Melis Siropyan
email: melis.siropyan@ogr.gsu.edu.tr

Ozan Celikel
email: ozan.celikel@ogr.gsu.edu.tr

Ozgun Pinarer
email: opinarer@gsu.edu.tr

Artificial Intelligence Driven Vertical Farming Management System

Keywords: smart agriculture, vertical farming, machine learning, smart systems

The change in the climate conditions and the increase of the consumption with the increased population, forced change in the agricultural field. This change brings a question of how to reach enough natural products even in small areas. Vertical farming option emerged as one of the sustainable options with the short supply chain processes. Besides, it helps to decrease the effects of climate change and improve the sustainability since it utilizes less water and avoids problems such as arid soil, soil infertility etc. Technological developments started to spread rapidly in the agricultural field and led to various digital transformations according to the needs. Recently, the new concept of smart agriculture makes agriculture more efficient and effective thanks to highprecision algorithms. The most significant farming applications are irrigation management, pest and disease control, greenhouse condition monitoring, soil and water quality monitoring, precision agriculture and dairy management. In this study, machine learning methods are applied such as CNN image processing model which can detect and identifying diseases on plants. In this research, AI based lettuce diseases detection system is proposed. An AI model is developed to identify different lettuce diseases. The model was built with ResNet50 and ImageNet on Tensorflow. Various over fitting prevention methods are also applied to the model to compensate for the limited training dataset and the results of the study are discussed. It will guide people towards enhancing awareness of the importance and necessity of using various machine learning techniques and various alternatives to traditional agriculture for the sustainability.

I. INTRODUCTION

Vertical agriculture is one of the valuable indoor methods to solve the food supply chain problem. This kind of agriculture is the method of growing crops in layers put together vertically. The main goal of these systems and the most important difference from other alternative systems is to produce more food per square meter of land, reducing the footprint. This innovative system consumes 95% less water than other systems and saves considerable space and soil [START_REF] Naskali | Vertical farming: Under climate change effect[END_REF].

Vertical farming is one of the valuable methods for food supply chain problem. Vertical farming is the practice of growing crops in vertically stacked layers. Major goal of such architecture is to produce more foods per square meter. This innovative system lowers the requirement of water to up to 70% and saves considerable space and soil. Nowadays, vertical farming is accepted as an attractive option for farmers. Due to its water and space requirements, it promises a much more sustainable way to produce foods. Since these systems are commonly constructed in indoor environments, a perfect combination of natural and artificial lights should be used to maintain the perfect light level in the room.

It has been an interesting agricultural approach in the world of agriculture as it saves water and requires less space. It is accepted as an attractive system for farmers thanks to these strengths. For the same reasons, it also provides the appropriate environment for sustainable agriculture. The most basic feature that distinguishes these systems from other competitors is the possibility of producing food in a closed area. The first requirement of growing food indoors is the proper adjustment of the light, humidity and temperature values in the indoor environment and following these settings optimally during food cultivation. Agriculture systems that require less soil and less water attract more attention and promise a much more sustainable way of producing food, as the land that can be cultivated in the world is decreasing day by day and the shortage of water worldwide is expected to start soon.

In conservative agriculture, the producer is dependent on many external factors, for example, unstable weather conditions, pests, diseases, infrastructure deficiencies, low rainfall, floods, etc. Many of these external factors are also dependent on to climate change. As climate change is real and could be seen by naked-eye, traditional agriculture has less advantage than vertical farming. In addition, air pollution that started to occur in rural areas also affects traditional agriculture significantly. The supply chain of conservative agriculture is long enough and polluting the air by swapping 3-4 different middlemen which causes climate change. By this swap, the prices rise and neither the farmer nor the consumer gains much. However, due to operational and investment costs, traditional farming has more advantages in prices than vertical farming.

Vertical farming can have many positive implications for the environment and climate change. Vertical farms tend to be more closed systems, the nutrients for the crops are recirculated. As these nutrient solutions are valuable resources, they are not dispersed into the environment. With conventional farming when crops are fertilized, the fertilizer can run down into the environment where high concentrations can have a negative impact on the ecology. As fertilizers are in essential nutrients, many species can take advantage of this abundance and reproduce beyond the capacity of their local environment.

Another advantage of vertical farming is that it does not have to take up any land area which can be utilized to regrow forests which may help the current situation with climate change. Besides, climate change with weather instability has the potential to wreck crops with hurricanes or cause drought. Vertical farms have the possibility of completely being isolated from their environment. In fact, if other planets are to be colonized, it is our strong belief that it will involve vertical farming.

Considering traditional agriculture, efficient cultivation of the crops depends on many external factors. Many factors such as unstable weather conditions, plant insects, plant diseases, air pollution, precipitation and floods directly affect the efficient growth of food. For this reason, farming practices, such as vertical agriculture, which are well sheltered and away from external factors, become more attractive by farmers. In addition, traditional agriculture has a long supply chain in its internal functioning. Throughout this supply chain, the crop changes 3-4 different intermediaries until it reaches the end consumer and the price increases with each change. However, due to operational and investment costs, traditional agriculture is currently more financially competitive than vertical agriculture at the cost of environmental change. Another disadvantage with vertical farming is that it is more power-intensive than other farming methods as sunlight is not used or needs to be supplemented for the lower layers. In fully closed systems the day/night cycle of the plants does not need to be synchronized with the day/night cycles. Therefore, they can be used as a means of balancing the electrical grid. Vertical farming environments are in industrial buildings, container farms, in-store farms where the consumption or the purchase is high like supermarkets or restaurants, and appliance farm which targeted for in-house environments.

AI is an efficient computing tool trains the machines to learn from experiences, adapt to new data, and perform human-like tasks. In this study by using neural networks, the algorithm is trained to detect the diseases in plants by processing large amounts of images and recognizing patterns in the data.

Due to operating and investment costs, traditional farming is currently more financially competitive than vertical farming at the cost of environmental change. Another disadvantage of vertical farming is that it is more energy intensive than other farming methods because sunlight is not used or needs to be supplemented for the lower layers. In completely closed systems, the day/night cycle of the installations does not need to be synchronized with the day/night cycles. Therefore, they can be used as a means of balancing the power grid.

Today, in smart agriculture systems, one of the important problems is that the crops of the agricultural farmers are wasted due to various diseases. However, diseases of plants also have stages, and if the disease is diagnosed in the early period, the spread of the disease to other plants can be prevented and in a more optimistic scenario, the plant can be treated. For this purpose, in this paper a smart system is presented that detects diseases by looking at the leaves of plants based on supervised learning ML method.

The remainder of the paper is structured as follows: Section II discusses the theoretical background of the research. Section III presents the methods used and the work produced. Experiment setup and performed experiments are presented in Section IV. Finally, conclusion and discussion are given in Section V.

II. RELATED WORK

Improving farm productivity is one of the main aims of farming applications and systems. Using machine learning (ML) techniques and improved artificial intelligence (AI) techniques may provide significant solutions to increase the productivity and crop harvest. In machine learning methodology, there are basic methods that can be applied on the acquired data (again basic data should be acquired from the environment): regression, clustering, Bayesian models, instance-based models etc. Besides, commonly a decision tree that is classification or regression models formulated in a tree-like architecture is constructed. With construction of such tree structure, the dataset can be progressively organized in smaller homogeneous subset.

In such ML applications support vector machine (SVM) and neural network (NN) are well known approaches. To separate two classes, support vectors define the discrimination function of linear SVM. In these approaches, k-mean is mostly preferred ML algorithm [START_REF] Behmann | A review of advanced machine learning methods for the detection of biotic stress in precision crop protection[END_REF].

Moreover, artificial neural network (ANN) approaches can also be applicable on farming data. Basically, ANN emulates the human brain functionality and use pattern generation for complex functions [START_REF] Liakos | Machine learning in agriculture: A review[END_REF].

With the gradual deepening and development of the machine learning methods, deep learning-based approaches are also involved in research. Especially deep learning methods are used for classification. Zhong et al. [START_REF] Zhong | Deep learning based multi-temporal crop classification[END_REF] propose a framework that covers multiple models to classify summer crops based on Long Short-Term Memory (LSTM), and one-dimensional convolutional layers. In the literature, these various machine learning models are applied in farming domain for crop and yield management, disease, weed, pest detection. Especially deep learning models bring high efficiency in plant classification, phenotyping, soil, and water management.

Disease detection has a critical effect on the crop production. Thus, much research are focused on the disease detection. etc [START_REF] Al-Sadi | Impact of plant diseases on human health[END_REF], [START_REF] Rehman | Current and future applications of statistical machine learning algorithms for agricultural machine vision systems[END_REF]. Authors proposes a monitoring system to detect the disease on the leaf and fruits. The study uses neural network and image processing to achieve a disease detection and to increase the harvest efficiency. As Jhuria et al., Bhange et al. [START_REF] Bhange | Smart farming: Pomegranate disease detection using image processing[END_REF], present that the uneven climatic conditions cause a decrease of agricultural yield. To avoid such effect, authors propose a web-based tool to identify fruit disease by image processing and feature extraction and several data analysis methods. Chung et al. [START_REF] Chung | Detecting bakanae disease in rice seedlings by machine vision[END_REF] introduce a SVM classifiers and a genetic algorithm to detect bakanae disease in rice seedlings. Similarly, Ebrahimi et al. [START_REF] Ebrahimi | Visionbased pest detection based on svm classification method[END_REF] propose using SVM classification methods and image processing for pest detection. Kim et al. [START_REF] Kim | Iot-based strawberry disease prediction system for smart farming[END_REF] introduce cloudbased prediction system for disease detection on strawberries. With the proposed approach, several information such as weather, distribution, pricing etc are collected and analyzed in agricultural decision makers services. Ferentinos et al. [START_REF] Ferentinos | Deep learning models for plant disease detection and diagnosis[END_REF] introduce CNN models to identify plant diseases through simple leaves images of healthy or diseased plants. al. [START_REF] Ahmad Loti | Integrated analysis of machine learning and deep learning in chili pest and disease identification[END_REF] used SVM, RF and an ANN for pest and disease detection from the chili that features were extracted using a deeplearning-based approach and were compared with the traditional approach.

