Quality Grading System Research Articles

Quality Variation of Pork Bellies by Cutting Manner and Quality Grade

In the meat industry, the quality grading system is commonly applied to classify carcasses based on quality and value. Presently, to facilitate consumer convenience, pork bellies are prepared into slices and retailed in supermarkets and butchers. The objective of this study was to assess the effect of quality grade (QG) and retail cutting manner on the quality properties of pork bellies. Thirty-two bellies with different QGs: QG1+, 1, 2, and off-grade (n = 8 each) randomly collected from a commercial slaughterhouse were used. Each belly was cut into 3 portions: A (5–10th rib, cranial edge), B (11–15th rib), and C (without rib, caudal edge) according to the commonly used retail cutting manner. Samples were subjected to chemical composition, quality traits, and aroma analysis. Fat content was highest in QG1+ and lowest in off-grade and distributed at a higher level in portions A and B than in portion C in all QGs (p < 0.05). Off-grade was associated with higher shear force and chewiness values and lower levels of palmitic and stearic acids, regardless of the cutting portion. The bellies used in this study exhibited variations in chemical composition and quality properties not only among the QGs but also across the cutting portions within each QG.

Does the quality grading system matter? Chinese consumer response to pork quality grading labels and the impact of information

PurposeA meat quality grading system is essential to meet consumers' increasingly diversified demand for food quality in the global market. This study aims to determine the effectiveness of the upcoming Chinese quality grading labels and examine the information effect of labeling standards on pork consumption choices.Design/methodology/approachUsing an online survey with choice experiments, this study estimates consumer valuation for the fat thickness of different pork primal cuts by simulating three scenarios. Generalized mixed logit models in WTP space are used to analyze the choice experiment data.FindingsChinese consumers prefer lean pork to fatty pork and this preference does not vary significantly between primal cuts. Consumer valuation for ungraded high-quality (lean) pork increases after the implementation of the quality grading. Meanwhile, they are willing to pay high premiums for labeled pork (including level 1, 2, 3), and there are higher premiums for pork with higher levels. Besides, incomplete information on labeling standards could achieve more premiums for pork than relatively complete information.Originality/valueThis study pays attention to essential but few-noticed pork quality grading. The findings provide references for pork industry practices and policy-making of the meat quality grading system in China and globally by examining incomplete and relatively complete information effects on consumer choices.

Mixed-integer programming models for a multi-period integrated reverse logistics network with multiple recovery options and different quality levels of returns

This paper addresses an integrated reverse logistics (RL) model with a production route and multiple recovery options, including repair, refurbishing, remanufacturing, recycling, and selling spare components. We aim to use the quality grading system method to analyse returns comprehensively. In this regard, quality levels are used to classify returns and quality thresholds are utilised to categorise them into certain quality groups. The proposed model deals with different activities such as locating, purchasing, transporting, processing, inventory holding, recovering, and production in a planning horizon with multiple periods. A two-stage optimisation model is proposed to facilitate the decision-making process. In Stage 1, a mixed integer linear programming (MILP) model is proposed to minimise the costs and determine the number of returned products directed into different recovery routes. In Stage 2, another MILP is proposed to maximise the network’s profit. Furthermore, 350 scenarios are defined to examine the network’s performance and decide the optimal quality thresholds. Finally, the proposed models are applied to a real dataset and solved by the CPLEX solver through Pyhton. According to the results, scenarios that use multiple recovery options earn higher profits and lower costs as most demands are satisfied and the stock level of inventories decreases.

Physical, colour, and mechanical properties of pearls (citrus caviar) from three finger lime (Citrus australasica) varieties: Implications for economic value, quality grading, and benchmarking

