Sexual and gender minority (SGM) populations are particularly vulnerable to poor COVID-19 outcomes and are more likely to experience stigma and medical mistrust that may impact COVID-19 vaccine acceptance. This study examined the prevalence of COVID testing and diagnosis and assessed COVID-19 vaccine acceptance among a large sample of SGM. Participants were recruited as part of an online cross-sectional study focused on an HIV biomedical prevention technology willingness in the United States at increased risk for HIV sero-conversion. Multivariate linear analysis was conducted to examine COVID-19 vaccine acceptance. The study sample included 1350 predominately gay (61.6%), Black (57.9%), cis-gender (95.7%) males with a mean age of 32.9 years. Medical mistrust and social concern regarding COVID-19 vaccine stigma were significantly associated with decreased COVID-19 vaccine acceptance, and altruism was significantly associated with increased vaccine acceptance. Black participants were significantly less likely to accept a COVID-19 vaccine, and Asian participants were significantly more likely to accept a vaccine, compared to White peers. As the planning of COVID-19 vaccine rollout efforts is conceptualized and designed, these data may inform equitable implementation strategies and prevent worsening health inequities among SGM populations. Equitable implementation of COVID-19 vaccine delivery is a national and global priority. The Centers for Disease Control and Prevention Advisory Committee on Immunization Practice highlighted allocation strategies that “aim to reduce existing disparities and to not create new disparities” . AttitudThe psychosocial and economic impact of the COVID-19 pandemic disproportionately affects SGM compared to cisgender heterosexual populations. Studies across international settings have demonstrated that SGM communities have experienced increased depression and anxiety as a result of social distancing measures and worrying about health status related to COVID-19 ,11. StudImproved understanding of COVID-19 vaccine acceptance among SGM can inform equitable implementation of vaccine delivery strategies. In a study of HIV vaccine trial acceptance, for example, Connochie et al. demonstrated that greater vaccine efficacy beliefs and altruistic attitudes were associated with increased vaccine acceptance while social concerns  decreased vaccine acceptance ,21. MediIn a recent national survey representative of the United States population, only 53.6% of people planned to get the COVID-19 vaccine . UnderstAs part of an online cross-sectional study focused on a HIV biomedical prevention technology willingness in the United States, we included a series of COVID-19 vaccine acceptance and medical trust questions in our study screener. These questions were the focus of our analysis, and included data collected between October 19 and December 16, 2020 . Participants were primarily recruited through advertisements on social and sexual networking sites. There were no set regional or state geographic quotas for the sample.Upon clicking on the study advertisement, participants were referred to a Qualtrics survey where they were asked to consent and complete a study screener. Interested individuals completed a 10-min screener including questions regarding their sociodemographic characteristics, HIV and COVID testing behaviors, medical mistrust, and COVID-19 vaccine acceptance. Participants were not compensated for screening in the study. Study data were protected with a 256-bit SSL encryption and kept within a university firewalled server. The study was approved by the University of Pennsylvania Institutional Review Board, approval ID #843161.COVID-19 Testing and Diagnosis. Participants were asked whether they had ever been tested for COVID-19  and whether they had been told by a health care provider that they had COVID-19 .COVID-19 Vaccine Acceptance. We assessed hypothetical efficacy based on scientific discussions prior to the results of the Pfizer-BioNTech and Moderna vaccines being released to the public and reviewed by the FDA. Specifically, participants were asked to rate how likely they would be to get a COVID-19 vaccine if it provided 85% protection. Participants used a 10-point scale  to answer these questions. Attitudes towards COVID-19 vaccines. We assessed attitudes towards COVID-19 vaccines with two subscales previously developed by Lee et al. [e et al. . These sMedical Mistrust. Using previously validated questions adapted from the Medical Mistrust Index [st Index ,27. We aIn addition to mistrust beliefs, participants were asked to note how often they had felt that they had been mistreated or felt ignored by health care providers, or whether they had felt that their health care was not as good as others. Participants were asked to answer these three statements using a four-point scale . We computed a mean composite score (α = 0.75) where higher scores meant greater frequency of negative experiences with health care providers in the past.Sociodemographic characteristics. Participants were asked to report their age (in years), sex assigned at birth  and current gender identity . For analytical purposes, gender identity  was dichotomized due to small numbers within some of the gender identity categories in our regression analyses. Participants also self-identified across their races  and Hispanic/Latinx ethnicity. Participants disclosed their sexual orientation .Descriptive statistics were conducted on demographics and variables of interest . A multiAs noted in p < 0.001; see p < 0.001) and medical mistrust . Vaccine acceptance was positively associated with altruistic motivations . In race comparisons, White participants were more willing to use a COVID-19 vaccine than Black, American Indian/Alaskan Native, and participants identifying with another race . In our multivariable regression  = 60.11, race see . Asian pSexual and gender minority (SGM) populations are disproportionately vulnerable to poor COVID-19 outcomes, yet little is known about COVID-19 vaccine acceptance among SGM. To address this gap, we examined COVID-19 vaccine acceptance among SGM. As hypothesized, increased medical mistrust and social concerns were significantly associated with lower rates of vaccine acceptance, and altruism was associated with higher rates of vaccine acceptance. Our results highlight the impact of medical mistrust and attitudes toward COVID-19 vaccines on vaccine acceptance at the intersection of gender, sexual, and racial minority identities, and can inform equitable vaccine implementation strategies.SGM who experienced medical mistrust were less likely to accept a COVID-19 vaccine. While negative experiences in healthcare were not associated with vaccine acceptance, medical mistrust may also stem from a long history of stigma and discrimination among SGM ,23. MediSGM participants in our study who identified as Black reported decreased COVID-19 vaccine acceptance. This finding is supported by prior studies ,5,6,30. Our study has limitations that deserve to be mentioned. First, the study sample was an online-recruited national convenience sample of SGM interested in being screened for a HIV prevention study. While this approach allowed for an analysis of a large sample size, it may be preemptive to extrapolate the current findings to the larger population of SGM in the United States. Second, while the sample includes SGM from across the United States, there was no specific regional geographic quota set for the sample, limiting generalizability of our findings to the entire United States. Third, vaccine acceptance was assessed before results of the two FDA-approved COVID-19 vaccines were released to the public. As the public becomes more aware of the efficacy and safety of the COVID-19 vaccine, we may see shifts in SGM vaccine acceptance and intention to use it. Moreover, acceptance to adopt a COVID-19 vaccine may differ based on the vaccine regimen , user characteristics and contexts, and treatment-related costs . We encourage future research to apply sociobehavioral perspectives to examine how these vaccine-related considerations affect COVID-19 vaccine adoption. Fourth, our study sample included few gender minority individuals (n = 58), who may be particularly vulnerable to pandemic harms, and our results may not be generalizable to larger gender minority populations . Lastly,Our analyses did not include measures of socioeconomic status, which can contribute to differences in COVID-19 vaccine acceptance, or other indicators of social vulnerability associated with increased risk of COVID-19 incidence and mortality ,6,33,43.Our study suggests that medical mistrust, social concern, altruism, and race were significantly associated with COVID-19 vaccine acceptance among a national online sample of SGM. COVID-19 vaccine uptake is essential to stopping the spread of COVID-19 and ending the COVID-19 pandemic. As the planning and implementation of COVID-19 vaccine rollout efforts are designed, administrators must address challenges and opportunities related to SGM vaccine acceptance.