Plant stress and the detection of the health status are another critical challenge [START_REF] Gopidas | Integrated deep learning based segmentation and classification method for boundary delineation of agricultural fields in multitemporal satellite images[END_REF]. Plant stress means where a plant is growing in non-ideal growth conditions. It can be occurred by different outside parameters such as drought or wind, over irrigation or root disturbance. The proposed ML based system includes various optical sensor devices to identify biotic and abiotic stresses. The ML model uses LSSVM and the sensor fusion for the detection. Such models are summarized in Behmann et al. [START_REF] Behmann | A review of advanced machine learning methods for the detection of biotic stress in precision crop protection[END_REF]. They introduce a brief summary of machine learning applications in the literature to improve crop yields.In the literature, there are many studies on machine learning based health conditions [START_REF] Pantazi | Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers[END_REF], corn grain yield health and prediction [START_REF] Geipel | Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system[END_REF], [START_REF] Sa | weednet: Dense semantic weed classification using multispectral images and mav for smart farming[END_REF], soil management [START_REF] Nahvi | Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature[END_REF]- [START_REF] Lottes | Uavbased crop and weed classification for smart farming[END_REF]. On the other hand, IoT devices are highly easy to use in agriculture domain, researchers are motivated to integrate IoT solutions combined with ML based approaches [START_REF] Rezk | An efficient iot based smart farming system using machine learning algorithms[END_REF], [START_REF] Grinblat | Deep learning for plant identification using vein morphological patterns[END_REF].

Thus, machine learning applications in farming require multidisciplinary approaches coming from IoT, big data and computation. A storage and real time data analyzing systems are highly crucial for these applications. Table I summarizes the existing machine learning applications in farming domain.

III. PROPOSED APPROACH

The concept of growing food in an indoor environment in a vertical manner creates several opportunities: handle future food demands, significantly less water, eliminate weather, crop effects, and diminish the diseases, less exposure to chemicals. On the other hand, the investment to build such a system can be costly. Pollination in such an environment is another problem to solve. Besides, these systems rely on technical infrastructure where any technical failure should be well considered [START_REF] Garg | Recent trends in agriculture: vertical farming and organic farming[END_REF]. Still, once more information are gathered and the workings of plants are discovered that need to be grown, the growing environment can be optimized to increase harvest size and reduce energy consumption.

The trend is that people want to eat healthier foods and lead healthier lives. This trend is leading people to look for alternatives to indoor farming. These types of agricultural alternatives are not affected by climate change or pests. Such approaches are used to develop a micro-based indoor system tool with AI function that detects diseases in vegetables. Artificial Intelligence (AI) is an effective computing tool that trains machines to learn from experiences, adapt to new data, and perform human tasks. In this study, using neural networks, the algorithm is trained to detect diseases in plants by processing large amounts of images and recognizing patterns in the data.

The objective of this project is to present an artificial intelligence system capable of detecting diseases on a closed vertical agricultural system. To accomplish this goal, things must be provided; on the one hand, a vertical farming system must be implemented, and on the other hand, an algorithm capable of detecting diseases.

Various techniques have been used to detect diseases in recent years. Studies by Jhuria et al. are a good example in this category, in this study, two apple diseases and three grapevine diseases are chosen to be detected and two image databases are used. One of the image datasets is used to detect for already stored diseases imaging and the other for query image implementation [START_REF] Jhuria | Image processing for smart farming: Detection of disease and fruit grading[END_REF]. Another study on the same subject [START_REF] Bhange | Smart farming: Pomegranate disease detection using image processing[END_REF] uses image processing to detect diseases on pomegranates. Once the different features of the images have been extracted, the k-means algorithm is used to cluster the data. And SVM is used for classification.

Regarding these studies, a closed vertical agricultural system is proposed that will be monitored by an image capture system to detect diseases on plants. This system will allow users to develop their bodies wherever they want while detecting and fighting diseases they encounter in their bodies more quickly. Anticipate that an image processing must be used on a vertical farming system that is implemented.

The vertical farming model is an indoor farm, adopting a multi-level high-rise factory design [START_REF] Benke | Future food-production systems: vertical farming and controlled-environment agriculture[END_REF], and by its nature, vertical farming gives us a very high yield without depending on external factors. However, building such a facility that can provide a system like this is more expensive compared to traditional [START_REF] Despommier | The vertical farm: feeding the world in the 21st century[END_REF] methods. Therefore, there are different variations of vertical farming models. Some of these models will be discussed in this part of the section.

One such model mentioned in the model proposed by Benke & Tomkins [START_REF] Benke | Future food-production systems: vertical farming and controlled-environment agriculture[END_REF]. In this model the basics of vertical farming can be seen. A closed vertical farming system must be self-sufficient by relying on a renewable energy source, and the system must be completely independent. To provide this independence, they considered the use of artificial intelligence for system monitoring, resulting in minimal human interaction. In addition to this, they offer the use of LED lights instead of sunlight. This paves the way for production that is completely independent of weather conditions and the day/night cycle. Another important aspect of this study is the vertical placement of the body. By placement at several levels, it is possible to have a very favorable yield per use of space.

The system proposed in this study is a happy medium between these examples, a closed vertical agriculture with LED lights and various IoT components to monitor. And the management of the system will be done by an image processing algorithm. The concept of micro-scale vertical farming is illustrated in Figure 1.

For software purposes, Keras is used as a framework. Keras has many advantages that make it more attractive than its peers and the biggest of these advantages is its speed and accessibility. Keras minimizes the number of user actions needed for common use cases and provides clear, actionable feedback in case of user error. Keras is one of the most popular and successful deep learning frameworks.

Keras uses the TensorFlow machine learning platform, which allows its users to easily configure CPU and GPU configurations while training a model. Besides TensorFlow, there are various other options to choose from as a model. Despite PyTorch is one such model that is very popular, in our study TensorFlow was preferred over it due to its compatibility with Keras.

ImageNet is an image database platform. It is free for researchers and is one of the largest image databases for researchers. In ImageNet there are over 100,000 synsets. With ImageNet it is possible to identify many different objects without any prior training, making training much faster. There are other databases like ImageNet which are used in classification like MS-COCO or CIFAR-10. However [START_REF] Geipel | Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system[END_REF] image processing and regression managing corn grain yield The Open Images dataset is another dataset considered for the pre-training, however it was much larger in size [START_REF] Kuznetsova | The open images dataset v4[END_REF] than ImageNet, which would be useless for this project. In this study, Conventional Neural Networks (CNN) and ResNet sre preferred on the sample images. With Convnational Neural Networks and ResNet, the model will be trained on sample images and once the training is complete, the model will process the live data. With each of these elements decided, the final schematic of the system is shown in Figure 2. Setup details are given in Table II.

A small-scale vertical farm capable of growing 16 heads of lettuce was designed to study different growth parameters of lettuce. To accommodate the lettuce, black plastic pots 30cm deep are used, large enough to hold 4 heads of lettuce each. The solution must not see the light to prevent the growth of unwanted organisms. The bins were covered with expanded polyurethane with holes for the mesh pots. The lettuce was planted in rockwool to provide stability. The 4 bins were stacked on 4 shelves on top of each other. For each level, the temperature and humidity of the top and bottom, the water temperature are monitored. The goal was also to monitor the pH and EC of each tank and the light level of each head of lettuce, however, this approach would have been prohibitively expensive for a larger system, these measures are measured manually. Besides, the light output is controlled for each lettuce, the airflow in each bin and the air ventilation for each head of lettuce. As the facility was in a temperature controlled indoor environment and there were no major temperature fluctuations, the temperature is not controlled. Each level was equipped with a camera to monitor the progress of the 4 heads of lettuce and detect any problems.

IV. EXPERIMENT In this study, we propose to train the model with a lettuce disease dataset [START_REF] Chandrasekhar | Lettuce plant leaf datasets[END_REF]. Lettuce is a very common plant that can be found all over the world and diseases are very common on lettuces. Additionally, growing lettuce is easier than its peers, which is important for providing additional data to the model once the system is ready to be deployed on the closed vertical system.

The dataset consists of 134 lettuce images. These images are separated into 4 subclasses as follows: bacterial [START_REF] Grinblat | Deep learning for plant identification using vein morphological patterns[END_REF], viral [START_REF] Nahvi | Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature[END_REF], fungal (76) diseases and healthy [START_REF] Rezk | An efficient iot based smart farming system using machine learning algorithms[END_REF] lettuces. The purpose of this subset is to familiarize the model to understand some of the common diseases encountered when growing lettuces. Fro the development, tensorflow framework and keras API are preferred. Dataset details are given in Table III.

One of the focal points of the project's software realization was data integrity. The first step to do this was to pre-process the image set. To do this, duplicate data and corrupted files are selected and removed from the image set. A part of the data preparation script is given in Example 1.

