Age-related macular degeneration (AMD) and Stargardt disease are the leading causes of blindness for the elderly and young adults respectively. Geographic atrophy (GA) of AMD and Stargardt atrophy are their end-stage outcomes. Efficient methods for segmentation and quantification of these atrophic lesions are critical for clinical research. In this study, we developed a deep convolutional neural network (CNN) with a trainable self-attended mechanism for accurate GA and Stargardt atrophy segmentation. Compared with traditional post-hoc attention mechanisms which can only visualize CNN features, our self-attended mechanism is embedded in a fully convolutional network and directly involved in training the CNN to actively attend key features for enhanced algorithm performance. We applied the self-attended CNN on the segmentation of AMD and Stargardt atrophic lesions on fundus autofluorescence (FAF) images. Compared with a preexisting regular fully convolutional network (the U-Net), our self-attended CNN achieved 10.6% higher Dice coefficient and 17% higher IoU (intersection over union) for AMD GA segmentation, and a 22% higher Dice coefficient and a 32% higher IoU for Stargardt atrophy segmentation. With longitudinal image data having over a longer time, the developed self-attended mechanism can also be applied on the visual discovery of early AMD and Stargardt features. Stargardt macular dystrophy is also called Stargardt disease or juvenile macular degeneration (JMD) which has been shown to be the leading cause of inherited vision loss in young adults/children and can worsen to cause legal blindness6. Geographic atrophy (GA), a form of late-stage AMD, manifests itself as a region of loss of the photoreceptors and retinal pigment epithelium (RPE), while early-stage AMD is largely asymptomatic2. Similarly, late-stage Stargardt disease can also manifest as region of atrophy, although distinct from that of AMD6. Fundus autofluorescence (FAF) imaging is a non-invasive, in-vivo technique which provides a 2-D mapping of the metabolically occurring fluorophores of the ocular fundus7. As atrophy ensues, there is a loss of RPE and hence, a loss of fluorophores in the lipofuscin which is normally located within the RPE cells. The loss of the RPE and corresponding fluorescence, creates well-demarcated regions of hypo-fluorescence which are representative of the region of atrophy. Due to the high image contrast for defining these regions of photoreceptor and RPE atrophy, FAF images have been widely used for the diagnosis and analysis of AMD GA and Stargardt atrophy in clinics.Age-related macular degeneration (AMD) and Stargardt macular dystrophy are two ophthalmic diseases affecting the retina, causing degeneration of the macula. AMD is the leading cause of vision loss in older adults, impacting roughly 200 million people around the worldAs AMD GA and Stargardt atrophy are the end-stage outcomes of these two retinal diseases respectively, efficient and accurate methods for the segmentation and quantification of these atrophic regions in FAF images are critical for clinical research studies attempting to better understand the disease progression. Furthermore, early detection and knowledge of key image features or biomarkers indicative of which eyes are likely to progress most rapidly is of importance in identifying which patients may benefit the most from early intervention with novel therapies before irreversible vision loss has occurred. Recognizing these biomarkers may also be of value in enhancing our understanding of disease pathophysiology.11. Likewise, AMD and Stargardt biomarkers have traditionally been identified by means which involve the direct input of clinicians and scientists. Manual involvement of these processes, however, is tedious and time-consuming. Automated approaches have become the preferred technique for image segmentation and analysis. We reported a traditional machine learning approach with 12. It utilized hand-crafted filters to extract image features, therefore requiring expertise, and was difficult to generalize to the large variations of ophthalmic image data. In contrast, deep learning algorithms automatically learn interesting image features for optimal segmentation results and have the capability to be generalized. In parallel with the evolution of deep learning convolutional neural networks (CNNs) in recent years, much research has been done regarding the efficiency of CNNs in automated medical imaging analysis, particularly in segmentation tasks33. Earlier deep learning algorithms for semantic segmentation utilized deep CNNs with sliding windows and fully connected layers.Traditionally, atrophy segmentation for AMD and Stargardt disease in clinical research studies have been completed manually or at best semi-automatically by trained graders15. A fully convolutional neural network architecture, the U-Net, has emerged as a state-of-the-art deep learning CNN approach for image segmentation in various eye-related assessment tasks including atrophic lesion segmentation22 for AMD21 and Stargardt disease22.which are slow and less efficient31 and via class activation mapping (CAM)33. Such post-hoc attention mechanisms helped visualize which parts in an image an already-trained CNN model deems important. Most recently, the focus has shifted to the exploration of trainable attention mechanisms which mimic how human eyes work35. Prior work has been done involving the trainable attention mechanism with CNNs for biomedical image analysis34. A trainable attention mechanism is supposed to help a CNN to attend key elements of images during training (as well as testing) and hence can help improve the CNN’s performance.In recent years, efforts have been made to understand and visualize where neural networks focus their “attention” on images in different CNN layers, while paying less “attention” elsewhere. There are in general two major types of attention mechanisms related to CNNs, categorized as trainable or non-trainable . Early efforts in exploration of attention mechanisms were more focused on post-hoc attention, for example, the heatmap visualization approaches via multi-layered deconvolutional network (deconvnet)In this project, we developed an improved atrophic lesion segmentation system which embeds the trainable attention mechanism into the regular U-Net. Moreover, our training attention mechanism U-Net system generates soft-labels, which can provide more informative probabilistic predictions than deterministic hard labels. Hence, our neural network features an integrated soft-labeled self-attended CNN system, which does not need to train complex external parameters, and can efficiently suppress feature activation of the background regions and highlight the important foreground information with higher probability towards enhanced CNN performance.To explore the potential applications and evaluate our self-attended neural networks, we performed several experiments. Firstly, we applied the self-attended neural networks for segmentation of AMD GA and Stargardt atrophic regions on baseline FAF images, against the corresponding manual ground truth based on baseline FAF images. Our previously developed regular U-Net is also applied on the same baseline datasets to compare the atrophy segmentation performance with the self-attended neural networks. Secondly, in an attempt to locate early features/biomarkers that indicate relevant information about the progression of atrophy in AMD and Stargardt disease, we applied the self-attended fully convolutional neural network on baseline FAF images, with their corresponding manually delineated ground truth depicting the development of the atrophic regions taken 12 months after the baseline imaging. Thirdly, visual heatmaps based on the trainable attention mechanism for the self-attended neural networks were generated. For comparison, visual heatmaps based on a post-hoc attention mechanism for the already trained regular U-Net model were also generated via the deconvnet.Figure For Stargardt data, 206 eyes from 127 patients were included. For AMD data, 90 eyes from 90 patients (one eye per subject) were included. The inclusion criterion for age for Stargardt patients was at minimum 6 years or older, and for the AMD patients was 60 years or older. Both Stargardt and AMD patients needed to have well-demarcated area of atrophy in the absence of fluid. The images for both AMD and Stargardt disease were centered on macular regions. The field of view (FOV) for AMD was 30° and the FOV for Stargardt disease was 20°. In total, there were 90 AMD eyes with GA and 206 Stargardt eyes with Stargardt’s atrophy included in this institutional review board (IRB)—approved project. All the data used for the image segmentation for both AMD and Stargardt data were from baseline visits and for prediction with additional longitudinal data. More specifically, all the AMD and Stargardt eyes had FAF imaging  performed at baseline (labeled as Month 0), and then had follow-up FAF imaging done 12 months after (labeled as Month 12). The Stargardt images had an original size of 512 × 512 pixels, and the AMD images had an original size of 768 × 868 pixels. All images were de-identified prior to the application of the algorithms according to the Health and Insurance Portability and Accountability Act Safe Harbor. Individual patient’ age and sex information were not accessible for this study.The GA regions on all the AMD FAF images and the Stargardt atrophy regions on all the Stargardt FAF images from both baseline and follow-up visits were manually delineated/labeled by certified reading center graders and were utilized as the ground truth. In the baseline atrophy segmentation algorithms’ training and testing for both the regular U-Net and the self-attended U-Net, the baseline images and corresponding manual labels are utilized. In the prediction algorithm’s training and testing of the self-attended U-Net for both AMD and Stargardt data, the original FAF images from the Month 12 visit were not used, only their manual labels  along with corresponding baseline images for ground truth for training and testing of the CNNs to predict progression of the diseases. Since the AMD FAF images included a black border on the bottom, we automatically detected and removed the black borders of these images  before algorithm training and testing, and then resized the images and manual labels to a constant size of 768 × 768 pixels. We used an eight-fold cross validation approach due to the relatively small size of the data set. The same 10 and 22 AMD and Stargardt eyes were used respectively as validation data. Eight rotating sets of 10 AMD eyes and 23 Stargardt eyes were used for testing, with 1 different set used for testing and the rest used for training for each fold.All methods were carried out in accordance with relevant guidelines and regulations. For AMD eyes, ethics review and institutional review board approval from the University of California—Los Angeles were obtained. For Stargardt eyes, the ethics reviews and institutional review board approvals were obtained from the local ethics committees of all the nine participating institutions, i.e., The Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland (JHU); Greater Baltimore Medical Centre, Baltimore, Maryland (GBMC); Scheie Eye Institute, University of Philadelphia, Philadelphia, Pennsylvania (PENN); Retina Foundation of the Southwest, Dallas, Texas (RFSW); Moran Eye Centre, Salt Lake City, Utah (MEC); Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio (CC); Moorfields Eye Hospital, London, UK ; Université de Paris 06, Institut national de la santé et de la recherche médicale, Paris, France ; and Eberhard-Karls University Eye Hospital, Tuebingen, Germany . Informed consent from all subjects and/or their legal guardian(s) for both study participation and publication of identifying images was obtained.36. The same spatial transformation was then also applied on the Month 12 manual label image to obtain the registered ground truth for the training and testing of the self-attended U-Net for the prediction of the atrophy progression over time for both AMD and Stargardt. A set of longitudinal AMD and a set of Stargardt FAF image and ground truth registration results are demonstrated in Fig. To predict the atrophy progression from AMD and Stargardt using the self-attended U-Net, we utilized the baseline FAF images and the corresponding manual labels of Month 12 for the algorithm training, which were longitudinally aligned. A feature-based image registration approach with rigid transformation which based on vessel branching, bifurcation, and crossover points was utilized to first align each Month 12 FAF image to its corresponding baseline imageWe embed the self-attended mechanism directly on different deep CNN layers of the proven state-of-the-art U-Net. Such a CNN system is concise without the dependency of complicated external network components and parameters, and yet more effective and efficient than the regular U-Net. Figure The U-Net is a fully convolutional neural network algorithm, without the need for fully connected layers. It includes a contracting auto-encoder path to capture context and a symmetric expanding auto-decoder path to enable precise localization. U-Net