Detained asylum seekers are at increased risk of self-harm, and the type of detention in which they are held may further exacerbate this risk. In Australia, there are four types of closed (or held) immigration detention for people seeking asylum, with varying levels of security and supports: Immigration Detention Centres [IDCs], Immigration Transit Accommodation [ITAs], Immigration Residential Housing [IRH], and Alternative Places of Detention [APODs]. The objective of this study was to examine the variation in the incidence and method(s) of self-harm among asylum seekers in Australian onshore immigration detention, according to the type of detention in which they are held.We obtained data on all self-harm incidents reported among asylum seekers in Australian onshore immigration detention according to held detention type, as well as individual facility, between 1 August 2014 and 31 July 2015, by Freedom of Information. We calculated self-harm episode rates per 1000 asylum seekers using the average population figures for held detention type, as well as for each individual facility comprising the main types of held detention. Method(s) used to self-harm was also extracted for the main sub-populations.The study included 560 episodes of self-harm. Individual facility rates of self-harm ranged from 91 per 1000 asylum seekers (95% CI 72–110) in Yongah Hill IDC to 533 per 1000 asylum seekers (95% CI 487–578) in Perth IDC. On average, calculated self-harm episode rates were highest among asylum seekers in: Immigration Transit Accommodation facilities, 452/1000 (95% CI 410–493); Alternative Places of Detention, 265/1000 (95% CI 233–296); and Immigration Detention Centres, 225/1000 (95% CI 195–254). The most frequently reported methods of self-harm across the main types of held detention were: cutting (35.2%), self-battery (34.8%), and attempted hanging (11.1%).Self-harm rates for asylum seekers in all types of closed immigration detention were many times higher than rates found in the general population. Average rates were not lower in facilities with lower security features. In response to recent surges in migration, many countries around the world apply various forms of administrative detention to asylum seekers who arrive on their territories . In AustThere are four main types of held immigration detention  in the Australian onshore detention network (which includes Christmas Island): (a) Immigration Detention Centres (IDCs); (b) Immigration Transit Accommodation (ITAs); (c) Immigration Residential Housing (IRH), and; (d) Alternative Places of Detention (APODs) . IDCs haIt is now well established that immigration detention has a deleterious impact on the psychological well-being of many asylum seekers –8, and tThe findings from our earlier study convey important information about the elevated risk of self-harm among asylum seekers in the onshore immigration detention population as a whole, in comparison with both asylum seekers in community-based arrangements and community detention , as wellWe obtained data on all self-harm incidents reported among asylum seekers in Australian onshore immigration detention between 1 August 2014 and 31 July 2015, by Freedom of Information [FOI] . These dWe undertook a content analysis study of all self-harm incidents, with self-harm defined as all forms of intentional self-injury (or self-poisoning), irrespective of suicidal intent or motivation , 19. ParWe calculated the episode rate of self-harm per 1000 detained asylum seekers for each held detention type, using the average adult population figures   by males, 90 16.0%) by females, and 239 episodes (42.6%) where gender could not be determined)  according to held detention type were ITAs: 452 episodes/1000 asylum seekers (95% CI 410–493); APODs : 265 episodes/1000 asylum seekers (95% CI 233–296); and IDCs: 225 episodes/1000 asylum seekers (95% CI 195–254). In individual facilities, the episode rate was highest in: Perth IDC, 533 episodes/1000 asylum seekers (95% CI 487–578); and lowest in Yongah Hill, 91 episodes/1000 asylum seekers (5% CI 72–110) (see Table Information regarding method(s) used to self-harm was extracted from 468 83.5%) of the 560 episodes of self-harm among asylum seekers in the Australian onshore immigration detention population. Of the nine different types of methods identified, the three most frequently reported were: cutting (35.2%), self-battery (34.8%), and attempted hanging (11.1%) (Table .5% of thThe site of bodily injury was determined in 346 (74.0%) of the 468 self-harm episodes among asylum seekers where method was known. The most common site of injury across all held detention types in Australian onshore immigration detention was the head , followed by the wrist and arm , and the neck   to 533 episodes/1000 asylum seekers (Perth IDC). On average, self-harm episode rates were highest among asylum seekers in ITAs (452 episodes/1000 asylum seekers), followed by APODs (265 episodes/1000 asylum seekers), and IDCs (225 episodes/1000 asylum seekers). Across all three types of held detention, cutting, self-battery, and attempted hanging were the three most frequently reported methods used to self-harm.To our knowledge, this is the first examination of the variation in the incidence and method(s) of self-harm among asylum seekers in the Australian onshore immigration detention population by held detention type, as well as for each individual facility comprising each type. Between 1 August 2014 and 31 July 2015, there were 560 episodes of self-harm among asylum seekers in Australian onshore immigration detentionThe episode rates of self-harm we have calculated for all three types of held immigration detention are considerably higher than the episode rates of hospital-treated self-harm – 1.2 per 1000 – in the general Australian community during a similar period . CalculaWhilst self-harm episode rates for asylum seekers across all three main types of held immigration detention were markedly higher than hospital-treated rates of self-harm in the general Australian population, interestingly, average self-harm rates were not lower in types of detention with lower security arrangements. Indeed, it might be expected that lower security facilities, with less stringent security features, and more flexible accommodation, for families or other vulnerable individuals, might be associated with a lowered risk of self-harm in asylum seekers. However, this was not the pattern observed in the present study: on average, self-harm episode rates were highest in ITAs (452 episodes/1000 asylum seekers), followed by APODs (265 episodes/1000 asylum seekers), the two types of detention with lower security, and purportedly more flexible accommodation arrangements. These findings expand upon earlier understandings of self-harm risk among detained asylum seekers , includiWhilst ITAs were originally designed as low security short-term detention facilities for people who arrived by air, but who were not immigration cleared , they wePrior research has also documented that the practice of transferring asylum seekers between or within facilities, without adequate time to inform family, friends or legal representatives, often resulting in the separation of family members, causes significant distress , and mayIn short, there are a range of distinct features associated with ITAs – diverse, mixed populations, detained for variable amounts of time, and for a range of reasons, and with frequent separations from family for unknown amounts of time. As these are features of detention environments that have previously been established to cause distress, and precipitate self-harm in detained populations, they are likely to also have influenced the high rates of self-harm found among those detained in ITAs in the current study. It is also conceivable that the lower levels of security in such facilities also provided individuals with greater opportunity to self-harm .APODs are places designated by the DIBP to be used for detaining asylum seekers that are less secure than other types of held detention, and purportedly have a more domestic environment , to allow more privacy, and the opportunity to do things together . ReportsFurthermore, as canvassed at length in the 2014 Australian Human Rights Commission [AHRC] National Inquiry into Children in Immigration Detention , closed The use of hotels, motels, and other temporary forms of accommodation as places of long-term detention for adults, as well as families with children, without adequate access to family, legal and social supports or mental or physical health treatment is another characteristic of APODs known to detrimentally impact on the mental health of those detained under such arrangements . The praWhilst average self-harm episode rates in IDCs (225 episodes/1000 asylum seekers) were lower than rates identified among asylum seekers in ITAs and APODs, both the lowest and highest self-harm episode rates were identified as occurring in IDC facilities, highlighting a large intra type range. The lower overall self-harm episode rates identified among asylum seekers in IDCs in the present study, compared with rates found in asylum seekers in ITAs, could relate to the fact that closed, secure environments, with more surveillance and staffing levels, mean that self-harm can be more closely monitored and controlled. More specifically, the stricter monitoring and surveillance of asylum seekers in IDCs may have provided fewer opportunities for self-harm attempts or threats to go unnoticed by staff. In addition to this, repeat occurrences of self-harm may be more easily ‘prevented’ in these more secure facilities, by using self-harm management practices, where security guards are placed ‘at arms-length’ of an asylum seeker for long periods following an episode of self-harm, to prevent another episode from occurring . Such prIndividual facility self-harm episode rates ranged from 91 per 1000 detained asylum seekers in Yongah Hill IDC to 533 per 1000 in Perth IDC, highlighting both a large inter- and intra-facility range. Perth IDC – the individual facility with the highest self-harm rates – is primarily used to house asylum seekers who were transferred to Perth for medical treatment . This inIt is important to note that the lowest individual facility self-harm episode rate identified in the present study among asylum seekers held in Yongah Hill IDC – 91 per 1000 detained asylum seekers – was calculated as 75 times the Australian community rate of hospital treated self-harm during the same period . These fThe three most frequently reported methods of self-harm across the three main types of closed immigration detention were cutting, self-battery and hanging. Similarly, the three most common sites of bodily injury were consistent across the three types of closed detention: head, wrist/arm, and neck. The current findings are consistent with previous research which has documented high rates of hanging among asylum seekers detained in onshore immigration detention , as wellOur study had a number of important strengths. Firstly, these are the first published data examining self-harm among asylum seekers in Australian onshore immigration detention, by held detention type, as well as individual facility. Secondly, we had access – under Freedom of Information laws – to all self-harm incidents from across the entire Australian onshore detention population, as reported to, and archived by, the  DIBP. Thirdly, our sample was large, and permitted us to examine not only the incidence of self-harm by detention type, and individual facility, but also the characteristics of self-harm, including method(s) used to self-harm, as well as the site of bodily injury – the first published data of this kind.Our study also had several limitations. The number of unrecorded self-harm episodes among asylum seekers in Australian onshore immigration detention is unknown. It is likely, therefore, that the rates reported here understate the incidence of self-harm among asylum seekers detained in Australia. Details regarding age or country of origin, including by each detention type, and at the individual facility level, were not able to be ascertained from the incident reports. Furthermore, gender was only able to be determined in just over half of all cases. This meant that we were not able to reliably calculate rates of self-harm by held detention type or individual facility, according to gender. Lastly, as the population figures in Perth IDC were very low, it is possible that a few frequently self-harming individuals could have increased the self-harm rate identified in this facility.Our findings illustrate the far-reaching detrimental impact that Australian onshore immigration detention has on the mental health of asylum seekers, specifically in relation to self-harm. They also highlight the particular influence that different types of held immigration detention, as well as individual facilities – and their associated features, practices and policies – may have on the health of detained asylum seekers. In conclusion, the independent surveillance and monitoring of self-harm among asylum seekers detained in Australian immigration detention – the first step in any national self-harm prevention strategy  – must b


