Medical Cyber-Physical Systems (MCPS) hold the promise of reducing human errors and optimizing healthcare by delivering new ways to monitor, diagnose and treat patients through integrated clinical environments (ICE). Despite the benefits provided by MCPS, many of the ICE medical devices have not been designed to satisfy cybersecurity requirements and, consequently, are vulnerable to recent attacks. Nowadays, ransomware attacks account for 85% of all malware in healthcare, and more than 70% of attacks confirmed data disclosure. With the goal of improving this situation, the main contribution of this paper is an automatic, intelligent and real-time system to detect, classify, and mitigate ransomware in ICE. The proposed solution is fully integrated with the ICE++ architecture, our previous work, and makes use of Machine Learning (ML) techniques to detect and classify the spreading phase of ransomware attacks affecting ICE. Additionally, Network Function Virtualization (NFV) and Software Defined Networking (SDN)paradigms are considered to mitigate the ransomware spreading by isolating and replacing infected devices. Different experiments returned a precision/recall of 92.32%/99.97% in anomaly detection, an accuracy of 99.99% in ransomware classification, and promising detection and mitigation times. Finally, different labelled ransomware datasets in ICE have been created and made publicly available. The increasing resilience to antibiotics, an ageing population, the epidemic obesity, or the impact of pollution are factors that increase the difficult for hospitals and care centres to effectively care for patients globally. Hospitals are constantly incorporating technological innovations to face these aspects and improve the quality of the healthcare provided within their borders in the hospital rooms of the future. In this context, new paradigms such as the Internet of Medical Things (IoMT) , and MedMCPS refer to safety-critical interconnected medical systems that analyse patients’ vital signs gathered from medical devices, infer the state of the patient’s health, and initiate treatments issuing information to doctors or directly to medical actuators. This disruptive vision has the potential to enable in a cost-efficient way the next-generation of healthcare, which requires systems able to interoperate efficiently, safely, and securely . In thisWith the goal of making possible the MCPS vision, the ASTM F2761 standard  proposesExisting protection mechanisms like anti-viruses, intrusion detection systems (IDS), or firewalls are not suitable to detect cyberattacks performed by novel or unseen ransomware. It is due to the fact that their functioning relies on having metadata like, for example, signatures of known cyberattacks.Not all the traffic patterns generated by ransomware and malware are distinguishable from the normal traffic patterns generated by medical devices and systems with networking capabilities. In this sense, both a malware encrypting a shared folder and an application compressing the same files have a similar traffic pattern. Similarly, normal changes in the clinical environment can be misinterpreted as attacks if the detection mechanisms do not adapt properly. In addition, malware developers are increasingly using encrypted traffic to avoid payload inspection.Achieving an acceptable balance between detection and false alarm rates is a difficult task. A high false alarm rate can be rather frustrating for the administrator and a low detection rate can make the system ineffective.As it has been demonstrated, ransomware attacks present a critical cybersecurity problem affecting the healthcare discipline. Below, we highlight some of the most important open challenges oriented to the detection and mitigation of ransomware affecting integrated clinical environments :ExistingIn this context, Machine Learning (ML) techniques  based onIn order to improve the previous challenges, the main contribution of this article is the design, implementation and validation of an automatic and intelligent system to detect, classify, and mitigate ransomware attacks affecting hospital rooms of the future equipped with ICE. The proposed solution detects and classifies in real time both well-known and unseen ransomware attacks in ICE by analysing the network flows generated during their spreading phase. In addition, once the ransomware has been detected and classified, the proposed mechanism automatically mitigates it by using NVF/SDN techniques to isolate and replace infected devices, avoiding the ransomware spreading across the clinical network. Specifically, we isolate infected medical devices through the SDN paradigm as well as replace their software controllers using NFV techniques. Both the detection and mitigation mechanisms are fully integrated into our previous work, the ICE++ architecture . The ICEAnother relevant contribution of this article is a set of experiments that demonstrate the effectiveness of our solution detecting some of the most recent and dangerous malware . In this sense, the selected techniques for both anomaly detection (One-Class Support Vector Machine) and ransomware classification (Naive Bayes) obtained a high precision with known and unseen ransomware samples. Additional experiments demonstrated the viability of the proposed solution in terms of time. In the worst case, our solution detected and mitigated a ransomware attack in less than 30 s, which is an acceptable time because the fastest ransomware required more than 1 min to spread and infect ICE devices. Additionally, as a final contribution, during these experiments, a publicly available labelled dataset was created containing the traffic capture and the resulting netflows from our ICE configuration for both clean and ransomware propagation traffic . In concThe remainder of the paper is structured as follows. This section gives an overview of the cybersecurity concerns found in the current state of ICE and MCPS solutions. Historically, medical devices have been developed as stand-alone systems without communication capabilities. However, the MCPS vision is emerging to provide interoperability, safety, and security to clinical