Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations. Most of the traditional CAD systems are performed through four consecutive stages: data preprocessing, Region of Interest (ROI) detection or segmentation, features extraction and selection, and classification. The detection stage (CADe) usually aims to localize the suspicious lesions in the input images, while the segmentation stage delineates the specific lesion boundaries. However, the diagnosis stage (CADx) utilizes the extracted features from the detected or segmented suspicious regions to differentiate between different diseases.Medical imaging is an approach that generates interior visual representations of the hidden internal structures inside the human body. This process could be applied noninvasively such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray, Ultrasound (US), endoscope, ophthalmoscopy, and dermoscopy modalities. Such imaging modalities play a crucial role in clinical analysis, diagnosis, and treatment planning. Computer-Aided Diagnosis (CAD) system is an indispensable tool that aims to provide assistance to clinicians through interpretations of the abnormalities existing in the medical images such as brain tumors in MR images6. In addition, segmentation of blood vessels from retinal images, also known as fundus images, is of significance for automatic screening of diabetic retinopathy7. Similarly, skin lesion boundary segmentation using dermoscopy images is an important process to support dermatologists of recognizing melanoma from other skin cancer types in its early stages through integrating specific knowledge such as lesion’s size and contour’s shape8. Figure Medical image segmentation is a fundamental preliminary step for any CAD system in medical image analysis applications. For example, the segmentation of brain tumors in MR images could considerably enable providing accurate quantitative analysis and diagnosis of ischemic stroke and Alzheimer’s diseasesEven though the last decades have witnessed numerous developments for providing automated semantic segmentation methods with lower cost and less user intervention, challenges still exist in many medical imaging segmentation tasks. More specifically, the low contrast, complex geometry, irregular boundaries, high inter- or intra-class variations, as well as the presence of noise and artifact in some imaging modalities added extra obstacles to biomedical segmentation tasks. Moreover, the difficulty of medical image analysis is related to the image quality and labeling variation. One example is illustrated in Fig. In this paper, we develop an end-to-end, pixel-to-pixel deep learning segmentation methodology called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse together the global context features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks, leading to generate dense predictions of multi-scale contextual information with minimal resolution loss. Furthermore, an extension to the test-time augmentation referred to as Inversion Recovery (IR) is presented. The IR scheme is able to accumulate all the predictions of the augmented testing data using logical “OR” and “AND” operators. The proposed method achieves state-of-the-art segmentation performance on three different medical imaging modalities .In the next section, we review the current approaches to semantic segmentation and previous works on medical imaging segmentation. Afterwards, the following sections describe the design of the proposed CMM-Net, the details of the utilized medical datasets, and explain the inversion recovery evaluation scheme. Finally, we present the experimental segmentation results of skin lesions, retinal blood vessels, and brain tumors.9, U-Net10, SegNet11, and DeepLab12. FCN was the first semantic segmentation method, which converted the conventional classification CNNs to pixel-wise segmentation by replacing the fully connected Neural Networks (NNs) layers into convolutional layers. Eventually, FCN exploited the up-convolution with the element-wise fusing from shallower layers to generate dense of predictions as the same size of the input. As an extension to FCN, U-Net, SegNet, and DeepLab were developed to improve the coarse output segmentation map by innovating encoder-decoder networks that share the hierarchy features. Lately, a lot of improvements using dilated convolution13, also known as atrous convolution, Spatial Pyramid Pooling (SPP)14, Atrous SPP (ASPP)12, and Pyramid Scene Parsing (PSP)15 have been introduced. The goal of these key ideas is to generate multi-level contextual information. For instance, dilated convolution enabled the network to control the resolution of the learned features by enlarging the receptive field of filters, equivalent to adding holes  between the elements of convolutional kernels. However, SPP eliminated the fixed input size constraint of CNN in image recognition and utilized local multi-level pooling to maintain the spatial information. In the PSP module, it was possible to incorporate hierarchical global context features from various pyramid scales to produce semantic parsing dense maps. It is noteworthy that all these components were only applied to the top layer of the network since the large pooling scales comprise more global information, while the small scales preserve the fine details.Recently, end-to-end deep learning segmenters based on Convolutional Neural Networks (CNNs) have been gaining attention due to their superior performance on different segmentation benchmarks. The most famous and impressive semantic segmentation methods include Fully Convolutional Networks (FCN)16 employed the atrous convolution of various rates along with ASPP in cascade or in parallel to extract rich multi-scale contextual information. An enhanced DeepLabv3 + 17 was extended by adding a decoder module to refine the detailed object boundaries. A depth-wise separable convolution was also added to both the ASPP and decoder modules, producing a faster and more robust network. In addition, various extensions have been made on the encoder-decoder U-Net. Attention U-Net18 aggregated Attention Gates (AGs) at each decoder level. The AGs added the feature maps from the relevant encoder level with features from the former decoder level resulting in suppressing the feature responses to irrelevant regions. GridNet19 interconnected multiple streams or paths at different resolutions in a grid pattern. These connections were performed horizontally and vertically throughout the encoder-decoder network, leading to share low and high resolutions and hence capturing more context information for full scene labeling. A Multi-Scale Densely Connected U-Net (MDU-Net)20 employed the well-known DenseNets21 concept by combining three different multi-scale dense connections at the encoder, decoder, and cross-connections. This architecture could fuse various scale feature maps from different resolution layers. Similar to GridNet, a Full-Resolution Residual Network (FRRN)22 was proceeded using two processing streams. The first stream carried the full-resolution information, while the other pooling stream extracted multi-scale contexts.The exploration of multi-scale feature fusion has been further studied for improvement of several semantic segmentation tasks. DeepLabv323 was proposed to address sufficient receptive fields while maintaining the same computational efficiency. This module was located at the transition layer between the encoder and decoder, where it consisted of global context network and depth-wise separable convolution. Context Encoder Network (CE-Net)24 was developed using three modules: encoder, context extractor, and decoder. The context extractor module contained two main blocks: dense atrous convolution block that captured deeper and wider context features by fusing cascaded paths and residual multi-kernel pooling block that encoded global context information at multi-size receptive fields. A Hyper-Densely connected CNN (HyperDense-Net)25 was proposed for multi-modal image segmentation. Inspired from DenseNet, HyperDense-Net densely connected not only the layers within the same path, but also these connections occurred across various paths. UNet +  + 27 extended the U-Net by redesigning the skip connections between encoder and decoder and fusing the features of different semantic scales. UNet +  + is an ensemble architecture that integrates multiple U-Nets of different depths into a single unified network. This could allow the network to aggregate the original, intermediate, and final features at decoder. As an extension to UNet +  + , UNet 3 + 28 replaced the nested convolution blocks at the encoder-decoder path by two different connections: densely inter-connection between encoder and decoder and densely intra-connection among decoder levels. These densely full-scale skip connections could fuse the low-level details with high-level features and maintain the full use of multi-scale features. In opposite to most existing state-of-the-art segmenters that first encoded the input into low-resolution features and then decoded and recovered the high-resolution features, High-Resolution Network (HRNet)29 preserved the high-resolution features throughout the whole network. This was achieved by connecting parallel multi-resolution  convolution streams and repeating this stage with multi-resolution fusions across the parallel streams, leading to obtain richer and more precise spatial representations.More recently, large kernel SPP30, object detection34, semantic segmentation36, artifact denoising37, and image reconstruction38. Also, many studies have been conducted for skin lesion boundary segmentation41, retinal blood vessel segmentation44, and brain tumor segmentation48. Further, some works have been conducted for multiple medical image segmentation50.In terms of applications, deep learning networks have been employed for several medical imaging tasks including image classification51 and Simonyan et al.52 averaged the predictions of multiple cropped patches around the object and horizontally flipped images to obtain the final score of an image classification model. These multiple predictions of a given test image help to achieve more robust inference. More recently, this procedure has been applied to medical image segmentation tasks to improve segmentation accuracy. The final segmented label was computed as an average or as a pixel-wise majority voting of the predicted pixels57. The disadvantage of TTA is its computational cost since the inference is performed many times depending on the number of augmentations. However, TTA is promising for medical image applications.In addition to training data augmentation, some deep learning researches have presented the effectiveness of Test-Time Augmentation (TTA), in which the following steps were used: test data augmentation, prediction, and merging or averaging of the results. Krizhevsky et al.We propose an end-to-end deep learning network for medical image segmentation named Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea of our CMM-Net is to generate global multi-level contextual information at every encoder convolutional level, which allows the network to learn various spatial scales of the target with minimal resolution loss. This is achieved by frequently promoting pyramid pooling and dilated convolution modules with different spatial rates throughout the networks.The proposed work achieves state-of-the-art performance on three different medical imaging benchmarks, including segmentation of skin lesion from dermoscopy images, retinal blood vessels from fundus images, and brain malignant glioma from MR images. We compare our results to recent state-of-the-art architectures such as U-Net, PSP-Net, DeepLabv3 + , CE-Net, and UNet +  + .We adopted a modified segmentation evaluation scheme called Inversion Recovery (IR), which can generate more accurate segmentation maps by utilizing augmented testing data using a combination of logic operations.https://github.com/Yonsei-MILab/Biomedical-Image-Segmentation-via-CMM-Net.We make the source code publicly available for researchers for validation and further improvement here: The main contributions of this paper are fourfold.13 is inspired by wavelet decomposition58, which is able to assemble multi-scale contextual features instead of using successive pooling layers. It controls the resolution of such information by exponentially enlarging the receptive field of filters. Consider Dilated convolution or atrous convolutionIt is of note that the resulting receptive field expands exponentially when the dilation rate gets increased. This relation can be formulated as entclass1pt{minimaThe key role of Pyramid Pooling Module (PPM) is to generate ensemble high-level feature maps, which represent global context information of multi-scales. In contrast to the SPP that passes the flattened and concatenated multi-level features into FC-NN in the classification tasks, PPM could reduce the loss of information between various sub-levels and extract effective hierarchical global representations. Figure The above two key modules have been exploited to design our proposed network as explained in the following section.10, we propose an end-to-end segmentation