Citrus australasica, the Australian finger lime or caviar lime have garnered interest both in culinary and scientific communities. Despite extensive studies on their metabolomic and volatilomic profiles, research on the physical properties of finger lime pearls, especially their diameter, colour, and mechanical properties, remains limited. Understanding these attributes is crucial for quality assessment, consumer preference, and marketability in the food industry. This study aims to investigate the diameter, visual appearance, and mechanical properties of finger lime pearls from three different varieties (‘Champagne Red’, ‘Emerald’, and ‘Chartreuse’), comparing them with similar foods and assessing their implications for culinary use and market value. Colour properties were analysed using the CIELAB system, and mechanical properties were assessed through texture analysis, focusing on bursting strength and strain. Finger lime pearl diameters ranged between 1.62 and 3.60 mm, aligning with sizes of comparative foods. Colour analysis revealed distinct differences among varieties. Mechanical properties, particularly bursting strength, and strain, varied significantly across varieties, with ‘Champagne Red’ and ‘Chartreuse’ showing higher bursting strengths. These properties suggest different culinary applications and potential for a grading system based on sensory experience. The study highlights the importance of physical and mechanical properties in determining the quality and marketability of finger lime pearls. It introduces a novel framework for assessing these attributes, suggesting that finger lime varieties can be differentiated and graded for culinary use based on pearl diameter, colour, and mechanical properties. This research lays the groundwork for future sensory-based studies and the development of quality grading systems for finger limes.

Growth period determination and color coordinates visual analysis of tomato using hyperspectral imaging technology

Growth period determination and color coordinates prediction are essential for comparing postharvest fruit quality. This paper proposes a tomato growth period judgment and color coordinates prediction model based on hyperspectral imaging technology. It utilizes the most effective color coordinates prediction model to obtain a color visual image. Firstly, hyperspectral images were taken of tomatoes at different growth periods (green-ripe, color-changing, half-ripe, and full-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colorimeter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were used to discriminate growth period. Results show that the LDA model has the best prediction effect with a prediction set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), and chromaticity prediction models were established using partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate. The results showed that hyperspectral imaging can non-destructively detect tomatoes’ growth stage and color coordinates, providing great significance for designing a tomato quality grading system.

Risk Factors for Atrial Fibrillation in the Dog: A Systematic Review.

Different risk factors for atrial fibrillation (AF) development have been identified in numerous studies on humans, but this information is less clearly available on the dog. The aim of this systematic review is to determine the risk factors for AF in the dog. Following the PRISMA 2020 guidelines, we conducted a comprehensive search using the Web of Science and Scopus databases for articles reporting on cases of spontaneously occurring AF in dogs. The level of evidence was assessed using the Evidence Quality Grading System of the National Institute of Health. One thousand forty-three studies were initially identified, and twenty of them were included in this systematic review involving 2,359,275 dogs, of which 4807 showed spontaneously occurring AF. Genetics, for the Irish Wolfhound, increased body weight, and left atrial enlargement were the main risk factors for the development of AF in dogs with different cardiac diseases, particularly myxomatous mitral valve disease (MMVD) and dilated cardiomyopathy (DCM). However, some differences were found between these two cardiac diseases regarding additional risk factors. In particular, the presence of congestive heart failure and echocardiographic evidence of increased left atrial pressure or the presence of right atrial enlargement emerged as risk factors in dogs with MMVD or DCM, respectively. Furthermore, significant differences in risk factors were observed between dogs and humans. In particular, advanced age and male sex are not reliable indicators of an increased risk of AF in dogs.

Improved deep belief network for estimating mango quality indices and grading: A computer vision-based neutrosophic approach

ABSTRACT This research introduces a revolutionary machinet learning algorithm-based quality estimation and grading system. The suggested work is divided into four main parts: Ppre-processing, neutroscopic model transformation, Feature Extraction, and Grading. The raw images are first pre-processed by following five major stages: read, resize, noise removal, contrast enhancement via CLAHE, and Smoothing via filtering. The pre-processed images are then converted into a neutrosophic domain for more effective mango grading. The image is processed under a new Geometric Mean based neutrosophic approach to transforming it into the neutrosophic domain. Finally, the prediction of TSS for the different chilling conditions is done by Improved Deep Belief Network (IDBN) and based on this; the grading of mango is done automatically as the model is already trained with it. Here, the prediction of TSS is carried out under the consideration of SSC, firmness, and TAC. A comparison between the proposed and traditional methods is carried out to confirm the efficacy of various metrics.

