It is especially important in the scope of dense prediction because pixel-level annotation requires substantial effort. This paper considers semi-supervised algorithms that enforce consistent predictions over perturbed unlabeled inputs. We study the advantages of perturbing only one of the two model instances and preventing the backward pass through the unperturbed instance. We also propose a competitive perturbation model as a composition of geometric warp and photometric jittering. We experiment with efficient models due to their importance for real-time and low-power applications. Our experiments show clear advantages of (1) one-way consistency, (2) perturbing only the student branch, and (3) strong photometric and geometric perturbations. Our perturbation model outperforms recent work and most of the contribution comes from the photometric component. Experiments with additional data from the large coarsely annotated subset of Cityscapes suggest that semi-supervised training can outperform supervised training with coarse labels. Our source code is available at  Most machine learning applications are hampered by the need to collect large annotated datasets. Learning with incomplete supervision ,2 presenDense prediction typically operates on high resolutions in order to be able to recognize small objects. Furthermore, competitive performance requires learning on large batches and large crops ,10,11. TThis paper studies semi-supervised approaches ,18,21,22T a perturbation to which the ideal model should be invariant, D between the two predictions:Let D corresponds to KL divergence.We argue that the clean teacher approach is a method of choice in case of perturbations that are too strong for standard data augmentation. In this setting, perturbed inputs typically give rise to less reliable predictions than their clean counterparts. One-way consistency is especially advantageous in the dense prediction context since it does not require caching latent activations in the teacher. This allows for better training in many practical cases where model capacity is limited by GPU memory ,23. In cThis paper is an extended version of our preliminary conference report . It expoThe consolidated paper proposes a simple and effective method for semi-supervised semantic segmentation. One-way consistency with clean teacher ,5,25 outThis paper proceeds as follows. Our work spans the fields of dense prediction and semi-supervised learning. The proposed methodology is most related to previous work in semi-supervised semantic segmentation.Image-wide classification models usually achieve efficiency, spatial invariance, and integration of contextual information by gradual downsampling of representations and use of global spatial pooling operations. However, dense prediction also requires location accuracy. This emphasizes the trade-off between efficiency and quality of high-resolution features in the model design. Some common designs use a classification backbone as a feature encoder and attach a decoder that restores the spatial resolution. Many approaches seek to enhance contextual information, starting with FCN-8s . UNet 3 improvesSemi-supervised methods often rely on some of the following assumptions about the data distribution : (1) simEntropy minimization  encouragMany approaches exploit the smoothness assumption by enforcing prediction consistency across different versions of the same input or different model instances. Introducing knowledge about equivariance has been studied for understanding and learning useful image representations ,44 and iClusterization of latent representations can be promoted by penalizing walks which start in a labeled example, pass over an unlabeled example, and end in another example with a different label . PiCIE  obtains MixMatch  encouragConsistency losses may encourage trivial solutions, where all inputs give rise to the same output. This is not much of a problem in semi-supervised learning since there the trivial solution is inhibited through the supervised objective. Interestingly, recent work shows that a variant of simple one-way consistency evades trivial solutions even in the context of self-supervised representation learning ,51.Virtual adversarial training (VAT)  encouragdictions . Unsuperdictions  also usedictions  shows thIn the classic semi-supervised GAN (SGAN) setup, the classifier also acts as a discriminator which distinguishes between real data (both labeled and unlabeled) and fake data produced by the generator . This apA recent approach enforces consistency between outputs of redundant decoders with noisy intermediate representations . Other rWe formulate dense consistency as a mean pixel-wise divergence between corresponding predictions in the clean image and its perturbed version. We perturb images with a composition of photometric and geometric transformations. Photometric transformations do not disturb the spatial layout of the input image. Geometric transformations affect the spatial layout of the input image and the same kind of disturbance is expected at the model output. Ideally, our models should exhibit invariance to photometric transformations and equivariance  to the gP, P is true; 0 otherwise. We denote cross-entropy with We typeset vectors and arrays in bold, sets in blackboard bold, and we underline random variables. th H(y_) . We use We denote the labeled dataset as We adapt one-way consistency ,5 for deA general semi-supervised training criterion D between the prediction in the perturbed image and its interpolated correspondence in the clean image. The proposed loss encourages the trained model to be equivariant to We formulate the unsupervised term Recall that the gradient is not computed with respect to n Ls cf. . Second,We use KL divergence as a principled choice for ion term  to the cion term ,5. This Note that two-way consistency ,21 wouldd models ,63.We argue that consistency with clean teacher generalizes better than consistency with clean student since strong perturbations may push inputs beyond the natural manifold and spoil predictions cf. . Moreoveb to all channels, (2) saturation is multiplied with s, (3) hue is shifted by addition with h, (4) contrast is modulated by multiplying all channels with c, and (5) RGB channels are randomly permuted according to We express pixel-level photometric transformations as a composition of five simple perturbations with five image-wide parameters Our training procedure randomly picks image-wide parameters T.We formulate a fairly general class of parametric geometric transformations by leveraging thin plate splines (TPS) ,66. We cf. When we determine TPS parameters We first consider images as continuous domain functions and later return to images as arrays from The resulting formulation is known as forward warping  and is t warping . It can ariables .In our experiments, we use H is the height of training crops.Our training procedure picks a random Algorithm 1. Evaluation of the gradient of the proposed semi-supervised loss given perturbationparameters . Line 17 triggers the backpropagation algorithm and accumulates the gradients of the consistency loss. The memory footprint briefly peaks due to temporary storage and immediately declines due to automatic deletion of the cached activations. At this point, the memory footprint is slightly greater than at line 4 since we still hold the supervised predictions in order to accumulate the recognition performance on the training dataset.The ratio between memory allocations at lines 16 and 9 reveals the relative memory overhead of our semi-supervised approach. Note that the absolute overhead is model independent since it corresponds to the total size of perturbed inputs and predictions, and intermediate results of dense KL-divergence. On the other hand, the memory footprint of the supervised baseline is model dependent, since it reflects the computational complexity of the backbone. Consequently, the relative overhead approaches 1 as the model size increases, and is around 1.26 for SwiftNet-RN34.Our experiments evaluate one-way consistency with clean teacher and a composition of photometric and geometric perturbations  or ResNet-34 (SwiftNet-RN34), and DeepLab v2 with a ResNet-101 backbone. We initialize the backbones with ImageNet pre-trained parameters. We apply random scaling, cropping, and horizontal flipping to all inputs and segmentation labels. We refer to such examples as clean. We schedule the learning rate according to nnealing . We use rameters . We haveNet-RN18  and  . The batIn half-resolution Cityscapes experiments the size of crops is Our full-resolution experiments only use SwiftNet models. The crop size is Classification setup. Classification experiments target CIFAR-10 and involve the Wide ResNet model WRN-28-2 with standard hyper-parameters . We augmBu)=128,60. Thus, Evaluation. We report generalization performance at the end of training. We report sample means and sample standard deviations (with Bessel’s correction) of the corresponding evaluation metric  both in terms of generalization performance and training footprint. We explain this by observing that predictions in perturbed images tend to be less reliable targets.The proposed perturbation model is a composition of a photometric transformation and a geometric warp. These two kinds of perturbations have to be treated differently, since we desire invariance to the former and equivariance to the latter. Our perturbation model outperforms CutMix both in standard experiments with DeepLabv2-RN101 and in combination with recent efficient models (SwiftNet-RN18 and SwiftNet-RN34).We consider two teacher formulations. In the simple formulation, the teacher is a frozen copy of the student. In the Mean Teacher formulation, the teacher is a moving average of student parameters. Mean Teacher outperforms simple consistency in low data regimes . However, experiments with more data suggest that the simple one-way formulation scales significantly better.To the best of our knowledge, this is the first account of semi-supervised semantic segmentation with efficient models. This combination is essential for many practical real-time applications where there is a lack of large datasets with suitable pixel-level groundtruth. Many of our experiments are based on SwiftNet-RN18, which behaves similarly to DeepLabv2-RN101, while offering about 9× faster inference on half-resolution images, and about 15× faster inference on full-resolution images on RTX 2080Ti. Experiments on Cityscapes coarse reveal that semi-supervised learning with one-way consistency can come close and exceed full supervision with coarse annotations. Simplicity, competitive performance and speed of training make this approach a very attractive baseline for evaluating future semi-supervised approaches in the dense prediction context.