network called CMM-Net. Similar to the original U-Net, the proposed network consists of contracting and expanding paths, also known as encoder and decoder networks. The contracting path contains sequential convolutional and subsampling layers, which are responsible to capture hierarchical features. Symmetrically, the expanding path involves up-convolution and up-sampling layers, in which all the features in the encoder are passed and concatenated with the feature maps in the decoder. This process increases the resolution of the output’s dense map and hence allows for more accurate localization. In this work, the exploited U-Net consists of four main levels, including eight convolutions and three pooling layers in the contracting path and seven up-convolutions, three up-sampling, and a single softmax layer in the expanding path. Details of the feature maps and convolution filter sizes are presented in Fig. Inspired by deep learning U-Net15, Deeplabv3 + 17, large kernel SPP23, and CE-Net24 that applied pyramid pooling at only the top of encoder network  are re-designed to make full use of global multi-scale contextual information through each network layer, which leads to capturing various spatial scales while minimizing the input resolution loss. This fusion scheme of the multi semantic scales enables to share coarse-to-fine details with localization information at every level between the encoder and decoder. More specifically, applying the PPM at the early encoder level, as the case of PPM #1 and 2, results in learning global fine-grained and structural details as well as preserving the input resolution, while the PPM at the top of the encoder  extracts coarser representations. Figure Unlike previous semantic segmentation methods such as PSPNetTo achieve superior segmentation performance, we have adopted three PPM units, involving four different scales varied in the range between 1 Furthermore, we have employed the dilated convolution strategy in all designed convolutional layers. To address the input size variation throughout the network , we assign different dilated rates, which directly related to input sizes. More specifically, the dilated rate All the convolutional layers in the proposed network have been appended by batch normalization (BN) process and activation function (ReLU). The extracted prominent representations at the last convolutional layer are fed into a softmax function. In our task, the softmax layer works as a binary classifier since each pixel is categorized into tumor or tissue as the case of skin and brain datasets and into vessel or background as the case of retinal images.59. The key role of the double cross-validation is to determine the tunable parameters and avoid any bias procedure during building the model. This process is accomplished by dividing the data into three subsets, namely the training, validation, and testing sets. The network optimization  is performed using the validation set, while the model evaluation is proceeded using the testing set. During network training, forward and backward propagation cycles are occurred, leading to compute the prediction maps and estimated segmented errors. The estimated error between the segmented maps , which is performed as a post-processing step applied to augmented test data. The main idea is to accumulate different segmented maps of various orientations for the same original testing image. This process requires to first augment each testing data multiple times using different rotations and flipping. After these augmented data have been tested via the proposed CMM-Net, we retrieve their prediction maps  into the original orientation by applying inverse augmented processes. Then, we fuse all the retrieved segmented maps along with the original map utilizing the logical “OR” and “AND” operators. In other words, for a given test image, multiple predictions or segmentation maps can be generated based on applying different augmentations to each test image. The final segmented result is computed as an ensemble of their prediction maps. This procedure allows to get more confident results as well as improving the overall segmentation performance. We illustrate the IR evaluation scheme in Fig. Formally, consider In this work, three public medical imaging benchmarks were utilized to evaluate the proposed CMM-Net segmentation method. Example images with their ground-truth segmentation masks are demonstrated in Fig. The most widely utilized and available database for skin lesion segmentation is known as the International Skin Imaging Collaboration (ISIC) Challenge 2017. The ISIC 2017 dataset involves a total of 2,750 RGB dermoscopy images, which are divided into 2,000 samples for training, 150 samples for validation, and 600 samples for testing. All the provided dermoscopy images are paired with the binary segmentation masks, which were delineated by expert dermatologists.8.Due to the large variation in image sizes ranged from 540 60. The DRIVE dataset consists of 20 retinal images for training and 20 images for testing obtained from the screening program for diabetic retinopathy in The Netherlands. This dataset is provided along with manually segmented annotations of the retinal vasculature by expert ophthalmologists and binary masks of the field of view (FOV). The DRIVE dataset contains 8-bit RGB images with a fixed size of 584 The experiments of how efficient our proposed CMM-Net on segmenting the retinal blood vessels are proceeded utilizing the Digital Retinal Images for Vessel Extraction (DRIVE) dataset61. Further, due to the limited size of this dataset, we considered a patching approach to enlarge the number of training samples. Firstly, we cropped the input data that exists in the FOV. Then, we extracted overlapped small patches from all training retinal images with a size of 128 To improve the visibility of the retinal images and in particular the small vessels, we applied the Contrast Limited Adaptive Histogram Equalization (CLAHE) method64. In this paper, we utilized the BraTS 2018 dataset, which contains 285 training cases and separate sets of 66 and 191 cases for validation and testing, respectively. It is of note that, the ground-truth annotations of the training cases are only accessible. However, it is possible to evaluate the proposed segmentation network using the validation cases throughout the online submission system. Regarding the testing set, it was only utilized to rank participants at the challenge time. Thus, we have randomly split the original training cases into 80% for network training and the rest 20% for a network assessment. In this paper, we call this 20% set as a local test data. Each case in this dataset includes four 3D MRI modalities, named as T1, contrast T1-weighted (T1Gd), T2-weighted, and T2 Fluid Attenuated Inversion Recovery (FLAIR), with a size of 240 Multimodal Brain Tumor Segmentation Challenge (BraTS) is the largest and publicly available dataset, which focuses on segmenting the brain tumors, namely gliomas (low- and high-grade), from multi MRI modalitiesTo reduce the network computations, we cropped the volumes to only contain brain data to 192 The original data distributions of the utilized medical imaging databases are summarized in Table All datasets are normalized between zero and unity. Moreover, to enlarge the training sets, which enables appropriate learning of the network and reduces the overfitting problem, we augmented all the databases eight times  using different rotation and flipping processes. An exception, we further utilized different color space features such as LAB  and SV  besides the RGB in the case of skin lesion database since it helps on illustrating more details.35.The proposed CMM-Net segmentation method is quantitatively evaluated using several performance evaluation measures, including sensitivity (SEN), specificity (SPE), accuracy (ACC), Dice similarity coefficient (DSC), Jaccard (JAC) index, and Matthew correlation coefficient (MCC). Moreover, we utilized the receiver operator characteristic (ROC) curve along with its area under the curve (AUC) as well as precision-recall (PR) curve. For the definitions and formulas of all these indices, refer to this articleThis section experiments the effect of key components of the proposed CMM-Net method on the skin lesion boundaries segmentation. It includes the segmentation of the original U-Net as a baseline, repeated PPMs, dilated convolution, augmentation of training data, and the IR evaluation process. In this experiment, we utilized the Jaccard index to assess the improvement of each added component since it was utilized by the ISIC 2017 challenge to rank the participants. Table As presented, we observed that all the proposed components in the network contributed to improve the segmentation performance. In the next section, we present the experimental results of our proposed CMM-Net on three different medical imaging tasks. To provide a further comparison, we implemented state-of-the-art segmentation methods such as U-Net, PSPNet based on VGG network, DeepLabv3 + , CE-Net, and UNet +  + using the same augmented training data. For all methods, we showed the segmentation results with and without applying the IR process.71 in Table 65) in the ISIC 2017 challenge by 5.19%, 0.53%, 0.88, and 1.15% in terms of overall segmentation sensitivity, accuracy, DSC, and Jaccard indices, respectively. It is of note that all the listed methods in Table This section presents the performance of the proposed CMM-Net method on segmenting the skin lesions using 600 test dermoscopy images of the ISIC 2017 dataset. Quantitatively, we report the experimental results of our proposed work against the latest state-of-the-art techniquesQualitatively, some examples of the segmented skin lesion boundaries via our CMM-Net (blue contours) compared to the ground-truth annotations (green contours) are illustrated in Fig. 77 with overall MCC, DSC, and accuracy scores of 78.57%, 80.27%, and 96.64%, respectively. In contrast, MCGU-Net50 achieved a higher DSC for the segmentation of the retinal blood vessels at 82.24%. Since the target in this task is small and thin segments of blood vessels, we found that training the proposed network without using dilated convolution . The results of brain tumors segmentation via the proposed CMM-Net are efficient and comparable with the top three methods in the challenge with a slightly lower DSC of 88.96%. The challenge set the average of DSC indices for the WT, ET, and TC to rank the participants. However, here we focused only on the whole tumor segmentation as further evaluation of our proposed segmentation method. As reported in Table We also evaluated the proposed CMM-Net segmentation method using both the 57 local testing and the 66 actual validation MR subjects of the BraTS 2018 dataset. Table Figure As a summary, the overall segmentation performances of skin lesions and retinal blood vessels were improved in the case of using the IR process. In the case of skin lesions, the proposed method achieved DSC of 83.13% without IR and 85.78% with IR. In the case of retinal blood vessels, the DSC also improved from 78.99% to 80.27% when applying the IR process. In contrast, this process did not provide improvement in the case of brain tumors segmentation neither for the local test where DSC dropped from 83.95% to 82.01% nor for the actual validation where DSC dropped from 88.96% to 88.32%. This may be due to that the structure of brain tumors is extremely irregular and in some cases exists as unconnected segments.The obtained segmentation results demonstrate the efficiency of the proposed CMM-Net over different medical imaging tasks. Indeed, the aggregation of multi-scale contextual features throughout all networks’ encoder levels along with the dilated convolutions foster to learn more robust multi-spatial global representations with location-awareness. The ablation experiments shown in Table Furthermore, Fig. We also show the overall evaluations for all medical image modalities including skin lesions, retinal blood vessels, and local testing and actual validation sets of brain tumors in Table The training and inference  computation times needed to accomplish the segmentation of different medical imaging applications via the proposed CMM-Net are listed in Table This paper proposed a deep learning segmentation method called CMM-Net for three different medical imaging modalities. The proposed method exploited both the dilated convolution and pyramid pooling modules recurrently in the encoder network of the U-Net. Further, a modified evaluation scheme, called inversion recovery, that retrieves all the segmented images from the augmented testing data into a single output using logical operators is developed. The proposed segmentation method achieved superior performances on three different biomedical imaging tasks, including the segmentation of the skin lesions, retinal blood vessels, and brain tumors, compared to the recent deep learning approaches. The proposed method could be feasible for future medical imaging analysis and clinical exam routine. In the future, we plan to develop a 3D version of the CMM-Net along with the residual and dense mapping to be used for different types of brain tumors.


