The performance of Bivariate Cut-HDMR is evaluated by two well-known error metrics: R-squared and Relative Average Absolute Error (RAAE). The results are compared with the performance of CCD. Bivariate Cut HDMR does not compromise the accuracy compared to CCD, although the former uses only one-fifth of sample points (201 sample points) required by the latter (1045 sample points). The sampling schemes and the predictions of cut-planes and boundary-planes are discussed to explain possible reasons for the outstanding performance of Bivariate Cut HDMR.A metamodeling technique based on Bivariate Cut High Dimensional Model Representation (Bivariate Cut HDMR) is implemented for a semiconductor packaging design problem with 10 design variables. Bivariate Cut-HDMR constructs a metamodel by considering only up to second-order interactions. The implementation uses three uniformly distributed sample points ( Numerous metamodeling techniques  have been developed and implemented for engineering design optimization . MetamodFor a typical engineering system, a metamodel considering up to second-order interactions is often sufficient to describe system responses ,3. For eAs the number of input variables increases, the computational cost may become prohibitively high due to an extremely large number of sample points required . This situation will be exacerbated when the modeling requires a computationally expensive analysis such as time-dependent and nonlinear analysis that is routinely encountered in complex semiconductor packaging architectures ,10,11.In order to build accurate and efficient metamodels for high dimensional input-output systems, numerous advanced metamodeling techniques such as the high-dimensional model representation (HDMR) technique ,13,14, rAmong these techniques, one family of HDMR, called Cut-HDMR, possesses two unique practical features: (1) it involves function evaluations only at sample points, and, more importantly, (2) it determines the number of sample points from a pre-defined function of the number of input variables regardless of the nature of engineering applications, i.e., selection of sample points is simple and straightforward ,17,18. BHDMR decomposes a multivariate function into multiple lower-order component functions, based on the hierarchical structure of interaction effects of the input variables. The high performance of some of the metamodeling techniques based on Cut-HDMR considering up to second-order component functions (this will be referred to as Bivariate Cut-HDMR) has been confirmed for nonlinear numerical test functions ,20 and sThe objectives of this paper are (1) to introduce the cut-HDMR to the semiconductor packaging design community and to help implement the Bivariate Cut-HDMR for those who are not familiar with the HDMR, and (2) to investigate the performance of Bivariate Cut-HDMR for a complex semiconductor packaging problem . The result is compared with the performance of CCD, which has been utilized widely in the semiconductor packaging industry.The fundamentals of HDMR are described first. A specific HDMR that uses the Dirac measure located at a cut center, called Cut-HDMR, is presented together with its approximated version, Bivariate Cut-HDMR, which considers up to second-order component functions.The concept of high-dimensional expansion was implemented originally to estimate the sensitivity of a function with respect to arbitrary groups of variables . Later, The HDMR expansion is performed based on the interaction effects of input variables. The term “interaction” employed here means that more than one variable act together to affect the performance function. This is distinctly different from the term “correlation” employed in statistics, which represents whether and how strongly a pair of random variables are related.A general form of HDMR is defined as :(2)y(x)=Each effect in the general form of HDMR is called a component function. The general form of component functions can be expressed as :(3)y0≡Myn-dimensional unit cube and The functions in the above equation are defined as: measure . A measuThere is no unique decomposition of the model output OVA-HDMR ,25.The ANOVA-HDMR is typically carried out by multi-dimensional Monte Carlo integration due to its complexity. The Monte Carlo integration needs a large number of sample points to attain good accuracy. It is impractical for the advanced semiconductor packaging applications where computational cost of each sample point is high. However, another approach of HDMR, Cut-HDMR, can tackle the challenge.Cut-HDMR uses the Dirac measure  located By combining it with Equations (3) and (4), the component functions of Cut-HDMR can be expressed as:Equation (6) shows that Cut-HDMR is an expression as a superposition of its values on lines, planes, and hyperplanes of higher orders passing through the cut center, er HDMRs ,27.For most well-defined physical systems, the high-order interactions are negligible ,28, and It has been proven that the mean values of input variables, nsidered . Accordinsidered :(7)y(x)≅The same procedure can be applied to obtain the main effect of The Bivariate Cut-HDMR technique is implemented to construct a metamodel for a semiconductor packaging application. The application involves warpage prediction of a thin flat ball grid array (TFBGA) package with 10 design input variables.®). The quarter symmetry model of boundary conditions and the die stack configuration are shown in (a) and (b); and the enlarged view of cross-section is shown in (c). The material properties and the nominal dimensions used in the model are summarized in A finite element (FE) model was constructed for warpage prediction. The TFBGA package was subjected to the EMC molding process at 175 °C, which was used as a stress-free temperature. The conventional lead-free solder reflow profile with the peak temperature of 260 °C was considered .In this implementation, 10 design variables were considered for the warpage prediction of solder pad areas. The details of design variables are summarized in The number of sample points to construct a Bivariate Cut-HDMR metamodel can be generally expressed as :(8)R=1+NFor the univariate terms ) are required to construct the Bivariate Cut-HDMR metamodel. The sample points consist of one mean sample point (cut center), 20 univariate sample points, and 180 bivariate sample points.For The mean sample point is the design point, which is the mean values of each design variables. The 20 univariate sample points are the sample points, where one of the design variables takes either maximum or minimum value in its design space while other design variables keep the mean values. The 180 bivariate sample points are the sample points, where two of the design variables take either maximum or minimum value in their design spaces while other design variables keep the mean values. The warpage values of 201 (=1 + 20 + 180) sample points were obtained by the FE model. Since the dimensions of the FE model varies with sample points, the FE model must regenerate different meshes for each sample point. The Select a design variable.Find the three sample points along the design variable that was obtained earlier, i.e., high, mid, and low values of the design variable and other design variables keep the mean values.Construct the 1D function of the design variable with the three sample points using quadratic spline interpolation. This can be done by using the built-in function that is available in commercial software .Select another design variable and repeat steps 2–3 until all 1D performance functions along each design variable are built.1D performance functions:Select a pair of design variables.Find the nine sample points along two design variables that were obtained earlier  as shown in the figure.Construct the 2D function of the design variable with the nine sample points using quadratic spline interpolation. This can be done by using the built-in function that is available in commercial software .Select another pair of design variables and repeat steps 2–3 until all 2D performance functions of each pair of design variables are built.2D performance functions:After the warpage values at the 201 sample points are obtained, the metamodel can be constructed by applying Equation (7). The quadratic spline interpolation scheme was adopted with all sample points to form the 1D performance functions (cut-lines), The performance functions are illustrated in Following is the example of determining the response of a random input by using the constructed metamodel. Assuming that a random input https://www.mathworks.com/matlabcentral/fileexchange/92890-bivariate-cut-hdmr (accessed on 25 May 2021). Those who are interested in implementing Bivariate Cut-HDMR metamodeling can run the script readily by following the instructions.The above Bivariate Cut-HDMR procedure was integrated in MATLAB (R2020b) codes, and they are available at The performance of Bivariate Cut-HDMR is evaluated using two well-known error metrics. The performance of CCD is also evaluated for comparison.Two error metrics employed to evaluate the performance are: R-squaredMetric 1: ith new sample point used for validity check; ith new sample point; and R-squared indicates the overall accuracy of a metamodel, and its maximum value is 1.RAAE)Metric 2: Relative average absolute error  was performed to produce 1000 additional sample points. They were used to evaluate the performance of Bivariate Cut-HDMR using the above metrics. The results of the performance metrics are summarized in R-squared and RAAE are 0.9662 and 0.1472, respectively.A metamodel based on CCD was also constructed for comparison. A total of 1045 sample points were required for the CCD metamodel, which built a 10D quadratic function to define the warpage behavior. The additional sample points obtained from MCS were utilized again to evaluate the performance of CCD metamodel. The results are also shown in More direct and quantitative comparisons are shown in The sampling points of Bivariate Cut-HDMR are utilized to construct the first-order and second-order component functions, i.e., every sample point is used to construct the 1D and 2D performance functions  in The surfaces in N) increases. On the boundary-planes of the TFBGA application (The example in The Bivariate Cut-HDMR surface (boundary-planes) of s = 3) in conjunction with quadratic spline interpolation allowed for comparing its performance with a metamodel based on Central Composite Design (CCD).Bivariate Cut-High Dimensional Model Representation (Bivariate Cut-HDMR) was implemented successfully for the warpage problem of a thin flat ball grid array package with 10 design variables. The implementation with three uniformly distributed sample points (R-squared and Relative Average Absolute Error (RAAE). The results were compared with the performance of CCD: the R-squared values of CCD and Cut-HDMR were 0.9662 and 0.9855, respectively; the RAAE values of CCD and Cut-HDMR were 0.1472 and 0.0880, respectively.The performance of both metamodels were evaluated by two well-known error metrics: The outcome was remarkable. Bivariate Cut HDMR used only one-fifth of sample points (201 sample points) required by CCD (1045 sample points); however, Bivariate Cut-HDMR did not compromise the accuracy in both error metrics compared to CCD, which was confirmed by more direct and quantitative comparisons using the absolute errors of the Monte Carlo simulation (MCS) sample points.(1)Sampling scheme: the sample points of Bivariate Cut-HDMR were utilized to construct the first-order and second-order component functions, while the sample points of CCD were aimed to cover the boundaries of a design domain.(2)Predictions of cut-planes and boundary-planes: Bivariate Cut-HDMR predicted cut-planes more accurately despite the smaller number of sample points, while both techniques produced similar accuracy for boundary-plane predictions.Two technical reasons for the outstanding performance of Bivariate Cut-HDMR were discussed:


Anode heel effects are known to cause non-uniform image quality, but no method has been proposed to evaluate the non-uniform image quality caused by the heel effect. Therefore, the purpose of this study was to evaluate non-uniform image quality in digital radiographs using a novel circular step-wedge (CSW) phantom and normalized mutual information (nMI). All X-ray images were acquired from a digital radiography system equipped with a CsI flat panel detector. A new acrylic CSW phantom was imaged ten times at various kVp and mAs to evaluate overall and non-uniform image quality with nMI metrics. For comparisons, a conventional contrast-detail resolution phantom was imaged ten times at identical exposure parameters to evaluate overall image quality with visible ratio (VR) metrics, and the phantom was placed in different orientations to assess non-uniform image quality. In addition, heel effect correction (HEC) was executed to elucidate the impact of its effect on image quality. The results showed that both nMI and VR metrics significantly changed with kVp and mAs, and had a significant positive correlation. The positive correlation is suggestive that the nMI metrics have a similar performance to the VR metrics in assessing the overall image quality of digital radiographs. The nMI metrics significantly changed with orientations and also significantly increased after HEC in the anode direction. However, the VR metrics did not change significantly with orientations or with HEC. The results indicate that the nMI metrics were more sensitive than the VR metrics with regards to non-uniform image quality caused by the anode heel effect. In conclusion, the proposed nMI metrics with a CSW phantom outperformed the conventional VR metrics in detecting non-uniform image quality caused by the heel effect, and thus are suitable for quantitatively evaluating non-uniform image quality in digital radiographs with and without HEC. Image quality is an essential requirement in digital X-ray imaging and is closely associated with the accuracy of disease diagnosis. The fundamental metrics of static image quality are contrast, spatial resolution, and noise, which can be evaluated through the measurements of modulation transfer function (MTF), point-spread function, and noise power spectrum (NPS) ,2,3. AltIn radiography, the “heel effect” causes less X-ray fluence and higher mean radiation energy in the anode direction due to the absorption of low-energy photons by the anode heel . The nonIn information theory, MI is a measure of mutual dependence between two random variables, and is calculated from their individual entropy and joint entropy, defined as entropy . As MI r entropy ,14. Howehttps://www.leedstestobjects.com (accessed on 30 March 2021)) [A commercial contrast-detail resolution (CDR) phantom was also used to evaluate the overall image quality of radiographic images. The phantom consists of 144 circular details with 12 sizes × 12 contrasts ) . Of the 2, dynamic range = 4096, and source-to-detector distance = 100 cm. For statistical analysis, image acquisition was repeated ten times at 40, 45, 50, 55, and 60 kV (5 mAs), and at 5, 10, 20, 25, and 40 mAs (40 kVp), respectively. A posterior-anterior right-hand radiograph was acquired with 52 kVp and 10 mAs to show the impact of anode heel effect on image quality. The human study was approved by the local institutional review board (KMUHIRB-E(I)-20200274).Image quality was evaluated using both CSW and CDR phantoms in a digital radiographic system (Toshiba/DRX-3724HD) that was equipped with a CsI flat panel detector . The X-ray images were acquired from the two phantoms with matrix size = 2800 × 3408, pixel size = 0.13 × 0.13 mmThis study estimated MI from an X-ray image of the CSW phantom using a home-made script on a MATLAB software. First, the center of the CSW phantom in the image was detected by the center of gravity. Second, 14 circular regions-of-interest (ROIs), each containing 1941 pixels, were automatically placed on the center of 14 steps, respectively, in one direction, as shown in This study measured visible ratio (VR) metrics using a TO16 CDR phantom with a commercial AutoPIA tool . The phantom was rotated counterclockwise every 30 degrees from 0 to 180 degrees to understand whether the CDR phantom can adequately reflect the anode heel effect on image quality. For each orientation, ten repeated X-ray images of CDR phantoms were acquired for comparisons and were analyzed automatically to detect all possible details. In this step, the software calculated the contrast-to-noise ratio (CNR) for each of 144 details, defined as ||/(background noise), and then those details with CNR higher than a predefined threshold were considered as visible details . FinallyThis study performed a retrospective correction method that minimizes the intensity inhomogeneity in the X-ray images by fitting the background signals to a 2nd order polynomial function in the anode-cathode direction to understand how the HEC impacts the image quality. Subsequently, the phantom image was subtracted by the fitted curve and added by a minimum value of the curve to keep similar image brightness, as shown in A one-way analysis of variance (ANOVA) was performed to understand whether the image quality metrics significantly changed with kVp, mAs, and orientations before and after HEC, respectively. A post-hoc Mann–Whitney U test was used to compare the differences between two exposure parameters and between two orientations. The Wilcoxon signed rank test was conducted to show the difference in nMI and VR metrics before and after HEC . MoreoveBy varying kVp, one-way ANOVA analysis showed that both nMI and VR metrics significantly changed with kVp between 40 and 60 kVp at a constant 5 mAs. It was also found that nMI changed more prominently than VR in X-ray images with and without HEC, as shown in By varying mAs, one-way ANOVA analysis showed that both nMI and VR metrics also significantly changed with mAs between 5 and 40 mAs at a constant 40 kVp, as shown in The post-hoc Mann–Whitney U test showed that both nMI and VR were significantly different between any two mAs in the images with and without HEC. The nMI metrics were significantly increased after HEC; however, no significant change was noted in the VR metrics at any of the mAs after HEC. Moreover, the averaged nMI and VR metrics significantly correlated in the images without HEC, as shown in By varying orientation in the measurement, without HEC, there were significant changes in nMI with orientations between 0 and 180 degrees (the results were symmetric around 180 degrees). However, without HEC, there were no significant changes in VR with orientations between 0 and 180 degrees, as shown in A posterior-anterior right-hand X-ray image  demonstrIn radiography, the heel effect causes less X-ray fluence and higher mean radiation energy in the anode direction, and results in non-uniform image quality. Although there have been some methods proposed to reduce the heel effect ,20,21, nIt is known that the anode heel effect may lead to heterogeneous X-ray exposure that can deteriorate overall image quality. The results of the present study demonstrated that the heel effect significantly deteriorated the overall image quality. Furthermore, the image quality reflected by the nMI metrics can be significantly improved with HEC in the anode direction; this correction resulted in improved homogeneity of image quality and higher conspicuity of bony structures in the hand X-ray images. However, the conventional VR metrics were not significantly changed with orientations before and after HEC, suggesting that the nMI metrics were more sensitive than the VR metrics to non-uniform image quality.The insensitivity of VR metrics to detect non-uniformity of image quality was likely attributable to the fact that the disk details were embedded in the central area of the CDR phantom, as shown in Some limitations, however, warrant discussion. First, a small range of exposure parameters was utilized in this study. A study using a broader range of exposure parameters may provide more comprehensive comparisons between the two metrics. Second, the nMI metrics with the CSW phantom have an intrinsic disadvantage of less sensitivity to changes in spatial resolution . HoweverIn conclusion, the nMI with the CSW phantom performs as well as VR does with the CDR phantom in evaluating overall image quality in digital X-ray images. Moreover, both metrics had a significantly high correlation at various exposure parameters. The nMI metrics further outperformed the VR metrics in detecting heel effects associated with non-uniform image quality. The nMI metrics also had higher sensitivity to changes in image quality after HEC. Therefore, we concluded that the proposed nMI metrics with the CSW phantom are suitable for evaluating overall and non-uniform image quality in digital X-ray images.