environments. In this context, key characteristics in the search for appropriate technology, regulations, and ecosystems to ICE are highlighted in . NowadayIn the literature, there are different solutions oriented to analyse and mitigate the security and privacy issues of healthcare and MCPS . In thisIn addition to the previous security mechanisms proposed in ICE, in the literature, we can find solutions in heterogeneous scenarios that leverage SDN to detect/mitigate ransomware attacks, some of them using different ML techniques for detection purposes. However, to the best of our knowledge, none of them has integrated the combination SDN/NFV with the ICE standard to be able to replace infected elements such as ICE Equipment Interfaces or ICE Supervisor in a few seconds. Moreover, the detection of the ransomware spreading in ICE with encrypted traffic has not been addressed yet.Among the ransomware mitigation solutions based on SDN, Ref.  uses SDNAnother relevant and recent paper related to SDN and ransomware is . In thisIn , the autRegarding other types of malware, the authors of  proposedThe previous solutions improve in a proactive and reactive way the cybersecurity challenges of clinical and heterogeneous environments. However, they do not consider the real-time and autonomic detection and mitigation of ransomware during its spreading phase. Additionally, these solutions are not integrated into an adaptive and flexible management architecture, which is able to manage the security mechanisms according to the contextual information . To the best of our knowledge, this paper proposes the first solution combining ML techniques to detect and classify ransomware as well as the use of NFV/SDN techniques combined with MCPS to provide a flexible management of the ICE resources in real time and on-demand.This section presents a clinical scenario that shows real issues caused by a ransomware attack affecting hospital rooms of the future. For the sake of clarity, we focus the explanation on the behaviour of the WannaCry ransomware family. However, it is important to note that the solution proposed in this paper is generic enough to detect and mitigate other families of ransomware, as it has been demonstrated in ICE Equipment Interfaces, which are attached to medical devices to enable their networking capabilities; the ICE Supervisor, focused on hosting medical applications that receive and control the patients’ vital signs; the ICE Network Controller, in charge of enabling the communications between the supervisor and the ICE equipment interfaces, as well as handling and maintaining the discovery of medical devices and their information; the Data Logger, focused on troubleshooting and forensic analysis; and External Interface, which enables the communication with external hospital resources such as Electronic Health Records (EHR). With the goal of facilitating the interoperability of the elements comprising the hospital rooms of the future and making possible the MCPS vision, the ASTM F2761 standard  proposesOver the last few years, ransomware has gained relevance as a devastating method of cybercrime in healthcare environments. Locky , SamSam To contextualize, ransomware is a type of malware that encrypts the files of infected devices (affecting the availability of data and/or device) and requests a ransom to decrypt the files and avoid information losses. Recent ransomware attempts to exploit system vulnerabilities to spread out rapidly through the networks and affect as many devices or machines as possible. This propagation process is much more harmful when the number of systems is significantly larger. Nowadays, there are several families of ransomware with important differences in terms of encryption and spreading behaviours. One of the most recent and malicious families is WannaCry. This ransomware spreads automatically across the network by exploiting a vulnerability in the MS Windows based Server Message Block (SMBv1). Despite this vulnerability having already been fixed by Microsoft, it is critical to have mechanisms able to detect unseen or known ransomware exploiting well-known or undetected vulnerabilities. Otherwise, hospital rooms of the future equipped with a large number of interconnected medical devices and computational systems will suffer ransomware attacks, affecting the patients’ safety and privacy.Spreading phase: The WannaCry ransomware attempts to spread itself across the hospital network to infect vulnerable medical devices and computers. For that goal, WannaCry uses EternalBlue, an exploit developed by the National Security Agency (NSA) that attacks a vulnerability of the MS Windows based Server Message Block (SMBv1) protocol. After a successful exploitation, the DoublePulsar payload is sent to run remote code and infect the medical devices and computers connected to the ICE network.Encryption phase: It can be performed before, in parallel, or after the spreading phase (depending on the ransomware version and family). During this phase, the medical database as well as other data files are encrypted. Once the process is completed, WannaCry asks for a ransom to decrypt the medical database. Ransomware belonging to the WannaCry family generates and saves a new RSA key pair, which is used to encrypt the medical database and the target data files. Once the previous files are encrypted, WannaCry deletes the original ones, and communicates with an Onion server using a Tor server to transfer the encryption keys. When the ransom is paid, WannaCry obtains the decrypted RSA private key from the Onion server and decrypts the ransomed files.As an example and to illustrate the ransomware impact in the hospital room of the future, we consider that the ICE Supervisor becomes infected by WannaCry. As it has been demonstrated at the beginning of this section, this situation is not unreal and it can happen due to plugging external devices with malicious or infected files, social engineering, weaponized files, or phishing. Once the ICE Supervisor is infected, WannaCry attempts to connect to a predefined domain. If the site was registered, the ransomware is not executed, acting as a killswitch. In contrast, if the