Estimates for dead space ventilation have been shown to be independently associated with an increased risk of mortality in the acute respiratory distress syndrome and small case series of COVID-19-related ARDS.Secondary analysis from the PRoVENT-COVID study. The PRoVENT-COVID is a national, multicenter, retrospective observational study done at 22 intensive care units in the Netherlands. Consecutive patients aged at least 18 years were eligible for participation if they had received invasive ventilation for COVID-19 at a participating ICU during the first month of the national outbreak in the Netherlands. The aim was to quantify the dynamics and determine the prognostic value of surrogate markers of wasted ventilation in patients with COVID-19-related ARDS.p < 0.001). The end-tidal-to-arterial PCO2 ratio was lower in non-survivors than in survivors (p < 0.001). As ARDS severity increased, mortality increased with successive tertiles of dead space fraction by Harris–Benedict and by direct estimation, and with an increase in the VR. The same trend was observed with decreased levels in the tertiles for the end-tidal-to-arterial PCO2 ratio. After adjustment for a base risk model that included chronic comorbidities and ventilation- and oxygenation-parameters, none of the dead space estimates measured at the start of ventilation or the following days were significantly associated with 28-day mortality.A total of 927 consecutive patients admitted with COVID-19-related ARDS were included in this study. Estimations of wasted ventilation such as the estimated dead space fraction (by Harris–Benedict and direct method) and ventilatory ratio were significantly higher in non-survivors than survivors at baseline and during the following days of mechanical ventilation (There is significant impairment of ventilation in the early course of COVID-19-related ARDS but quantification of this impairment does not add prognostic information when added to a baseline risk model.Trial registration: ISRCTN04346342. Registered 15 April 2020. Retrospectively registered.The online version contains supplementary material available at 10.1186/s13054-021-03570-0. Since the outbreak of coronavirus disease 2019 (COVID-19) in the City of Wuhan, Hubei Province, China, caused by the transmission of the novel coronavirus SARS-CoV-2, millions of individuals have been infected and more than one million have died. Severe disease requiring admission to intensive care unit (ICU) occurs in approximately 5% of infections [VD/VT) [VD/VT is a predictor of death and is one of the few lung-specific physiological variables independently associated with mortality [VD/VT do not require quantitative assessment of exhaled carbon dioxide, are less complicated to perform and easier to calculate at the bedside compared with calculations made by volumetric capnography [VD/VT [Patients with COVID-19 pneumonia meeting criteria for ARDS usually present with a high respiratory drive and minute ventilation, potentially due to hypercapnia and an increased dead space fraction (VD/VT) . In patiortality , 5. Methnography . In recenography . In patiy [VD/VT  and may y [VD/VT .2 removal due to increased physiologic dead space [VD/VT and VR with 28-day mortality in patients undergoing invasive ventilation because of COVID-19 ARDS. We hypothesized that these markers of impaired ventilation are independently associated with 28-day mortality.At least two independent groups have described series of patients with COVID-19-related ARDS who may have inefficient COad space , 9. Howead space . TherefoPRoVENT-COVID is an investigator-initiated, multicenter, observational cohort study undertaken at 22 ICUs in the Netherlands. The study protocol including the statistical analysis plan is available . The appConsecutive patients ≥ 18 years of age were eligible for participation in PRoVENT-COVID if they were admitted to one of the participating ICUs and had received invasive ventilation for COVID-19 ARDS. COVID-19 infection was defined by a confirmed reverse transcriptase-polymerase chain reaction (RT-PCR) .D/VT or VR on the first day of ventilation, and patients with no data about 28-day mortality.PRoVENT-COVID had no exclusion criteria, but for the current analysis, we excluded patients transferred from a non-participating hospital who had been receiving invasive ventilation for more than 2 calendar days, patients without complete data to calculate the VDemographics and data regarding premorbid diseases and medication were collected at baseline. Ventilator settings and parameters were collected after one hour of invasive ventilation and every 8 h thereafter, for the first 4 calendar days. In the present study, the first day of ventilation is called ‘at start of ventilation.’D/VT calculated using the Harris–Benedict formula as described in Eq.  [2 predicted is the predicted PaCO2 determined as 37.5 mmHg.VR is the ventilatory ratio,  weight) , and PaC2 [A post-hoc analysis was performed using Corrected Minute Ventilation as additional parameter of wasted ventilation. This parameter is calculated using the following formula:of PaCO2 . This ismentclass2pt{minimmentclass2pt{minimAll variables were calculated three times per day, and the values were aggregated as the mean in the respective day. Primary analyses focused on the values obtained on the day on which ventilation was started.The outcome assessed in this study was death at 28 days, defined as the mortality 28 days after the start of ventilation. Other clinical outcomes are reported only to describe the cohort but were not used to test their association with the exposures described above.The amount of missing data was low for most of the variables  were aggregated as the mean per day. In addition, to compare variables at each day, the day variable was entered as a categorical variable in the model described above, and the P value for the daily difference was extracted using pairwise comparisons after Bonferroni correction.Trends in markers of impaired ventilation were presented in boxplots between survivors and non-survivors over the first 4 calendar days. The direction of effect over time of the variables was assessed with mixed–effect linear models with center and patients treated as random effect to account for clustering and repeated measurements, and with 28-day vital status , time (as a continuous variable) and an interaction of 28-day vital status and time as fixed effect. Overall We examined the risk of death for each tertile of the lung-specific physiological variables used to evaluate whether the predictive ability of each variable varied by level. In addition, a simple stratification creating two groups according to the median of the variables was also assessed. The comparison of the two groups was presented in Kaplan–Meier curves and compared using Log-rank tests.2/FiO2 ratio, plasma creatinine, hypertension, diabetes, use of angiotensin converting enzyme inhibitors, use of angiotensin II receptor blockers, use of a vasopressor or an inotrope drug, fluid balance, pH, mean arterial pressure, heart rate, respiratory system compliance and PEEP. Multicollinearity was assessed through the analysis of the variance inflation factors, and the final model was assessed for discrimination using c-statistics, and for calibration using the Brier-Score.Univariable mixed-effect generalized linear models considering a binomial distribution and with center as random effect were used to estimate the unadjusted effect of each variable on 28-day mortality. A multivariable mixed-effect generalized linear model considering a binomial distribution and with center as random effect were used to test the association of each of the exposures described above with 28-day mortality. A list of candidate confounders was determined a priori, and based on clinical relevance rather than statistical significance. The following baseline variables (measured at baseline or within 1 h after intubation or ICU admission with ventilation) were considered in the models: age, gender, body mass index, PaOIn addition to the odds ratio (OR) and its 95% confidence interval, the predictive accuracy of the lung-specific physiological variables was measured by the area under the receiver operating characteristics curve (AUC-ROC). Also, to estimate whether these variables improved predictive accuracy on top of that of the base model described above, the net reclassification improvement (NRI) and the integrated discrimination index (IDI) were assessed.For the primary analysis, covariates with less than 3% of missing values were imputed by the median value of the overall cohort. Since respiratory compliance was missing in 8.2% of the patients  represents the increase in one standard deviation of the variable. All analyses were conducted in R v.4.0.2  , and sig2, had higher levels of lactate and creatinine and lower levels of pH and end-tidal-to-arterial PCO2. There were no differences in the radiographic findings between survivors and non-survivors (Table S15).From March 1, 2020, through June 1, 2020, 31 ICUs were invited for participation in PRoVENT-COVID, and 22 of them met inclusion criteria. A total of 1340 individuals were screened. A total of 218 were not enrolled; 62 did not have COVID-19-related ARDS, 150 never received invasive ventilation, and 6 were excluded for other reasons , but not different in the groups considering the dead space fraction by direct estimation , and the ventilatory ratio  .Mortality over the first 28 days was higher in patients in the high group of dead space fraction by the Harris–Benedict estimation  or prone positioning during the first day of mechanical ventilation, we did not observe large changes in effect sizes  to measure Vd/Vt. Estimated measures for calculating VD/VT are more frequently utilized and a wide array of these indices were included in this study [VD/VT by volumetric capnography [Despite  the poteis study , 20. VR is study , 9 and inography . We foun2 ratio (PETCO2/PaCO2) has been described as another surrogate for VD/VT in ARDS patients [2 coincides with decreasing PETCO2. Both shunt and low cardiac output states are known determinants of VD/VT. It is worth noting that that the impact of cardiac output exists only when measuring VD/VT the Enghoff modification of Bohr's original formula is used. In the case of shunt, the increase in venous admixture will elevate the PaCO2 increasing dead space fraction [VD/VT is high, where physiologic dead space can be contaminated by the large shunt fractions present in any type of ARDS. In low cardiac output states, a decrease in pulmonary blood flow leads to a reduced alveolar CO2 delivery