We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at  In some contexts, rapid detection of COVID-19 from CT scans can be crucial for optimal patient management. Here, the authors present a Deep Learning system for this task with multi-center data, human reader comparison and age stratified results. It is important to detect COVID-19 as quickly and accurately as possible for controlling the spread of the disease and treating patients. Even though reverse transcription-polymerase chain reaction (RT-PCR) is still ground truth of COVID-19 diagnosis, the sensitivity of RT-PCR is not high enough for low viral load present in test specimens or laboratory error2. In addition, the supply of kits of RT-PCR varies from place to place and some developing countries are in short supply of it3.The SARS-CoV-2  as a first-line investigation and patient management tools7 and modern GPUs, AI, especially deep learning technique8, has achieved excellent performance in several computer vision tasks, such as image classification9 and object detection10. Recent research shows that AI algorithms can even achieve or exceed the performance of human experts in certain medical image diagnosis tasks, including lung diseases17. Comparing to other lung diseases, such as lung nodule detection20, tuberculosis diagnosis21, and lung cancer screening15, differentiating COVID-19 from other pneumonias has unique difficulty, i.e., high similarity of pneumonias of different types  and large variations in different stages of the same type. Hence, developing AI diagnosis algorithm specific to COVID-19 is necessary. The AI diagnosis algorithm also has the advantages of high efficiency, high repeatability, and easy large-scale deployment.Artificial intelligence (AI) may be the unique preparation to take up this challenge. Powered by large labeled datasets23. Here, we briefly review several representative studies employing relatively large datasets. Zhang et al.24 developed COVID-19 diagnosis system on a database consisting 4154 patients, and it can differentiate COVID-19 from other common pneumonias and normal healthy with AUC of 0.9797. In their system, the classification was based on lesion segmentation result, and the lesion segmentation DICE index was ~0.662, which is not an accurate representation of lesions. Another drawback is manual annotation of training segmentation masks is a very expensive procedure. Li et al.25 developed an AI system and yielded AUC of 0.96 for COVID-19 detection on dataset consisting of 3322 subjects, including COVID-19, CAP, and healthy people. Their system extracted features on slices and fused them into volume level, which increased much memory demand, while without extracting more informative 3D features. Several slice-level diagnosis methods27 were proposed which were quite similar to Li et al.’s work. Some AI systems employed 3D convolution neural networks, but only considered the relatively simple two-category classification29. There are also a few COVID-19 detection systems using CXR30, but the number of subjects with COVID-19 in these studies is much smaller than that in the studies using CT, and no study has quantitively compared performances of CXR and CT using paired data.There are already some published studies on CT-based COVID-19 diagnosis systemsIn this work, we construct a clinically representative large-scale dataset with 11,356 CT scans from three centers in China and four publicly available databases, which is much larger than previous studies. To understand relative performances of CT and CXR for detecting COVID-19, we develop both CT-based and CXR-based diagnosis systems, and test them using paired data, which has not been studied before. We compare the diagnostic performance of our CT-based diagnosis system with that of five radiologists in reader studies, and the results show that the performance of this system is higher than that of experienced radiologists. In addition, based on prediction score on every slice of CT volume, we locate the lesion areas in COVID-19 patients and perform a statistical study of different subsets of patients. The specific phenotypic basis of the diagnosis output is also traced by an interpretation network, and radiomics analysis is applied to understand the imaging characteristics of COVID-19.31, Tianchi-Alibaba32, MosMedData33, and CC-CCII24 .We proposed a deep learning-based AI system for COVID-19 diagnosis, which directly takes CT data as input, to perform lung segmentation, COVID-19 diagnosis, and COVID-infectious slices locating. In addition, we hope that the diagnosis results of AI system can be quantitatively explained in the original image to alleviate the drawback of deep neural networks as a black box. The system consists of five key parts Fig. , (1) lunThe workflow of deep learning-based diagnosis model is shown in Fig. The trained AI system was evaluated on the test cohort. We used the receiver operating characteristic (ROC) curves to evaluate the diagnostic accuracy. On the test cohort, the ROC curve Fig.  showed ACOVID-infectious slice locating results are shown in Fig. We conducted a reader study with five board-certified radiologists . The separate tasks helped us to analyze the COVID-19 distinguishing ability with different negative classes. Compared with human radiologists, the ROC curves in Fig. Unlike Zhang et al.Figure For an in-depth understanding of the AI system and characteristics of different populations with COVID-19, we evaluated the AI system on subsets of test cohort divided by gender, age, and stage and show them in Fig. A subset of the patients in the database have multistage CTs. We compared the diagnostic performance of stage I and stage II and fusion of them. The experiment suggested that the performance of the AI system was independent of the progress of the disease because there were no significant qualitative differences between performances of different stages. We did not test more complex fusion methods which may overestimate the performance, since most non-pneumonia and CAP subjects have only one stage CT.p < 0.001). Representative examples are given in Fig. A subset of COVID-19 and CAP subjects in the database have localizer scans along with CT scans. The localizer scans of CTs are very similar to CXR, but typically noisier. We used this subset to study performances of CT vs. CXR. We developed a CNN-based classification algorithm to discriminate COVID-19 from CAP using these localizer scans (described in “Methods” and Supplementary Methods). Experiments on subjects with both types of data showed that CT-based system performed significantly better ane Fig. , and the36 feature extraction on these attentional regions, and obtained a total of 665-dimensional imaging features. The Least Absolute Shrinkage and Selection Algorithm (LASSO) were used to find the most discriminative 12 features in distinguishing COVID-19 from other pneumonia. Three additional features were also extracted for the attentional regions, distance feature, 2D margin fractal dimension, and 3D grayscale mesh fractal dimension (“Phenotype feature extraction” in “Methods”). The statistics of these features were consistent to previous literature37 on the pathogenesis and morphology of COVID-19. The selected features were used to explain the imaging characteristics in CT , and t-test and Kolmogorov–Smirnov (KS) test were performed to statistically analyze those features  of subjects Table , indicat38 that women have higher antiviral immunity than men, so that it was harder for AI to find out diagnostic clues in CTs of women. The results of different stages showed that the performance of the AI system had little correlation with the stages of CT scans. Fusion of stages can slightly improve the performances.To further understand the performance of the AI system, we evaluated it on subsets divided by gender, age, and stage Fig. , which cCXR is also considered as a possible way to diagnose COVID-19. According to Figs. T-SNE clearly showed that deep features provided by our AI system can divide different types of pneumonias into different clusters, as shown in Fig. t-SNE was a conjecture for extracting features from the network, we can clearly find that patients of COVID-19 may be divided into different subclasses.According to Grad-CAM, we found that the AI system focused on different regions depending on the types of pneumonias. For CAPs, it generally ignored GGO which might also occur in COVID-19, and focused on effusion and consolidation adjacent to the pleura. On the other hand, the AI system focused on GGO rather than consolidation for most COVID-19 subjects. For influenza and COVID-19, Grad-CAM displays similar concerns, such as stripe consolidation and GGO, but the AI system is still able to distinguish the two correctly. We speculated that the AI system will focus on specific regions where other types of pneumonias may be rare. 39, and we can make pathophysiological and anatomical speculations on the viral infection process .Further, we provided a visual interpretation of the system’s decision by performing a radiomics analysis to obtain diagnostically relevant phenotypic characteristics of the attentional regions that are mapped to the original CT image. By visualizing the diagnostic results and the phenotype analysis, we found that the spatial distribution of the attentional region, morphology, and the texture within it are consistent with the characteristics of COVID-19 as reported in previous manual diagnosis studiesThere are still some drawbacks and future works of this research. Firstly, collecting more data on more subtypes of pneumonias or other lung deceases is useful for exploring AI system with higher diagnosis capability. Secondly, Guided Grad-CAM can only extract attention region rather than lesion segmentation, while phenotype feature analysis would better be done on accurate segmentation. Finally, constructing a large dataset with linked CT and clinical information, especially with information of underlying diseases, will enable additional analysis of the diagnosis system and development of more functionality, such as decease severity evaluation.Overall, our AI system has been comprehensively validated on large multi-class datasets with higher diagnosis performance than human experts in diagnosing COVID-19. Unlike classical black box deep learning approaches, by visualizing the AI system and applying radiomics analysis, it can decode effective representation of COVID-19 on CT imaging, and potentially lead to the discovery of new biomarkers. Radiologists could perform an individualized diagnosis of COVID-19 with the AI system, adding new driving force for fighting the global spread of outbreak.We retrospectively collected data from three centers in Wuhan, which are Wuhan Union Hospital, Western Campus of Wuhan Union Hospital, and Jianghan Mobile Cabin Hospital. The study has been approved by the institution review board of Wuhan Union Hospital, which is in charge of three centers. As a retrospective study, the need for informed consent was waived by the institutional review board. In total, 4260 CT scans  from 3177 subjects  were collected from multicenters  were assigned to training cohort which contained 1230 non-pneumonia, 666 CAP, 41 influenza-A/B, and 751 COVID-19 subjects. In this cohort, 198 CAP subjects (198 scans) and 468 COVID-19 subjects (725 