The COVID-19 pandemic has prompted the re-emergence of staycations to the fore, as many people were forced to spend their vacations at or close to home due to travel restrictions. This phenomenon first went mainstream during the 2008 financial crisis, and has now been further accelerated by the COVID-19 pandemic. This study investigated the growth and practice of staycations during the first two years of the pandemic by analyzing social media and internet search data using Latent Dirichlet Allocation (LDA) topic modeling and Google Trends analytics. Key findings suggest that, while spatially close to home, people tried to achieve a psychological distance away from home. This was demonstrated by a strong global search interest in spending staycations at hotels close to home. The optimal LDA topic model produced 38 topics which were classified under four aggregate dimensions of antecedents, attributes, activities, and consequences of staycations. The findings provide useful insights to managers and policymakers on boosting revenue through this practice, and the role of staycations in promoting leisure activities close to home and sustainable tourism. •We investigate the practice of staycations during the COVID-19 pandemic from conversations on Twitter and analysis of Internet search trends.•The LDA topic model produced 38 topics which were classified under four aggregate dimensions.a•Findings point to a strong interest in spending staycations at hotels to achieve a psychological distance away from home.•Critical insights are drawn on the role of staycations in promoting tourism closer to home and sustainable tourism. Twitter; Topic modeling; User-generated content (UGC); Construal level theory; Big data; Social media; Social media analytics; Google trends analytics. As a reLeisure travel and tourism has been hit hard by the COVID-19 pandemic . CompareThe COVID-19 infection rate in different countries came in waves, and as the first wave of infection subsided with a decline in positive cases around June 2020, some control measures were relaxed that allowed some forms of tourism to resume . HoweverHence, staycations have boomed during the COVID-19 pandemic and have been part of the emergent customer behavior inspired by the pandemic . HoweverThis study analyzes user-generated content (UGC) from Twitter using topic modeling to understand the discourse about staycations on social media and internet search data from Google to study the search interest and behavior about staycations. As a first step, this study explores the identifying elements of staycation and its differentiators from adjacent concepts like domestic tourism, based on the extant academic and industry literature, in order to propose a definition and highlight its benefits. The results of the study are interpreted using Construal Level Theory (CLT), described in the theoretical framework of this study. Finally, the implications of the insights for leisure travel and sustainable tourism are discussed.22.1Vacations (used interchangeably with holidays in the literature) provide a means for macro-recovery to help people recover from work and everyday stress . EmpiricCincinnati Enquirer newspaper from 1944: “Take a Stay-cation instead of a Va-cation, this year” . The use of the word went mainstream during the global financial crisis as soaring fuel prices pushed up airfares, room rates, and other miscellaneous expenses while the global economy was in a recession, which made going for a vacation prohibitively expensive  is the theory behind the notion of psychological distance. This theory from psychology proposes that human beings only directly experience the present, here, and now. The experience of all other objects beyond the immediate situation is done by forming abstract mental construals of these objects. Hence, psychological distance is a subjective experience that something is close or far away from the self in the present. Psychological distance is egocentric with the self as the reference point, while how an object may be removed from this reference are distance dimensions in time , space , social distance, and hypothetical distance . ResearcCLT postulates that we focus on the abstract at a higher level of construal and on the concrete at lower levels. The perceived distance influences decisions and behavior, i.e. when making choices in psychologically distant situations, people focus on the central or global features of an object . SeveralVacation travel is a beloved pastime and when it was not possible because of the pandemic, people settled for various activities within their usual environment. CLT offers a theoretical foundation for understanding the relationship between psychological distance and these activities and behaviors. Furthermore, previous research has explored COVID-19 leisure activities during lockdown as a way of distancing from the present . Therefo3This research consists of two studies, the first is an analysis of Twitter UGC and the second based on analytics of Google Trends data. In the first study, topic modeling of tweets containing staycation was conducted to get the major topics in the data. Topic modeling is a text analytical technique to extract the main features from a body of text that has been applied extensively in the literature, including hospitality and tourism . The tweIn the second study, data from Google Trends is analyzed to obtain insights from the search behavior around staycations. Google Trends is an open platform that shows an unbiased sample of anonymized, categorized, and aggregated search queries of a keyword or topic on Google Search, the most popular search engine. It can measure the level of interest in a topic across the globe, in a specific geographical location, or over a specific period . AnalysiThe data for study 1 and study 2 were obtained without applying any geographical filters, hence, they are both presumed to cover the whole world, in order to provide complementary data to spot worldwide trends on staycations and to enhance the validity of this work. In addition, study 2 includes more recent data than study 1, which covers only 2020. This allows us to compare the search trends data between 2020 and 2021, and provides the search trend index up to the time of writing in 2022.44.1Topic modeling enabled the identification of the major topics in the Twitter UGC to primarily provide insight on the features of COVID-19 staycations and also about new staycation-related activities. 7,729 tweets containing staycation posted by 6,059 unique users were obtained. The mean, median, and standard deviation of tweets per user were 1.276, 1, and 1.929 respectively. This indicates that most of the tweets were probably from personal accounts, and not repeated posts from the same account as typical with news media or blog accounts. Manual examination to identify irrelevant tweets like adverts was carried out, which were then removed . 6,067 tw1,…, wn used to describe the topic, which are usually the top n words by frequency p(w|k) (Of the pair-wise scores of words y p(w|k) . After cy p(w|k) . The topThe interpretation of the topic model is presented in The aggregate dimension on antecedents comprised of tweets on the causes of growth in staycations and is made up of seven topics as follows; the rise in the number of daily cases of COVID-19 (Topic 1), health rules or measures to control the spread of the virus (Topic 4), cancellation of vacation trips (Topic 7), spending holiday in country (Topic 9), protecting the community (Topic 21), travel restrictions (Topic 32), and travel fear (Topic 29).The aggregate dimension on attributes comprised of tweets on the characteristics of staycations and is made up of eight topics as follows; duration of staycations (Topic 5), staycation deals and hotel staycation packages (Topic 6 & 24), staying home (Topic 3), hotels reopening and encouraging staycations (Topic 2), staying at hotels (Topic 23), hotels mentioning their safety measures (Topic 22), staycation within a drivable distance (Topic 36).The aggregate dimension on activities comprised of the activities people engaged in and is made up of 15 topics as follows; relaxation (Topics 13 &15), watching movies (Topic 37), reading (Topic 26), virtual travel (Topic 28), walking (Topic 12), backyard activities (Topic16), outdoor activities/cottage stays/swimming pools (Topic 20), camping/caravans/campervans/road trips (Topics 19&34), boating (Topic 25), staying on the beach (Topic 27), Belgians staycationing in tents nested in trees (Topic 30), Hemester (Swedish name for staycation) (Topic 35), “Extreme staycation” in South Korea (Topic 38).The aggregate dimension on consequences comprised of topics on the consequences of staycations and is made up of eight topics. These include tweets discussing the staycation trend or boom in staycations (Topics 8&10), family connection from staycations (Topic 11), reduction in carbon emissions (Topic 14), support for local businesses (Topic 17), warnings about staycation scams (Topic 18), rise in domestic tourism (Topic 31), and providing employees with an opportunity to rest (Topic 33).4.2The Google search trends provided insights on the search behavior around staycations on how staycations have changed since 2008 in terms of growth and new activities, as well as the features of COVID-19 staycations. The historical search query for staycation from 2004 till the middle of 2022, showing the search interest in staycations over time, is presented in Tables The most significant observation from Tables 5This study investigated staycations during the COVID-19 pandemic with data from Twitter and Google Trends to provide insights on how staycations have changed since 2008 in terms of growth and new activities and the principal features of staycations during the first two years of the pandemic. The findings indicate that there has been a dramatic increase in internet search interest in staycations worldwide since 2008, including unprecedented growth during the pandemic. The most significant aspect of COVID-19 staycations, as shown by the findings, is a dominant association between staycations and hotels, as demonstrated by four of the topics in the topic model and the internet search analysis. This indicates that many people had staycations at hotels during the pandemic based on the Twitter analysis or were interested in doing so based on the Google search analysis. To interpret this key finding using the Construal Level Theory (CLT), a principal benefit of vacation travel is the provision of psychological distance and freedom from everyday routines and bothersome chores . CLT conIn the same vein, the numerous 5-star hotels and luxury hotel amenities like spa, swimming pool, and buffet in the related search tables which people searched for with staycation indicate a desire for something different by users from what is available at home. All these fit into the mold of people trying to create experiences that can generate high-level construal, as suggested by CLT. Safety measures by the hotels, such as increased cleaning regimes to encourage patronage for staycations, was an important topic in the context of the COVID-19 pandemic. Other studies during the pandemic have shown that these safety measures were of prime importance to attracting customers . The strOther key aspects of COVID-19 staycations include staycation-related Google searches for deals, packages, discounts, allowances, vouchers, and tax credits, including two topics in the topic model show people were interested in staycation opportunities at a reduced cost. Benefits related to travel like breaking the routine and enjoying free time are facilitated when they are affordable, making it easy to form a mental construal of travel according to CLT. These offers, deals or discounts can be divided into two; those provided by hotels and those implemented by governments. Hotel managers are encouraged to provide staycation packages for locals, which may compensate for part of the shortfall from international visitors during the ongoing pandemic . After tStaycation offers through vouchers or tax credits can also be deployed by governments to stimulate demand and encourage people to visit local. Even after the pandemic, these tools remain