decreasing PECO2, thereby increasing VD/VT [2 because of abnormal ventilation-perfusion matching giving a good global index of a lung’s gas exchange efficiency [Recently, the end-tidal-to-arterial PCOpatients . Each offraction . This cong VD/VT . In bothficiency , 24.VD/VT) was elevated during the first week after start of invasive ventilation [ETCO2/PaCO2 at the start and at day 1 of ventilation. This contrasts with several studies in ARDS that showed increased dead space ventilation to be a robust and independent predictor of mortality risk [ETCO2/PaCO2 was also independently associated with mortality risk in one study [Dead space estimations were significantly increased in non-survivors in the first four days of mechanical ventilation compared to survivors. This is line with previous studies in all patients with ARDS (not only those with COVID19), in which dead space . Prone position facilitates shape matching, which helps minimizing injurious ventilation and frequently improves gas exchange through better VD/VT further increased during the first days of invasive ventilation, especially in patients who did not survive. Altered hemostasis and thrombosis are postulated to be a key element of ARDS, with the endothelium playing a key role by promoting microthrombogenesis [In the current study in patients with COVID-19-related ARDS, impaired ventilation was already present in the first days of invasive ventilation. The studied estimations for ogenesis –30. Endoogenesis . Autopsyogenesis  in additThe strengths of this study include the size of the multi-center cohort, careful data collection and with few missing data, the pre-specified analysis plan, and the evaluation of multiple estimations for impaired ventilation. The central limitation of this study is that we did not quantify dead space ventilation directly by volumetric capnography or another technique. This was not possible in the setting of a pandemic, where the critical care systems were overwhelmed with patients. A second limitation is the observational nature of the study. Therefore, this study does not provide insight into potential mechanisms that may contribute to the association between high dead space estimations and mortality in COVID-19-related ARDS patients. Another important aspect to take into account is the aspect of the instrumental dead space. Use of heated humidifiers of HMEs (heat and moisture exchangers) is heterogeneous in the clinical practice, and different HMEs have different dead space volumes. Instrumental dead space may significantly affect the total dead space, mainly when using low tidal volume ventilation, and we have commented on this previously .VD/VT computed by the Harris–Benedict formula, we assumed an RQ of 0.8 for VCO2 calculation based on a previous study [2) and dead space measured with volumetric capnography [For the estimated us study . Althougnography .VD/VT in patients with ARDS [The results of this study indicate that estimations for increased dead space may not be independently associated with mortality. The observed effect sizes were remarkably similar to those observed in non-COVID-19-related ARDS. This contrasts several reports that have hypothesized that profound endothelial injury and coagulopathy may be central mediators of lung injury in COVID-19 . We acknith ARDS , making 2 concentration versus the respective expired tidal volume of a single breath. Since volumetric capnography offers a more in-depth representation of the kinetics of CO2 elimination per breath, the application of longitudinal or time-series models to analyze the effect of CO2 elimination impairment on outcome warrants further research.At the moment, no data exists on the measurement of physiologic dead space in COVID-19-related ARDS by integrating volumetric capnography plots of eliminated COEstimates for wasted ventilation are abnormal at the start of invasive ventilation in patients with COVID-19-related ARDS and worsen during consequent days. Ventilation impairment seems to be more extensive in non-survivors than in survivors, but they may not yield prognostic information when added to a baseline risk model. In the absence of bedside capnography, dead space estimates may serve as an important tool to assess the severity of COVID-19-related ARDS along with other variables such as oxygenation abnormalities and respiratory mechanics.Additional file 1. Impaired ventilation is not associated with 28-day mortality in COVID-19 ARDS


SERPINE2) is a very powerful thrombin inhibitor and can also inactivate plasminogen activators and plasmin. Studies have shown that this serpin is expressed by all cell subpopulations in the vascular wall and by circulating cells but is barely detectable in plasma in the free form. PN-1 present in platelet granules and released upon activation has been shown to present strong antithrombotic and antifibrinolytic properties. PN-1 has a broad spectrum of action related to both hemostatic and blood vessel wall protease activities. Different studies showed that PN-1 is not only an important protector of vascular cells against protease activities but also a significant actor in the clearance of the complexes it forms with its targets. In this context, PN-1 overexpression has been observed in the pathophysiology of thoracic aortic aneurysms (TAA) and during the development of atherosclerosis in humans. Similarly, in the heart, PN-1 has been shown to be overexpressed in a mouse model of heart failure and to be involved in cardiac fibrosis. Overall, PN-1 appears to serve as a “hand brake” for protease activities during cardiovascular remodeling. This review will thus highlight the role of PN-1 in the cardiovascular system and deliver a comprehensive assessment of its position among serpins.The balance between proteases and protease inhibitors plays a critical role in tissue remodeling during cardiovascular diseases. Different serine protease inhibitors termed serpins, which are expressed in the cardiovascular system, can exert a fine-tuned regulation of protease activities. Among them, protease nexin-1 (PN-1, encoded by  SERPINE2 gene on human chromosome 2 (Protease Nexin-1 (PN-1) is a 50-kDa glycoprotein encoded by the mosome 2 . This reIn vitro kinetic assays showed that PN-1 reacts rapidly with trypsin and thrombin, with an association rate constant (Ka) of ~2.106 M−1.s−1  particles in the vessel wall where they are susceptible to oxidation. Monocytes are also implicated and transmigrate into the intima where they differentiate into macrophages, becoming foam cells after ingestion of oxidized LDL. VSMC proliferation and migration from the media to the intimal layer, as well as their phenotypic shift into foam cells, are also important features of atherosclerosis development. vSMCs present in the intimal layer form a fibrous cap that contains the plaque. The rupture of the fibrous cap leads to thrombus formation causing blockage of the blood flow .An unbalanced ratio between proteases and their inhibitors is involved throughout the pathophysiology of atherosclerosis. Excessive thrombin, uPA/tPA or plasmin activities are indeed involved in the chronic evolution of the plaque. An important question thus concerns the regulation of these proteases in the vessel wall. In this context, serpins increasingly appear to be critical in regulating protease activity in arterial lesions. Among them, PN-1 has emerged as a key regulator in vascular biology even though its precise mechanism of action remains to be deciphered.via LRP-1  o assays , (ii) ino assays . Taken to assays .Given its ubiquitous expression in the atheromatous lesions and its inhibitory activity against numerous deleterious proteases present in the atheroma, PN-1 can undoubtedly regulate the characteristics of the atherosclerotic plaque at different stages of development.in vitro pericellular plasminogen activation  share common pathophysiological features leading to structural deterioration of the aortic wall. VSMCs apoptosis and the degradation of collagen and elastic fibers are the two principal modifications occurring within the medial layer characterizing TAA. The relevance of the antiprotease activity of PN-1 expressed by vSMCs has been emphasized by its ability to regulate tivation  and thertivation . This wareatment . Indeed,reatment . Togethein vivo during TAA development, as discussed for atherosclerosis, but further experimental animal models are required to fully understand its impact on TAA pathophysiology.The clearance of PN-1/plasmin complexes has also been addressed specifically in the TAA context. PN-1, LRP-1 and plasmin were shown to colocalize in the media of human TAA where PN-1 amounts correlated with plasmin activity . The uptIn contrast to TAA, the role of PN-1 in abdominal aortic aneurysms (AAA) has not yet been addressed. Previous reports have shown that the enzymes of the fibrinolytic system are also involved in AAA progression , 39 and Myocardial fibrosis is an important pathophysiological process defined as an excessive accumulation of matrix proteins and is a well-established morbi-mortality marker. It increases myocardial stiffness, alters systolic function and contributes to malignant arrhythmias .in vivo heart failure models (in vivo mouse model of cardiac fibrosis induced by transverse aortic constriction (TAC), that collagen deposition was increased after 4 weeks, associated with a slight increase in PN-1 expression in the heart (in vitro in human pulmonary fibroblasts (PN-1 in the heart has received less attention although it has been reported to be present in mouse heart . Moreovee models . Li et ae models . They shhe heart . Moreoveroblasts . Reciproroblasts . HoweverPN-1 appears to be importantly involved in fibrotic processes. Interestingly, depending on the affected tissue, PN-1 displays either anti-fibrotic properties as described in pulmonary fibrosis  or in coMore detailed studies are required to decipher the role of PN-1 in cardiac fibrosis. Indeed, in pathological conditions, such as pressure overload models or myocardial infarction, inflammation plays an important role in adaptative and inadaptive responses, where monocytes and macrophages are key components of the inflammatory pathophysiology . BecauseThe close relationships between PN-1 and proteases of the coagulation and fibrinolytic systems, as well as between PN-1 and the endocytic receptor LRP1, explain the impact of this serpin in the cardiovascular system . EssentiCM and BR generated the figure and the table. VA and M-CB provided critical feedback and edited the review. All authors contributed to the review.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.