Acute high-altitude diseases, including acute mountain sickness (AMS), high-altitude cerebral edema (HACE), and high-altitude pulmonary edema (HAPE), have been recognized as potentially lethal diseases for altitude climbers. Various preconditioning stimuli, including hyperbaric oxygen (HBO), have been proposed to prevent acute high-altitude diseases. Herein, we reviewed whether and how HBO preconditioning could affect high-altitude diseases and summarized the results of current trials. Evidence suggests that HBO preconditioning may be a safe and effective preventive method for acute high-altitude diseases. The proposed mechanisms of HBO preconditioning in preventing high-altitude diseases may involve: 1) protection of the blood-brain barrier and prevention of brain edema, 2) inhibition of the inflammatory responses, 3) induction of the hypoxia-inducible factor and its target genes, and 4) increase in antioxidant activity. However, the optimal protocol of HBO preconditioning needs further exploration. Translating the beneficial effects of HBO preconditioning into current practice requires the “conditioning strategies” approach. More large-scale and high-quality randomized controlled studies are needed in the future. With the rapid development in transportation from low to high altitudes and the growing interest in adventure travel, more than 100 million people now travel to high altitudes for work or pleasure annually. The decreased barometric pressure and ambient oxygen tension at high altitudes can trigger a range of physiological responses, leading to maladaptive reactions and acute altitude illness , includiHyperbaric oxygen therapy (HBOT) refers to the inhalation of 100% oxygen at a pressure higher than normal atmospheric pressure . A growiAccording to the 2018 Lake Louise AMS Score , AMS wasHAPE may occur within one to five days after reaching an altitude above 2,500 to 3,000 m, with the symptoms of excessive exertional dyspnea, cough, chest tightness, and decreased exercise performance . It woul2) and tension (PaO2) are a bit lower and the alveolar-arterial oxygen tension is a bit greater at high altitudes compared to healthy controls  and prostaglandin E2  can causA slow ascent is the primary strategy for preventing altitude diseases . FurtherNumerous studies have explored the effectiveness of HBO preconditioning for high-altitude diseases  70-mediated HBO preconditioning could prevent high-altitude brain edema, cognitive deficits, and hippocampal oxidative stress in rats . Zhou etAs for the preventive effects of HBO preconditioning on HAPE, the mechanisms may involve HSP70 and aquaporin (AQP) . AQP1 anBased on the existing evidence, we propose some potential mechanisms for HBO preconditioning in the prevention of high-altitude disease .HBO preconditioning may play an important role in inhibiting inflammatory responses to prevent high-altitude diseases. HIF-1 is a crucial modulator for inducing genes that promote cellular adaptation and survival under hypoxic conditions . Some ph2 level in the HBO chamber. Hence, repeated HBO preconditioning may generate a hyperoxia and hypoxia cycle and induce an accumulation of HIF-1α  level . PreviouThe common HBO preconditioning pressure in these included studies was two ATA. The number of HBO preconditioning sessions ranged from one to seven, while two to five sessions were the common choices. Almost every study chose to do the HBO preconditioning once a day. The neuroprotective effect induced by HBO preconditioning is short-term and usually disappears in approximately two weeks . TherefoHBOT is used in an increasing number of conditions and is seen to be a generally safe treatment. The possible side effects of HBO involve barotrauma, oxygen poisoning, visual abnormalities, and claustrophobia . Middle To translate HBO preconditioning paradigm to the clinic, it is critical to identify vulnerable groups at high risk for acute high‐altitude diseases. Previous studies have shown that AMS history, level of residence, or recent high-altitude exposure and ascending rate are significant predictors of high-altitude diseases . ObesityFor climbers, it is convenient to take HBO preconditioning. Hyperbaric chambers can be categorized into monoplace chambers and multiplace chambers. The monoplace chamber can only accommodate one person, and the multiplace chamber allows for the treatment of several patients at the same time . There aTo date, HBOT has been widely used in the treatment of high-altitude disease; however, the preventive role of HBOT pretreatment has been largely overlooked. To the best of our knowledge, this is the first study to review the effect of HBO preconditioning in preventing acute high-altitude diseases. Both animal studies and clinical studies showed that HBO preconditioning may be an effective modality to prevent high-altitude diseases. The proposed mechanism of HBO preconditioning in preventing high-altitude diseases may involve: 1) the protection of the BBB and prevention of brain edema, 2) the inhibition of inflammatory responses, 3) the induction of HIF-1α and its target genes, and 4) the increase in antioxidant activity. Based on the current results, multi-sessions of hyperbaric oxygen preconditioning may have a better preventive effect on acute altitude disease compared to a single session. The combined effect of pre- and post-climbing HBO use also warrants further study. With further confirmed role and the development of an optimal protocol of hyperbaric oxygen preconditioning, it will be beneficial to lower the morbidity of altitude diseases, and reduce the life, health and economic loss.


In COPD, (a combination of) β2-agonists and muscarinic M3-antagonists (anticholinergics) are used as bronchodilators. We studied whether differential receptor expression in large and small airways affects the response to β2-agonists and anticholinergics in COPD. Bronchoprotection by indacaterol (β2-agonist) and glycopyrrolate (anticholinergic) against methacholine- and EFS-induced constrictions of large and small airways was measured in guinea pig and human lung slices using video-assisted microscopy. In guinea pig lung slices, glycopyrrolate  concentration-dependently protected against methacholine- and EFS-induced constrictions, with no differences between large and small intrapulmonary airways. Indacaterol  also provided concentration-dependent protection, which was greater in large airways against methacholine and in small airways against EFS. Indacaterol (10 μM) and glycopyrrolate (10 nM) normalized small airway hyperresponsiveness in COPD lung slices. Synergy of low indacaterol (10 nM) and glycopyrrolate (1 nM) concentrations was greater in LPS-challenged guinea pigs (COPD model) compared to saline-challenged controls. In conclusion, glycopyrrolate similarly protects large and small airways, whereas the protective effect of indacaterol in the small, but not the large, airways depends on the contractile stimulus used. Moreover, findings in a guinea pig model indicate that the synergistic bronchoprotective effect of indacaterol and glycopyrrolate is enhanced in COPD.Expression of bronchodilatory β Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease characterized by chronic progressive airflow obstruction that is only partially reversible and is caused by an abnormal inflammatory response to external stimuli, like cigarette smoking. The underlying airway inflammation in COPD is associated with epithelial changes, mucus hypersecretion, airway smooth muscle contraction, peribronchial fibrosis and emphysema, all contributing to development of airway obstruction ,2. There2-adrenoceptor agonists (β2-agonists) and muscarinic M3 acetylcholine receptor antagonists (anticholinergics)  and to the anticholinergic tiotropium on carbachol-induced constriction of small airways [2-adrenoceptor expression in large vs small airways [2-adrenoceptor by acetylcholine-activated M3-receptors, which would explain the stronger bronchoprotective effect of indacaterol in the small airways compared to the large airways. However, the β2-adrenoceptor-mediated response in the small airways was not reduced compared to the large airways when using methacholine as a contractile agent instead of EFS. Therefore, the difference in responsiveness to indacaterol in the small airways between methacholine and EFS may result from the fact that the EFS-induced constrictions are caused by a combination of cholinergic and non-adrenergic, non-cholinergic receptor activation whereas methacholine only activates muscarinic receptors. This suggests that non-adrenergic, non-cholinergic mediators may preserve more β2-adrenoceptor function than methacholine.Bronchodilator responses to β airways  has been airways . However airways , changes50-value to either olodaterol or tiotropium [max) of large and small airways cannot be compared. Since we used the same technique to study the constriction of large and small airways while normally embedded in lung tissue, the Emax and pEC50-values can be compared. Moreover, the fact that both airways sizes were still embedded in the lung slice ensured that morphological and functional characteristics were preserved at both the macroscopic and microscopic level. When comparing the highest concentration of indacaterol with the highest concentration of glycopyrrolate, no statistical significant differences were observed on the bronchoprotective effect against methacholine or EFS on either the large or the small airways. This is in line with the fact that no statistical significant effects were observed on the maximal bronchodilator response to either olodaterol or tiotropium in the subsegmental human bronchi or the human small airways [Calzetta et al. compared the bronchodilator effects of olodaterol and tiotropium on isolated subsegmental bronchial rings and small intrapulmonary human airways in lung slices precontracted with a submaximal concentration of methacholine . No signotropium . However airways .50-value) towards methacholine compared to the response observed in non-COPD controls. The prevention of small airway hyperresponsiveness in COPD by indacaterol as well as glycopyrrolate is in line with clinical studies using inhaled treatments. Thus, glycopyrrolate monotherapy in