Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. Most MOT methods use a tracking-by-detection pipeline, which includes both the object detection and data association tasks. However, many approaches detect objects in 2D RGB sequences for tracking, which lacks reliability when localizing objects in 3D space. Furthermore, it is still challenging to learn discriminative features for temporally consistent detection in different frames, and the affinity matrix is typically learned from independent object features without considering the feature interaction between detected objects in the different frames. To settle these problems, we first employ a joint feature extractor to fuse the appearance feature and the motion feature captured from 2D RGB images and 3D point clouds, and then we propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames and learn a deep affinity matrix for further data association. We finally provide extensive evaluation to reveal that our proposed model achieves state-of-the-art performance on the KITTI tracking benchmark. PointNets [AB3DMOT [Multi-object tracking in a 3D environment (3D MOT) plays a crucial role in the environmental perception of autonomous systems ,2,3,4. TointNets ,11 allow[AB3DMOT  and Hung[AB3DMOT  over the[AB3DMOT  and Hung[AB3DMOT  only incGNN3DMOT [JRMOT [mmMOT [In order to improve the reliability and safety, recent tracking-by-detection approaches ,14,15 fiT [JRMOT  to assigT [mmMOT  learns aIt is easy to observe that capturing the discriminative feature, which is beneficial for distinguishing different objects, is the critical process when learning the affinity matrix and the following data association. One efficient method to solve this problem is to represent the objects in the adjacent frames as a directed graph. Specifically, each object can be treated as a node in the graph, and the relationship between an object pair is the edge between related two nodes. As a result, the problem of exploiting the discriminative feature between two objects is convert to learn the relations between the nodes in a directed graph. Consequently, we propose a deep neural network by employing the graph neural network into the 3D MOT to better exploit discriminative features.Furthermore, considering the fact that the nodes in the graph are not structured and unordered, we are unlikely to leverage the advantages of convolutional neural networks (CNNs) to exploit the features. Prior works for both 2D MOT and 3D MOT ,8,14 useWe represent the detected objects as the nodes in a directed acyclic graph and propose a graph neural network to exploit the discriminative features of the objects in the adjacent frames for 3D MOT. Specifically, a directed graph is constructed by treating each object feature as the node. Consequently, the Graph Neural Network (GNN) technique could be employed to update node feature by combining the features of other nodes.We propose a novel joint feature extractor to learn both 2D/3D appearance features and 2D motion features from the images and the point clouds in the sequence. In particular, the 2D/3D appearance features are learned from the image and the point cloud respectively by applying corresponding feature extractors. The motion model uses the parameters of the 2D bounding box as the motion cues to capture the motion features, which are finally aggregated with the 2D/3D appearance features to obtain the joint feature.We propose the RelationConv operation to efficiently learn the correlation between each pair of objects for the affinity matrix. The proposed operation is similar to the kernel of Convolutional Neural Network (CNN) that is used on the standard grid data, but our operation is more flexible and can be applied over the irregular data .In order to learn temporal-spatial features for the objects in the sequence, we also propose a feature extractor that jointly learns appearance features and motion features from both 2D images and 3D point clouds. Specifically, we use the off-the-shelf 2D image and 3D point cloud detectors to extract respective features for the appearance features of the objects. We learn the motion features by building a subnetwork that just takes the 2D bounding boxes in the images as input. We finally fuse the appearance feature and motion feature together for further data association. We summarize our contributions as below.MOT methods can be categorized into online and offline methods. Online method envisage the prediction of data association between the detection in current frame and a few past frames. Online are the standard for real-time applications. Early 2D trackers ,19,20,21On the other hand, offline methods aim at finding the solution as an optimization problem for the whole data sequence. Some models ,27 buildmmMOT [GNN3DMOT [3D object detection has achieved great success in the recent years, especially given PointNets ,11 are cmmMOT  builds ammMOT  and a 3DmmMOT  to captuGNN3DMOT  proposesFaF [Joint multi-object detection and tracking method has become popular as it might lead to a sub-optimal solution if the detection task and the tracking task are decoupled. Recent methods ,32 constFaF  first coJPDA [Data association is an essential problem in the MOT task to assign the same identity for the same objects in the sequence. Traditionally, the Hungarian algorithm  minimizeJPDA  considerJPDA ,34 firstjoint feature extractor to capture the appearance feature and the motion feature from the 2D images and 3D point clouds, (b) a feature interaction module that takes the joint feature as input and uses proposed RelationConv operation to exploit the correlation between the pairs of objects in the different frames, (c) a confidence estimator predicts if a certain detected object is a valid detection, and (d) a data association module to compute the affinity matrix for associating the objects in the adjacent frames.Our proposed 3D MOT network follows the tracking-by-detection paradigm. As shown in t with N detected objects as M detected objects as t.As an online 3D MOT method, our model performs objects association in every two consecutive frames from a given sequence. Assume that a new identity number will be assigned if an object reappears in the frame due to the occlusion or misdetection conditions. Consequently, our model focuses on correlation learning without considering the re-establishment of the associations for the objects across the temporal gaps. We refer to the current frame at time PointRCNN [PointPillar [RRC-Net [For what concerns the object detection, there are two main methods to localize the objects in the frames. One method is to use existing, state-of-the-art, 3D detectors  to predntPillar  to estim[RRC-Net  as our 2VGG-16 [PointNet [In order to exploit sufficient information for the detected objects in the frames, we propose a joint feature extractor see a to learVGG-16  to extraPointNet  over theVGG-16 [VGG-16 network  for each detected object. We propose three fusion operators: 1) an intuitive method is to add 2D and 3D appearance features together after forcing them to have the same feature channels; (2) another common approach is to concatenate 2D appearance feature and 3D appearance feature, after which a 1-layer MLP is used to adapt the dimension of the fused features; and (3) the last operator is an attention-based weighted sum. Specifically, as defined in Equation .After the joint feature extractor, we gain the fused features for F dimension, and In order to efficiently represent the objects in the different frames, we treat the feature of each object as a node in a graph structure, and the edge between two nodes in the graph can indicate the relationship between two objects in the different frames. As a result, we first construct a directed acyclic graph structure to represent the objects in two adjacent frames as shown in t and all the objects in the frame N objects at current frame t, and M objects at previous frame t, i-th object node t and j-th object node i-th object node in current frame t and all the M neighborhood nodes in previous frame However, as we only learn the correlation between each object in the frame Equation :(3)Xt=xiPointCNN [PointNets [Considering irregular and unordered properties of the nodes in the graph, we are unlikely to use regular CNN filters . In standard CNNs, to obtain the abstract features Equation .(4)fX=WSimilarly, as defined in Equation , our Relraph see . It is en RELU . fEt areIt is likely that the same objects in different frames should learn similar discriminative features. Therefore, the feature similarity should be dependent. In contrast, the feature similarity of two different objects should be low. As a result, the discriminative feature is beneficial for avoiding confusion while matching the objects.Given the obtained fused features containing 2D/3D appearance features and 2D motion features, we propose a feature interaction module to learn the correlation between each object pair in the different frames as shown in As shown in fined as . The outIn order to associate and match the objects in the different frames, given the correlation representation after feature communication among objects, we use a 3-layer MLP subnetwork to learn an affinity matrix with a 1-dimension output feature t . The start-estimator and the end-estimator use an average pooling over the deep correlation representation to summarize the relations, and then employ the respective MLP network to learn the scalar scores for all the objects.Furthermore, inspired by the work in , we leari-th object is a true positive detection. j-th object in previous frame i-th object in current frame t are the same objects. j-th object in previous frame i-th object in current frame t ends a trajectory in the frame t. Similar to t and t, respectively.We obtain several binary variables for prediction scores from our proposed neural network as shown in Equation , denotesEquation , sjstartEquation .(7)sijAWe finally aggregate all the prediction scores to a new vector Considering the graph structure for all the detected objects, the data association problem can be formulated as the min-cost flow graph problem ,27. Spect, or it starts a new trajectory. As a result, we define the linear constraint as Equation (There are two circumstances for a certain true positive object in previous frame Equation .(9)sjdet can be matched to another object in previous frame Similarly, a certain true positive object in current frame Equation .(10)sidFinally, we formulate the data association problem asquations  and 10)(11)argmiWe first evaluate our neural network on the KITTI object tracking benchmark ,44. The i-th object and corresponding matched hypothesis in the frame t. t.The evaluation metrics to assess the performance of tracking methods are based on CLEAR MOT ,46. Spect.MOT accuracy (MOTA) measures the total tracking accuracy for all the frames as defined in Equation .(13)MOTBesides, the work in  introduc−4. Besides, the super convergence training strategy is employed to boost the training processing and the maximum learning rate is set to 6 × 10−4.Our model is implemented using Pytorch v1.1. We trained our model on a ThinkStation P920 workstation with one NVIDIA GTX 1080Ti, and use Adam as a training optimization strategy with the initial learning rate 3 × 10MASS [RRC-Net [MASS , but out[RRC-Net . As a reAB3DMOT [Compared to the 3D tracking methods, our model achieves the best performance. However, Kalman filter method AB3DMOT  could peGNN3DMOT [GNN3DMOT uses the 3D detections to obtain the 2D information by projecting the point cloud to the image, which leads to only 80.40% mmMOT [Compared to the 2D&3D tracking methods, it is easy to observe that our MOTA and MOTP results outperform those of GNN3DMOT , whose fWe also investigate the effectiveness of the appearance feature, motion feature, and different convolutional operations as shown in It is worth highlighting that the Equation . Besidestion see , as longWe also study the effectiveness of different nonlinear activation functions in Equation  for our We finally provide some examples of the tracking results as shown in We investigate different hyperparameter settings to evaluate the effectiveness of our model on the KITTI object tracking benchmark .We proposed a deep affinity network, named Relation3DMOT, able to learn discriminative features and associate the objects in adjacent frames for 3D MOT. We employed a joint feature extractor to capture the 2D/3D appearance feature and 2D motion feature from 2D images and 3D point clouds, respectively, followed by a feature interaction module able to enhance the feature communication among objects in different frames. We also proposed a novel and efficient convolutional operation, named RelationConv, to abstract semantic and contextual relations for each object pair. Finally, we carried out extensive experiments on the KITTI object tracking benchmark to demonstrate the effectiveness of our Relation3DMOT tracker.In the future, we plan to improve our model by considering objects with hard occlusion between frames. Further to this, it would be interesting to develop an end-to-end framework for the joint task of object detection and tracking, as we believe that it could avoid the decoupling issues that might arise when we deal with the object detection and tracking separately.