Control of the novel COronaVIrus Disease-2019 (COVID-19) in a hospital setting is a priority. A COVID-19-infected surgeon performed surgical activities before being tested. An exposure risk classification was applied to the identified exposed subjects and high- and medium-risk contacts underwent active symptom monitoring for 14 days at home. All healthcare professionals (HCPs) were tested for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) at the end of the quarantine and serological tests were performed. Three household contacts and 20 HCPs were identified as high- or medium-risk contacts and underwent a 14-day quarantine. Fourteen HCPs and 19 patients were instead classified as low risk. All the contacts remained asymptomatic and all HCPs tested negative for SARS-CoV-2. About 25–28 days after their last exposure, HCPs underwent serological testing and two of them had positive IgM but negative confirmatory swabs. In a low COVID-19 burden area, the in-hospital transmission of SARS-CoV-2 from an infectious doctor did not occur and, despite multiple and frequent contacts, a hospital outbreak was avoided. This may be linked to the adoption of specific recommendations and to the use of standard personal protective equipment by HCPs. The CoronaVIrus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is still affecting the whole planet, with over 110 million confirmed cases and 2.4 million related deaths . Several studies have shed light on the infection dynamics at the population level, but still little is known regarding the in-hospital transmission, although hospitals and healthcare facilities have been acting as epicentres of the pandemic [Herein, we report the dynamics of how an infected otorhinolaryngologist, who performed inpatient and surgical activity during the window period and during his first symptomatic days in an Italian University hospital located in Florence, Tuscany, did not provoke a hospital outbreak. For the infected surgeon, the initial exposure to the virus is assumed to have occurred around 26 February 2020, in the very early weeks of the so-called ‘first wave’. At that time, Tuscany was not considered a ‘red zone’, as only a limited number of SARS-CoV-2 infections were detected in the area. However, after Italian Public Health authorities' advice, HCPs were asked to wash their hands with an alcoholic solution after each visit and to use basic personal protective equipment , as most of the head and neck procedures are known to possibly aerosolise droplets; moreover, there should be as few people as possible in the same room at any one time. However, whereas all the staff always used basic PPE when dealing with patients, we admit they did not during meetings or in shared environments where only distancing measures were taken. Another critical issue was constituted by the lack of any triage protocol, especially for external consultations because neither formal screening programme was conducted nor thermoscans or other filtering measures were available. Furthermore, systematic testing for SARS-CoV-2 was not immediately implemented, in particular in terms of surgical pathways.For research aims, the date of symptoms onset for the index surgeon is considered as the day of investigation 1 (DOI 1), while ‘contacts’ were defined as people who reported or were identified to have had potential exposure to the index on or after DOI 1.2 was gradually reduced. As from DOI 27, the surgeon was no longer sedated and only required low flow oxygen. Tracheostomy was removed on DOI 32 and he was finally discharged from the ICU after two consecutive (DOI 32 and DOI 33) negative nasopharyngeal swabs.The index case is a 61-year-old male who spent 9 days in Northern Italy until 1 March 2020. He has a personal history of arterial hypertension, previous myocardial infarction and asthma. He drinks alcohol occasionally and he has never smoked. His medications include clopidogrel, atorvastatin and ramipril/hydrochlorothiazide. He came back to work on 2 March 2020 (DOI 1) and he took part in major oncological surgical procedures. A fluctuating, mild, dry cough was the only symptom he complained of on those four days, but this was attributed to his long history of asthma. On the night between 5 and 6 March (DOI 4-5), the index physician started to feel ill and decided to rest at home for the next few days. His clinical conditions quickly worsened and he went to the Emergency Department on 8 March (DOI 7). Chest radiography showed bilateral reticular and patchy infiltrates. Sputum, oropharyngeal and nasopharyngeal swabs were obtained and all the specimens tested positive for SARS-CoV-2. He was promptly hospitalised and, in the following days, he developed respiratory distress that required endotracheal intubation and mechanical ventilation, so he was transferred to the intensive care unit (ICU) on DOI 10. On DOI 19, a worsening of gas exchange was noticed. A computed tomography pulmonary angiography revealed acute pulmonary embolism and the dosage of enoxaparin was therefore doubled. Subsequently, clinical status gradually improved and FiOImmediately after index case confirmation, a prompt epidemiological investigation was undertaken to track all the in-hospital and familiar contacts. All the movements, encounters and clinical and surgical activities performed by the physician from DOI 1 to the day of COVID-19 diagnosis (DOI 6) were retrospectively investigated, in order to pinpoint as many contacts as possible. Hospital records were reviewed to identify all the patients that attended our Department for in-hospital examination and surgical interventions. All HCPs who worked on the same floor of the building were tracked and interviewed to evaluate individual exposure, as well as the three persons who lived with him. From DOI 6, epidemiological data were prospectively collected by the first author.Contacts' exposure risk was stratified according to the three-tiered classification system which was proposed by the Region of Tuscany COVID-19 Emergency Task Force, and that was in force at the time of the virological diagnosis . Such crThe analysis identified 56 contacts that were staged for exposure risk as explained above . ConsideThirty-three persons (69%) were instead classified as ‘low-risk’ contacts. In this group, there were 10 physicians belonging to other departments, 4 nurses and 19 patients. All the patients who had close contact with the index doctor in the period 2−5 March were classified as ‘low-risk’ because he performed all procedures  while wearing appropriate PPE . Furthermore, apart from physical examination, everyone kept a distance of at least 2 m from one another. HCPs in the low-risk group were allowed by the hospital's authorities to keep on working with PPE and strict protection measures. Moreover, they were obliged to measure body temperature twice a day for 14 days and to alert if any of the aforementioned symptoms developed. None of them complained of any symptoms during the following 2 weeks. Finally, low-risk non-HCPs contacts were invited to stay at home taking the usual precautions and to alert in case of any symptom onset during the subsequent 14 days. Nobody called and, on 26 March, 16 of 19 non-HCPs contacts were all phoned back again to assess their clinical condition in the previous two weeks by a standardised questionnaire. Even in this case, none complained of any COVID-19-associated manifestations. Since the three remaining patients were already scheduled for imminent surgery, it was decided to test one patient on 23 March (DOI 22) and two patients on 1 April (DOI 31). All the samples eventually tested negative and patients could undergo surgery.® COVID-19 IgG/IgM test , a qualitative chromatographic lateral flow immunoassay was used for anti-SARSCoV-2 IgG and IgM detection. Among the HCPs included in the medium-risk group, one individual  had positive IgM results (++−−) with no detectable IgG. In the low-risk group, only a weak positivity in IgM (+−−−) was detected in one nurse (7.1% according to both classifications). Both the contacts obtained two confirmatory nasopharyngeal and oropharyngeal swabs, 24 h-distanced from one another, on DOI 31 and DOI 32, but no viral RNA was detected. Nevertheless, all the people with a positive or uncertain serological result were further obliged to 14 days of quarantine with serial control swabs.Regardless of the initially assigned exposure risk category, on DOI 26, 29 HCPs obtained serological analysis. NADALet al., for instance, reported a very low prevalence (0.4%) of positive healthcare workers and this was possible thanks to the application of a general prevention and reporting system [In this paper, we have presented a detailed examination of a potential COVID-19 hospital outbreak caused by an infected working surgeon. Actually, neither the patients nor the HCPs who worked with the COVID-19-positive surgeon were apparently infected. During his whole working period, the surgeon was always wearing a simple facemask and it is conceivable that such simple PPE may have been protective both for the patients and the other HCPs . Other gg system . Anotherg system . A very g system . The effg system . In addiThe favourable outcome in this study might also depend on the relatively low COVID-19 cases burden present in our hospital at the time of these events. On 2 March (DOI 1), the number of confirmed COVID-19 cases in the whole Tuscany region was 123, of whom only 23 were admitted to our hospital and we believe our risk estimate is not applicable to settings where a greater proportion of HCPs is infectious. According to the literature, the RNA detection rate in samples collected before day 7 or during days 15−39 after illness onset decreases from 66.7% to 45.5%, while the presence of antibodies increases rapidly to 100% tests would strongly suggest the lack of COVID-19 transmission in exposed HCPs but, because of the many commercially available serological tests, it is not possible to draw any conclusions until more definite evidence is published [In conclusion, we have conducted a thorough analysis of a potential surgeon-to-patient and surgeon-to-HCPs transmission of SARS-CoV-2. The absence of COVID-19 RNA and of an antibody response among patients treated by the surgeon as well as among HCPs suggest that distancing measures, hygiene practice and PPE may be very useful for in-hospital control. Future studies, especially in different hospital settings, are needed but at present widespread use of PPE, the rapid isolation and testing of all HCPs potentially infected seems to be the only way to minimise the risk of a hospital outbreak.