COPD patients improved trough forced expiratory volume in one second (FEV1) after 12 and 24 weeks, improved dyspnea  and reduces the need for rescue medication [1 and dyspnea [We previously demonstrated small airway hyperresponsiveness in lung slices obtained from COPD patients (GOLD I and II) compared to non-COPD control . We now dication , whereas dyspnea ,34.50 values in the naïve animals in the multiple dose study. The low doses of indacaterol and glycopyrrolate alone were effective in reducing the sensitivity, but not the maximal responsiveness, to methacholine in the large airways of the LPS-challenged guinea pigs. These results indicate that the sensitivity to β2-agonists and anticholinergics was increased in the LPS model compared to saline controls, which could have important beneficial therapeutic implications in COPD. Importantly, combined treatment with indacaterol and glycopyrrolate also had stronger protective effects on airway responses of LPS-challenged guinea pigs than in saline-challenged controls. Whereas the combination changed the sensitivity (pEC50), but not maximal responses, in the large airways of saline-challenged animals only, in LPS-challenged animals the sensitivity was reduced in both large and small airways—with the protection being larger in the large airways. Moreover, the combined treatment also reduced small airway hyperresponsiveness by 42%—compared to a non-significant reduction of 4.6% and 18.8% by indacaterol and glycopyrrolate alone. These findings highlight the importance of targeting the small airways and improving the drug delivery to the small airways, particularly since most end-points in studies in patients do not see a synergistic effect between indacaterol and glycopyrrolate compared to the individual effects, although the combination is superior to the individual treatments are often observed [2-agonists and anticholinergics on bronchodilator responses have been observed in animals, as well as in human tissue and in patients  depend on the amount inhaled but range between ~0.5 nM and ~2.5 nM for each drug  [The concentrations of indacaterol (10 nM) and glycopyrrolate (1 nM) used in this study to determine possible synergistic effects are expected to be in the range of the local concentration of these drugs upon inhalation. The bioavailabilities of indacaterol and glycopyrrolate are 57% and 43%, respectively ,43. Maxirrolate) ,45,46,4750) of small airways is greater than that of large airway in rat lung slices [We observed some differences between the responses of the naïve animals in the multiple dose protocol and the saline-challenged control animals in the combination protocol. Whereas the maximal methacholine-induced airway constriction of the small airways was significantly less than that of the large airways in the saline challenged animals, in line with our previous finding , the airg slices . A lower3-mediated as well as EFS-induced airway constriction of large and small intrapulmonary airways that are embedded in their normal matrix in lung slices, demonstrating that the lower expression of muscarinic M3-receptors as the airways get smaller does not result in a lower therapeutic effect of anticholinergics. The bronchoprotective effect of the β2-agonist indacaterol is similar for large airways independent on the stimulus used, but the protective effect on small airways is larger against EFS than against methacholine. Indacaterol and glycopyrrolate each also normalize hyperresponsiveness of small airways in lung slices obtained from COPD patients. The synergistic effect of the combination of low concentrations of indacaterol and glycopyrrolate is more pronounced in large and small airways in lung slices obtained from a guinea pig model of COPD than in control animals. Taken together, these findings show that the bronchoprotective effect of glycopyrrolate does not de-pend on airway size, whereas the protective effect of indacaterol is different for large and small airways depending on the contractile stimulus used. Moreover, the findings in the guinea pig model indicate that the synergistic bronchoprotective effect of indacaterol and glycopyrrolate is enhanced in COPD.In naïve guinea pigs, the anticholinergic glycopyrrolate similarly protects against muscarinic M


Large observational studies showed a U-shaped association of clinic blood pressure (BP) with mortality among patients undergoing peritoneal dialysis (PD). Whether ambulatory BP provides a more direct risk signal in this population remains unknown. In a prospective cohort of 108 PD patients, standardized clinic BP was recorded at baseline with the validated device HEM-705  and 24-h ambulatory BP monitoring was performed using the Mobil-O-Graph monitor . Over a median follow-up of 16 months (interquartile range: 19 months), 47.2% of the overall population reached the composite outcome of non-fatal myocardial infarction, non-fatal stroke, or all-cause death. In Cox-regression analysis, systolic but not diastolic BP was prognostically informative. Compared with the reference quartile 1 of 24-h systolic BP (SBP), the multivariate-adjusted hazard ratio for the composite outcome was 1.098 : 0.434–2.777) in quartile 2, 1.004 (95% CI: 0.382–2.235) in quartile 3 and 2.449 (95% CI: 1.156–5.190) in quartile 4. In contrast, no such association was observed between increasing quartiles of clinic SBP and composite outcome. The present study shows that among PD patients, increasing ambulatory SBP is independently associated with higher risk of adverse cardiovascular events and mortality, providing superior prognostic information than standardized clinic SBP. Hypertension is an established cardiovascular risk factor both in the general population and in patients with chronic kidney disease not yet on dialysis . Unlike The reverse epidemiology of hypertension among patients on dialysis requires a closer examination. Prior studies revealed that confounding factors with an opposing effect on BP, such as underlying congestive heart failure (CHF), inflammation, or the level of illness, magnify the inverse relation of BP with mortality, limiting the ability of hypertension to predict risk . Other sBased on the above observations, we hypothesized that among patients on PD, ambulatory BP provides a more precise reflection of the patient’s actual BP load over the entire 24-h period and may therefore be of greater prognostic significance than clinic BP. Accordingly, the aim of this study was to explore for first time the association of BP with the risk of future cardiovascular events and all-cause mortality using standardized clinic BP recordings and the reference-standard method of ambulatory BP monitoring (ABPM) as risk predictors in a prospective cohort of 108 prevalent PD patients.2; (v) arteriovenous fistula in both arms that was formerly used as vascular access for hemodialysis; (vi) patients who had a change either in the PD regimen or in the prescribed antihypertensive medications within 2 weeks before enrolment were also excluded from the study.The cross-sectional data on part of this cohort have previously been reported elsewhere . Adult EThe protocol procedures of our study were accordant with the Declaration of Helsinki and its latest Amendments and all patients gave written informed consent before enrolment. The protocol of our study received approval by the ethics committee of School of Medicine, Aristotle University of Thessaloniki .Clinic BP was recorded at baseline visit by nurses trained in this technique under standardized conditions with the validated self-inflating oscillometric device HEM-705 CP  . In detaAfter the completion of baseline assessment at clinic, all patients underwent ABPM with the oscillometric Mobil-O-Graph device . A brachial cuff of appropriate size was fitted to the non-dominant arm and ABPM was performed for 24 h. The BP-detection unit of this device was validated according to the criteria of ESH/ESC and British Society of Hypertension ,14. CompThe primary outcome of our study was prespecified as the composite of time to first occurrence of non-fatal MI, non-fatal stroke, or death from any cause. Patients were prospectively followed up from the day that they successfully completed the baseline evaluations through 28 February 2021. Patients were censored on the date that they received their last PD treatment, if they received a kidney transplant or when they were switched to hemodialysis. The adjudication of deaths and non-mortal cardiovascular events was performed by an independent member of the investigator team (V.V.) after review of medical records that were provided by the 3 participating PD centers.p < 0.05.Continuous variables are presented as mean ± standard deviation (mean ± SD) or median (range), according to the normality of the distribution of each variable assessed using the Kolmogorov–Smirnov test. Categorical data are expressed as absolute frequencies and percentages. The study population was divided into quartiles according to the level of clinic and 24-h ambulatory systolic BP (SBP). Differences in baseline characteristics among quartiles were evaluated using one-way analysis of variance (ANOVA) for continuous variables or with the chi-squared (χ2) test for categorical variables, respectively. Kaplan–Meier curves were created, and the log-rank test was applied to explore the equality in the risk of the primary composite outcome between quartiles of clinic and 24-h ambulatory SBP. Cox proportional hazards regression analysis was applied to explore the prognostic association of baseline BP with the prespecified composite outcome of non-fatal MI, non-fatal stroke, or all-cause death. The analysis was initially performed using unadjusted models. Subsequently, we generated multivariate models that provided adjustment for the following variables: age, sex, time on PD, mode of PD (continuous ambulatory vs. automated), diabetic status, history of pre-existing cardiovascular disease , BP medication use (yes vs. no), the presence of substantial residual diuresis (yes vs. no), hemoglobin, and serum albumin levels at baseline. To calculate the adjusted hazard ratios (HRs), continuous variables  were centered at their group means. All analyses were performed with the Statistical Package for Social