous rotation of particles such that their three-dimensional features can be captured. The relationships between running torque and channel height and particle size are analysed to determine the channel height. An infinite fluid field is considered to make sure that the particles rotate around the same axis to capture 360 degree views. Based on this, the cross section of the microchannel is determined at 5 mm 0.2 mm (height width) to capture the wear debris under 200 mm. A CMOS sensor is used to image the particles in multiple views and then three-dimensional features of wear debris (e.g. thickness, height aspect ratio and sphericity) are obtained. Two experiments were carried out to evaluate the performances of the designed system. The results demonstrate that (1) the microfluidic device is effective in capturing multiple view images of wear particles various in sizes and shapes; (2) spatial morphological characteristics of wear particles can be constructed using a sequence of multi-view images