HLA class I molecules are critical regulators of NK cell activation through the interaction with inhibitory- as well as activating NK cell receptors, hence, HLA molecules act as critical immune checkpoints for NK cells. In the current review, we evaluate how NK cell alloreactivity and anti-viral immunity are regulated by NK cell receptors belonging to the KIR family and interacting with classical HLA class I molecules, or by NKG2A/C and LILRB1/KIR2DL4 engaging non-classical HLA-E or -G. In addition, we provide an overview of the methods to determine genetic variation in these receptors and their HLA ligands.Natural killer (NK) cells are innate lymphocytes that can kill diseased- or virally-infected cells, mediate antibody dependent cytotoxicity and produce type I immune-associated cytokines upon activation. NK cells also contribute to the allo-immune response upon kidney transplantation either by promoting allograft rejection through lysis of cells of the transplanted organ or by promoting alloreactive T cells. In addition, they protect against viral infections upon transplantation which may be especially relevant in patients receiving high dose immune suppression. NK cell activation is tightly regulated through the integrated balance of signaling  Kidney transplantation is considered to be the best treatment option for patients with end-stage renal failure since transplantation gives better survival outcome and improved quality of life compared to dialysis . After tAllograft rejection is the result of concerted actions of several immune effector cells. During allograft rejection, the immune cells of the recipient get activated by alloantigens of the donor leading to immune responses against the graft and subsequently pathological changes, that can destroy the graft if not controlled . RejectiNK cells are innate lymphocytes that represent 5-15% of lymphocytes in peripheral blood, they are derived from common lymphoid progenitors, they can mediate both cytotoxic and cytokine producing effector functions, and they have been recognized primarily for their contribution to the immune response against intercellular pathogens and malignant cells . Howevervia NK cell mediated cytotoxicity or through secretion of pro-inflammatory cytokines   while CD56dim NK cell infiltrates characterized biopsies from patients with antibody mediated rejection (AMR)  . Since C T cells . Moreove T cells . Infiltr surface , 37. CD1 surface . In a mo surface . In huma surface , 40. Conin vitro and in vivo models, missing-self by itself was not sufficient for the NK cells to damage graft endothelial cells. However, in combination with Poly-I:C  or ischemia/reperfusion as provoking factor for NK cell activation and expression of activating ligands on endothelial cells, microvascular inflammatory lesions and allograft rejection occurred in an NK cell dependent manner  or three (KIR3D) extracellular immunoglobulin domains. Activating family members (aKIR) have a short (KIRxDSx) intracellular domain with immunoreceptor tyrosine-based activation motifs (ITAMs) and, although some of them recognize HLA class I, for several of the aKIRs the ligands remain elusive. Inhibitory family members have a long (KIRxDLx) intracellular domain with immunoreceptor tyrosine-based inhibitory motifs (ITIM) and most of them have HLA class I as ligands . The so called framework KIRs  are present in every haplotype ; for KIR2DL2/3 that predominantly interact with HLA-C alleles with the C1 epitope (asparagine at AA position 80); and for KIR3DL1 binding to HLA-B alleles with a Bw4 epitope and to HLA-A*23/*24/*32 . When assessing the presence vs absence of the HLA ligands it is especially for Bw4 important to also consider the HLA-A alleles as HLA-A*23, -*24 and -*32 encode for the binding site for KIR3DL1 and this is not always consistently done between studies. Since NK cell can express one or more KIRs, and because there is a large inter-individual variation, the size of the alloreactive NK cell population (i.e. the size of the population of NK cells expressing only mismatched KIRs) can be very different on a per individual basis  alleles carrying the HLA-Bw4 epitopes are recognized by KIR3DL1, the Bw6 epitope is not a ligand for KIRs. The HLA-C molecules carry either a C1 or a C2 motif, based on a dimorphism at residue 80. The C1 motif is defined by the presence of an asparagine at position 80, whereas the C2 motif has a lysine at position 80 of the mature protein .https://www.ebi.ac.uk/ipd/imgt/hla)  method. Since Bw4 and Bw6 are potent public antibody epitopes, specific antibodies against these supratypes are present and can be used to distinguish these motifs. However, nowadays HLA typing is merely determined by molecular typing methods using genomic DNA. Due to the high polymorphism a special HLA allele nomenclature has been developed for molecular HLA typing results, an example and explanation of this nomenclature is depicted in mgt/hla) . TherefoFor high resolution typing, sequencing is the most reliable method, because then the complete nucleotide sequence of at least exons 2 and 3 is determined. Other methods are PCR-SSP, based on amplification of genomic DNA with sequence specific primers analyzed by gel electrophoresis, and PCR-SSO, based on amplification of the locus of interest followed by hybridization with sequence specific oligonucleotide probes. Although typing can be performed with these methods, the problem exists that not all nucleotides are determined, only the ones for which polymorphism has been identified in the past. However, for determination of NK ligand positions all of these methods can be used.Determining the nucleotide sequences can be done with either Sanger sequencing or with next generation sequencing. For both methods the HLA gene of interest is amplified and this amplification comprises at least exons 2 and 3, but can be extended to the full length gene. Subsequent sequencing by the Sanger method might result in an ambiguous allele assignment, because the two alleles were not separated before sequencing and therefore, the cis-trans positions of nucleotides cannot be determined. To circumvent this problem, an allele-group specific amplification can be performed as described previously . AlternaSince sequencing used to be an expensive and labor intensive method and since NK ligand binding of HLA class I is limited to the amino acid region 77-83, several other methods, focusing only on the polymorphism defining the KIR ligand specificity, were developed in the past. Among them are: (1) a SNP (single nucleotide polymorphism) assay , amplifyHowever, all these methods, that only identify the crucial positions for KIR binding, are not able to detect if the allele present will be really expressed or not. Among both HLA-B and -C (as well as -A) there are now many alleles identified that are not expressed, the so called null alleles. Although their frequencies in the population are very low , the impThe KIR genes are comparable to HLA, they also show a high degree of polymorphism, correlated with population groups of specific geographic ancestry, and the more they are studied, the more different alleles are identified , 106. HoMost methods that exist for KIR typing focus on the gene content. Determination of KIR genes present can be performed by PCR-SSO, preferably with Luminex technology so that many samples can be analyzed simultaneously , multip–115, reaFor defining the number of copies (CNV = copy number of variation) of the different KIR genes different methods have been used, among them quantitative PCR , droplethttps://www.ebi.ac.uk/ipd/kir/)   with rulpd/kir/)   or in a soluble- (HLA-G5-7) form. Soluble isoforms are the result of alternative splicing of the transmembrane region encoded by exon 5 and can form HLA-G dimers that can signal more potently than monomeric variants , 177. ThIn addition to the above mentioned mechanisms, HLA-G expression levels can be influenced by genetic variation in the 3’-untranslated region (3’ UTR). Best characterized example, is the 14-base pair insertion or deletion fragment (14-bp INDEL) in exon 8 that, in 14-bp insertion allele variants, leads to the extra deletion of a 92-bp region at the start of exon 8 and enhanced mRNA stability . Anothervs 82 renal transplant recipients, a SNP in LILRB1 (rs1061680) has been shown to associate with increased carotid intimal media thickness  and KIR2DL4 are the most well described receptors for NK cells. LILRB1 interacts with β2M-associated HLA-G molecules and exclusively acts as an inhibitory receptor for NK cells . KIR2DL4hickness  but addihickness . They alSince typing of the classical class I molecules has been mandatory for transplantation purposes, many different DNA typing techniques and commercial kits are available. In contrast, typing of the non-classical HLA class I, HLA-E, -F and G genes, has been rather fragmentary.For HLA-E, the typing method has long been limited to resolve the dimorphic amino acid at position 107 (R or G) by either PCR-SSP . In the present IPD-IMGT/HLA database (3.44.0) a total of 45 different alleles has been identified, but they only encode 6 different proteins. The amino acid differences are located at the start or the end of the protein, leaving the middle part (amino acids 51 – 250) identical for all hitherto known HLA-F alleles. There is also no null allele yet identified, implicating an important role for this conserved protein. Since there is only limited polymorphism identified, the methods to type for HLA-F have often been limited to the known polymorphism or to a fraction of the gene, enabling only identification of the known alleles. The method dealing with the known polymorphism that has been used for HLA-F typing is PCR-SSP , whereasComparable to HLA-E and HLA-F also HLA-G exhibits few polymorphic sites along the sequence , althougvs. mismatching is greatly influenced by the exact transplantation protocol and beneficial effects of KIR-ligand mismatching were primarily seen in the severely T cell depleted setting (vs. absence of HLA epitopes while in other studies this was complemented with data on the genotypic- or even phenotypic presence of the corresponding KIR genes. Since HLA and KIR are encoded on different chromosomes and KIR null alleles exist it would be relevant to address the relevance of KIR-ligand mismatching in larger cohorts using different models to determine matching status. In addition, functional studies could be used to further dissect the importance of KIR-ligand matching and this could also be related to the different immunosuppressive regimen. Most pronounced were the effects observed for the KIR gene repertoire and KIR haplotype and they were identified by various studies as important determinants of the immune response against the certain serotype of viruses. This illustrates that assessing genetic profiles of NK cell receptor with specificity for HLA class I may be useful to improve transplantation outcome. Given the dual role of NK cells in transplantation, it would be relevant to perform follow up studies to further evaluate the predictive value of those genetic profiles for combined endpoints, i.e. taking into account the occurrence of both graft rejection and infectious disease. The advancement in molecular typing methods for both HLA and NK cell receptors provides better discrimination of subtype of HLA alleles and specific types of KIR receptors and enables this type of analysis. Combined with functional- and spatial analysis of immune cell infiltration and -function and the identification of additional ligand-specificity for aKIR, this will facilitate the deeper understanding of the role of NK cell immune checkpoints in kidney transplantation, which may guide the exploitation of targeting NK cells for therapeutic benefits.In the present review, we discussed the role of classical and nonclassical HLA class I molecules as immune checkpoints for NK cells and the relevance for two important determinants of kidney transplantation outcome: graft survival/rejection and viral infection. While several studies showed that expression levels- and soluble variants of non-classical HLA-E and -G are associated with rejection, data on the direct effects on NK cells and the contribution of NK cell receptors with specificity for non-classical HLA class I molecules in the kidney transplantation setting is rather limited. The contribution of KIR genes and iKIR-HLA class I matching status have been studied much more frequently. Multiple studies support the hypothesis that incompatibility between iKIRs in the recipient and HLA ligands in the graft may be detrimental for allograft survival. From the stem cell transplantation setting, it is becoming more and more clear that the impact of KIR-ligand matching  setting . This ma setting . MoreoveBD, TO, MG, CV, and LW wrote sections of the manuscript and reviewed it. 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.


