COVID-19 is considered to be very contagious as it can be spread through multiple ways. Therefore, exposure risk of healthcare workers (HCWs) treating COVID-19 patients is a highly salient topic in exposure risk management. From a managerial perspective, wearing personal protective equipment and the risk of accidents occurring during aerosol generating procedures applied to COVID-19 patients are two interconnected issues encountered in all COVID-19 hospitals.The study was conducted to understand the realistic impact of exposure risk management on HCWs exposed to risks of SARS-CoV-2 virus infection in a healthcare unit. In particular, this study discusses the role of personal protective equipment (PPEs) used in aerosol generating procedures (AGPs) to protect HCWs, and the related risk of accidents occurring when performing AGPs.This is a cross-sectional single-hospital study conducted at the “Sf. Ioan cel Nou” Hospital in Suceava, Romania, that had to ensure safety of healthcare workers (HCWs) getting in contact with COVID-19 cases. Data used in the study were collected between 10.12.2020–19.03.2021 by means of a questionnaire that collected information on risk assessment and healthcare workers’ exposure management, and which was translated and adapted from the World Health Organization (WHO) and applied to respondents online. For this purpose, ethical approval was obtained, doctors and nurses from all hospital departments being invited to complete the questionnaire. Data processing, as well as descriptive, correlation and regression analyses have been done by using the 21.0 version of the Statistical Package for Social Sciences software.Most of the 312 HCWs reported having always used disposable gloves (98.13%), medical masks N95  (92.86%), visors or googles (91.19%), disposable coverall (91.25%) and footwear protection (95.00%) during AGPs. The waterproof apron had always been worn only by 40% of the respondents, and almost 30% of staff had not used it at all during AGPs. Over the last three months, the period when the questionnaire was completed, 28 accidents were reported while performing AGPs: 11 accidents with splashing of biological fluids/ respiratory secretions in the eyes, 11 with splashing of biological fluids/ respiratory secretions on the non-idemn skin, 3 with splashing of biological fluids/ respiratory secretions in the oral/ nasal mucosa and 3 with puncture/ sting with any material contaminated with biological fluids/ respiratory secretions. Also, 84.29% of respondents declared having changed their routine, at least, moderately due to COVID-19.An effective risk exposure management is based on wearing protective equipment. The only protection offered by the disposable coverall, as it results from our analysis, is related to splashing of biological fluids/ respiratory secretions on the non-idemn skin. In addition, the results show that the number of accidents should decrease due to the fact that disposable gloves and footwear protection are used while performing AGPs on patients with COVID-19 and hand hygiene is practised before and after touching a patient with COVID-19 (regardless of glove wearing). Because of the high number of COVID-19 infections reported across the globe, on March 11, 2020, was declared the pandemic [COVID-19 can be spread through aerosol/aerated solids, fluids from human secretions, droplets from normal breathing, coughing, sneezing and surface contact, and therefore is considered very contagious . AccordiAlthough a lot of effort was put into vaccine introduction and use, COVID-19 still has an impact on healthcare services, while personnel working in medical systems are at higher risk of infection , 7. FronThis study was conducted using a questionnaire translated and adapted from the World Health Organization (WHO)  with theAerosols are small liquids or solid particles present in the air, ranging from 0.001 μm to 100 μm . Small pNurses have been exposed to COVID-19 since they have a direct contact with COVID-19 patients, especially in the context of AGPs . In ChinIn Zhongnan Hospital of Wuhan University, out of 103 infected employees, before getting the infection, almost half worked over 7 hours per day in a COVID-19 contaminated environment . In the An adequate PPE is strongly recommended against the risk of HCW getting infected with COVID-19 , 20. A sOne study conducted in Australia, identified such barriers as supplier availability, workplace rationing, delivery delays, high costs, no PPE supply at the workplace, denial of PPE based on a low perception of risk or inappropriateness by management . The majPPE has fulfilled for over 100 years its main purpose of protecting the patient and the surgeon, and it includes: eye protection, gowns, gloves, surgical face masks, non-powered filtering face piece respirators, and powered air-purifying respirators . Full PPIn the context of COVID-19, wearing different pieces of PPE has become very questionable . HoweverDuring this pandemic, PPE supply was a concern. In a busy tertiary care hospital in the United Kingdom, a negative factor in the fight against the virus was the supply of PPE for HCWs . In a stThe case of South America was analysed in one study. The rates of SARS-CoV-2 infection among HCW in São Paulo city ranged from 5.5% (IgG ELISA) in a private hospital to 14%  in a large public hospital in 2020 due to adoption of high-quality hospital infection control and provision of complete PPE in the early stages of the COVID-19 pandemic .Not only PPE is important during AGPs, it seems that HCWs’ hand hygiene is also significant, and should be monitored –34. In tIn a study conducted on 80 frontline nurses in COVID-19 hospitals in the north of Saudi Arabia, 3.8% of the respondents reported such accidents related to biological material handling as splashes of a biological fluid (into the eyes) . Also, tIn laboratories, SARS-CoV-2 aerosols live on average 5.6 hours on stainless steel, and 6.8 hours on plastic . The numFace shields are recommended as a substitute to goggles, and offer protection against splashes and splatter of aerosols on a larger area of face . GogglesIn procedures involving splashing of body fluids, blood, etc., use of goggles and protective face shields/ face screens are mandatory . Shoe coTo formulate the research hypotheses, this study started from data provided by earlier studies, which have been described above, following three main research lines: AGPs applied to COVID-19 patients, PPE during AGPs applied to COVID-19 patients, and accidents occurring during AGPs for COVID-19 patients and ways of protecting HCWs. In the first phase, the assumption was made that when performing AGPs, the coverall is worn with all the other accessories for ensuring the highest protection. Therefore, the following H1 hypothesis was formulated:H1. There is a positive correlation between wearing disposable coverall when performing aerosol generating procedures (AGPs) on a COVID-19 patient and wearing disposable gloves, medical masks N95 , visor or goggles, and footwear protection.Also, it has been assumed that the waterproof apron is worn during AGPs with all the other accessories for providing the highest protection. So, the following H2 hypothesis was formulated:H2. There is a positive correlation between wearing waterproof apron when performing aerosol generating procedures (AGPs) on a COVID-19 patient and wearing disposable gloves, medical masks N95 , visor or goggles, disposable coverall, and footwear protection.We started from the premise that coverall equipment, which offers the highest and complete protection, should lead to a decrease in accident frequency when performing AGPs, so the H3 hypothesis was formulated, as follows:H3. There is a negative correlation between wearing disposable coverall when performing aerosol generating procedures (AGPs) on a COVID-19 patient and the occurrence of the following accidents: splashing of biological fluids/ respiratory secretions in the eyes; splashing of biological fluids/ respiratory secretions in the oral/ nasal mucosa; splashing of biological fluids/ respiratory secretions on the non idemn skin; puncture/ sting with any material contaminated with biological fluids/ respiratory secretions.Although we have found in the literature that disposable gloves can spread infection with the SARS-CoV-2 virus, we started from a positive hypothesis, which underlines the role of disposable gloves in preventing the infection with the virus:H4. The average number of accidents  decreases when the use of disposable gloves (AGP_GLOVES) increases when applying the aerosol generating procedures (AGPs) on a COVID-19 patient.This study was conducted using a questionnaire translated and adapted from the WHO  questionAs a result, 312 answers were collected between 10.12.2020–19.03.2021. Given the fact that the pandemic generated by the SARS-CoV-2 virus appeared globally in December 2019, and its first effects began to appear in Romania in March 2020, the time period of data collection (until March 2021) enabled us to provide a plausible interpretation of responses due to long time period for analysis of what had happened at the “St. Ioan cel Nou” County Emergency Hospital in Suceava, Romania during the pandemic. This study was given an ethical approval by the “St. Ioan cel Nou” County Emergency Hospital before data collection, an ethical approval from Grigore T. Popa University of Medicine and Pharmacy, Iași, Romania , the collaboration protocol having been signed by three parties–National Authority for Quality Management in Health, Grigore T. Popa University of Medicine and Pharmacy, Iași, and Alexandru Ioan Cuza University of Iași . The consent of the respondents in the research was informed, and implicitly. Before completing the questionnaire, was mentioned that the answers are confidential, and that respondents are kindly asked to continue only if they agree to participate in the study.The instrument includes a complex questionnaire comprising several parts that were applied to medical staff who had been in contact with patients diagnosed with COVID-19. All questions referred to the last 3 months of activity prior to questionnaire completion. As it can be seen from the table above, 82.05% and 17.95% of respondents are female and male, respectively. Most respondents are nurses (81.73%) belonging to the following departments: ER (27.25%); Obstetrics—Gynaecology (8.34%); ICU (7.69%) and Infectious diseases (7.37%). The dispersion of answers obtained for 21 departments is quite high for the 312 answers, as we aimed to cover all hospital disciplines that would generate most realistic results.An open-ended question asking respondents to specify their age was also included into the questionnaire. The average age of medical staff included in the study was 41.75 years.For all 22 items used in this research and presented in The study used 312 answers to perform descriptive, correlation and regression analyses. Descriptive analysis was used to provide an overview of PPEs used during AGPs on COVID-19 patients. The actions of medical staff during AGPs applied to patients with COVID-19, the episodes of accidental exposure to biological fluids/ respiratory secretions during interactions with COVID-19 patients, and personnel’ satisfaction with COVID-19 measures implemented in the hospital–were analysed using correlation and regression analyses, which helped us validate the research hypotheses.The main descriptive results are shown in Over 50% of respondents reported having taken part in performing AGPs on COVID-19 patients in the previous 3 months. In this regard, the questionnaire included an item filtering the respondents before applying the main questions that helped us obtain valid results for our analysis.Most respondents reported always having used disposable gloves (98.13%), medical masks N95  (92.86%), visor or goggles (91.19%), disposable coverall (91.25%) and footwear protection (95.00%) during AGPs. The waterproof apron had been always used by only 40% of staff, while almost 30% had not used it at all during AGPs.As for the actions that are required when PPE is used, most respondents reported always having done the following: hand hygiene before and after any procedure requiring asepsis conditions (96.93%).We expected that a higher percentage of respondents would report having always removed and replaced their PPE in line with the protocol (82.82%), and