useful to promote local tourism. These staycation vouchers can be a win-win for all parties as reported in the literature, the government, tourism operators, and the locals, while making the people that redeem such staycation vouchers to spend more money locally . It coulSeveral leisure activities surfaced in the topic model such as camping, boating, walking, relaxation, watching television and movies, visiting the shopping mall, outdoor activities, hiking, road trips, backyard activities, visiting the countryside, eating out at restaurants, visiting local attractions, lounging by swimming pools etc. were similar to the leisure activities reported in previous literature and confirms them . AccordiPreviously unreported leisure activities in the literature connected to staycations surfaced in the findings were virtual travel and staycation cruises. Virtual travel involves visiting unknown places through an online immersion process via the information and videos available on the internet. In more sophisticated implementations; this immersion is carried out through Virtual Reality (VR) headsets . The COVThere were also activities or concepts that were unique to some locations in the topics, such as people in Belgium spending their staycations in tents constructed in trees. An activity named “extreme staycation” in South Korea, which involved the transformation of the home into a chosen popular destination like Bali, for example. Hemester is the Swedish word for staycation and it was captured as a topic of its own from tweets about Swedes engaging in staycations. According to CLT, these activities facilitate building a mental image and reduce the psychological distance to destinations and are associated with positive attitudes . These aPerhaps, the most important topic surfaced under the consequences of staycations is how it can help to reduce the carbon emissions due to leisure travel and tourism. As frequent extreme weather events in different parts of the world continues to highlight the reality of a changing climate , the rol5.1This study has made some conceptual and theoretical contributions. First, it has proposed a definition for staycation, which we hope is clearer based on the standing conventions of the UNWTO. This study has also used CLT to explain its findings, demonstrating its applicability and an avenue for further experimental research on CLT and staycations. To the knowledge of the authors, this study is the first to analyze related web search queries and topics data in hospitality and tourism research. Many of the past research in this area using Google Trends data has been on forecasting studies. The interesting findings from this data demonstrate that this is a useful method for acquiring insight from Google-search activity on a global or local scale. Finally, this study also contributes to the literature on the impact of COVID-19 on hospitality and tourism.Previous research has demonstrated the value of internet-generated data and UGC for hospitality and tourism businesses . From a 6This study has proposed a definition for staycation based on the standing convention of the UNWTO and extant literature. The most significant finding about the characteristics of the staycations during the COVID-19 pandemic was the uncovering of a dominant interest to spend staycations at hotels, which was interpreted using CLT as people trying to achieve a psychological distance away from home while being spatially close to home. New staycation-related leisure activities, like virtual travel and staycation cruises and specific practices in some locations, were revealed in the findings.At the time of writing in 2022, cross-border travel in some places remains with COVID-19 related requirements for tests or vaccination. The current high inflation and fuel prices in several OECD countries may also impact on the ability to engage in international leisure travel by some consumers in the near term. Tourism experts have stated that they expect a recovery in international tourism to 2019 levels in 2024 or later . Researc6.1This study has the following limitations. Social media data is noisy while containing useful information and it is possible that some valuable information was removed in the process of data cleaning. Social media data might also not be generalizable sometimes and may be biased towards the demographic of people using a particular social media network, hence, it is usually a good idea to complement the data from social media with those from other sources as done by this study. Also, the 38 topics surfaced by the LDA topic model were not the only topics present in the data, but the dominant topics, as the algorithm needs to stop at a particular threshold. If not, it is possible to have an extensive but weak model with many more topics. In order to obtain the staycation-related tweets, a single search term of “staycation” was employed because there are no close synonyms at this point and searching for “home” and “vacation” or “holiday” would have yielded too many false positives. This search process could have missed out on some relevant tweets in which staycation was not explicitly mentioned and this is a limitation of this study. Furthermore, Google does not provide the absolute number of search queries for the Google Trends data, as all the data provided are normalized and relative to the maximum in each category and is therefore not suitable for robust statistical manipulation.This study has interpreted the findings using CLT, but the method employed cannot directly prove the theory. Future experimental studies that can prove the theory are recommended using the aggregate dimensions of antecedents, attributes, activities, and consequences as research variables. Future studies exploring how to exploit staycations in particular locations to derive more economic benefits in terms of revenues and profits, as well as to produce cultural benefits for citizens increasing their knowledge about their local environment, are recommended to expand the research in this area. Studies into the market segments likely to be interested in staycations, the motivations to engage in staycations in different geographical areas of the world, as well as surveys about people’s staycation preferences, are also recommended.Babajide Abubakr Muritala; Ana-Beatriz Hernández-Lara; Maria-Victoria Sánchez-Rebull: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713679 and from the Universitat Rovira i Virgili (URV).This work was supported by the Data included in article/supp. material/referenced in article.The authors declare no conflict of interest.No additional information is available for this paper.


We explored a new artificial intelligence-assisted method to assist junior ultrasonographers in improving the diagnostic performance of uterine fibroids and further compared it with senior ultrasonographers to confirm the effectiveness and feasibility of the artificial intelligence method. In this retrospective study, we collected a total of 3870 ultrasound images from 667 patients with a mean age of 42.45 years ± 6.23 [SD] for those who received a pathologically confirmed diagnosis of uterine fibroids and 570 women with a mean age of 39.24 years ± 5.32 [SD] without uterine lesions from Shunde Hospital of Southern Medical University between 2015 and 2020. The DCNN model was trained and developed on the training dataset (2706 images) and internal validation dataset (676 images). To evaluate the performance of the model on the external validation dataset (488 images), we assessed the diagnostic performance of the DCNN with ultrasonographers possessing different levels of seniority. The DCNN model aided the junior ultrasonographers (Averaged) in diagnosing uterine fibroids with higher accuracy , sensitivity , specificity , positive predictive value , and negative predictive value  than they achieved alone. Their ability was comparable to that of senior ultrasonographers (Averaged) in terms of accuracy , sensitivity , specificity , positive predictive value , and negative predictive value . The DCNN-assisted strategy can considerably improve the uterine fibroid diagnosis performance of junior ultrasonographers to make them more comparable to senior ultrasonographers. Since CT and MRI examinations are costly and cannot be popularized, ultrasonography is currently the first imaging method used for the clinical diagnosis of uterine fibroids, as it possesses high sensitivity and specificity. Nevertheless, the following problems still exist in ultrasound uterine fibroid diagnoses. The first problem is the confusion between subplasmic and giant fibroids and between pelvic and adnexal masses2. Second, the current lack of standardized image acquisition views and the performance differences among types of ultrasound equipment impact the accuracy of fibroid detection. In addition, the accuracy of ultrasound uterine fibroid diagnosis depends on the knowledge and experience of ultrasonographers3.Uterine fibroids are by far the most common type of benign tumor in women9. It is well-recognized by both researchers and clinicians that DL methods are already playing a substantial role in radiology because of their powerful image feature extraction capabilities10. DL algorithms for ultrasound image formation are rapidly garnering research support and attention, quickly becoming the latest frontier in ultrasound image formation11. A large number of previous studies on DCNN models based on ultrasound images mainly focused on the diagnosis of benign and malignant masses in superficial organs, such as the thyroid gland15 and breasts19. However, the diagnosis level of junior ultrasonographers with the assistance of this model has not only reached that of senior ultrasonographers but has also shortened the required diagnosis time. Deeper organs, such as the uterus, have been less studied. Therefore, it is necessary to develop a uterine fibroid detection DCNN model based on DL algorithms.Deep convolutional neural networks (DCNNs) based on deep learning (DL) techniques are new computer-aided diagnosis (CAD) tools that enable the automatic capture of targeted areas after trainingIn this study, to improve the accuracy of ultrasound diagnosis for uterine fibroids, we developed a DCNN model that automates the detection of uterine fibroids in ultrasound images and discriminates between the presence and absence of fibroids and internally and externally validated the model. We also compared the DCNN model against eight ultrasonographers with different levels of seniority and explored whether the diagnostic performance of junior ultrasonographers can be improved with the assistance of this DCNN model.In this retrospective, noninterventional, case‒controlled study, we collected ultrasound images of patients who had been diagnosed with uterine fibroids according to surgical and pathological findings and those with normal uteri. Using these ultrasound images, we developed a DCNN model to automatically detect uterine fibroids in the images and to assist junior ultrasonographers in diagnosing uterine fibroids. To protect patients’ privacy, all identifying information, such as name, sex, age, and ID, on the ultrasound images was anonymized and omitted when the data were first acquired. This retrospective study was approved by the hospital ethics committee . Additionally, all experiments were performed in accordance with relevant guidelines and regulations. All of the data in the study were obtained without any conflicts of interest.A total of 3870 ultrasound images  from 667 patients (mean age: 42.45 years ± 6.23 [SD]) with a pathological diagnosis of uterine fibroids and 570 women (mean age: 39.24 years ± 5.32 [SD]) without uterine lesions were included in the analysis. The DCNN was trained and developed on 3382 ultrasound images, which were randomly divided into a training dataset  and an internal validation dataset . The model performance was tested on an external validation dataset containing 488 ultrasound images . The medical record system of the hospital provided us with the patients' clinical data . The information and imaging data of the patients with uterine fibroids are summarized in Tables The ultrasound images and clinical information data of patients with