ObjectivesThis study aimed to evaluate quantitative and qualitative screening measures for anomalous computed tomography (CT) scans in cancer patients with potential coronavirus disease 2019 (COVID-19) as an automated detection tool in a radiation oncology treatment setting.MethodsWe identified a non-COVID-19 cohort and patients with suspected COVID-19 with chest CT scans from February 1, 2020 to June 30, 2020. Lungs were segmented, and a mean normal Hounsfield Unit (HU) histogram was generated for the non-COVID-19 CT scans; these were used to define thresholds for designating the COVID-19-suspected histograms as normal or abnormal. Statistical measures were computed and compared to the threshold levels, and density maps were generated to examine the difference between lungs with and without COVID-19 qualitatively.ResultsThe non-COVID-19 cohort consisted of 70 patients with 70 CT scans, and the cohort of suspected COVID-19 patients consisted of 59 patients with 80 CT scans. Sixty-two patients were positive for COVID-19. The mean HUs and skewness of the intensity histogram discriminated between COVID-19 positive and negative cases, with an area under the curve of 0.948 for positive and 0.944 for negative cases. Skewness correctly identified 57 of 62 positive cases, whereas mean HUs correctly identified 17 of 18 negative cases. Density maps allowed for visualization of the temporal evolution of COVID-19 disease.ConclusionsThe statistical measures and density maps evaluated here could be employed in an automated screening algorithm for COVID-19 infection. The accuracy is high enough for a simple and rapid screening tool for early identification of suspected infection in patients treated with chemotherapy and radiation therapy already receiving CT scans as part of clinical care. This screening tool could also identify other infections that present critical risks for patients undergoing chemotherapy and radiation therapy, such as pneumonitis. As of January 5, 2021, there have been over 86 million cases of coronavirus disease 2019 (COVID-19), with more than 1.8 million deaths, worldwide . RespiraInitial signs of COVID-19 in the lungs might not be detected by chest radiography as they can develop in areas difficult to visualize on radiographs . CT scanHowever, the high sensitivity of CT scans can be leveraged as a screening tool, especially for patients with lung cancer, esophageal cancer, or other pulmonary indications who undergo routine CT scans as a part of their diagnostic work-up and treatment. With PCR tests readily available in most hospital systems, the high sensitivity of a CT-based screening tool can be used to identify suspected cases early and determine diagnosis using PCR testing. Furthermore, the ability to automate the rapid detection of any infection leading to respiratory disease in cancer patients would allow early intervention in this highly vulnerable patient population.Here, we propose using statistical measures derived from Hounsfield Unit (HU) intensity volume histograms and lung density maps as a potential rapid screening tool for COVID-19 in radiation oncology patients already receiving chest CT scans as part of their clinical workflow.Selection of patients and chest CT scansAlbert Einstein College of Medicine Institutional Review Board (IRB) issued approval for this study  by expedited review under 45 CFR 46.110 and 21 CFR 56.110. Based on the IRB-approved study protocol, we identified patients with chest CT examinations. One patient cohort consisted of patients receiving CT scans between January 1, 2019 and August 31, 2019, before the COVID-19 pandemic in the United States. Here, we selected an equal number of patients with and without contrast-enhanced CT scans as the control non-COVID-19 cohort. The other cohort consisted of patients with suspected COVID-19 with CT scans acquired between February 1, 2020 and June 30, 2020, and any CT scans for those patients acquired in the first eight months of 2019 as internal controls. Following patient selection, the CT scans were exported to MIM software  for lung segmentation.Lung segmentation and data processingThe chest CT scans consisted of 5-mm slice thickness images with 512 x 512 pixels, with each scan consisting of approximately 50 to 70 slices covering the entire chest. The lungs (right and left lung together) were then segmented in MIM using a semi-automatic region-growing algorithm and manually adjusted as necessary to include all lung tissue. If there existed known non-COVID-19 pathologies such as a lung tumor, this was not included in the lung contours. Once completed, the CT scan data and lung segmentation were anonymized and exported to MATLAB  for data processing and analysis, masking the CT data to only include the segmented lung contours as a three-dimensional (3D) image structure.Data analysisThe CT data for the cohort of control non-COVID-19 scans were used to generate a normal mean histogram of CT HUs. Before this, any HUs above 200 were excluded to avoid the inclusion of higher density anatomy at the fringes of the lung contours. To make the histograms of lung HUs comparable between different CT scans and patients, we normalized the histograms to 100,000 data points binned into 1,200 bins ranging from -1000 to 200 HUs. The normal mean histogram was then generated by adding all of the normalized histograms of non-COVID-19 CT scans and dividing by the number of scans included.To quantitatively differentiate between control scans and scans showing COVID-19, we included simple statistical measures such as the mean, median, and skewness of the lung HU histogram and the proportion of data points falling above the mean. We also included statistical distance measures, such as the Kolmogorov-Smirnov (KS) distance, the Earth Mover’s Distance (EMD), and the Cramér-von-Mises (CvM) distance as further quantitative measures to differentiate the CT scans. These statistical distance measures account for all information in a lung HU histogram and compare it to the mean normal histogram.The data from the normal mean histogram of the non-COVID-19 cohort were used to define thresholds for all the study metrics to designate scans as either normal or abnormal. A scan was classified as abnormal for a given study metric if it exceeded the 99% confidence limit of the mean normal histogram. Subsequently, all suspected COVID-19 CT scans were normalized and processed in the same way as described above, and the statistical measures for each scan were computed and compared to the established threshold levels.We found that the mean normal cumulative lung histograms generated from CT scans of patients that received contrast compared to noncontrast scans were not meaningfully different Figure . TherefoVisual density maps were constructed from the lung contour data using Tool for OPerations on Catalogues And Tables (TOPCAT) version 4.8  freeware astrophysics graphing tool. For noncontrast CT scans, the threshold below which all HU values were excluded in the maps was -150 HU. For contrast scans, this threshold was -100 HU. The lungs were compressed in two dimensions along the cranial-caudal axis, plotting the count of voxels above the threshold along this axis in the transverse plane. These density maps were then visualized on a logarithmic scale.Statistical analysisThe baseline for COVID-19 status and disease severity at the time of CT scan was established by retrospectively examining the patient’s medical records and PCR test results. This was performed by investigators blinded to any CT scan data. Cases were classified as either COVID-19-negative or positive at the time of CT scan, and positive cases classified as asymptomatic, mild, moderate, or severe disease. To classify the performance of the statistical measures, we computed the sensitivity, specificity, negative predictive value, positive predictive value, and accuracy at the given threshold level. The performance of each metric was determined by plotting the receiver operating characteristics (ROC) curve and calculating the accompanying area under the curve (AUC).Seventy patients with 70 chest CT scans were identified for the non-COVID-19 cohort, and 59 patients with 80 chest CT scans were identified for the cohort of suspected COVID-19 cases; patient characteristics are presented in Table Figure The statistical measures and classification of all 80 evaluated CT scans are shown in Figures The mean HU and skewness metrics showed good separation between positive and negative COVID-19 cases at the given threshold levels. Positive COVID-19 CT scans incorrectly classified as normal corresponded with asymptomatic or mild disease, whereas CTs corresponding to moderate or severe disease were classified as abnormal. The statistical measures could not distinguish well between moderate and severe cases because there was typically widespread pneumonia with a considerable increase in lung HU numbers in most of these instances. The threshold levels shown represent the 99% confidence limits from the average normal scan described in the methods and appear to be well suited for distinguishing positive from negative cases.Table The ROC curves for each of the studied statistical measures are presented in Figure Figure Figures This study focused on developing a quick and straightforward potential screening tool for COVID-19 in vulnerable patients treated in a radiation oncology setting. We found that simple statistical measures of mean HUs and skewness of the intensity histogram of chest CT scans performed well in discriminating between positive and negative cases of COVID-19. Skewness had the highest sensitivity of 0.919 in the testing cohort, while mean HUs had the highest specificity of 0.944, as they offer some complimentary classification performance. This screening tool could be implemented as a quick semi-automated test applicable to any patient for whom a chest CT scan is acquired. As a correlate, the density maps generated from the segmented lungs in the CT scan act as visual aids to further characterize COVID-19 disease progression and resolution. CT scans are sometimes difficult to use for characterizing lesion growth due to the extent of disease consolidation, whereas density maps may illustrate density differences between seemingly similar regions on a CT scan more easily. More severe cases typically show consolidation at the posterior of the lungs and are suggestive of inflammation of the vasculature in milder cases ,8. FurthA decline in pulmonary function and changes in lung morphology are associated with natural aging . There wThe high accuracy of the presented point measures would allow CT scans to be used as rapid screening tools, especially at the height of an outbreak when testing resources and labs are overburdened. A recent study supports this, with rapid diagnosis of COVID-19 from CT scans using convolutional neural networks showing promising results that align with our findings, especially when combined with clinical symptoms and laboratory serology . The chaThe statistical point measures evaluated in this study showed good classification performance and could be implemented as part of an automated screening tool algorithm for COVID-19, especially during the height of a pandemic. With this type of methodology, an automated algorithm could be trained on several virtually generated density maps that mimic the patterns of COVID-19 of varying case severity to develop algorithms with higher specificity. The results of this study also suggest the potential for this type of screening tool to discover infections that present critical risks for patients undergoing chemotherapy and radiation therapy, such as pneumonitis. Early identification of such infections would allow rapid intervention that is key for improving the outcome of these patients as their immune system is often compromised following their cancer treatment.


In April 2021, Sunnybrook Health Sciences Centre opened a Mobile Health Unit  under the direction of the Ontario Ministry of Health and Long Term Care in response to a surge in hospitalized patients with COVID-19 during wave three of the pandemic. Providing care to patients in non-conventional spaces is not new, however, experience in safely caring for COVID-19 patients in these settings is lacking. Our aim is to describe the implementation of our MHU and associated outcomes of these COVID-19 patients.A multidisciplinary clinical and operations team was created to plan, execute and operate a safe environment for COVID-19 patients and healthcare workers within the MHU. Patient selection was restricted to patients with COVID-19 who were clinically recovering from severe COVID-19 pneumonia. Ventilation was optimized with air flow directed away from patient areas, velocity reduced to below 0.25 meters per second, and air exchanges of 24-28 per hour. All healthcare workers working in the MHU were offered COVID-19 vaccine and required to complete mandatory education if they declined (vaccination rate of 87% was achieved among dedicated staff). Universal masking and eye protection was used throughout the MHU with designated areas for donning and doffing personal protective equipment. In total, 32 patients with COVID-19 were managed in the MHU between 26 April and 21 May, 2021. Table 1 provides the summary of patient characteristics. All patients had a median of one-day of transmission-based precautions remaining in their course and were infected with Alpha variant with exception of one patient with the Gamma variant. Among those patients with genotyping available, all were infected with SARS-CoV-2 carrying the N501Y mutation. Four of the 32 patients required transfer to the main hospital for medical indication while the others were discharged home or to rehabilitation. None of the healthcare workers who worked within the MHU developed COVID-19 infection. We safely cared for patients recovering from COVID-19 infection in an MHU to support system healthcare capacity. Our experience, including the specific hierarchy of controls implemented, may be helpful for future pandemic planning.All Authors: No reported disclosures