A review of some aspects of goat meat quality: future research recommendations

The global goat meat sector is advancing and contributes to long-term food security, especially in meeting the protein demands of the growing human population in developing countries. Spanning all countries, Australia, is the largest exporter of goat meat, although it has negligible consumption. However, Australia does potentially have a secure future as an innovative, profitable, and resilient world leader in goat production, provided some challenges are addressed. These challenges facing the goat meat sector require suitable strategies and interventions for better profitability and acceptance of goat meat consumed in Australia and as an export product. Limited research on goat meat quality and the lack of an adequate grading system for goat meat quality are two of the major issues that need attention from the industry and researchers. Some of the most critical areas that need further research to enable growth of the goat meat industry are the influence of genetics and age of animals on meat quality, standardisation of the ageing period of various goat meat cuts, cooking innovations, consumer acceptance and sensory analysis of goat meat (both farmed and rangeland goats). This paper reviews the status of the goat meat sector and identifies the opportunities for the goat meat sector, particularly in Australia. In addition, we highlight several key issues requiring further research and interventions to enhance the growth of the goat meat industry.

Design and Experimental Research of Intelligent Inspection and Classification System for Yuba Skin Quality

At present, the surface quality of Yuba skin is determined by sensory methods. In order to realize the intelligent classification detection of Yuba skin quality, this study designed a system that automatically determines the quality of Yuba skin surfaces based on image processing and support vector machine (SVM) approaches. Specifically, the system uses image preprocessing to extract the grayscale eigenvalues, gray level co-occurrence matrix (GLCM) eigenvalues, and gray level run length matrix (GLRLM) eigenvalues of the sample image and uses them as input values for a quality grading system. Through model evaluation of three classification models, the SVM classification model was selected according to the evaluation results, and different kernel functions were used in the model for sample training. Based on Matlab, the quality grading software of Yuba skin was developed and designed. Intelligent detection and grading were realized through the radial basis kernel function support vector machine (RBF-SVM) grading model. The best penalty factor (c = 3.50) and kernel parameter value (g = 0.98) were obtained through cross-validation. The accuracy of the model was 95.31% and 94.16% for the training and test sets, respectively. The grading accuracy of the RBF-SVM grading system was 93.56%, and the error was less than 5% compared with the traditional sensory method of grading; thus, the quality classification method based on the SVM classification system for Yuba skin is feasible and can be used for quality detection.

Inspection and Grading of Dried Coconut – an Automatic Method Using SVM Classification

Coconut production and consumption rate of India being very high, contributes significantly towards the Indian economy; thus, ensuring the quality of allied products proves to be a necessity. Intelligent quality evaluation and grading system for copra images is proposed to classify copra, the dried coconut kernel into various categories using an image processing approach. The proposed method is useful in the current scenario, as quality deterioration during drying and storing of copra is a challenging task. Inferior quality of copra due to the presence of moisture, mold, fungi, bacteria and sulphur may adversely affect the shelf life thus deteriorating the quality of final products and human health. The grading system automatically segments the region of interest of copra images using SUSAN filter and classifies them into usable and unusable categories. The unusable categories of copra considered in the proposed method include copra with moisture, mold, wrinkles and sulphur. Various features of copra images were collected and analyzed. The selected features of copra images were used for training and classification using SVM classifier. The proposed method has been evaluated using a real database and the results are promising.

French consumer evaluation of eating quality of Angus x Salers beef: Effects of muscle cut, muscle slicing and ageing

Based on the Meat Standards Australia (MSA) methodology, the objectives of this study were to evaluate the eating quality of grass-fed Angus x Salers beef by French consumers, across different muscle cuts (m. triceps brachii caput longum, m. longissimus dorsi et thoracis and m. rectus abdominis), ageing times (5 and 14 days) and muscle slicing and grilling methods (being distinguished by 2 different sides on the grill), and to further quantify whether the MSA model could predict the eating quality of the meat produced by these lean crossbred animals. The results showed that French consumers can discern significant differences in eating quality traits across muscle cuts but not between ageing times and muscle slicing methods. In this study, the accuracy of eating quality score (MQ4)-assigned MSA grade was 64.5%. Due to the lack of MSA data on these lean crossbred animals, the prediction of eating quality from carcass characteristics was less accurate than usual. However, these results do indicate that the MSA sensory testing protocol, the MQ4 calculation and the MSA quality grades could be used to support a consumer-based quality grading system in France.