Owing to the increasing use of permeable pavement, there is a growing need for studies that can improve its design and durability. One of the most important factors that can reduce the functionality of permeable pavement is the clogging issue. Field experiments for investigating the clogging potential are relatively expensive owing to the high-cost testing equipment and materials. Moreover, a lot of time is required for conducting real physical experiments to obtain physical properties for permeable pavement. In this paper, to overcome these limitations, we propose a three-dimensional microstructure reconstruction framework based on 3D-IDWGAN with an enhanced gradient penalty, which is an image-based computational system for clogging analysis in permeable pavement. Our proposed system first takes a two-dimensional image as an input and extracts latent features from the 2D image. Then, it generates a 3D microstructure image through the generative adversarial network part of our model with the enhanced gradient penalty. For checking the effectiveness of our system, we utilize the reconstructed 3D image combined with the numerical method for pavement microstructure analysis. Our results show improvements in the three-dimensional image generation of the microstructure, compared with other generative adversarial network methods, and the values of physical properties extracted from our model are similar to those obtained via real pavement samples. In recent years, permeable pavement has been widely used in developed countries as part of low-impact development practices . The maiHowever, there are some drawbacks to using permeable pavement. First, it is expensive owing to its special design. Permeable pavement consists of aggregate components with different materials such as concrete mixtures with cement, polymer, and plastic . Second,Several experimental studies related to clogging in permeable pavement have been reported by researchers over the years to develop an ideal pavement design ,7,8,9. ATo analyze the clogging potential in permeable pavements, some important properties of pavement microstructure need to be measured. Microstructure is the small-scale structure of a material that can be viewed through a microscope. The properties of microstructure are very important for understanding the material structure and studying the mechanical behavior of a material. In permeable pavement, three crucial properties for analysis are (1) porosity, (2) permeability, and (3) hydraulic conductivity ,10. PoroTraditionally, to measure the clogging potential properties, we needed to conduct field experiments ,13 usingA virtual experiment is another alternative to estimate the clogging potential properties ,15. It iHowever, there are some limitations to the basic machine learning techniques and statistical approach. They require predefined knowledge of the materials , many diTo overcome these limitations, we propose a deep-learning-based 3D microstructure reconstruction framework for permeable pavement analysis as a virtual experiment method. Our system first takes a two-dimensional image to extract the latent features of the image. Then, it generates the corresponding 3D microstructure image that can be obtained through the concept of the generative adversarial network ,23. SincWe propose a deep-learning-based 3D microstructure reconstruction framework for permeable pavement analysis. Our proposed framework can effectively reconstruct a 3D microstructure image after taking only one 2D image. For this purpose, our framework applies several image preprocessing techniques to the input 2D images and extracts the latent features from them via the variational autoencoder (VAE) method.In comparison to other generative adversarial network methods, we obtained more realistic 3D microstructure images of porous pavements and stable outputs. This stability is the main result of using 3D-IWGAN with an enhanced gradient penalty.The accuracy of our results has been verified by evaluating some physical properties extracted from the generated 3D samples. On observing nine different output samples, the average error difference in hydraulic computation was found to be less than 5%.The main contributions of this paper are summarized as follows:The remainder of this paper is organized as follows. In this section, we provide a brief review of the related work. There are three categories to be discussed: (1) microstructure reconstruction approaches, (2) general 2D/3D object reconstruction approaches, and (3) deep-learning-based microstructure reconstruction approaches.Microstructure is the small-scale structure of a material that can be viewed through a microscope. In the fields of materials science and civil engineering, microstructure properties are very important to understand the structure of a material and study its mechanical behavior.The most common and earlier work for 3D microstructure reconstruction uses the two-point correlation method ,20,21. IAfter the successful results shown by convolutional neural networks (CNNs) in the field of image classification and computer vision, researchers started considering using CNNs and other deep learning methods in their research on permeable pavement.Since the core functionality of GAN enables us to generate a set of realistic (2D) images from the given sample, thus it is extended to generate 3D images (objects) . AlthougThere were a few research works on deep learning applications for microstructure reconstruction before the GAN technique. One example of such applications is a research work by Cang, Ruijin, et al. . It propOwing to the proliferation of GAN, several research approaches exploit the generative power of GAN. Since GAN is used for microstructure reconstruction , it suffRecently, more complex methods have been combined with GAN-based methods ,33,34. AOur proposed system is different from the aforementioned recent approaches in the following aspects. First, we focus on reconstructing the 3D microstructure of permeable pavements and obtaining 3D images with a given 2D image. In this regard, an end-to-end framework  has the The generative adversarial network (GAN) is a deep learning method that learns the representation of high-dimensional probability from a given dataset, and was introduced by Ian Goodfellow et al. . For imax is a real object input in discriminator D and z denotes the normally distributed latent vectors that represent the random input for generator GAN is a generative system that comprises two networks, a generator network and a discriminator network. The generator network converts latent vectors with a normal distribution into samples that can be classified as either real or fake by the discriminator network. The training process is stopped and the discriminator is discarded after a proper sample image is obtained from the generator. GAN follows a minmax game with a loss function that is formally defined asIn the original GAN method, the Kullback–Leibler divergence is minimized when training the networks to generate 3D objects. The drawback of using this method is the slow and unstable training process. A recent study introduced a method called 3D-improved Wasserstein GAN (3D-IWGAN)  that attThe deviation of the discriminator’s gradients is penalized from unity in 3D-IWGAN. It provides a more essential method for enforcing the Lipschitz constraint. The gradients of a differentiable function are at most one if and only if it is a 1-Lipschitz function. In the formal definition, the loss function for the discriminator in 3D-IWGAN is written asThe issue of the instability of GAN training can be tackled by employing the IWGAN method. Our system utilized the IWGAN with an enhanced gradient penalty for 3D microstructure reconstruction. In comparison to previous approaches, our system demonstrated more stable results in training of the GAN. A latent vector is needed as an input to the generator network in the 3D-IWGAN system. In a general way, this latent vector is generated randomly using normal or uniform distribution. Meanwhile, our system generates this latent vector using a variational autoencoder (VAE). A VAE  consistsThe proposed system for the virtual experiment consists of three components: preprocessing, 3D model generator, and pavement analysis. The architecture of the system is illustrated in In this section, we provide a brief overview of each component of the system. The preprocessing part has several tasks such as cropping, converting, downsizing, and resampling the images. In the model generation part, we use an adversarial network to generate a 3D image from the 2D images of the porous pavement microstructure. This 3D image is used as the input for pavement analysis, which further provides the numerical values of physical properties such as porosity, permeability, and hydraulic conductivity.Before implementing the 3D-IWGAN, we need to prepare the image data to be fixed with the requirement of GAN input. The first step of preprocessing is binary image conversion. In this step, the raw CT images of the microstructure are converted into binary images with the dimension of 1130 × 1130 pixels. This binary conversion is based on Otsu’s thresholding method.In the second step, the image is cropped into a square with the dimension of 800 × 800 pixels, which is the maximum square size inside the circle of the converted binary image. The squared image is then resized into a smaller size of 300 × 300 pixels to fit the system requirement. This is the third step, known as image downsizing. The illustration of binary conversion, image cropping, and downsizing is shown in The fourth step of preprocessing consists of volumetric image construction. In this step, a 3D microstructure image is created by stacking the binary images. The stack consists of 800 images of the permeable pavement microstructure and the size of each image is 800 × 800 pixels. The volumetric image of this generated microstructure is shown in Image resampling is the fifth step of preprocessing. Since we only have one training image per sample for the generator network, we need to create appropriate images for the training process by extracting sub-volumes from the voxelized binary images. For this experiment, we made approximately 24,389 images of In this subsection, we shall present details of the 3D model generation including the 3D-IWGAN process and enhanced gradient penalty.The 3D model of the pervious pavement microstructure is generated using GAN in the first part of our system (3D model generator). The network takes a 2D image as the input in the generator network. This 2D image is transformed into a 3D image by the deconvolutional neural network. From the generator, the image is delivered to the discriminator network and it is tested against the real sample image of the microstructure to check whether the generated 3D model is real or fake using the discriminator.G and discriminator D. A 400-dimensional latent vector with normal distribution serves as the input for the generator network. The 3D-IWGAN network architecture used for 3D microstructure reconstruction in module one consists of two independent networks, the generator The latent vector was generated using the VAE, whose architecture is identical to that of discriminator network in the 3D image generation of GAN. The discriminator network D takes an input image of size 20 × 20 × 20 voxels. This input passes through four 3D convolutional layers, followed by a final fully connected layer. It is then condensed to a single value known as the discriminator output. The features of the architecture of the generator and discriminator networks are listed in Various types of gradient penalty are used to improve the convergence and stability of GAN training. One of the most commonly used gradient penalties is the one-centered gradient penalty , which iIn this paper, we used an enhanced gradient penalty for our 3D-IWGAN system. The original 3D-IWGAN gradient penalty is being modified to enhance the training stability of our GAN. Its formal definition is as follows: The gradient penalty used in this study is a form of zero-centered gradient penalty. In the zero-centered gradient penalty, we want