What are the associations of cohorting, staffing, and testing interventions with COVID-19 transmission in nursing homes?In this decision analytical modeling study in a simulated nursing home with 100 residents and 100 staff, routine screening testing and strategies that prioritized pairing recovered staff and recovered residents with susceptible residents were associated with a reduction in transmission of COVID-19 in nursing homes.These findings suggest that frequent testing and immunity-based staffing interventions may reduce transmission of SARS-CoV-2 in nursing homes and protect this vulnerable population. This decision analytical modeling study examines the associations of cohorting, staffing, and testing interventions with COVID-19 transmission in nursing homes. Nursing homes and other long-term care facilities have been disproportionately impacted by the COVID-19 pandemic. Strategies are urgently needed to reduce transmission in these high-risk populations.To evaluate COVID-19 transmission in nursing homes associated with contact-targeted interventions and testing.This decision analytical modeling study developed an agent-based susceptible–exposed–infectious (asymptomatic/symptomatic)–recovered model between July and September 2020 to examine SARS-CoV-2 transmission in nursing homes. Residents and staff of a simulated nursing home with 100 residents and 100 staff split among 3 shifts were modeled individually; residents were split into 2 cohorts based on COVID-19 diagnosis. Data were analyzed from September to October 2020.In the resident cohorting intervention, residents who had recovered from COVID-19 were moved back from the COVID-19  cohort to the non–COVID-19  cohort. In the immunity-based staffing intervention, staff who had recovered from COVID-19 were assumed to have protective immunity and were assigned to work in the non–COVID-19 cohort, while susceptible staff worked in the COVID-19 cohort and were assumed to have high levels of protection from personal protective equipment. These interventions aimed to reduce the fraction of people’s contacts that were presumed susceptible  and replaced them with recovered (immune) contacts. A secondary aim of was to evaluate cumulative incidence of SARS-CoV-2 infections associated with 2 types of screening tests  conducted with varying frequency.Estimated cumulative incidence proportion of SARS-CoV-2 infection after 3 months.Among the simulated cohort of 100 residents and 100 staff members, frequency and type of testing were associated with smaller outbreaks than the cohorting and staffing interventions. The testing strategy associated with the greatest estimated reduction in infections was daily antigen testing, which reduced the mean cumulative incidence proportion by 49% in absence of contact-targeted interventions. Under all screening testing strategies, the resident cohorting intervention and the immunity-based staffing intervention were associated with reducing the final estimated size of the outbreak among residents, with the immunity-based staffing intervention reducing it more  than the resident cohorting intervention . The estimated reduction in transmission associated with these interventions among staff varied by testing strategy and community prevalence.These findings suggest that increasing the frequency of screening testing of all residents and staff, or even staff alone, in nursing homes may reduce outbreaks in this high-risk setting. Immunity-based staffing may further reduce spread at little or no additional cost and becomes particularly important when daily testing is not feasible. In addition, nursing homes have high risk of transmission because residents live in close quarters and have frequent close contact with staff.4 Furthermore, many facilities face understaffing, which may lead to even higher contact rates between staff and residents, and larger outbreaks.5In the United States, nursing homes and other long term care facilities have been disproportionately affected by the COVID-19 pandemic.6 Long-term separation after recovery keeps the non–COVID-19 cohort highly susceptible, and thus has the potential unintended effect of preventing institutional herd immunity. Additionally, there is relatively little evidence about the potential of staffing strategies based on infection histories. Although guidelines require testing in nursing homes,6 the frequency and type of testing varies.The ability to develop evidence-based responses to this crisis has been constrained by limited data and research in these facilities prior to the COVID-19 pandemic. SARS-CoV-2 infection control in many nursing homes has relied on isolating residents who were infected in a COVID-19 cohort, per recommendations.7 A primary driver may be presymptomatic and asymptomatic transmission, the latter of which occurs in an estimated 40% of SARS-CoV-2 infections.9 For this reason, public health screening testing  is an important intervention to control transmission. In addition, control measures that reduce the frequency of contacts among those who are susceptible and those who are infected but not yet identified could reduce transmission. Owing to a lack of resources, it is critical to identify interventions that will most effectively control infection in these settings at minimal cost.Despite guidelines on cohorting and testing, nursing homes have experienced large outbreaks.In this study, we use an agent-based model representing a nursing home to estimate the associations of a range of interventions with COVID-19 outbreak size. We focus on 2 contact-targeted interventions to group staff and residents based on their infection and immunity status and examine testing interventions that reflect currently available virological tests. Our proposed strategies involve colocating susceptible nursing home residents with staff and residents who have recovered and are assumed to be immune, and examining whether screening with rapid antigen testing outperforms polymerase chain reaction (PCR)–based testing strategies.This decision analytical modeling study did not require institutional review board review or approval because only simulated data are used. In this study, we developed a stochastic, agent-based, discrete-time susceptible–exposed–infectious (asymptomatic/symptomatic)–recovered model to examine SARS-CoV-2 transmission in nursing homes. We modeled daily time steps over 3 months, beginning with a fully susceptible population and 1 resident with SARS-CoV-2 exposure. Once identified, residents who were infected were separated into a distinct cohort where they have no interactions with residents who are susceptible or undiagnosed . Individ10 We made the conservative assumption that new residents continually replaced those who died or were discharged, keeping capacity at 100% and leading to a constant inflow of susceptible residents. On entry into the nursing home, each resident was assigned to a 2-person room and remained in there unless identified as infected and moved to the COVID-19 cohort or discharged.We used 2016 Centers for Medicare & Medicaid Services data to set the distribution for the length of stay for residents.11Staff were split between COVID-19 and non–COVID-19 cohorts in proportion to the number of residents in each. Staff who became infected were sent home and replaced by temporary workers while they recovered. Staff had a constant daily risk of infection from the community, which was set to reflect different levels of community prevalence  was weighted to represent different contact intensities. These did not incorporate proximity and duration of contact explicitly, but aimed to capture heterogeneity in transmission risk. The lowest intensity was between staff, with a baseline probability of transmission per contact of 0.02. Owing to closer contact necessitated by provision of care, staff-resident contacts had a 3-fold higher risk of transmission (0.06) than baseline. Roommates had the highest risk of transmission, which was 10-fold higher (0.20) than baseline.Daily contacts were modeled between residents, staff, and between the 2 populations. We assumed transmission only occurred on contact and did not consider strictly airborne transmission. The frequency of contacts between residents and staff were based on rates in a Massachusetts network of nursing homes, as reported by R. Anglo, RN, MSN, MBA , which was consistent with analyses of nursing homes across the country, as reported by of M. Samore, MD . With the exception of roommates, who were modeled explicitly, we assumed homogeneous  mixing. The daily risk of infection for each individual was binomial, with a probability determined by the mean infectiousness of the population they contacted multiplied by the number of contacts. At baseline, staff contacted 6 residents per day and 2 other staff. As nursing homes have been under strict restrictions during the pandemic,14 Following the latent period, viral load increased rapidly for 2 to 5 days before peaking, then immediately declined. For individuals who were symptomatic, symptom onset occurred after 2 days of increasing viral load, resulting in an incubation period of 5 to 7 days.16 We made the conservative assumption of the same viral load distributions for asymptomatic and symptomatic infections.17 The peak viral load was drawn from a normal distribution on the log scale.18 The duration of detectable viral load was normally distributed and could be longer than the infectious period.19 The relationship between viral load and infectiousness is not fully understood.20 In this model, we based infectiousness categorically on viral load: not infectious, moderately infectious, and fully infectious . The code used for this study is available online.In a simulated nursing home with 100 residents and 100 staff, the estimated mean (SD) cumulative incidence proportion across 100 simulations after 3 months in the absence of testing or cohorting interventions was 0.41 (0.06) . The meaIn the absence of testing, the estimated effectiveness associated with the immunity-based staffing intervention compared with no intervention was 19% for residents, 10% for staff, and 12% overall. The estimated effectiveness associated with this intervention was lower when testing frequency increased. Combining the resident cohorting and immunity-based staffing interventions was not associated with higher estimated effectiveness than the immunity-based staffing intervention alone  , we do not explicitly model different staff roles, which may have different levels of risk. We made this simplification in order to make the results of this model more generalizable, as nursing home staff structure varies widely.This study has some limitations. In developing and analyzing our model, we have made several simplifying assumptions. We assume no spillover between the COVID-19 and non–COVID-19 cohorts. In settings with understaffing, this assumption may be violated owing to compounding effects associated with outbreak-related stressors. As a conservative assumption, we assume no difference in viral load dynamics between symptomatic and asymptomatic individuals and ignore the persistent low viral loads after individuals are no longer infectious27 While we incorporate length of stay data for residents and temporary staff for staff who are isolating at home, we do not model staff turnover. Recent research suggests nursing home staff turnover may be very high.28 The impact of this assumption for the results of our model would depend heavily on the levels of immunity in new staff: new staff could come from other nursing homes that have experienced outbreaks or from the general community, which would likely have lower seroprevalence. In addition, available data are from before the COVID-19 pandemic, and there may be large heterogeneity in how the pandemic affected staff employment trends. In an effort to minimize the implications of this assumption on our results, we limit our analysis to only 3 months.We also assume that all individuals with symptoms are infected with SARS-CoV-2 and do not model symptoms from other diseases. Given low rates of other infectious diseases, such as flu, due in part to non-pharmaceutical interventions, we think this is a reasonable assumption.This decision analytical modeling study found that simple interventions involving testing and staffing were associated with greatly reducing the burden of SARS-CoV-2 in nursing homes. In practice, the choice of interventions would depend on the resources available, which include testing, PPE, and staff time. Our model further underscores the importance of rapid screening testing.