scans) had localizer scans, which were considered as paired CXR and the CXRs were used to train CXR diagnosis network.Test cohort: 2688 subjects (3199 scans) were assigned to test cohort, which contained 1229 non-pneumonia subjects, 668 CAP, 42 influenza-A/B, and 751 COVID-19 subjects. In this cohort, 220 CAP subjects (220 scans) and 469 COVID-19 subjects (802 scans) had localizer scans, which were considered as paired CXR. This part of data were used to evaluate CT performance compared with CXR.CC-CCII test cohort: due to different category definition of CC-CCII database, we evaluated our system on it as an external independent test cohort. All its 2539 subjects (3784 scans) were used only in the test, we took the “common pneumonia” of CC-CCII as CAP together with influenza of our system in experiments. Since CC-CCII database shares only processed image slices, from which the original CT values cannot be obtained, and the definition of categories is different, the diagnosis performance could be a little lower.MosMedData test cohort: in this database, COVID-19 cases were separated into four categories: mild, moderate, severe, and critical. Since our AI system was trained to perform COVID-19 detection, all four categories were labeled as COVID-19. As a result, there are 254 non-pneumonia and 856 COVID-19 cases in this cohort.All above data (except CC-CCII and MosMedData) were randomly divided into training and test cohorts, with no overlapping subjects. The ratio of division is roughly 1:1. As a result, the cohort division is:40, which is a 2D semantic segmentation network. All CTs are in 3D, so we trained and tested the segmentation model slice by slice. The training slices were extracted from chest CTs in the training cohort and annotations of lung segmentation were obtained manually. Lung segmentations worked as masks and region boxes in diagnosis module, which were merged with raw images into a multichannel image as the input of classification network (described in Supplementary Methods).The lung segmentation module is implemented based on U-Net41). The parameters of ResNet152 were pretrained on a huge dataset ImageNet9 for better and faster convergence. We tested a 3D classification network, but this 2D scheme showed much better performance and 3D network might not work if GPU memory is limited (even 11 G memory might not be able to process one volume of high resolution). In order to eliminate the influence of different scanners and factors on the diagnosis, the inputs of slice diagnosis module were lung-masked slices, which had been cropped out along their lung bounding boxes, and segmentation mask. The outputs of the classification network were four scores, respectively, representing confidence levels of being four categories (three categories for CC-CCII database). Slices for training this network were extracted from training scans, and the extraction process is explained in Supplementary Methods.The slice diagnosis module is a 2D classification deep network whose backbone is ResNet152  in all slices and the non-pneumonia score is obtained by averaging all slices. More details of the hyper-parameter choice are stated in Supplementary Methods. If the task is specific to distinguish non-pneumonia and pneumonia subjects, all pneumonia scores of a slice will be summed up. If the task is specific to distinguish COVID-19 from other pneumonia, the scores of other classes will be muted in the fusion block. To measure the diagnostic performance, AUC, sensitivity, and specificity (with default threshold 0.5) are computed on test cohort.A task-specific fusion block is used to get a volume/case-level prediction from slice-level results. Because one volume is regarded as pneumonia infected if any one of its slices is diagnosed as infected, the fused scores of three pneumonia classes  were obtained by averaging the top-COVID-infectious slice locating module has the same structure as the slice diagnosis module, but it was trained and evaluated only on a set of COVID-19-positive subjects whose slices with lesions have been marked manually. All training samples of this module came from training cohort, and evaluation samples came from test cohort.We used Guided Grad-CAM to obtain attentional regions as interpreting block of our system. Guided Grad-CAM has the advantage that it not only generates a heat map to locate the relevant area, but also produces a coarse localization map highlighting the important regions in the image for predicting the result. Guided Grad-CAM may also give more detailed diagnosis suggestions in addition to classification results. Also, the attentional regions were used in latter feature extraction and analysis to get more detailed information about lesion areas. We extracted region of attention by binarizing the output of Guided Grad-CAM followed by some morphological operations.T-SNE was done based on features from slice diagnosis module, which are obtained by max-pooling feature maps of all slices before the last fully connected layer. Therefore, every CT volume was mapped to a 2048-D latent feature vector for t-SNE analysis.42. T-SNE was implemented using scikit-learn (version 0.22) package. Numpy (version 1.15.3) and scipy (version 1.3.3) were also used in analysis.All the deep learning blocks were implemented using PyTorch (version 1.3.1)Pneumonia-or-non-pneumonia cohort: 100 subjects  were assigned. This cohort was used to compare results of the AI system with radiologists in diagnosis of pneumonia.CAP-or-COVID-19 cohort: 100 subjects (50 CAP subjects and 50 COVID-19 subjects from three centers) were assigned. This cohort was used to compare results of the AI system with radiologists in distinguishing COVID-19 from CAP.Influenza-or-COVID-19 cohort: 50 subjects (20 influenza-A/B subjects and 30 COVID-19 subjects from three centers) were assigned. This cohort was used to compare results of the AI system with radiologists in distinguishing COVID-19 from influenza-A/B.In order to analyze the performance, we set up three different reader study cohorts for different diagnosis tasks, which were acquired by randomly selecting subjects from test cohort:We invited five experienced radiologists in our experiments from the radiology department of Wuhan Union Hospital, which is in the center of the epidemic area with the most patients in this outbreak in China. They all have read over 400 CTs of COVID-19 in the past 3 months. Five radiologists had an average of 8 years of clinical experience in the imaging diagnosis of pulmonary diseases, as detailed in Table The human radiologists were aware of the tasks and the possible classes in reader cohort when reading. For example, they were informed that only CAP and COVID-19 subjects were collected in CAP-or-COVID cohort. Besides, readers can choose any window of gray value, and zoom in or out when reading CT volumes using Slicer 4.10.2 software while our system used fixed size resample images (224 × 224 × 35) with fixed gray value window  for all volumes.To analyze the differences of imaging phenotype between different pneumonia, features were extracted in the attentional regions determined by binarizing Grad-CAM maps followed by morphological processing. We only kept the regions with valid size (>200 pixels within margin of lung mask). We extracted attentional regions only in pneumonia subjects in test cohort.36.Radiomics features widely used in tumor diagnosis were extracted. These features were composed of different image transforms and feature matrix calculations. We adopted three image transforms: original image, transformed image by Laplacian of Gaussian operator, and transformed image by wavelet. For each image after the operation of a transform, six series of features are extracted, including first order features, Gray Level Co-occurrence Matrix, Gray Level Size Zone Matrix, Gray Level Run Length Matrix, Neighboring Gray Tone Difference Matrix, and Gray Level Dependence Matrix. Radiomics analysis was performed using python version 3.6 and the pyradiomics (version 2.2.0) packageThe distance feature was defined as the distance between the center of gravity of the region of interest (obtained by Grad-CAM) and the edge of the lung (obtained by lung segmentation results). Besides, 2D contour fractal dimension and 3D grayscale mesh fractal dimension of the attentional region were extracted. The fractal dimension describes the degree of curvature of a curve or surface.LASSO logistic regression model was used to choose most discriminative features in all extracted ones. LASSO analysis was performed using python version 3.6 and the scikit-learn package.44. These areas have well-established immune system and well-developed pulmonary lobules, leading to a strong inflammatory response46. Secondly, because region of attention obtained by Grad-CAM could not delineate the lesion accurately, there were little differences among three types of pneumonia in fractal dimension  Vascular thickening: it was consistent with the rules of inflammation production. The inflammatory increased vascular permeability, caused telangiectasia, further caused pulmonary artery thickening48. (IV) The fine mesh feature of large area: the COVID-19 mainly invaded the interstitium in the lobules, so it appeared as confluent fine mesh (crazy paving). (V) The density-increased GGO. This kind of GGO was transforming to consolidation. The consolidation edges were flat or contracted, and fiber strands appeared. Compared with COVID-19, influenza showed higher density on the lesion area, which can be inferred that they caused slightly less alveolitis and GGO patterns. As a result of bronchiolitis, influenza virus pneumonia was more likely to form tree-bud signs, and occasionally hyperlucent lung.According to all selected features, we can describe in depth the relationship between the medical findings and typical patterns of COVID-19 (some examples are shown in Supplementary Fig. Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


Unplanned hospitalization following ureteroscopy (URS) for urinary stone disease is associated with patient morbidity and increased healthcare costs. To this effect, AUA guidelines recommend at least a urinalysis in patients prior to URS. We examined risk factors for infection-related hospitalization following URS for urinary stones in a surgical collaborative.Reducing Operative Complications from Kidney Stones (ROCKS) is a quality improvement (QI) initiative from the Michigan Urological Surgery Improvement Collaborative (MUSIC) consisting of academic and community practices in the State of Michigan. Trained abstractors prospectively record standardized data elements from the health record in a web-based registry including patient characteristics, surgical details and complications. Using the ROCKS registry, we identified all patients undergoing primary URS for urinary stones between June 2016 and October 2017, and determined the proportion hospitalized within 30 days with an infection-related complication. These patients underwent chart review to obtain clinical data related to the hospitalization. Multivariable logistic regression analysis was performed to determine risk factors for hospitalization.1817 URS procedures from 11 practices were analyzed. 