Listing 1: Data preparation import t e n s o r f l o w a s t f d a t a _ d i r = ' d a t a s e t _ l e t t u c e ' t r a i n _ d s = t f . k e r a s . u t i l s .

i m a g e _ d a t a s e t _ f r o m _ d i r e c t o r y ( d a t a _ d i r , v a l i d a t i o n _ s p l i t = 0 . 2 , s u b s e t = " t r a i n i n g " , s e e d =123 , i m a g e _ s i z e = ( i m g _ h e i g h t , i m g _ w i d t h ) , b a t c h _ s i z e = b a t c h _ s i z e ) v a l _ d s = t f . k e r a s . u t i l s .

i m a g e _ d a t a s e t _ f r o m _ d i r e c t o r y ( d a t a _ d i r , v a l i d a t i o n _ s p l i t = 0 . 2 , s u b s e t = " v a l i d a t i o n " , s e e d =123 , i m a g e _ s i z e = ( i m g _ h e i g h t , i m g _ w i d t h ) , b a t c h _ s i z e = b a t c h _ s i z e ) c l a s s _ n a m e s = t r a i n _ d s . c l a s s _ n a m e s One of the other problems with the database is its size, the number of images inside the database is not enough to feed the model. In this example, the training set has better accuracy scores and lower loss at each epoch, however, the same cannot be said for the validation set. This is the result of overfitting the model to the dataset. Because the example dataset is very small, the model tends to overfit the training data, making it sensitive to new data. To overcome this problem, randomly inverted, rotated, contrasted, and cropped the data is preferred. In this way, increase the amount of data illustrated in Example 2 can be managed.

Listing 2: Data augmentation d a t a _ a u g m e n t a t i o n = t f . k e r a s . S e q u e n t i a l ( [ t f . k e r a s . l a y e r s . RandomFlip ( ' h o r i z o n t a l ' ) , t f . k e r a s . l a y e r s . RandomFlip ( ' v e r t i c a l ' ) , t f . k e r a s . l a y e r s . R a n d o m R o t a t i o n ( 0 . 3 ) , t f . k e r a s . l a y e r s . R a n d o m C o n t r a s t ( 0 . 5 ) , t f . k e r a s . l a y e r s . RandomCrop ( 2 2 4 , 2 2 4 ) ] )

The network learning rate is 0.001. Setting a low learning rate such as 0.0001 prevented the algorithm from adjusting to a lower number of epochs, after 50 epochs the model could only have an accuracy of 50%. And a high learning rate causes the model to be optimized too fast.

Due to these factors, the learning rate is reduced gradually, starting from a high value. Every 20 epochs, the model reduces the learning rate by 10%. This method gave better results compared to the other methods.

The dropout is given as 0.2 to avoid overfitting. With these values, after 15 epochs, the pre-trained ResNet50 model gave more accurate result in for the example dataset. Training accuracy is not significantly affected, training set accuracy remains relatively the same in each epoch. On the other hand, the accuracy of validation increases with epochs. However, both accuracy values remain below the desired margin which is greater than 90%.

After the base model is created, the model is trained with the sample dataset and trained for 125 epochs. After the training was done, the output layer is frozen to add another layer created by us for fine tuning. While the output layer was frozen, the ResNet50 model could not continue training, the added layer trained itself with the train dataset for 25 epochs. After applying all these methods, an accuracy of 0.869565 is acquired. With (learning rate = 0.001), 125 training epochs on ResNet50 and 25 additional training epochs on the new layer, the model gave an accuracy of about 86%.

V. CONCLUSION

In this study, we focus on one of the global problems: the limited resources such as land and food. Researchers on alternative methods for providing food to cities and growing plants in indoor environments.

The presented model has the potential to impact the agriculture industry, as it is possible to achieve reliable disease identification accuracy with a very limited data set. Applying various over-fitting prevention methods solved the problem of lack of variety and small size of the training dataset by .

The field of AI is growing at a rapid pace, and it is obvious that many application areas arise every year. In its current form, our study may be a reliable starting point for other researchers interested in applying image processing or machine learning solutions to an agricultural problem. Currently, our model can detect the type of disease in the image, however, it is not able to further diagnose the given image. An important improvement that can be made to this project would be to fulfill its deployment goals, a self-feeding image recognition system would be a very important study point and help to provide new sets of images for studies later.

Fig. 1 :

 1 Fig. 1: Model flowchart

Fig. 2 :

 2 Fig. 2: Model flowchart

TABLE I :

 I Usage of Machine Learning Techniques in Vertical Farming

	Study	Methodology	Application
	Jhuria et al. (2013) [22], Moshou et al.	prediction model, neural Network, SVM	disease detection
	(2014) [23], Bhange et al. (2015) [7], Chung	classifier and image processing
	et al. (2016) [8] , Ebrahimi et al. (2017) [9],
	Kim et al. (2018) [10]
	Ahmad et al. (2020) [12]	SVM, RF and ANN	pest and disease detection
	Geipel et al. (2014)

TABLE II :

 II Environment Details

	Specification	Value
	Electrical Conductivity	1.4 mS/cm
	pH	5.5-6.0
	Daytime Temperature	22 C
	Nighttime Temperature	18 C
	Daily Light Integral	17 mol/m2d
	Period	12-12 hours

TABLE III :

 III Dataset Details

	Class	Number of images
	Bacterial	21
	Fungal	76
	Healthy	20
	Viral	17

This work is supported by Galatasaray University Research Foundation under the Grant No. FBA-2021-1041.


Jonathan Douxfils

Julien Favresse

Jean-Michel Dogné

Thomas Lecompte

Sophie Susen

Charlotte Cordonnier

Aurélien Lebreton

Robert Gosselin

Pierre Sié

Gilles Pernod

Yves Gruel

Philippe Nguyen

Caroline Vayne

François Mullier

Hypotheses behind the very rare cases of thrombosis with thrombocytopenia syndrome after SARS-CoV-2 vaccination

Keywords: COVID-19, Vaccines, Thrombosis, Cerebral venous sinus thrombosis, AZD1222, ChAdOx1 nCov-19, COVID-19 Janssen vaccine, Ad.26.COV2.S

As of 4 April 2021, a total of 169 cases of cerebral venous sinus thrombosis (CVST) and 53 cases of splanchnic vein thrombosis were reported to EudraVigilance among around 34 million people vaccinated in the European Economic Area and United Kingdom with COVID-19 Vaccine AstraZeneca, a chimpanzee adenoviral vector (ChAdOx1) encoding the spike protein antigen of the SARS-CoV-2 virus. The first report of the European Medicines Agency gathering data on 20 million people vaccinated with Vaxzevria® in the UK and the EEA concluded that the number of post-vaccination cases with thromboembolic events as a whole reported to EudraVigilance in relation to the number of people vaccinated was lower than the estimated rate of such events in the general population. However, the EMA's Pharmacovigilance Risk Assessment Committee concluded that unusual thromboses with low blood platelets should be listed as very rare side effects of Vaxzevria®, pointing to a possible link. The same issue was identified with the COVID-19 Vaccine Janssen (Ad26.COV2.S).

Currently, there is still a sharp contrast between the clinical or experimental data reported in the literature on COVID-19 and the scarcity of data on the unusual thrombotic events observed after the vaccination with these vaccines. Different hypotheses might support these observations and should trigger further in vitro and ex vivo investigations. Specialized studies were needed to fully understand the potential relationship between vaccination and possible risk factors in order to implement risk minimization strategies.

Introduction

As of 4 April 2021, a total of 169 cases of cerebral venous sinus thrombosis (CVST) and 53 cases of splanchnic vein thrombosis were reported to EudraVigilance among around 34 million people vaccinated in the European Economic Area (EEA) and United Kingdom (UK) with COVID-19 Vaccine AstraZeneca (AZD1222, Vaxzevria®, AstraZeneca, Cambridge, United Kingdom), a chimpanzee adenoviral vector (ChAdOx1) encoding the spike protein antigen of the SARS-CoV-2 virus [START_REF]'s COVID-19 vaccine: EMA finds possible link to very rare cases of unusual blood clots with low blood platelets[END_REF]. The first report of the European Medicines Agency (EMA) gathering data on 20 million people vaccinated with Vaxzevria® in the UK and the EEA concluded that the number of post-vaccination cases with thromboembolic events as a whole reported to EudraVigilance in relation to the number of people vaccinated was lower than the estimated rate of such events in the general population [2]. However, the EMA's Pharmacovigilance Risk Assessment Committee (PRAC) concluded that unusual thromboses with low blood platelets should be listed as very rare side effects of Vaxzevria®, pointing to a possible link. The COVID-19 subcommittee of the WHO Global Advisory Committee on Vaccine Safety (GACVS) also reviewed reports of rare cases of thromboses with low platelets following vaccination with the AstraZeneca COVID-19 vaccine and concluded in a public statement that based on available information, a causal relationship between the vaccine and the occurrence of thromboses with low platelets was considered plausible but not yet confirmed [START_REF]World Health Organization Global Advisory Committee on Vaccine Safety (GACVS) review of latest evidence of rare adverse blood coagulation events with AstraZeneca COVID-19 Vaccine (Vaxzevria and Covishield)[END_REF]. Specialized studies were needed to fully understand the potential relationship between vaccination and possible risk factors. The EMA's Committee for Medicinal Products for Human use (CHMP) has further analyzed available data to put the risk of these very rare thromboses in the context of the vaccine's benefits for different age groups and different rates of infection. Graphic representations of the findings assuming an 80% vaccine effectiveness over a period of four months, are available on the EMA's website [START_REF]European Medicines Agency AstraZeneca's COVID-19 vaccine: benefits and risks in context[END_REF].