having practised hand hygiene after touching objects in COVID-19 patient proximity  (87.12%). We also expected a higher percentage that could indicate that most touched surfaces had been decontaminated, at least, 3 times a day (84.05%). All these expectations appeared since all the preventive actions had been in place in case of COVID-19 infections.Asked if they had any episodes of accidental exposure to biological fluids or respiratory secretions during the period in which respondents had interacted with COVID-19 patients for their care in the previous 3 months, they mentioned only 28 accidents: 11 accidents with splashing biological fluids/ respiratory secretions in the eyes, 11 accidents with splashing biological fluids/ respiratory secretions on the non-idemn skin, 3 accidents with splashing biological fluids/ respiratory secretions in the oral/ nasal mucosa and 3 accidents with puncture/ sting with any material contaminated with biological fluids/ respiratory secretions. We cannot sort these types of accidents by categories of more or less risky but we are aware of the fact that any type of accident with exposure to COVID-19 infection attracts a risk of COVID-19 infection for anyone not wearing PPE.During the study (December 2021-March 2022), there were 116 cases of Covid-19 throughout the hospital, of which 60 cases were in the departments involved in the study. Moreover, in the same period, 58 employees from the entire hospital who came into contact with the infected with Covid 19 staff were quarantined, of which 33 employees were coming from the departments participating in the study.In the According to our collected data, personnel’s’ satisfaction with COVID-19 measures implemented in the health unit was ranked as very good by only 50.32% of the respondents, and, at least, as moderate by 92.63%. This means that the measures implemented in the hospital still needed improvements at that time.The access to PPE was very good for 73.08% of respondents, and, at least, moderate for 94.87%. The COVID-19 test access was very good only for 55.45% of respondents, and, at least, moderate for 89.75%. All in all, these statistics lead us to the idea that improvements are required to have better COVID-19 measures in place in the organization. Also, 57.70% of respondents are, at least, moderately worried that they may become infected with COVID-19 because of the work they do, 66.35% are, at least, moderately worried that they might spread COVD-19 to their family members. Basically, they are more concerned about avoiding the COVD-19 spreading to their family members than about getting the infection themselves. More than half of respondents (50.32%) reported having changed significantly their routine due to COVID-19, and 84.29%, at least moderately.According to the results presented in This result may not necessarily reflect the reality due to low number of male respondents . For this reason, a further research could analyse in detail gender differences in wearing PPE items.All Pearson coefficients presented above for the variables mentioned in Hypothesis H1 indicate positive correlations, most of them having medium to strong intensities: there is a positive correlation between wearing disposable coverall (AGP_COVERALL), when performing AGPs on a COVID-19 patient, and wearing disposable gloves (AGP_GLOVES) , medical masks N95  (AGP_MASK) , visor or goggles (AGP_VISOR) , and footwear protection (AGP_FOOTPROT) . Therefore, hypothesis H1 has been confirmed:H1. There is a positive correlation between wearing disposable coverall when performing aerosol generating procedures (AGPs) on a COVID-19 patient and wearing disposable gloves, medical masks N95 , visor or goggles, and footwear protection.The main idea, from which stemmed this hypothesis, was that when medical doctors participate in AGPs and wear a coverall, they also wear disposable gloves, a surgical mask, a visor or goggles and protective footwear, which offer the highest caution equipment for preventing medical staff against COVID-19 infections.All Pearson coefficients presented in H2. There is a positive correlation between wearing waterproof apron, when performing aerosol generating procedures (AGPs) on a COVID-19 patient and wearing disposable gloves, medical masks N95 , visor or goggles, disposable coverall, and footwear protection.These results are abnormal in our opinion, since the waterproof apron should be completed with disposable gloves, medical masks, visor or goggles, and footwear protection, except disposable coverall. Once a doctor wears a disposable coverall, they cannot wear at the same time a waterproof apron. So, in this case, there should have appeared a significant negative correlation, and it was not noted. We believe the reason that led to the appearance of these statistics that invalidated hypothesis H2 was the high dispersion of the respondents’ declarations on wearing or not of a waterproof apron during AGPs  when performing AGPs on a COVID-19 patient and splashing biological fluids/ respiratory secretions on the non-idemn skin (SPLASH_SKIN) . It shows that the frequency of accidents, namely, splashing biological fluids/ respiratory secretions on the non-idemn skin (SPLASH_SKIN) lowers when the PPE disposable coveralls are worn often. Consequently, we agree that the disposable coverall is the more efficient type of PPE preventing possible infections with COVID-19.The H3 hypothesis was formulated starting from the premise that the disposable coverall is the best method for medical staff to protect themselves against any type of accident. However, the correlations between the variables of this hypothesis are mostly statistically insignificant (p>0.05), showing that even if the disposable coverall is worn when AGPs are performed on patients with COVID-19, there is still exposure to the risk of infection. The only protection offered by wearing disposable coverall, in line with our analysis, and as it results from the correlations, is related to splashing biological fluids/ respiratory secretions on the non-idemn skin.All these being said, the hypothesis H3 has not been confirmed:H3. There is a negative correlation between wearing disposable coverall when performing aerosol generating procedures (AGPs) on a COVID-19 patient and the following accidents: splashing biological fluids/ respiratory secretions in the eyes; splashing biological fluids/ respiratory secretions in the oral/ nasal mucosa; splashing biological fluids/ respiratory secretions on the non idemn skin; puncture/ sting with any material contaminated with biological fluids/ respiratory secretions.The validation of hypothesis H4 is shown in Tables The dependent variable in the model is the average number of accidents reported by respondents, in numbers, during AGPs on patients with COVID-19. Initially, 11 independent variables were introduced into the model. These are listed in detail in 2 of 0.384, adjusted R2 of 0.367, and an estimated Std. Error of 0.09507. As it can be seen, the value of R (0.620) indicates a relatively good correlation between the average number of accidents, as opposed to the elements of PPE, and preventive protective measures against COVID-19. The values of R2 (0.384) and adjusted R2 (0.367) indicate a relatively low to average proportion of the variation in the dependent variable explained by the regression model.The eighth variant of the model is the best, with R of 0.620, RThe final model (Model no. 8) is explained in proportion of 36.7%, which is a realistic percentage, if we consider the number of reported accidents , the number of accidents should decrease.All these being said, the hypothesis H4 has been confirmed:H4. The average number of accidents  decreases when the use of disposable gloves (AGP_GLOVES) increases during performing aerosol generating procedures (AGPs) on a COVID-19 patient.The problem in this model is that the results indicate that the number of accidents increases as the coefficient of AGP_HANDH_ASEPSIS increases. So, as long as there is hand hygiene before and after any procedure requiring asepsis conditions , the number of accidents increases. We think this could be possible only if hygiene before and after any procedure requiring asepsis conditions was not properly done. Another reason for such an output could also be the low number of accidents , medical masks N95  (92.86%), visor or googles (91.19%), disposable coverall (91.25%) and footwear protection (95.00%). The waterproof apron was always used by only 40% of staff, and almost 30% not using it at all during AGPs. More than half of respondents (50.32%) reported having changed very much their routine because of COVID-19, and 84.29%, at least, moderately.We derived from the WHO recommendations  no less We found no positive correlation between wearing a waterproof apron when performing AGPs on COVID-19 patients, and wearing disposable gloves, medical masks N95 , visor or goggles, disposable coverall, and footwear protection. We believe this result has a statistical reason, namely the high dispersion of the respondents’ declarations on wearing or not of a waterproof apron during AGPs. Thus, we consider hypothesis H2 a future interesting investigation topic. Also, we found no negative correlation between wearing disposable coverall when performing AGPs on COVID-19 patients, and the analysed accidents.The only protection offered by a disposable coverall, as it results from our analysis, is related to splashing biological fluids/ respiratory secretions on a non-idemn skin. Also, the results show that as disposable gloves and footwear protection are used in performing AGPs on patients with COVID-19 and hand hygiene is also done before and after touching a patient with COVID-19 (regardless of wearing gloves), the number of accidents should decrease.According to the results, there were no differences between male and female participants when using specific PPE items while performing AGPs on COVID-19 patients, except the case when the waterproof apron is used. For this reason, a further research could analyse in detail gender differences in wearing PPE items.We found in the literature that COVID-19 could be spread through aerosol/ aerated solids, fluid from human secretions, droplets from normal breathing, coughing, sneezing and surface contact, and, thus, it is considered to be very contagious , and thaIn line with the exposure risk management recommendations, in our study we expected that a higher percentage of respondents had taken off and replaced their PPE, in accordance with the protocol (82.82%), and had always performed hand hygiene after touching an object found in close proximity to a COVID-19 patient  (87.12%).The present research was carried out at the “Sf. Ioan cel Nou” Hospital in Suceava, Romania, which was the first outbreak of infection in Romania. Thus, the novelty of our research consisted in suggestions for risk management, extremely complicated at the intersection of three large areas of analysis: PPE, AGP, Covid-19.According to Girma et al., 2020, HCWs’ risk perceptions may be a factor that could influence the wearing of PPE . This stPrevious studies adressed the challenge to further analyzes related to PPEs , others The results presented in our study are useful for risk management, when the following are put together: PPE, AGP, and Covid-19. Given the fact that AGPs are procedures through which infections can easily spread, and the management of any hospital wants to prevent the spread of Covid-19 infections, we conclude that only with adequate PPEs, HCWs could avoid accidents.Because we found no positive correlation between wearing a waterproof apron when performing AGPs on COVID-19 patients, and wearing disposable gloves, medical masks N95 , visor or goggles, disposable coverall, and footwear protection, we consider this result a starting point for future investigation. Also, given the fact that between wearing disposable coverall when performing AGPs on COVID-19 patients, and the analysed accidents no correlation were found, we expect further research results related to this conclusion.Future research perspectives on the mix composed by PPE, AGP, and Covid-19 could also be analyzed in the post pandemic times. An interesting comparative analysis between the using of PPE during AGPs in Covid and non-Covid times would be also useful for hospitals management in the future.S1 File(SAV)Click here for additional data file.