Sciences version 23.0 . The probability values reported are 2-sited and considered to be statistically significant at As shown in p < 0.001).The baseline demographic and clinical characteristics of study participants are shown in p < 0.001). p = 0.001) as well as between quartiles of 24-h ambulatory SBP and composite outcome   . However< 0.001) .Large observational studies using conventional clinic BP recordings as risk predictors showed a “reverse epidemiology” of hypertension among patients undergoing long-term PD ,3,7,17. These observations are in line with the results of 2 separate cohort studies showing that the timing or the technique of BP measurement inserts variation in the prognostic association of BP with mortality in the ESRD population ,10. In tThe diverse prognostic association of clinic versus ambulatory SBP that was evident in the present study could be theoretically attributed to the greater number of BP recordings that the technique of ABPM provides. However, we believe that this potential explanation is an oversimplification of the overall value of ABPM in the assessment of hypertension. This issue was illustrated in a prior observational study showing that among hemodialysis patients, interdialytic ambulatory SBP retained its strong prognostic association with all-cause mortality, even when a small subset of randomly selected ambulatory BP measurements was averaged and used as risk predictors . TherefoThe superior predictive value of ambulatory over clinic BP may be explained by the fact that ABPM provides a more precise reflection of the patient’s actual BP burden over the entire 24-h period ,16,20. EStrength of our study is the careful assessment of BP at baseline with the use of standardized clinical BP recordings and the reference-standard method of ABPM. However, the present work has also some limitations that need to be acknowledged. First, the observational design of this study precludes the opportunity to derive direct cause-and-effect associations between BP and risk of future cardiovascular events and all-cause death. However, this limitation is commonly shared across all prior observational studies that attempted to explore the prognostic significance of BP in patients on dialysis. Second, patients with infectious/bleeding complications shortly before study enrolment or those with a recent hospitalization due to acute coronary syndrome or acute stroke were excluded from our analysis. Therefore, larger studies also including patients with more severe underlying illness are warranted to confirm whether our observations are applicable to the whole spectrum of the PD population. Third, even though the survival analysis was adjusted for several established risk factors, we acknowledge that the possibility of residual confounding is still not fully eliminated. Finally, since the assessment of clinic and ambulatory BP was performed only in a single occasion at baseline, our analysis could not provide time-varying risk-associations between BP and clinical outcomes.In conclusion, the present study shows that among patients on long-term PD, increasing ambulatory SBP was associated with increased risk of adverse cardiovascular events and all-cause death independently from several established risk factors. In contrast, the HR for the composite outcome was not significantly higher in the highest quartile of clinic SBP as compared with the reference quartile 1, suggesting that increasing clinic SBP could not provide a direct risk signal. Future studies are warranted to fully elucidate whether the adequate assessment of hypertension with the use of ABPM can improve cardiovascular risk stratification in the PD population.


Children with cancer are not at increased risk of severe SARS-CoV-2 infection; however, adults with haematological malignancies have increased risk of severe infections compared with non-haematological malignancies.We compared patients with haematological and non-haematological malignancies enrolled in the UK Paediatric Coronavirus Cancer Monitoring Project between 12 March 2020 and 16 February 2021. Children who received stem cell transplantation were excluded.Only 2/62 patients with haematological malignancy had severe/critical infections, with an OR of 0.5 for patients with haematological compared with non-haematological malignancies.Children with haematological malignancies are at no greater risk of severe SARS-CoV-2 infection than those with non-haematological malignancies. Children have a very low risk of developing severe SARS-CoV-2 infections. Early in the pandemic it became clear from the experiences in China, Italy and Spain that this was also true for children with cancer. Not so for adult patients particularly those with haematological malignancies who were considered to be 'extremely vulnerable'. The recommendation however, from the UK's national Childhood Cancer and Leukaemia group (CCLG) in 2020 was that, other than particularly high risk patients (those with multiply relapsed disease or undergoing bone marrow transplant or CAR-T therapy) children with cancer, including those with haematological malignancies, need not be considered extremely vulnerable. This clearly written, concise paper by Millen et al compares patients with haematological and non-haematological malignancies enrolled on the UK Paediatric Coronavirus Cancer Monitoring Project from March 2020 until Feb 2021 and supports that recommendation. Children with cancer are not at increased risk of severe infection with SARS-CoV-2 compared with the normal paediatric population. Adults with haematological malignancies are at increased risk of severe forms of SARS-CoV-2 infection and death compared with adults with other malignancies.Children with haematological malignancies are not at any increased risk of severe SARS-CoV-2 infection than children with other malignancies. This is clearly different from adult patients and supports the continuation of routine care for these patients.Data previously published by our group have suggested that children with cancer are at no greater risk of severe infection with the coronavirus SARS-CoV-2 than other children.Given that approximately 40% of children with cancer are diagnosed with a haematological malignancy, we performed a sub-analysis on our dataset to examine the severity of SARS-CoV-2 in this population.The UK Paediatric Coronavirus Cancer Monitoring Project (UKPCCMP) is a national database of children less than 16 with an underlying diagnosis of malignancy attending hospital with evidence of COVID-19 infection.Patients were eligible for inclusion if they had a positive SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) test between 12 March 2020 and 16 February 2021 and had an underlying diagnosis of any subtype of haematological malignancy including (but not limited to) all subtypes of leukaemia and lymphoma as well as myelodysplastic syndromes. Patients with other malignancies in the UKPCCMP cohort were used as a comparator. The cohort included both symptomatic and asymptomatic patients screened prior to elective admission to hospital.https://www.ebmt.org/covid-19-and-bmt).Patients who had undergone an allogeneic stem cell transplant were excluded from the database as their information is being reported separately by the European Society for Blood and Marrow Transplantation  had a haematological malignancy. In total, 2/62 patients (3.2%) with haematological malignancies had severe/critical infections. Both patients had pre-B acute lymphoblastic leukaemia; one was receiving delayed intensification and the other was receiving blinatumumab for non-remission after induction.Also, 3/48 patients (6.3%) with non-haematological malignancies had severe/critical infections. One had a hepatoblastoma on a background of chronic lung disease. One patient had an osteosarcoma and was receiving postoperative chemotherapy. The final patient had a refractory stage 4 neuroblastoma.Most patients with a positive SARS-CoV-2 test and haematological malignancy had acute lymphoblastic leukaemia (ALL) .In the UKPCCMP cohort, we did not identify any increased risk of severe/critical infection in children with haematological compared with non-haematological malignancies. The OR for severe/critical disease in patients with haematological compared with non-haematological disease was 0.50 (95% CI 0.08 to 3.11). Also, 80% of patients in the haematological cohort were receiving their first line of chemotherapy compared with 83% of patients in the non-haematological cohort.In this updated analysis on our initial review of the severity of COVID-19 in children with cancer in the UK, we focus specifically on comparing patients with haematological and non-haematological malignancies.Since the beginning of the pandemic, a number of groups including our own have published data on the severity of COVID-19 in children with cancer and on the severity of COVID-19 in patients with different haematological malignancies.The adult UKCCMP reported that patients with haematological malignancies have a significantly higher case-fatality rate than patients with non-haematological malignancies .In our haematological malignancy cohort, there were no deaths related to COVID-19 and only two patients required intensive care support. Also, 31% of patients were asymptomatic and were identified either through targeted screening prior to a general anaesthetic or were admitted for another reason and incidentally found to be SARS-CoV-2 positive. In the non-haematological cohort, 36% were asymptomatic. Thus, there was no evidence of a higher rate of asymptomatic cases in the haematological cohort despite a higher rate of general anaesthetics, suggesting that the estimated OR of 0.50 for developing severe or critical disease was not due to higher rates of detection by asymptomatic screening. In the adult UKCCMP cohort, 119/227 patients with haematological malignancies (52.4%) had severe or critical infections. They had an increased risk of severe/critical infections compared with those with non-haematological malignancies .Given the OR for severe/critical disease in patients with haematological malignancies compared with non-haematological malignancies was 0.5, there is a possibility that the risk of severe/critical infection