Importantly, the same issue was identified for a human adenovirus vector vaccine (Ad26.COV2.S, COVID-19 Vaccine Janssen, Janssen-Cilag International NV, Beerse, Belgium). In the clinical trial program, a case of CVST with thrombocytopenia occurred in a Janssen vaccine recipient which led to a pause in the clinical program. No clear causality was established, and the data and safety monitoring board agreed that the study could restart. The vaccine recipient was subsequently found to have had antibodies against platelet factor 4 (PF4) at the time of the event. Venous thromboembolism was added as an important potential risk in its risk management plan due to an imbalance of thrombotic events in general during clinical trials [START_REF]European Medicines Agency Signal assessment report on embolic and thrombotic events (SMQ) with COVID-19 Vaccine (ChAdOx1-S [recombinant]) -COVID-19 Vaccine AstraZeneca (Other viral vaccines)[END_REF]. The Food and Drug Administration (FDA) and the Centers for Disease Control and Prevention (CDC) recommended a pause in vaccination with COVID-19 Vaccine Janssen in the United States to allow further study. At that moment, six cases were reported among >7.2 million persons who had been vaccinated with COVID-19 Vaccine Janssen globally [START_REF] Sadoff | Thrombotic thrombocytopenia after Ad26.COV2.S vaccination -response from the manufacturer[END_REF]. During the pause, the FDA and the CDC examined available data to assess the risk of CVST with thrombocytopenia (Table 1 ). During March 2-April 21, 2021, the Vaccine Adverse Event Reporting System (VAERS), the US vaccine safety monitoring system, had received 15 reports of thrombosis with thrombocytopenia syndrome (TTS) after Janssen COVID-19 vaccination, with clots located in the cerebral venous sinuses and other unusual locations, including in the portal vein and splenic vein, and a combination of venous and arterial thromboses. On April 23, 2021, the Advisory Committee on Immunization Practices (ACIP) voted in favour of using of the COVID-19 Vaccine Janssen in all persons aged above or equal to 18 years. The importance of providing education for vaccination providers and the public about the risk for TTS and availability of other COVID-19 vaccine options, particularly for women aged 18-49 years was emphasized [START_REF] Macneil | Updated recommendations from the advisory committee on immunization practices for use of the Janssen (Johnson & Johnson) COVID-19 vaccine after reports of thrombosis with thrombocytopenia syndrome among vaccine recipients -United States, April 2021[END_REF]. The FDA and the CDC later lifted the recommended pause on COVID-19 Vaccine Janssen use and added a warning to the Janssen COVID-19 vaccine EUA and fact sheets regarding rare clotting events [START_REF]Food and Drug Administration FDA and CDC lift recommended pause on Johnson & Johnson (Janssen) COVID-19 vaccine use following thorough safety review[END_REF]. The EMA's PRAC also concluded that a warning about unusual thrombosis with low blood platelets should be listed as very rare side effects in the product information for COVID-19

Vaccine Janssen [START_REF]COVID-19 vaccine Janssen: EMA finds possible link to very 12[END_REF]. [START_REF] Blauenfeldt | Thrombocytopenia with acute ischemic stroke and bleeding in a patient newly vaccinated with an adenoviral vector-based COVID-19 vaccine[END_REF]].

Among the 15 cases of TTS with COVID-19 Vaccine Janssen reported by the CDC [START_REF] Shimabukuro | Thrombosis with thrombocytopenia syndrome (TTS) following Janssen COVID-19 vaccine[END_REF] including the six cases reported by Muir et al. [START_REF] Muir | Thrombotic thrombocytopenia after Ad26.COV2.S vaccination[END_REF], the median age was 37 (range: 18 to 59 years of age). Thirteen occurred among women aged 18-49 years, and two occurred among women aged ≥50 years. All affected persons were women (i.e. 100%). The time to symptom onset ranged from 5 to 16 days (median: 8 days) and 12 cases presented with CVST.

The platelet nadir ranged from 9000 to 127,000 per cubic millimeter. Eleven cases were positive for PF4-heparin antibodies and results were not available for the 4 remaining cases [START_REF] Shimabukuro | Thrombosis with thrombocytopenia syndrome (TTS) following Janssen COVID-19 vaccine[END_REF]. Among subgroups by age (18-29, 30-39, 40-49, 50-64, and ≥65 years), the reported rate was highest among women aged 30-39 years, with 11.8 TTS cases per 1 million Janssen COVID-19 doses administered [START_REF] Macneil | Updated recommendations from the advisory committee on immunization practices for use of the Janssen (Johnson & Johnson) COVID-19 vaccine after reports of thrombosis with thrombocytopenia syndrome among vaccine recipients -United States, April 2021[END_REF] (Table 1). All 15 patients were hospitalized, and 12 were admitted to an intensive care unit. Three patients had died. Although there is disproportionality in the occurrence of these TTS events and the rare associated of thrombotic events compared to the normal population, it has to be noted that COVID-19 is also associated with a near 10-fold increased risk of developing CVST (Table 2 ). antibodies and heparin-induced platelet aggregation (HIPA) tests with various experimental conditions, it has been concluded that the observed cases were resembling to a rare form of spontaneous HIT (i.e. autoimmune HIT without proximate heparin exposure) [START_REF] Greinacher | Autoimmune heparin-induced thrombocytopenia[END_REF], and possibly due to an immune process where the inducer is not heparin but rather a component of the vaccine, through the exposure to a polyanionic macromolecule [START_REF] Greinacher | Towards Understanding ChAdOx1 nCov-19 Vaccine-induced Immune Thrombotic Thrombocytopenia (VITT)[END_REF]. The vaccine or some of its components could also have been an inducer of a broad immune response, including the activation and expansion of autoreactive B-cells [START_REF] Greinacher | Towards Understanding ChAdOx1 nCov-19 Vaccine-induced Immune Thrombotic Thrombocytopenia (VITT)[END_REF].

Another recent report mentioned bilateral superior ophthalmic vein thrombosis and immune thrombocytopenia along with an ischaemic stroke but did not observe the presence of PF4specific antibodies [START_REF] Bayas | Bilateral superior ophthalmic vein thrombosis, ischaemic stroke, and immune thrombocytopenia after ChAdOx1 nCoV-19 vaccination[END_REF]. Therefore, and importantly, the presence of any confounders in spontaneous cases is needed all cases since the etiology remains uncertain. Different hypotheses should be investigated, and adequate laboratory tests performed (Table 3 ). Activation/consumption/dysregulation of the complement system cannot be excluded and deserve further laboratory investigations.

Open in a separate window Abbreviations: CAR, coxsackievirus and adenovirus receptor; FcɣRIIa, Fc ɣ receptor IIa;

IgG, immunoglobulin G; PCR, polymerase chain reaction; PF4, platelet factor 4; RBD, receptor binding domain.

a The type of serological testing is not always reported. In the presence of vaccination, testing should target the nucleocapsid protein of the virus to avoid cross-reactivity with the antibodies generated after vaccination. Importantly the temporal association between serological positivity against SARS-CoV-2 (i.e. +14 days) and the appearance of the event may generate false negative results. Testing should be done at least two weeks after the appearance of symptoms.

b Different findings are listed in the rationale column, sometimes contradictory, or pointing to mechanistic heterogeneity.

Anti-PF4 antibodies in COVID-19

Anti-PF4 antibodies have been frequently detected in COVID-19 patients suggesting that heparin administration in severe COVID-19 patients could lead to classical HIT. However, many patients developed anti-PF4 antibodies that were not able to activate platelets in dedicated assays [ [START_REF] Daviet | Heparin-induced thrombocytopenia in severe COVID-19[END_REF], [START_REF] Deruelle | Immune thrombocytopenia in a patient with COVID-19[END_REF], [START_REF] Minet | Assessment of the performances of AcuStar HIT and the combination with heparin-induced multiple electrode aggregometry: a retrospective study[END_REF], [START_REF] Delrue | Contrast between prevalence of HIT antibodies and confirmed HIT in hospitalized COVID-19 patients: a prospective study with clinical implications[END_REF]]. This confirms that, among patients who had received heparin, the distinction between non-HIT and HIT antibodies is not always evident and that functional assays are mandatory for HIT confirmation [START_REF] Warkentin | Studies of the immune response in heparin-induced thrombocytopenia[END_REF].

Immunoassays for anti-PF4 antibodies are not very specific to HIT, since for instance they detect such antibodies in up to 50% of patients after cardiac surgery without any thrombocytopenia or thrombosis [START_REF] Warkentin | Studies of the immune response in heparin-induced thrombocytopenia[END_REF][START_REF] Greinacher | Me or not me? The danger of spontaneity[END_REF]. These tests can also show cross reactivity with other types of IgG antibodies recognizing epitopes present on the test antigen sequence that resembles to PF4/heparin complex [START_REF] Warkentin | Laboratory diagnosis of heparin-induced thrombocytopenia[END_REF]. This is especially true if a low optical density result is obtained. In addition, due to the lack of standardization for HIT antibody testing, the interlaboratory variations, and dependence on experimental conditions, it is mandatory to specify which technique has been used to better appreciate the true positivity of the cases [START_REF] Greinacher | Heparin-induced thrombocytopenia: towards standardization of platelet factor 4/heparin antigen tests[END_REF][START_REF] Bui | Physicochemical characteristics of platelet factor 4 under various conditions are relevant for heparin-induced thrombocytopenia testing[END_REF].