to set the gradient to zero as the generator distribution PermSolver and (2) MorphAnalyzer.In the final part of our system, we analyzed the physical properties of the microstructure, such as porosity and permeability, using the 3D model output from the model generator part. The pavement analysis comprised two sub-parts: (1) PermSolver is an open-source framework developed by NIST [ by NIST . It takeK) with this equation:k is the permeability value (from Permsolver), g is the acceleration due to gravity, and Other than permeability values, we can also calculate the hydraulic conductivity  CPU E5-2609 v4 @ 1.70 GHz x 16, with 64 GB memory, NVIDIA GeForce GTX 1060 6 GB, and runs on a 64-bit Ubuntu 16.04 operating system.We implemented our approach in Python 2.7 using Tensorflow-GPU 1.2.1 performed with CUDA Toolkit 8.0 and cuDNN 6.0.The dataset used for this work was extracted from nine different samples of permeable pavement. The sample image was scanned using a CT machine with a voxel size of 88.194 μm. The size of one sample image after binary conversion to 8-bit resolution was The reconstruction process started by defining some hyperparameters for the generator and discriminator networks. We set We trained the network and calculated the loss for the discriminator network. The calculated loss is used to track the convergence and quality of generated objects, depicted in The result of the 3D reconstructed image using GAN is displayed in We reconstructed the microstructure for nine different samples, whose results are presented in The final part of our system is pavement analysis. After obtaining the 3D image of the pavement microstructure, we attempted to analyze the physical properties of the generated images. We attempted to evaluate five properties: porosity, permeability, hydraulic conductivity, specific surface area, and Euler characteristic.In porous media such as permeable pavement, flow properties can be related to porosity, which is required to determine other properties such as permeability and hydraulic conductivity. Therefore, it needs to be estimated first. According to the ASTM standard, the porosity for permeable pavement is approximately 15–35%. We also calculated other properties such as the specific surface area and Euler number. The specific surface area is used to define the adsorption and dissolution processes in porous media, while the Euler number is used to characterize the connectivity of porous media. This is an important factor that determines the ability of fluids to flow. A less negative Euler number indicates a reduction in pore network connectivity. The values of surface area and Euler number are presented in After measuring the morphological values, we tackled the final part in our system: computing the permeability and hydraulic conductivity for each sample. The results of this computation can be used to determine the clogging potential of permeable pavement. The result for hydraulic conductivity is illustrated in The results of pavement analysis demonstrate that the proposed 3D model yielded better values than the model generated using 3D-GAN and original 3D-IWGAN. From the nine different samples, the average error difference in hydraulic conductivity computation was found to be below 5%. Moreover, the values of physical properties extracted through the proposed 3D model using our GAN method are closer to the baseline (theory) values than other GAN methods.We summarize our two major finding based on our experimental results. First, our framework can successfully generate 3D images of porous pavement microstructure from a single 2D image using 3D-IWGAN with enhanced GP. There are some limitations that restrain us from obtaining 100% accuracy despite getting decent results in 3D image generation and the computations of physical properties. Some limitations arise from the limited 2D image dataset as the input whereas others arise from the computational resources for our proposed framework. To provided a 2D input image for our framework, scanned images of pavement microstructure are needed at the pre-processing step. Since we only had a limited set of scanned images, we applied image resampling to obtain enough 2D images for our experiments. Powerful computational resources are important for generating 3D images with high resolution. For future experiments, several machines can be utilized as a distributed way to obtain large 3D images with higher resolutions.Although permeable pavement has been extensively used to allow rainwater to pass through the pores into the ground, to control water quality, and to reduce the surface temperature, it has some obstacles during utilization: obtaining samples, maintenance cost, and clogging issues.To overcome above-mentioned problems, in this paper, we proposed a three-dimensional microstructure reconstruction framework for permeable pavement analysis as one of the virtual experiment solutions. Since we utilized the generative model of 3D-IWGAN with an enhanced gradient penalty, our framework can effectively generate 3D microstructure reconstruction from a single 2D image of permeable pavement. To the best of our knowledge, 3D-IWGAN has not been used for the 3D model reconstruction of permeable pavement materials. From the visualization of the generated 3D microstructure images from nine samples, we have observed that our framework generates realistic 3D images.When analyzing the flow in porous media, it is crucial to improve the values of porosity. Incorrect values of porosity will affect the other physical properties of permeable pavements such as permeability and hydraulic conductivity. These two properties are also important to assess the quality of generated images that are later used for pore network analysis. We have demonstrated that our framework generates more realistic 3D microstructure images, maintaining the values of these physical properties within the range of the standard values. In particular, the porosity values of the generated 3D images from 3D-IWGAN with enhanced GP were improved by 66.67% compared to the previous methods (3D-GAN and 3D-IWGAN).Our proposed 3D microstructure reconstruction framework can be extended in two directions. First, we can add an additional module that generates and analyzes the pore network to conduct comprehensive virtual experiments. Second, we can combine some empirical methods with our framework to increase the quality of the generative model and avoid random errors.


Indeed, the RER mag was significantly different between the subgroups stratified by Weber’s fitness class or a ventilatory efficiency class. Our study is the first to investigate recovery of the RER in a population of KTRs, which correlates well with known prognostic CPET markers of cardiorespiratory fitness, determining the RER mag as the most meaningful RER overshoot parameter. Thus, the RER recovery might be included in CPET evaluations to further improve prognostic risk stratifications in KTRs and other chronic diseases.The overshoot of the respiratory exchange ratio (RER) during recovery from exercise has been found to be reduced in magnitude among patients with heart failure. The aim of this study is to investigate whether this phenomenon could also be present in patients with peripheral, and not cardiac, limitations to exercise such as kidney transplant recipients (KTRs). In this retrospective cross-sectional study, KTRs were evaluated with maximal cardiopulmonary exercise testing (CPET) assessing the RER overshoot parameters during recovery: the RER at peak exercise, the maximum RER value reached during recovery, the magnitude of the RER overshoot (RER mag = (RER max-peak RER)/peak RER%) and the linear slope of the RER increase after the end of exercise. A total of 57 KTRs were included in the study , all of them showing a significant RER overshoot (RER mag: 28.4 ± 12.7%). Moreover, the RER mag showed significant correlations with the fitness of patients (peak VO Moreover, a CPET evaluation has an important role in driving clinical decisions in end-stage heart or lung disease, guiding the selection process of candidates for transplantation [Cardiopulmonary exercise testing (CPET) is currently recognized as a useful clinical tool to assess the physical fitness and exercise limitations of patients with cardiac, pulmonary and musculoskeletal diseases ,2,3. Itsantation . However2 after maximal exercise testing is delayed in patients with heart failure and that this delay correlates with the severity of the disease [2 recovery delay has been correlated with a worse prognosis in more than one study [2 deficit from exercise [At present, there seems to be a reasonable agreement on the fact that the recovery of VO disease ,6. Moreone study ,8. The rexercise  whereas exercise .2 at the end of exercise [2 during the first part of recovery and has been attributed to a paradoxical increase in the stroke volume due to a reduction in systemic vascular resistances. Speculations have been made about whether this impaired relationship between the stroke volume and vascular resistances could be due to a slow decrease in the adrenergic tone or to the repayment of the accumulated O2 deficit [Another associated phenomenon that has been observed in patients with heart failure is the overshoot of VOexercise . This is deficit ,11.2/VO2), ventilation to oxygen consumption ratio (VE/VO2) and the end-tidal partial pressure of oxygen (PETO2) commonly observed in healthy subjects after maximal exercise testing were found to be reduced in magnitude in patients with cardiac disease (left ventricular ejection fraction (LVEF) < 40%). Takayanagi et al. demonstrated how the magnitude of the overshoot  of the aforementioned indices was lower in cardiac patients than in healthy subjects. The explanation for this phenomenon has been attributed to the delayed recovery of VO2 typically present in these patients. Moreover, the authors found no correlation between the overshoot magnitude of the studied parameters and the LVEF whereas significant correlations were found between the overshoot magnitude and several CPET indices of cardiorespiratory fitness, suggesting a relationship between the recovery metrics and the cardiopulmonary function during exercise but not with the resting cardiac function [Recently, attention has been drawn to the overshoot of several other CPET indices during recovery after maximal exercise. In particular, the overshoot of the respiratory exchange ratio  factors in the genesis and modulation of this phenomenon, the behavior of these respiratory gas indices during recovery should also be studied in a population of patients with peripheral limitations to exercise in the absence of major cardiac limitations ,13,14. I2 and VCO2.The aim of the present study was thus to characterize the recovery of the respiratory gas indices in a population of patients with peripheral limitations to exercise in the absence of significant heart disease ,14. The This was a retrospective cross-sectional study that included all KTRs who underwent a functional evaluation at the Sports and Exercise Medicine Division of the Padova University Hospital between 2015 and 2018 for a cardiovascular screening, an individual assessment of physical fitness and/or an exercise prescription.The exclusion criteria for exercise testing were an abuse of psychotropic substances, orthopedic problems that contraindicated the performance of an exercise test, a recent myocardial infarction, a known aneurysm of the thoracic aorta, recent (less than 2 months) thoracic and/or abdominal surgery and a recent pneumothorax. Patients with less than two minutes of monitored gas exchange during recovery after the test were also excluded. All subjects underwent a functional evaluation after signing their informed consent. The presented data are secondary study outcomes obtained from an action research project that derive from a diagnostic-therapeutic pathway of clinical assistance routinely performed for these patients and approved by the local Ethics Committee (protocol n. 4777/AO/19).For each patient, medical and drug histories were taken, a physical examination was performed and the most recent blood tests were used to determine