43 (2.4%) patients were hospitalized with an infection-related complication, and the mortality rate was 0.2%. Median time to admission and length of stay was 4 and 3 days, respectively. Nine (20.9%) patients did not have a pre-procedure urinalysis or urine culture, which was not different in the non-hospitalized cohort (20.5%). In hospitalized patients, pathogens included gram-negative (61.5%), gram-positive (19.2%), yeast (15.4%), and mixed (3.8%) organisms. Significant factors associated with infection-related hospitalization included higher Charlson comorbidity index, history of recurrent UTI, stone size, intra-operative complication, and procedures where fragments were left in-situ.One in 40 patients are hospitalized with an infection-related complication following URS. Awareness of risk factors may allow for individualized counselling and management to reduce these events. Approximately 20% of patients did not have a pre-operative urine analysis or culture, and these findings demonstrate the need for further study to improve urine testing and compliance Ureteroscopy (URS) is now the most common treatment modality for treating upper urinary tract stones in North America , 2. Due Prior studies investigating infection-related complications after URS have provided some insights on risk factors, which include stone, patient, and operative characteristics. However, most are single institution series from tertiary referral or academic medical centers –10, whicIn the state of Michigan, we have developed a quality improvement (QI) initiative and a clinical registry—Reducing Operative Complications from Kidney Stones (ROCKS)—to better understand processes of care, outcomes, and quality indicators for patients undergoing URS for urinary stones. A strength of this registry is its diversity of patients and practicing urologists. In our drive to improve outcomes for URS, we sought to better understand risk factors for infection-related hospitalization using data from this surgical collaborative. We also sought to assess care in relation to guideline-based practice. We hypothesize that there are modifiable factors which lead to infection related morbidity. Identifying high-risk patients may allow for individualized counseling, and development of QI interventions that reduce adverse events, and the associated patient morbidity and healthcare costs.The Michigan Urological Surgery Improvement Collaborative (MUSIC) was established in 2011 in partnership with Blue Cross Blue Shield of Michigan. The ROCKS QI initiative within MUSIC comprises diverse community and academic urology practices in the state of Michigan and started in 2016. For patients with urinary stones undergoing URS, trained abstractors prospectively record standardized data elements in a web-based registry including patient and stone characteristics, surgical details and complications. Patient data are entered into the registry 60 days after a URS procedure, and data entry is guided by standard variable definitions and collaborative-wide operating procedures. To ensure data quality, the coordinating center performs on-site data audits on a semi-annual basis.We identified all patients undergoing URS for primary treatment of urinary stones between June 2016 and October 2017. During this period, ROCKS consisted of 11 practices. To be included in the ROCKS registry, a patient had to be at least 18 years of age and undergone unilateral URS for urinary stones. Patients who underwent bilateral URS, had an ipsilateral nephrostomy at the time of URS, or underwent URS after percutaneous renal surgery were ineligible. We identified all patients who were discharged after surgery and then subsequently hospitalized (at any institution) within 30 days of their procedure. An infection-related hospitalization was determined by chart review, based on the presence of SIRS criteria with or without bacteriuria. Patients admitted for other indications  were classified as a non-infectious hospitalization. Stone-free rate (SFR) was defined as absence of any fragment on X-ray, CT or ultrasound reports obtained within 60 days. Chart review was performed on all patients with infection-related hospitalizations, including urine culture pathogen data, length of stay, and timing from surgery.t tests were performed for categorical and continuous variables, respectively, to compare demographic and operative factors between the two groups. Significant pre-operative and operative variables were then used as covariates in a multivariable analysis to determine which factors were associated with higher odds of an infection-related hospitalization. Multivariable analysis was performed using a logistic regression model. The odds ratios and 95% confidence intervals were reported. Significant variables with less than 10 events were not included in the multivariable final model. All analyses were performed with SAS 9.4  at a 5% significance level.We generated descriptive summary statistics of all patients in the analytic sample. Chi-square tests and student’s A total of 1817 URS procedures in 1737 patients from 11 practices were analyzed. In total, 80 (4.4%) patients were hospitalized within 30 days of their URS. Forty-three (2.4%) patients were hospitalized with an infection-related complication . 12 (27.9%) patients who were hospitalized with an infectious indication had a positive urinalysis or urine culture prior to surgery, compared to 261 (15.4%) in the non-hospitalized patients (p = 0.08). None of the 12 patients with abnormal pre-operative urine studies who were hospitalized were treated with antibiotics prior to surgery.Pre-operative, intra-operative, and post-operative characteristics among infection-related hospitalized, and non-hospitalized patients, are provided in Table Patients who were hospitalized for infectious reasons were more likely to have an intra-operative complication (7.0%). Complications included inability to complete procedure due to bleeding or perforation. There was no difference in the rate of ureteral stent placement, ureteral dilation, or use of ureteral access sheath at the time of surgery between the infection-related hospitalization group and the non-admitted group. Those hospitalized with an infection-related complication were more likely to have lithotripsy with fragments left in situ at the conclusion of the operation.On multivariable analysis Table , signifiWe found that in 11 diverse urology practices across the state of Michigan, 1 in 40 patients were hospitalized with an infection-related complication following URS for urinary stones. During admission the most commonly identified organisms were gram-negative, however a small proportion of patients had yeast identified. Risk factors for an infection-related admission were higher Charlson comorbidity index, history of recurrent UTI, larger stone size, intra-operative complication and cases where lithotripsy was performed with fragments left in-situ. Overall, 20% of all patients did not have a documented urinalysis or urine culture prior to URS. Collectively, these findings represent an opportunity for the development of QI initiatives to decrease the risk of infection and sepsis after URS, as well as better adherence to American Urological Association (AUA) guidelines.Previous investigators have examined risk factors for infectious complications following URS. Zhong et al. examined 250 patients that underwent URS for stone treatment, and found an 8.1% incidence of systemic inflammatory response syndrome (SIRS) following the procedure. Risk-factors included stone size, smaller caliber ureteral access sheath, higher irrigation flow rate, and presence of struvite calculi , 13, hisAwareness of risk factors can allow for an individualized approach to pre-operative antibiotic selection, adoption of intra-operative technical factors such as considering a ureteral access sheath or limiting the irrigation flow rate, and post-operative antibiotic therapy in patients at risk for developing sepsis. Since there was a strong relationship between an intra-operative complication and subsequent hospitalization, patients who suffer this event could be considered for prolonged observation in the recovery room or even admission and observation. Likewise, patients with a history of recurrent UTI should be considered for pre-operative urine culture  and be managed with culture-directed pre-operative antibiotics. While almost all patients received peri-operative antibiotics, more patients in the hospitalized group had an abnormal urine study prior to surgery, and none of these patients were treated with antibiotics. There are a very small number of patients in both groups with untreated positive urine cultures prior to ureteroscopy. This represents a focus for subsequent quality improvement initiatives with the goal to improve pre-operative testing and follow-up.We found that in patients with a positive urine culture during hospitalization, only 34.6% had a positive UA or urine culture prior to surgery. It is possible this discordance lies in our definition of a positive urinalysis (nitrite positivity), which can be altered by medications such as pyridium. Additionally, any positive pre-op culture, regardless of organism or colony count, is deemed positive. These represent limitations of our study, however, previous studies have also reported discordance between pre-operative, intra-operative and post-operative urine cultures in patients undergoing stone surgery. Paonessa et al. examined pre-operative urine cultures and intraoperative stone cultures in patients undergoing percutaneous nephrolithotomy and found that 9.7% of patients with negative pre-operative urine cultures had positive stone cultures. In patients with both positive pre-operative urine and intra-operative stone cultures, the organisms differed in 13.3%, representing an overall discordance in almost a quarter of cases . Marien The AUA Guidelines on Surgical Management of Stones advises clinicians to obtain a urinalysis prior to URS, and in patients with clinical or laboratory signs of infection, a urine culture should be obtained . EAU GuiLithotripsy with fragments left in-situ was associated with an increased risk of infection-related hospitalization in our cohort. This variable is determined by review of the operative notes by data abstractors based on key phrases, such as “all fragments were removed,” or “all remaining fragments were 1 mm of less.” The database does not detect specific stone treatment technique, and it is difficult to ascertain if this indicates dusting technique, or a hybrid technique of basketing and dusting. It is possible that our results are confounded by patients with large stone burden. Also, while patients in the non-hospitalized group were more likely to be on pre-operative alpha-blockers, this was not significantly associated with a lower risk of an infection-related hospitalization on multi-variate analysis. In a recent study, 1 week of pre-operative alpha-blocker therapy was associated with lower overall complications after URS . The samOur work has several limitations. First, our patients are located in a single state and it is possible these results are not applicable to all patients in the United States or outside the country. Our registry does not collect information on pre-operative ureteral stent dwell-time, technical intraoperative details such as size of access sheath, irrigation rate, surgical time, and other factors that may place patients at higher risk for developing an infectious complication. In addition, stone cultures are not captured by registry. Additionally, it is possible we are underreporting the number of events based on our study design. From the registry we were able to identify all patients that were hospitalized within 30 days. We then made a determination if the hospitalization was due to infectious or non-infectious etiologies  based on chart review. Along with the admission notes, SIRS criteria were used to determine if the admission was due to infectious-indication, as culture data was not available or obtained after administration of antibiotics. Therefore it is possible that some patients were admitted with infectious-indications without SIRS criteria. Finally, the small number of hospitalization events may alter the fit of our multi-variable model.Our findings do have several implications. URS is among the most commonly performed urologic surgeries, and unplanned healthcare encounters following URS are not uncommon. We demonstrate suboptimal statewide compliance with guidelines regarding pre-operative urine screening. Efforts should be taken to comply with best practice statements, and this will be the subject of future QI initiatives in MUSIC. In particular we are considering collecting information on which specific pre-operative urine study was performed to determine whether urinalysis is insufficient as a screening tool to mitigate the risk of sepsis after URS.We found that nearly 1 in 40 patients are hospitalized with an infection-related complication following URS for urinary stones in diverse practices in Michigan. Awareness of risk factors may allow for individualized counselling and management to reduce these events. Approximately 20% of patients did not have a pre-operative urine analysis or culture, and these findings demonstrate the need for further study to improve urine testing and compliance.