Accounting for regional water recyclability or scarcity using Machine Learning and Pinch Analysis

Water stress is becoming a major concern worldwide because of the lack of fresh resources to meet growing water demand in the face of climate change. Resources recycling is a viable option, but the main dilemma is to define a proper water quality grading system. This paper proposes a hybrid framework combining Machine Learning (ML) with Process Integration (PI) tools for assessing the regional water scarcity and recycling potential. The procedure involves defining the quality of water resources using supervised or unsupervised ML. Supervised ML (Classification) is employed when the data samples' origins or quality levels are known. The data can be sampled from an existing recycling system. The unsupervised ML (Clustering) method is used when quality levels are unknown. Data dimensionality reduction or expansion methods are used on the dataset to yield better classification or clustering outcomes. Once the hierarchical quality classes/clusters are revealed, the PI approach of Pinch Analysis is applied with the defined quality categories for planning water exchange systems (e.g., urban water networks or industrial parks). The method not only identifies the quality bottleneck of the system but also reveals the fresh resources deficit or excess of system supplies based on the defined quality clusters. This novel concept is demonstrated with case studies featuring different water sources and scenarios. Results show that the hybrid approach can categorise the water sources effectively, and depending on the number of defined clusters/categories, the water recycling potential can be different (e.g. with 5 clusters, the recyclability rate is 44%, while with 2 clusters, the recyclability rate can increase to 78% for the case study). The framework could serve as a guideline for regional authorities to manage the water resources according to their own water resources and properties database.

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

In order to solve the problems of image perception and quality decision-making of wood defects with typical bionic intelligent algorithms, the existence of multidimensional degradation factors causes serious real problems of image distortion; the author proposes a wood defect image reconstruction and quality evaluation model based on deep reinforcement learning. The model introduced the deep learning mechanism and realized real-time and efficient reconstruction of multidimensional defect images of different wood by using the deep residual network for iterative training. In this model, a panoramic autonomous perception model was constructed for the fine segmentation and feature extraction of multidimensional defects of different wood and a shared resource pool of wood defect features of the magnitude of big data was constructed. Introduce the reinforcement learning mechanism, use the deep deterministic policy gradient algorithm, and establish a high-dimensional decision mapping between the iterative update of defect features, autonomous decision-making, panoramic visualization, depth prediction, and wood quality evaluation; it realizes the horizontal sharing integration of multidimensional difference wood defect image reconstruction and quality evaluation. The results obtained are as follows: in a typical environment, systematic wood quality evaluation, and autonomous intelligent decision-making performance, the coincidence rate with artificial defect recognition and evaluation efficiency can reach 90% and the loss of the training set can be controlled below 0.2%. Compared with the traditional wood quality grading system, the wood defect image reconstruction, and quality evaluation model system designed by the author, the quality evaluation decision-making efficiency rate was 90.19%, an increase of about 20%, and the system decision-making operation and maintenance loss was 2.23%, a decrease of about 10%. It is proved that the system designed by the author can realize the timely detection of wood quality defects very effectively and save a lot of manpower and material resources.

O-066 CSCM-NXC media containing a low lactate concentration seems to benefit expansion grade compared to traditional continuous media