Interestingly, Brodard et al. reported COVID-19 patients with high levels of PF4-specific antibodies as assessed with an enzyme immunoassay (EIA), but these were not plateletactivating antibodies. Among the twelve COVID-19 patients with suspected HIT studied, the EIA performed were positive in nine cases but functional assays in only three. The hypothesis that a factor present in the serum of COVID-19 patients, and inhibiting platelet activation induced by anti-PF4 antibodies, was evaluated and not confirmed [START_REF] Brodard | COVID-19 patients often show high-titer non-platelet-activating anti-PF4/heparin IgG antibodies[END_REF]. In addition, another report also showed that among COVID-19 patients, the rate of positive EIA is significantly higher than in the general population [START_REF] Warrior | Heparin induced thrombocytopenia in patients with COVID-19[END_REF].

Another study of ten COVID-19 patients with HIT suspicion revealed that samples were tested positive with the serotonin release assay (SRA), even in absence of heparin [START_REF] Nazy | Platelet-activating immune complexes identified in critically ill COVID-19 patients suspected of heparin-induced thrombocytopenia[END_REF]. Since anti-PF4/heparin antibodies could not be detected in these patients, the diagnosis of HIT was ruled out [START_REF] Nazy | Platelet-activating immune complexes identified in critically ill COVID-19 patients suspected of heparin-induced thrombocytopenia[END_REF]. The addition of IV.3 (anti-human CD32 (FcɣRIIa), Clone IV.3) antibody inhibited platelet activation, indicating that immune complexes containing IgG likely triggered the platelet activation in vitro via FcγRIIa. Of note, convalescent plasma from noncritically ill COVID-19 subjects (n = 8) did not activate platelets in the SRA, despite having high levels of anti-SARS-CoV-2 antibodies, indicating that antibodies alone were insufficient for platelet activation [START_REF] Nazy | Platelet-activating immune complexes identified in critically ill COVID-19 patients suspected of heparin-induced thrombocytopenia[END_REF]. The authors of this study concluded that a subset of critically ill COVID-19 patient sera contains immune complexes that mediate platelet activation via FcγRIIa. The antigen in the immune complexes remains elusive, but may be part of SARS-CoV-2, similar to what was reported with H1N1 viral infection [START_REF] Boilard | Influenza virus H1N1 activates platelets through FcgammaRIIA signaling and thrombin generation[END_REF]. This is further supported by the inhibition of platelet activation observed with therapeutic concentrations of heparin, which may bind the SARS-CoV-2 receptor binding domain (RBD) and induce a conformational change with altered binding specificity, potentially disrupting immune complexes [START_REF] Yang | The utility of native MS for understanding the mechanism of action of repurposed therapeutics in COVID-19: heparin as a disruptor of the SARS-CoV-2 interaction with its host cell receptor[END_REF]. Also, interaction with syndecan-1 and endocan, two polyanionic proteoglycans released by damaged endothelial cells, may also form complexes with PF-4 then stimulating the production of anti-PF4 antibodies.

Therefore, these results suggest that in COVID-19 patients, immune complexes unrelated to PF4 could also activate platelets via FcγRIIa.

4. Anti-PF4 antibodies and spontaneous HIT HIT antibodies are typically induced by heparin exposure, which causes the formation of macromolecular complexes associated with a conformational change in PF4, thereby creating neoantigens [START_REF] Greinacher | Close approximation of two platelet factor 4 tetramers by charge neutralization forms the antigens recognized by HIT antibodies[END_REF]. HIT IgG antibodies activate platelets via FcɣRIIa, leading to the release of platelet-derived procoagulant microvesicles [START_REF] Mullier | Platelet microparticle generation assay: a valuable test for immune heparininduced thrombocytopenia diagnosis[END_REF].

Even if it is confirmed that recent (i.e. 3 months) heparin exposure cannot be involved in the advent of TTS after vaccination, we have to be cautious about possible confounders regarding the trigger of the immune response toward PF4. Indeed, it has already been reported that socalled 'spontaneous' HIT may occur in patients who had proximate episodes of infection, or major surgery such as total knee or hip replacement surgery

[ [START_REF] Warkentin | A spontaneous prothrombotic disorder resembling heparin-induced thrombocytopenia[END_REF], [START_REF] Krauel | Platelet factor 4 binds to bacteria, [corrected] inducing antibodies cross-reacting with the major antigen in heparin-induced thrombocytopenia[END_REF], [START_REF] Warkentin | Spontaneous heparin-induced thrombocytopenia syndrome: 2 new cases and a proposal for defining this disorder[END_REF], [START_REF] Jay | Fatal heparin-induced thrombocytopenia (HIT) during warfarin thromboprophylaxis following orthopedic surgery: another example of 'spontaneous' HIT?[END_REF], [START_REF] Pruthi | Heparin-induced thrombocytopenia (HIT) during postoperative warfarin thromboprophylaxis: a second example of postorthopedic surgery 'spontaneous' HIT[END_REF], [START_REF] Hwang | Cerebral venous sinus thrombosis associated with spontaneous heparin-induced thrombocytopenia syndrome after total knee arthroplasty[END_REF], [START_REF] Okata | Spontaneous heparin-induced thrombocytopenia syndrome without any proximate heparin exposure, infection, or inflammatory condition: atypical clinical features with heparin-dependent platelet activating antibodies[END_REF], [START_REF] Poudel | Spontaneous HIT syndrome post-knee replacement surgery with delayed recovery of thrombocytopenia: a case report and literature review[END_REF]]. Although the nature and pathogenesis of spontaneous HIT is still a matter of debate, Nguyen et al. showed that some patients with autoimmune HIT developed two types of antibodies, with different specificities: anti-PF4/H antibodies, with moderate affinity for modified PF4, and high affinity antibodies, capable of recognizing PF4 whether complexed or not with heparin [START_REF] Nguyen | Anti-platelet factor 4/polyanion antibodies mediate a new mechanism of autoimmunity[END_REF]. In vitro data showed that high affinity anti-PF4 antibodies were able to promote an approximation between adjacent PF4 tetramers allowing the binding of anti-PF4/heparin antibodies to PF4 even in the absence of heparin. This could permit inducing conformational changes on PF4 like those induced by heparin [START_REF] Nguyen | Anti-platelet factor 4/polyanion antibodies mediate a new mechanism of autoimmunity[END_REF]. However, this immune response varies widely according to the clinical context.

Indeed, non-activating anti-PF4 antibodies can be found in the general population, but also in up to 50% of non-HIT patients after cardiac surgery as well as in COVID-19 patients [START_REF] Daviet | Heparin-induced thrombocytopenia in severe COVID-19[END_REF][START_REF] Pouplard | Antibodies to platelet factor 4-heparin after cardiopulmonary bypass in patients anticoagulated with unfractionated heparin or a low-molecular-weight heparin : clinical implications for heparin-induced thrombocytopenia[END_REF].

In addition, while heparin-dependent platelet-activating IgG antibodies are typically evidenced in classical HIT, they are found in up to 20% of non-HIT patients after major surgery. Atypical anti-PF4 IgG antibodies able to strongly activate platelets in the absence of heparin but in a PF4-dependent manner are also sometimes observed [START_REF] Greinacher | Me or not me? The danger of spontaneity[END_REF].

Moreover, even in the general population not treated with heparin during the 12 months preceding blood sampling, anti-PF4/heparin IgG antibodies can be detected with a relatively high incidence rate of 6.1% [START_REF] Krauel | Platelet factor 4 binds to bacteria, [corrected] inducing antibodies cross-reacting with the major antigen in heparin-induced thrombocytopenia[END_REF]. This is consistent with the concept that the general population is frequently exposed to PF4/heparin-like antigen complexes, resulting in a continuum of the immune response. PF4 can bind to the surface of various bacteria that cause infections in humans, such as S. aureus, S. pneumonia, and E. coli. This interaction is mediated by polyanionic structures present on their surface, and particularly for Gramnegative bacteria, the phosphate residues of lipid A of their lipopolysaccharide [START_REF] Krauel | Platelet factor 4 binding to lipid A of Gram-negative bacteria exposes PF4/heparin-like epitopes[END_REF]. In addition, circulating DNA, potentially released from bacteria or viruses during infections, also induces conformational changes on the surface of PF4, which are identical to those observed in the presence of heparin and make it immunogenic inducing, at least in mice, the synthesis of anti-PF4/DNA antibodies that cross-react with PF4/heparin complexes [START_REF] Jaax | Complex formation with nucleic acids and aptamers alters the antigenic properties of platelet factor 4[END_REF]. These notions support the understanding that anti-PF4 immunization can occur without exposure to heparin, since other molecules can modify the structure of this chemokine and potentially its immunogenicity leading to false positive EIA assays directed against anti-PF4/heparin IgG antibodies.

Antibodies found in the general population can result from repeated challenges with bacteria, which elicit production of anti-PF4/heparin antibodies. While the epitopes involved in the binding of classical HIT antibodies to PF4 are partially known [START_REF] Cai | Atomic description of the immune complex involved in heparin-induced thrombocytopenia[END_REF][START_REF] Li | Defining a second epitope for heparin-induced thrombocytopenia/thrombosis antibodies using KKO, a murine HIT-like monoclonal antibody[END_REF], those involved in autoimmune HIT syndromes have not yet been documented, and might be very different [START_REF] Huynh | Characterization of platelet factor 4 amino acids that bind pathogenic antibodies in heparin-induced thrombocytopenia[END_REF].