The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolutional neural network (CNN) models were employed to improve the classification accuracy of ITS by fully utilizing the feature information contained in different seasonal images. Among the three CNN models, DenseNet yielded better performances than ResNet and GoogLeNet. It offered an OA of 75.1% for seven tree species using only the WorldView-3 image and an OA of 78.1% using the combinations of WorldView-3 and autumn Google Earth images. The results indicated that Google Earth images with suitable temporal detail could be employed as auxiliary data to improve the classification accuracy. Forest resources are an essential resource that has significance for the sustainable development of human society ,2. ForesHyperspectral data have many spectral bands, ranging from visible to near-infrared to shortwave infrared (SWIR) . AirbornHigh-resolution aerial images have higher spatial resolution and richer texture information than satellite images . CurrentWith the development of satellite imaging technology, the resolution of satellite imagery has been continuously improved. For example, the panchromatic image resolution of Worldview-3 data could reach 0.31 m ,43. The In this work, the potential of several CNN models  for ITS classification was explored using high-resolution satellite imagery covering a natural forest area. The ITS classification accuracies obtained using a single Worldview-3 image (recorded in summer), two Google Earth images , and the combinations were also compared to explore the potential of Google Earth images used as an auxiliary data source to the Worldview-3 imagery for ITS classification. The results of this work were helpful for selecting appreciative CNN models for ITS classification and also provided a valuable reference for using Google Earth images as an auxiliary data source for ITS classification.2. In the continental monsoon climate area of the temperate zone, the study area has an annual average temperature of 16.2 °C and annual average precipitation of 483.9 mm. With a forest coverage rate above 96%, the study area contains both natural forests and artificial forests. Tree species are rich in the study area [Cupressus), pine (Pinus), locust (Sophora japonica), maple (Acer spp.), oak (Quercus L.), ginkgo (Ginkgo biloba L.), and goldenrain tree (Koelreuteria paniculate Laxm.), were considered in this study. Cypress and pine are evergreen conifers, whereas locust, oak, and ginkgo are deciduous broad-leaved trees.The study area is located in the western suburb of Beijing, China  , coverinLaunched on 13 August 2014, the WorldView-3 satellite  provides single-band panchromatic images and multispectral images with eight visible and near-infrared bands and eight shortwave infrared bands . The spaFirst, radiometric calibration and RPC orthorectification of the WorldView-3 images were performed in ENVI 5.3. Second, the eight multispectral bands and a panchromatic band were fused by using the reduced misalignment impact (RMI) fusion method  to obtaiGoogle Earth is a virtual earth software developed by Google that arranges satellite photographs, aerial photographs, and GIS on a three-dimensional earth model . Google Due to the topography in West Mountain, the ITS samples were collected where they were accessible. The field survey dataset contains attributes for each tree, such as the stem coordinates, common species name, crown diameter at breast height, and stem diameter at breast height. The GPS receiver acquired the stem coordinates, and the crown and stem diameters at breast height were measured by a meter ruler. The position accuracy of an individual tree fell within the submeter. In total, 695 trees were sampled, including 195 cypresses, 120 pines, 90 locusts, 70 maples, 100 oaks, 60 ginkgoes, and 60 goldenrain trees.The production of ITS samples requires ITC images, and obtaining complete and accurate ITC images is essential. Traditional ITC images were mainly obtained by manual sketching or multiscale segmentation, but these methods had a low automation degree, and the segmentation accuracy was not high. Therefore, the crown slices from imagery (CSI) crown delineation method  was emplAccording to distribution c of sampTo expand the scale of the ITS sample set, the data augmentation method was performed on the original sample sets. Data augmentation was a method to expand the dataset in deep learning methods . The traTo fully utilize the spectral characteristics of trees in different seasons, the Worldview-3 image and Google Earth images were combined to obtain three merged images, which were denoted as WV3SprGE, WV3AutGE, and WV3SprAutGE. Therefore, with three single images, six images were utilized for ITS classification. Based on the three single images  and three merged images , six sample sets were constructed for ITS classification. The six sample sets were denoted as WV3, SprGE AutGE, WV3SprGE, WV3AutGE, and WV3SprAutGE. All six sample sets contained cypress, pine, locust, maple, oak, ginkgo, and goldenrain tree, with a total of 4170 samples. The six sample sets contained the same tree species and the same number of samples for each tree species, and all samples were resized to 32 × 32. The sample sets were randomly divided into a training set, validation set, and testing set. As shown in The CNN is a kind of deep neural network with convolutional structures. The multilayer, neural network structure could thoroughly learn and extract the features in the image for image classification. Commonly employed CNNs, including GoogLeNet, ResNet, and DenseNet, were chosen for ITS classification.GoogLeNet was proposed by Szegedy et al. in 2014. GoogLeNet won first place in the 2014 ImageNet Large-scale Visual Recognition Challenge (ILSVRC). To maintain the sparsity of the network structure and utilize the high computational performance of the dense matrix, GoogLeNet designed the inception module . To adapDesigned and proposed by He et al. in 2015, the ResNet model won the title of 2015 ILSVRC classification task champion. Traditional convolutional networks or fully connected networks have problems such as loss and wastage of information. They also cause gradients to disappear or explode, resulting in deep networks being unable to train. The core of ResNet was the design of the residual block. The residual block introduced an identity skip connection, which relieved the problem of vanishing gradients in deep neural networks and allowed the network to learn more features in images .ResNet with a depth of 34 (ResNet_34) was selected for classification experiments. The input of the CNN models are tree crown sample images with a size of 32 × 32, which is relatively small. When the sizes of the output features of a pooling layer were reduced to 1 × 1, no additional features could be learned by the following layers. Thus, ResNet models with a relatively shallow network, such as ResNet models with depths of 18, 34, and 50, were suggested for ITS classification by Li et al. . They alDenseNet was proposed in 2017 and won the best paper award of CVPR 2017. DenseNet’s designer proposed a dense connection mechanism: all layers were connected . Each laDenseNet with a depth of 40 (DenseNet_40) was employed for this study. The structure of DenseNet_40 is shown in The deep learning environment was constructed based on Tensorflow2.0.0 and Keras2.3.0. All CNN models were trained in the same deep learning environment.