uterine fibroids and normal uteri were collected from Shunde Hospital of Southern Medical University between 2015 and 2020. We controlled the quality of the ultrasound images based on the associated pathological findings. The inclusion criteria for the abnormal patient group were as follows: (1) The preoperative ultrasound suggested the presence of a uterine mass; (2) no other combined uterine masses were present; (3) the ultrasound images were in black and white; and (4) the patients were diagnosed with uterine fibroids by two senior ultrasonographers . The exclusion criteria were as follows: (1) the patient lacked ultrasound data from our institution; and (2) images showing mass locations that did not match the clinical data. Finally, a total of 3870 ultrasound images  were acquired in this study.All ultrasound images were acquired in.jpg format using a color Doppler ultrasound machine. The models included the APLI300 TUS-300, APLI400 TUS-400, APLI500 TUS-500, and LOGIQ S8. The operation routes were transabdominal or transvaginal, with the abdominal ultrasound probe set at 2–7 MHz and the vaginal ultrasound probe set at 5–7 MHz.We randomly grouped the 3382 ultrasound images  according to a training dataset  and an internal validation dataset  for model training and development. The ground truths (GTs) of the training and validation datasets were labeled with the Visual Geometry Group Image Annotator software. Ultrasonographers with more than 10 years of experience labeled the ultrasound images of uterine fibroids through each patient’s clinical information and ultrasound image reports. If data samples with excessive labeling biases were generated, the final results were voted on again by the three ultrasonographers to determine the GT results. The system automatically generated json files, which included the image and information such, as the size and location of the GTs. The flow diagram is shown in Fig. 20 detection network used to detect lesion regions in the ultrasound images and (2) another ResNet5021 network that was used to classify the images as normal or abnormal.In this study, we developed a two-stage DCNN model to detect uterine fibroids in ultrasound images. The network structure in this study consisted of two parts: (1) the YOLOv322 classification task and fine-tuned on our training dataset. The original ultrasound images and the bounding boxes outlined by the radiologist, which covered the entire lesion area, were used as input data for training the YOLOv3 detection network.YOLOv3 is a state-of-the-art object detection network that uses features from the entire input image to predict a bounding box for each region of interest. ResNet50, a unique residual module, was used to replace the original network in YOLOv3 to learn more complex feature representations from the ultrasound images of uterine fibroids. The ResNet50 backbone was pretrained on the ImageNet23. Four graphics processing units  were used, and the total training time was 10 h. The Adam24 optimizer was initialized, and each mini-batch contained 12 images. The weight decay was set as 0.0005, and the momentum was set as 0.9. A summary of the layers and output sizes of the ultrasound images produced by the DCNN during training is illustrated in Supplementary Table All training and testing procedures were developed with Paddle (version 2.0.2), CUDA (version 10.1) and Python version 3.7).723. FouIn our local institution, the role of ultrasonographers is to examine the patient and make a diagnosis using a diagnostic ultrasound machine. In our study, four junior ultrasonographers (with less than 5 years of experience) and four senior ultrasonographers (with more than 10 years of experience) were selected to participate in determining the validation dataset, and none of them had previously been involved in the examination and interpretation of the validated ultrasound images. In the experiment, all identifying information, such as name, sex, age, and ID, on the ultrasound images were anonymized and omitted to ensure double-blindness. In addition, we scheduled the 8 ultrasonographers to perform the test at the same time in different locations to ensure their relative independence and temporal consistency. Each ultrasonographer independently interpreted the ultrasound image data contained in the external validation dataset and provided results, and four junior ultrasonographers interpreted the ultrasound image data again with the assistance of the DCNN model after interpreting the ultrasound image data individually.P < 0.05 was considered a statistically significant difference. All statistical analyses were conducted using the software SPSS 22.0.To compare the performance of DL methods with that of each ultrasonographer, we calculated their sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy metrics. Sensitivity was calculated as the percentage of correctly detected uterine fibroid images, and specificity was calculated as the percentage of correctly detected normal uterine images. The number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) findings yielded by the described methods were determined based on the pathological result. According to the TP rate (sensitivity) versus the FP rate (1-specificity), we calculated the areas under the receiver operating characteristic (ROC) curve and the 95% confidence interval (CI) for the junior ultrasonographers, the senior ultrasonographers, the DCNN model alone, and the DCNN-assisted junior ultrasonographers. Additionally, we used the pearson's chi-square test to test the difference in the interpretations of diagnosis of uterine fibroids between junior ultrasonographers and the DCNN model, senior ultrasonographers and the DCNN model, the DCNN-assisted junior ultrasonographers and senior ultrasonographers, junior ultrasonographers with and without the DCNN’s assistance, and This study was performed in accordance with the Declaration of Helsinki and was approved by the hospital ethics committee . All research was performed in accordance with relevant guidelines and regulations. We confirmed that informed consent was obtained from all participants and/or their legal guardians.The diagnostic performance of the DCNN model was evaluated on an internal validation dataset of ultrasound images and compared with that of currently popular detection models. The intersections over union , we selected four junior ultrasonographers (with 5 years of experience or less) and four senior ultrasonographers (with more than 10 years of experience) to participate in this study. We compared the performance of the four junior ultrasonographers (Averaged), the four senior ultrasonographers (Averaged), the DCNN model and the four junior ultrasonographers (Averaged) with the DCNN-assisted in diagnosis of uterine fibroids, and the corresponding ROC graphs are shown in Table On the external validation dataset, the DCNN model had higher accuracy , sensitivity , specificity , PPV , and NPV  than the junior ultrasonographers (Averaged). Moreover, the DCNN model had comparable accuracy , sensitivity , specificity , PPV , and NPV  to those of the senior ultrasonographers (Averaged).On the external validation dataset, the four junior ultrasonographers (with the assistance of the DCNN) showed substantial improvement in identifying the presence or absence of uterine fibroids. The DCNN model assisted junior ultrasonographers in diagnosing uterine fibroids with higher accuracy , sensitivity , specificity , PPV , and NPV  than they achieved alone. In terms of accuracy , sensitivity , specificity , PPV , and NPV , the DCNN-assisted junior ultrasonographers were comparable to the senior ultrasonographers (Averaged). Performance comparisons between the four junior ultrasonographers with and without the DCNN assistance are provided in Table In this study, we developed a DCNN model for the automated detection of uterine fibroids in ultrasound images and compared this DCNN with several current and more advanced DL detection models in terms of their uterine fibroid diagnosis performance on internal and external validation datasets. With the assistance of the DCNN model, the junior ultrasonographers performed better in diagnosing uterine fibroids than they did without the model, and the resulting diagnostic performance was comparable to that of senior ultrasonographers. In addition, the DCNN model was able to simultaneously and quickly detect multiple masses of different sizes in a single image.27, and DCNN–based image detection methods have been commonly applied for classifying and identifying lesions30. However, no studies have been reported on the DCNNs in diagnosing uterine fibroids in ultrasound images and comparing with ultrasonographers thus far33. Even if artificial intelligence (AI) has superior performance, real world decisions should be supervised by ultrasonographers. Therefore, the most important role of an AI system is to assist junior ultrasonographers in improving the detectability of uterine fibroids.To the best of our knowledge, this study is the first to evaluate a DL strategy as an aid for the diagnosis of uterine fibroids. In recent years, DL algorithms have been widely used in the field of medical image analysis2. However, the DCNN aids junior ultrasonographers in diagnosing uterine fibroids and is expected to improve this situation. The DCNN model developed in this study assisted junior ultrasonographers in diagnosing uterine fibroids in ultrasound images with higher sensitivity, specificity, and accuracy than they achieved alone. This suggests that the proposed DCNN AI model can be used to assist junior ultrasonographers in improving their diagnostic performance, thereby shortening the growth cycle of ultrasonographers.The lack of both medical resources and senior ultrasonographers leads to poor uterine fibroid diagnostic performance and causes some patients to miss their optimal treatment opportunities34. In the future, multicenter studies should be conducted to improve the database required for training AI systems to improve the diagnostic capability of DCNN models. In addition, a combination of semisupervised and unsupervised network learning should be explored to reduce the data requirements of our model36. Second, we only trained and tested a single image of uterine fibroids instead of obtaining volume data from ultrasound images, such as CT or MRI. Selecting an image was itself influenced by the experience and skills of the performing ultrasonographers, but it does not mean that the images were purposely selected to be representative, only that there are factors that are objectively influenced. However, in real practice, ultrasonographers usually evaluate uterine fibroids using real-time ultrasound information, not an ultrasound image37. Thus, in the future, we need to study the real-time automated application of the DCNN or the DCNN with improved technology allowing the collection of ultrasound volume data, and we will gradually extend these studies to clinical work. Third, the current treatment plan for uterine fibroids is mainly based on clinical symptoms and does not rely exclusively on ultrasound diagnosis. Therefore, the DCNN model established in this study can only assist ultrasonographers in improving the diagnostic capability of uterine fibroids but not the clinical outcome of patients.This study has several limitations. First, this study is a single-center study, and the data may have selection bias38, it can reduce the burden of ultrasonographers in reading images. The proposed DCNN detection system can identify the presence of fibroids in ultrasound images, and it can also serve as a learning tool for junior ultrasonographers to learn to correctly differentiate uterine fibroids. In conclusion, the DCNN-assisted strategy considerably improved the uterine fibroid diagnostic performance of junior ultrasonographers. Further studies with larger sample sizes, enriched types of uterine masses, and generalized model structures are needed so that the proposed approach can be applied to other medical image analysis tasks in a quicker and more flexible manner.AI is unable to replace ultrasonographers by automating the diagnosis of diseases. However, as a tool for medical aid diagnosisSupplementary Information.