The present study was performed to investigate the validity of a wireless earbud-type inertial measurement unit (Ear-IMU) sensor used to estimate head angle during four workouts. In addition, relationships between head angle obtained from the Ear-IMU sensor and the angles of other joints determined with a 3D motion analysis system were investigated. The study population consisted of 20 active volunteers. The Ear-IMU sensor measured the head angle, while a 3D motion analysis system simultaneously measured the angles of the head, trunk, pelvis, hips, and knees during workouts. Comparison with the head angle measured using the 3D motion analysis system indicated that the validity of the Ear-IMU sensor was very strong or moderate in the sagittal and frontal planes. In addition, the trunk angle in the frontal plane showed a fair correlation with the head angle determined with the Ear-IMU sensor during a single-leg squat, reverse lunge, and standing hip abduction; the correlation was poor in the sagittal plane. Our results indicated that the Ear-IMU sensor can be used to directly estimate head motion and indirectly estimate trunk motion. Athletes and active people have continued exercising during the coronavirus disease 2019 (COVID-19) pandemic quarantine period to maintain their physical fitness, decrease pain and fatigue, and prevent injury . WearablInertial measurement units (IMUs) are commonly used in wearable devices to capture and monitor human motion ,6. The vIn a study in which measurements were taken using IMU sensors placed inside a baseball cap and attached to the waist of healthy subjects while walking, the head motion was strongly correlated with trunk motion based on acceleration data . When viWearable sensors have been used to prevent injury in healthy athletes, which is an important element of modern healthcare . IncorreThe study population consisted of 20 healthy volunteers from XX University, all of whom regularly engaged in exercise training or workouts . The inc2 each were placed on each participant: on the second metatarsal head, posterior calcaneus and fibular lateral malleolus of both feet, both shins, lateral epicondyles of both knees, both thighs, anterior and posterior superior iliac spine, 7th cervical and 10th thoracic vertebrae, sternum, clavicle, right scapula, superior to both acromioclavicular joints, both upper arms, both lateral epicondyles of the elbow, both forearms, medial and lateral styloid processes of both wrists, and 2nd metacarpals bilaterally; four other markers were attached to a headband [The experimental study was carried out in a laboratory setting. While participants performed four exercises, kinematic data in sagittal and frontal planes were recorded simultaneously using both the earbud-type IMU sensor and a 3D motion analysis system . The 3D motion analysis system was used as a reference to collect kinematic data via six infrared cameras at a frequency of 100 Hz. Signals emitted from the Vicon camera were reflected from markers attached to anatomical landmarks on the participant according to the full-body plug-in-gait marker model. In this study, 39 markers measuring 15 mmheadband .x-, y- and z-axis. Acceleration outputs were scaled to be a proportional value of Earth gravity which are A single IMU  was embedded into the right wireless earbud  . To measThe orientation of the IMU can be defined with its roll was randomised using Excel . Each workout was performed three times at a self-selected speed with a 1 min break time between workouts. The supporting leg during a single-leg squat, reverse lunge, and standing hip abduction was regarded as the supporting side when kicking a soccer ball. To synchronise the two datasets obtained with the 3D motion analysis system and IMU sensor, participants were asked to perform rapid neck flexion at the beginning of each workout . Each woSquat: The participant standing with hands placed on the waist and the feet shoulder-width apart was asked to perform a mini-squat to a self-determined depth within an angle of 90° knee flexion, then return to the starting position .Single-leg squat: The participant stood on the single supporting leg with the hands placed on the waist, then squatted down and up while maintaining balance. The squat depth was not controlled because this study focused on capturing the true movement patterns of the participants .Reverse lunge: The reverse lunge was initiated by moving the non-supporting foot backwards, while the supporting foot remained planted. The front knee continued to flex until achieving a position parallel to the floor; the back knee was brought closer to the floor using a 6 cm foam pad as a tactile depth cue. The distance between the front foot and back foot was approximately 75% of leg length. The participant was then asked to return to the starting position .Standing hip abduction: The participant was asked to abduct the arm of the non-supporting side to 90° while standing with the feet shoulder-width apart. Subsequently, the participant was asked to abduct the hip of the non-supporting leg as far as possible and then return to the starting position .The raw data of head, trunk, pelvis, hip, and knee obtained from the 3D motion analysis system were analysed using Nexus software ver. 1.8.5 ; they were filtered using a low-pass, zero-lag, 4th order Butterworth filter with a cut-off frequency of 6 Hz .Statistical analysis was performed with SPSS . The alpha level for statistical significance was set at 0.05. According to the results of the Shapiro–Wilk test for the normality of the data distribution, the Spearman rho correlation coefficient (ρ) was used to investigate the concurrent validity of head angle data between the IMU sensor and the 3D motion analysis system; the pairwise correlations between head angle (determined with the IMU sensor) and the angles of the trunk, pelvis, hip, and knee ; the pairwise correlations between the head angle and the angles of the trunk, pelvis, hip, and knee . The correlations were expressed as perfect (ρ = 1), very strong (0.8 ≤ ρ < 1), moderate (0.6 ≤ ρ < 0.8), fair (0.3 ≤ ρ < 0.6), or poor (0 < ρ < 0.3) .The observed concurrent validities of the head angle determined with the earbud-type IMU sensor, compared with the angle determined with the 3D motion analysis system, were very strong during the squat, single-leg squat, and standing hip abduction exercises; the concurrent validity was moderate during reverse lunge in the sagittal plane. In addition, the head angle measured with the earbud-type IMU sensor showed moderate validity in the frontal plane, compared with the 3D motion analysis system, during all workout activities .We investigated the pairwise correlations between the head angle (determined with the IMU sensor) and the angles of other joints  to determine which joints had movement similar to head movement during the workout. In the frontal plane, the trunk angle showed a fair correlation with IMU sensor head angle during the single-leg squat, reverse lunge, and standing hip abduction. In addition, the pelvis and knee angles showed fair correlations with head angle during the single-leg squat and standing hip abduction exercises, respectively. The pairwise correlations between the head angle and the angles of other joints in the sagittal plane were poor .For comparison, we also investigated the pairwise correlations between head angle and the angles of other joints using only the 3D motion analysis system data. These correlations were similar to the results when using both the IMU sensor and the 3D motion analysis system. Specifically, the trunk angle in the frontal plane showed a moderate or fair correlation with the head angle during the single-leg squat, reverse lunge, and standing hip abduction exercises; the pelvis angle in the frontal plane showed a fair correlation with the head angle during the single-leg squat exercise. The pairwise correlations between the head angle and the angles of other joints in the sagittal plane were poor .sensorsThis study confirmed the moderate-to-very-strong validity of measuring the head angle during four types of workouts using an earbud-type IMU sensor, compared with a 3D motion analysis system. The head angle determined with the IMU sensor in the frontal plane showed a fair correlation with trunk angle determined with the 3D motion analysis system during the single-leg squat, lunge, and standing hip abduction activities. The earbud type-IMU sensor could be useful for measuring the motion of the head and trunk, as an alternative to the use of a 3D motion analysis system for workout motion-focused coaching, for active people who prefer to listen to music during workouts.Comparison of head angles measured using the earbud-type IMU sensor and the 3D motion analysis system confirmed moderate-to-very-strong validity in the sagittal and frontal planes during the squat, single-leg squat, reverse lunge, and standing hip abduction exercises. The validity in the sagittal plane was higher than the validity in the frontal plane in this study. Previous studies suggested that the higher validity in the sagittal plane, compared with the frontal plane, was caused by greater angles in the sagittal plane during squats and single-leg squats, as determined using a Kinect camera and 3D motion capture system ,35,36. AWe found that head movement was similar to trunk movement in only the frontal plane, based on data obtained from the 3D motion analysis system during the single-leg squat, reverse lunge, and standing hip abduction exercises. Consistent with these results, the frontal head angle measured by the earbud-type IMU sensor showed a similar pattern to the frontal trunk angle measured with the 3D motion analysis system, based on the fair correlation between head and trunk angles. There are two plausible reasons for these observations. First, the trunk is closer to the head than to the hips and knees, resulting in a fair correlation between the trunk and head angles. The second reason may be explained by vestibular function in healthy people. Our observations are consistent with previous findings that healthy people have a greater head–trunk correlation than people with unilateral vestibular hypofunction while walking on a treadmill because the vestibular system controls movements of the head and trunk together through the vestibulo-cervical reflex and vestibulospinal reflex, respectively . In peopAn earbud IMU sensor could be used for measuring the trunk motion indirectly in clinical and sports settings. Specifically, earbud IMU sensors can be used to prevent knee injury by assessing frontal trunk motion during functional performance tests, such as single-leg squats and forward step-down tests, because frontal trunk motions can directly influence the knee moment in the frontal plane; people with knee pain reportedly exhibited greater ipsilateral trunk lean than did healthy controls ,42. An eAn earbud IMU sensor may also be useful for vestibular rehabilitation using visual and auditory feedback. A previous study provided visual feedback on the anteroposterior and mediolateral displacement of the trunk on a TV screen using two webcams and showed that a 4-week visual feedback program significantly improved balance in older people with vestibular loss or sensory impairment by increasing their awareness of body motion while walking. In another auditory feedback study, the volumes of four loudspeakers increased linearly, from 60 to 95 dB, towards the swaying side depending on the trunk angle velocity, improving postural control in patients with vestibular dysfunction . In an eThis study found poor pairwise correlations between frontal head angle (measured by the earbud-type IMU sensor) and the hip and knee angles . To our knowledge, there have been no studies regarding direct correlations of movement between the head and other joints. However, some studies reported the pattern of trunk motion, compared with lower limb motion, in terms of compensatory mechanisms ,45. In pThere was no significant correlation between head and trunk motions in the sagittal plane during workout activities in this study, meaning that the earbud IMU sensor cannot inform excessive trunk flexion or extension during squats, single-leg squats, lunges, and standing hip abduction. Participants had to gaze at the front during performing workout activities in this study, so the head was maintained without excessive head flexion or extension even though the trunk was flexed or extended during performing squat and lunge. This may be the reason why no significant correlation was found between head and trunk motions in the sagittal plane in the current study. This study found correlations only in the sagittal and frontal planes because a systematic review found low validity for the rotation components of the trunk and lower limb joints , and truFirst, the participants in this study were young active people who exercised regularly and had no musculoskeletal pain. Therefore, the results cannot be generalised to sedentary or inactive people, older adults, and people with musculoskeletal pain. Second, only a gyroscope and an accelerometer were used to maximise battery life, with the aim of facilitating the commercial development of the wireless earbud-type IMU sensor. A previous study suggested that using a gyroscope, accelerometer, and magnetometer together can overcome the limitations of each sensor type and improve measurement accuracy, although sensor fusion could not completely compensate for all sensor limitations . FurtherThe wireless earbud-type IMU sensor showed moderate-to-very-strong concurrent validity in head angle measurement with 3D motion analysis results in sagittal and frontal planes during the squat, single-leg squat, lunge, and standing hip abduction exercises. The earbud-type IMU sensor can be used for indirect measurement of frontal trunk motion; it showed a fair correlation between the head and trunk in the frontal plane during workout activities. These findings suggest that a wireless earbud incorporating a single IMU sensor could be useful for measuring the head motion directly or frontal trunk motion indirectly in workout and sport settings.