−4, the learning rate factor was set to 1 × 10−3, and the offline setting of the learning rate was 5 × 10−7. Three CNN models utilized for classification experiments were trained by adjusting the parameters during the training process, and the CNN models which achieved the highest accuracy were saved, employing the CNN models saved to the test dataset for acquiring the classification accuracies.As introduced in For comparing the classification accuracy obtained by using CNN models, we also performed ITS classification experiments with the RF method. Before the CNNs were applied in image classification, the RF method was one of the most commonly used machine learning methods and was widely applied in image classification ,59. As wSix metrics, including overall accuracy (OA), precision, recall, F1-measure, user accuracy (UA), and producer accuracy (PA), were utilized to evaluate the ITS classification accuracy. Among them, OA, precision, recall, and F1-measure were utilized to evaluate the overall classification results, whereas UA and PA were employed for the comparisons of each tree species.The OA is calculated through the total number of correctly classified samples divided by the total number of samples . PrecisiThe classification metrics including the overall accuracy, precision, recall, and F1- measure of the three CNN models  based on the six sample sets  are shown in Among the three combined sample sets , the WV3AutGE sample set yielded the highest OA, precision, recall, and F1-measure values. By observing the classification results of WV3 and WV3SprGE using the same three models, it was found that the classification accuracy of WV3 was higher than that of WV3SprGE. WV3SprGE compared to WV3, the OAs yielded by GoogLeNet, ResNet, and DenseNet decreased by 1.3%, 3.9%, and 4.6%. The precision values yielded by GoogLeNet, ResNet, and DenseNet decreased by 0.06, 0.04, and 0.03. The recall values obtained by GoogLeNet did not change while those obtained by ResNet and DenseNet decreased. Moreover, the F1-measure values yielded by GoogLeNet, ResNet, and DenseNet also decreased by 0.01, 0.05, and 0.04, respectively. In contrast, the classification accuracies obtained from the WV3AutGE sample set were higher than those obtained from the WV3 sample set. The OAs yielded by GoogLeNet, ResNet, and DenseNet improved by 15.1%, 4.8%, and 3%, respectively. The precision values yielded by GoogLeNet, ResNet, and DenseNet improved by 0.16, 0.11, and 0.08, respectively. The recall values yielded by GoogLeNet, ResNet, and DenseNet increased by 0.19, 0.04, and 0.03, respectively. In addition, the F1-measure values yielded by GoogLeNet, ResNet, and DenseNet also increased by 0.21, 0.06, and 0.06, respectively. The WV3SprAutGE sample set achieved an improvement in OA compared to the WV3 sample set, while compared to the WV3AutGE sample set, only ResNet_34 achieved an improvement, and the other two CNN models obtained a slight decrease in classification accuracies.Three CNN models including GoogLeNet, ResNet_34, and DenseNet_40 were employed for ITS classification. For the WV3AutGE and WV3SprAutGE sample sets, DenseNet_40 also provided the highest accuracies, followed by GoogLeNet and ResNet_34. For WV3AutGE and WV3SprAutGE, the OAs yielded by DenseNet_40 were 78.1% and 76.5%, respectively, whereas the precision values of DenseNet_40 were 0.8 and 0.78, respectively. The recall values yielded by DenseNet_40 were 0.76 and 0.75, respectively, whereas the F1-measure values yielded by DenseNet_40 were 0.78 and 0.76, respectively. ResNet_34 did not provide high accuracies in experiments utilizing WV3AutGE and WV3SprAutGE, the OAs offered by ResNet_34 were 72.3% and 74.2%, respectively. In the other three accuracy metrics, ResNet_34 also did not provide higher values than DenseNet_40.By observing the classification results, it was evident that the performances of CNN models were different. Each CNN model had its own unique structure design, which led to the features extracted by model training being various. The various features brought about the difference in classification performance when distinguishing similar tree species or sample sets.As shown in By comparing the classification accuracies obtained by the three CNN models and RF, it was evident that the CNN models offered higher classification performance than RF. CNN models could extract features in deep layers from several ITS images; more and deeper features of ITS images made it easier to distinguish different tree species. It can be seen from The UAs and PAs of the seven tree species for the three CNN models  are shown in As shown in The differences between the classification accuracies of the seven tree species were also related to the spectral features of the seven tree species. In addition, by observing the classification results of seven tree species, it was found that the accuracies of maple and gingko yielded by WV3AutGE and WV3SprAutGE were significantly higher than those of WV3 and WV3SprGE. The leaves of maple and ginkgo presented different colors in summer and autumn, respectively; thus, the spectral characteristics presented by images differed. However, a mono-temporal image could not contain the spectral characteristics in two seasons. By combining the images in summer and autumn, the merged image could contain the spectral characteristics of tree species in two seasons, which made it easier to distinguish ginkgo and maple, thus the classification accuracies of maple and gingko could be improved. Compared with the classification accuracies yielded by WV3, the classification accuracies achieved by WV3SprGE did not increase. The results showed that the spectral information contained in the spring Google Earth image was not helpful to improve the classification accuracies.In the ITS classification experiments, six ITS sample sets constructed using a Worldview-3 image and two Google Earth images, were classified using three CNN models to explore the potential of several typical CNN models and multitemporal, high-resolution, satellite imagery for ITS classification.In this study, a Worldview-3 image and two Google Earth images were utilized for ITS classification. In the experiments using the SprGE, AutGE, and WV3 sample sets, the WV3 sample set offered higher classification accuracies than the others. This is due to the fact that the Worldview-3 image used in the experiments had higher spatial resolution and more spectral bands than the GE images, which is helpful for distinguishing different tree species. Several previous studies also used Worldview imagery for ITS classification. For example, Deur et al.  employedThe forest coverage rate of the study area is also an essential factor influencing the classification accuracy. The research area of this study was located in a forest area with a forest coverage rate of 96%. The classification accuracies offered in this study were close to some studies conducted in urban regions ,64. Liu Multitemporal Google Earth images were combined with the Worldview-3 imagery for ITS classification. The results showed that the inclusion of the autumn Google Earth image could improve the classification accuracy. Madonsela et al.  utilizedIn addition, DenseNet_40 and ResNet_34 yielded better performances than GoogLeNet, due to the excellent structural design of DenseNet and ResNet_34. All layers of DenseNet were connected, which can enhance the transfer of features and allow the extracted features to be used more effectively. Hartling et al.  also utiThe classification of ITS is significant to the management and protection of forest resources. A detailed and accurate survey of tree species distribution will help forest resource managers manage and utilize forest resources. By employing CNN models for classification in six sample sets, the classification performances of several CNN models  were compared, and the potential of multitemporal high-resolution satellite images in different seasons for ITS classification was also explored. The three CNN models in this study offered high classification accuracies, which were higher than those of RF. Among the three CNN models, DenesNet_40 provided the highest classification accuracy, followed by ResNet and GoogLeNet. Therefore, the three CNN models have a great application potential in ITS classification. The utilization of mono-temporal Worldview-3 image and multitemporal Google Earth images offered higher classification accuracy compared to employing a single data source. The classification results demonstrated that the utilization of Google Earth and Worldview-3 images could improve the classification accuracies of some tree species, such as ginkgo and maple. Thus, the overall classification accuracy was improved. In the experiment using Worldview-3 and autumn Google Earth images, the OA reached 78.1%, which was 3% higher than that using Worldview-3 only. Compared with multitemporal, high-resolution, satellite images, the combination of Worldview-3 and Google Earth images improved the classification accuracy and reduced the cost. The employment of Google Earth images could offer an effective way of using features contained in multiple seasons for ITS classification. In future ITS classification research, the classification accuracy could be improved in the following aspects: (1) A larger number of samples could improve the ITS classification accuracy. When CNN models are employed for ITS classification, more samples will help the CNN models extract more features of trees for ITS classification, improving classification accuracy. (2) The potential of other low-cost high-resolution satellite imagery used as auxiliary data can also be explored to improve the classification accuracy. For example, time sequence GaoFen-2 (GF-2) and GaoFen-7 (GF-7) imagery can be combined with WorldView-3 imagery for ITS classification in further work.


Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant state for a spectrum of lymphoplasmacytic malignancies. The risk of progression of MGUS to a symptomatic therapy requiring plasma cell dyscrasia is about 1% per year. Studies carried out over the previous 10 years have improved risk stratification of MGUS based on serologic and genomic evaluations, which has led to better management of patients. In this review, we address the epidemiology, diagnosis, and pathogenesis of MGUS and discuss risk-adapted best practice approaches to monitor patients. Though fundamentally similar, each MG has its unique symptoms, diagnostic criteria, treatments, and disease trajectories. The focus of this review is to showcase the recent advances in the diagnosis, risk stratification, and management of MGUS.Monoclonal gammopathy (MG) encompasses several conditions defined by the increased proliferation of a clone of plasma cells that produce an abundance of a monoclonal immunoglobulin: monoclonal (M) protein. Within this category fall both benign hematologic conditions such as monoclonal gammopathy of undetermined significance (MGUS) and lymphoplasmacytic malignancies (LPMs), including multiple myeloma (MM), Waldenström macroglobulinemia (WM), and amyloid light-chain amyloidosis (AL)2. However, in 1978, Robert Kyle coined the current term “monoclonal gammopathy of undetermined significance” after his observational retrospective study of 241 patients showed that some patients with MGUS progressed to MM, WM, or AL3. Since that time, population studies from Olmsted County (Minnesota) and Ghana, as well as the National Health and Nutrition Examination Survey (NHANES) in the United States and the Prostate, Lung, Colorectal, And Ovarian (PLCO) cancer screening trial, have significantly advanced our knowledge of MGUS and the disorders it precedes8.MGUS was first described in 1960 by Jan Waldenström as “essential hyperglobulinemia” or “benign monoclonal gammopathy.” He firmly believed that benign MG was unrelated to MM9. It is defined by the presence of serum or urine M protein, <10% clonal plasma cells in the bone marrow, and the absence of a diagnosis of MM or related LPMs1. Three distinct subtypes of MGUS are classified based on the M protein isotype: immunoglobulin M (IgM) MGUS, non-IgM  MGUS, and light-chain MGUS (LC-MGUS). The risk of progression into an LPM is different for each of these subtypes of MGUS10.MGUS is a clinically asymptomatic, premalignant, clonal plasma cell disorder and is an obligatory precursor for several LPMs, including MM, WM, and AL11. For patients aged <40 years, MGUS is a relatively rare event with a prevalence of <0.3%, and this patient population represents only 2% of all patients with MGUS12. The incidence and prevalence of MGUS are also higher in men than women and are two- to three-fold higher in Blacks than Whites. The incidence of MGUS in men is 120 per 100,000 at age 50 and increases to 530 per 100,000 by the age of 9013. The corresponding rates for women are 60 per 100,000 population at age 50 and 370 per 100,000 at age 9013. The prevalences of MGUS among a well-defined, predominantly White population in persons ≥50, ≥70, and ≥85 years of age are estimated to be 3.2%, 5.3%, and 7.5%, respectively4. The age-adjusted prevalences of MGUS using population-based racial surveys are 3.7%, 2.3%, 1.8%, and 2.1% in Blacks, Whites, Hispanics, and Japanese, respectively15. 6. In a recent population-based analysis, the prevalence of MGUS detected using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was 17% in Blacks who are >50 years old16. In that study, high-risk individuals were categorized as Black patients and those with first-degree relatives with a diagnosed hematologic malignancy, and the prevalences of MGUS were 4.9% and 13% in patients between the ages of 40 and 49 and between 70 and 79, respectively16.The prevalence of MGUS increases with age and is observed in nearly 3% of the population ≥50 years old and 5% of those ≥70 years17. Owing to defined genomic aberrations, monoclonal plasma cell populations emerge from a polyclonal background, and with fluorescence in situ hybridization (FISH), chromosomal abnormalities have been identified in the development of MGUS20. About 50% of patients with MGUS demonstrate aneuploidy, particularly hyperploidy. This is compared with chromosome aneuploidy seen in about 75% of patients with MM or LPM21. Translocations involving the immunoglobulin heavy chain (IGH) locus on chromosome 14q32 and one of five partner chromosomes — 11q13 , 4p16.3 (FGFR-3 and MMSET), 6p21 (cyclin D3 gene), 16q23 (c-maf), and 20q11 (mafB) — have been identified in close to half of the patients with MGUS  deletion, 1q-gain, and hyperdiploidy; however, the frequency of these abnormalities is lower in MGUS than in MM24. Consistent with the primary lesion hypothesis, one study found IGH translocations comprising , t, t, and t to be around 27%, which is similar to that seen in MM. In another study, t, t, t, t, and t were seen between 0.5% and 16% of patients with MGUS18. Moreover, comparable to myeloma, mutations in genes such as RAS, NRAS, DIS3, HIST1H1E, EGR1, and LTB were also found in MGUS26. Importantly, mutations or deletions of TP53 and MYC translocations were not detected in patients with MGUS as one may see in MM or LPM, suggesting that these events could happen later in the disease course and may lead to MGUS progression to more advanced stages25. The genetic alterations in IgM MGUS, unlike those in non-IgM MGUS, seem to be different, where MYD88L265P and CXCR4 were the main mutations seen in 60% and 9%, respectively27. These cytogenetic changes could be the primary events in initiating the process of plasma cell immortalization. Therefore, the fate of MGUS, as in its persistence as an indolent malignancy or its progression to aggressive cancer, is dictated by the different cytogenetic changes acquired by the plasma cell clone over time29.Plasma cells are terminally differentiated B lymphocytes derived from post-germinal center B cellsith MGUS 10,22. Inet al. identified myeloma-defining genomic events, which include chromothripsis, templated insertions, mutations in driver genes, aneuploidy, and canonical apolipoprotein B mRNA-editing catalytic polypeptide (APOBEC) mutational activity to be associated with MGUS progression36. In that study, it was found that patients with MGUS without progression had a lower burden of myeloma-defining genomic events when compared with patients who had a high number of myeloma-defining genomic events in whom MGUS progression was imminent37.The genomic landscape of myeloma precursor conditions such as MGUS and smoldering myeloma has expanded with the use of whole genome sequencing. A study by Oben 38. The recent advances in molecular and genomic techniques, including whole exome sequencing (WES), single-nucleotide polymorphism array, and global gene expression profiling, have uncovered these myeloma-defining genomic events and have enhanced our understanding of the disease biology of MGUS, smoldering myeloma, and MM. Of these genomic aberrations, hyperdiploidy and canonical IGH translocations are seen more commonly in MGUS, but other genomic alterations are rare. Another study looked at the tumor mutational burden (TMB), single-base substitution (SBS), and activation-induced cytidine deaminase (AID)-induced somatic mutagenesis in MGUS and MM. In that study, the TMB and SBS were high in MM compared with MGUS, and the presence of the APOBEC signature was a predictor of poor overall survival in patients with MM. Additionally, patients with MM who had poor outcomes had high TMB compared with patients with better outcomes39. Detecting these genomic aberrations early in the clonal evolution of plasma cells has enabled us to better understand the progression of MGUS to MM40. The advent of genomic techniques may pave the path to better risk-stratify patients based on genomic alterations and predict disease progression before the appearance of clinical and laboratory indicators. However, these sophisticated genomic analysis techniques are not universally available, and most of the findings based on genomic alterations have yet to be prospectively validated. Hence, the practice patterns should still be based on clinical and laboratory parameters along with chromosomal alterations, which have already been established and incorporated in MM risk stratification models41.Myeloma-defining genomic events include structural variants, alterations in driver genes, clonal IGH translocations, genomic copy number changes, hyperdiploidy, and MYC translocation1. IgM MGUS is defined by serum IgM M protein, and non-IgM MGUS is characterized by serum IgG, IgA, and (rarely) IgD or IgE M proteins1. LC-MGUS is defined by the presence of an abnormal FLC ratio , increased level of involved FLC more than the upper limit of normal (ULN), urinary monoclonal protein of less than 500 mg/24 hours, and the presence of the aforementioned three criteria42.Based on the current diagnostic criteria from the 2014 International Myeloma Working Group (IMWG), MGUS is diagnosed when all three of the criteria are met: serum M protein less than 3 g/dL or the presence of abnormal free light chain (FLC) ratio, bone marrow plasma cells less than 10%, and the absence of end-organ damage attributed to plasma cells1. Following the diagnosis of MGUS, risk stratification of patients should be done based on the presence or absence of risk factors that increase the rate of progression of MGUS to LPM.The absence of end-organ damage translates to the lack of hypercalcemia, renal insufficiency, anemia, and bone lesions (referred to as CRAB features) attributable to an underlying plasma cell disorder. Hypercalcemia is defined by serum calcium greater than 1 mg/dL (>0.25 mmol/L) of ULN or higher than 11 mg/dL (>2.75 mmol/L). Renal insufficiency is defined as a creatinine clearance of less than 40 mL/min or a serum creatinine greater than 2 mg/dL. Anemia is defined by a hemoglobin value greater than 2 g/dL below the lower limit of normal or a value less than 10 g/dL. Bone lesions are osteolytic lesions that can be detected on skeletal radiography, computed tomography (CT), or positron emission tomography-CT1. The