blood hemoglobin, glycemia, total cholesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, triglycerides and creatinine concentration. Exercise testing was performed on a cycle-ergometer  or a treadmill (COSMOS model T170 DE-med) based on the preferences or comorbidities of the patients [2, VCO2) and ventilation (VE) were monitored breath by breath during the whole test and the first 2–6 min of recovery . The peak VO2 was defined as the highest value of VO2 attained in a 30 s interval at peak exercise. The age-predicted heart rate (HR) was calculated as: (220-age) bpm. The ventilatory threshold (VT) was identified using the simplified V-slope method; in case the VT was not clearly detectable with this method, it was evaluated by two expert physicians considering the behavior of ventilatory equivalents. The respiratory compensation point (RCP) was also evaluated by two expert physicians considering the behavior of ventilatory equivalents and PETCO2. The VE/VCO2 slope was calculated as the coefficient of the linear regression obtained by plotting the VE and VCO2 data from the beginning of the exercise to the RCP. For patients who performed cycle-ergometer testing, the VO2/Watt slope was calculated as the linear regression coefficient of the relationship between VO2 and workload of the entire exercise phase. The oxygen uptake efficiency slope (OUES) was calculated as the coefficient of the linear relationship between oxygen uptake and the logarithm of total ventilation [The ramp test protocol was adapted based on the characteristics of each patient and their reported level of physical activity with the aim of reaching exhaustion within 8–12 min of incremental exercise. This maximal CPET was performed until patients reached a Borg rating of perceived exertion (RPE) ≥ 18/20. Continuous monitoring of the electrocardiogram was performed throughout the test and blood pressure was measured both during exercise and at recovery. The respiratory gas exchange , the maximum RER value reached during recovery (RER max), the magnitude of the RER overshoot (RER mag) and the linear slope of the RER increase after the end of exercise.Breath by breath data were analyzed on an average of 5 s to avoid possible confounding values due to inconstant ventilation or sampling errors. The peak RER was defined as the highest value of the RER reached during exercise and the RER max was defined as the highest value of the RER reached during recovery. The RER slope was calculated by linearly regressing the RER data points against the time from the peak RER to the RER max. Finally, the magnitude of the RER overshoot was calculated as the percentage increase in the RER during recovery compared with the peak RER, as previously described . Figure 2 slope into ventilatory class I (VE/VCO2 slope < 30) and II (VE/VCO2 slope between 30 and 35.9). In addition, patients in Weber’s class A (peak VO2 > 20 mL/kg/min) were compared with their counterparts in classes B and C .Two subpopulations of KTRs were further derived from the initial KTR cohort in order to investigate the behavior of the RER mag in the subgroups with potential functional and prognostic differences. Firstly, patients were divided based on the VE/VCOt-test for the normally distributed variables and a Mann–Whitney U test for the non-normally distributed variables. The correlations were evaluated with Pearson’s correlation index if they were normally distributed and Spearman’s correlation index if they were non-normally distributed. A standard multiple regression was performed to assess the possible determinants of the RER mag. A statistical analysis was performed using IBM SPSS Statics software version 25. A statistical significance level of p ≤ 0.05 was used for all analyses.The normality was assessed using the Shapiro–Wilk test. Data are expressed as a mean ± the standard deviation. The difference between the subgroups was assessed with a A total of 79 patients were included in the study at first. Of these, 9 were excluded because a clear overshoot could not be defined in the recorded recovery interval; i.e., the RER descent after the maximum RER reached during recovery (RER max) was not observed. Of the remaining subjects, 2 patients with a documentation of an impaired LVEF in their medical history were excluded to avoid possible confounding effects due to cardiac limitations. Finally, 11 patients with submaximal tests  were also excluded. The study population therefore consisted of 57 KTRs; 40 performed exercise testing on a treadmill and 17 on a cycle-ergometer. The characteristics of the patients are described in This sample included 16 patients (28.1%) with a history of chronic glomerulonephritis, 11 (19.3%) with polycystic kidney disease (PKD), 8 (14.0%) with diabetic nephropathy, 5 (8.8%) with a congenital malformation or renal genetic syndrome, 2 (3.5%) with interstitial nephritis, 2 (3.5%) with a combination of more than one disease and 1 (1.8%) patient for each of the following categories: nephroangiosclerosis, vasculitis, lupus nephropathy, focal segmental glomerulosclerosis, neoplasia, Alport’s syndrome and nephrotoxicity. In 6 patients (10.5%), the etiology of the renal disease was not known.Patients exercised until perceived exhaustion  without reporting any adverse events. The results of the cardiopulmonary exercise testing are described in The average time interval of the recorded recovery was 205.9 ± 56.3 s. All included patients displayed an overshoot of the RER after exercise. Patients showed a RER max of 1.60 ± 0.18 within, on average, 132.9 ± 44.7 s of recovery. The magnitude of the RER overshoot was 28.0 ± 4.1% and the RER recovery slope was 19.8 ± 14.6 .2, VO2 at the VT, the OUES and the VE/VCO2 slope , the latter showed no significant correlation with the RER mag. The main correlations of the RER mag are shown in Moreover, although the RER max correlated with the peak RER . The results of the multiple regression analyses are shown in At the multiple linear regression, the peak VOp = 0.03; With regard to the analyses performed in the KTR subgroups, the RER mag as well as the other overshoot parameters were significantly higher in the patients of ventilatory class I compared with those of ventilatory class II  and a few significant differences in peak VO2  and VO2 at the VT , the RER mag was not significantly different between the two subpopulations , nor were the other recovery metrics.Finally, in our population of KTRs, 26 patients (46%) were taking beta-blockers at the time of the CPET evaluation. A comparison between these patients and those not taking beta-blockers showed that, despite an expected lower peak HR  was comparable with that found in healthy subjects (29.3 ± 10%) [The present study confirmed the occurrence of an overshoot of the RER during recovery from maximal exercise testing in all (100%) evaluated patients and it was the first to provide values of the RER mag, RER max, time to reach RER max and RER recovery slope in a population of KTRs. Most of these metrics are poorly described in the literature but could help in understanding the recovery kinetics of VOs 28.4 ± .7% was c3 ± 10%) ,16, are 2, VO2 at the first VT, the OUES and the VE/VCO2 slope. The correlations with the VO2/WR slope, found in a previous study on the RER overshoot [2 , VO2 at the VT  and the VE/VCO2 slope  were comparable with those previously described for cardiac patients  [2, hemoglobin and age. However, it stands to reason that future studies, including other clinical variables related to the study subjects in addition to the cardiorespiratory function parameters, may help to explain other aspects of this phenomenon.Additionally, the analysis of the RER during recovery in this population provided interesting findings, showing that this parameter correlates with physical fitness and cardiorespiratory efficiency even in KTRs, thus suggesting its possible integration in the interpretation of CPET in clinical populations. The RER overshoot showed significant correlations with other important indices of cardiopulmonary functions such as peak VOvershoot , might h < 0.05) . This fa2 > 20 mL/kg/min) and patients belonging to classes B and C . Patients with low fitness levels were pooled due to the small sample size. The inter-group comparisons showed that the RER mag was significantly different . In addition, the patients were also divided according to their ventilatory classes, which reflected the respiratory efficiency and degree of the ventilation-perfusion mismatch during exercise. Patients belonging to ventilatory class I (VE/VCO2 slope < 30) showed significant differences compared with those belonging to ventilatory class II (VE/VCO2 slope between 30 and 35.9) in peak VO2 and also in the RER mag . Therefore, the RER mag values were altered in those patients belonging to the worse prognostic classes , in line with what has been previously reported in cardiac patients with a reduced LVEF [In an attempt to assess the potential clinical value of the RER overshoot, KTRs were divided into subgroups based on two classifications used in the prognostic risk stratification of patients with heart failure . Moreove2 but also VCO2 plays a role in reducing the RER overshoot, possibly through CO2 retention within exercising muscles, therefore showing higher VE/VCO2 slopes during exercise. However, our data impose caution with this consideration as peak VCO2 was not a determinant of the RER mag in the multiple regression analyses. It is possible that the spread of a ventilation-perfusion mismatch in this population was too limited to give strength to the correlation between the RER mag and peak VCO2 at the multiple regression. Indeed, no patients were found in ventilatory classes > II. In the perspective of future studies, it could therefore be specifically investigated what causes the RER to move in the recovery phase of exercise testing in healthy and diseased patients.Looking at these data, it might be tempting to speculate that not only VOSo far, the recovery phase after exercise has been studied in relatively few clinical populations and often by applying different methods and metrics. The present data seem promising in the perspective of integrating a CPET evaluation with different variables of recovery, which might help to improve the risk stratification of these patients. The RER mag appeared to be the most useful and feasible metric that can also be evaluated in the clinical setting . FurtherThis was a retrospective trial evaluating the recovery phase after maximal exercise by CPET evaluations performed for clinical purposes. There was, therefore, a lack of standardization in the protocol used and in the monitored gas exchange recovery time interval, which could have led to a possible underestimation of the RER overshoots for patients with a shorter monitored recovery. Indeed, future specifically designed experimental studies should investigate how exercise modality, test protocols and training interventions might affect these overshoot parameters . FurtherThe present study adds new evidence to a promising issue for a functional evaluation in patients with chronic diseases. The recovery of the respiratory gas exchange parameters was studied in KTRs, evaluating the RER overshoot after maximal exercise testing. Our data showed the occurrence of a RER overshoot during recovery in all evaluated KTRs with a magnitude of 28.4 ± 12.7%, which is comparable with that of healthy subjects previously reported in the literature. A significant correlation of the RER overshoot with the CPET indices of cardiorespiratory fitness and efficiency was confirmed. Moreover, the magnitude of the RER overshoot was lower in patients with decreased cardiovascular fitness and in those with a significant ventilation-perfusion mismatch but it was not affected by beta-blocker therapy. Finally, the magnitude of the RER overshoot appeared the most meaningful metric to assess the recovery of patients, being independent from the peak RER. Thus, the evaluation of the RER recovery should be included in clinical CPET interpretations to provide more scientific data and further improve prognostic risk stratification in KTRs and other chronic diseases.