Abstract Study question Does lactate presence in the culture medium have an effect on embryo development, specifically during blastocyst formation? Is it an age related effect? Summary answer There is a statistical significative reduction of early blastocyst formation and a trend towards higher proportion of expanded and hatching blastocysts in lower lactate concentration. What is known already Embryos after embryonic genome activation start an active consumption of glucose to convert it into pyruvate and lactate for ATP production through the TCA cycle. Conversion from pyruvate to lactate to produce NADH through LDH (lactate dehydrogenase) can be counterbalanced by increased amounts of lactate, which then can alter the NAD+/NADH ratio resulting in a higher oxidative stress and reduced metabolic activity. Recently, a new commercial media formulation with a substantial reduction of lactate has come in to the market reporting increased efficiency of blastocyst formation and amount of euploid embryos compare to its version with a traditional lactate concentration. Study design, size, duration From September to November 2021 embryos were cultured in two different continuous media (SAGE 1-STEP and FUJIFILM IRVINE SCIENTIFIC CSCM-NXC). A total of 260 correctly fertilized embryos were allocated in both types of media (119 in 1-STEP and 141 in CSCM-NXC). Patients containing more than one zygote had their embryos allocated as equally as possible in both types of media. Participants/materials, setting, methods Age of participants ranged from 24-45 years. The embryos were cultured in embryoscope+ and their destination was assigned as Transfer, Vitrification or Discard (if by day 6 they did not reach a good quality blastocyst). Morphokinetic parameters were measured and Gardner's grading system for blastocyst quality annotated. For blastocyst quality comparison, only day 5 was considered. Variables studied were utilisation and blastocyst formation rates, expansion grade, ICM and TE quality, patient age, and pregnancy rates. Main results and the role of chance Incubation of embryos in reduced lactate concentration reduced significantly (p-value=0.013) the proportion of embryos with expansion grade 1 (14.3% 1STEP vs 3.4% CSCM-NXC), in contrast, a trend towards higher expansion grades, more importantly grade 5 (7.8% 1STEP vs 14.9% CSCM-NXC) was observed. A slight reduction in proportion of discarded embryos (47.52% CSCM-NXC vs 54.62% 1STEP) and increased proportion of vitrified embryos (34.75% CSCM-NXC vs 27.73% 1STEP) was also observed. Regarding ICM and TE qualities, in general there was also a trend in benefit of use of CSCM-NXC medium (55.3% CSCM-NXC vs 50.7% 1STEP for grade A in ICM; 36.8% CSCM-NXC vs 30.1% 1STEP for grade A in TE). Morphokinetic parameters were not different between groups, except T3 which showed to be significative (p-value=0.026) earlier in 1STEP than CSCM-NXC (37.13 h vs 38.71 h respectively). Age subgroups of patient showed no difference in utilisation rates for ages < 37 years old, however, for ages ≥37 there was a higher proportion of utilised embryos (56.3 CSCM-NXC vs 41.9% 1STEP for 37-40 years old; 60% CSCM-NXC vs 42.3% 1STEP for >40 years old). Finally, pregnancy rates were equal for those fresh transfers between both groups. Limitations, reasons for caution The study has a reduced sample size, so statistical power is reduced. Furthermore, it cannot be discarded that the effect is not a contribution to other differences in formulation of both media. Time to birth or cumulated pregnancy parameter would be more indicative of benefits. Wider implications of the findings Two main hypotheses that require further validation: First, excess of certain metabolites in culture can impair development of preimplantation embryos. Second, embryos may be more sensitive to certain medium composition depending on age, so medium designed according to this may be a near future direction to improve IVF outcomes. Trial registration number NA

Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis

• Quality-oriented recycling of plastic waste is proposed. • Data-driven Plastic Pinch Analysis to evaluate plastic waste recycling potential. • Machine learning approaches to define quality grades of plastic polymers. • The study showed maximum recyclability is 38% for PET, 100% for PE and 92% for PP. • Environmental impacts of recycling and treatment technologies are compared. 1 1 The main corresponding author address (chin@fme.vutbr.cz) is going to be idle as the author is moving, so another corresponding author is proposed (Petar Sabev Varbanov) for future correspondence (two corresponding authors) Abstract The worldwide plastic waste accumulation has posed probably irreversible harm to the environment, and the main dilemma for this global issue is: How to define the waste quality grading system to maximise plastic recyclability? This work reports a machine learning approach to evaluating the recyclability of plastic waste by categorising the quality trends of the contained polymers with auxiliary materials. The result reveals the hierarchical resource quality grades predictors that restrict the mapping of the waste sources to the demands. The Pinch Analysis framework is then applied using the quality clusters to maximise plastic recyclability. The method identifies a Pinch Point – the ideal waste quality level that limits the plastic recycling rate in the system. The novel concept is applied to a problem with different polymer types and properties. The results show the maximum recycling rate for the case study to be 38 % for PET, 100 % for PE and 92 % for PP based on the optimal number of clusters identified. Trends of environmental impacts with different plastic recyclability and footprints of recycled plastic are also compared.

Quality assessment pathway for respiratory oscillometry.

A flow-chart-driven procedure is presented to facilitate respiratory oscillometry operator competency and measurement quality. A novel feature is a quality grading system, in line with other standards of lung function. https://bit.ly/3G4r0X1

Automated Quality and Process Control for Additive Manufacturing using Deep Convolutional Neural Networks

Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internal defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption.