three major risk factors for the progression of MGUS are an abnormal serum FLC ratio , non-IgG MGUS, and a high serum M protein level (≥1.5 g/dL)  11,43. Ba11. The risk of progression among patients with IgM MGUS is 2% per year in the first 10 years after diagnosis and 1% per year thereafter11. In contrast, non-IgM MGUS is associated with a risk of progression to MM at a rate of 0.5–1% per year11. Though rare, both IgM MGUS and non-IgM MGUS can progress to AL, and the risk is about 1% over 35 years of follow-up11. LC-MGUS can progress to light-chain MM and AL with a risk of 0.3% per year1.IgM MGUS has a higher risk of progression than non-IgM and is typically associated with progression to lymphoplasmacytic lymphoma/WMet al. found that the risk of progression was higher when there are >5% clonal plasma cells in the bone marrow and low concentrations of two uninvolved immunoglobulins11. However, this heightened risk was not observed with a lower concentration of only one uninvolved immunoglobulin level11. The age, sex, presence of hepatosplenomegaly, hemoglobin values, serum creatinine, serum albumin, and quantitative measurements of a monoclonal urinary light chain were not predictors of MGUS progression11.In addition to the aforementioned risk factors, a study by Kyle 45. The antigens that are most frequently used to identify aberrant plasma cells include CD19, CD45, and CD56 in combination with CD38/CD13847. The validated immunophenotypic approach to identify aberrant phenotypes in plasma cells is the absence of CD19 or CD45, the decreased expression of CD38, and the overexpression of CD5649. According to the Spanish study group, the two risk factors for the progression of MGUS are the percentage of aberrant plasma cells to bone marrow plasma cells of above 95% and DNA aneuploidy. When these independent variables are used, the rates of progression-free survival at 5 years for MGUS patients with zero, one, and two risk factors are 2%, 10%, and 46%, respectively45.Conversely, the Spanish study group recognized multiparametric flow cytometry as a valuable tool to identify aberrant plasma cell populations and predict the risk of MGUS progression to MM18.Another study classified patients with MGUS into three risk categories based on the cytogenetic risk factors: high risk  and chromosome 17p deletion), intermediate risk (trisomies without translocations), and standard risk , all translocations other than t and chromosome 13 abnormalities). In that study, the median time-to-progression of MGUS was 4.7 years for patients with high-risk cytogenetics and was not reached for other risk groups in a 4.2-year follow-upet al. showed that young patients with MGUS had an average progression rate of 1.4% per year, similar to older patients12. Furthermore, autoimmune diseases have been associated with an increased risk of MGUS development with a relative risk of 1.42, and the strongest association was seen with pernicious anemia with a relative risk of 1.6750. It has been hypothesized that the constant stimulation of the immune system in immune-related conditions can cause B-cell dysfunction and clonal plasma cell disorders51. Interestingly, although the prevalence of MGUS is increased with autoimmune conditions, patients with immune-related disorders may have a relatively lower risk of progression. In one study, the M protein level was found to be higher in patients without autoimmune conditions as compared with patients with autoimmune conditions12. However, in that study, the size of the M protein was the strongest risk factor, and the presence of an autoimmune condition by itself was not an independent risk factor for MGUS progression12.Earlier onset of MGUS does not imply a more aggressive or indolent disease. Pang 9.In addition to these risk factors, one should pay close attention to clinical and laboratory “red flags” when following patients with MGUS . In the 11. Therefore, MGUS is generally found incidentally while evaluating a patient with signs and symptoms suggestive of a possible LPM or any of the associated conditions. Clinicians also look for MG by testing for M protein in patients with a nonmalignant disease known to cause or be associated with MG53.Currently, owing to the lack of evidence supporting the clinical benefit of early detection, screening of asymptomatic MGUS in the general population is not recommended54. The top five indications for testing were neuropathy (19.8%), renal disease (13.7%), anemia (12.8%), bone disorders or connective tissue pain (12.8%), and cutaneous diseases (5.8%). The subsequent diagnoses of the common indications were neuropathy-no other source (NOS), chronic kidney disease-NOS, iron deficiency, and osteoporosis/osteopenia, respectively. In that study, neuropathy was associated with IgM MGUS in close to 19% of patients54. Initial screening of MGUS was predominantly done by general internal medicine (31.3%), neurology (10.3%), and cardiology (9.7%)54.A recent study at the Mayo Clinic found that monoclonal protein testing is commonly performed for signs and symptoms not typically associated with LPM55. The recent results of the iStopMM study involving 75,422 total participants with a 5% prevalence of MGUS found that active screening of patients with MGUS could identify a higher number of patients with progression than patients who are followed by current established guidelines56. However, the study is still evolving, and results pertaining to the survival of these patients are pending. While these data are maturing, the study’s authors advised against preemptive MGUS screening in otherwise-healthy individuals56. In the meantime, experts believe that it may be beneficial to screen for MG in high-risk patients who have two or more first-degree relatives with MM, AL, or WM9.The Iceland Screens, Treats, or Prevents Multiple Myeloma (iStopMM) study is the first population-based, prospective screening study and randomized controlled trial based in Iceland to evaluate the potential harm and benefit of MGUS screeninget al.9, in the absence of concerning clinical or laboratory features along with the presence of low-risk MGUS , one could omit a bone marrow biopsy and skeletal survey as only 2% of these low-risk patients progress during their lifetime44.At the time of initial evaluation of a patient with suspected MGUS, complete blood count, serum M protein, serum FLC, and IGH evaluation should be performed. Based on the 2018 publication by Go 9.Currently, there is no role in initiating any disease-specific treatments with chemoimmunotherapy or targeted therapy for patients with MGUS. However, clinical follow-up in conjunction with focused laboratory evaluations based on the risk of progression as well as establishing patient expectations and proper education is paramount in managing patients with MGUS. The monitoring parameters for patients with MGUS should be based on the risk factors and “red flags” for the progression of MGUS. All patients should undergo repeat laboratory evaluations within 3–6 months from the time of initial MGUS diagnosis. These laboratory evaluations include complete blood count, serum protein electrophoresis, serum FLC assessment, and assessment of calcium and serum creatinineet al., including 17,963 patients with a follow-up of 46,276 person-years, identified that the majority of progression (2%) occurs within the first 2 years and gradually declines thereafter with a rate of 0.8% at 5 years51. The seminal study by Kyle et al. identified the risk of progression as 2% per year for the first 10 years, decreasing to 1% per year thereafter11. For patients with an abnormal FLC ratio and ≥1.5 g/dL serum M protein, 3.6% of patients per 100 person-years had MGUS progression, as compared with 1.1 per 100 person-years of patients in whom neither of these risk factors was seen11.The data for monitoring patients with MGUS are derived from a few retrospective and population-based studies. A study by Go 57. The International Expert Consensus has recommended monitoring patients every 4–6 months for the first 2 years and every 6–24 months thereafter for all patients with MGUS58. The European Myeloma Network recommends re-evaluating patients at 6 months from diagnosis and yearly thereafter. For low-risk patients, no follow-up is recommended if the disease is stable at 6 months from diagnosis24. Recommendations from the IMWG suggest following low-risk patients at 6 months from the diagnosis and then every 2–3 years if the disease is stable. For patients with higher-risk disease, follow-up should be done annually after an initial 6-month follow-up from diagnosis59.Many of the governing bodies have put forward guidelines to monitor patients with MGUS. The UK Myeloma Forum/Nordic Myeloma Study Group has recommended monitoring patients with low-risk MGUS every 3–4 months for the first year and 6–12 months thereafter if no disease progression is detected. In the absence of low-risk MGUS , patients should be followed every 3–4 monthsDuring these follow-up evaluations, laboratory studies focusing on the complete blood count, creatinine, and calcium should be commenced along with quantification of the serum M protein. Routine imaging studies and bone marrow assessment should not be done without clinical or laboratory “red flags,” which could signify disease progression. MGUS is a precursor state for LPMs, including MM, AL, and WM. When patients with MGUS are evaluated, accurate assessment should be done to risk-stratify patients, as this will guide future monitoring. It is important to keep in mind that the majority of patients with MGUS will never progress to an aggressive malignancy during their lifespan, and having a diagnosis of a precancerous state could be a psychological burden for these patients. With the advent of genetic sequencing techniques such as WES, the field is rapidly evolving and new molecular and genetic signatures that will enable better and early risk stratification of MGUS are looming. With future studies, we hope to be able to better understand the pathobiology of MGUS that leads to disease progression and to be able to better risk-stratify patients and streamline the evaluation, thus improving the physical and psychological well-being of patients.