No previous systematic review has examined the effect of high-intensity interval training (HIIT) interventions on body composition, cardiometabolic risk factors and cardiorespiratory fitness (CRF) in healthy schoolchildren from 5 to 12 years old.This study will be conducted by following the guideline of the preferred reporting items for systematic review and meta-analysis protocols. An electronic search in MEDLINE (via PubMed), EMBASE (via Scopus), SPORTDiscus, Cochrane Library and Web of Science databases of all dates from inception will be conducted. We will include randomized controlled trials aimed to assess the effectiveness of HIIT to improve cardiometabolic risk factors, body composition, and CRF in children. Two authors will perform the study selection and data collection; disagreements will be solved by a third reviewer. The methodological quality of studies will be assessed by the Cochrane Collaboration's tool for assessing risk of bias (RoB2). Data analysis and synthesis will be performed by Comprehensive Meta-analysis Software and StataSE software, version 15.The results should be disseminated through publication in a peer-reviewed journal. Since the data used in systematic reviews of this type will be extracted exclusively from published studies, approval form and ethics committee will not be required. However, the training duration of continuous aerobic exercise must be sufficiently long to induce the desirable effect and thus, long exercise duration leads to poor exercise adherence due to monotony.Traditionally, continuous aerobic exercise in children, characterized as moderate intensity exercise without rest intervals, has been the most common type of exercise recommended to improve body composition, cardiorespiratory fitness (CRF) and overall health-related parametersRecently, interest in higher intensity interval training (HIIT) research has increased. HIIT has been identified as a time-efficient type of exercise to promote health benefits and improve fitness. and CRF–11 in healthy adolescents and adults. Besides, HIIT may improve physiological adaptations and may induce greater health benefits. Greater reduction in total body mass, fat mass, trunk fat, and fasting plasma insulin levels has been reported in several intermittent and continuous exercise comparison studies in obese adults.,12Numerous meta-analyses have reported that HIIT leads to improvements on body composition However, HIIT performed in healthy children is significantly less investigated than in obese children, adolescents, adults or chronic disease patients; so, it remains uncertain whether HIIT is the best form of exercise for children to maintain or improve cardiometabolic health.Particularly, there is limited evidence related to outcomes of intermittent exercise. It is discussed that it may be more appropriate for children because their physical activity patterns are naturally intermittent.Therefore, the aim of this study protocol is to provide a clear, standardized and transparent methodology for performing a systematic review and meta-analysis aimed to assess the effect of HIIT programs versus non-training Control Groups (CGs) on body composition, cardiometabolic risk factors and CRF in healthy children.22.1 and following the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions.This systematic review and meta-analysis protocol is based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statementThe meta-analysis study was registered with the PROSPERO International Prospective Register of Systematic Reviews Database (CRD42018093432).2.2Relevant randomized controlled trials (RCTs) will be searched in MEDLINE (via PubMed), EMBASE (via Scopus), SPORTDiscus, Cochrane Library, and Web of Science databases RCTs with language restriction in Spanish and English. In addition, the literature search will be complemented by manual screening references included in articles that are considered eligible for the systematic review. Study records will then be managed using the Mendeley reference manager. The search strategy will include free text terms, combining Boolean operators from the relevant concepts presented in Table 2.32.3.1All relevant RCTs assessing HIIT to improve body composition, cardiometabolic risk factors and/or CRF in children, in which the CG did not receive any PA intervention will be included. Observational, prospective cohort studies systematic reviews, and RCTs performed with adolescents or adults will be excluded.2.3.2The studies must include healthy schoolchildren aged 5 to 12 years.2.3.3The included studies must involve interventions of HIIT, prescribing high intensity exercise if (VO2peak);(1) the intensity was >= 90% peak oxygen consumption; and/or ensuring that the participant's heart rate (HR) was >= 90% of their peak HR (HRpeak).,19 The studies also could be included if HIIT is combined with nutritional intervention in the CG.(2) had an intensity that was >= 100% maximal aerobic speed2.3.42max), VO2peak, maximal HR (HRmax) and HRpeak.The primary outcomes will be the following: changes in body composition assessed with body mass index (BMI) and body fat percentage; changes in the levels of some of cardiometabolic risk factors such as systolic and diastolic blood pressure, total cholesterol, low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), glucose, triglyceride and changes in the CRF assessed as maximal oxygen consumption first author's name,(2)year of publication,(3)characteristics of participants ,(4)HIIT and CG protocols , and(5)results of the means and standard deviations of body composition, cardiometabolic risk factors and CRF variables from pre- to post-intervention between groups (HIIT vs CG).2.5When we encounter missing or unclear data, we will try to contact corresponding authors for additional information.2.6 This version includes 5 domains: bias arising from the randomization process, bias due to deviations from intended interventions, bias due to missing outcome data, bias in measurement of the outcome, and bias in selection of the reported result. Each domain is scored as high risk, some concerns or low risk. A sixth domain, overall bias, will have a low risk result if the rest of the domains are of low risk, some concerns if there are some domains assessed with some concerns; and high risk if there are 1 or more domains with high risk of bias.Two independent researchers will be blinded to the authors, titles and years of publication of the included studies to evaluate the risk of bias of each included study. The risk of bias assessment will be conducted using the RoB 2.0 tool developed by the Cochrane Collaboration.2.7Meta-analyses will be conducted to determine the effect of HIIT on body composition, cardiometabolic risk factors, and CRF variables, in comparison to nontraining CGs. For studies that include both non-training CGs and continuous moderate intensity exercise comparison groups, only the CG data  will be included in the meta-analysis. (DerSimonian-Laird approach). Finally, the ES of all studies included will be combined to estimate an overall summary ES, with a 95% confidence interval and a random-effects model.The effect size (ES) of each study will be calculated as the standardized mean differences in the variables from pre- to post-intervention between groups (HIIT vs CG) using random-effects modelI2 statistic, and the following values will be used for its interpretation: 0% to 30% “not important” heterogeneity; >30% to 50% “moderate” heterogeneity; >50% to 80% “substantial” heterogeneity, and >80% to 100% “considerable” heterogeneity. The corresponding P-values will be also considered.The heterogeneity results across studies will be evaluated using the For each pooled ES, we will be conducted a sensitivity analysis by removing the studies one by one to assess the robustness of the summary estimates and to detect whether any study accounted for a large proportion of heterogeneity. Finally, visual inspection of funnel plots and the Egger test will be conducted to detect publication bias.A lineal meta-regression analysis will be used to explore whether covariates could be associated with the magnitude of the effect and could explain the observed statistical heterogeneity.In addition, subgroup analyses will be performed, if it is possible, to assess whether the weight status influences the overall ES. All analyses will be perform using Comprehensive Meta-analysis Software  and StataSE software, version 15 .3To the best of our knowledge, this is the first systematic review and meta-analysis protocol to examine the effect of HIIT interventions on body composition, cardiometabolic risk factors and CRF in healthy schoolchildren aged 5 to 12 years. HIIT could be considered a more effective and time-efficient intervention for improving cardiometabolic health maskers in children compared with a non-exercise CG. and CRF in children. However, the majority of meta-analysis aimed to assess the effectiveness of HIIT to improve the cardiometabolic health have been conducted in adolescents or adults, which justifies the need for this study.Several meta-analyses have assessed the effectiveness of physical activity interventions to improve cardiometabolic risk factorsFurthermore, the results of this study will provide rigorous summary evidence about the effects of HIIT interventions on body composition, cardiometabolic risks factors such as blood pressure, BMI, HDL-c, LDL-c, triglycerides, glucose, total cholesterol, and CRF in healthy children.4 and the Cochrane Collaboration Handbook.Different issues should be acknowledged as potential limitations: information and publication bias, insufficient methodological quality, scarce of statistical analysis, inclusion of articles in English and Spanish exclusively and inadequate reporting of methods and results. To try to avoid these possible limitations, we will take into account potential risks of bias for each study and we will follow the existing guidelines included in the PRISMA5Due to the nature of systematic review and meta-analysis, patients’ informed consent and ethic approval will not be required. The results obtained of this study could be particularly useful because they will provide rigorous evidence regarding the effect of HIIT interventions to improve the children's cardiometabolic health.Conceptualization: Solera-Martínez, M, Pozuelo-Carrascosa DP.Data curation: Solera-Martínez M, Díez-Fernández A, Pozuelo-Carrascosa DP.Formal analysis: Solera-Martínez M.Investigation: Solera-Martínez M, Manzanares-Domínguez I, Pozuelo-Carrascosa DP.Methodology: Solera-Martínez M, Díez-Fernández A, González-García A, Pozuelo-Carrascosa DP.Project administration: Solera-Martínez M, Pozuelo-Carrascosa DP.Supervision: Martínez-Vizcaíno V.Writing – original draft: Solera-Martínez M, Pozuelo-Carrascosa DP.