Peritonitis is the leading cause of morbidity and technique failure in peritoneal dialysis (PD) patients. The International Society for Peritoneal Dialysis (ISPD) recommends each centre to monitor the peritonitis rates and the causative organisms in order to guide local empiric antibiotic protocols. The aim of this study was to report on the peritonitis rates and describe the causative microorganisms and the antibiotic susceptibility in continuous ambulatory peritoneal dialysis (CAPD) adult patients at the Universitas Academic Hospital.A single-centre, retrospective descriptive survey was conducted to determine the peritonitis rates in PD patients (January–December 2016). All CAPD patients aged ≥18 years, who presented with clinical features of PD-associated peritonitis, were included. The peritonitis episodes were studied per patient, and the causative microorganisms and the antibiotic susceptibility of the organisms were described.n = 122) were mono-microbial. Gram-positive organisms accounted for 73.0% (n = 116) of the peritonitis episodes, coagulase-negative Staphylococcus being the most common. Gram-negative organisms accounted for 15.7% (n = 25) of the peritonitis episodes, and the common pathogens was Enterobacteriaceae.One hundred and twenty-eight patients underwent CAPD. The peritonitis rate was 1.45 episodes per year at risk. The prevalence of CAPD patients affected by at least one episode of CAPD-associated peritonitis during 2016 was 56.3%. The majority of episodes (76.7%) (The peritonitis rate was alarmingly high, with 1.45 episodes per year at risk; this is three times more than the recommended 0.5 episodes per year according to the ISPD guidelines. The culture-negative rate of 8.8% is within ISPD-acceptable limits. There is a need to strengthen preventive measures with regard to peritonitis. Haemodialysis (HD) and peritoneal dialysis (PD) are the other two forms of renal replacement therapy, mostly utilised as either lifelong renal replacement therapies in patients who are not eligible for kidney transplantation or as bridging treatment for patients awaiting kidney transplantation.The increasing prevalence of chronic kidney disease (CKD) remains a worldwide public health problem, especially in developing countries.In the South African public health care sector, renal replacement therapy is rationed because of limited resources and limited HD slots. At the Universitas Academic Hospital (UAH) and the rest of the Free State Province hospitals, the PD-first rule applies. This means that the patients who are potentially transplantable and therefore eligible for renal replacement therapy programme only have access to PD as their initial treatment modality, unless there is a compelling contraindication as judged by the panel. Patients who are sometimes not keen to have continuous ambulatory peritoneal dialysis (CAPD) as their treatment modality are selected, and enrolment of patients with suboptimal socio-economic background onto CAPD may predispose them to a high risk of PD-associated peritonitis.2 Peritonitis can be defined as ‘the inflammation of the serosal membrane that lines the abdominal cavity and the organs confined therein’.3 Gram-positive organisms such as Staphylococcus epidermidis and other normal skin flora are amongst the most common causes of bacterial peritonitis.4 It may occur because of contamination of the PD catheter (touch contamination) or translocation of bacteria from the gut or from haematogenous spread of infections.5 Knowledge of local susceptibility data is important to guide a protocol-driven approach to empiric antimicrobial therapy.Peritonitis remains the most common complication in PD patients, contributing significantly to mortality and morbidity.6 Just after 2000, various centres have reported a peritonitis rate of 0.2–0.6 episodes per year of treatment, which translates to about one episode in 20–60 patient-months, which is a significant improvement.7 However, in South Africa, higher rates have been reported, with a study conducted by Isla et al.8 reporting an overall peritonitis rate of 0.82 per year with 1-, 2- and 5-year patient survival rates of 86.7%, 78.7% and 65.3%, respectively.8Over the past two decades, the rate of CAPD-associated peritonitis has significantly reduced in most parts of the world. Between the early 1990s and the early 2000s, the incidence of peritonitis in Hong Kong was reported to have declined to 0.46 episodes from 1.10 episodes per year.Peritonitis associated with peritoneal dialysis, as well as microbial susceptibility patterns have not yet been described for the CAPD patient population being treated at UAH.This is a retrospective audit aimed to describe the local peritonitis rate as well as the microbiological profiles of causative organisms in order to guide local antimicrobial management protocols.This study was conducted at the CAPD clinic located in UAH in Bloemfontein, Free State, South Africa. This is a tertiary hospital with a capacity of 636 beds and all CAPD patients from the Free State province state sector are managed in the hospital’s nephrology clinic.The research was conducted as a retrospective, descriptive review of microbiological and medical records of CAPD patients who had CAPD-associated peritonitis in 2016 (January–December) as well as the causative bacterial species for peritonitis.9 The microbial analysis data of the PD effluent sent for both WCCs and microscopy, culture and sensitivity (MC&S) testing were extracted from the National Health Laboratory Service (NHLS) capturing system, TrakCare. Medical records from clinic files and electronic notes were used to obtain clinical and demographic information. The data were collected and entered into an Excel spreadsheet after each patient had been allocated a numerical code to ensure confidentiality. The data extracted included demographical data such as age and gender. Microbial and clinical information included PD effluent WCC, number of peritonitis episodes, the causative bacterial species for peritonitis and antibiotic susceptibility profiles of all.The study population included adult patients (aged 18 years or older) who were on CAPD treatment and had been diagnosed with CAPD-associated peritonitis at the UAH during 2016 (January–December). Peritonitis was diagnosed when a patient met at least two of the following criteria: (1) clinical features consistent with peritonitis, that is, abdominal pain plus cloudy dialysis effluent; (2) dialysis effluent white cell count (WCC) ˃ 100 µL/with over 50% polymorphs in the differential count; and (3) identification of infective organisms from the dialysis effluent using Gram stain or culture.The PD effluent was typically inoculated into a blood culture bottle for better yield. All microbiological results that did not fulfil the inclusion criteria were excluded .9 The prevalence of peritonitis in CAPD patients affected by at least one episode of CAPD-associated peritonitis during the study period (January–December 2016) was calculated by dividing the number of patients who experienced at least one episode by the total number of patients. A relapsing case was defined as an episode that occurs within 4 weeks of completion of therapy of a prior episode with the same organism. The patient laboratory data were correlated with clinical records to confirm the clinical presentation of patients. Exclusion criteria entailed patients who did not meet the full diagnostic criteria defined above, and duplicate entries. ‘Duplicate entries’ was defined as having a specimen collected within 3 days and having the same causative organism. Culture-negative peritonitis episode refers to the patients that presented with clinical features suggestive of peritonitis  with a dialysis effluent WCC ˃ 100 µL/with over 50% polymorphs in the differential count but no growth on culture and Gram stain.In this study, peritonitis rates were defined according to ISPD guidelines that defined peritonitis rates as the number of infections by an organism for a time period, divided by dialysis years’ time at risk, expressed as episodes per year.Statistical analysis was conducted by the Department of Biostatistics of the University of Free State using SAS 9.3 for Windows Software. Categorical variables were described as frequencies and percentages. Numerical variables were summarised by medians and interquartile ranges owing to skew distributions.Ethical approval was obtained from the Free State Department of Health, the National Health Laboratory Services and the University of the Free State (UFS-HSD2017/0429). All the personnel involved in the study handled all data and medical records with the highest confidentialityDuring the year 2016 (from January to December), 128 patients underwent CAPD at UAH.The median age of this study’s population was 41 (IQR 19–63).n = 32) and 55.6% were women (n = 40).Of the 128 patients, 38 patients were peritonitis free and 18 did not meet the inclusion criteria. A total of 72 patients presented with clinical features suggestive of CAPD-associated peritonitis and confirmed laboratory evidence and therefore met the inclusion criteria see . Of the The peritonitis rate was 1.45 episodes per year at risk, whereas the prevalence of CAPD patients affected by at least one episode of CAPD-associated peritonitis during 2016 was 56.3% ([72/128] × 100).Majority of microbial infections were mono-microbial .We identified 159 episodes of peritonitis that ranged between one and six episodes per patient , with 16Staphylococcus epidermidis was the most prevalent species followed by Staphylococcus haemolyticus, Staphylococcus hominis and Staphylococcus lugdunensis.Most infections were caused by Gram-positive bacterial species , the mosStaphylococcus aureus was the most prevalent. Lastly Streptococcus mitis oralis (2.5%) was the leading cause of peritonitis amongst the Streptococcus species.Of the coagulase-positive staphylococci, Klebsiella spp., Escherichia coli and Neisseria were the most common of the Gram-negative organisms, with a prevalence of 6.3%, 5.0% and 1.3%, respectively.Candida species.Four episodes were caused by the n = 14).The culture negativity rate was 8.8%  isolates were detected in the 12-month period. Amikacin was not routinely tested in the Gram-negative isolates; however, no resistance was detected for the ones that were tested.n = 10) patients were transferred to haemodialysis. Seven patients (10%) died, but the cause of death was not documented. During the time that the study was undertaken (in 2016), 76% (n = 55) of the patients were still alive and were undergoing CAPD at the end of the year.During the study period, 14%  producing or carbapenem resistant.Gram-negative organisms are known to be gastrointestinal contaminants and can commonly cause peritonitis by translocation from the gut into the peritoneal space. They are known to be associated with prolonged hospitalisation and higher risk of technique failure and increased mortality, compared to Gram-positive peritonitis.15 who conducted the study based on information from the Andalusian transplant autonomic coordination registry and found that females experienced significantly higher infection-related mortality rate.15 Hypothetical explanations could include the increased risk of ascending Gram-negative infections from the female genitourinary tract or impaired immune response resulting from uremic hypogonadism.16A higher number of females were affected, with a prevalence of 55.6%. This study was not powered to evaluate risk factors for CAPD peritonitis; therefore, it is not possible to explain as to why females were more affected than males with peritonitis in this study population. These findings are however in accordance with the findings of Ros et al.n = 14). This is lower than the ISPD standard, which stated that <10% of all episodes of PD peritonitis should be culture-negative.9 This success could possibly be attributed to the improved technique of inoculating PD effluent into the blood culture bottles instead of just sending a small sample in a specimen bottle as is routinely done for other body fluid samples.The culture negativity rate amounted to 8.8%  is endemic in South Africa, and the dialysis population has been identified as one of the populations at risk owing to an impaired immune system. This calls for a more targeted testing so as to ensure that cases are not missed out, as TB culture requires specific samples to be inoculated in the specific TB culture medium.17 Intra-abdominal fluid locations can also impair the penetrance of the antibiotics and therefore increase the risk of relapse.18The number of patients who had relapsed infections was high. This is a cause for concern. Possible reasons for the relapses could be pharmacodynamics and pharmacokinetics that are altered by ESKD and inaccurate dosing, which may lead to sub-therapeutic drug levels.This study did not investigate the risks for peritonitis.The most common cause of infections in this study were CNS bacteria. No vancomycin-resistant organisms were detected; thus, vancomycin is still a drug of choice. Clindamycin and cloxacillin were less effective as there was high resistance to these drugs. We recommend that preventative measures be revisited such as developing more intense programmes for educating patients on hygiene; re-training patients when using the dialysis; evaluating PD programmes regularly; and comparing outcomes with international and local standards. We also recommend that the government officials in charge of housing consider prioritising these patients for placement in more suitable living conditions with access to clean water and optimal space where possible. We recommend an ongoing annual peritonitis registry to monitor the peritonitis trends and emergence of resistant organisms. More research is needed to study the risk factors of CAPD-associated peritonitis in this population so that more targeted preventive measures can be devised.