may actually be higher in children with non-haematological malignancies, but the small numbers make it difficult to draw any conclusion.In the UKPCCMP cohort, 63.6% of patients had a mild infection that either did not require admission to hospital or had a short stay with no need for additional therapy such as supplemental oxygen. This figure remains consistent with our previously reported cohort in which 63% of patients had a mild infection, highlighting the stability of the data over time.Approximately 40% of children diagnosed with cancer across the UK have a haematological malignancy,The major strength of our study is that all principal treatment centres caring for children with cancer in the UK were enrolled. While some patients with positive SARS-CoV-2 PCR results may not have been registered on the UKPCCMP database, rigorous and repeated follow-up with participating centres has ensured near complete capture of childhood cancers attending hospital in the reported timeframe.The major limitation of our study is the relatively small numbers of patients involved. However, the results shown here are remarkably similar to the data we published for all children with cancer last year. A specific limitation with regard to our haematological cohort is our elective omission of patients who had undergone an allogeneic stem cell transplant as they were being captured by another registry study. This decision does limit our ability to generalise our findings to all patients with haematological malignancies, but the conclusion that paediatric patients with cancer are at no greater risk of severe COVID-19 infection remains relevant for those who have not had an allogeneic transplant. Furthermore, less than 1% of patients with haematological malignancies in the adult UKCCMP cohort had allogeneic stem cell transplants . This therefore means that, although we excluded patients who were post-allogeneic transplant from our dataset, the two sets are largely comparable.Our data suggest that children with haematological malignancies who have not had an allogeneic stem cell transplant are not at greater risk of severe infection with SARS-CoV-2 than other children with cancer. This supports the recommendations that children with haematological malignancies need not be considered extremely vulnerable and should continue with standard of care therapy without modification attendant on the SARS-CoV-2 pandemic.


The ongoing coronavirus pandemic has major impacts on both patients and healthcare systems worldwide, thus creating new realities. Patients on maintenance dialysis listed for renal transplantation are a vulnerable subgroup with many comorbidities and recurring contacts with the healthcare system. Due to the COVID-19 pandemic transplant numbers have dropped considerably, further increasing waiting times in this high-risk population. On the other hand, knowledge of the severity of SARS-CoV-2 infection in immunocompromised patients, development and persistence of neutralising antibodies in such patients is just emerging. It is unclear how best to address the dilemma of postponing the life-saving transplantation.We present a case report of a successful kidney transplantation only 65 days after the recipient was hospitalized for treatment of COVID-19 pneumonia. In a follow up of 9 months, we observed no signs of recurrent disease and transplant function is excellent. Monitoring SARS-CoV-2 antibody response demonstrates stable IgG levels.This reassuring case provides guidance to transplant centers how to proceed with kidney transplantation safely during the pandemic. Careful consideration of risks and benefits of the organ offer, full recovery from COVID-19 symptoms and the presence of a positive SARS-CoV-2 IgG antibody test, qualifies for kidney transplantation. The rapid global spread of SARS-CoV-2 with over 115 million cases and over 2.5 million deaths of the resulting coronavirus disease (COVID-19) reported worldwide by March 2021, has led to urgent and widespread efforts to contain and mitigate transmission. The resulting disruption of many aspects of private and public life with significant socioeconomic sequelae also affected hospitals worldwide: non-life saving operations and treatment of non-life threatening illnesses were postponed. Transplant centers were also affected by various measures to contain the spread of the virus and subsequently transplant numbers fell remarkably [The ongoing COVID-19 pandemic poses specific risks for waitlisted patients, patients who are already transplanted and on the transplant centers themselves: a reduced number of donated organs will affect waitlisted patients for a considerable time. Patients not yet vaccinated might defer a life-saving organ offer due to concerns over infection risks. Many hospitals, on the other hand, have their activities still not fully resumed during the second or in the face of the third wave. At this point, the number of patients who have received vaccinations varies considerably in different countries, such that many patients in need for transplantation worldwide are still forced to decide together with their physicians whether to accept an organ offer or not.As numbers of infections are still high despite various lockdown strategies, percentages of patients vaccinated vary greatly and as mutant strains of SARS-Cov-2 spread more widely, a considerable portion of patients on dialysis will still be infected by SARS-CoV-2 with a symptomatic or sometimes asymptomatic course of COVID-19. This increasing patient cohort faces difficult decisions such as when it is safe to be transplanted or whether susceptibility for re-activation or re-infection under immunosuppressive therapy poses an unacceptable risk.We present a patient with a successful renal transplantation 65 days after hospital discharge for COVID-19 pneumonia. Follow up of the first 100 days are very reassuring without signs of recurrent or de-novo infection. The serial detection of SARS-CoV-2 specific IgG before and after transplantation indicates that immunosuppression does not alter the B cell response to SARS-CoV-2.A 65-year-old female hemodialysis patient with ESRD due to IgA nephritis was offered a kidney from a 70-year-old deceased donor on May 27, 2020. Her waiting time was nearly 6 years. Donor eGFR was 94 ml/min, the cause of brain death was intracerebral bleeding. Additional donor risk factors were hypertension and history of smoking.2-therapy or non-invasive ventilation (NIV) was not necessary. The two following PCR-tests in April were negative, and she had been discharged without respiratory symptoms 7 weeks before the organ offer. She did not report intercurrent infections.The recipient herself was hospitalized for 7 days due to Covid-19 pneumonia from March 23 to April 7, 2020. Her symptoms at onset were fever and shortness of breath, and she subsequently tested positive with polymerase chain reaction (PCR) for SARS-CoV-2 in a nasopharyngeal swab on March 16, 2020  for acute T-cell-mediated rejection, mild acute tubular damage with isometric vacuolization of tubular epithelial cells, the latter suggestive of calcineurin-inhibitor toxicity at a tacrolimus trough level of 10 ng/ml. No viral particles could be found with electron microscopy in tubular epithelial cells or podocytes (not shown). Under a three-day course of high dose steroids and intravenous fluid, serum creatinine levels reached baseline again. At present, more than 9 months after transplantation, the patient has an uncomplicated follow-up with a stable serum creatinine of 1.6 mg/dl.At the onset of COVID-19 pneumonia, the patient tested PCR-positive for SARS-CoV-2 on March 16, 2020 .This is one of the few cases of kidney transplantation after COVID-19. In contrast to the first report , our casTo this date, it is not known whether SARS-CoV-2 can actually recur. There have been several reports –6, howevA strength of our study is the longitudinal documentation of the serologic response: IgG and IgM-antibodies against SARS-CoV-2 were still detectable weeks after transplantation. Gradual weakening of IgM is normal for an antibody response. As the test is not quantitative and it is not known whether the antibodies display neutralizing properties, this finding has to be interpreted with caution. Interestingly, SARS-CoV-2 infection on primates has been shown to induce immunity , which iOur study has several limitations: obviously, the follow-up time is relatively short. A substantially longer time will be necessary to draw conclusions regarding patient and graft survival and whether COVID-19 associated complications might occur. However, with the level of immunosuppression highest at the time of transplantation, infectious complications are expected to emerge at this point in time; also because the recovery from COVID-19 was only recent. From a clinical perspective, as already described in other case reports, we did not know the antibody status at time of reperfusion. This has prompted us to implement screening strategies on the waiting list, which facilitates decision making at the time of the organ offer.This case has several important implications for transplant centers worldwide.First, it demonstrates that transplantation and immunosuppressive therapy is possible already 7 weeks after SARS-CoV-2 infection. We conclude that this is reassuring for patients who have suffered from a more severe course of COVID-19 even with hospitalization. As Massie et al. have demonstrated, there are only very few scenarios when postponing transplantation is - from the patients’ perspective - warranted  especialTaken together, our case suggests that, after careful consideration of risks and benefits of the organ offer, full recovery from COVID-19 symptoms and the presence of a positive SARS-CoV-2 IgG antibody test qualifies for kidney transplantation. Even though knowledge of the efficacy of the antibody response is currently not clearly defined in the dialysis population, we suggest patients on the waiting list for kidney transplantation should undergo routine serologic testing for SARS-CoV-2. In consideration of the ongoing pandemic and the protracted vaccination programs globally, this might facilitate decision making at the center level to proceed with transplantation.