However, it is not excluded that these IgG antibodies directed against other epitopes are also able to activate platelets. In addition, the fact that these cases were mainly reported in young women should question on the possibility that we are facing previously immunized women.

Such immunization, i.e. human platelet alloantibodies (HPA) or anti-PF4, could be observed in women during pregnancy or having received platelet transfusions [START_REF] Lubenow | Very low platelet counts in post-transfusion purpura falsely diagnosed as heparin-induced thrombocytopenia[END_REF], possibly exposing them to an increased risk of the vaccine -associated thrombotic complications.

On the other hand, recent data from Greinacher et al., may support the fact that some constituents from the ChAdOx1 nCov-19 vaccine can form antigenic complexes with PF4 as biophysical analyses allowed to visualize complexes between PF4 and the vaccine components, including virus proteins. Importantly, those complexes were recognized by antibodies from TTS patients [START_REF] Greinacher | Towards Understanding ChAdOx1 nCov-19 Vaccine-induced Immune Thrombotic Thrombocytopenia (VITT)[END_REF]. However one need to substantiate these findings by the fact that these cases manifest as early as five days post vaccination. Thus, it is unlikely that the vaccine itself generates an independent immune reaction but rather reactivate a previous immune response. Indeed, there is typically not enough time for immunological tolerance to break and to generate high titers and class-switched, high affinity IgG antibodies to trigger the proposed mechanism. However, it is possible that anti-PF4 is a byproduct of an initial mechanism which in turn can eventually lead to thrombocytopenia and amplify a vicious cycle [START_REF] Kadkhoda | Post adenoviral-based COVID-19 vaccines thrombosis: a proposed mechanism[END_REF].

The SARS-CoV-2 infection status of the patients should be considered

It is important to note that only Norwegian Vaxzevria® cases and 2 US cases have reported the information on the previous nucleocapsid serological status (i.e. a serological marker which becomes positive 12-14 days after SARS-CoV-2 infection) of the patients. Among the 7 cases, none were positive for antibodies directed against the nucleocapsid protein [11,52]. In the other case series, the presence of antibodies directed against the nucleocapsid protein is unknown or the type of serological testing is not reported, precluding the exclusion of concurrent COVID-19 disease [START_REF]European Medicines Agency Signal assessment report on embolic and thrombotic events (SMQ) with COVID-19 Vaccine (ChAdOx1-S [recombinant]) -COVID-19 Vaccine AstraZeneca (Other viral vaccines)[END_REF]10,[START_REF] Shimabukuro | Thrombosis with thrombocytopenia syndrome (TTS) following Janssen COVID-19 vaccine[END_REF][START_REF] Bayas | Bilateral superior ophthalmic vein thrombosis, ischaemic stroke, and immune thrombocytopenia after ChAdOx1 nCoV-19 vaccination[END_REF]. We have to keep in mind that we have to wait for ±14 days for nucleocapsid test to become positive and in light of the temporal occurrence of thrombotic event, it is possible that these patients have not yet become seropositive against the nucleocapsid protein [START_REF] Tre-Hardy | The role of serology for COVID-19 control: population, kinetics and test performance do matter[END_REF].

It has already been proven that viruses like dengue [START_REF] Simon | Dengue virus binding and replication by platelets[END_REF], influenza [START_REF] Koupenova | The role of platelets in mediating a response to human influenza infection[END_REF], and HIV [START_REF] Real | Platelets from HIV-infected individuals on antiretroviral drug therapy with poor CD4(+) T cell recovery can harbor replicationcompetent HIV despite viral suppression[END_REF] can invade platelets, and there is preliminary evidence of the presence of SARS-CoV-2 in platelets of COVID-19 patients either through ACE-2 receptor, although controversial, or via other receptors like the αIIbβ3 integrin [ [START_REF] Pryzdial | Platelets in Thrombotic and Non-thrombotic Disorders[END_REF], [START_REF] Manne | Platelet gene expression and function in patients with COVID-19[END_REF], [START_REF] Zaid | Platelets Can Associate with SARS-Cov-2 RNA and Are Hyperactivated in COVID-19[END_REF], [START_REF] Zhang | SARS-CoV-2 binds platelet ACE2 to enhance thrombosis in COVID-19[END_REF], [START_REF] Bury | Search for SARS-CoV-2 RNA in platelets from COVID-19 patients[END_REF]]. It might also be suggested that pre-infection with SARS-CoV-2 could generate, in certain patients, IgG antibodies, probably directed against the spike protein, which are able to interact with FcɣRIIa on the platelet surface possibly leading to platelet activation and subsequent thrombocytopenia. induces procoagulant platelets and thus contribute to the increased risk for thromboembolic complications [START_REF] Althaus | Antibody-induced procoagulant platelets in severe COVID-19 infection[END_REF]. In particular, sera from COVID-19 patients in the ICU cause IgGmediated platelet apoptosis via cross-linking FcɣRIIa [START_REF] Althaus | Antibody-induced procoagulant platelets in severe COVID-19 infection[END_REF]. To explore the mechanism of platelet apoptosis in COVID-19, patients' sera were incubated with washed platelets from healthy donors, and these samples induced a significantly higher mitochondrial inner membrane potential depolarization compared with sera from healthy donors [START_REF] Althaus | Antibody-induced procoagulant platelets in severe COVID-19 infection[END_REF]. More importantly, IgG-mediated platelet apoptosis was inhibited by blocking FcɣRIIa with the IV.3 monoclonal antibody [START_REF] Althaus | Antibody-induced procoagulant platelets in severe COVID-19 infection[END_REF]. These data also indicate that IgG contribute to the increased phosphatidylserine expression on platelets of patients with severe COVID-19 infection.

Although Althaus et al. interestingly found an association between the IgG binding to the spike protein of SARS-CoV-2 and the phosphatidylserine externalization after incubation with patient sera [START_REF] Althaus | Antibody-induced procoagulant platelets in severe COVID-19 infection[END_REF], it remains unclear whether this results from a direct effect of IgG-virus complex such as previously reported in H1N1 infection and dengue fever [START_REF] Boilard | Influenza virus H1N1 activates platelets through FcgammaRIIA signaling and thrombin generation[END_REF][START_REF] Alonzo | Platelet apoptosis and apoptotic platelet clearance by macrophages in secondary dengue virus infections[END_REF].

Interestingly, blockade of the FcɣRIIa by infusion of a large amount of immunoglobulins (IVIg) has been proposed as a therapeutic option for inhibiting excessive platelet activation in COVID-19 patients similarly to the attitude suggested for severe cases of HIT and autoimmune HIT with life-threatening complications [START_REF] Goldmann | Oral Bruton tyrosine kinase inhibitors block activation of the platelet Fc receptor CD32a (FcgammaRIIA): a new option in HIT?[END_REF]. .Targeting the signaling pathway downstream platelet FcɣRIIa by inhibitors of Bruton Tyrosine Kinase (BTK) or Splenic Tyrosine Kinase (SYK), as suggested for HIT and more recently for cerebral venous thrombosis after SARS-CoV-2 vaccination [START_REF] Lhermusier | The Syk-kinase inhibitor R406 impairs platelet activation and monocyte tissue factor expression triggered by heparin-PF4 complex directed antibodies[END_REF][START_REF] Von Hundelshausen | Vaccine-induced immune thrombotic thrombocytopenia (VITT): targeting pathomechanisms with Bruton tyrosine kinase inhibitors[END_REF], could be an alternative or complementary therapeutic option.

Moreover, the work from Nazy et al. showed that some severely affected COVID-19 patients, in whom HIT had been suspected due to the occurrence of thrombocytopenia and thrombosis, developed antibodies able to strongly activate platelets without addition of heparin in vitro.

Interestingly, when studying the specificity of these antibodies, they found that they were not directed against PF4/heparin but rather against the RBD domain of the SARS-CoV-2 spike protein [START_REF] Nazy | Platelet-activating immune complexes identified in critically ill COVID-19 patients suspected of heparin-induced thrombocytopenia[END_REF]. However, as mentioned above, convalescent plasma from non-critically ill COVID-19 subjects did not activate platelets despite having high titers of anti-SARS-CoV-2

antibodies, but platelet activation was independent of heparin which even inhibited platelet activation at both therapeutic and high doses. The development of such antibodies after vaccination may potentially contribute to the rare cases of thrombotic events observed.

Additional data through RNA sequencing coming from severely SARS-CoV2 infected patients have further demonstrated, distinct changes in the gene-expression profile of circulating platelets of COVID-19 patients [START_REF] Manne | Platelet gene expression and function in patients with COVID-19[END_REF]. Pathway analysis revealed differential geneexpression changes in pathways associated with protein ubiquitination, antigen presentation, and mitochondrial dysfunction [START_REF] Manne | Platelet gene expression and function in patients with COVID-19[END_REF]. In addition, mRNA from the SARS-CoV-2 N1 gene was detected in platelets from two of 25 COVID-19 patients of this study, suggesting that platelets may take up SARS-COV-2 mRNA [START_REF] Manne | Platelet gene expression and function in patients with COVID-19[END_REF]. However, among these studies, there was no clear correlation between the presence of SARS-CoV-2 mRNA in platelets and thrombocytopenia.