A review of unboiled arecanut drying process and its correlation with mechanical properties

It has been revealed through literature review that, numerous challenges are still to be addressed in developing scientific grading methods for agricultural produce like fruits, vegetables, nuts, seeds etc. due to their physical nature and mechanical behavior. Their classification is primarily based on visual attributes and human expertise. The present methods of the manual grading of arecanuts are tedious, labour intensive and the grading equipments are mainly based on size, shape color and texture. Further, they do not take into account the physical and mechanical properties that have a strong correlation with the moisture content of the nuts, that largely impacts their inherent characteristics. The potential medicinal and industrial applications of arecanuts call for scientific and automated quality grading systems. India being the largest producer of Arecanut in the world, accounting for more than 50% of the world’s output has several sorting centers in need of accurate and reliable quality grading solutions. In this paper an attempt is made to study and review the existing drying processes which largely determine the quality characteristics of unboiled type arecanuts widely grown in coastal states of India as well as other parts of Asia. Research publications pertaining to the investigation of the physical and mechanical properties with respect to the moisture content in the context of dehusking, cutting operations and evaluation of kernel properties for sorting and other post processing operations are also reviewed. This work articulates the use of statistical techniques in quality characterization with minimal error, emphasizing on the future scope of research in optimization of grading and drying parameters for processed arecanuts of unboiled category. Further these studies could help in the development of quality grading and drying systems.

1643 Quality Assessment of Intra-Oral Radiography Within Two Hospitals in South Wales, UK: A Two-Cycle Audit and Introduction to The Updated 2020 Public Health England Quality Grading System

Abstract Aim To evaluate the quality of intra-oral radiographs taken within two hospitals in South Wales, United Kingdom, and assess the impact of radiographer training, with the aim of reducing the percentage of undiagnostic radiographs to 10% or less in accordance with national performance targets. Method Details of intra-oral periapical and occlusal radiographs taken in the two hospitals between the 1st July 2015 and the 30th June 2016 were retrieved. 100 radiographs were randomly selected and assessed. Change was then implemented in the delivery of radiographer training workshops, which involved two hours of both didactic teaching and hands-on skills training. There were 10 workshops and 52 attendees. A second audit cycle was completed, with a further 100 radiographs randomly selected from the 1st December 2018 and 30th April 2019. Results There was a 33.2% reduction of undiagnostic radiographs, bringing the total down to 6.3%, meeting the standards of the audit. Feedback from the workshops were also extremely positive; 50% of radiographers grading the quality of the workshops at 10/10. Conclusions The workshops were successful in improving the quality of intra-oral radiographs, therefore improving the quality of this service. This blueprint can be shared amongst other hospitals, to sustainably improve the overall quality of dental radiography in the hospital setting. Recent updated guidance in 2020 introduced a new two-point scale radiograph grading system: ‘Acceptable’ and, ‘not acceptable’. Radiographs graded as, ‘not acceptable’, should account for no more than 5% of radiographs. Future audit cycles will follow this new grading system and standard.

Efficacy and safety of Xinkeshu in the treatment of angina pectoris of coronary heart disease: A protocol for systematic review and meta-analysis.

Background:The incidence of angina pectoris (AP) of coronary heart disease (CHD) is increasing in the world, which seriously affects people's lives and brings a huge economic burden. The clinical research on Xinkeshu (XKS) in the treatment of AP of CHD has been increasing. However, there is no systematic review and meta-analysis. This study intends to provide a basis for systematically evaluating the efficacy and safety of XKS combined with conventional western medicine in the treatment of AP of CHD.Methods:CNKI, Wanfang, VIP, Web of Science, PubMed, Cochrane Library, and EMbase databases were searched for the period from the establishment of the database to August 31, 2021. The clinical randomized controlled trials of XKS in the treatment of AP of CHD were collected. Two systematic reviewers independently selected the literature, extracted the data, and evaluated the quality according to the inclusion and exclusion criteria. The methodological quality of the literature was evaluated using Cochrane Handbook 5.3.0 bias risk assessment tool, RevMan 5.3.0 software was used for meta-analysis and GRADE3.6 evidence quality grading system was used to evaluate the quality.Results:This study intended to evaluate the efficacy and safety of XKS in the treatment of AP of CHD from many aspects, including the frequency of AP, the duration of AP, the dosage of nitroglycerin, and the efficacy of ECG (total effective rate = markedly effective + effective). The secondary indicators included the efficacy of AP (total effective rate = significant + effective), blood lipids (triglyceride, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol), hemorheology (whole blood viscosity, plasma viscosity, and fibrinogen), serum factors (C-reactive protein, endothelin-1, homocysteine, and nitric oxide), and adverse drug reactions.Conclusion:The conclusion of the systematic review intended to provide clear evidence of clinical application of XKS combined with conventional western medicine in the treatment of AP of CHD, which can be widely used in the clinic.