Pn1-specific antibody (Ab) levels of neonatal mice immunized with Pn1-CRM197 alone were low. The adjuvants enhanced IgG Ab responses up to 8 weeks after immunization, more after s.c. than i.n. immunization. On the contrary, i.n. immunization with either adjuvant enhanced serum and salivary IgA levels more than s.c. immunization. In addition, both dmLT and mmCT enhanced germinal center formation and accordingly, dmLT and mmCT enhanced the induction and persistence of Pn1-specific IgG+ Ab-secreting cells (ASCs) in spleen and bone marrow (BM), irrespective of the immunization route. Furthermore, i.n. immunization enhanced Pn1-specific IgA+ ASCs in BM more than s.c. immunizatiofimmu.2022.1078904n. However, a higher i.n. dose of the Pn1-CRM197 was needed to achieve IgG response comparable to that elicited by s.c. immunization with either adjuvant. We conclude that dmLT and mmCT enhance both induction and persistence of the neonatal immune response to the vaccine Pn1-CRM197, following mucosal or parenteral immunization. This indicates that dmLT and mmCT are promising adjuvants for developing safe and effective early life vaccination strategies.Immaturity of the neonatal immune system contributes to increased susceptibility to infectious diseases and poor vaccine responses. Therefore, better strategies for early life vaccination are needed. Adjuvants can enhance the magnitude and duration of immune responses. In this study we assessed the effects of the adjuvants dmLT and mmCT and different immunization routes, subcutaneous (s.c.) and intranasal (i.n.), on neonatal immune response to a pneumococcal conjugate vaccine Pn1-CRM AlthougNeonatal immunizations induce low antibody (Ab) responses and multiple vaccinations are needed to maintain protective immunity and immunological memory . MultiplH. influenzae, induces both mucosal and systemic immune responses, providing protection against carriage, otitis media and invasive disease in a variety of challenge models  immunization elicits antigen-specific immune responses in the respiratory tract . Mucosale models . Conjugae models , 12. Impe models . This ise models . Therefoe models .S. pneumoniae of serotype 1   –40. In t22.1Adult NMRI mice (5-6 weeks old) were purchased from Taconic . They were kept in microisolator cages with free access to commercial food pellets and water and housed under standardized conditions at ArcticLAS vivarium facility , with regulated temperature, daylight and humidity. Breeding cages were checked daily for new births, and pups were kept with their mothers until weaning at 4 weeks of age. The protocol was approved by the Experimental Animal Committee of Iceland, according to regulations 279/2002.2.2197 was provided by the Serum Institute of India (India) and consisted of a pneumococcal polysaccharide of serotype 1 (Pn1) conjugated to diphtheria toxoid (CRM197)  . The adj(CRM197) , 31 and 197, neonatal (7 day old) mice (8 mice per group) were immunized once s.c. at either side at base of tail with 0.5, 0.75 or 1 µg of Pn1-CRM197 and Pn1-specific IgG Abs were measured bi-weekly up to 8 weeks. Based on the results  into the mouth. After 5 min, the sticks were transferred to phosphate-buffered saline (PBS) containing 10.0 µg of protease inhibitor  per ml to prevent proteolysis. The dissolved saliva samples for each group were pooled and stored at -70°C.2.4Pn1-specific serum and salivary Abs (IgG and IgA) were measured by ELISA as described previously . Briefly2.5+ and IgA+ ASCs were enumerated by ELISpot, as previously described  overnight at 4°C, and developed by 5-bromo-4-chloro-3-indolylphosphate and NBT in AP development buffer . The number of spots (each representing a cell secreting specific Abs) were counted by ELISPOT reader ImmunoSpot R S6 ULTIMATE using ImmunoSpot R SOFTWARE (Cellular Technology Limited (CTL) Europe, Bonn, Germany).Pn1-specific IgGescribed , in sple2.6+ staining were measured from all pictures using the Zenblue Software.GC formation was studied by staining spleen sections with peanut agglutinin (PNA) and anti-IgM, as described elsewhere . Spleens2.7Statistical analyses between groups were performed using the Mann Whitney U test or Kruskal Wallis H test  using Graphpad Prism 6.0 . P-values less than 0.05 were considered statistically significant.33.1197 with or without dmLT or mmCT or injected with saline as a control. Since a different pneumococcal conjugate, Pn1-CRM197, was used in this study a pilot experiment was performed where several doses of Pn1-CRM197 s.c. were tested and 0.75 µg was found to be optimal  than Pn1-CRM197 alone , where the difference becomes pronounced after week 4. Accordingly, 54% and 76% of neonatal mice immunized s.c. with Pn1-CRM197 and 5 µg dmLT or mmCT respectively reached Pn1-specific IgG levels known to be protective against pneumococcal bacteremia , whereas 2 µg of the adjuvants had less effect, similar to what was observed for IgG responses. The effects of the adjuvants on IgA levels were most pronounced at early time points  and GC B cells (PNA+) in spleen sections collected 2 weeks after immunization, the peak time for GC reaction in neonatal mice  with 5 µg of dmLT or mmCT, and evaluated the immune response, by measuring Pn1-specific IgG and IgA Abs in serum bi-weekly, IgG+ and IgA+ ASCs in spleen and BM 8 weeks after immunization, as well as salivary IgA.Mucosal delivery of vaccines faces several obstacles, including stability and dosage for optimal vaccine uptake at the mucosal inductive sites and since i.n. immunization with 0.75 µg of Pn1-CRM197 alone were low and comparable to those of saline-injected control mice  and was comparable to s.c. immunization with mmCT or dmLT, respectively (197 + dmLT) and 88% (4µg Pn1-CRM197 + mmCT) of the mice reached protective IgG Ab levels against pneumococcal bacteremia. As previously observed, the inclusion of either adjuvant with Pn1-CRM197 enhanced the number of Pn1-specific IgG+ and IgA+ ASCs in BM  vaccination could also be an alternative to the i.n. route. It has been shown to induce both systemic and mucosal immune responses and importantly and contrary to nasal administration it does not seem to redirect antigens and/or adjuvants to the brain  –72. Usinocytosis , 74 and ocytosis . Herein,s LT-K63 . Similars LT-K63 , 33. Liks LT-K63 . Also ins LT-K63 .Although all immunized groups assessed here showed good protection against pneumococcal bacteremia, IgG levels were lower than needed for protection against pneumococcal lung infection, and there is a wide variation in immune responses after one immunization of neonatal mice, even when adjuvants are included in the vaccine formulation . The vac197 by s.c. or i.n. routes similarly enhanced GC reaction, that are crucial for production of high affinity, class-switched Abs needed for protective immunity. However, the i.n. route was clearly superior to s.c. route for both induction and persistence of Pn1-specific salivary IgA, serum IgA and IgA+ ASCs in BM, although Pn1-specific IgG levels were lower at most timepoints, despite comparable numbers of IgG+ ASCs in BM 8 weeks after immunization. However, by increasing the i.n. dose of Pn1-CRM197 with either dmLT or mmCT, protective serum IgG levels could be induced to comparable levels to those elicited by the lower dose of vaccine and adjuvant by the s.c. route. Our study indicates that dmLT and mmCT are promising adjuvants for development of safe and effective mucosal and parenteral vaccination strategies against respiratory pathogens in early life.Taken together, we have shown that both dmLT and mmCT enhanced induction and persistence of the neonatal immune response to a pneumococcal conjugate vaccine, by only one i.n. or s.c. immunization. Both adjuvants given with Pn1-CRMThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by Experimental Animal Committee of Iceland (license number 2021-01-04), Austurvegi 64, Selfoss, Iceland. The study was carried out in accordance with Act No. 55/2013 on animal welfare and regulations 460/2017 on protection of animals used for scientific research.JM, AA, IJ, and SB conceived and designed the study, interpreted the results and wrote the manuscript. IJ and SB supervised the study. JM, AA, PF and SB performed the experiments. JM analyzed the data. All authors contributed to and approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.