The level of impulsivity, hyperactivity, and inattention (IHI) is higher among patients with substance use disorder (SUD) than in the general population. However, the prevalence of such symptoms in patients seeking treatment with an opioid antagonist, such as extended-release naltrexone (XR-NTX), is unknown. We screened 162 patients with opioid use disorder (OUD) seeking treatment with XR-NTX in Norway using the Adult ADHD Self-Report Scale (ASRS) to estimate the prevalence of IHI alongside an assessment of mental and physical health and substance use. Sixty-six patients scored above the clinical cut-off on the ASRS. Higher levels of IHI were significantly associated with a longer history of frequent amphetamine use, current alcohol use, and greater mental distress. Mental distress was the strongest factor associated with higher levels of IHI. The introduction of screening for IHI and mental distress in opioid maintenance treatment and XR-NTX would likely improve the quality of care and enable clinicians to tailor interventions to the needs of patients with high levels of IHI to prevent treatment discontinuation. A high prevalence of psychiatric comorbidity, mental distress, and attention deficit hyperactivity disorder (ADHD) has been observed in a number of patient populations with substance use disorder (SUD) ,2. HowevBy 2019, there were approximately 7900 patients enrolled in Norwegian OMT programs, and the primary medical treatment option was opioid agonist therapy, such as methadone, buprenorphine, or buprenorphine/naloxone .Medical treatment with an extended-release opioid antagonist is an alternative to OMT medication for select patients and may reduce some of the concerns associated with OMT . HoweverTo explore the burden of IHI, it is relevant to better understand the impact of these risk factors on treatment trajectories . To the We estimated the prevalence of patients scoring above the clinical cut-off on a screening test for IHI in a population qualifying for OMT, but with a preference for opioid antagonist treatment. To broaden our understanding of the challenges encountered by these patients, particularly in treatment settings in which non-adherence to treatment incurs increased risks, we examined the reported level of IHI and its association with SUD complexity and mental health burden. Lastly, we explored which factors had the strongest association with elevated levels of IHI.®) was administered every 4 weeks. The total study period was 24 weeks, followed by a 28-week optional follow-up treatment period. Study interviews, assessments of mental distress, and urine drug screens were performed at each visit together with the XR-NTX injection.This study was built upon baseline data from the Norwegian NaltRec study  , an openThe study was conducted in accordance with the ethical principles of the Declaration of Helsinki , which aPatients were recruited between September 2018 and September 2020 from addiction clinics and detoxification units at the participating hospitals. A number of patients were also referred from the municipal health services and by word of mouth between opioid users. Men and women aged 18–65 years with a current diagnosis of opioid dependence were eligible for participation . PatientAll screening procedures were carried out by trained staff at the participating hospitals. Demographic data were obtained using the European version of the Addiction Severity Index interview (Europ-ASI). Opioid dependence was verified by The Mini International Neuropsychiatric Interview (MINI), version 6.0 .IHI was measured using the self-administered Adult ADHD Self-Report Scale 18-item version (ASRS-18) v1.1 . The ASRCurrent mental distress was measured using the 25-item Hopkin’s Symptom Checklist (H-SCL-25) . This seCurrent severity in other life areas  was measured using the Europ-ASI ,25. CompWe used descriptive statistics to characterize the sample. Some data were not normally distributed and were reported as medians and interquartile ranges (IQRs).β) with a 95% confidence interval (CI). Preliminary bivariate analyses were performed and variables with significant p-values were included in a multivariable linear regression analysis. The R-squared (R2) value was used to assess the percentage of variation in the level of IHI explained by the model. We also report the standardized βs to compare the relative strength of the relationship between variables. The threshold for significance was set at p < 0.05. All statistical analyses were performed using IBM SPSS, version 26 [Non-parametric statistics (Mann–Whitney U-test) were applied to compare the groups in regard to continuous variables and cross-table analysis with the chi-squared test for categorical variables. We used linear regression to examine the association between the IHI score and independent variables, such as socio-demographic characteristics and severity variables. The results of these analyses are presented as unstandardized beta coefficients . This is in line with previous studies indicating a preference for stimulants among the patients with symptoms of IHI ,22. CompLooking at the lifetime prevalence of psychiatric symptoms, a larger fraction of the patients with ASRS-18 scores above the cut-off reported cognitive challenges , non-drug-related hallucinations, violent behaviour, and suicidal behaviour. The same applied to current mental distress, as a significantly higher proportion of those scoring above the ASRS-18 cut-off reported clinical levels on the H-SCL-25 (70% vs. 48%). Similar findings have been reported in previous studies of patients with SUDs, as well as in patients in OMT .In a multivariable analysis, high levels of mental distress were more strongly associated with IHI than other variables. One increased point on the H-SCL-25 was associated with a 2.29-point increase on the ASRS 18-item scale when controlling for all the other factors in the model. This is a substantial increase and implies a need to focus on current mental distress when considering feasible treatment options for patients with OUD. A previous randomized study conducted in Norway found that patients receiving opioid antagonist treatment did not experience any increase in mental distress over a 3-month period compared to patients on BUP/NLX . Thus, aRegarding IHI, the ASRS-18 does not provide information on any threshold for neurocognitive impairment or the symptom aetiology. It does, however, hint to a state of executive functioning that is prone to being challenged in the process of recovering from SUD, taking into consideration the effort needed to resist relapse . PatientThe cross-sectional design limits the possibility of drawing conclusions about causality concerning the relationship between variables. The study could potentially have been strengthened by an in-study control group, such as a general SUD sample, to compare the prevalence of high ASRS scores. However, we have related our findings to comparable studies in this field. The IHI scores rely on subjective data, as the ASRS-18 is a questionnaire expressing the patient’s experience with IHI. No neuropsychological tests were applied to obtain more objective measures of IHI. Mental distress measured using the H-SCL-25 focused mainly on symptoms experienced during the past 2 weeks. At the time of screening and treatment initiation, the patients could have been influenced by residual withdrawal symptoms from drugs or excitement over entering a new treatment, influencing the H-SCL-25 scores . During The high coexistence of IHI and mental distress warrants attention to improve the chance of successful treatment trajectories. With knowledge of this double burden among opioid-dependent patients seeking treatment with XR-NTX, a screening routine could help clinicians identify patients with an extended need for additional follow-ups in order to better facilitate the patient’s recovery process.


The term spondyloarthritis pertains to both axial and peripheral arthritis including ankylosing spondylitis (AS) and psoriatic arthritis (PsA), which is strongly linked to psoriasis and also the arthritis associated with inflammatory bowel disease. The argument supporting the role for IL-23 across the spectrum of SpA comes from 4 sources. First, genome wide associated studies (GWAS) have shown that all the aforementioned disorders exhibit IL-23R pathway SNPs, whereas HLA-B27 is not linked to all of these diseases-hence the IL-23 pathway represents the common genetic denominator. Secondly, experimental animal models have demonstrated a pivotal role for the IL-23/IL-17 axis in SpA related arthropathy that initially manifests as enthesitis, but also synovitis and axial inflammation and also associated aortic root and cutaneous inflammation. Thirdly, the emergent immunology of the human enthesis also supports the presence of IL-23 producing myeloid cells, not just at the enthesis but in other SpA associated sites including skin and gut. Finally, drugs that target the IL-23 pathway show excellent efficacy for skin disease, efficacy for IBD and also in peripheral arthropathy associated with SpA. The apparent failure of IL-23 blockade in the AS which is effectively a spinal polyenthesitis but evidence for efficacy of IL-23 inhibition for peripheral enthesitis in PsA and preliminary suggestions for benefit in axial PsA, raises many questions. Key amongst these is whether spinal inflammation may exhibit entheseal IL-17A production independent of IL-23 but peripheral enthesitis is largely dependent on IL-23 driven IL-17 production. Furthermore, IL-23 blocking strategies in animal models may prevent experimental SpA evolution but not prevent established disease, perhaps pointing towards a role for IL-23 in innate immune disease initiation whereas persistent disease is dependent on memory T-cell responses that drive IL-17A production independently of IL-23, but this needs further study. Furthermore, IL-12/23 posology in inflammatory bowel disease is substantially higher than that used in AS trials which merits consideration. Therefore, the IL-23 pathway is centrally involved in the SpA concept but the nuances and intricacies in axial inflammation that suggest non-response to IL-23 antagonism await formal definition. The absence of comparative immunology between the different skeletal sites renders explanations purely hypothetical at this juncture. The seminal clinical observations by Moll and Wright in the 1970s classified several diseases under the umbrella term of Spondyloarthorpathy (SpA) based on shared clinical and immunological features , 3. The Following on shortly after the Moll and Wright’s SpA concept was the discovery of HLA-B27 that was associated with AS, PsA axial disease, IBD related axial arthritis, anterior uveitis and reactive arthritis –8. HowevThe current therapy options in AS include an anti-TNF agents for subjects that fail to respond to NSAIDS . If anti-TNF is contraindicated or if there is loss of efficacy to anti-TNFs, one of two anti-IL-17A blockers can be considered with the provision that these agents should not be used for active associated IBD . The JAKWhen naïve T-cells encounter a cognate antigen in lymphoid tissues, they have the ability to differentiate into effector T-cells, depending on the local microenvironment. This involves MHC peptide presentation to the T-cell receptor (TCR)  and then co-activation with CD80/86 binding to CD28  on T-cells . In humaRemarkably, IL-23R polymorphisms have been reported across all of the aforementioned categories of disease but not in classical autoimmunity , 9, 19. It is well established that the synovium is the primary target of inflammation in RA with autoimmunity directed against citrullinated synovial proteins driving an inflammatory reaction culminating in periarticular joint destruction and erosion, with the well-recognised polyarticular joint destruction phenotype. In the mid-1990s, MRI studies showed that enthesitis was evident in both swollen small and large joints in PsA and SpA in general . This reA seminal paper by Sherlock et al. demonstrated that the normal murine enthesis harboured an IL-23R expressing cell population . This moOf course, there are several independent animal models showing the pivotal role of IL-23 in experimental gut inflammation and reactive type arthritis , 40. A bClinical trials in man dissect human disease immunopathogenesis and it is important to turn to these, in order to better understand human enthesitis. First, IL-17A blockers have proven efficacy for both peripheral and axial SpA including evidence for efficacy for isolated enthesitis as a secondary outcome measure –50. Likein vitro in MSC from the spinal enthesis  . Indeed,in vivo , 86. HenMost of the spinal inflammation in AS occurs in the peri-entheseal bone where disease localisation to this site is related to the HLA-B27 genetic status . Our worThe failed trial of ustekinumab in AS used the 45mg and 90mg dosing regimen but the higher dose was associated with a non-significant CRP reduction and minor improvements in MRI lesions . The dosIt must be clearly articulated that translational therapy in man, and not laboratory experimental science is leading the understanding of these pathways. It is noteworthy that p40 blockers were associated with efficacy for axial symptoms in PsA, but it must be acknowledged that HLA-B27 negative axial PsA might represent a different disease from AS . A cliniFor the purposes of this article the term SpA was taken to include the protean manifestations associated with axial inflammation including skin and gut involvement where it has clearly been shown that IL-23 SNPs are a common denominator across the different conditions. It is also clearly evident in experimental and human systems that the IL-23/IL-17 axis is involved in skin, gut and entheseal biology . A diffeThis non-linearity between IL-23 and IL-17 also appears to exist in the human spine but this knowledge is presently very rudimentary. Nevertheless, there is preliminary evidence suggesting that the downstream IL-17A pathway in axial biology is regulated in both IL-23 and IL-23 independent pathways. Further work is needed in man including IL-23 posology and careful assessment of disease subtypes and objective measures of inflammation including CRP and MRI appearances. The emergent biology of the IL-23/17 axis in the human skeleton strongly suggests that hidden within the current complexity is an IL-23 pathway, there may be a SpA subgroup with axial inflammation that might still be exploitable therapeutically with antagonism of this pathway.CB, AW, KS and DM all contributed to scientific discussion, writing and figure making for the paper. All authors contributed to the article and approved the submitted version.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.