At the beginning of genomic selection, some Chinese companies genotyped pigs with different single nucleotide polymorphism (SNP) arrays. The obtained genomic data are then combined and to do this, several imputation strategies have been developed. Usually, only additive genetic effects are considered in genetic evaluations. However, dominance effects that may be important for some traits can be fitted in a mixed linear model as either ‘classical’ or ‘genotypic’ dominance effects. Their influence on genomic evaluation has rarely been studied. Thus, the objectives of this study were to use a dataset from Canadian Yorkshire pigs to (1) compare different strategies to combine data from two SNP arrays (Affymetrix 55K and Illumina 42K) and identify the most appropriate strategy for genomic evaluation and (2) evaluate the impact of dominance effects  and inbreeding depression effects on genomic predictive abilities for average daily gain (ADG), backfat thickness (BF), loin muscle depth (LMD), days to 100 kg (AGE100), and the total number of piglets born (TNB) at first parity.The reliabilities obtained with the additive genomic models showed that the strategy used to combine data from two SNP arrays had little impact on genomic evaluations. Models with classical or genotypic dominance effect showed similar predictive abilities for all traits. For ADG, BF, LMD, and AGE100, dominance effects accounted for a small proportion (2 to 11%) of the total genetic variance, whereas for TNB, dominance effects accounted for 11 to 20%. For all traits, the predictive abilities of the models increased significantly when genomic inbreeding depression effects were included in the model. However, the inclusion of dominance effects did not change the predictive ability for any trait except for TNB.Our study shows that it is feasible to combine data from different SNP arrays for genomic evaluation, and that all combination methods result in similar accuracies. Regardless of how dominance effects are fitted in the genomic model, there is no impact on genetic evaluation. Models including inbreeding depression effects outperform a model with only additive effects, even if the trait is not strongly affected by dominant genes.The online version contains supplementary material available at 10.1186/s12711-022-00760-4. Genomic selection (GS) , 2 has bbiological) effect (d) in linear mixed models. For example, SNPs are coded as 0, 1, and 2 for genotypes AA, Aa, and aa, respectively, and the coding of dominance effects is equal to 0, 1, and 0 for genotypes AA, Aa, and aa, respectively [AA, Aa, and aa, respectively [statistical). In our study, we used the terms ‘genotypic dominance effect’ and ‘classical dominance effect’ to refer to the dominance effects coded in either a genotypic manner or a dominant deviation manner, respectively, to avoid potential confusion.Although previous studies have demonstrated that dominance effects are not negligible , they arectively , 9. In cectively . Vitezicectively  referredAn increasing number of studies have investigated the influence of including dominance effects in prediction models on genomic evaluations of livestock , 11–18. Thus, the objectives of our study were: (1) to explore an appropriate strategy and procedure for integrating genomic information from different SNP arrays (Affymetrix 55K and Illumina 42K) for further genomic evaluation; (2), to evaluate the impact of dominance effects and inbreeding depression effects on the estimates of the genetic variance and the genomic prediction of four production traits and one reproduction trait in Canadian Yorkshire pigs raised in China; and (3) to compare the models including different dominance effects  in terms of the accuracy of the prediction of breeding values. The work was performed in two stages: first, we determined the optimal imputation strategy and chip, and then, after collecting more data, we addressed the models including dominance effects.Genesus)): 38,785, 38,667, 38,644, 38,785, and 10,504 records were available for ADG, BF, LMD, AGE100, and TNB, respectively. All the phenotypic records for the four production traits were obtained at the same time point, allowing a 10-kg deviation from the final bodyweight (–7) were removed. After quality control, 31,654 and 35,710 SNPs were retained from the Affymetrix 55K and Illumina 42K arrays, respectively. To avoid possible confusion in the subsequent analyses, we refer to the remaining genotypic data as Affymetrix 31K SNPs and Illumina 35K SNPs. Among these remaining SNPs, 23,430 (23K common SNPs) were shared by the two SNP arrays. In other words, after quality control, for the 1208 pigs genotyped with the Affymetrix array there were 31K SNPs, i.e. 23,430 (23K) common SNPs and 7724 (8K) Affymetrix array-specific SNPs; and for the 1106 pigs genotyped with the Illumina array there were 35K SNPs, i.e. 23,430 (23K) common SNPs and 12,280 (12K) Illumina array-specific SNPs.All the data were obtained from a national pig nucleus herd in North China. The herd’s purebred Yorkshire pigs were originally imported from Canada in 2014, and since then, the Yorkshire population within this herd has been continuously selected based on selection indices for five traits: average daily gain (ADG) in the range of 30 to 100 kg, backfat thickness (BF) at 100 kg body weight, loin muscle depth (LMD) at 100 kg body weight, days to 100 kg (AGE100), and the total number of piglets born (TNB) at first parity; in this study, we used phenotypic records for these five traits collected from 2012 to 2019  were used as the reference set, and the remaining 1208 pigs genotyped with the Affymetrix array  were used as the imputation set  between the genotyped and imputed SNPs. To reduce the systematic bias of evaluating imputation accuracy, we repeated the above procedure three times with different random seeds, leading to different subsets of the 10% masked SNPs. We obtained similar results across these repeats, and the average accuracies are presented in the “As mentioned above, 23K common SNPs were obtained in this study. To evaluate the imputation accuracy in Scenarios 1 and 2, 10% of the 23K common SNPs were masked (setting them to an ungenotyped state), and the masked SNPs were then imputed. After imputation, imputation accuracy was estimated by the concordance rate (CR) and the squared Pearson correlation coefficient (rcombinations to univariate single-step genomic best linear unbiased prediction (SSGBLUP) models for the five traits recorded in Canadian Yorkshire pigs [To evaluate the effects of each strategy to combine the genomic information from two arrays on genomic evaluation, we applied the abovementioned four ire pigs , 22 and For the four production traits, the model included sex, herd-year-season, and final body weight as fixed effects and an additive genetic effect, a common litter effect and a residual as random effects. For the reproduction trait, the model included herd-year-season as a fixed effect and an additive genetic effect and a residual effect as random effects. These analyses were carried out using the restricted maximum likelihood (REML) algorithm in the software DMU .Individual model-based reliabilities were computed as follows :\documendel used . Note thp matrix , 22; in p matrix . Our appCombination 3 was the optimal strategy for combining the information from the two arrays; thus, we advised the pig company to use the Illumina 42K SNP array for genotyping additional pigs so that we could estimate the dominance variance more accurately, and 4300 additional pigs with phenotypic records obtained between 2019 and 2020 were genotyped. These pigs were selected based on the following criteria: (1) they had progeny; (2) they were phenotyped for at least three of the target traits ; and (3) the total number of males or females within a litter was limited to four. Based on these criteria, we obtained 6614 genotyped pigs. The numbers of phenotypic records for ADG, BF, LMD, AGE100, and TNB were 41,367, 41,249, 41,224, 41,367, and 10,811, respectively , each paired group showed similar The mean genomic-based inbreeding coefficient was 0.67 (ranging from 0.55 to 0.763). Estimates of the inbreeding depression parameter . For the production traits, compared to model MA, the predictive ability was increased by ~ 1.5% with model MAI, ~ 1.5% with model MAID, and ~ 1.6% with model MAID*. For TNB, the predictive ability increased by ~ 6.0% with model MAD, ~ 6.6% with model MAD*, ~ 3.0% with model MAI, ~ 7.8% with model MAID, and ~ 8.4% with model MAID*. In terms of the regression coefficients of the corrected phenotypes on estimated genetic values . As shown in Table In this study, first we investigated different strategies to combine the genomic information from two SNP arrays. Subsequently, we examined the impact of the nonadditive effects on genomic predictive ability and further explored two models of fitting dominance effects  in the genomic prediction models. For the five traits studied, fitting inbreeding depression effects yielded the highest predictive abilities, and for one trait (TNB), the inclusion of dominance effects in the genomic model slightly increased the predictive ability.The imputation accuracy in Scenario 1 was slightly higher than in Scenario 2. Previous studies have shown that imputation accuracy is affected by the MAF of the imputed SNPs and by the top relatedness between animals in the reference and imputed populations –35. ThusIn this study, the estimated narrow-sense heritability confirmed that ADG, BF, LMD, and AGE100 were moderately heritable and that TNB was lowly heritable, in line with many other reports , 15, 36.