The main objective of this systematic review is to investigate the expression level of the cyclooxygenase-2 (COX-2) in rectal cancer treated with either preoperative radiotherapy or radiochemotherapy. In addition, we have summarized the effects of preoperative treatment of rectal cancer with regards to the expression levels of COX-2. A systematic literature review was performed in The Cochrane Library, PubMed, Web of Science, and Scopus databases on 1 January 2021 with the usage of the following search string—(cyclooxygenase-2) OR (COX-2) AND  AND (preoperative radiochemotherapy) OR (preoperative radiotherapy). Among the 176 included in the analysis, only 13 studies were included for data extraction with a total number of 2095 patients. The results of the analysis are based on the articles concerning the expression of COX-2 in rectal cancer among patients treated with preoperative radiotherapy or radiochemotherapy. A COX-2 expression is an early event involved in rectal cancer development. In cases of negative COX-2 expression, radiotherapy and radiochemotherapy might contribute to the reduction of a local recurrence. Therefore, COX-2 may be considered as a biologic factor while selecting patients for more effective, less time-consuming and less expensive preoperative treatment. However, the utility of the administration of COX-2 inhibitors to patients with COX-2 overexpression, in an attempt to improve the patients’ response rate to the neoadjuvant treatment, needs an assessment in further clinical trials. In the last decades, a key enzyme in prostaglandin synthesis, cyclooxygenase-2 (COX-2), has been considered to play a key role in cancer development and progression ,2. DiscuCyclooxygenase (COX) regulates a key step in prostanoid synthesis. COX catalyzes the conversion of membrane phospholipids fraction—arachidonic acid (AA)—into an unstable prostaglandin H2 (PGH2) . This inA huge emphasis on the role of COX-2 in carcinogenesis was primarily made since the early 1990s when the first epidemiological reports regarding the effectiveness of the regular non-steroidal anti-inflammatory drugs (NSAIDs) in the prevention of colorectal cancer have appeared. It was established that COX-2 promotes pro-tumorigenic activity by several mechanisms, including angiogenesis development and resistance to apoptosis, modulation of host immune surveillance, increased DNA mutagenesis, activated peroxidase activity and xenobiotic carcinogens, which are all involved in the cancer invasiveness ,16,17,18So far, there are numerous reports describing tumorigenesis induced by the overexpression of COX-2. Liu et al. have observed an intensified COX-2 expression in the epithelial cells of the mammary glands, which was associated with a higher risk of hyperplasia and carcinomatosis . One of Recently, a wide range of different phenotypes and molecules  have been studied as potential biological markers and prognostic factors of colorectal cancer. However, data regarding their association with the survival rate of colorectal cancer patients remains slightly inconsistent either considering those factors as insignificant ,30,31 orPreoperative radiotherapy or chemoradiotherapy followed by a complete tumor resection are both well-established treatment strategies for locally advanced rectal cancer ,40,41. MIn this systematic review, we aim to evaluate whether COX-2 expression might act as a prognostic/predictive factor for overall survival after neoadjuvant radio(chemo)therapy in rectal cancer and whether COX-2 inhibitors might act as additional drugs except for standard therapy. In addition, this paper aims to establish the guidelines for rectal cancer treatment regarding COX-2 expression levels and determine the utility of the assessment of COX-2 expression while predicting the treatment response in patients with rectal cancer who are undergoing preoperative radiotherapy or chemoradiotherapy.Colorectal cancer is currently the second leading cause of cancer-related deaths and the third most prevalently diagnosed cancer, according to GLOBOCAN 2020 data . The higCurrently, surgery remains the main treatment strategy of rectal cancer with radiation and chemotherapy provided either before or after the surgery. Early rectal cancers and most of the polyps can be removed during a colonoscopy by polypectomy or local excision. More complex types of surgery for rectal cancer include the transanal excision (TAE), transanal endoscopic microsurgery (TEM), low anterior resection (LAR), proctectomy with coloanal anastomosis, abdominoperineal resection (APR), diverting colostomy, or pelvic exenteration. When rectal cancer metastasizes, ablation or embolization might be useful. For rectal cancers, radiation therapy is commonly applied in form of neoadjuvant treatment or intraoperative treatment therapy (IORT). Further, chemotherapy might be provided at different stages of rectal cancer treatment; the most common drugs currently used include 5-fluorouracil, irinotecan, capecitabine, oxaliplatin, and trifluridine and tipiracil ,54,55,56In the last three decades, numerous therapeutic strategies for rectal cancer have evolved. At first, two landmark studies—the National Surgical Adjuvant Breast and Bowel Protocol RO1 trial and the Gastrointestinal Tumor Study Group Protocol 7175 trial—proved the benefits of the adjuvant chemoradiation therapy for local control and long-term survival for patients with advanced carcinoma ,61. SevePreoperative radiotherapy followed by TME reduces local recurrence rates and is recommended in intermediate cases . In locally advanced, sometimes in non-resectable colorectal cancer cases, preoperative chemoradiotherapy followed by radical surgery even up to 12 weeks later should be provided. Neoadjuvant chemoradiation therapy is considered as a standard treatment aiming to reduce the local recurrence of cancer as well as to improve the marginal overall survival; however, its duration as well as type depends on the recommendations that slightly differ between countries  62,63].,63.62,63Notwithstanding the benefits, the outcome of patients is still poor with a 5-year disease-free survival estimated at only 66%; therefore, there is an urgent need for further improvements. The main therapeutic problems include the choice of the best standard of preoperative treatment, as well as tumor resistance .Currently, the main clinical objective is to search for the potential predictors that could identify those patients who would be the most vulnerable to preoperative treatment. The research is focused on the identification of molecular differences between the pre-treatment tumor biopsies of either responders or ‘non-responders’ to treatment. If a prediction of a patient’s response to radiochemotherapy or radiotherapy was possible (in the early phase of treatment), the ‘non-responders’ (radio-resistant) might be selected for the alternative treatment, which aims at improving their response.A systematic search of the PubMed, Cochrane, Web of Science and Scopus electronic databases was performed on 1 January 2021 with the usage of the following search string—(cyclooxygenase-2) OR (COX-2) AND  AND (preoperative radiochemotherapy) OR (radiochemotherapy) OR (preoperative radiotherapy) OR (radiotherapy). The above-mentioned terms were chosen in accordance with the Medical Subject Headings. An additional free search term has limited the results only to the articles related to the colorectal cancer. Terms were combined with Boolean operators. A non-mesh search was also performed. The final analysis included studies of any design that were specifically investigating the COX-2 expression assessment in prediction of response for treating rectal cancer with preoperative radiotherapy or chemoradiotherapy. The language of the articles was limited to English while there were no restrictions regarding the year of the publication. All of the analyzed articles were published and peer-reviewed and included only adult patients; the pre-prints were not taken into consideration. The articles with less than 27 patients in a study group as well as the case studies were excluded from the final analysis. Eventually, 13 articles were included in a qualitative synthesis with a time range 2005–2020.The methodological quality of the included studies was assessed by M.B. and A.F. All studies were graded according to the EBLIP critical appraisal checklist developed by Glynn L that takes into consideration such variables as study population, data collection, study design, as well as results . An overThe papers included in the final analysis were analyzed in terms of the relationship between COX-2 expression and the effects of rectal cancer treatment while applying radiotherapy or radiochemotherapy.The search revealed 176 articles; after duplicates were removed, 48 articles were screened while 38 of them were assessed for eligibility. The analyzed articles included only those that concerned the studies performed on humans. The time range of the analyzed articles was 2005–2020. Two major inclusion criteria were the expression of COX-2 and either preoperative radiotherapy or preoperative radiochemotherapy applied in rectal cancer patients. The search revealed 38 unduplicated articles, among which all of them have undergone a detailed assessment for eligibility providing a total number of 13 articles included in the final analysis. The analysis is based on all of the articles published as full article research  investigating among others the expression of COX-2 in rectal cancer treated with preoperative