Since the Nobel Prize-winning work of Huggins, androgen ablation has been a mainstay for treatment of recurrent prostate cancer. While initially effective for most patients, prostate cancers inevitably develop the ability to survive, grow, and metastasize further, despite ongoing androgen suppression. Here, we briefly review key preclinical studies over decades and include illustrative examples from our own laboratories that suggest prostate cancer cells titrate androgen signaling to optimize growth. Such laboratory-based studies argue that adaptations that allow growth in a low-androgen environment render prostate cancer sensitive to restoration of androgens, especially at supraphysiologic doses. Based on preclinical data as well as clinical observations, trials employing high-dose testosterone (HDT) therapy have now been conducted. These trials suggest a clinical benefit in cancer response and quality of life in a subset of castration-resistant prostate cancer patients. Laboratory studies also suggest that HDT may yet be optimized further to improve efficacy or durability of response. However, laboratory observations suggest that the cancer will inevitably adapt to HDT, and, as with prior androgen deprivation, disease progression follows. Nonetheless, the adaptations made to render tumors resistant to hormonal manipulations may reveal vulnerabilities that can be exploited to prolong survival and provide other clinical benefits. Prostate cancer is the most common non-skin cancer in men. While most prostate cancers are relatively indolent and are cured by local treatment, recurrent cancers and cancers that are advanced at diagnosis are responsible for over 30,000 deaths annually in the United States alone. In this work, we briefly address the current status of clinical management of recurrent or advanced prostate cancer. Ever more efficacious suppression of androgen signaling has been the central focus of therapeutic development for decades. However, progress in the treatment of men whose disease has progressed in the face of continued androgen suppression has been limited. Recent trials employing the approach of high-dose testosterone (HDT) therapy in castrate-resistant prostate cancer (CRPC) show clinical benefit and improved quality of life in a subset of CRPC patients who otherwise have few treatment options, all with potential for significant side effects. We review preclinical studies that supplied support for the heretical (to some) concept of administering testosterone to prostate cancer patients. Using previously unpublished data from our laboratories, we illustrate the types of findings that have led to the reassessment of the potential clinical utility of manipulation of the androgen environment in prostate cancer. Finally, we discuss the implication of such findings on the future of HDT and how this approach may be optimized further.Targeting of the androgen receptor (AR) axis has been the foundation of therapy for advanced prostate cancer for over 75 years. The seminal work by Huggins and Hodges in the 1940s led to the widespread use of surgical orchiectomy for symptomatic metastatic prostate cancer . Other rp < 0.0001). In the combination group, a significant long-term benefit was realized, with the median time to subsequent prostate therapy and time to secondary progression-free survival in excess of 4 years.The use of more-intensive strategies to block androgen signaling has now been extended into the metastatic, hormone-sensitive setting. For example, in the LATITUDE trial, men with metastatic, castration-sensitive prostate cancer were randomized to ADT with or without abiraterone as initial systemic therapy . PatientDespite more aggressive upfront therapy, the outcomes of metastatic prostate cancer remain poor, especially after the development of castrate-resistant disease. For example, in later-stage patients with castration-resistant prostate cancer who have progressed after docetaxel chemotherapy and an androgen-signaling-targeted inhibitor , the use of secondary chemotherapy versus an additional androgen-signaling-targeted inhibitor has been studied. In this setting, the use of secondary chemotherapy was superior to an additional androgen-signaling-targeted inhibitor; however, the median overall survival was 13.6 months with cabazitaxel versus 11.0 months with the androgen-signaling-targeted inhibitor, which highlighted the limits of treatment efficacy in later-stage prostate cancer patients.While both preclinical and human studies have identified potential mechanisms for resistance to ADT, such as expression of AR variants  and overThe centrality of androgen signaling in prostate cancer was highlighted by the report of Chen et al. in that a moderate increase in AR gene expression was the only consistent change in gene expression in CRPC, and that this increase in receptor mRNA and protein was required for resistance . There aMore recently, there has been renewed clinical interest in the potential therapeutic and quality-of-life benefit of HDT in CRPC. Much of the recent work has been spearheaded by Sam Denmeade and colleagues at Johns Hopkins University, including a small pilot clinical study in patients with CRPC published in 2015. In this study, investigators administered HDT as bipolar androgen therapy (BAT), in which patients were given a high-dose injection of testosterone cypionate monthly . This prIn these clinical reports, several patterns of response to BAT have been described ,23. SomeAt initial clinical presentation, most prostate cancer patients’ tumors are dependent on androgens for growth. Unlike hormone-dependent mammary cancers, there is a paucity of reliable cell models of hormone-dependent prostate cancer. Indeed, key early studies on androgen-dependency employed the androgen-dependent Shionogi mouse mammary tumor model . Many suIn the sections that follow, we offer illustrative examples utilizing unpublished preclinical studies from our own laboratories that emphasize the observations we and others have made that have led to insights into the role of androgens in prostate cancer, and how these insights may be exploited clinically.The LNCaP prostate cancer cell line is derived from a lymph node metastasis and is dependent on androgens for growth in cell cultures and in xenografts . When swWhen growth medium is supplemented with a high level of R1881 (10 nM), LNCaP cell growth is partially inhibited, and a subset of cells undergo apoptosis. Nonetheless, LNCaP cells adapt to continuous exposure to high levels of R1881 in growth medium and will resume growth. R1881-adapted cells exhibit a dose response that is right-shifted to become a classic S-shaped curve , blue, uLike many investigators, we selected LNCaP cell variants as models of CRPC through long-term culturing in low-androgen medium. The CRPC LNCaP variant, P1, was derived from a parental LNCaP line (P0) that had previously been engineered to contain an androgen-responsive luciferase reporter gene. Two other independent lines  were selected similarly from the M0 parental line . A fourth CRPC model, 4D1enz1, was derived by selection of M0 in the androgen antagonist, enzalutamide, followed by further selection in low-androgen medium . As modeThe adaptation of CRPC LNCaP variant lines to a low-androgen environment was evidenced by their growth properties in vivo. The sensitivity of CRPC cells to androgens is highlighted in the xenograft data shown in The response of the P1 CRPC variant to androgen supplementation was evaluated further in xenograft studies. In the experiment shown in With the exception of the relapsed tumor in the experiment above, there was insufficient tumor to analyze in the testosterone-implant cohort. Where palpable, the tissue appeared to consist predominantly of stromal elements and fibrous extracellular material. The relapsed tumor was still relatively modest in size, having been one of the pair with the smallest tumor volumes at the initiation of treatment. Analysis of the relapsed tumor by hematoxylin and eosin staining and immunohistochemistry (IHC) for AR and Ki67 revealed some notable features  with no sign of recurrent tumor can be seen  Figure . This maTo test this hypothesis, we subjected the four high-testosterone-resistant lines to a second round of withdrawal of androgens in the culture medium. Unlike the case with the initial selection of CRPC lines, the second round of selection in low-androgen medium resulted in a rather transient and partial level of growth suppression as the cells adapted readily to the low-androgen environment. These lines were named P1R1/R *, M1R1/R *, M2R1/R *, and 4D1enz1/R * . We agaiTo substantiate the in vitro data suggesting the capacity of high-testosterone-resistant P1R1/R cells to respond to a further round of androgen manipulation was limited, P1R1/R cells were injected into the flanks of castrated male mice with implanted testosterone pellets. Tumors were allowed to develop; at which time the testosterone pellets were removed from half of the mice. Androgen withdrawal had only a modest, transient effect on tumor growth, supporting the in vitro observations that these cells were relatively resistant to further rounds of manipulation of their androgen environment. Histochemical analysis  did showProstate cancer cell line LNCaP was obtained from the University of Colorado Cancer Center  Tissue Culture Shared Resource and were authenticated by single tandem repeat analysis. These cells were the ultimate parent of all the variants described in The P0 variant was derived by transducing LNCaP cells with an ARE-luciferase reporter lentivirus  and selection for puromycin resistance. The M0 variant was selected independently in the same way as P0. The enzalutamide-resistant 4D1 line was derived by repeated passaging of LNCaP cells in increasing concentrations of enzalutamide between 1.25 and 2.5 μM for 3 months.The P0, M0, and 4D1 variants were then used to generate models of CRPC by culturing cells in low-androgen medium . Growth slowed, and the cells acquired a dendritic morphology. After 8–10 months of continuous culture in low-androgen medium and passaging as necessary, the cells exhibited a stable phenotype featuring robust growth in low-androgen medium, increased AR expression, and growth inhibition and cell death in response to treatment with 1 nM R1881. These variants were named P1, M1, M2, and 4D1enz1. M1 and M2 were independently derived from M0. While the overall behavior of the four CRPC models with respect to androgen signaling was similar, there were clear differences between them in response to certain drug treatments, reflecting the heterogeneity seen in human CRPC.These CRPC variants were subjected to selection by growth in low-androgen medium supplemented with 1 nm R1881. This initially inhibited growth and stimulated apoptosis, but the cells eventually adapted, and after repeated