overcomes the bottleneck limitations of the classic auto-encoder by adding skip connections that concatenate the higher resolution features from the downsampling with the upsampled features for more efficient algorithm performance. For our self-attended U-Net, the output of the skip connections depends on the self-attention mechanisms. By embedding the self-attended mechanism in the regular U-Net, the self-attended mechanism highlights features to pass through the skip connections and, during the backward pass gradients originating form the background region are down weighted. The embedded self-attended mechanism progressively suppresses feature responses in regions that are irrelevant in the native CNN layers, allowing the neural network to focus on key details. As such, there is no need for training additional preceding image localization models to improve accuracy.ali ∈  are generated for each pixel i of the feature maps for each CNN layer l through the concatenation of the self-attended mechanism Fint×1 to be mapped to an intermediate space with The output after the application of each self-attended mechanism is an element-wise combination of the input feature maps from the corresponding CNN layer of the U-Net, along with the self-attention coefficients of that layer. The feature maps Fint and As a result, the self-attention coefficients The atrophy segmentation is a problem for two classes which classify each pixel on a FAF image to atrophy or non-atrophy. The sigmoid function converts the combined self-attention signals 31. The reconstruction blocks operated in reverse, so that the inputs to the reconstruction blocks were first passed through the activations before the transposed convolutions. The inverse operation could place the maximal values kept after the forward max pooling back in the stored location and zero out remaining smaller values. Through recovering the inputs to each block in this way, we produced reconstructions of the notable signals for each CNN layer of the forward pass.As mentioned above, we first applied the self-attended U-Net for the segmentation of atrophic lesions from AMD and Stargardt disease on baseline Month 0 FAF images against their corresponding baseline manual ground truth. Meanwhile, we also performed same experiments using our regular U-Net. Feature maps were obtained based on the self-attended mechanism as described above for the visualization of significant self-attended CNN signals. Additionally, image features for the regular U-Net were reconstructed and visualized via transposed convolutions replacing the standard convolutions by deconvnet as described inFurthermore, the self-attended U-Net was also applied to predict progression of AMD GA and Stargardt atrophy. As such, the baseline FAF images of AMD GA and Stargardt atrophy were trained with ground truth that was their corresponding manual segmentation from a follow-up visit 12 months after the baseline images were taken. The self-attended U-Net maps were also generated to attend the notable features for the atrophy progression after 12 months. The self-attended U-Net using baseline Month 0 images and manual ground truths gave us a comparison against the regular U-Net, and using baseline 0 images and the corresponding follow-up Month 12 manual ground truths can possibly reveal information about early features/biomarkers which predict atrophy progression after 12 months.For the algorithm development, an open-source deep learning framework Keras, which provides a Python interface for neural networks and runs on top of TensorFlow, is utilized. A semiautomated software tool RegionFinder (Heidelberg Engineering) was used for the grading of both AMD and Stargardt atrophic lesions’ area on FAF images. More specifically, the images were first graded by a certified reading center grader using RegionFinder and then reviewed or adjusted by a senior grader. When there was discrepancy between the two graders, an adjudication was performed by a senior investigator.Generally, the performance of a CNN is greatly increased when the size of the dataset is sufficient and suffers when the data set is small. To overcome this problem with our relatively small data set, we performed an eightfold cross validation. For the models trained on FAF images containing AMD, each fold had ten test images, 70 training images, and ten validation images. While the ten selected test images were swapped with ten unique images from the set of training images on each fold, the validation images were kept the same. For the models trained on FAF images with Stargardt’s disease, each fold had 23 test images, 161 training images, and 22 validation images. This was done to keep the ratio of test, training, and validation images nearly the same between the models trained on AMD images and the models trained on Stargardt images. Likewise, the 23 test images were swapped out with 23 unique training images for each fold, while the validation images were kept constant. The same dataset  for every fold for both U-Net and self-attended U-net was used in this study. In Stargardt dataset, some patients had images for both eyes. To avoid bias in the testing for Stargardt data, for any two images of both eyes from a same patient, they were either both included in the training dataset or in the testing dataset but not included in training and testing separately.Five metrics were used to measure the results: accuracy, sensitivity, specificity, Dice coefficient, and the intersection over union.The accuracy measures the proportion of pixels on FAF images with correctly identified atrophy  and without atrophy , over the total of all identified atrophy and non-atrophy .The sensitivity measures the amount of atrophy that is correctly identified, over all regions of atrophy. All regions of atrophy include those that were falsely identified as not having atrophy.The specificity measures the amount of non-atrophy that was correctly identified, over all regions of non-atrophy. All regions of non-atrophy include those that were falsely identified as atrophy.The Dice coefficient and intersection over union (IoU) both measure the spatial overlaps of an atrophic region A on an FAF image from manual delineation against the corresponding atrophy region B generated by algorithm segmentation.We performed four comparisons, i.e., the self-attended U-Net trained on baseline atrophic AMD FAF images and baseline manual delineation against a regular U-Net trained on baseline atrophic AMD FAF images and baseline manual delineation, the self-attended U-Net with baseline Stargardt images and baseline manual segmentation for ground truth compared to the regular U-Net trained on baseline Stargardt images and baseline ground truth, the self-attended U-Net trained on baseline AMD images and ground truth compared to the self-attended U-Net trained on baseline Stargardt images and baseline segmentation as ground truth, and the self-attended U-Net trained on baseline AMD images with 12-month progressed atrophy manual segmentation as ground truth compared to the baseline Stargardt images with 12-month progressed atrophy manual segmentation as ground truth, in percent differences for the above five evaluation metrics. Mann–Whitney test was also performed for each comparison for the analysis of statistically significant difference.The performance results of the five measures for the automated segmentation and prediction of atrophic lesions with AMD and Stargardt are provided in Table In this paper, we reported an automated system for the segmentation and progression prediction of AMD GA lesions and Stargardt atrophic lesions. The automated system utilizes a self-attended neural network which embeds the trainable attention mechanism into a fully convolutional neural networks, the U-Net, to focus “attention” on the notable image features for more efficient performance. The neural network generates soft-labels, which allows for more informative probabilistic predictions than deterministic hard labels. Such soft-labeled self-attended CNN system can efficiently highlight the important foreground information with an enhanced algorithm performance compared to a regular U-Net and does not need to use complex external training components.6. The specificity results were also extremely high in each model, with a specificity of 0.99 for all tests except for AMD self-attended U-Net Month 12 and the baseline regular U-Net, which both had specificities of 0.98. This is likely due, in part, to the large amount of non-atrophy in the original images, meaning that false positives account for a relatively small portion of all regions of non-atrophy. Certain background-regions of non-atrophy are also much easier to detect, due to their distinct differences from regions of atrophy.Based on the quantitative performance evaluation results as shown in Tables Several visual results of the AMD GA and Stargardt atrophy segmentation and progression prediction with the self-attended U-Net and the regular U-Net are provided in Figs. While this study demonstrates the advantages/improvements in segmentation and progression prediction, it also has some drawbacks. (1) The nature of the deep learning algorithms requires larger training datasets for optimal algorithm performance. While the fully convolutional neural networks used in this study work well with smaller datasets, larger datasets are preferred to achieve optimal performance. Our datasets are relatively small as we required patients to have longitudinal visits. (2) AMD and especially Stargardt progression are generally slow. The growth of the atrophic regions for both AMD and Stargardt disease within 12 months are not distinct as shown in Fig. In summary, in this paper, we reported a deep learning system using a self-attended neural network for the automated segmentation and progression prediction of AMD GA lesions and Stargardt atrophic lesions. Compared with the start-of-the-art regular U-Net, the self-attended neural network demonstrates a consistent enhancement of performance for both AMD and Stargardt diseases. The developed self-attended mechanism can be applied on the visual discovery of early AMD features based on longitudinal image data with longer visit times.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


This research aimed to determine the levels of COVID-19 booster dose vaccinations in Thai populations in areas with environmental risk exposure during the Omicron outbreak. Five of twenty provinces in Thailand were selected by assessing environmental risk exposure for study settings. A total of 1038 people were interviewed by a structured questionnaire. The predicting factors of COVID-19 booster dose vaccinations were analyzed by univariate and multivariate analysis. The results showed that 69.4% (95% CI 66.5–72.1) of the population was vaccinated with COVID-19 booster doses. Multiple logistics regression revealed that the female gender , all age groups from 38 to 60 years old, all education levels of at least secondary school, high income , populations having experience with COVID-19 infection , knowledge of vaccine , and trusting attitude  were factors among those more likely to take COVID-19 booster dose vaccinations in high-environmental-risk-exposure areas. Therefore, an effective booster dose campaign with education programs to increase attitudes toward booster vaccinations should be implemented for the resilience of COVID-19 prevention and control. Various countries around the world have been concerned about the largest public health crisis since the first confirmed case of a novel coronavirus disease 2019 (COVID-19) in December 2019 . Hence, Shekhar et al.  found thThe onerous effects, including economic crisis, psychological attack, and lost social interaction, were ongoing during the global COVID-19 pandemic . The mutThe Universal Coverage Scheme (UCS) has been implemented in Thailand since 2002, enhancing the total health expenditure from 63 to 77% and reducing out-of-pocket expenses from 27 to 12% . ApproxiFor vaccine uptake in Thailand, 75%, 65%, and 18% of the population have taken a first dose, a second dose booster, and a third dose booster of COVID-19 vaccines, respectively. The Thai Ministry of Public Health (MOPH) reported and confirmed the first case of the Omicron variant in early December 2021. This situation caused significant concern for the government regarding the capacity of the Thai healthcare system to handle the new variant outbreak . In ThaiBontempi et al.  stated tDuring data collection in this study, there was an Omicron variant wave of the COVID-19 outbreak in Thailand, and over 25 thousand COVID-19 cases were admitted to the hospital, with more than eighty deaths/day reported during the highest epidemic peak in Thailand from 27 March to 2 April 2022 . VaccineTherefore, the objective of this study was to determine the predictive factors of COVID-19 for booster dose vaccinations among the population in environmental risk exposure areas of Thailand during the Omicron variant outbreak. The environmental risks of exposure in areas of Thailand were investigated and adapted using the measurement by Mario Coccia . These fk = score of a province assigned to each factor Fi, with values between 0 and 3.The study setting in this research was selected