The study of construction and demolition waste (CDW) has attracted more and more attentions with the increasing CDW pollution caused by the large-scale infrastructure construction. This study used the Latent Dirichlet Allocation (LDA) combined with topic intensity to discover hot topics and development trends in the study area of CDW. First, the LDA was used for topic modeling to extract the existing topics from textual data. Second, the topic intensity was calculated for the extracted topics and the numerical values of the topic intensity represented the popularity of the topics. In this study, 4 topics were extracted from 1,849 relevant articles through the LDA modeling and topic intensity calculation. The results showed that the topic of “CDW management” had an upward trend. Topics such as “recycled aggregate,” “environmental impact,” and “study of CDW on soil” all showed a downward trend. The methods of this study can dig into the topics of CDW study and help scholars to engage in this field for better understanding the prevalence and evolution trends of these topics. With the constant advancement of the construction industry, the problems that construction brings to the environment are becoming more and more obvious –3 and thIn recent years, to grasp the research trend of CDW, relevant scholars have discussed the development trend of CDW from the perspective of the whole or partial field of CDW , 11. TheThe LDA model is a generative probabilistic topic model. Given that the potential subject is the likelihood appropriation of a progression of words in the corpus, then, at that point, the record is the likelihood dispersion of a progression of expected themes. Unlike the overall bunching technique, the LDA permits a record to contain different themes simultaneously, so it is more suitable to extract study topics from scientific articles . At the To sum up, this study extracts the topics of CDW articles with the help of the LDA classic model. It makes a detailed analysis of the content and intensity of each topic to deeply understand the study status of CDW in China from 2011 to 2020.The next part of this article is organized as follows. Section 2 introduces the previous study work on the data source and implementation of the LDA-based topic modeling. Section 3 describes the analysis results. The last part is the conclusion.When retrieving from the WoS database, TS = “demolition and construction waste” or “C&D waste” or “C&D wastes” or “CDW” or “demolition waste” or “construction waste” . The stop word list of the NLTK is relatively standard and complete, covering the stop word list in 22 languages. By passing language name parameters to the library's stop words constructor, stop words in different languages can be removed to improve the reliability of results. The lemmatization operation uses the setting and morphology to decide the variety of related words and uses different normalization rules to obtain connected roots based on the grammatical feature . In thisThe choice of the number of topics is the first step in building a model. The topic of the LDA model is an abstract concept. According to different granularities, the topics in the corpus can be divided into various quantities of themes. The quantity of subjects in the LDA needs to be given in advance. In general, larger corpora represent more topics.Furthermore, the LDA gives a bunch of words compared to themes based on the number of points. Hence, it is essential to investigate the appropriate number of themes. If not, it will affect the advantages and disadvantages of the entire topic model. Suppose that the selected value of the number of topics is too small. In this case, the ability of the model to describe the data will be limited. Instead, it will prolong the training time of the model and hinder the effect of data analysis. Perplexity is often used to determine the number of topics .Moreover, although perplexity is widely used, it is not the best way to evaluate the number of topics because it does not consider the semantic association between word contexts. This study selects the ideal number of themes in light of the coherence score. It uses the method provided by Mallet to calculate a coherence score, depending on the level of co-event between words removed from the subject to work out 10 expressions of every point. Topic modeling gives a bunch of 10 words to encapsulate a subject and the theme is created by subjectively examining the collection of words introduced in the topic displaying results. Then, the most noteworthy top articles of each topic are analyzed and referred to make the theme title.The topic coherence score compels the number of topics K and when the coherence score is the highest, K takes the optimal value. This article employs the assessment index coherence score in the statistical language model show to decide the ideal number of topics .The formula for the coherence score is described as follows:i and νj are any two words belonging to V, respectively. p  represents the co-occurrence of νi and νj probability. The consistency score is positively correlated with the sentence similarity, as obtained by calculating the co-event recurrence of words in the sentence. Therefore, a higher consistency score could give a better result  of the topic j, the more significant the proportion of the topic j in the document set and the more critical the topic j. The study frontier has the characteristic of great attention, hence the frontier topic has a relatively high proportion in the document set, with a large topic intensity value. The average topic intensity value of each topic in the document set can be calculated according to the formula. Topics with a greater topic intensity value than the average can be filtered out to obtain popular topics.In the formula, M represents the complete number of reports. θWhen individual topics extracted from topic modeling, the corresponding study trends of these topics over time are investigated. The weight upsides of the multitude of investigated articles were acquired and the year-to-year mean weight was charted. The exploration of preparation can be partitioned into five steps:(1) Obtain the article data of CDW from the past 10 years.(2) Preprocess the collected article data set to form a document set for running the LDA algorithm.(3) Select the appropriate topic to run the LDA and get the topic and subject terms.(4) Establish the topic intensity model, calculate the intensity of each hot topic, and analyze hot topics.(5) According to the weight of each topic, analyze the study trend of each hot topic.Python language was used to implement the LDA modeling, extract the potential topic distribution, and present it in the topic-word and document-topic distributions. After estimating the consistency score, the ideal number of points is 4. The LDA topic recognition generated document-topic probability matrix that is based on the probability of the 4 × 1,849 matrix. This study visualizes each document in the subject probability of topic information and intuitively investigates each document's relevant information. Four study topics reflect the current study status in the field of CDW over the past decade. Based on the words in the topic-word matrix and the top articles of each topic in the document-topic distribution, the study content in the field is analyzed and summarized, mainly focusing on the following areas:Category I: Study on material recycling technology. The study related to material recycling technology focuses on recycled aggregate (topic 0). This category highlights material-related stability studies, which are essential for technical study on various recycled materials in CDW. In this topic, Sasanipour et al.  with theCategory II: Study on the impact of CDW on the ecosystem is the environmental impact (topic 1). This category investigates the environmental factors of CDW and proposes some measures to reduce CDW in the environment. The highest contribution rate in this topic is that Dahlbo et al.  used a cCategory III: Study on CDW management (topic 2), mainly on policy support, reduction, and production calculation. This category favors CDW management and policy study, which is closely related to stakeholders. With the highest contribution rate in this topic, Kim et al.  used queCategory IV: Study on CDW in other areas, such as the study of CDW on soil (topic 3). CDW is used as a new substrate to study the ability of microorganisms in the soil to purify sewage, increasing the recycling of CDW to help protect the environment. Therefore, study in other fields is another effective way to reuse CDW resources. With the highest contribution rate in this topic, Chen et al.  used CDWAfter obtaining the topic through experiments, the focus is on evaluating the intensity of the topic and further analyzing its hot topic. Based on the LDA topic modeling, this study uses the topic-document probability matrix obtained by the model to calculate the topic intensity, reflecting the degree of attention paid to the topic and discovering hot topics.As shown in The average annual weights of articles per topic from 2011 to 2020 are shown in As shown in Three topics  show a downward trend. Among them, topic 0 is declining, but accounting for a large proportion of all the topics, with a weight of 0.73–0.80. This indicates that the recycling of CDW has always been an important core technology or application field. The decline in study on recycled aggregates may be due to limited technical conditions, lacking study equipment, and high costs. Moreover, there are currently fewer CDW recycling companies and limited study subjects. From the topic intensity of CDW recycling, it can be seen that its value is relatively high, indicating that the weight of CDW recycling declines, but continues to develop, which is a study hotspot. Similarly, Liu  found thFuture study is suggested to pay attention to the impact of CDW on the environment from the perspective of the whole life cycle. The governance of CDW should be seen as a sustainable system . Most ofAnother future study is suggested to conduct more extensive study on different kinds of CDW. There are many types of CDW, each with different recycling routes and process modes. A single CDW processing model may be too general and not suitable for all the types of CDW. Therefore, more in-depth study on different types of CDW should be carried out, such as study on recycled glass and recycled concrete, which can provide more valuable suggestions for CDW recycling.Apply new technologies to study related to the entire field of CDW. By analyzing the literature and combining with the current development trend, it is found that there is still a relatively lack of study on new technologies. With the rapid development of information technology, more and more new technologies have been applied to engineering construction study, such as big data, artificial intelligence, building information model, geographic information system, and so on –47. For Furthermore, CDW study can benefit from additional views, even after the findings of this study are considered. Study articles can be also expanded to incorporate additional databases . A second option is to add earlier years to the list of publications. The history of CDW study trends is uncovered, which can shed light on the fads and features of CDW study evolving through time. In CDW field, these viewpoints provide patterns that can be used to anticipate future developments. Topic modeling is also feasible through the use of multiple algorithms, which allow for more refined or comparative identification of topics, with the potential to provide a test of the topics found. A corpus with additional metadata, such as author-offered keywords, abstracts, and titles, can be used for topic modeling. Finally, the extracted topics can also be utilized to understand and evaluate the progress of CDW study over time.Tracking the study hotspots of CDW can help scholars to understand the current study status and the law of development over time. This study proposed a method to obtain short-text topics through the combination of the LDA and topic intensity. The topics and abstracts of articles in the WoS published from 2011 to 2020 were chosen as the study subject. First, we use the NLTK and spaCy to obtain the professional terminology of CDW articles. Second, the LDA model of Mallet, a generative probabilistic topic model, was used to capture potential topics in articles of CDW to obtain the topics and topic words. Third, the topic intensity was calculated to obtain hot topics. Finally, the periodic changes of topics to year-to-year mean weight were analyzed in different periods, discussing each topic's development trend and mapping the trend of CDW study over time.The results show that the topic effect is the best when the number of topics is four. One topic is trending up and the other three topics are trending down. Results based on the LDA have revealed these topics. Only one topic is on the rise . The rise is relatively straightforward, indicating that CDWM is receiving an increasing attention in society, while others are decreasing gradually . Such decline may be affected by the economy, technology, and policy to a certain extent, but according to the topic intensity, it can be seen that topic 0 and topic 1 are both the hot topics. The results show the trends of CDW study and provide more valuable information for real-time, detailed, and scientific tracking of its study. The LDA can more accurately extract the study topics of CDW articles, which is helpful for researchers to have a preliminary understanding of the development status of the field, while grasping the future study direction and looking for emerging topics. Compared with the existing similar studies, previous studies have found that the study in the field of CDWM has attracted more and more scholars' attention. However, in addition to finding that CDWM has attracted much attention, this study also found that recycled aggregate and environmental impact are also hotspots in CDW study. In addition, some scholars have studied the impact of CDW on soil. It is important to consider the study's limitations when interpreting findings. First, the titles and abstracts provided by authors are studied. Previous study has focused on the article's title, abstract, keywords, and text. Titles and abstracts may not sufficiently convey the study, despite that authors are proficient in their field and in a position to do so. Second, the sample size of 1,849 articles is large, which has specific practical significance. However, only CDW articles of the WoS are considered. CDW articles published by scholars in comprehensive excellent domestic journals are not considered. Future study can consider increasing the example size for subject analysis to completely comprehend the current development status of CDW field. Third, the analysis is based on the LDA algorithm, which does not look at different calculations for point demonstration (LSI/LSA). The themes and findings of this study may vary slightly, if other topic modeling techniques are used. Fourth, the LDA model assumes that topics are independent of each other. There are always inseparable links between topics in the same discipline. Therefore, the related topic model can be added to the model in future study. Finally, this study only considers the external characteristics of the publication time. In future study, the structural topic model can be used to add external information, including authors and journal categories to understand the study status more accurately.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.PX and ZW contributed to conception and design of the study. QH organized the database. BZ performed the statistical analysis. PX wrote the first draft of the manuscript. ZW and JZ wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version of the manuscript.This study was supported by the National Natural Science Foundation of China (No. 72001148), the Foundation for Basic and Applied Basic Research of Guangdong Province (No. 2019A1515110247), the Research Start-up Funding in Shenzhen (No. 000376), and the Natural Science Foundation of Shenzhen University (SZU).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.