Misconceptions about the contagion may have given rise to adverse risk behaviours across population groups. This study evaluates risk perception among 2,244 residents of seven countries in sub-Saharan Africa  in relation to socio-demographic determinants.an online survey was conducted via social media platforms to a random sample of participants. Risk perception was evaluated across six domains: loss of income, food scarcity, having a relative infected, civil disorder, criminal attacks, or losing a friend or relative to COVID-19. A multivariable ordinal logistic regression was conducted to assess socio-demographic factors associated with the perceived risk of being affected by COVID-19.595 (27%) respondents did not consider themselves to be at risk, while 33% perceived themselves to be at high risk of being affected by the pandemic with respect to the six domains evaluated. Hospital-based workers had the highest proportional odds  high perceived risk. Teenage respondents had the highest predictive probability  of perceiving themselves not to be at risk of being affected by COVID-19, while Zambia residents had the highest predictive probability  for high-risk perception.this study reveals the need to increase awareness of risks among socio-demographic groups such as younger people and the unemployed. Targeted risk communication strategies will create better risk consciousness, as well as adherence to safety measures. In December 2019, the coronavirus disease 2019 (COVID-19) was identified as a novel contagion in Wuhan, China. The outbreak rapidly spread across the world, necessitating the declaration of a public health emergency of international concern (PHEIC) under the International health regulations by the World Health Organization on January 30, 2020, and as a pandemic in March 2020 . Yet, noStudy design and sampling: this study was a cross-sectional study across seven sub-Saharan African countries: Botswana, Kenya, Malawi, Nigeria, Zambia, Tanzania and Zimbabwe. The study used a random sample of respondents from 7 countries cutting across West (1), East/Central (2), and Southern Africa (4). These countries were selected to give a geographic representation across the different sub-Saharan African blocs which typically differ in national culture and context. Sample size was calculated using OpenEpi software [software . The minThe elements of this formula as relates to this study are described as follows: i) population size (for finite population correction factor or fpc); (N): population of each country; ii) hypothesized % frequency of outcome factor in the population (p): 50%+/-5; iii) confidence limits as % of 100(absolute +/-%)(d): 5%; iv) design effect (for cluster surveys-DEFF): 1.Data collection tools and procedure: the survey was administered online, between the 17th May and 15th June 2020 via a structured questionnaire that assessed respondents' socio-demographic characteristics and individual perception of being affected by the COVID-19 pandemic using a 5-point Likert scale. Perceived risk was assessed with respect to the following domains: loss of income, food scarcity, having a relative infected, civil disorder, criminal attacks, or losing a friend or relative to COVID-19. The questionnaire was created with appropriate skip patterns, pretested, and was administered via Google forms  online. These forms were then distributed via email, Facebook, Twitter, Telegram and WhatsApp using the following approach: initial call; i) Twitter-short survey intro plus link shared via Twitter adverts targeting the countries, broadcasts using hashtags of trending topics in each country and push through influencers in these countries; ii) Facebook-short survey intro plus link shared as broadcasts targeting each country; iii) WhatsApp and Telegram - short survey intro plus link broadcasts to individuals and groups by survey team in each country; iv) Telegram-short survey intro plus link shared using the groups nearby feature of Telegram that allows you to locate and share info with groups in a geographic location; v) Email listservs-short survey intro plus link shared to email listservs. Reminders: i) Repeated reminders till sample size was complete for the country via same channels as above; ii) Maximum of two via individual reminders to those who started but did not complete the survey. Data collection was closed after 28 days of initial launch or when the sample size had been completed. Respondents were eligible for inclusion into the study if they were, resident in one of the 7 countries considered in our study, aged ≥15 years, and able to communicate in English. We excluded non-residents and/or respondents who inadvertently gained access to the survey despite not meeting these criteria.Variables: variables of interest in this study include socio-demographic profiles of respondents . The primary response variable of interest was perceived self-risk of being affected by the COVID-19, assessed on a scale of zero to five, with zero representing “no perceived risk” and five representing very high perceived risk. Responses were then recategorized during data analyses into no perceived risk (0), low (1-2), moderate (3), and high (4-5) perceived risk). Other response variables collected include “What is the single most important factor that made you rate your risk this way?” with five categorical responses, as well as perceived likelihood of experiencing potential risks due to COVID-19  were assessed on five-point Likert scale .Data analyses: data analyses were conducted using STATA 14.1 and R (version 4.0.2). All variables were initially summarized in frequency tables. Thereafter a multivariable ordinal logistic regression was conducted to estimate associations between socio-demographic variables and the primary response variable (perceived risk of being affected by COVID-19). Given the objectives of our study, we included all socio-demographic variables collected into the regression models. Post-estimation predicted probability modelling of perceived risk was conducted for each socio-demographic variable while holding all other variables constant at their respective means.Ethical considerations: the survey protocol was approved by the Health Research Development Committee (HRDC) of the Ministry of Health and Wellness, the local institutional review board of Botswana (REF Number HPDME 13/18/1). A brief information page was the first section of the online questionnaire, and the respondents were allowed to consent electronically before completing the survey. Participation was voluntary and those who consented were allowed to exit the survey at any time by simply closing the browser page.Socio-demographic characteristics of participants: descriptive characteristics of the respondents are provided in COVID-19 risk perception among respondents:Predictors of the perceived risk of COVID-19: outputs from the ordinal logistics regression are presented in Adjusted predicted probabilities of perceived risk categories with respect to each predictor: et al. [This study generates and compares empirical evidence from seven African countries which could help improve social strategies to curb the ongoing pandemic. Like some other studies in similar African settings ,18, thiset al. -21. Ordiet al. . This iset al. ,21. Hospet al. .Just 10% of respondents thought they would not be able to practice safety protocols. Key concerns that raised risk perception among the respondents were the potential for loss of income, relatives, and friends being infected or dying from COVID-19, and criminal attacks associated with the pandemic. Across the world, many have lost loved ones or income. However, perhaps counterintuitively, studies indicate that crime may not be on the increase following the pandemic . One stuThis study estimates the risk perception among residents in selected sub-Saharan African countries and highlights potential gaps that may be targeted by interventions to address the ongoing pandemic. Specifically, there is a need to increase awareness of risks among young people, as well as the unemployed. These categories of people, who may consider themselves to be less vulnerable to serious forms of the disease, are more at risk of the socioeconomic impact of the pandemic. Further, they are still susceptible to infection and unconscious spread of the contagion. Targeted enlightenment programs will go a long way to create better consciousness of risk, as well as the importance of pandemic control measures.Sub-Saharan Africa remains at high risk of high morbidity and mortality due to COVID-19, given the poor adherence to pandemic control protocols;Misconceptions about the contagion may have given rise to adverse risk behaviours across population groups.This study reveals the need to increase awareness of risks among socio-demographic groups such as younger people and the unemployed;Targeted risk communication strategies will create better risk consciousness, as well as adherence to safety measures.