from five provinces with the highest areas of environmental risk exposure level in Thailand. Coccia  identifiThe value of the environmental risk of exposure level ranged from 1 to 0. A value close to 1 implies a high environmental risk of exposure area to COVID-19, and a rank close to 0 indicates a low environmental risk of exposure area to COVID-19.The survey by face-to-face interviews was conducted on the populations in five provinces with the highest levels of environmental risk exposure from the past. The sample size of participants was calculated using a confidence interval of 95% and an acceptable error of 20. Permission for data collection was approved by the health officers in each area. According to this method, 1038 people were recruited as subjects in this study. The participants were included with the following eligibility criteria: aged over 18 years, Thai citizens, and living in the study setting for more than 6 months. The proportional random sampling by gender and age group was conducted to be representative of citizens of Thailand. Participants voluntarily agreed to enroll in this survey and take part in the research; they provided written informed consent after receipt of the recruitment procedure and research participation instructions provided by a trained research assistant. Anonymity and confidentiality of data were strictly maintained. During data collection, a research assistant followed the government policy for COVID-19 prevention and control. This research was approved by the Committee for Research Ethics , Faculty of Social Sciences and Humanities, Mahidol University with certificate approval number 2022/033.2802 and MU-SSIRB number 2022/35(B2).The questionnaire as the measurement tool in this study was a modified version of a previous survey ,39,40. Tp < 0.05 was identified for multivariate logistics regression. An odds ratio of association was performed by the confidence intervals of logistic regression. SPSS software version 21 was used to calculate all the statistical analyses, and the analysis was conducted at a 5% significance level.Descriptive statistics including percentages, means or medians, standard deviations, or quartile deviations were used for data analysis in this study. A univariate analysis, the Chi-square test, was used to identify the variables associated with COVID-19 booster dose vaccinations. A strong predictor for COVID-19 booster dose vaccination with A summary of COVID-19 booster dose vaccinations among populations in the Thai areas with the highest environmental risk exposure is presented in The prediction factors for COVID-19 booster dose vaccinations in the areas with the highest environmental risk exposure during the Omicron variant outbreak were identified using multiple and logistic regression analyses within the interest variables from the bivariate analysis with a p-value of 0.05. The results are shown in The predictive factors associated with COVID-19 booster dose vaccination were investigated with a multivariate logistic regression analysis. The results showed that female sex  predicted the COVID-19 booster dose vaccination uptake among the population at the highest level of the environmental risk exposure areas. Being in the age groups of 38 to 45 years , 46 to 53 years , and 54+ years  contributed to COVID-19 booster dose vaccination. The education levels of secondary school , diploma degree , and bachelor’s degree or higher  were associated with COVID-19 booster dose vaccinations. Populations with monthly income levels >USD 154  were more willing to receive the COVID-19 booster doses. In addition, the COVID-19 booster doses were significantly higher in the populations testing positive for COVID-19 . Only COVID-19 knowledge on vaccination  and COVID-19 attitudes on trust  were significantly associated with the uptake of COVID-19 booster doses.The Omicron variant of the COVID-19 epidemic outbreak also caused rapid transmission with populations in many countries including the United States , the UniThe levels of environmental risk exposure in highly endemic provinces of Thailand were assessed by environmental factors adapted from Coccia , includiThe current survey found that nearly 70% of populations in the areas with environmental risk exposure took COVID-19 booster dose vaccinations. This uptake rate of COVID-19 vaccination was higher compared with the findings from a current systematic review and meta-analysis in the world setting on COVID-19 booster dose vaccination by Abdelmoneim et al. . The uptThe results from multiple logistics regression analysis found that the groups of population characteristic factors including female gender, all age groups from 38–60 years, all education levels of at least secondary school, and high income were predictors of COVID-19 booster dose vaccination uptake among the populations with the highest levels of environmental risk exposure in Thailand. Some research ,48 identThe current study found that a strong predictor of COVID-19 booster vaccination among populations in the areas with environmental risk exposure was knowledge level concerning vaccination. Consistent with the previous research in Thailand  and ItalThis study highlighted the need to develop a specific recommendation for the enhancement of booster vaccinations in high-risk areas for infection transmission in the context of environmental exposure. The significant benefits on environmental, public health, social, and economic issues in cities can be enhanced by pollution reduction, and the risk of transmission in cities can be reduced for any new wave or future pandemics of COVID-19 . In addiThe recommendation for booster dose vaccination uptake was a global approach to COVID-19 outbreak prevention during the Omicron variant. The rapid spread of COVID-19 infections in a city was stimulated by social interaction as well as the environmental risk exposure factor. Thus, the widespread distribution of COVID-19 booster dose vaccination was provided by the Thai government during that crisis. The findings from this survey determined the crucial knowledge on prediction factors for the booster dose of the COVID-19 vaccination in the high-risk areas of Thailand in the environmental exposure context. To sum up, good knowledge of booster dose vaccination and a good attitude with trust were found to be strong predictors of COVID-19 booster dose vaccination among populations in the areas with a high risk of environmental exposure. Therefore, an educational campaign on the effectiveness and safety of vaccinations was still demanded to expand public attitudes toward COVID-19 booster dose vaccinations  and was


Molecular mimicry between human and microbial/viral/parasite peptides is common and has long been associated with the etiology of autoimmune disorders provoked by exogenous pathogens. A growing body of evidence accumulated in recent years suggests a strong correlation between SARS-CoV-2 infection and autoimmunity. The article analyzes the immunogenic potential of the peptides shared between the SARS-CoV-2 spike glycoprotein (S-protein) and antigens of human endocrinocytes involved in most common autoimmune endocrinopathies. A total of 14 pentapeptides shared by the SARS-CoV-2 S-protein, thyroid, pituitary, adrenal cortex autoantigens and beta-cells of the islets of Langerhans were identified, all of them belong to the immunoreactive epitopes of SARS-CoV-2. The discussion of the findings relates the results to the clinical correlates of COVID-19-associated autoimmune endocrinopathies. The most common of these illnesses is an autoimmune thyroid disease, so the majority of shared pentapeptides belong to the marker autoantigens of this disease. The most important in pathogenesis of severe COVID-19, according to the authors, may be autoimmunity against adrenals because their adequate response prevents excessive systemic action of the inflammatory mediators causing cytokine storm and hemodynamic shock. A critique of the antigenic mimicry concept is given with an assertion that peptide sharing is not a guarantee but only a prerequisite for provoking autoimmunity based on the molecular mimicry. The latter event occurs in carriers of certain HLA haplotypes and when a shared peptide is only used in antigen processing At the beginning of the 20th century, a Russian biologist and alumnus of Saint Petersburg University, Konstantin S. Merezhkovsky , suggMuch later, this idea was adopted by immunologists and gave birth to the molecular mimicry concept as a prerequisite for pathological autoimmunity provoked by shared antigens of exogenous pathogens.The role of this phenomenon was first identified for streptococci antigens and the etiology of rheumatic fever , particu3 and D4 positive individuals, the epitopes from some viruses  may serve as viral diabetogens or triggers of autoimmune insulitis with a subsequent diabetes mellitus type I )In this article, we report some data based on the bioinformatic analysis on possible molecular mimicry between SARS-CoV-2 S-protein and several autoantigens of human endocrinocytes, all of them being typical targets in most important autoimmune endocrinopathies.Peptide sharing between the human endocrinocytes proteins  and spike glycoprotein  from SARS-CoV-2 was analyzed using pentapeptides as sequence probes. Pentapeptides were used since it is a peptide grouping formed by at least five amino acid residues, which defines a minimal immune determinant able for highly specific antibodies induction and frames antigen-specific immune cell receptors interactions . A libraWe selected the following proteins most commonly serving as targets in several frequent endocrinopathies according to current clinical and experimental data : thyroidThe S-protein primary sequence was dissected into pentapeptides offset by one residue , and the resulting viral pentapeptides were analyzed for occurrences within the human proteins mentioned above. The occurrences and corresponding proteins were annotated.The peptides immunological potential shared between the SARS-CoV-2 spike glycoprotein and endocrinocytes proteins was analyzed by searching the Immune Epitope Data Base and Analysis Resource  proteinsQuantitatively, the SARS-CoV-2 spike glycoprotein was found to share 14 minimal immune determinants, i.e., pentapeptides with 9 human proteins expressed by endocrinocytes and involved in the pathogenesis of clinical autoimmune endocrinopathies .The shared pentapeptides are described in Other β-cell autoantigens did not share any pentapeptides with the SARS-CoV-2 S-protein.Exploration of the Immune Epitope Data Base revealed that all shared pentapeptides described in Half of the shared pentapeptides identified in our study belong to marker autoantigens of the autoimmune thyroid disease, namely both of its forms: Hashimoto’s thyroiditis and Graves’ disease. Not surprisingly, provocation of new cases of these diseases and exacerbation of existing autoimmune thyroid disorders are not rare in COVID-19 patients. For example, Turkish authors recently summarized clinical descriptions of at least 20 such cases .The relations between type 1 diabetes mellitus and COVID-19 are somewhat contradictory because a new onset of diabetic cases should be clearly distinguished from the simple stress-related and glucocorticoid-derived iatrogenic hyperglycaemia . NeverthOf special interest is the presence of a common immunogenic epitope between the marker autoantigen of autoimmune adrenalitis, 21-hydroxylase, and the SARS-CoV-2 protein. The proper adrenocortical response in acute COVID-19 is a critically important defensive mechanism against the vicious consequences of a cytokine storm. Otherwise, failure of adrenal response may result in hemodynamic shock. Glucocorticoids are effective in treating severe COVID-19 . We haveThere are several suspicions of pituitary involvement in COVID-19  and evenOf course, just the presence of any shared peptide in a pathogen, even within the epitopes considered to be immunoreactive, is not a guarantee of autoimmunity excess provocation. It is only a prerequisite for it. Most essential is in what individual HLA context these peptides will be present. Different HLA haplotypes cause various processing of the same protein by different individuals. Figuratively speaking, the difference between self and non-self proteins, being relative, can be more or less obvious to lymphocytes, depending on HLA set, similar to in jewelry art—the impression produced by the same precious stone depends greatly on its mounting design and metal chosen, not only on the properties of gem itself.Perhaps this is why sequence homologies, even when extensive and confirmed by computational biology, may not necessarily result in immunologic cross-reactivity, which is confirmed by the “wet lab” methods. It has been demonstrated, for example, in autoimmune primary biliary cirrhosis with respect to the homology of human pyruvate dehydrogenase and urease-β from helicobacter pylori . MoreoveAristo Vojdani et al.  tested tWe consider bioinformatic analysis to be an essential step in the preliminary evaluation of autoimmunity risks and spectrum in COVID-19 complications, including post-COVID-19 syndrome. Additionally, it may be useful in epitope selection for elaboration of the safest anti-COVID-19 vaccines.