*), which confirmed the equivalence of the estimates of dominant variation generated in this study. The standard error of the estimates of dominance variation was still relatively large, which indicates that the size of our dataset was not sufficient to accurately estimate dominance variation. Therefore, more data are needed to further investigate the dominance effects in the current population.In this study, the proportions of dominance variation to the total genetic variance in production traits were relatively low (ranging from 1.9 to 10.5%) and generally lower than those found in other studies on production traits in pigs , 16. TheIn this study, we used the pre-corrected phenotypes of the genotyped pigs as the response variables to estimate dominance variances. These genotyped pigs were not randomly sampled from the population, and most of them showed high EBV and were selected as parents for producing the next generation. Xiang et al.  reportedAs shown in Table  backfat , and the backfat . NotablyThe ratio of the estimated inbreeding depression effect divided by the phenotypic standard deviation for the trait is an indicator of the importance of the inbreeding depression effect . In modeOur study is the first to report the proportion of additive genetic effects that is contributed by inbreeding depression effects. According to the formula for calculating the additive variance, the proportion contributed by inbreeding depression is mainly affected by allelic frequencies, the magnitude of the estimated inbreeding depression parameter, and the number of SNPs used. As shown in Additional file Overall, the inclusion of the inbreeding depression effect in the genomic model had no significant effect on the estimation of variance components for all traits, although all of the dominance variances were slightly reduced, as also reported by Aliloo et al. .The goodness-of-fit of the six genomic models showed that those with inbreeding depression effects  presented a better goodness-of-fit than the model with only additive effects (MA) for all traits, in line with Aliloo et al. . This reOur results revealed that the inclusion of an inbreeding depression effect in the genomic model increased its predictive ability for the four production traits  and the reproduction trait (TNB) studied and that when the tested trait was strongly affected by dominance genes, the inclusion of the dominance effect in the model further improved its predictive ability. Even when the trait was little affected by dominance, the inclusion of the dominance effect in the model did not decrease its predictive ability.Additional file 1: Table S1. Estimates of variance components, standard error (SE) of parameters, − 2 log likelihood (− 2LogL), AIC (Akaike’s Information Criterion) from models MA, MAD, MAD*, MAI, MAID and MAID*. Table S2. Contributed additive genetic variance from the inbreeding depression effect. Table S3. P-value of likelihood ratio test based on model MA. Table S4. P-value of likelihood ratio test based on models MAI, MAD and MAD*. Table S5. P-value of inbreeding depression effect based on the Wald test. Table S6. Average genomic relationships between animals in the imputed and reference sets. Table S7. Converted classical variance components based on the genotypic variance component in models MAD and MAID.Additional file 2: Figure S1. Distribution of the minor allele frequencies of the Illumina array-specific SNPs in Scenario 1 and the Affymetrix array-specific SNPs in Scenario 2. Figure S2. Effect of allele frequency on


This study aimed to analyze the cerebrospinal fluid (CSF) parameters affecting the outcomes of patients with tuberculous meningitis (TBM).This is a multi-center, retrospective, cohort study involving 81 patients who were diagnosed with TBM and treated in Haihe Clinical College of Tianjin Medical University, Tianjin Medical University General Hospital, and General Hospital of Air Force PLA from January 2016 to December 2019. Baseline data, Glasgow Coma Scale (GCS) score, and clinical presentations of all patients were collected at admission. CSF samples were collected at 48 h, 1, 2, and 3 weeks after admission. CSF lactate, adenosine deaminase, chloride, protein, glucose levels and intracranial pressure were measured. After a follow-up of 16.14 ± 3.03 months, all patients were assessed using the modified Rankin Scale (mRS) and divided into good (mRS scores of 0–2 points) and poor outcome groups (mRS scores of 3–6 points). The differences in patients' baseline data, GCS score, clinical presentations, and levels of CSF parameters detected at 48 h, 1, 2, and 3 weeks after admission between two groups were compared. Statistically significant variables were added to the binary logistic regression model to identify the factors impacting the outcomes of patients with TBM. Receiver operating characteristic (ROC) curve was used to assess the predictive ability of the model.The CSF lactate level exhibited a decreasing trend within 3 weeks of admission in the two groups. For the within-group comparison, statistically significant differences in the lactate level was found in both groups between four different time points. A binary logistic regression model revealed that CSF lactate level at 48 h after admission, age, and GSC score on admission were independently associated with the outcomes of patients with TBM. ROC curve analysis showed that the area under the ROC curve (AUC) was 0.786 for the CSF lactate level (48 h), 0.814 for GCS score, and 0.764 for age.High CSF lactate level at 48 h after admission is one of the important factors for poor outcomes in patients with TBM. Mycobacterium tuberculosis, accounts for about 1–5% of TB cases, approximately one half of patients with TBM die or suffer severe disability  in China, with an incidence rate of 59/100,000. China has the world's second largest TB cases, accounting for 8.5% of the global incidence cases. The number of TB deaths among HIV-negative people was about 30,000, and the TB mortality rate was 2.1 deaths per 100,000 persons  and written informed consent was obtained from all patients.Mycobacterium tuberculosis was detected in CSF, or nucleic acid amplification test was positive for Mycobacterium tuberculosis; in the absence of evidence of pathogens in the CSF, patients were diagnosed with TBM based on a combination of clinical manifestations, CSF examination, and brain imaging examination (diagnostic score of ≥6), and had a positive result from the T-SPOT.TB test (Inclusion criteria were: (1) patients who were aged ≥15 years old; (2) patients who were diagnosed with confirmed TBM according to the TBM diagnostic criteria, i.e., .TB test –12.Exclusion criteria were: (1) patients who were HIV positive; (2) female patients who were pregnant; (3) patients who were diagnosed with suppurative, cryptococcal, viral, syphilitic, brucella meningitis, brain parasites, malignant tumors, and autoimmune diseases; (4) patients who failed to complete the 1-year follow-up.All patients were followed up for an average of 16.14 ± 3.03 months. The modified Rankin Scale (mRS) was used to assess the outcomes of patients at 1 year after disease onset. All patients were assessed face to face by two senior neurologists who were trained in the use of the mRS. Patients were divided into a good outcome group (mRS scores of 0–2 points) and a poor outcome prognosis group (mRS scores of 3–6 points) , 14.g for 10 min, and analyzed with a Beckman Coulter AU5800 chemistry analyzer , and reagents from Beckman Coulter . The levels of CSF parameters lactate, adenosine deaminase (ADA), chloride, protein, glucose were detected, and intracranial pressure (ICP) was measured simultaneously.2 ml of CSF samples were collected by lumbar puncture at 48 h, 1, 2, and 3 weeks after admission, which was then sent to the laboratory of our hospital immediately. Samples were centrifuged at 1,300 All patients were treated according to the guidelines for the treatment of TBM, i.e., the duration of intensive-phase treatment for TBM should be > 2 months, the total duration of treatment should be at least 12 months. Patients should receive at least four antitubercular medications during the intensive phase, isoniazid, rifampicin, and pyrazinamide should be used as the preferred antitubercular medications. During consolidation phase, at least two effective antitubercular medications should be given, with isoniazid and rifampicin being used as the preferred medicines , 16.t-tests, and categorical variables were compared using the Chi-square test or Fisher's exact test. Statistically significant variables were added to the binary logistic regression model. The predictive ability of the model was verified by the receiver operating characteristic (ROC) curve. A P-value of < 0.05 was considered statistically significant.Statistical analysis and graphing were conducted using SPSS 24.0 software, and GraphPad Prism version 9.0. Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables were expressed as number and percentage (%). Continuous variables were compared using the independent or paired P < 0.001).The average age of patients in the good and poor outcome groups was 54.2 ± 17.6 years old and 38.1 ± 15.1 years old, respectively. Patients in the poor outcome group was significantly older than those in the good outcome group (P < 0.001), and were more likely to present with limb weakness and neck stiffness  score compared to the good outcome group , whereas no statistically significant difference was found at 3 weeks  for the CSF lactate level (48 h) was 0.786, with a sensitivity of 76.0%, and a specificity of 76.8%. The AUC value for GCS score was 0.814, with a sensitivity of 72.0%, and a specificity of 82.1%. The AUC value for age was 0.764, with a sensitivity of 60.0%, and a specificity of 83.9%. The ROC curves indicated that the model can accurately predict the outcomes of patients with TBM .A series of previous studies have demonstrated that TBM had a mortality approaching 20–50% –20. A raCSF lactate can be used as a diagnostic tool for many CNS disorders, such as intracranial infections, epileptic seizures, cerebral infarction, and mitochondrial diseases, which is more accurate, and has a higher accuracy than conventional CSF parameters , 24. TheAmong the prognostic factors of TBM, previous studies have demonstrated that patients' age and GCS score on admission were prognostic factors in patients with TBM –44. ResuMycobacterium tuberculosis. A previous meta-analysis has shown that ADA test is a method for rapid diagnosis of TBM with high sensitivity and specificity, the pooled sensitivity and specificity were 89 and 91%, respectively, indicating that ADA test had adequate accuracy for diagnosing TBM  (The results of the present study showed that the ICP assessed by lumbar puncture was lower in the poor outcome group than that in the good outcome group. A previous study showed that there was no correlation between ICP changes and prognosis in patients with TBM . The mosoutlets) . The preoutlets) . HoweverA limitation of this study is the small sample size, which can result in wide confidence intervals. Despite these limitations, we believe that early detection of CSF lactate level is important for predicting the outcomes in patients with TBM.The present study demonstrated that the levels of CSF parameters at different time points after admission in patients with TBM, especially CSF lactate level at 48 h of admission, are important indicators for predicting the outcomes in patients with TBM. High CSF lactate level at 48 h of admission is an important factor for poor outcomes in patients with TBM.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.The studies involving human participants were reviewed and approved by Ethics Committee of Haihe Clinical School, Tianjin Medical University. The patients/participants provided their written informed consent to participate in this study.CL, RH, YX, and XH analyzed data and wrote the manuscript. ZC, YLi, XL, HL, HZ, SW, LH, YB, and YLu were responsible for the data collection. YiyW and YiW designed the study and revised the manuscript. All authors have read and approved the final version of the manuscript.This study was supported by Tianjin Science and Technology Project (No. 19ZXYXSY00070) and Tianjin Health Science and Technology Project (No. ZC20145).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.