passaging in these conditions, reacquired robust growth. These lines represented models of high-testosterone resistance (HTR) and were named P1R1/R, M1R1/R, M2R1/R, and 4D1enz1/R. They featured greatly reduced levels of AR expression compared to their CRPC progenitors.Finally, to test whether repeated rounds of manipulating the androgen environment continued to inhibit growth, the testosterone-resistant lines were cultured in low-androgen medium without R1881 supplementation. At this point, growth inhibition was far less pronounced than in the initial round of selection in low-androgen medium. Cells adapted within a few passages and resumed robust growth in low-androgen medium. Further manipulation of the androgen environment of these lines had only minimal transient effects on growth and were, therefore, defined as androgen-independent (AI) for growth. See Cell pellets were collected and resuspended in a lysis solution. Twenty micrograms of protein were separated by polyacrylamide gel electrophoresis and transferred to a nitrocellulose membrane . The membrane was blocked and incubated overnight with the primary antibody at 4 °C. The AR N-20 antibody sc-816 was purchased from Santa Cruz Biotechnology . The GAPDH antibody, 1574S, was purchased from Cell Signaling Technology. Blots were washed 3 times in PBST followed by 1 hour of incubation with secondary antibodies conjugated to horseradish peroxidase (Invitrogen). GAPDH was used as a loading control. Band signals were visualized with a LI-COR system .Histological analyses were performed on formalin-fixed cells or tissues. For the latter, 5 µM sections were used. The sources of primary and secondary antibodies and methodological details for their use follows: androgen receptor , 1:200, secondary: ; Ki67  1:400; secondary: horse anti-rabbit, .The 4- to 6-week-old mice were purchased from Charles Rivers Laboratories . Tumor xenografts were generated by injecting cells into the flanks at 8–9 weeks. In some experiments, testosterone pellets  were implanted in the treated group using a 10 gauge trochar  according to the manufacturer’s instructions. All procedures were carried out under protocol 495 approved by the Institutional Animal Care and Use Committee of the University of Colorado.The successful clinical application of BAT ,22,23 brA better understanding of the impact of androgen levels in prostate cancer may have many clinical applications. For example, patients with advanced prostate cancer have traditionally been started on gonadotropin-releasing hormone (GnRH) agonist therapy . This approach does produce a transient testosterone spike or flare in the first few weeks, before a decline in the testosterone levels. The sensitivity of prostate cancer cells to manipulation of their androgen environment suggests that the testosterone flare associated with initial implementation of GnRH agonist therapy could potentially enhance the response to the subsequent reduction in testosterone levels. Since AR levels rapidly fall in response to elevated testosterone, the testosterone flare has the potential to prime cancer cells for a strong response when testosterone levels then decline to castrate levels. On the other hand, since part of the adaptation of prostate cancer to manipulation of the androgen environment is to increase heterogeneity of AR expression, it may be that any potential benefit of the testosterone flare is counterbalanced by the consequences of this first round of adaptation to the spike in testosterone levels that accompany the initial response to GnRH agonists.Despite the questions that remain, HDT is a strategy that may have great benefit to a subset of men who have progressed to castrate-resistant prostate cancer and is worthy of additional investigation. It is imperative that predictive markers be found to best identify patients with castrate-resistant disease that are likely to respond to HDT. Human CRPC often exhibits elevated AR expression much like the variants developed from LNCaP cells, but tumors from other individuals may express modest or even no AR expression, and therefore are unlikely to achieve any sustained benefit from HDT.The new clinical state of resistance to testosterone therapy means that models to understand the adaptations acquired by testosterone-resistant prostate cancer must be exploited to optimize the benefits of testosterone therapy. One arena in which the experimental advantages of preclinical models may be exploited is to use an integrated omics approach to identify pathways that are associated with adaptation to testosterone resistance. These adaptations could reveal vulnerabilities that are amenable to targeting with existing drugs. Current studies in our laboratories have identified candidate strategies that are being evaluated at this time.The work of many investigators has highlighted the balancing act that prostate cancer must maintain with respect to androgen signaling. Preclinical models have been instrumental in overcoming the mindset that androgens cannot be included in the armamentarium of prostate cancer therapy. High-dose testosterone has now been shown to be of potential therapeutic value in a subset of men whose cancer has progressed while on ADT. Preclinical models offer the prospect of identifying the mechanisms that underlie adaptation of tumors to HDT. Understanding these mechanisms, it is hoped, will lead to improvements in HDT and patient selection, as well as new approaches to overcome resistance.


The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has wreaked havoc on millions of people around the world. Although China quickly brought the Coronavirus disease (COVID-19) under control, there have been several sporadic outbreaks in different regions of China since June 2020. This article described the chronological nosocomial COVID-19 infection events related to several sporadic outbreaks of SARS-CoV-2 in different regions of China. We have reported epidemiological characteristics and management measures of sporadic nosocomial COVID-19 infections from June 2020 to June 2021 and specially focused on the domestic COVID-19 breakthrough infection in China, such as domestic COVID-19 breakthrough infection—a vaccinated healthcare professional working in the isolation ward of a designated COVID-19 hospital. The outbreak of the Coronavirus-2019 (COVID-19) virus was first reported in Wuhan, China, in December 2019. The COVID-19 pandemic led China to quarantine the population to protect them  lineage,” and Boolean operator “AND,” we periodically searched the published medical literature using the PubMed service maintained by the U.S. National Library of Medicine of NIH. Confirmed COVID-19 cases are defined as persons who tested positive for SARS-CoV-2 and had clinical symptoms. Asymptomatic carriers refer to persons without clinical symptoms who tested positive for SARS-CoV-2.From June 2020 to June 2021, the regional distribution of the sporadic nosocomial COVID-19 infections is shown in From June 2020 to June 2021, six cities in China had reported over 45 cases with nosocomial COVID-19 infections, i.e., 39 confirmed cases and 6 asymptomatic cases . Among tSevere acute respiratory syndrome coronavirus 2 gene sequencing is crucial work for nosocomial COVID-19 infections in new sporadic outbreak regions. The virus strain of nosocomial COVID-19 infection found in Qingdao city is the virus strain lineage B.1.1 imported from overseas . Gene seA report from Fang et al.  showed tThis study provides us with an inspiring vision regarding the current COVID-19 pandemic. The main findings of sporadic nosocomial COVID-19 infections from June 2020 to June 2021 are listed below: (1) the importance of rolling out an overall nucleic acid test campaign to all staff in healthcare settings is a crucial part of the COVID-19 surveillance; (2) the case of coronavirus transported onto the workplace warns us that the current hospital disinfection concept urgently requires updating, especially in designated hospitals, and there is an urgent demand of the management of staff accompanying patients in non-designated hospitals; (3) The phenomenon about COVID-19 vaccine breakthrough infection deserves our great attention.For healthcare workers, patients/accompanying staff, and cleaners, their COVID-19 infection may be from a hospital  or community (community-acquired infection). Distinguishing nosocomial infection and community-acquired infection is an important base to control the epidemic in nosocomial COVID-19 infections. Because healthcare workers are at the interface between hospitals and the community, where there is significant COVID-19 transmission, they may be infected by asymptomatic carriers from the community with COVID-19 or patients with COVID-19. These healthcare workers (infected in the community) do not belong to nosocomial infection . For example, in Among nine sporadic nosocomial COVID-19 infections, our results showed that eight series cases were found by rolling out overall a nucleic acid test campaign to all hospital staff, suggesting the importance of regular screening for all staff in healthcare settings. As part of efforts to control the COVID-19 infections, one of the effective prevention measures taken by local authorities is that all hospital personnel labeled a key population is closely monitored . WhetherOur data also showed that a vaccinated healthcare professional who received inactivated vaccine was infected with COVID-19 while working in the isolation ward of a designated COVID-19 hospital, and the coronavirus strain was determined to be the imported COVID-19 variant strain B.1.1.7 , suggestVaccine breakthrough cases with SARS-CoV-2 were reported in many countries –24. An aSevere acute respiratory syndrome coronavirus 2 virus delta variants have drawn worldwide attention. The WHO proposed three labels for global SARS-CoV-2 variants, namely, a variant of concern (VOC), variant of interest (VOI), and variant under monitoring, to be used alongside the scientific nomenclature in communications about variants to the public . The B.1To conclude, our findings add to the accumulating evidence regarding the importance of regular screening for all staff in healthcare settings. Furthermore, our study highlights the need for an update of the current hospital disinfection procedures in designated hospitals to prevent the nosocomial spread of SARS-CoV-2 infection. Finally, the epidemiological exposure of vaccinated medical staff should draw concern to minimize the impact of a new outbreak induced by virus mutants.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.All the authors contributed to the paper's presented methodology, conceptualization, data analysis, and paper writing.This work was funded by the National Natural Science Foundation of China .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. 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