The abritAMR platform utilises NCBI’s AMRFinderPlus, as well as additional features that classify AMR determinants into antibiotic classes and provide customised reports. We validate abritAMR by comparing with PCR or reference genomes, representing 1500 different bacteria and 415 resistance alleles. In these analyses, abritAMR displays 99.9% accuracy, 97.9% sensitivity and 100% specificity. We also compared genomic predictions of phenotype for 864 Salmonella spp. against agar dilution results, showing 98.9% accuracy. The implementation of abritAMR in our institution has resulted in streamlined bioinformatics and reporting pathways, and has been readily updated and re-verified. The abritAMR tool and validation datasets are publicly available to assist laboratories everywhere harness the power of AMR genomics in professional practice.Realising the promise of genomics to revolutionise identification and surveillance of antimicrobial resistance (AMR) has been a long-standing challenge in clinical and public health microbiology. Here, we report the creation and validation of  The implementation of genomics for identification and surveillance of antimicrobial resistance (AMR) in clinical laboratories remains challenging. Here, Sherry et al. present a bioinformatics platform for detection of AMR determinants from whole-genome sequencing data, suitable for clinical and public-health microbiology reporting. A clear understanding of the genomic and mechanistic basis for AMR is required to inform clinicians and public health teams, from the level of individual patients through to population-level surveillance5. By providing additional, timely data on acquired AMR genes or gene mutations that confer resistance, genomic sequencing has the potential to significantly enhance AMR surveillance and inform patient treatment beyond conventional phenotypic susceptibility testing methods7.Antimicrobial resistance (AMR) is an increasingly well-recognised threat to global health8. However, technological advances in whole genome sequencing (WGS) means the process is becoming more cost-effective and the turnaround time for sequencing a microbial genome is decreasing significantly9.The use of genomics in the detection and surveillance of bacterial AMR is lagging behind other applications of genomics, such as strain typing and phylogenetic analysis. Contributors to the lack of uptake include the fact that phenotypic testing can be performed more rapidly than genotypic testing for many common pathogens, and the correlation between genotype and phenotype can be variable due to incomplete knowledge of AMR mechanisms that impact function11. To facilitate implementation, the development of standardised and extensive open-access AMR databases and the validation of bioinformatic analytical tools for the detection of AMR is crucial12. Another hurdle to the acceptance and implementation of AMR genomics is how the data can be meaningfully reported outside of research or reference laboratory settings4. If the implementation of WGS for AMR is going to be accepted for the detection of AMR resistance, it is important to consider the way in which complex genomic data is presented to clinicians, nurses, public health surveillance teams, and other stakeholders with varying understanding of genomics, and thus how to interpret findings13. This is a gap in the bioinformatic tools currently available for AMR, as outputs are not usually tailored for clinical reports, or easily modifiable to suit local reporting requirements.A lack of international standards for genomic detection of AMR mechanisms means it is difficult to compare results between laboratories14. These standards require the implementation of standardised operating procedures, quality management systems, staff training and rigorous validation of all processes used to generate results and reports in each laboratory15. Currently, the relevant standards for medical laboratories (last released in 2012) are not designed to assess the performance of bioinformatic tools, making validation to meet these standards difficult for laboratories, in addition to the paucity of publicly-available validation datasets4.In many countries, clinical and public health microbiology laboratories are required to meet International Standards Organization (ISO), or ISO-equivalent, standards to be accredited to operateabritAMR, a wrapper for the NCBI AMRFinderPlus tool16 for the detection of AMR determinants from whole genome sequencing data16, with outputs adapted for clinical and public health microbiology reporting. We envisage that this pipeline, extensive validation methods and validation dataset could be adopted for use in public health and clinical sequencing laboratories to assist those involved in AMR surveillance and clinical applications.Here we design and validate a bioinformatic tool, abritAMR is a pipeline for characterisation and reporting of AMR determinants from bacterial sequences, adapting the AMRFinderPlus tool and database for use in clinical and public health microbiology. Using the outputs from AMRFinderPlus, AMR mechanisms are further classified by antimicrobial class and/or mechanism to suit clinical and public health microbiology (CPHM) needs, and subsequently filtered according to local reporting requirements, with results ready for incorporation into sample reports , sensitivity 97.9% (97.5-98.4%), and specificity 100% (100-100%)  in Australia17, with 99.9% accuracy (95% CI 99.9–100%), 98.9% sensitivity (98.3–99.3%) and 100% specificity (100–100%) across these classes , vancomycin resistance genes, and oxazolidinone and phenicol resistance ).abritAMR pipeline was highly accurate compared to PCR  with 1179/1184 (99.6%) resistance genes correctly detected, and compared well to Sanger sequencing  with 355/356 (99.7%) alleles correctly identified by WGS. After discrepancy resolution , five discrepancies between PCR and WGS results remained, including three potential false negatives  consisting of one CTX-M and two CMY genes not detected by abritAMR; at least one of these was due to the presence of a contig break in the gene leading to smaller fragments not detected by AMRFinderPlus. Additionally, two potential false positives  were identified, one CMY-42 and one IMP-62, confirmed by repeat PCR and sequencing; both genes were reported to be within the inclusivity range of the assay as per the manufacturer’s instructions, although this was validated by in silico PCR by the manufacturer (and observed in our dataset). Alternatively, these discrepancies may be due to plasmid dropout in culture (which is commonly observed with suspected CPE isolates such as these), as different colonies with and without the ESBL/AmpC gene may have been picked for PCR and WGS, potentially explaining these discrepancies. No discrepant results were detected for mecA and van gene detection, and only one allele was incorrectly assigned compared to Sanger sequencing (99.7% accuracy)(Table abritAMR against PCR yielded 99.6% accuracy (95% CI 99.0-99.9%), 99.6% sensitivity (99.0-99.9%) and 99.4% specificity (97.9-99.9%).The abritAMR on synthetic reads from complete reference genomes, and comparing to the (native) AMRFinderPlus results on the complete genome, considered the ‘gold standard’ , 97.5% sensitivity (96.9-98.0%), and 100% sensitivity (100-100%)  short-read data, which likely accounts for at least a proportion of the discrepant results.The presence or absence of 415 AMR genes across 321 genomes  was tested by running aac(6’)-Ib family, implicated in 18 false negatives, and specifically the aac(6’)-Ib-cr5 allele (11/18). Some of these were detected as partial genes at the site of contig breaks, possibly related to slightly higher GC content (leading to lower sequence coverage) in these genes. The other major theme was difficulty resolving sequences with multiple alleles of the same gene family, which were often collapsed into a single gene detection by abritAMR or miscalled as a different allele. For example, this included a sequence with CTX-M-3, CTX-M-14 and CTX-M-65 identified by AMRFinderPlus, and called as CTX-M-3 and CTX-M-24 by abritAMR. Four out of the five ‘false positive’ detections were actually allele miscalls within the same gene family. Collapse of repeated regions or duplicate alleles is often a feature of short-read sequencing, hence the discrepancies here may be a feature of comparing (synthetic) short-read data to complete genomes, rather than a feature of abritAMR. Notably, use of alternative genome assembly tools (SKESA18 and SPAdes19) did not resolve the discrepancies, with similar performance to Shovill20 , especially the abritAMR pipeline was assessed to determine the minimum average sequencing depth for acceptable accuracy of AMR gene detection . Accuracy was found to be consistent (99.9%) across the 40X to 150X range, with 40X being the minimum coverage accepted by our accredited quality control (QC) pipeline. Repeatability and reproducibility (precision) were assessed (replicates within and across sequencing runs) and found to be 100% concordant.The limit of detection of the 21.Validation of inferred phenotype against phenotypic AST data demonstrated 98.9% accuracy (95% CI 98.7–99.1%), 98.9% sensitivity (98.4–99.3) and 98.9% specificity (98.7–99.1%) overall Table . Accuracn = 30/716 (4.2%) isolates). The AMR genes detected in phenotypically susceptible isolates were also detected in non-susceptible isolates, although the non-susceptible isolates more often had >1 AMR gene , suggesting that these AMR mechanisms had small but additive effects on phenotype. Evaluation of phenotype-genotype concordance for azithromycin identified five ‘false negatives’  and two ‘false positives’ ; one carried mef(B), an efflux pump with variable activity, and one carried ere(A), an esterase with lower affinity for azithromycin22).A number of ‘false positive’ results were identified for streptomycin , whilst isolates with ≥2 AMR genes were all phenotypically resistant. Despite these discordances, the best correlation between genotype and phenotype (S/I/R) was determined according to the number of AMR mechanisms detected (any type). This was coded into the reporting logic: absence of AMR mechanisms, ‘susceptible’, one AMR