Patient-reported outcomes—symptoms, treatment side effects, and health-related quality of life—are important to consider in chronic illness care. The increasing availability of health IT to collect patient-reported outcomes and integrate results within the electronic health record provides an unprecedented opportunity to support patients’ symptom monitoring, shared decision-making, and effective use of the health care system.The objectives of this study are to co-design a dashboard that displays patient-reported outcomes along with other clinical data  within an electronic health record and conduct a longitudinal demonstration trial to evaluate whether the dashboard is associated with improved shared decision-making and disease management outcomes.Co-design teams comprising study investigators, patients with advanced cancer or chronic kidney disease, their care partners, and their clinicians will collaborate to develop the dashboard. Investigators will work with clinic staff to implement the co-designed dashboard for clinical testing during a demonstration trial. The primary outcome of the demonstration trial is whether the quality of shared decision-making increases from baseline to the 3-month follow-up. Secondary outcomes include longitudinal changes in satisfaction with care, self-efficacy in managing treatments and symptoms, health-related quality of life, and use of costly and potentially avoidable health care services. Implementation outcomes  during the co-design process and demonstration trial will also be collected and summarized.The dashboard co-design process was completed in May 2020, and data collection for the demonstration trial is anticipated to be completed by the end of July 2022. The results will be disseminated in at least one manuscript per study objective.This protocol combines stakeholder engagement, health care coproduction frameworks, and health IT to develop a clinically feasible model of person-centered care delivery. The results will inform our current understanding of how best to integrate patient-reported outcome measures into clinical workflows to improve outcomes and reduce the burden of chronic disease on patients and health care systems.DERR1-10.2196/38461 Within objective 2, we hypothesize that patients who use the dashboard will experience increases from baseline to the 3- and 6-month follow-ups in perceived quality of shared decision-making, satisfaction with care, self-efficacy in managing treatments and symptoms, and health-related quality of life. We also hypothesize that, compared with historically and demographically matched controls, patients who use the dashboard will have a greater reduction in use of potentially avoidable, high-cost, and low-value health care services (see the Methods section for specific indicators that will be tested). By carefully delineating the dashboard design and evaluation process, this study will enable the dashboard’s scalability and inform future investigations aiming to adapt the patient-reported outcome dashboard to other health care systems.The Northwestern University Institutional Review Board approved all procedures described in this manuscript and a detailed data security plan . Depending on the assessment point, survey data will be collected and stored securely through the health care system’s electronic patient portal or through a REDCap   server hThe dashboard will build on an existing infrastructure called the Northwestern Medicine Patient-Reported Outcomes system. This is the health care system’s current technological framework for administering patient-reported outcome measures electronically and integrating data into the electronic health record to inform clinical care delivery . SpecifiIn this study, this system will be adapted, with patient and other stakeholder input, into a clinical dashboard to facilitate symptom management and shared decision-making during scheduled health care visits for patients with advanced cancer and chronic kidney disease. Patient and care partner input will maximize the dashboard’s patient-centeredness. By including clinicians in the design process, attention will be paid to the possible impact of dashboard participation on digital fatigue. To direct attention to and encourage discussion of the more potentially bothersome “hard-stop” alerts, alert thresholds on the patient-reported outcome measures will be set relatively high. The dashboard will be viewable by both patients  and physicians  and will automatically populate electronic health record–linked data, including patient-reported outcomes, laboratory test results, medications, and vital signs.The first objective of the study is to collaborate with stakeholders who represent “end users” to co-design our clinical dashboard. End users include patients with advanced cancer or advanced chronic kidney disease, their care partners, and their clinicians. Clinician participants will include 2 oncologists , a nephrologist, a nephrology physician assistant, and 2 primary care physicians. Patients will be eligible to participate if they have a history of receiving care from one or more of the participating clinicians at Northwestern Memorial Health Care. Patients with gastrointestinal cancer must have a confirmed diagnosis of stage 4 gastrointestinal cancer and have been receiving intravenous chemotherapy for at least three months. Patients with lung cancer must have a confirmed diagnosis of stage 3C or 4 lung cancer and have been receiving first- or second-line chemotherapy for at least 3 months. Patients with chronic kidney disease must have a confirmed diagnosis of at least stage 3 . Care partners will be eligible to participate if they assist in the care of a patient who would meet the inclusion criteria.For each of the two disease groups of interest (advanced cancer and chronic kidney disease), we will convene a co-design team comprising approximately 20 highly engaged stakeholders, including investigators, patients, care partners, clinicians, and health IT professionals. Co-design teams will iteratively develop the dashboard over the course of monthly meetings held during the first year of the study. Co-design meetings will involve team-based working sessions with predefined objectives and deliverables for each meeting . The co-In parallel to these intensive co-design team meetings, we will also hold supplemental focus groups. The primary purpose of these focus groups is to validate the evolving consensus and work product of the co-design teams with additional stakeholders. In tandem with phases 1 and 2 of co-design, we will conduct a focus group with 24 patients and care partners to gather an initial assessment of attitudes, beliefs, and perceptions to inform the dashboard’s development. In tandem with phase 3 of co-design, we will conduct focus groups with a total of 72 stakeholders separated by disease group (cancer vs chronic kidney disease) and role (patients and care partners vs clinicians) to provide additional feedback on the appropriateness and desirability of the proposed dashboard design and content. In tandem with phase 4 of co-design, we will conduct focus groups to confirm the acceptability and usability of the dashboard (n=16-24 patients and care partners per disease group). Trained members of the research team will facilitate the discussions according to guides containing semistructured questions to follow in each session. Research staff will take notes during these sessions, which will also be audio recorded in case project staff are unable to manually document all responses.Appropriateness will capture stakeholders’ perceptions of the compatibility between the proposed dashboard and the target clinical setting’s goals and needs, whereas feasibility will assess perceptions of whether and how it can be successfully implemented into the clinical infrastructure. These 2 outcomes will be captured qualitatively through phase 1 and 2 co-design meetings and focus groups as well as through quantitative and qualitative responses provided by clinicians in a prelaunch survey conducted just before dashboard implementation. The acceptability of the near-final dashboard will reflect stakeholders’ satisfaction with its design, content, and functionality. This outcome will be captured qualitatively through feedback provided during co-design meetings, co-design postmeeting surveys, and phase 4 focus groups’ usability evaluations of the co-designed dashboard. The fidelity of the co-design process, or how well it was carried out as intended, will be assessed quantitatively through responses to postmeeting evaluations sent to each member of the co-design teams.The primary outcome of the co-design process is to build a fully functional dashboard that can be implemented for clinical testing in objective 2 of this study. However, we also anticipate the development of a rich source of quantitative and qualitative data on the dynamics of development and implementation outcomes . Data soEach of the outcomes will be summarized through qualitative analysis or descriptive statistics depending on the data type. For qualitative analyses, transcripts and notes from the sessions will be analyzed using a directed content analysis approach  guided bThe second objective of this study will include testing the co-designed dashboard in the flow of clinical care service delivery. First, as a quality improvement initiative, the dashboard will be implemented at the clinic level with all eligible patients who receive care from participating clinicians. This step will allow us to collect clinic-wide quality metrics associated with dashboard implementation. Second, the project will include a longitudinal follow-up study among a subset of patients to assess changes in patient outcomes associated with using the dashboard. The co-designed dashboard will be enabled in the health system’s electronic health record software within the clinical departments of all participating clinicians. The first month of dashboard implementation will constitute a “soft launch” period where participating clinicians and their eligible patients will be trained on using the dashboard and will work with the project team to troubleshoot and resolve any technical and practical issues. After the soft launch period, clinicians will be encouraged to use the dashboard during clinical encounters with all their patients who meet the inclusion criteria (as described previously in objective 1). As outlined in The dashboard will display results from the patient’s most recent symptom and goals assessment and other clinical measures stored in the electronic health record. The included assessments will be determined through extensive stakeholder input during the dashboard co-design process described in objective 1. The symptom and goals assessment will include a mix of items and scales selected to optimize clinical relevance and feasibility. For instance, each assessment will include the PROMIS Global01 health perception item , the FunIn addition to rating health-related quality of life domains, patients will respond to 5 open-response questions in the symptom and goals assessment