These separations were driven by metabolites that reflect known species differences in their biochemistry and pathways. Unexpectedly, there was also considerable diversity between the C3 species. Falling atmospheric CO2 and changing temperature, nitrogen, and water availability have driven evolution of C4 photosynthesis in multiple lineages. We propose that analogous selective pressures drove lineage-dependent evolution of the CBC in C3 species. Examples of species-dependent variation include differences in the balance between the CBC and the light reactions, and in the balance between regulated steps in the CBC. Metabolite profiles also reveal conserved features including inactivation of enzymes in low irradiance, and maintenance of CBC metabolites at relatively high levels in the absence of net CO2 fixation. These features may be important for photosynthetic efficiency in low light, fluctuating irradiance, and when stomata close due to low water availability.Improving photosynthesis is a promising avenue to increase crop yield. This will be aided by better understanding of natural variance in photosynthesis. Profiling of Calvin–Benson cycle (CBC) metabolites provides a top-down strategy to uncover interspecies diversity in CBC operation. In a study of four C There is considerable diversity in photosynthesis across life forms, species, and even within species  that support CBC operation in our low CO2 world. We then survey the evidence for species diversity in photosynthesis, in particular in how the CBC operates. ‘Operation’ refers to the balance between different reactions or the poising of metabolic state in a pathway; species variation can result from differences in the relative abundance of enzymes, in the kinetic and other properties of enzymes, or in the regulatory network that coordinates flux at different sites in and around a pathway. Until now, most of the evidence for CBC diversity came from analyses of whole-leaf photosynthetic traits, and studies of the kinetic and regulatory characteristics of individual CBC enzymes, especially Rubisco. The main part of this review explains how metabolite profiling provides a complementary top-down approach to detect interspecies diversity in CBC operation and to identify features that are conserved across species. This provides a starting point to formulate testable hypotheses about the underlying mechanisms and biological reasons for this combination of diversity and conservation.The Calvin–Benson cycle (CBC) uses NADPH and ATP from the light reactions to drive the incorporation of CO2 by Rubisco to form two molecules of glycerate 3-phosphate (3PGA), reduction of 3PGA to triose phosphate (triose-P) using NADPH and ATP from the light reactions, and a series of reactions that convert triose-P to ribulose 1,5-bisphosphate (RuBP) (2 into 6NADP++9ADP+8Pi (inorganic phosphate), and one triose-P. The remaining Pi is recycled during the conversion of triose-P to end-products. In source leaves of terrestrial plants, the main end-products are carbohydrates such as sucrose and starch, as well as smaller amounts of organic acids and amino acids.The CBC consists of three subprocesses; fixation of COe (RuBP) . Most of2 by Rubisco; in C3 species, this generates the diffusion gradient that drives CO2 entry, and in species that operate a CCM it decreases wasteful back-leakage of CO2. Rubisco has a low catalytic rate (kcat) and represents a large part of the total protein in photosynthetic cells (see below for more details). Entry of CO2 into the leaf is therefore best promoted by ensuring efficient use of Rubisco rather than having even higher Rubisco abundance. The third reason is to deactivate Rubisco in conditions, such as low irradiance, in which RuBP cannot be regenerated quickly enough to keep all of the Rubisco-binding sites saturated. This is important because there is a high concentration of Rubisco-binding sites. Unless surplus active sites are rendered temporarily inoperative, they will sequester other CBC metabolites and impair pathway flux (see below for more details). The fourth reason is to coordinate fluxes at different reactions around the CBC. A combination of high flux and small metabolite pool sizes means that most CBC metabolites have half-lives of well under 1 s . 3PGA reduction and the remaining reactions in the regeneration of RuBP are essentially reversible , sedoheptulose-1,7-bisphosphatase (SBPase), and phosphoribulokinase (PRK)] and Rubisco (versible . Plastidversible . All thrkcat (see below for more discussion), Rubisco is present at much higher concentrations than other CBC enzymes. A recent quantitative proteomics analysis in the green alga Chlamydomonas reinhardtii , and its kinetic characteristics (see below). In some conditions, photosynthesis may also be limited by the rate of end-product synthesis and the associated recycling of Pi. This is termed triose-P utilization limitation of photosynthesis. It is seen most clearly after transients that increase the rate of photosynthesis, with a balance often being re-established in the mid-term in which the rate of end-product synthesis somewhat exceeds the rate of photosynthesis (The model of Farquhar and colleagues  providesng light . The cenynthesis .2 (see above). These considerations were formulated by Notwithstanding the crucial role of Rubisco, photosynthesis depends on efficient conversion of triose-P to RuBP. Efficient removal of triose-P will promote 3PGA reduction and the associated consumption of NADPH and ATP from the light reactions, and efficient formation of RuBP is essential to saturate Rubisco-binding sites and promote entry of COAnalysis of the FCCs of CBC enzymes confirmed an important role for Rubisco, but also underlined that Rubisco is seldom fully limiting for photosynthesis . Many otThese analyses also revealed that the distribution of control  depends strongly on both current conditions and past history  , about every fourth reaction of Rubisco is with O2   that mus ammonia . The oxy2 and rising O2 concentration in the atmosphere, as well as changes in temperature and the water and nutrient supply . Higher selectively for CO2 is thought to be mechanistically linked to a decrease in kcat  to generate a high concentration of CO2 around Rubisco  carboxylase in the mesophyll to generate 4-carbon metabolites that diffuse to the bundle sheath where they are decarboxylated to release CO2 and 3-carbon metabolites, which move back to the mesophyll . There is considerable diversity in the rate of photosynthesis between terrestrial C3 species including closely related species and even cultivars or accessions from the same species  and, for leaves, instantaneous freezing between two metal columns that have been pre-cooled to liquid N2 temperature . This means that even temporary changes in irradiance or CO4 species  and five C3 species . This set of species included monocot and dicot species for each photosynthesis type, several model species for photosynthesis research  and several important crops . The analyses focused on CBC intermediates, in order to avoid distortion by species differences in ancillary or unrelated pathways.3 species. This could lead to apparent differences in metabolite profiles that are driven by changes in leaf composition rather than the balance of metabolism in the CBC. To detect and avoid such secondary effects, metabolites were normalized on different parameters; not only fresh weight but also chlorophyll and protein content. They were also normalized by expressing the carbon present in a given metabolite as a fraction of the total carbon in all CBC metabolites. The latter was termed the ‘dimensionless’ normalization and provides information on the distribution of carbon between the CBC metabolites.Cross-species comparisons face potential pitfalls. One is the choice of the growth and harvest conditions. Optimal growth conditions and response of photosynthesis to light and temperature vary between species. 4 species were separated from the five C3 species. This is expected as the CBC adjusts to the high CO2 environment provided by the C4 CCM (see above). Inspection of the metabolite vectors that drive the separation of C4 from C3 species  in C4 species than in C3 species due to the higher kcat of C4 Rubisco (see above). Third, within the C4 species, Z. mays and S. viridis were separated from the Flaveria spp. This was driven by higher 3PGA and triose-P in maize and S. viridis. As already mentioned, all four C4 species belong to the NADP-ME subtype. Decarboxylation of malate by NADP-ME in the plastids of bundle sheath cells delivers not only CO2 but also about half of the NADPH that is required by the CBC. Depending on the species, this has allowed partial or complete loss of PSII in the bundle sheath chloroplasts. Maize has bimorphic chloroplasts lacking PSII in the bundle sheath. The other half of the NADPH that is required by the CBC is imported from the mesophyll cells via an intercellular 3PGA/triose-P shuttle. This requires elevated overall contents of these metabolites to generate the necessary intercellular concentration gradients (see above). The high 3PGA and triose-P in S. viridis indicate that a similar shuttle operates in this species. The two C4Flaveria species have PSII activity in their bundle sheath chloroplasts to a varying extent depending on conditions  analysis  leaves , whereas RuBP plateaued at irradiances at which photosynthesis was still increasing in wheat (Spinacea oleracea (spinach) (Raphanus sativus (radish)  , and Rap(radish) . It appe3 species. As already mentioned, C3 species will have been subject to selective pressure by falling CO2 as well as varying temperature and water and nutrient availability. Such pressures combined to drive the evolution of C4 photosynthesis in >65 different plant lineages (4 photosynthesis and (ii) that, just as in the evolution of C4 photosynthesis, any resulting evolution of the CBC would have occurred in a lineage-dependent manner. Indeed, it is extremely unlikely that only one evolutionary route would have been taken in all C3 species. The response in any given species would have been influenced, for example, by (iii) prior conditioning factors both within the CBC and elsewhere, for example, in leaf anatomy; (iv) the light regime, temperature, water availability, and nutrient supply in the ecological niche occupied by the species; and (v) random events. It is also of course possible, and even likely, that some of the current-day variation of CBC operation in C3 plants has an even older origin.The question arises of why there is so much variation in CBC operation between different Clineages . We can CBC metabolite profiling opens up a new research avenue to discover genetic and mechanistic features that underlie interspecies variation in CBC operation, and to understand what specific ecological and evolutionary pressure drove their emergence.3 species. This would include (i) sets of species that are characteristic of different habitats; (ii) species with short-lived and long-lived leaves and the associated changes in