Correctional and detention facilities have been disproportionately affected by coronavirus disease 2019 (COVID-19) because of shared space and movement of staff members and detained persons within facilities , and Cermak Health Services. CCSO staff members who worked at least one shift at the jail during the same period were also included. A case of COVID-19 was defined as detection of SARS-CoV-2 by real-time reverse transcription–polymerase chain reaction (RT-PCR) in a specimen from a detained person, and, among staff members, as reported COVID-19–compatible symptoms or detection of SARS-CoV-2 in specimens by real-time RT-PCR. Detained persons who reported symptoms or who were a close contact of someone with a positive test result were tested; those who were not tested  or who received a negative test result  were grouped together for analyses and visualizations. Although staff members were not systematically tested, they were required to report symptoms of COVID-19 or receipt of positive test results immediately to CCSO; staff members reporting positive test results (confirmed case) or symptoms (probable case) were considered to have COVID-19. Staff member test results were confirmed through the Illinois National Notifiable Disease Surveillance System.In the person-division networks, divisions represent housing buildings within the jail. Connections occur between persons (detained persons or staff members) and the divisions to which they are assigned, either for housing or a work shift. The number of connections between persons and divisions is calculated based on a value of one for each new bed assignment for detained persons or shifts within a given division for staff members.To determine whether the number of connections to a given division was associated with a higher proportion of cases among staff members and detained persons, the linear correlation between the proportion of persons meeting the case definition during the study period and the number of connections was compared for each division, representing all persons linked to each division through at least one work shift (staff member) or cell or bed assignment (detained person). The strength of correlation was determined by calculating the correlation coefficients for each line of best fit, with statistical significance assessed at α = 0.05.The person-person networks show persons present at the same location and time based either on shift or bed assignment. To determine whether connections between persons with COVID-19 in both groups were occurring more frequently than expected, the proportion of persons meeting the case definition was used to calculate ratios of observed-to-expected proportions of positive-positive, positive-negative (or not known to be positive), and negative-negative connections, and these ratios were compared using chi-squared tests of independence; significance was assessed at α = 0.05.A joined network of detained persons and staff members was constructed but did not demonstrate clear patterns of clustering or spread. Detained person and staff member networks, displayed separately as unique patterns for each network, were more easily visualized. Data management and analyses were conducted using SAS  and R  statistical software; visualizations were performed using Gephi . This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy.The average daily census of detained person during March 1–April 30, 2020, was 4,884, with 3,834 (79%) included in the analysis based on availability of cell or bed assignment. Among the 1,080 detained persons tested during the same period, 472 (12.3%) received a positive SARS-CoV-2 test result. CCSO had 2,370 staff members assigned to Cook County Jail on March 1, 2020, most of whom worked on site. During the outbreak, 270 staff members were added, totaling 2,640. Among these, 2,041 (77%) were included in the analysis based on availability of shift and division assignments, 198 (9.7%) of whom had COVID-19; staff members with negative test results could not be identified through available data sources. During the outbreak, interventions were used to limit spread, including cessation of visitation (March 15), suspension of programmatic activities (March 23), conversion of cells to single occupancy, and universal masking for staff members (April 2) and detained persons (April 13).Person-division networks. Visualization of networks among detained persons and staff members indicates that COVID-19 cases occurred in all jail divisions. The staff member network did not demonstrate a discernable pattern with distribution of persons with COVID-19 throughout divisions  (p<0.001) (p<0.001) . Among dp<0.001) . The obsOverall, 3,278 connections across all divisions were observed between staff members and divisions, ranging from 80 connections in division 16, to 625 in division 2 . The perNetwork analyses and visualization of a large outbreak of COVID-19 at Cook County Jail demonstrate the complex transmission dynamics that can propagate disease spread, especially in a congregate setting. Connections among persons with COVID-19 occurred more often than expected in staff members and detained persons (p<0.001). The observed connections among detained persons with COVID-19 were significantly lower than expected when considering only the housing units in which initial transmission occurred, suggesting the systematic isolation of those with COVID-19 is effective at limiting subsequent transmission. These findings support the importance of rapid detection and isolation of persons with COVID-19 and limitation of movement between divisions as critical elements in reducing spread . This likely reflects transmission among staff members; however, additional SARS-CoV-2 exposures not recorded in this analysis might also contribute, including staff members returning to their household and community after each work shift. The higher than expected percentage of staff members with COVID-19 reinforces the need for cohorting staff members , maintaining consistency in shift assignment locations, and enforcing mask use for source control.This is the first known report using network analyses and visualization techniques to describe a COVID-19 outbreak in a U.S. correctional or detention facility. Use of network analyses in China revealed disease occurring in clusters , or other non-CCSO staff members on site . Persons not included in the staff member shift data might have interacted with persons in the staff member network. Third, staff members were not systematically tested; thus, this analysis possibly underrepresents the number of staff members with COVID-19 and their connections. In addition, although detained person and staff member networks are displayed separately to more easily visualize the patterns, interactions between these groups likely play an integral role in transmission in a detention facility. Finally, COVID-19 contacts and exposures occurring outside the jail or in the surrounding community, or from staff members with asymptomatic COVID-19, were not assessed in this analysis.Network analyses and visualizations provide insight into disease spread, illustrating the effectiveness of certain control measures. This study demonstrates the consistent use of cohorting among detained persons and suggests effectiveness of employing this strategy. A network-informed approach can identify likely points of high transmission by demonstrating when transmission is higher than expected, allowing for interventions targeted at these groups or locations .Network analyses and visualization can provide information about outbreak transmission dynamics.Analysis of detained person and staff member movements during a COVID-19 outbreak at Cook County Jail in Illinois found fewer connections among detained persons with COVID-19 than expected, suggesting that interventions and medical isolation practices were effective at reducing transmission. Higher than expected connections were identified in staff member networks, suggesting occurrence of additional transmission and areas of focus for transmission interruption.A network-informed approach can identify likely points of high transmission, enabling targeted interventions to reduce transmission, such as by reducing convening of staff members, closing breakrooms, and cessation of contact sports.