regarding their goals of care that will populate the dashboard. These questions will prompt patients to specify (1) the top 1 or 2 concerns they would like to discuss during the visit, (2) their most concerning side effects, (3) overall goals regarding their cancer or kidney disease treatment, (4) personal goals and values, and (5) how they can work together with their care team to achieve their goals. Finally, the dashboard will display the most recent results on other clinical measures typically collected during the care process and stored within the electronic health record. For patients with cancer, the additional clinical data will include medications, weight, white blood cell count, absolute neutrophils, hemoglobin, albumin, and an appointment schedule. For patients with chronic kidney disease, the additional clinical data will include weight, blood pressure, glomerular filtration rate, hemoglobin A1c, microalbuminuria, urine protein, and an appointment schedule. Patients and clinicians will view the patient’s individualized dashboard on a computer screen during the visits. Patients will also be able to view their dashboard in between visits using their own devices  that can access their electronic patient portal.To assess the longitudinal changes in our primary and secondary study outcomes, a subset of approximately 200 patients will participate in a longitudinal follow-up study. Patients will be eligible to participate if they meet all other study inclusion criteria, have completed at least two dashboard-eligible visits, and provide informed consent to complete a follow-up survey at 3 and 6 months. In parallel to collecting these follow-up patient outcomes, all participating clinicians will also complete surveys at 3 and 6 months to report on their experience with the dashboard’s implementation . All surFor analyses focusing on health service use , we will identify two cohorts of patients who were not exposed to the dashboard: (1) a cohort of eligible patients with cancer and chronic kidney disease who received care concurrently from participating Northwestern Medicine clinicians but who did not enroll in the dashboard study and (2) a matched cohort of patients with cancer and chronic kidney disease treated contemporaneously by nonparticipating Northwestern Medicine clinicians. Health service use variables will be the only study outcome data available for these 2 comparison groups.The central outcome of the study is shared decision-making in the treatment of patients with advanced cancer or chronic kidney disease. We will use the CollaboRATE measure  to assesSatisfaction with health care will be assessed at baseline, 3 months, and 6 months with a single item from the Functional Assessment of Chronic Illness Therapy-Treatment Satisfaction measure . PatientA total of 2 subdomains of the PROMIS Self-Efficacy for Managing Chronic Conditions domain will be assessed at baseline and at the 3- and 6-month follow-ups. A 4-item custom short form created from the PROMIS Self-Efficacy for Managing Medications and Treatment Item Bank version 1.0  will assTo evaluate changes in health-related quality of life as an outcome associated with using the dashboard, a subset of health-related quality of life domains included in the symptom and goals assessment will be assessed again at the 3- and 6-month follow-ups  unplanned all-cause hospital admissions, (2) potentially avoidable all-cause emergency department use, (3) excess  days in acute care within 30 days of hospital discharge, and (4) 7-day readmissions. In addition, among patients with cancer, we will assess the following disease-specific indicators: (5) admissions and emergency department visits for patients receiving outpatient chemotherapy, (6) chemotherapy within the last 14 days of life, (7) use of a triage clinic, (8) completion of an advance directive, and (9) hospice use of >3 days. Among patients with chronic kidney disease, the following additional indicators will be collected: (10) use of emergency start dialysis; (11) chronic kidney disease–related emergency department or inpatient use; and (12) progression from chronic kidney disease stage 3 to stage 4, stage 4 to stage 5, or stage 3 to stage 5.reach, we will extract data from the electronic health record on patient enrollment rates in the demonstration trial and response rates to the symptom and goals assessments. To assess the fidelity of the dashboard’s use during the study period, patients will report whether the dashboard was discussed during their eligible clinical encounter in each dashboard visit evaluation survey, which is completed immediately after the appointment. Moreover, the study staff will conduct regular clinician observations to assess whether the dashboard is being used consistently and as intended. To assess adoption, clinicians will self-report in their 3- and 6-month surveys the frequency with which they use the dashboard with eligible patients. To assess usability, acceptability, and adoption, both patients and clinicians will rate the dashboard at the 3- and 6-month follow-ups on the System Usability Scale [sustainability of the clinical dashboard at the 6-month follow-up using the Clinical Sustainability Assessment Tool [Patient- and clinician-reported survey measures will capture relevant implementation outcomes  of the dty Scale , the Strty Scale , and thety Scale . Finallyent Tool . In addiP value of <.05 will be considered statistically significant. Unless otherwise noted, analyses will be conducted on the full analysis set and stratified by populations with chronic kidney disease versus cancer.For all statistical tests, a nominal 2-sided t tests. We will supplement the paired t tests with multivariable regression models to adjust for key covariates . Regarding health service use, we will first use paired-sample t tests to compare rates in the occurrence of each indicator described in the Health Service Use outcome section between the 6 months before and after the date of first dashboard exposure. On a secondary basis, we will model these outcomes as count variables using zero-inflated Poisson regression to adjust for covariates [t tests and zero-inflated Poisson regression. To do so, we will use propensity score matching to create comparable cohorts of patients with and without exposure to the dashboard. We will match on age, sex, cancer type (where applicable), and disease severity or staging [The primary analyses for objective 2 are within-group mean changes in shared decision-making (CollaboRATE), self-efficacy for managing chronic conditions (PROMIS), satisfaction with care , and health-related quality of life and symptoms . First, we will test whether within-group means have changed significantly from the baseline and symptom assessments (as appropriate) to the 3-month follow-up (primary) and 6-month follow-up (secondary) using paired samples variates ,72. Fina staging .r=0.30; (2) a common SD of 3.5 points; (3) a 2-sided P value of .05; and (4) mean changes of 1.0, 1.5, and 2.0 points. These analyses suggested that sample sizes of 137, 62, and 36 patients would be needed to detect mean changes of 1.0, 1.5, and 2.0 points, respectively, using a paired t test.Sample size considerations for objective 2 were informed by statistical power analyses for differences in within-group changes in mean responses to the CollaboRATE shared decision-making measure and PROMIS measures from baseline to the 3-month follow-up. Within-group mean changes in the CollaboRATE 5-point response scale version ranged from approximately 2.0 to 4.5, with SDs of approximately 3.5 . For ther=0.50; (2) a common SD of 10.0 points; (3) a 2-sided P value of .05; and (4) mean changes of 3.0, 4.0, and 5.0 points. These analyses suggested that sample sizes of 90, 52, and 34 patients would be needed to detect mean changes of 3.0, 4.0, and 5.0 points, respectively, using a paired t test.As noted in the Methods section, all PROMIS measures are scored as a T-score metric with a mean of 50 and SD of 10. Changes as low as 3 points can be clinically meaningful . For powThe co-design sessions (objective 1), which focused on collaboratively designing the dashboard’s content and format for initial clinical testing, concluded in May 2020. Focus groups supplementing the co-design sessions were completed in October 2019, February 2020, and October 2021. Data collection for the demonstration trial is anticipated to be completed by the end of July 2022. Study investigators currently meet monthly as an entire team to discuss progress on manuscript development for results dissemination. Each study objective will have at least one resulting publication: (1) co-design process and outcomes and (2) demonstration trial process and outcomes.In this study, we hypothesize that co-designing and implementing a patient-reported outcome clinical dashboard will be associated with improved processes underlying health care coproduction among adults with advanced cancer or chronic kidney disease, including shared decision-making, satisfaction with care, engagement in health care, self-efficacy in managing symptoms and treatments, health-related quality of life, and use of health services. Collaborating with patients, care partners, and clinicians to create scalable clinical dashboards can help improve coproduction outcomes ,33. ThusThe dashboard’s design and evaluation processes have several strengths worth noting. First, the dashboard will be co-designed by investigators and key stakeholders . Collaborating with stakeholders promotes person-centered care delivery and increases the likelihood that a new health care element can be successfully implemented and optimally impactful . UnfortuDespite these strengths, this project has limitations that warrant discussion. First, the study will focus only on patients in the advanced stages of 2 chronic conditions and on those who regularly access their electronic patient portals. Although advanced cancer and chronic kidney disease are two of the most common and burdensome illnesses to individuals and the health care system ,21,23, wThis study demonstrates how to combine stakeholder engagement, a health care coproduction framework, and health IT to develop a clinically feasible, acceptable, and scalable model of patient-centered care delivery. This study’s premise integrates emerging research indicating that better patient outcomes and appropriate health care use can be enhanced through patient-clinician collaboration in the context of well-designed workflows and systems. Thus, this study focuses on dealing with the manifold challenges of implementing significant changes in workflow and clinical approach in the real world of ambulatory care of patients with a serious chronic illness. We aim to build on implementation research to understand the needed adoption, acceptability, and feasibility of new tools to optimize a successful and sustainable transformation of the clinical encounter over time. Our work serves as an example for transforming care delivery in a way that embraces value-based care and has the potential to improve the lives of patients with chronic conditions.