mechanism, ‘intermediate’, two or more AMR mechanisms, ‘resistant’. In this application, the use of an ‘intermediate’ category implies that MICs are likely to be borderline for these samples, i.e. may test susceptible or resistant on AST. Note that these results are only used for epidemiologic purposes (not for patient treatment), and hence over-calling resistance is more suitable for this purpose than non-detection of AMR mechanisms.abritAMR pipeline: (i) Detailed Report output, where AMR genes or mutations are shown by enhanced subclass, as classified by the abritAMR database, and (ii) Final AMR Gene Report output (binned into reportable and non-reportable genes) after reporting logic is applied  is also shown in Supplementary Data Two different outputs are used in the ied Fig. . An examabritAMR, and clients were educated about the change  before full implementation into routine workflows. Implementation led to streamlined workflows, including rapid bioinformatic processing of large sequencing runs (AMR gene detection for a 96-sample run completed in <3 min with 256 CPUs), and less manual re-classification of AMR gene results by laboratory scientists .After the validation process, implementation processes included modifying report outputs, integration with the existing LIMS, and documentation of the standard operating procedure (SOP). Multiple groups were consulted on the proposed report outputs, including reporting scientists with domain expertise (ensure results were easily interpretable and met reporting obligations across different pathogens), quality management staff , and end-users . Subsequently, all staff involved in detection, reporting or interpretation of AMR results were trained in the use and interpretation of 23. Detection of AMR from WGS data has somewhat lagged behind other applications of WGS, likely due its inherent complexity in comparison to simple and effective phenotypic AST4. This complexity is multi-faceted but includes the vast array of resistance mechanisms for testing (a single phenotype may be encoded by many different AMR mechanisms), and the limitations of phenotype-genotype correlation, particularly for less-common organisms and drug classes25. If not addressed systematically, these issues may render genomic AMR difficult to identify comprehensively across all pathogens seen in a CPHM laboratory, and difficult to communicate to clinicians and public health units26.The use of genomics in clinical and public health microbiology (CPHM) has increased substantially in the last decade, particularly in the fields of pathogen typing and outbreak investigations27. Here, we have designed and validated a bioinformatic platform for genomic detection of AMR determinants across bacterial species focusing on reporting requirements for clinical and public health microbiology, performed a rigorous validation, and implemented it to achieve an ISO-ccertified genomic workflow for AMR. This was achieved by adapting an existing software tool and database (AMRFinderPlus), and adding a modified classification step plus reporting logic to produce tailored reports for a CPHM audience.Globally, the paucity of highly accurate, reproducible bioinformatic tools for detection of AMR mechanisms has been recognised as one of the main limiting factors to wider application of genomics in the CPHM settingAMRFinderPlus, as well as the excellent software tool, which uses multiple search methods to best identify AMR genes and mutations 16. Notably, outputs for other large AMR databases, such as CARD28 and ResFinder29, could be modified to achieve similar tailored reports to abritAMR; our choice was based on the ease of integration into our existing workflows and reporting. abritAMR’s speed allows for rapid detection of AMR genes in routine high-throughput workflows, with AMR gene detection completed on a 96-sample sequencing run within 3 min. The addition of mutations to AMRFinderPlus for an increasing number of species has been very useful in our early applications, enabling our public health laboratory to move to a fully-genomic workflow for Salmonella surveillance, as all samples were sequenced for typing and phylogenetic analysis (a sub-sample of isolates still undergo AST to ensure new AMR mechanisms are detected). The capacity to include AMR genes or mutations of local significance would be a welcome addition to AMRFinderPlus, further extending its utility.This platform relies heavily on the comprehensive, well-curated and frequently updated AMR database behind AMRFinderPlus database, hence we created our own classification database, which has also evolved in parallel with the great advances made by the AMRFinderPlus team. There are (now) a small number of essential differences in the drug class classifications that we feel are important to enhance its utility for CPHM. Key examples include separating carbapenem resistance into different groups based on their mechanisms; separating into ‘carbapenemase’, ‘carbapenemase (MBL)’ and ‘carbapenemase (OXA-51 family)’ enables reporting each group separately, as antibiotic choices differ with metallo-beta-lactamases (MBLs) compared to non-MBL carbapenemases, and OXA-51 family carbapenemases are weak and intrinsic to Acinetobacter spp. and routine reporting is not required (coded as part of reporting logic). This combination of tailored classification and reporting logic allows the vast and complex array of AMR mechanisms to be distilled into results and reports that can be understood by scientists, clinicians and public health teams alike, without a great deal of prior knowledge. Future development will focus on restructuring the database to include different levels  to take advantage of the higher resolution of classifications now included in AMRFinderPlus.When this work commenced, there were no classifications of AMR mechanisms into drug classes in the AMRFinderPlus are also limitations of both ResFinder30 and CARD-RGI31 tools in different ways; ResFinder classifies AMR determinants into a small number of antimicrobial classes, but lacks the resolution needed for the more difficult classes described above (particularly beta-lactams), whilst CARD-RGI maintains an ontologic focus, where antimicrobial targets and mechanisms are identified at varying levels, but not grouped in a way that facilitates CPHM reporting and clinician understanding. However, both tools offer the accessibility of a graphical user interface (GUI) and the option of using raw reads as inputs for analysis, which are particularly important considerations for laboratories without dedicated bioinformatic expertise. Ideally, all large AMR databases and tools should facilitate clinically-relevant reporting of AMR determinants through ‘interpretation’ of outputs for clinical needs, and modifiable reporting logic to tailor outputs to reporting requirements, whilst maintaining a balance between accessibility and accuracy to enable validation and accreditation.Notably, the key limitations of 12. However, formal test validation and accreditation procedures are based on wet-lab assays, and not always easily transferable to new methods such as WGS32. This may require some creative thinking about different ways to validate a new genomic test33, as demonstrated here with the use of synthetic sequencing reads generated from complete reference genomes. Ideally, a broad range of publicly-available reference datasets with genotypic and phenotypic data would be made freely available to assist with validation and bench-marking for databases, tools and new pipelines such as this, greatly advancing the development of AMR genomics34. Initiatives such as the NCBI National Database of Antibiotic Resistant Organisms (NDARO)35 and PATRIC36 are promising, but currently limited in scale, and further global data sharing is required here to advance phenotype-genotype correlations. Here, we have contributed a dataset that may be used for validation of genomic detection of AMR determinants against PCR results, and a method for validating against synthetic genomic data, to assist other laboratories to validate their own AMR workflows.In a CPHM setting, it is critical to validate any new test or analytical process to ensure the veracity of results, and that the results (outputs or reports in this case) are fit-for-purposeabritAMR was highly accurate overall, a small proportion of discrepant results were identified, of which the majority were false negative results. Most of these discrepancies are likely due to the comparison of synthetic short-read data to complete genomes, where contig breaks within a gene result in non-detection, or plasmid dropout in culture, leading to non-identification of plasmid-borne AMR determinants in WGS data. In our validation, use of a different genome assembly tool had minimal impact, although it is critical to include this as a consideration in the validation process, particularly where discrepancies are present.As with all attempts to validate new WGS pipelines and workflows, our study has limitations. The absence of a ‘gold-standard’ dataset to compare results from our pipeline to means that we must compare to imperfect standards, such as existing testing methods with lower resolution (PCR) and use synthetic sequencing data to compare targets not covered by PCR in our laboratory. Until these issues are addressed globally, laboratories will have to persist with these challenging comparisons, and rely on new initiatives such as proficiency testing programs (PTPs) for WGS (with participation being a requirement for test accreditation in our setting) to start to standardise results across laboratories and countries. Whilst abritAMR pipeline will most likely be applied in CPHM settings, and hope that it may assist sequencing laboratories address the difficult question of how to best report these data to clinicians and public health teams with limited AMR knowledge. However, it may also have utility in other settings including research, particularly where complex AMR data need to be binned into functional classes to facilitate understanding when the user is less familiar with AMR. In our view, it is critical for medical microbiologists, scientists and bioinformaticians to continue to work together to navigate the