radiotherapy or radiochemotherapy. The articles with less than 27 patients were not taken into consideration. No case reports were finally included, six articles were retrospective analyses of COX-2 expression in pretreatment biopsy (paraffin blocks) compared to post-treatment/post-irradiation of COX-2 expression.The presented systematic review investigated a total of 2095 patients. The studies included in a final analysis were published between 2005 and 2020 and all of them were analyzing the significance of COX-2 expression in either preoperative radiotherapy or radiochemotherapy of rectal cancer, predicting the response of given treatment. In all of the studies, immunohistochemistry was used for COX-2 expression staining. The preparation for quantification of COX-2 was typical—paraffin-embedded tissue from pre-treatment biopsy was prepared and sectioned; then the membrane was blocked and incubated with primary antibodies against COX-2; afterwards, incubation with secondary antibody and detection reagent was performed. In some of the studies, apart from the COX-2 expression, the expression of other molecular markers was performed , which indicates the need for a further search of treatment predictors, including the molecular markers that are involved in cell growth, as well as in the prostanoid and apoptotic pathways ,72,73,74p < 0.0001), but no difference was observed between primary tumors and metastases. Heer et al. also investigated the COX-2 expression in relation to apoptosis with similar results—COX-2 expression in non-irradiated surgical specimens was negatively correlated with the apoptosis (p = 0.05–0.13); however, it was significantly associated with the decreased level of apoptosis in irradiated tumors (p = 0.001) [The majority of the tumors (94%) expressed COX-2 only to some degree. COX-2 expression was mainly observed within the cancerous area. Within the tumor cells, the most common immunohistochemical staining pattern of COX-2 was brown, diffuse, granular, cytoplasmic staining. COX-2 expression was also proved to be increased in primary tumors compared with the non-cancerous mucosa (= 0.001) . COX-2 op = 0.06) between the COX-2 expression and grading; a poor grade of differentiation was associated with high COX-2 expression levels in pretreatment specimens; a high level of COX-2 expression was more often observed both in irradiated and non-irradiated adenocarcinomas [Only the research by de Heer et al. has found a significant association (rcinomas . Shinto rcinomas . Accordip = 0.02); Heer et al. proved a correlation with a significantly higher rate of distant recurrences (p= 0.005) but not with the local recurrence [p = 0.004) as well as poor overall survival (p= 0.006) [p = 0.03) [p = 0.003, p = 0.026, p < 0.031, p < 0.001) [Firstly, weak COX-2 expression was associated with the lower rate of local recurrence after radiation in a study by Pachkoria et al.  . In addi = 0.03) . Wen et  = 0.03) . Moreove< 0.001) ,82,83,85< 0.001) . AdditioDuring the last decades, therapeutic strategies for rectal cancer treatment have significantly evolved. The initial step that aimed to change the treatment guidelines regarding rectal cancer were two trials conducted by the National Surgical Adjuvant Breast and Bowel Protocol RO1 and Gastrointestinal Tumor Study Group Protocol 7175, which have established the beneficial impact of adjuvant radiation and chemoradiation therapy for local control and long-term survival. These results were confirmed by several subsequent prospective randomized trials.The most important attribute of radio/radiochemotherapy is the ability to induce a complete pathological response, which equals the complete absence of tumor in surgical specimens. Despite the huge role of adjuvant treatment in decreasing local recurrence and improving overall survival, the results of treatment are varying from complete response to little or even no response. Usually, only 15–30% of patients show a favorable response to treatment with the usage of pCR. Thus, to quantify tumor response for neoadjuvant therapy, several techniques as well as numerous researches investigating different molecular mechanisms and molecules have been provided. In order to reduce a variation in tumor response, neoadjuvant regimens were also standardized. A significant number of patients have their surgical intervention delayed and those are unnecessarily exposed to the toxic effects of rectal cancer treatment that is usually ineffective, time-, and cost-consuming. The response to the combined neoadjuvant therapy predicts the final outcome. The clue task is to discover specific biomarkers or provide a method that could be potentially useful to differentiate the tumor ‘responders’ from the ‘non-responders’. The most important disadvantage of the neoadjuvant treatment is the fact that patients with a worse response cannot be identified until the time of pathologic analysis; all in all, the ‘non-responders’ (radio-resistance) are not selected for alternative treatment, which can improve their response. What is crucial is the histopathological assessment; however, imaging studies should also be provided in the case of rectal cancer—an MRI performed after radiotherapy aims to assess patients’ response to treatment and further qualification for potential surgery. At present, no means of predicting treatment response exists and all patients must undergo empirical preoperative treatment and surgery. Preoperative treatment is a well-suited model to evaluate and compare the expression of the biological factors . Currently, available therapeutic strategies are fundamentally based on the T and N stages and often do not consider a tumor’s biological genotype and phenotype ,91. ThisThis systematic review investigated a total of 2095 patients. The studies included in a final analysis were published between 2005 and 2020 and were focused on the analysis of the significance of COX-2 expression in rectal cancer preoperative radiotherapy or radiochemotherapy by predicting the response of given treatment. 80% of the studies were based on the retrospective analysis of COX-2 expression in pretreatment biopsy (paraffin blocks) compared to post-treatment/post-irradiation COX-2 expression. In each case, immunohistochemistry was used for COX-2 expression staining. In some of the studies, apart from COX-2 expression, the expression of other molecular markers was also investigated , which indicates the need of searching for other treatment predictors among molecular markers involved in cell growth, as well as in the prostanoid and apoptotic pathways. All of the presented methods of treatment were administrated according to the individual staging  and in accordance with the ruling guidelines for rectal cancer treatment.It should be considered that our study has some limitations that are mainly the consequences of the available literature. Although the systematic search was performed in The Cochrane Library, PubMed, Web of Science, and Scopus databases, only 13 researches were found. Each of the analyzed studies had a similar methodology but the study groups were not easy to collate due to the lack of important data  or lack of expression of COX-2 measurement details. Another difficulty was a standardization of given preoperative treatment—in four cases, preoperative radiotherapy was provided  and in four cases radiochemotherapy was administrated preoperatively. Some of the results seem to be inconclusive, probably due to the small study population. Further, it should be considered that COX-2 expression is slightly different interpreted amongst studies; the interpretation whether its expression was high or low was primarily based on the results of the ones studies rather than reference values, which de facto remain not constant as previously mentioned. Another limitation is the fact that the stages in which the samples were collected remained different between studies and further were sometimes not clearly defined by the authors.Based on the systematic review, apart from investigating the role of COX-2 expression in neoadjuvant rectal cancer treatment response, we have proposed a novel pretreatment proceeding in rectal cancer, which is based on the well-established guidelines for rectal cancer treatment and the most recent research regarding the COX-2 inhibition during preoperative treatment (Diagram 1). Since it was observed that the increase of COX-2 activity amongst colorectal cancer patients is associated with greater progression of carcinogenesis, it is suggested that COX-2 expression might act as a prognostic factor in some aspects . FurtherTo the best of our knowledge, there are no accessible studies that summarize the effects of rectal cancer preoperative treatment in relation to the levels of COX-2 expression, which makes the presented study a novel review. Nevertheless, to confirm our results and validate the predictive power of COX-2 as a diagnostic marker, further additional independent research is needed.A COX-2 expression is an early event involved in rectal cancer development. In cases with negative COX-2 expression, radiotherapy and radiochemotherapy may reduce the local recurrence. Taking those facts into consideration, COX-2 may be considered as a biological factor in selecting patients for more effective, less time-, and cost-consuming preoperative treatment. Administration of COX-2 inhibitors to patients with COX-2 overexpression, in an attempt to improve the response rate to neoadjuvant treatment, needs an assessment in further clinical trials.