Schizophrenia is a lifelong illness that requires long-term treatment and caregiving. Family psychoeducation (FP) has been shown to lessen caregiver burden, improve caregiver functioning, and improve outcomes in patients. However, the impact of FP delivered specifically to caregivers on patient outcomes has not been well explored, particularly for early schizophrenia. Furthermore, there is a lack of research examining the benefits of telehealth-based psychoeducation for caregivers on either patient or caregiver outcomes.The Family Intervention in Recent-Onset Schizophrenia Treatment (FIRST) study is a randomized controlled trial of patients with schizophrenia spectrum disorders and their caregivers, which is designed to evaluate the effect of telehealth-based, caregiver-focused, study-provided psychoeducation versus usual care (UC) on patient treatment failure (TF). The impact of study-provided psychoeducation on caregiver burden is also investigated.Eligible patients and their designated caregivers were randomly assigned to either the study-provided psychoeducation  or UC group, stratified by antipsychotic treatment . The major TF events  were assessed at 3, 6, and 12 months after baseline. A proportional means model using mean cumulative function was used to assess between-group differences in the mean cumulative number of TF events over 12 months. Caregiver burden was assessed using the Involvement Evaluation Questionnaire and 12-item Short Form Health Survey.P=.90) or measures of caregiver burden . However, post hoc analyses revealed lower relapse rates in patients who received paliperidone palmitate than in those who received oral antipsychotics at all time points. Although the FIRST study did not meet the primary end point, several key lessons were identified to inform future caregiver-focused, telehealth-based FP interventions. Lack of study-provided psychoeducation, focus on caregiver-only intervention, difficulties with enrollment, and caregiver–treatment team coordination may have affected the outcomes of the FIRST study.A total of 148 pairs of participants were enrolled in the study, of whom 96 (64.9%) patients and 94 (63.5%) caregivers completed the 12-month follow-up. The mean number of sessions in the study-provided psychoeducation group was 7.7 (SD 5.9). No differences were observed between the study-provided psychoeducation and UC groups in patient outcomes (rates of TF: 70% vs 67%; Key insights from the FIRST study suggest the potential importance of supporting sufficient caregiver engagement; communication between clinicians, patients, and family members regarding treatment plans; and solidifying the relationship between clinicians providing psychoeducation to the caregiver and patient treatment team.ClinicalTrials.gov NCT02600741; http://clinicaltrials.gov/ct2/show/NCT02600741 Schizophrenia is a complex, lifelong illness that typically develops in young adults  and requTo address this unmet need, web-based or telehealth-based models of psychoeducation that offer private at-home sessions have been developed -18. CompThe Family Intervention in Recent-Onset Schizophrenia Treatment (FIRST) study was designed to evaluate the impact of FP given specifically to caregivers on the outcomes of patients with schizophrenia spectrum disorder under their care and family burden. In the FIRST study, FP was delivered using Healios Inc, doing business as MyHealios, a telehealth-based study-provided psychoeducation (SPPE) and skills training intervention. MyHealios was developed to incorporate common components of efficacious caregiver-oriented FP interventions during the patients’ early phase of illness; the FP program was individualized to each caregiver to include education about schizophrenia and its treatment and skills training to improve communication, problem solving, and coping -23. The The FIRST study (NCT02600741) was a randomized controlled trial of patients with schizophrenia spectrum disorders and their caregivers, that was conducted to evaluate the overall effect of caregiver-focused study-provided psychoeducation and skills training compared with UC on the number of treatment failure (TF) events in patients . The stuThe study protocol was approved by an institutional review board (ID #5146C) and conducted in accordance with the Declaration of Helsinki and was consistent with Good Clinical Practices and applicable regulatory requirements. Patients and their legally acceptable representatives provided written informed consent. Further details of the study design can be accessed on the ClinicalTrials.gov page for the FIRST study .Caregivers randomly assigned to the study-provided psychoeducation group were invited to attend up to 16 live web-based sessions of MyHealios, a telehealth-based FP and skills training program for caregivers of patients with schizophrenia over a 6-month period. Each caregiver was assigned a trained and certified masters-level clinician who was independent of, and had no communication with, the patient’s UC team. All MyHealios clinicians received formal training and a training manual. They also underwent a certification process before conducting FP sessions with the caregivers enrolled in the study. The clinician who developed the FP curriculum and supervised the caregiver sessions was a PhD-level clinical psychologist with expertise in FP interventions for schizophrenia. Regular  supervision was provided to the certified clinicians throughout the study. The adherence of clinicians to the FP and skills training program and manual was routinely evaluated using a 10-point fidelity scale based on observations of recorded caregiver sessions , with fidelity ratings provided as feedback to clinicians and incorporated into supervision.The MyHealios clinicians worked with the caregivers through live web-based sessions on a one-on-one basis throughout the program. Each session was 40 minutes in length and was conducted on the web at a time convenient for the caregiver. The web interface included live videos of both the caregiver and clinician, as well as a chat window to facilitate communication and caregiver participation in interactive activities. The number of delivered sessions and topics were determined jointly by the caregiver and clinician, with the teaching information and skills individually tailored to the caregiver. During each session, the caregiver presented problems that arose from caring for the patient and elaborated with specific examples. The clinician offered training and guidance on the appropriate methods to manage the identified problems.Sessions were planned to occur weekly at the beginning of the program and decrease in frequency over the next 6 months as participants learned how to apply the skills in their day to day lives. A total of 3 modules were identified for initial completion by all caregivers . Caregivers could then elect to complete any of the other modules in any order .The UC group received support routinely provided by caregivers at the study sites. In both groups, patients and their caregivers were followed up for ≤12 months after the baseline assessment.Assessments, including those of TF events, were evaluated at baseline and at 3, 6, and 12 months. Patient illness self-management was evaluated using the self-reported Illness Management and Recovery (IMR) scale . This seCaregiver-reported assessments were conducted at the same time as patient assessments. The Involvement Evaluation Questionnaire (IEQ)  was usedSafety was assessed based on reported adverse events (AEs) and serious AEs (SAEs). AEs and SAEs were reported for patients, and only SAEs were reported for caregivers.Medical resource utilization, including hospitalizations, emergency department visits, and outpatient services for patients and caregivers, was recorded on a patient health resource utilization form by chart abstraction and an interview or questionnaire if data were missing.The primary efficacy end point was the mean cumulative number of TF events experienced by patients over the 12-month study period. A proportional means model using the mean cumulative function was used to assess between-group differences in the mean cumulative number of TF events over 12 months. The mean cumulative function, as a function of time, was defined as the expected (mean) number of TF events in a given time interval since study day 1. The mean cumulative function for recurrent events and Kaplan–Meier (for time to the first event) analyses were performed for overall TF because of any event and for TF because of each of the events specified in the definition of TF. For secondary outcomes, changes from baseline to 3, 6, and 12 months in IEQ, IMR, SF-12, and CGI-S scores were analyzed using a mixed model repeated measures methodology with terms for study group, time, study group by time interaction, and baseline score. In addition, treatment-emergent AEs (TEAEs) were presented according to the treatment group .The TF rate in the control group was assumed to be 0.50 based on a previous study with a similar end point . The effOwing to difficulties in study enrollment, recruitment was discontinued before the target enrollment of 300 pairs was met, resulting in underpowered statistical analyses. A total of 170 patient–caregiver pairs were screened in the study; 19 pairs had screening failures . As a rPatients’ and caregivers’ demographics and baseline characteristics were generally balanced across the study-provided psychoeducation and UC groups  and 2. TCaregivers who discontinued participation early tended to be nonparent relatives with lower self-reported health on the SF-12 and high burden scores on the IEQ at baseline . The basengagement and goal setting module, 52 (85%) received the communications module, 40 (66%) received the problem solving and goal achievement module, and 35 (57%) received the coping module. The other modules were assigned to <50% of the caregivers , and the median was 8 (range 0-16). Of the 73 participants, 40 (55%) caregivers who were randomly assigned to the study-provided psychoeducation intervention group received at least half of the modules ; 12 (16%) caregivers did not receive any sessions, and 7 (10%) caregivers received only 1 session; 9 (12%) caregivers received 15 training sessions, and 3 (4%) received a maximum of 16 sessions . Of the regivers  and 4. Cregivers . Of the P=.90; A total of 89 TF events occurred during the study. Approximately 37% (23/63) of participants in the study-provided psychoeducation group and 37% (25/67) of participants in the UC group had at least 1 TF due to any event. TF rates were not associated with baseline CGI-S scores and did not differ between the study-provided psychoeducation and UC groups P=.90; . Most TFP=.76). In the study-provided psychoeducation group, TF rates were notably higher in patients whose caregivers received at least one session than in patients of caregivers who received 15 to 16 sessions . Similar decreases from baseline in health resource use at months 6 and 12 were observed in the study-provided psychoeducation and UC groups.Caregiver IEQ total scores, SF-12 PCS and MCS scores, patient IMR total scores, and CGI-S scores improved from baseline to the follow-up assessments for both the study-provided psychoeducation and UC groups -8. HowevOf the 148 patients, 84 (56.8%) reported at least 1 TEAE during the study . No TEAENo differences were observed over the 12-month study period between the study-provided psychoeducation and UC groups in either patient outcomes  or caregiver outcomes , with both groups showing significant improvement. This study aimed to fill the gap in the evidence base for FP by providing information on the effects of FP delivered specifically to caregivers using a telehealth-based platform. FP programs share several common characteristics but can vary considerably in length, setting, and content . AlthougStudies of caregiver-directed psychosocial interventions with positive outcomes have typically been longer (mean 57 weeks) and have provided more overall sessions mean 28 sessions) than this study . The dur sessionsMost published studies on FP have evaluated models that include patients in the intervention. Since the inception of FP in the 1970s, several models have evolved to meet the needs of families, including FP and support ,36, behaAn unexpectedly large percentage of caregivers  discontinued participation in the study. The most common reasons for discontinuation were withdrawal of consent , others , lost to follow-up , and physician’s decision . Although caregiver demographic factors were similar between those who discontinued the study and those who completed the study, 80.6% (83/103) of caregivers who completed the study were parents of the patient compared with only 64% (29/45) of caregivers who dropped out. It is possible that the parents of patients may have been more committed and motivated to continue the study than caregivers who were not parents of the patient. In addition, per protocol, when a patient discontinued participation in the study, their caregivers were also discontinued. This may have also contributed to the high discontinuation rate among the caregivers.worrying, the baseline IEQ total and subscale scores were lower among those who received >8 sessions, indicating lower caregiver burden. It is possible that caregivers with a higher burden may have been too distressed to engage in the program, regardless of the convenience of internet-based access to interventions, and dropped out early. As noted earlier, caregivers who discontinued participation within the first 12 months of the FIRST study were also more likely to be nonparent relatives with poorer health  may be a particularly vulnerable period that requires an additional level of support not examined in this study to facilitate better outcomes. Furthermore, the median age of the patients in the FIRST study was 25.0 years, indicating that they were also early in the course of their illness. Typically, many patients have difficulty accepting their diagnosis  and expeThe study-provided psychoeducation intervention was delivered across many study sites , which dThe results of this study coincide with a critical moment for telehealth interventions. Although telehealth interventions were only used by 8% of Americans in 2019, engagement with telehealth has grown dramatically in acceptance during the COVID-19 pandemic ,42. For The findings from this study provide valuable insights into a supplemental telehealth-based FP provided in the treatment of patients with early-phase schizophrenia spectrum disorders receiving paliperidone palmitate or oral antipsychotic medication. Key insights include the potential importance of supporting sufficient caregiver engagement; communication between clinicians, patients, and family members regarding treatment plans; and ensuring a link between clinicians providing psychoeducation to patients and the rest of their treatment team. Future studies in which telehealth interventions include caregiver–patient sessions and multicaregiver group sessions are warranted . Meanwhi


Klebsiella pneumoniae are a leading cause of mortality and morbidity among hospitalized patients. In neonatal intensive care units (NICU), blood stream infections by K. pneumoniae are one of the most common nosocomial infections leading to poor clinical outcomes and prolonged hospital stays. Here, we describe an outbreak of multi-drug resistant K. pneumoniae among neonates admitted at the NICU of a large tertiary care hospital in India. The outbreak involved 5 out of 7 neonates admitted in the NICU. The antibiotic sensitivity profiles revealed that all K. pneumoniae isolates were multi-drug resistant including carbapenems and colistin. The isolates belonged to three different sequence types namely, ST-11, ST-16 and ST-101. The isolates harboured carbapenemase genes, mainly blaNDM-1, blaNDM-5 and blaOXA-232 besides extended-spectrum β-lactamases however the colistin resistance gene mcr-1, mcr-2 and mcr-3 could not be detected. Extensive environmental screening of the ward and healthcare personnel led to the isolation of K. pneumoniae ST101 from filtered incubator water, harboring blaNDM-5, blaOXA-232 and ESBL genes (blaCTX-M) but was negative for the mcr genes. Strict infection control measures were applied and the outbreak was contained. This study emphasizes that early detection of such high-risk clones of multi-drug resistant isolates, surveillance and proper infection control practices are crucial to prevent outbreaks and further spread into the community.Infections caused by multi-drug resistant  Klebsiella pneumoniae, a Gram negative bacterium, is a member of the order Enterobacterales. It is a dreaded pathogen owing to its potential for carbapenemase production and harbouring multiple resistance genes. The evolution and rapid dissemination of carbapenem resistant K. pneumoniae is a significant problem worldwide as it is responsible for high mortality and morbidity in hospitalized and immuno-compromised patients  at Sanjay Gandhi Post Graduate Institute of Medical Sciences, a 900 bed tertiary care referral hospital in North India, during the last week of June till second week of July, 2021.K. pneumoniae isolates were recovered. All the isolates were identified by conventional biochemical tests and later confirmed by automated MALDI-TOF-MS system .The blood samples from the neonates were received at our laboratory for routine bacterial culture and sensitivity testing. A total of 5 multi-drug resistant All the demographic and clinical data of the patients were collected from the hospital information system of the institute.The antibiotic susceptibility pattern and minimum inhibitory concentration (MIC) was determined by liquid broth microdilution method. The results were interpreted according to Clinical and Laboratory Standards Institute (CLSI) guidelines  except fK. pneumoniae isolates were analyzed using polymerase chain reaction to detect the genes encoding carbapenemases , ESBLs (blaCTX-M), other class A β-lactamases  and colistin resistance genes mcr-1, mcr-2 and mcr-3 using previously described methods and primers  digested DNA of 5 clinical isolates and one environmental isolate of K. pneumoniae according to a previously described method  using d method . The resK. pneumoniae isolates was performed as described previously (http://bigsdb.pasteur.fr/klebsiella/klebsiella.html/.Multi-locus sequence typing (MLST) of 5 eviously . The sevScreening cultures (swabs) were obtained from different sites in NICU like entrance of room, incubator, oxygen connectors, IV stand, 10% dextrose, normal saline, reverse osmosis water, ringer lactate. The samples were inoculated onto Blood agar and MacConkey agar and incubated for 16-18 hours aerobically. Samples were also inoculated in the Robertson cooked meat broth for 16-18 hours. Sub-cultures were done on blood agar and MacConkey agar after 48 hours. Air samples were also taken by the air sampler for air bio-load monitoring.In order to control outbreaks and hospital acquired infections, we have a hospital infection control committee comprising of hospital administrators, microbiologists, physicians, nurses, technical staff and sanitation workers. During the outbreak we applied strict infection control measures including a) temporary evacuation of the infected ICU with transfer of all patient to the alternative ICU area; b) the empty infected ICU was cleaned by fogging and sodium hypochlorite; c) use of shared medical equipment were stopped; d) sanitation workers were educated about cleaning processes, cleaning protocol were established which included use of disposable cloths for each area using sodium hypochlorite solution kept in closed container to prevent evaporation and replacing it in every 24 hours; e) hand hygiene inspection and observation of infection control nurses and resident doctors were increased, educational sessions of standard precautions and hand hygiene practices were carried out by infection control physicians and nurses; f) environmental screening of bacterial cultures from different sites and hands of hospital personnel were continued;Besides the NICU and its staff, the infection control measures were implemented in other areas of the hospital especially other ICUs and critical care units. The adherence to infection control measures in the NICU was approximately 70% before the outbreak but it was enhanced to 100% to contain the outbreak and was continued for the next one month, to prevent any further outbreaks.blaNDM and blaOXA-232 genes were submitted in the NCBI GenBank database under accession numbers OL544134-OL544138 and OK351259-OK351263 respectively.The nucleotide sequences of the Klebsiella pneumoniae isolates  as confirmed by MALDI-TOF-MS. Among the environmental samples, filtered incubator water was positive for multi-drug resistant K. pneumoniae (KPNIC2). Antibiotic sensitivity testing revealed that all the isolates, including the environmental isolate, were resistant to first generation cephalosporins, carbapenems, aminoglycosides and colistin except KPNIC6 which was sensitive to ciprofloxacin  while blaNDM-1 was present in 2 isolates (KPNIC4 and KPNIC5). The gene blaOXA-232 (blaOXA-48 like variant) co-existed with blaNDM-1 and blaNDM-5 in all these 4 isolates. Among other carbapenemases, blaIMP gene was absent in all while blaVIM was present in one isolate (KPNIC5). Screening of other β-lactamases revealed that blaSHV gene was present in all isolates which is intrinsic to K. pneumoniae while blaTEM was present in 3 isolates . Among ESBLs, blaCTX-M co-existed with blaNDM-1 in 2 isolates (KPNIC4 and KPNIC5) and was absent in others. Colistin resistance genes mcr-1,-2 and-3 were absent in all isolates. Other variants of the mcr gene and the chromosomal mutations were not investigated. The environmental isolate of K. pneumoniae was positive for blaNDM-5, blaOXA-232, blaSHV, blaTEM, and blaCTX-M but negative for mcr. Clonal lineage profiling by MLST revealed that out of 5 clinical isolates 2 each belonged to ST101 and ST16 respectively while 1 isolate belonged to ST11. The environmental isolate belonged to ST101  producing K. pneumoniae involving neonates admitted in NICU at a large tertiary care hospital in north India. The outbreak had a serious bearing on the clinical outcomes leading to mortality and prolonged hospital stays of the neonates. Molecular epidemiology of the clinical isolates revealed presence of sequence types ST16, ST101 and ST11 but the PFGE pattern indicated that the ST16 and ST101 isolates were closely related. While carbapenemase associated ST16 and ST101 are internationally spread clones and have been associated with outbreaks of K. pneumoniae in various countries including India, ST11 is not a prevalent sequence type in India. One major limitation of our study was lack of whole genome sequencing of the isolates that provides detailed information about the resistome profiles and also helps to understand the dissemination pattern of the high-risk clones. In addition to the transmissible mcr gene, chromosomal mutations in mgrB, phoP/phoQ, pmrA and pmrB genes are responsible for colistin resistance but we did not analyze the alternative mechanisms and it was a limitation of our study. Further, we could track the source of the isolates belonging to ST101 to the incubator water but the source of ST11 could not be traced. Furthermore, we could not isolate ST16 from the environmental samples and after surveillance of the healthcare personnel although it was found to be closely related to the ST101 obtained from the incubator water in our study, indicating that more rigorous surveillance and environmental screening should have been performed. However, owing to the close relatedness of five out six isolates in the current outbreak we believe that the incubator water was the source of infection. Further, after taking corrective measures for proper autoclaving of the jar by the concerned staff the outbreak was contained. Though infection control measures were not robust in our set up but our surveillance actions spread more awareness among the staff. Early detection of such high-risk clones of multi-drug resistant isolates, surveillance and proper infection control practices are crucial to prevent outbreaks and further spread into the community.This study investigates an outbreak of colistin resistant carbapenemase (blaNDM and blaOXA-232 genes were submitted in the NCBI GenBank database under accession numbers OL544134-OL544138 and OK351259-OK351263 respectively.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The nucleotide sequences of the The studies involving human participants were reviewed and approved by Institutional Ethics Committee, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.CS, AP and NT conceived and designed the study. AP and NT wrote the manuscript. CS critically revised the manuscript. All authors carried out the experiments and collected the data. All authors contributed to the article and approved the submitted version.