challenges of communicating complex AMR data to clients, to advance the reach of genomic AMR and maximise the benefits of this potentially transformative technology.We envisage that the Enterobacterales (CPE), Acinetobacter spp., Pseudomonas spp., vancomycin-resistant enterococci (VRE) and enteric pathogens39. The laboratory has a strong emphasis on genomics, primarily for epidemiologic surveillance, with increasing applications for clinical purposes. In conjunction with the Department of Health Victoria, we have embarked upon a broad program to increase implementation of pathogen genomics for public health purposes, either enhancing or superseding current laboratory methods.The Microbiological Diagnostic Unit Public Health Laboratory (MDU PHL) is a state reference laboratory for bacterial pathogens, including carbapenemase-producing 42. Details of this workflow can be found in Supplementary Methods. Briefly, single colonies from overnight pure bacterial sub-cultures were selected and placed in lysis buffer. DNA extraction was performed on the QIAsymphony using the DSP Virus/Pathogen Mini Kit, and library preparation performed using Nextera XT (Illumina Inc.) according to manufacturer’s instructions. WGS was performed on NextSeq 500/550 or MiSeq platforms (Illumina Inc.), generating 150 bp or 300 bp paired-end reads respectively. Reads were assembled de novo using Shovill20. QC requirements for fastq reads to be included in subsequent analysis were (i) Q-score ≥30, (ii) data with a minimum estimated average genome coverage of >40X, and (iii) estimated genome size within range for observed species (see Supplementary Methods for detailed descriptions).Our existing genomics workflow , and quality control (QC) of reads including de novo genome assembly) has already been validated and accredited by the National Association of Testing Authorities Australia abritAMR bioinformatic platform takes a genome assembly from short-read data, long-read data or hybrid assemblies (fasta file) as input (once it has met defined QC parameters), and includes five main components  – abritAMR implements this tool to identify AMR genes in genome sequences, using a combination of BLASTx (matching the protein sequences of AMR genes to the protein sequence of the query isolate) and Hidden Markov Models (HMMs)16.NCBI’s ii.AMRFinderPlus database – abritAMR uses this frequently updated database (https://github.com/ncbi/amr/wiki/AMRFinderPlus-database), which is a comprehensive and extensively curated database of AMR gene sequences. Current functionality includes mainly AMR genes (‘core’ database), with point mutations (species-specific) and virulence genes increasingly being included in the ‘plus’ database. In more recent iterations, AMR genes and point mutations include information about the antimicrobial class and subclass  that they confer resistance to.NCBI’s iii.AMRFinderPlus database includes some information about the antibiotic class and subclass affected for each AMR gene, these classifications are not always easily translatable for clinical and public health practice. For example, the beta-lactam subclass ‘cephalosporin’ includes AMR genes conferring resistance to first-generation cephalosporins , or third-generation cephalosporins (such as blaCTX-M ESBLs), which have very different implications for AMR surveillance and patient management. The local abritAMR classification database is based on the current version of the AMRFinderPlus database, with an added field (‘Enhanced subclass’) to translate the NCBI subclasses into more functional versions for our purposes , and differentiating between AMR genes that are only reportable in certain species , while always reporting significant AMR genes that are not limited by species (e.g. carbapenemase and mcr genes). Non-reportable genes are also made available to the reporting pathologists and senior scientists and recorded in the laboratory information management system (LIMS), enabling detailed review of all detected AMR genes, correlation with phenotype, and movement between reportable and non-reportable categories when required as part of any routine results review process before reporting.Species-specific reporting logic  – Currently, most AMR genes detected by this pipeline are not required to be reported for surveillance or clinical purposes; reporting data on v.– The pattern of AMR genes and mutations detected can be used to infer phenotype for a given isolate. In abritAMR, this is currently validated for Salmonella spp., and reported for epidemiologic purposes in our laboratory, replacing routine antimicrobial susceptibility testing (AST) of Salmonella spp. for public health surveillance  The aims for development of this bioinformatic pipeline were to detect AMR genes and mutations accurately and reliably from bacterial whole genome sequencing (WGS) data, which could be validated against PCR and other data sources, implemented in a public health or clinical microbiology laboratory, and successfully accredited by governing bodies. The nts Fig. :i.NCBI’sabritAMR outputs include a Detailed Report output, consisting of a table  of AMR genes or mutations detected for each sample, listed by enhanced subclass (e.g. “Carbapenemase (MBL)”, “Colistin”), or a Final AMR Gene Reports, a table of AMR genes detected for each sample binned into ‘reportable’ or ‘not reportable’ fields, when the species-specific reporting logic is applied , abritAMR also produces an Inferred Antibiogram Report. All alleles listed in these outputs are either ‘exact matches’ (100% identity and 100% sequence coverage compared to the reference protein sequence) or ‘close matches’ , as defined by AMRFinderPlus. Partial matches  are listed separately, and must be examined further if deemed suitable for reporting. Where an internal stop codon (i.e. truncated gene) or HMM match are recorded by AMRFinderPlus, no result is reported by abriTAMR. Examples of abritAMR pipeline outputs are shown in Fig. AMRFinderPlus output is modified by abritAMR .ied Fig. . AdditioabritAMR, results from the pipeline were compared to results from PCR testing, Sanger sequencing and synthetic read sets as detailed below. For the purposes of validation, both ‘exact’ and ‘close’ matches were considered as ‘detected’. Pre-specified sensitivity and specificity thresholds were defined for successful validation prior to analysis.To validate All isolates used in validation were obtained as part of routine AMR surveillance under public health laboratory functions, and hence were exempt from requiring ethics approval. Data were de-identified for the validation study .n = 1020 isolates, AusDiagnostics 16-well CRE panel, catalogue no. 21098, version 03; Sydney, Australia), van gene PCR  and mecA PCR , that had previously been tested by PCR, including a carbapenemase and ESBL real-time multiplex PCR (This dataset included 347 isolates (20 species) with carbapenemase resistance genes detected by a range of carbapenemase and ESBL PCR assays across six different carbapenemase resistance gene families   AMRFinderPlus results from complete genome sequences. This allowed direct comparisons of presence or absence of AMR genes, therefore avoiding the problem of discrepancies in AMR gene nomenclature that may lead to false discordance if two different AMR gene databases were compared.For the remaining AMR gene targets where PCR was not readily available to compare with QC) Fig. . This daQC) Fig.  & 10. ababritAMR results from a test panel of 13 organisms  were generated at a range of sequencing coverages, from the minimum average coverage accepted for our routine QC (40 X) up to 150 X coverage.Salmonella spp. with antimicrobial susceptibility (AST) data generated by agar dilution from 2018-2019. For the fluoroquinolone drug class, the S/I/R phenotypes associated with combinations of AMR genes and mutations were analysed to determine the relative weighting of each AMR mechanism to infer a phenotype most reliably from in silico analysis.We validated phenotypic inference  against an existing dataset of 864 sequenced Discordant results were divided into two categories: firstly, PCR negative, WGS positive  - this may be due to the AMR gene detected by WGS not being included in the range of the PCR panel. If the gene was known to be included in the range of the PCR panel (as stated by the manufacturer), the isolate was retested by PCR and WGS to resolve this discrepancy. Secondly, PCR positive, WGS negative  – this may be due to an AMR gene being fragmented across two or more contigs, hence partial matches were assessed; if no partial matches were found, the sequence was interrogated using alternative tools; if this failed to resolve the discrepancy, the isolate was retested by PCR and WGS. Where possible, discrepancies between phenotypic and genotypic results were investigated through repeat phenotypic testing and/or repeat sequencing of the isolate.abritAMR pipeline must be re-verified after each database or tool update. Database updates are reverified by confirming that the updated database performs to the same criteria as was defined in the original validation, using the synthetic dataset described above (‘abritAMR test suite’). Updates to the abritAMR software may take the form of minor patches or major updates. Minor patches are changes that do not impact underlying structure or core logic of the pipeline, such as fixes for typographical errors or addition of functionality which does not impact the core logic of the tool, e.g. changes to log outputs. In these cases, a full reverification is deemed unnecessary and running of the abritAMR test suite is sufficient. However, other changes which may impact the core logic or structure of the outputs require a complete reverification as described for database updates. Any change in performance is assessed, the cause identified, and modifications made before the changes are implemented for reporting. All changes to abritAMR are tracked in GitHub and the versions managed using conda.In accordance with ISO standards, the Test performance characteristics  were calculated using the epiR package for R (version 4.1.1), used in RStudio (version 1.4.1717).Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesDataset 1Dataset 2Reporting Summary