If SARS-CoV-2 enters platelets, this seems to be a rare phenomenon, but this cannot be excluded.

One alternative hypothesis could be that, in those patients, administration of the vaccine would reactivate previous immune response to certain antigens such as PF4, leading to massive platelet activation and thrombotic state. The reasons for the unusual location of these thrombosis are not yet elucidated but these types of thrombosis shared similar risk factors like inflammation or autoimmune disease [START_REF] Silvis | Cerebral venous thrombosis[END_REF][START_REF] Valeriani | Splanchnic vein thrombosis: current perspectives[END_REF], It cannot be excluded that adenoviruses have a tropism for cell types encountered in these areas or for CSF therefore stimulating a pro-inflammatory state in these sites. The spike protein produced locally may then disturb the tight equilibrium between ACE-1 and ACE-2 (by inhibiting/occupying ACE-2) thus increasing locally the production of angiotensin leading a localized hypertensive area.

One cannot exclude that some patients suffering from TTS could have developed an immune response before the vaccination and that the vaccine itself has just reactivated a previous immunization against PF4. Indeed, one issue is that these cases manifest as early as 4 days post vaccination. This is typically short for immunological tolerance to generate high levels [START_REF] Kadkhoda | Post adenoviral-based COVID-19 vaccines thrombosis: a proposed mechanism[END_REF]. It is therefore possible that anti-PF4 is a byproduct of an initial mechanism.

Active or recent infection (infection since <14 days) should also be excluded. However, in this case scenario, one would expect to observe the same event rate with the different COVID-19 vaccines currently on the market.

Other platelet activation hypotheses? A link between SARS-CoV-2 or adenovirus and possible platelet infection

Platelets express only one class of receptors for the Fc domain of IgG antibodies, the FcγRIIa.

Once virus-directed antibodies are generated by the adaptive immune response, "bridged" interactions can be facilitated by platelet FcγRIIa, as demonstrated for influenza A virus [START_REF] Boilard | Influenza virus H1N1 activates platelets through FcgammaRIIA signaling and thrombin generation[END_REF].

Although antibody-bridged binding to platelets has not been specifically documented for many viruses, it is reasonable to speculate that such viral immune complexes and interactions with platelets are common. Similar to engagement of other receptors on the platelet surface, these multivalent adducts could crosslink FcγRIIa, causing platelet activation [START_REF] Pryzdial | Platelets in Thrombotic and Non-thrombotic Disorders[END_REF]. Many receptors on platelets that associate with viruses have been identified and include α5β1, and glycoprotein VI [START_REF] Pryzdial | Platelets in Thrombotic and Non-thrombotic Disorders[END_REF]. Interestingly, the SARS-CoV-2 spike protein exhibits the tripeptide Arg-Gly-Asp (RGD) motif that is necessary for interaction with integrins [START_REF] Hussein | Beyond RGD: virus interactions with integrins[END_REF][START_REF] Sigrist | A potential role for integrins in host cell entry by SARS-CoV-2[END_REF]. The conservation of the motif and its location in the receptor-binding region of the SARS-CoV-2 spike protein suggests that integrins may be alternative receptors for this virus [START_REF] Sigrist | A potential role for integrins in host cell entry by SARS-CoV-2[END_REF]. Thus, if patients presenting with TTS are currently infected by SARS-CoV-2 or another viral infection, platelet activation induced by SARS-CoV-2 or this other virus cannot be excluded.

Integrins are critical in platelets-leukocytes interactions. In addition to their role in facilitating numerous direct platelet-virus interactions, they are also important in processes leading to immune clearance of blood-borne viruses. One example is through the recruitment of dendritic cells by the interaction of platelet surface junctional adhesion molecule-C (JAM-C) and dendritic cell integrin αMβ2 [START_REF] Langer | Platelets recruit human dendritic cells via Mac-1/JAM-C interaction and modulate dendritic cell function in vitro[END_REF]. Trafficking of cytotoxic T lymphocytes (CTLs) to sites of infection can be mediated by platelet activation [START_REF] Iannacone | Platelets mediate cytotoxic T lymphocyte-induced liver damage[END_REF]. Also, some viruses induce platelet exposure of CD40 ligand, which promotes platelet-monocyte aggregates via platelet Pselectin and monocyte P-selectin glycoprotein ligand-1 (PSGL-1) [START_REF] Singh | Characterization of platelet-monocyte complexes in HIV-1-infected individuals: possible role in HIV-associated neuroinflammation[END_REF]. Regarding adenoviruses, identified receptors on platelets concerns CAR, αIIbβ3 and α5β3 among others.

The adenoviruses are correspondingly responsible for thrombocytopenia, also seen when adenovirus gene therapy vectors are intravenously administered to rhesus macaques and mice [START_REF] Othman | Adenovirus-induced thrombocytopenia: the role of von Willebrand factor and P-selectin in mediating accelerated platelet clearance[END_REF][START_REF] Wolins | Intravenous administration of replication-incompetent adenovirus to rhesus monkeys induces thrombocytopenia by increasing in vivo platelet clearance[END_REF]. Current literature indicates that CAR mediates the binding of adenovirus to platelets, enabling subsequent entry [START_REF] Othman | Adenovirus-induced thrombocytopenia: the role of von Willebrand factor and P-selectin in mediating accelerated platelet clearance[END_REF][START_REF] Shimony | Analysis of adenoviral attachment to human platelets[END_REF]. The virus-platelet interaction is predominantly localized to sites of intercellular complex formation, implying that CAR expression is enhanced in response to platelet activation [START_REF] Gupalo | Human platelets express CAR with localization at the sites of intercellular interaction[END_REF]. Vaccination with chimpanzee adenoviruses is quite recent and the evaluation of the risk of interaction with platelets or other cell types involved in hemostasis regulation is so far incomplete. The experience with Ebola vaccination (i.e.

ChAd3-EBO-Z, a chimpanzee adenovirus type-3 (ChAd3)-vectored vaccine) has not reported to date a risk of thrombocytopenia, but the number of patients included is limited and therefore, further investigations are required [START_REF] Tapia | Safety, reactogenicity, and immunogenicity of a chimpanzee adenovirus vectored Ebola vaccine in children in Africa: a randomised, observerblind, placebo-controlled, phase 2 trial[END_REF][START_REF] Tapia | Safety, reactogenicity, and immunogenicity of a chimpanzee adenovirus vectored Ebola vaccine in adults in Africa: a randomised, observer-blind, placebo-controlled, phase 2 trial[END_REF].

Platelets circulate in a resting state and are stimulated by ligand-receptor engagement. Protein and carbohydrate receptors on the surface of platelets bind viruses from many distinct families and it is not surprising that the interactions known to exist between viruses and platelet receptors can facilitate platelet activation. For example, dengue virus has been demonstrated to cause the exposure of P-selectin and procoagulant phospholipid on the platelet surface [START_REF] Simon | Dengue virus binding and replication by platelets[END_REF].

It finally initiates apoptosis-like markers, including caspase activation and mitochondrial permeability changes [START_REF] Hottz | Dengue induces platelet activation, mitochondrial dysfunction and cell death through mechanisms that involve DC-SIGN and caspases[END_REF]. Through their activation, platelets may also release several other components like ADP, polyphosphate or serotonin via their α-granule [ [START_REF] Packham | Historical perspective on ADP-induced platelet activation[END_REF], [START_REF] Smith | Polyphosphate: a new player in the field of hemostasis[END_REF], [START_REF] Morrell | Emerging roles for platelets as immune and inflammatory cells[END_REF]]. It has further been shown that polyphosphate/PF4 complexes can mediate heparin-independent platelet activation in HIT [START_REF] Cines | Polyphosphate/platelet factor 4 complexes can mediate heparin-independent platelet activation in heparin-induced thrombocytopenia[END_REF][START_REF] Mailer | Polyphosphate as a target for interference with inflammation and thrombosis[END_REF].

Thus, such alternate components could be part of thrombogenic mechanisms if platelets are activated by the interaction between the adenovirus and the receptor CAR, αIIbβ3 and α5β3 among others. However, pharmacokinetics investigations of these vaccines need to support this hypothesis since it implies that the adenovirus should spread into blood circulation at a sufficient concentration to interact with platelets and activate them sufficiently to generate massive platelet activation. Nevertheless, the presence of EDTA as excipients of the vaccine composition could lead to microvascular leakage as demonstrated by experiments done on endothelial cells and mouses [START_REF] Greinacher | Towards Understanding ChAdOx1 nCov-19 Vaccine-induced Immune Thrombotic Thrombocytopenia (VITT)[END_REF][START_REF] Duan | Regulatory mechanisms, prophylaxis and treatment of vascular leakage following severe trauma and shock[END_REF]. This should be seen in relation to the level of calcium in the microenvironment [START_REF] Duan | Regulatory mechanisms, prophylaxis and treatment of vascular leakage following severe trauma and shock[END_REF].