T-cell epitope matching according to the TCE3 algorithm classifies HLA-DPB1 mismatches in permissive and non-permissive. This classification has been shown to be predictive for mortality and acute GvHD (aGvHD) events in large international cohorts. We retrospectively genotyped HLA-DPB1 in 3523 patients transplanted in Germany between 2000 and 2014 and in their unrelated donors using an Illumina amplicon-NGS based assay. Aim of the study was to evaluate DP-compatibility beyond the established TCE3 algorithm by assessing the combined effect of several DP-mismatch parameters on post-transplant outcome. We implemented an extended DP-mismatch assessment model where TCE3, DP allotype expression with respect to rs9277534, mismatch vector and number of mismatches were conjointly taken into consideration. In this model, non-permissive HLA-DPB1 mismatches showed significantly increased aGvHD risk if they were accompanied by two HLA-DPB1 mismatches in GvH direction (HR: 1.46) or one mismatched highly expressed patient allotype (HR: 1.53). As previously reported, non-permissive HLA-DPB1 mismatches associated with a significantly higher risk of aGvHD and non-relapse mortality , which in turn translated into worse GvHD and relapse free survival (HR 1.13). Effects on GvL and GvHD appeared strongest in GvH-directed non-permissive mismatches. Our study results support the consideration of additional HLA-DPB1 mismatch parameters along with the established TCE3 matching algorithm for refinement of future donor selection. In particular, our findings suggest that DP non-permissiveness associated with two HLA-DPB1 mismatches or at least on highly expressed mismatched patient allotype should be avoided. Allogeneic hematopoietic stem cell transplantation has become an established clinical treatment for various, otherwise often incurable diseases of the lympho-hematopoietic system. Improvements in treatment protocols as well as donor selection procedures have led to increasing numbers of patients undergoing hematopoietic stem cell transplantation (HSCT) . AlthougEarly studies had shown that the impact of HLA-DPB1 differences on the incidence of GvHD was balanced by a lower relapse rate and therefore did not translate into better survival outcomes . It was This study included patients transplanted for various hematological diseases with peripheral blood stem cells (PBSC) or bone marrow (BM) from an unrelated donor at German centers. The transplants were performed from 2000 to 2014. All searches were conducted by the search unit in Ulm. Only transplants with first allogeneic transplantation were included. Disease status at time of transplantation was classified according to the definitions used in the establishment of the EBMT risk score . MyeloabFor all patients high resolution HLA-typing was available for the gene loci HLA-A, -B, -C, -DRB1 and -DQB1, defining all polymorphisms within the ARD – exons 2 and 3 for HLA-class I, and exon 2 for HLA-class II molecules . Non-expHLA-DPB1 TCE3 matching was performed according to the revised TCE3 matching procedure based on functional distance . DPB1 miEndpoints of interest were overall survival (OS), GvHD and relapse-free survival (GRFS), non-relapse mortality (NRM), aGvHD incidence and relapse incidence. OS was defined as time to death or last follow-up. GRFS was defined as time to aGvHD, relapse or death, whichever occurred first. NRM was defined as time to death from any cause except relapse. A relapse event was treated as competing risk. The endpoint aGvHD incidence was defined as time to first diagnosis of aGvHD (grades II-IV). An additional subanalysis for aGvHD (grades III-IV) was conducted. Death from any cause without prior aGvHD was considered as competing risk. Relapse incidence was defined as time to relapse and death from any cause without prior relapse was treated as competing risk. Patients alive and/or free from the event of interest were censored at last follow-up .For descriptive statistics, the chi-squared test was used for categorial variables and the Mann-Whitney-U-Test for continuous variables. For survival analyses of the endpoints OS and GRFS Kaplan-Meier estimates were used and comparisons were performed with the log-rank test . For theThe cohort consisted of 10/10  and 9/10 HLA  matched transplant pairs . The distribution of diagnoses was similar in both groups, median age was slightly lower in the 9/10 matched transplants. Details regarding the cohort’s features are shown in Retrospective genotyping of HLA-DPB1 locus in patients and their respective donors confirmed the high prevalence of HLA-DPB1 mismatches in both, 10/10 and 9/10 HLA-matched transplantations already reported elsewhere , 25, 26.Regarding the effect of HLA-DPB1 mismatch on outcome endpoints, our results are in line with those previously reported. Specifically as to GRFS, 10/10 HLA-matched transplant pairs with HLA-DP non-permissive mismatches compared to HLA-DP matched or permissively mismatched cases exhibited a clearly higher composite risk of relapse, GvHD II-IV or death as presented in the GRFS outcome endpoint. These results are graphically depicted in Non-relapse mortality was also significantly higher in DP non-permissive mismatched transplant pairs, both in the 10/10 HLA-matched p=0.010, Table 4.Subanalysis of the vector of non-permissiveness against DP matched/permissively mismatched showed significantly higher risks for both GvH and HvG directed mismatches in the GRFS endpoint. . The detrimental effect of the non-permissive mismatches on NRM was again independent of the mismatch directionality as both, GvH and HvG vector non-permissive mismatches, associated with increased NRM risk . Contrary to the previous endpoint analyses, the mismatch vector appeared to indeed play a role in aGvHD incidence, as the higher risk observed was mostly driven by non-permissive mismatches in GvH direction . In line with the results for aGvHD, the effect of non-permissive mismatches on relapse incidence appeared to be mainly driven by the GvH vector  model, an interesting finding was that apart from TCE3 permissiveness also the overall number of DP mismatches as well as the anticipated expression level of the mismatched allotype with a GvH vector contributed to the overall mismatch effect. Specifically, a significantly higher risk of GRFS, NRM and aGvHD was found for DP non-permissive mismatches with two overall DP-allele-mismatches in GvH direction Table 5.Several factors seem to contribute to the alloreactivity is induced by HLA-DPB1 differences. These are linked to the intrinsic immunogenicity on account of T-cell epitopes, the numbers and vectors of mismatches as well as the expression level of the mismatched allele. HLA-DPB1 mismatching represents therefore a multilevel variable and any individual model represents only a simplification of the true biological relationship. In this retrospective study we sought to conjointly assess the effect of the aforementioned factors with the aim to offer a more unified approach as to DP mismatch evaluation for donor selection. Through our analysis we were able to confirm previously described associations, while we also showed that consideration of additional factors might be meaningful for histocompatibility assessment and for improving predictiveness.Regarding the prevalence of HLA-DPB1 mismatches in 10/10 as well as 9/10 HLA-matched HSCTs, no differences were observed between our findings and those seen in other studies , 25, 26.In line with previously reported associations of HLA-DPB1 non-permissive mismatches with outcome endpoints, we also observed a clearly higher risk of aGvHD and NRM , 14. AltMultivariate Models were checked for interaction between HLA-DPB1 and other classical HLA-mismatches by forming an interaction term, which showed not statistical significance. This is also shown in the separate analysis of 10/10 HLA and 9/10 HLA matched cases, where the respective effect of HLA-DPB1 mismatch did not appear to be influenced by the prevalence of an additional HLA mismatch with the exception of relapse as already mentioned previously. This implies that HLA-DP mismatches confer their effect on outcome rather independently from additional HLA-mismatches. As far as mismatch directionality is concerned, our analysis revealed that non-permissive mismatches in GvH direction mainly drove the overall effect of higher aGvHD but also lower relapse risk when compared to non-permissive mismatches in HvG direction. The fact that no such effect was observed in NRM suggests that DPB1-mismatch-induced morbidity is not only restricted to aGvHD but may also affect other pathophysiological pathways such as conditioning associated toxicity or infections early after hematopoietic stem cell transplantation . A mechaIn our analysis we explored the impact of HLA-DPB1 mismatches on GRFS, a composite endpoint now increasingly used for assessing the success of HSCT, as it simultaneously measures the proportion of patients free from disease and GvHD . We consAim of this study was to conjointly assess different DP mismatch alloreactivity predictive models so that an extended predictive model can be proposed. To this end we included in our analysis, along with the TCE3 algorithm, the HLA-DPB1 expression model as proposed by Petersdorf et al. . AssignmLimitations of our analysis are the small sample size in some sub-analyses particularly in the combined DP mismatch model. Missing data has also been a substantial problem for CMV status and blood group as well as for date of development of acute and chronic GvHD in the EBMT promise registry database although in direct collaboration with the transplant centers we were able to collect a substantial proportion of these missing data. Still missing data in the final analysis showed a completely random pattern, indicating no data collection bias. Furthermore, our cohort represents patients transplanted in Germany and shows a large proportion of patients treated with ATG as part of the conditioning treatment as well as a low proportion of patients treated with mTOR inhibitor based immunosuppression, which may limit comparability with other cohorts showing different features.In conclusion, our study confirms the previously reported detrimental effect of non-permissive HLA-DPB1 mismatches according to the TCE3 model in a large cohort of patients having been treated with unrelated HSCT in German transplant centers between 2000 and 2014. This effect was similarly present in 10/10 and 9/10 HLA- matched transplantations. The results of our combined assessment of distinct DP mismatch alloreactivity models indicate that the effect mediated by rs9277534 may be independent from the immunogenicity model underlying the TCE3 model. Furthermore, an additional dose effect of mismatched HLA-DPB1 allotypes in GvH direction is implied, at least for aGvHD and relapse incidence. The aforementioned findings support an extension of the TCE3 model for refined donor selection avoiding the putatively detrimental combinations of non-permissive DP mismatches with overall 2 DP mismatches as well as with a high expressed mismatched patient allotype (rs9277534-G). Larger future studies are anticipated to offer a clearer insight into the multifaceted immunogenicity features of HLA-DPB1 mismatches addressed in this study.joannis.mytilineos@zkrd.de.The data analyzed in this study are subject to the following licenses/restrictions: If required, the data can be reanalyzed by other groups within our premises. Requests to access these datasets should be directed to The studies involving human participants were reviewed and approved by the ethical committee of the University of Ulm. The patients/participants provided their written informed consent to participate in this study.DF, DM, CT, JM, and HS are principal investigators. They designed the study, performed data analysis/interpretation, and wrote the manuscript. DM and CT as well as DF and JM contributed equally. MH, CM, SaF, CN, and CT contributed to the data analysis and in writing of the manuscript. CN, UP, DB, MG, ED, GW, NK, NM, HE, KE, SeF, JC, MK, MD, BH, SK, and MR contributed the patients, reviewed the data, and edited the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the Wilhelm Sander-Stiftung (Grant No 2018.092.1).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.