Despite their proven efficacy and huge contribution to the health of humankind, vaccines continue to be a source of concern for some individuals around the world. Vaccinations against COVID-19 increased the number of distressed people and intensified their distrust, particularly as the pandemic was still emerging and the populations were encouraged to be vaccinated under various slogans like “back to normal life” and “stop coronavirus”, goals which are still to be achieved. As fear of vaccination-related adverse events following immunization (AEFIs) is the main reason for vaccine hesitancy, we reviewed immune and autoimmune AEFIs in particular, though very rare, as the most worrisome aspect of the vaccines. Among others, autoimmune AEFIs of the most commonly administered COVID-19 vaccines include neurological ones such as Guillain-Barre syndrome, transverse myelitis, and Bell’s palsy, as well as myocarditis. In addition, the newly introduced notion related to COVID-19 vaccines, “vaccine-induced immune thrombotic thrombocytopenia/vaccine-induced prothrombotic immune thrombotic thrombocytopenia” (VITT/VIPITT)”, is of importance as well. Overviewing recent medical literature while focusing on the major immune and autoimmune AEFIs, demonstrating their rate of occurrence, presenting the cases reported, and their link to the specific type of COVID-19 vaccines represented the main aim of our work. In this narrative review, we illustrate the different vaccine types in current use, their associated immune and autoimmune AEFIs, with a focus on the 3 main COVID-19 vaccines . While the rate of AEFIs is extremely low, addressing the issue in this manner, in our opinion, is the best strategy for coping with vaccine hesitancy. Considered one of the fastest processes of manufacturing vaccines ever, “Coronavirus Disease 2019” (COVID-19) vaccines were discovered, studied, and produced in terms of months since the declaration of COVID-19 as a pandemic by the World Health Organization (WHO) . InteresWe hereby present a detailed, narrative literature review covering the various types of vaccines, their immune-mediated AEFIs, and we focus on the autoimmune AEFIs described in correlation with the main COVID-19 vaccines in use worldwide, that is to say, those most administered (approximately 95% of those delivered). The rarity of the AEFIs among vaccinated people, and the risk of severe complications that COVID-19 carries, are the key factor for fighting against vaccine hesitancy.Vaccines that were approved or in use against COVID-19 can be classified as follows:Inactivated or killed vaccines contain a pathogen that has been inactivated after being grown in a culture. The first utilized was the cholera vaccine introduced in 1896 . There aViral vectors were first introduced in relation to gene and cancer therapy but, since then, have been adapted to vaccine development . The utiin vitro transcribed (IVT) mRNA in animals was described, when reporter gene mRNAs were injected into mice and protein synthesis was detected  recommended that GBS should be added as a warning sign on the ChAdOx1 vaccine product information, despite being unable to decisively conclude about a causal association . This acTM is an immune-mediated acute or a subacute inflammatory disease of the spinal cord accompanied with motor, sensory, and autonomic symptoms . The cliActually, little evidence with no obvious association exists regarding the appearance of TM after the administration of BNT162b2 COVID-19 vaccine. Case reports include a 75-year-old Japanese patient who presented with TM 3 days following the first dose of the BNT162b2 vaccine .Concerning the mRNA-1273 COVID-19 vaccine, case reports of ADEM , neuromyDuring the phase 3 clinical trial of the ChAdOx1 vaccine, 3 participants were diagnosed with TM . As a reBell’s palsy, or facial nerve palsy, is the partial (paresis) or total  loss of function of the facial nerve . The etiIn a case-control study from Switzerland which aimed to assess the correlation between the inactivated intranasal influenza vaccines and Bell’s palsy , a signiThe safety database of the BNT162b2 vaccine revealed a slight increase in the cases of Bell’s palsy in vaccinated individuals . There wThree cases of Bell’s palsy were reported following vaccination with mRNA-1273 vaccine –101. TheWhile Bell’s palsy is a lower motor neuron disease manifesting as a unilateral facial paralysis , GBS mayPostvaccinal encephalitis was described in regard to COVID-19 vaccines. In a case series of three patients who presented with symptoms suspected of encephalitis in a range of 7-11 days of receiving the ChAdOx1 vaccine was previously documented . The symIn addition, a case report of a Japanese lady admitted who developed diplopia the next day following the first dose of the BNT162b2 vaccine administration was described in the literature . The symIn regard to the mRNA-1273 COVID vaccine, a case report of a patient diagnosed with acute encephalitis, myoclonus and Sweet syndrome was described after receiving the first dose of the mRNA-1273 vaccine . The symMyocarditis is defined as the presence of inflammatory cellular infiltrate in the myocardium alongside tissue necrosis, which is not caused by coronary heart disease, and diagnosed by a combination of histological, immunological and immunohistochemical criteria . Based oIn another study designed to determine the incidence of cardiac symptoms and subclinical myocarditis/pericarditis after smallpox and trivalent influenza vaccine, out of 1081 individuals who received the smallpox vaccine, 4 Caucasian males were diagnosed with probable myocarditis and 1 female with suspected pericarditis. The study did not find any possible or probable cases of myocarditis/pericarditis following trivalent influenza vaccine however, some patients developed new cardiac symptoms like chest pain, dyspnea, and palpitations . One stuMyocarditis was documented by several case reports in individuals after receiving the COVID-19 vaccines –126. MonMyocarditis is a rare post-mRNA-vaccine sequela, and was seen mostly among young, vaccinated males. Up to June 2021, around 1226 reports of myocarditis have been reported in the US after more than 296 million doses of mRNA COVID-19 vaccines administered . SymptomWhile myocarditis has been extensively reported following mRNA COVID-19 vaccines, very few reports exist regarding the development of myocarditis in relation to the ChAdOx1 vaccine . In the Since the appearance of the vaccine-induced immune thrombotic thrombocytopenia (VITT)/vaccine-induced prothrombotic immune thrombotic thrombocytopenia (VIPITT) was mainly attributable to the adenoviral vector vaccines, the ChAdOx1 (AstraZeneca) and the Ad26.COV2.S (Janssen/Johnson & Johnson) vaccines, the current section starts with the AstraZeneca vaccine, then the mRNA vaccines follow.In early 2020, as the newly authorized COVID-19 vaccinations were being distributed worldwide, cases of thrombotic events as well as thrombocytopenia following vaccination with the ChAdOx1 vaccine began to emerge –141. WhiSince the mass vaccination campaign began with the BNT162b2 vaccine, several case reports have been published regarding the combination of thrombotic events together with thrombocytopenia, in a form of cerebral venous sinus thrombosis, VITT/VIPITT, and acquired thrombotic thrombocytopenic purpura (TTP) several days following vaccination with the BNT162b2 vaccine –162. In Cases of ITP and TTP after mRNA-1273 COVID-19 vaccine have been documented since the early vaccination campaign –168. A fITP is a well-known autoimmune hematological condition characterized by a substantial reduction in peripheral platelet count to less than <100,000/microL due to platelet destruction by antiplatelet antibodies . PatientMinimal change disease (MCD) is a histologically based renal pathology which is the leading cause of idiopathic nephrotic syndrome in adults and children . As caseSeveral case reports have been published regarding new-onset or relapse of MCD following vaccination with the BNT162b2 vaccine. A 50-year-old healthy male presented with nephrotic syndrome and acute kidney injury four days following the first dose of the BNT162b2 vaccine . The diaThough autoimmune renal AEFIs of the mRNA-1273 COVID-19 vaccine were reported, MCD was not common among them. For instance, Thappy et al.  presenteSeveral cases of MCD were described in the context of the ChAdOx1 vaccine –194. AllAs for vasculitis, two cases of antineutrophil cytoplasmic antibody (ANCA) associated vasculitis after the second dose of the mRNA-1273 COVID-19 vaccine were reported. As a result of the new onset ANCA vasculitis, one patient became dependent on dialysis , while iAutoimmune hepatitis was reported as well among individuals vaccinated with various types of COVID-19 vaccines –202. In A brief summary of the studies addressing autoimmune AEFIs of the most utilized COVID-19 vaccines, including the study population and the conclusion of the works, is presented in Immune and autoimmune AEFIs after COVID-19 vaccines are rare and mostly non-life-threatening. Their rate follows 1 out of 3 possibilities, either it is very low, or similar to the occurrence rate in the general population, or even much lower in comparison to the COVID-19 infection itself. Of AEFIs mentioned, Bell’s palsy and myocarditis seemingly have the greatest risk when it comes to the mRNA-based COVID-19 vaccines however, in addition to their rarity, the disease course is mild and full recovery is the rule. In turn, GBS and VITT/VIPITT were found to be associated mainly with the adenovirus vector based COVID-19 vaccines, still with a low rate. Addressing this sort of AEFIs, their severity, and long-term effects of the COVID-19 vaccines while fighting against vaccine hesitancy is of great importance. Doubtlessly, such efforts will decrease public concern, increase vaccination coverage rate, and guide physicians towards the rapid identification of AEFIs, reassuring patients, and applying appropriate treatment in a timely manner.NM: Supervision, Writing- Review and Editing. NL: Writing - Original Draft, Software. AO: Writing - Original Draft. RS: Writing - Original Draft. AA: Writing - Original Draft. MA: Writing - Original Draft. MZ: Writing - Original Draft. NB: Conceptualization, Writing- Review and Editing. All authors contributed to the article and approved the subitted 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. 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