It can also be hypothesized that other cells such as monocytes, macrophages and endothelial cells which also express many Fc receptors, including the FcɣRIIa, can be activated by immune complexes formed after adenovirus vector vaccination and trigger thrombin generation [START_REF] Tutwiler | Platelet transactivation by monocytes promotes thrombosis in heparin-induced thrombocytopenia[END_REF]. Thrombin generation is probably less regulated in extra cerebral vessels due to the very low level of thrombomodulin [ [START_REF] Wenzel | Endogenous THBD (thrombomodulin) mediates angiogenesis in the ischemic brain-brief report[END_REF], [START_REF] Krenzlin | The importance of thrombin in cerebral injury and disease[END_REF], [START_REF] Ishii | Thrombomodulin, an endothelial anticoagulant protein, is absent from the human brain[END_REF]], and this could contribute to the particular clinical expression observed in these cases. A recent report also suggests that the spike protein stimulates the phosphorylation/activation of the extracellular signal-regulated kinase 1/2 (ERK1/2) through the CD147 receptor in cardiac pericytes potentially leading to endothelial disturbance [START_REF] Avolio | The SARS-CoV-2 Spike Protein Disrupts the Cooperative Function of Human Cardiac Pericytes -Endothelial Cells Through CD147 Receptor-mediated Signalling: A Potential Non-infective Mechanism of COVID-19 Microvascular Disease[END_REF]. Although interesting, this last hypothesis implies that these thrombotic events should have been observed with mRNA vaccines as well.

Another group has also hypothesized that immune complexes present in the pulmonary circulation of severely ill patients might activate platelets and contribute to high rates of thrombosis by a similar mechanism that has been identified in severe H1N1 infection, whereby immune complexes present in the lungs activate platelets via FcɣRIIa [START_REF] Boilard | Influenza virus H1N1 activates platelets through FcgammaRIIA signaling and thrombin generation[END_REF][START_REF] Bye | Aberrant Glycosylation of Anti-SARS-CoV-2 IgG Is a Pro-thrombotic Stimulus for Platelets[END_REF]. It has also been suggested that afucosylated IgG may be common to immune responses against all enveloped viruses [START_REF] Larsen | Afucosylated IgG characterizes enveloped viral responses and correlates with COVID-19 severity[END_REF] and it has been demonstrated that immune complexes containing recombinant SARS-CoV-2 spike protein and anti-spike IgG are able to enhance plateletmediated thrombosis on Von Willebrand Factor (VWF) in vitro, but only if the Fc domain of the IgG had an aberrant glycosylation state, similar to that previously observed in patients with severe COVID-19 [92,[94], [START_REF] Goshua | Endotheliopathy in COVID-19-associated coagulopathy: evidence from a single-centre, cross-sectional study[END_REF], [START_REF] Escher | Severe COVID-19 infection associated with endothelial activation[END_REF]]. Finally, two patient which has been vaccinated

with Vaxzevria® and presenting with TTS improved after eculizumab administration, while other treatments, i.e. anticoagulation and IVIg, failed [97]. The first patient presented with the full picture of thrombotic microngiopathy, reminiscent of atypical haemolytic uremic syndrome. In the second case, evidence of complement consumption was noted. This suggests that it was due to uncontrolled complement activation supporting the hypothesis that FcɣRIIA-dependent platelet activation may not be the only prothrombotic mechanism in this syndrome [98,[START_REF] Mastellos | Is complement the culprit behind COVID-19 vaccine-related adverse reactions?[END_REF].

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  to polyanionic structures like DNA/RNA, which are released from viruses during infections.  -Some IgG antibodies directed against the spike protein may be able to interact with FcɣRIIa.  -Sera from COVID-19 patients caused platelet apoptosis via cross-linking with FcɣRIIa. immune complexes containing IgG might trigger platelet activation in vitro.  -NETosis can be triggered via the interaction between SARS-CoV-2 and platelets leading to P-selectin secretion and neutrophils activation. 19 patients have developed antibodies directed against the RBD of the spike protein and able to activate platelet in vitro without addition of heparin.  -Vaccinated patients could have developed such an immune response before the vaccination (due to e.g. pregnancy, blood

  It has been reported by Althaus et al. that severe COVID-19 was associated with antibodymediated upregulation of platelet apoptosis and that IgG from severe COVID-19 patients

  αIIbβ3, and α5β3 integrins; the lectin, dendritic cell-specific intercellular adhesion molecule-3-grapping non-integrin (DC-SIGN); Toll-like receptors (TLRs) 2 and 4; coxsackieadenovirus receptor (CAR) of note, ChAdOx1nCoV-19 is from adenovirus species E and uses the CAR while Ad26.COV2.S is from adenovirus species D and can engage CD46 as its cellular receptor -; complement receptor 2 (CR2); C-X-C chemokine receptor type 4 (CXCR4); C-type lectin domain family 2 (CLEC-2); chemokine C-C motif ligand (CCL);

7 .

 7 Concluding remarks SARS-CoV-2 infection may lead to COVID-19 which is associated with an important risk of hospitalization and death. The overall benefits of Vaxzevria® and COVID-19 Janssen Vaccine in preventing COVID-19 is well established. Thrombosis with thrombocytopenia syndrome is a very rare, but clinically serious and potentially life-threatening adverse event that has been observed in association with these vaccines. This strongly argues for pathogenesis shared by the two adenoviral vector-based vaccines, although important differences exist between the two vaccines. Currently, there is still a sharp contrast between the clinical or experimental data reported in the literature on COVID-19 and the scarcity of data on the unusual thrombotic events observed after the vaccination with these vaccines. Different hypotheses might support these observations and should trigger further in vitro and ex vivo investigations. The work currently done by the hematologists and the clinical pathologists is of upmost importance to try to elucidate the mechanisms behind the observed TTS in those patients and to potentially identify risk factors and allow adequate risk minimization. Detailed clinical reports with fully documented time-related data (including platelet count changes after diagnosis and anticoagulant management) are eagerly awaited. The role of IVIG infusion also deserves to be fully investigated. ://www.ema.europa.eu/en/news/covid-19-vaccine-janssen-ema-finds-possible-linkvery-rare-cases-unusual-blood-clots-low-blood 10. Greinacher A., Thiele T., Warkentin T.E., Weisser K., Kyrle P.A., Eichinger S. Thrombotic thrombocytopenia after ChAdOx1 nCov-19 vaccination. N. Engl. J. Med. 2021 doi: 10.1056/NEJMoa2104840. [PMC free article] [PubMed] [CrossRef] [Google Scholar] 11. Schultz N.H., Sorvoll I.H., Michelsen A.E., Munthe L.A., Lund-Johansen F., Ahlen M.T., Wiedmann M., Aamodt A.H., Skattor T.H., Tjonnfjord G.E., Holme P.A. Thrombosis and thrombocytopenia after ChAdOx1 nCoV-19 vaccination. N. Engl. J. Med. 2021 doi: 10.1056/NEJMoa2104882.

Table 1

 1 Summary of the risk of thrombosis with thrombocytopenia syndrome (TTS) according to age group with Vaxzevria® and COVID-19 Vaccine Janssen. Data were obtained from the report of the European Medicines Agency a and the Centers for Disease Control and Prevention (CDC) b .

	Age

group Absolute risk per 1,000,000 persons after first dose of Vaxzevria® Age group Absolute risk per 1,000,000 persons with COVID-19 Vaccine Janssen

	20-29	19	18-29	5.2
	30-39	18	30-39	11.8
	40-49	21	40-49	4.3
	50-59	11	50-64	1.5
	60-69	10	65+	0.0
	70-79	5.0
	80+	4.0
	2. Description of the cases
	Among the 40 cases reported in the literature presenting with the thrombosis with
	thrombocytopenia syndrome (TTS) after Vaxzevria® vaccination, the median age was 40
	(range: 21 to 77 years of age) with a relative predominance of females (i.e. 70%). The onset

was within 5 to 24 days after vaccination and reported initial symptoms were headache, backache, abdominal pain, visual disturbance and leg or arm weakness. Thromboses were found at cerebral, abdominal, deep vein, or pulmonary sites or were even arterial. Platelet nadir varied between 7000 and 113,000 platelets per cubic millimeter. The majority, but not all, of cases were positive for PF4-heparin antibodies and were reactive to functional platelet activation assays [[10], [11], [12],

Absolute risk per 1,000,000 within 2 weeks after COVID-19 diagnosis (hospitalized) Highest baseline absolute risk per 1,000,000 over any 2 weeks period

	Table 2
	Summary of the risk of cerebral venous thrombosis associated or not with COVID-19
	disease. a
	39	0.41
	With laboratory assays primarily used for detecting heparin-induced thrombocytopenia (HIT)

Table 3

 3

	Summary of the different hypotheses for the risk of cerebral venous sinus thrombosis
	following vaccination with AZD1222. The following hypotheses all need to be supported by
	further pre-clinical and clinical laboratory investigations. Additional clinical information on
	potential confounders is also needed, underlying the need for complete and documented
	reporting of serious adverse events in pharmacovigilance databases (see text).
	Hypothesis	Rationale	Current limitations
			 -
	VIPIT	Multiple confounders not investigated like the presence of recent infection  -Insufficient laboratory confirmation  -Platelet activation already Temporal association (events observed  -IgG Anti PF4/heparin antibodies present  -No proximate exposure to heparin  -"Severe" thrombocytopenia  -observed with the buffer 14 days after vaccination) condition and addition of  -human PF4 had clearly a FcɣRIIa dependent platelet activation potentiating effect (resembles 'spontaneous' -'autoimmune'  -HIT) Laboratory data only
			available in some cases
	 -Previous or COVID-19 patients may develop high levels  -Majority of cases reported current of IgG anti-PF4 antibodies (no polyanionic negative results for SARS-SARS-CoV-2 macromolecule). CoV-2 PCR and serological infection b investigations. a

 -

 -COVID-19 patients were reported positive with SRA even in absence of heparin.