TIL-Maps-23, for 7983 WSIs spanning 23 types of cancer with complex and diverse visual appearances, which will be publicly available along with the code to evaluate performance.The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures  with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as Code Available at:https://github.com/ShahiraAbousamra/til_classification. Tumor infiltrating lymphocytes (TILs) have gained importance as a biomarker in translational cancer research for predicting clinical outcomes and guiding treatment. As our collective understanding of tumor immune responses in cancer expands, clinical research studies have shown that high densities of TILs correlate with favorable clinical outcomes , such asComputational image analysis of whole slide images (WSIs) of cancer tissue samples has become a very active area of translational biomedical research. The goals are to gain novel insights into cancer and the tumor microenvironment, including tumor immune responses, through the search for biomarkers to predict outcomes and treatment response. Modern digital microscopes scan whole slide tissue samples at very high image resolutions, ranging from 50,000x50,000 pixels to over 100,000x100,000 pixels. The increasing availability of such gigapixel WSIs has stimulated the development of image analysis methods for detection, segmentation, and classification of microanatomic regions, structures, cells, and other objects in tissue images. Therefore, we utilized advances in computer vision and machine learning to quantitatively characterize TILs to complement qualitative microscopic evaluation of cancer tissue samples by pathologists. Deep learning has become the preferred approach for a variety of image analysis tasks in recent years –17 sinceSeveral projects have implemented methods to detect and classify lymphocytes in tissue images. Eriksen et al.  employedMethods were also developed to study TILs in Hematoxylin and Eosin (H&E) stained tissue images. Budginaite et al.  developeDespite an increasing number of projects, there are few large scale datasets of WSIs that are publicly available to study TILs. Moreover, most of the previous projects targeted specific types of cancer from particular organ sites. The classification of TILs can be challenging in large datasets of WSIs across multiple types of cancer from different organ sites for many reasons. Deep learning models need to distinguish TILs from cancer cells that are intrinsically complex across a wide spectrum of growth patterns, cellular and nuclear morphologies, and other histopathologic features associated with specific types of cancer, which vary by organ site, state of cellular differentiation, and stage of cancer (e.g. primary organ site versus a metastatic tumor deposit). Computational image analysis of pathology WSIs is also complicated by variations in image properties from differences in scanning with different types of digital slide scanners and varying tissue staining laboratory protocols. In this paper we describe a deep learning workflow that was utilized to generate a large dataset of TIL maps, referred to here as the TIL-Maps-23 dataset. Unlike the previous work that studied TILs in mostly common types of cancer, we trained a deep learning model with the goal of analyzing WSIs from a much wider range of different types of cancer. We adopted the same approach of patch-wise classification as in , where eThe work presented in this manuscript focuses on an improved deep learning workflow for patch-level TIL prediction and generation of a large dataset of TIL predictions across multiple cancer types. We plan to carry out additional studies to ascertain the clinical relevance of TIL predictions in future works. Our work improves on the earlier work done by Saltz et al.  in severThe overall analysis workflow is illustrated in We created a training dataset by combining manually annotated patches (strong annotations) from 18 TCGA cancer types  and model-generated annotations from 4 TCGA cancer types . For the model-generated annotations, we sampled a set of patches classified by the model in . The modThe manual annotations are generated in 2 ways. First, patches of 150 x 150 square microns are randomly sampled from the WSIs. Pathologists annotate the center 50 x 50 square micron sub-patch in each patch. The annotation indicates whether the center sub-patch is TIL-positive or TIL-negative. Using a 150 x 150 square micron patch allows pathologists to see the surrounding tissue for a more informed decision on the label of the center sub-patch. Only the center sub-patch is used in training. A patch is labeled TIL-positive if it has at least 2 lymphocytes or plasma cells in the center sub-patch. Second, pathologists mark TIL-positive and TIL-negative regions on WSIs, where TIL-positive regions are regions with a significant amount of lymphocytes and/or plasma cells. Patches of 50 x 50 square microns are randomly sampled from these regions, where each patch is assigned the same label as the source region.The model-generated annotations are collected from classifications produced by the previous model in . This moWe trained 3 models with different networks: VGG-16 , ResNet-via small variations to brightness and color in the hue, saturation, and lightness (HSL) space. All of the networks were trained end-to-end using the cross entropy loss.Each network is initialized with weights from the respective pre-trained model on ImageNet. The batch normalization layers are dropped. Each input image (patch) is scaled with bilinear interpolation to match the network’s pre-training input size . The input image is normalized to the range  for VGG-16 and Inception-V4 by The trained models output a probability value for each patch in an input WSI. This creates a probability map for the entire WSI. The final binary prediction (TIL positive or TIL negative) is obtained by thresholding the probability map. If the probability of a patch is greater than or equal to the threshold value, the patch is classified as TIL-positive. Otherwise, it is classified as TIL-negative.hold-out dataset. We evaluated two methods for selecting the threshold value for each model. The first method relies on the true positive rate (TPR) and the false positive rate (FPR) . The length of the line from the  point and intersecting the curve at  isA default threshold value of 0.5 was used during training to evaluate a model’s performance in each training epoch. At the end of the training phase, the threshold value was fine-tuned for the inference phase. A threshold value in the range  was selected for each model based on the performance of the model on a small te (FPR) . The optPR - FPR . In our The WSIs in the image dataset are loaded to a software platform, called Quantitative Imaging in Pathology (QuIP), for training data generation and review of the model predictions. QuIP consists of multiple services, implemented as micro-services with software containers, and a set of Web-based applications that support viewing of WSIs, annotation of image regions and patches, and interactive viewing of model predictions as heatmaps overlaid on WSIs .One of the web applications is a markup and annotation tool with multiple class label selections . It is computed as:super-patches. Super-patches make it easier to collect a large number of annotations from multiple pathologists. This evaluation method provides a higher level of evaluation that is beyond individual patches and offers a quantification of the correlation between a model’s predictions and a pathologist’s perception of TIL distribution.For the region categorical classification performance, we adopted the evaluation method implemented in . We evalA super-patch is defined as a large 800 x 800 square pixel patch at 20x magnification . The deep learning models classify 100 x 100 square pixel patches at 20x magnification. Hence, each super-patch is divided into an 8 x 8 grid, and each patch (of 100 x 100 square pixels) is classified as TIL-Positive or TIL-Negative. score of a deep learning model for a given super-patch is the number of patches classified as TIL-positive by the model. Hence, each super-patch gets assigned a score between 0 to 64.In our work, each super-patch was annotated by one to three pathologists as Low TIL, Medium TIL, or High TIL, based on the perceived fraction of the area of the TIL-positive patches. The We use the polyserial correlation method , 43 to qThe number of patches in training and test sets are given in We trained three models with popular networks, namely Inception V4 , VGG-16 Baseline model in the tables. The columns LUAD, BRCA, SARC, and OV show the performance numbers in each metric for the patches collected from these four cancer types. The columns Other and All show the performance values with the 888 patches from the other cancer types and with all of the patches, respectively. The column 13 Cancer Types shows the performance comparison between the Baseline model and the newer models with patches from the 13 cancer types  analyzed in the previous work  and National Cancer Institute (NCI) grants UH3-CA22502103, U24-CA21510904, 1U24CA180924-01A1, 3U24CA215109-02, and 1UG3CA225021-01 as well as generous private support from Bob Beals and Betsy Barton. AR and AS were partially supported by NCI grant R37-CA214955 (to AR), the University of Michigan (U-M) institutional research funds and also supported by ACS grant RSG-16-005-01 (to AR). AS was supported by the Biomedical Informatics & Data Science Training Grant (T32GM141746). This work was enabled by computational resources supported by National Science Foundation grant number ACI-1548562, providing access to the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center, and also a DOE INCITE award joint with the MENNDL team at the Oak Ridge National Laboratory, providing access to Summit high performance computing system. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Early detection of SARS-CoV-2 variants enables timely tracking of clinically important strains in order to inform the public health response. Current subtype-based variant surveillance depending on prior subtype assignment according to lag features and their continuous risk assessment may delay this process. We proposed a weighted network framework to model the frequency trajectories of mutations (FTMs) for SARS-CoV-2 variant tracing, without requiring prior subtype assignment. This framework modularizes the FTMs and conglomerates synchronous FTMs together to represent the variants. It also generates module clusters to unveil the epidemic stages and their contemporaneous variants. Eventually, the module-based variants are assessed by phylogenetic tree through sub-sampling to facilitate communication and control of the epidemic. This process was benchmarked using worldwide GISAID data, which not only demonstrated all the methodology features but also showed the module-based variant identification had highly specific and sensitive mapping with the global phylogenetic tree. When applying this process to regional data like India and South Africa for SARS-CoV-2 variant surveillance, the approach clearly elucidated the national dispersal history of the viral variants and their co-circulation pattern, and provided much earlier warning of Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529). In summary, our work showed that the weighted network modeling of FTMs enables us to rapidly and easily track down SARS-CoV-2 variants overcoming prior viral subtyping with lag features, accelerating the understanding and surveillance of COVID-19. The severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has been running rampant all over the world since December 2019. The current pandemic has triggered an unprecedented scale of whole-genome sequencing and sharing of the virus’s genome. Surveillance of SARS-CoV-2 variants using sequence data provides insight into disease virulence, pathogenesis, host range or immune escape, as well as the effectiveness of SARS-CoV-2 diagnostics and therapeutics . Viral sIt is well known that new SARS-CoV-2 variants with their specific mutation features gradually dominate through spatial and temporal expansion . The frevia tracking the variants circulating in two COVID-19 hotspots, India and South Africa.In this paper, we developed a module-based variant surveillance method that enables real-time tracking of historical and circulating SARS-CoV-2 variants without designating their subtypes in advance allowing newly emerging variants or the descendants of circulating variants to be tracked earlier. This method views mutations represented by FTMs as nodes of a network and describes their relationships using network connections. We found that closely connected nodes in the network forming a biologically meaningful module indicate a potential variant, and module clusters indicate potential contemporaneous variants. We demonstrate the FTM network construction and interpretation through analysis of worldwide data of SARS-CoV-2 genomes and validate its variant surveillance capability A comparison of the workflows between subtype-based and FTM-based variant surveillance methods has been shown in SARS-CoV-2 genomes were retrieved from GISAID database . Only viWhole genome genetic variations, including single nucleotide polymorphisms (SNPs) and insertions/deletions (INDELs), were determined and annotated using a bioinformatic framework proposed by yst, at a sampling week t on a specific site s was calculated as the fraction of genomes with the mutation of all genomes sampled at that week. Then the frequency trajectory of a mutation s(1 = s = S) can be denoted asMutations present at least once across all genomes were extracted and their frequency time series were generated according to calendar weeks of sampling. Specifically, a mutation frequency, denoted by t denotes the week number and t = 1 represents the first complete calendar week of 2020 . When aggregating the mutation events for each mutation site, a large amount of multi-directional mutations  were detected (where detected . All posS) were detected across the viral genome but most were less informative with the temporal frequency pattern of fluctuating near zero  of the sth mutation is the sum of the connection strengths with the other mutations, ks = ∑i≠sAsi. The summation performed over all mutations in a particular module is the intra-modular connectivity .The network connectivity  instead. The topological overlap is defined by:l≠i,jAilAlj quantifies the indirect connection strengths between i and j through their shared neighbors and the denominator serves as a normalization factor. The topological overlap between mutation i and j reflects their relative interconnectedness as mediated through other mutation nodes (TOM = (1−TOMij) coupled with average linkage hierarchical clustering. Modules corresponded to branches of the resulting hierarchical clustering tree. We used a dynamic cut-tree algorithm to determine the branches  and visualized using the R igraph package . To distAccording to our hypothesis, modules in our network are expected to be sets of synchronous FTMs that represent variants. Emerging variants develop mutations quickly, but they are characterized by a highly correlated set of characteristic mutations. These characteristic mutations form densely connected intra-modular sub-networks. These sub-networks represent the “core” of a module and are detected using a high-pass adjacency score threshold. The threshold value is determined empirically by mapping benchmark modules to the global phylogeny (see below) with statistical evaluation of specificity and sensitivity. The historical classification and nomenclature for these variants were extracted from the GISAID metadata.We assessed variants determined by our module “core” mutations against a global reference dataset provided by GISAID using the pipeline proposed by Nextstrain . First, A total of 5,043,950 SARS-CoV-2 sequences during our study period were retrieved. After excluding those with probable sampling date error, 5,042,287 (>99.9%) were eventually included. These viral sequences have been accumulating over time at an unprecedented speed, from a few to hundreds of thousands a week according to their sampling time . ChangesTo show the association of epidemic variants and genetic variations of SARS-CoV-2 across time, a clustering process using Ward’s method was done for the FTMs. Due to ultra-high analytic dimensionality, the cluster having randomly fluctuated series was firstly identified and excluded . In consequence, 158 time series were left. The clustering analysis showed that mutations with consistent temporal change patterns were clustered together and some of these clusters were clearly linked to variant features . This suβ). Mutations with similar patterns of connection strengths are speculated to form network modules while each node represents an FTM-related mutation. Topological overlaps are used to assess the similarity of the synchronous relationship of two FTMs with all the other FTMs in the network. Modules with high topological overlaps are detected using average linkage hierarchical clustering coupled with a dynamic tree-cutting algorithm. Each module is analyzed separately to identify “core” mutations for variant determination.The weighted network workflow for SARS-CoV-2 variant tracking has been summarized in We used the 158 most frequent mutations from worldwide data for module detection and variant identification to show the capability of the method to track variants using a weighted network. This may lead to some information loss about the endemic variants, but we will illustrate later that this workflow will be more sensitive when it is applied to regional data. As showed in Network graphs were used to further demonstrate the relationships among nodes within a module as well as to inspect how any module is related to the rest of the network and how closely any two modules are related. The continuous network topology was dichotomized by different cutoffs, and modules were individually colored. These network graphs highlighted our FTM-based weighted network conglomerated variant-specific mutations as modules with contemporaneous variants forming module clusters. First, mutations pointing to the same variant were clustered together to form closely connected modules . Second,Variants determined by core mutations  were evaAfter showing the capability of weighted network analysis of FTMs in module-based variant identification, we applied this workflow for SARS-CoV-2 variant surveillance in regional data and further tested its efficacy. All 59,069 SARS-CoV-2 genomes in the study period from India were first included. Since the genome numbers in the sampling weeks showed a high fluctuation , from zeThe same pipeline was applied in South Africa for SARS-CoV-2 variant tracking. The weighted network modeling for FTMs generated by the total available 17,778 SARS-CoV-2 genomes showed viral population in South Africa has gone through four prevalent stages with variant cluster pattern , includiScientists are keeping their eyes open for the mutating SARS-CoV-2 virus and making every effort to detect, investigate, and monitor clinically important variants . In thisThe ad hoc viral classification may delay the detection of newly emerging variants or their descendants. Viral subtyping followed by their characterization, prevalence monitoring and risk assessment is continuing to be used in SARS-CoV-2 variant surveillance . Either Our investigation also reveals other advantages of module-based variant monitoring. First, the surveillance system will automatically divide the whole epidemic period into multiple stages and detect variant co-circulation pattern during each stage , 4A, 5A.Our approach can be an alternative method for rapid investigation and early detection of prevalent variants to facilitate regional SARS-CoV-2 genomic surveillance. An efficient variant surveillance is firstly dependent on the timely availability of viral genomes . To compSome limitations are also acknowledged. First, the mutation modules detected by our workflow may not represent a nominated lineage, but the analysis offers perceptive insights into novel variants which could be causing more transmission. Second, the independence between FTMs were assumed in the analysis. This might not be true especially for multiple direction mutations at the same nucleotide sites. However, as we can see in our analysis, the assumption may not highly influence our results. Lastly, the threshold value of FTM filtration is empirically chosen. This may result in the loss of less frequent variants. We believe it is a trade-off between detectability and discriminability in variant monitoring. When more samples are available and the cutoff is thought to be too big, analysis at a higher spatial resolution is recommended.In summary, an efficient and easy-to-use weighted network framework was proposed for SARS-CoV-2 variants tracing that could help to accelerate the understanding, surveillance, and control of the emerging viral variants.The original contributions presented in the study are included in the article/YL and YH conceived, designed, and supervised the project. QL and FZ collected the data. QH, QZ, YW, and YL performed computations, analyzed the results, and drafted the manuscript. PB and YH provided critical revision for important intellectual content. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.