investment strategies  species that represent intermediate stages in the evolution of C4 photosynthesis; and (vii) cyanobacteria and eukaryotic algae to learn how operation of their CCMs impacts on CBC function.On the one hand, it will be instructive to widen the empirical base by investigating CBC profiles in a wider set of Crategies  and in cIn parallel, it will be important to investigate the mechanistic reasons for species differences. These might include investigating expression patterns, protein abundances, and kinetic characteristics of enzymes that act on CBC metabolites or sets of CBC metabolites that show divergent responses between different species, for example, such as those seen between FBPase and SBPase in Arabidopsis and rice . Relatively high levels of triose-P and other CBC intermediates were also found at the CO2 compensation point in Arabidopsis  can be discerned in earlier more fragmentary analyses of the metabolite levels in wheat, spinach, bean, radish, and sugar beet  optimizes CBC function in different light regimes and (ii) enables energy dissipation in a cycle involving the CBC, the oxygenase reaction of Rubisco, and photorespiration when stomata close and the CO2 concentration decreases inside the leaf.The relatively high levels of triose-P and other CBC metabolites in very low light  and in l2 fixation to (i) ensure that enough RuBP is regenerated to allow continued CO2 fixation, whilst (ii) avoiding overaccumulation of phosphorylated intermediates, depletion of Pi, and inhibition of ATP synthase (2 fixation and (ii) drives a strong increase in flux to sucrose in response to a rising rate of CO2 fixation without this requiring a large increase in the levels of CBC metabolites or a concomitant depletion of free stromal Pi. In addition to sucrose synthesis, flux to starch needs to be restricted when net photosynthesis is low and increased when it is high. This is especially so in species where starch is a major product of photosynthesis. Allosteric regulation of ADP glucose pyrophosphorylase by the 3PGA/Pi ratio (S. oleracea (spinach) in the 1980s. The conserved response with substantial levels of triose-P and other CBC metabolites in low light or low CO2 in many species (see above) is consistent with ‘threshold’ regulation of end-product synthesis representing a general strategy to optimize CBC operation in low light or low CO2 across many species.The mechanisms that regulate removal of carbon from the CBC are summarized in 2 conditions  is induced in a Redox Responsive Transcription Factor 1 (RRTF1)-dependent manner  in wild-type plants, whereas mutants that are deficient in plastidic α-glucan phosphorylase do not show this increase in G1P and G6P, and also have lower levels of CBC metabolites than wild-type plants in low CO2. As in low irradiance, possible substrates for plastidic α-glucan phosphorylase might be nascent glucans that have not yet been incorporated into the starch granule and, later in the day, glucans formed as starch degradation speeds up. However, there is also strong genetic evidence for a dedicated pathway for starch degradation in the light under water stress, which is the condition under which the concentration of CO2 in the leaf will fall in the field. It involves an initial attack on the starch granule by α-amylase3 and β-amylase1  increase starch mobilization, maintain CBC metabolite levels, and support CBC operation in the mesophyll cells, allowing continued photorespiration and energy dissipation.Starch degradation probably also provides a source of carbon in low COet al., 1999; 2, the plastid envelope membrane glucose 6-phosphate:phosphate translocator (GPT2), is induced in a Redox Responsive Transcription Factor 1 (RRTF1)-dependent manner. This finding points towards a link with oxidative stress, possibly due to overenergization in the light reactions in low CO2 conditions. This could be relieved by maintaining CBC pools at a high enough level to allow energy dissipation in a photorespiratory cycle; that is, rapid regeneration of RuBP in the CBC to support oxygenation and the operation of photorespiration with concomitant dissipation of energy .Another potential source of carbon to maintain CBC pools during rapid transients to low light or low CO2. Their relative contribution may depend on the species and conditions. For example, whereas ‘threshold’ regulation of end-product synthesis may suffice to maintain a basal level of CBC metabolites in low irradiance, additional mechanisms including recycling of carbon into the CBC . It is also important that the network around F2,6BP that regulates cytosolic FBPase (In the future it will be interesting to profile CBC metabolites in wild species that are strongly adapted to extreme fluctuating light regimes, as well as in sets of crop cultivars with differing photosynthetic efficiency in fluctuating light (3 species. In retrospect, this diversity is not surprising. All plant lineages have been subject to strong evolutionary pressure from changing CO2 and other environmental factors, and in the lineages that did not evolve C4 photosynthesis or crassulacean acid metabolism, these pressures will have impacted directly on a CBC that was not shielded by a CCM. Any evolutionary adaptations of the CBC in C3 species will have occurred independently in each plant lineage, and will probably have been influenced by specific factors relating to prior history and environmental niche. One important implication is that the best strategy to improve C3 photosynthesis might vary from crop to crop. In the future, it will be instructive to apply the top-down approach of CBC metabolite profiling to a much wider range of photosynthetic life forms. This will provide insights into the adaption of the CBC to various types of CCMs, to different environments, and to different plant life histories. The approach can also be applied to panels of closely related species or cultivars of a single species with a varying rate of photosynthesis to learn whether there are accompanying changes in CBC operation and, if so, whether these contribute to the variance in photosynthetic performance. Profiling of CBC metabolites has not only uncovered diversity, it has also highlighted some conserved features of CBC operation. These are likely to be important for photosynthetic performance in conditions where stomata close and internal CO2 concentrations fall, a scenario that will be increasingly frequent with future climate change. These conserved features may also be important for photosynthetic performance in low light, and in the fluctuating light regimes that plants experience in the field. Finally, by pinpointing reactions or processes that are divergent or conserved, CBC metabolite profiling provides a starting point for focused biochemical, molecular, and genomics analyses to identify the underlying changes in protein abundance, protein characteristics, or network wiring.Profiling of CBC metabolites has uncovered an unexpected degree of diversity in the operation of the CBC in C


BackgroundObsessive-compulsive disorder (OCD) is a common condition that has a significant impact on people’s lives. COVID-19 pandemic imposed a challenging situation for the general population with new precautionary measures. All that can have serious implications for those who already have intense concerns about cleanliness and hygiene and those diagnosed with OCD. The aim of this study was to examine the impact of the coronavirus disease 2019 (COVID-19) pandemic on the emergence and severity of obsession and compulsion symptoms in Saudi Arabia.MethodsThis was a cross-sectional study with 1,190 participants who completed an online three-part questionnaire that included sociodemographic data, the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) checklist, and (Y-BOCS) severity scale. On account of the fact that OCD requires a clinical evaluation to confirm the diagnosis, screening positive for OCD was defined based on criteria A of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), which is selecting at least one of either obsession or compulsion symptoms or both. Screening positive for obsession was defined as selecting at least one of the obsessional symptoms while screening positive for compulsion was defined as selecting at least one of the compulsion symptoms.ResultsOverall, OCD screening was positive in 82% of participants. Previous diagnosis of OCD was reported by 2.6% (N=36) of the participants and 55.2% of them reported that their symptoms did not change during the pandemic, while 41.1% reported that their symptoms increased. Positive OCD screening was significantly higher in participants who reported previous psychological illness (87.6% vs. 80.9%), those who followed news related to COVID-19 on a daily basis (88.7% vs. 76.1%), and participants who had not acquired the infection (82.9%) compared to those who were infected with COVID-19 (72.3%).ConclusionThe aim of this study was to determine the impact of the pandemic on OCD screening and symptoms. New OCD symptoms were reported in a high proportion of the participants. The results of this study can provide guidance for psychiatrists and psychologists in the clinical approach and management of patients with OCD. Further focused research on the factors affecting the emergence or severity of OCD symptoms needs to be conducted in the future. At present, coronavirus continues to spread in nations across the world affecting millions of people, without any indication of how or when the disease will come to an end. COVID-19 is a new strain of the coronavirus infection that mainly affects the respiratory system similar to the previous six types, among which were the well-known SARS-CoV and MERS-CoV . By MarcSince the onset of COVID-19 in late 2019, a considerable number of studies have focused on the impact of the COVID-19 pandemic on mental health. Several studies have suggested that the COVID-19 pandemic has significant implications for mental health, including anxiety disorders, post-traumatic stress disorder, behavioral disorders, and depression -8. A ranObsessive-compulsive disorder (OCD) is a common condition that has a significant impact on people’s lives. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), obsessions are defined as “recurrent and persistent thoughts, urges, or images that are experienced as intrusive and unwanted,” whereas compulsions are defined as “repetitive behaviors or mental acts that an individual feels driven to perform in response to an obsession or according to rules that must be applied rigidly.” Globally, 1.1-1.8% of people suffer from obsessive-compulsive disorder. In the United States, the 12-month prevalence of OCD is 1.2% . HoweverThe content of obsessions and compulsions varies between individuals, but common obsessional themes include contamination, symmetry, excessive doubt, or intrusive thoughts that are aggressive, sexual, or intend to harm oneself or others. Common compulsions are repetitive cleaning, ordering, counting, and repetitive checking . Fear ofThe guidelines from ministries of health affirm that the way to protect oneself from the virus is adherence to cleanliness and social distancing. This can have serious implications for those who already have intense concerns about cleanliness and hygiene and those diagnosed with OCD. The literature showed that fear of COVID-19 has a significantly impacts OCD patients; one indicator can be seen in hoarding; patients start displaying such maladaptive behavior due to fear of unavailability or shortage in the supply of protective measures and equipment. It can also lead to the development of OCD, particularly contamination fear, which can arise from hand hygiene recommendations that are an important protective measure against infection. Although OCD responds well to psychological and pharmacological treatments, the worsening of OCD symptoms may occur during stressful times, particularly under the influence of the frequent reminders emphasizing the importance of cleanliness and hygiene by various sources . It is nA study from Saudi Arabia found that the emergence of new-onset obsessions and compulsion symptoms was higher among employees, housewives, students, and quarantine disciplines. Interestingly, it has been found that those not employed in healthcare had increased obsession compared to medical field workers, while stress levels were higher among those who work in healthcare . AnotherWe hypothesize that people with or without OCD diagnosis would have experienced the worsening or emergence of obsession and compulsion symptoms during the current COVID-19 pandemic. The main aim of this study was to determine the impact of the COVID-19 pandemic on obsession and compulsion symptoms in Saudi Arabia. Additionally, we examined the association between OCD symptoms during the COVID-19 pandemic with participants' previous diagnosis of psychiatric or chronic medical illness and the magnitude of the COVID-19 news follow-up.This was a cross-sectional study targeting adults aged > 18 years living in Saudi Arabia. An online questionnaire was used and a link to it was posted on social networking sites  and cross-platform messaging applications (WhatsApp). The link to the questionnaire was active for two months, from September to November 2020. Informed consent was obtained at the beginning of the questionnaire, which indicated the purpose of the study and the right of the participant to withdraw at any time. The questionnaire had three parts: sociodemographic data, the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) checklist, and (Y-BOCS) severity scale. We collected general sociodemographic data such as gender, age, nationality, city of residence, education, and marital status. In addition to the presence of physical or mental health conditions, being infected with or having contact with a person infected with COVID-19. Participants were also asked how frequently they watched news related to COVID-19. In this section, we added a question for those who were diagnosed with OCD before the pandemic asking them to report their perception of the severity of their OCD symptoms before and during the pandemic. The second part of the questionnaire included the Y-BOCS symptoms checklist. We only used the symptoms related to contamination obsessions and cleaning/washing compulsions for simplicity and relevance. Screening positive for obsession was defined as selecting at least one of the obsessional symptoms, while screening positive for compulsion was defined as selecting at least one of the compulsion symptoms. Screening positive for OCD was defined as selecting at least one of either obsession or compulsion symptoms. The questionnaire was translated into Arabic from English and then verified. The internal consistency assessed by Cronbach’s alpha coefficient was α =.85 for obsessional symptoms and α =.78 for compulsion symptoms. The overall alpha coefficient was α =.90 which indicates excellent reliability. In addition, the validity results showed good construct validity .The third part of the questionnaire was the Arabic version of the Y-BOCS, which is a 10-item scale designed to measure the severity of obsession and compulsion symptoms over the past seven days. This scale was validated in Arabic, and the internal consistency assessed by Cronbach’s alpha coefficient was α =.73, indicating good reliability. In addition, the validity results showed good construct validity .2) test and continuous data by Student’s t-test, and appropriate multivariate analysis was performed using multinomial logistic regression to assess demographic and socioeconomic influences on obsession and compulsion symptoms. Statistical significance was set at p ≤0.05. Ethical approval for this study was obtained from the Research Ethics Committee (Reference No 476-20).The analysis was performed using the Statistical Package for the Social Sciences . Categorical variables were presented as percentages. Comparisons between groups were performed using the chi-square , 796 (66.9%) were female, and 582 (48.9 %) were young adults aged between 18 and 30 years. Detailed characteristics of the participants, including their employment status, educational level, and distribution across regions of Saudi Arabia, are listed in Table The proportion of participants who were previously diagnosed with any psychological illness was 19.5%. Depression was the most prevalent illness (7.9%), followed by anxiety (5.6%) Table . Among those with known cases of OCD (N=36), more than half (55.2%) declared that the symptoms did not change during the pandemic, and 41.1% admitted that the symptoms of OCD had increased during the pandemic.Strikingly, OCD screening was found to be positive in 82% of the participants. In particular, 78% screened positive for obsession and 43.9% screened positive for compulsion. Positive screening was significantly higher among those who were following news and updates about COVID-19 on a daily basis (88.7%) than among those who did not follow the news (76.1%) (P<0.001). Participants who did not acquire the infection had a higher proportion of positive screening for OCD (82.9%) than those who were infected with COVID-19 (72.3%) (P<0.008)  (P<0.001). The present study aimed to determine the impact of the COVID-19 pandemic on obsession and compulsion symptoms in Saudi Arabia. The most important clinically relevant finding was a high prevalence of contamination obsessions and/or cleaning compulsions symptoms during the pandemic . This high proportion was observed among all the participants, keeping in mind that only 3% of the participants reported having a diagnosis of OCD prior to the COVID-19 pandemic. This finding is consistent with other studies conducted in Saudi Arabia, which reported a high prevalence of new-onset obsessions and compulsions during the pandemic ,22. A stIt is interesting to note that 55% of the participants with a previous diagnosis of OCD reported no change in the severity of symptoms during the pandemic. Moreover, the Y-BOCS scores showed that 77.4% of the participants with or without OCD were in the subclinical category of the scale, and 22.6 % showed mild to extreme OCD severity. This finding was surprising; higher OCD severity in general and worsening of OCD symptoms among those diagnosed with OCD was expected; however, the results in our study showed the contrary. Even though current studies have mixed results regarding the extent of impact the COVID-19 pandemic has on OCD symptoms, in a recent study, patients with OCD diagnosis showed greater resilience to the worsening of OCD symptoms during the pandemic . In anotThe median age of onset of OCD is between 17-22 years. We predicted that the age group between 18-30 would screen positive for OCD more than other age groups . HoweverAlthough the results were not statistically significant, it was surprising that healthcare professionals were the least likely to report OCD symptoms during the pandemic compared to unemployed and non-healthcare employees. Healthcare professionals may benefit from their medical knowledge about the transmission of COVID-19, in addition to the nature of their work that causes repetitive exposure to other infectious agents during their careers. This finding was also reported by Alateeq et al. . This isSurprisingly, no significant correlation was found between chronic medical conditions and OCD positivity. Given the nature of the disease and its ability to affect patients with chronic illness more severely, we expected to find a difference in OCD symptoms between those with chronic illness and those without. We found that the majority of participants who reported having a mental illness diagnosis prior to the pandemic were screened positive for OCD. This also accords with other studies conducted during the pandemic that reported high levels of stress, anxiety, depression, and other illnesses during the COVID-19 pandemic among people with a previous history of mental illness .Another important finding was that the more the participants followed COVID-19-related news, the more likely they were to screen positive for OCD. This finding is consistent with the case report of a 30-year-old female, who reported that the main reason behind her relapse into severe OCD symptoms during the pandemic was increased media reporting of COVID-19 news [The generalizability of these results is subject to certain limitations. For instance, this was a cross-sectional study that indicated the presence of OCD symptoms at a particular time. Therefore, it was beyond the scope of this study to find a causal relationship between the emergence of OCD and the COVID-19 pandemic. Another limitation is that although we used a valid questionnaire for screening and measuring the severity of OCD, the diagnosis of OCD requires a clinical evaluation to confirm the diagnosis. In addition, the questionnaire was self-reported, which carries the risk of recall bias. The generalizability of the results is also limited by the recruitment method, as the questionnaire was distributed through online platforms, so only people who had access to social media were able to participate. Further research should be undertaken to investigate the long-term effects of the COVID-19 pandemic on obsession and compulsion symptoms, in addition to comparing the severity level of OCD pre-, during, and post-pandemic. The main goal of the current study was to determine the effect of the COVID-19 pandemic on obsession and compulsion symptoms in Saudi Arabia. The most significant finding was the high proportion of participants (82%) who reported new OCD symptoms; positive screening was higher for obsessions than compulsions. The findings also showed that positive screening was significantly higher among those who were following news related to COVID-19 on a daily basis, had no prior COVID-19 infection, and reported a previous psychiatric diagnosis. An implication of these findings is that they could enable healthcare professionals is to recognize those at higher risk of developing or worsening obsession and compulsion symptoms during the current pandemic and other health-related crises.