connection fails, the ransomware starts with the following two phases:As it has been explained in This section presents the design details of our intelligent and automatic solution to detect, classify and mitigate ransomware attacks in hospital rooms of the future. Having in mind the clinical scenario defined in The following subsections, together with The Monitoring module is in charge of acquiring in real time the network traffic generated by medical devices and systems belonging to ICE, calculating network flows, grouping them in a time sliding window and computing a feature vector from each group or batch of flows.The Offline Model Generation module, in contrast to the rest of modules, requires human supervision. It is only executed during the system bootstrapping, or when it is required to update the existing ML models due to the detection of a new ransomware. This module receives feature vectors for a given period of time , generates a dataset suitable for the training process, selects the proper ML algorithms to detect and classify ransomware in ICE, and trains those algorithms.The Analyser module receives the trained ML models from the previous module and uses them to evaluate in real time the current feature vectors. The anomaly detection method will identify traffic pattern novelties , whereas the classifier will label the traffic coming from known ransomware.The Decision & Reaction module combines the evaluations of the two ML models to estimate the risk of having an active ransomware attack. For that, it uses rules, predefined by the system administrator, to decide in real-time proper mitigation actions like, for example, the isolation and replacement of infected medical devices, or the retraining of ML models.This is the first module of our system and focuses on monitoring in real time and continuously network packets exchanged between the medical devices and databases of ICE. After that, network packets are processed and translated to network flows. The main reason for using network flows is because some pieces of clinical data travel encrypted, even certain ransomware cyphers its spreading traffic, which makes it useless to inspect the network packets. In this point, it is important to consider that we define a network flow as all the traffic exchanged in both directions, during a given connection sharing the same values of source IP, destination IP, source port, destination port and protocol.The Flow Exporter  and the medical application (ICE Supervisor). In these cases, our flow collector is configured to generate a new network flow periodically (triggered by a timeout that prevents a large flow from appearing only once), which would affect the performance and detection time of our solution see . In our The Flow Collector  is to label the datasets according to its purpose, and to divide them into training, validation and test. This is clearly necessary in the case of our supervised classification method. On the other hand, for the anomaly detection method, there is no need of an explicit labelling for the training. However, in this case, it is required an explicit labelling to evaluate the performance of both the detector and the classifier. More specifically, the anomaly detector needs a binary label , whereas the classifier needs as many labels as classes. Subsequently, a first feature selection is done by computing the Pearson’s correlation coefficient for each pair of components in the feature vector. This correlation matrix of the dataset allows us to identify sets of highly-correlated features and keep just one feature of each set. This procedure is done in both 3.3A and 3.3B steps. An additional feature selection stage could reduce significantly the number of features of the datasets. However, this phase is not carried out on the datasets used with the anomaly detection methods. The main reason is that a more aggressive feature selection procedure based on the already-known ransomware classes could discard features useful for detecting future anomalies. This additional feature selection is only carried out on the dataset for the supervised ransomware classifier (3.3A). To this end, we use a Random Forest (RF)  supervisThis component is in charge of determining the most suitable detection and classification techniques for ICE in terms of performance (steps 4.A and 4.B). For that goal, below we show the variety of initial methods considered for both classification and anomaly detection.Anomaly detection techniques–One-class Support Vector Machine (OC-SVM), –Local Outlier Factor (LOF), –Isolation Forest (IF), Probabilistic classification techniques –Neural Network (NN),–Naive Bayes (NB),–Random Forest (RF).Regarding the anomaly detection (step 4.A), our system uses the OC-SVM technique. OC-SVM belongs to the class of boundary-based classification algorithms. The training process of this method consists of finding a function which defines a boundary surrounding the normal traffic patterns of our ICE. This function returns a positive value for points belonging to the inner area defined by the boundary, and a negative value for outsiders. The anomalous traffic pattern generated by the ransomware propagation ought to fall in the negative side.On the other hand, with regard to the probabilistic classification techniques (step 4.B), the goal is to determine to what extent they can deal with ransomware sharing key spreading aspects with the samples used in training. In this sense, probabilistic classification methods, whose output is an estimation of the likelihood of the vector belonging to each class that can help to reach this goal. Even if an input vector does not belong to any known class, the probability distribution provided by the algorithm can give some useful information to the system administrator about the behaviour of the potential malware. In this sense, the proposed system uses Naive Bayes because it provided the best probability estimation of the belonging of the new samples to the class with the most similar traffic pattern. Analyser module.Once the best ML techniques have been selected, this component is in charge of re-training the ML techniques  with the whole dataset (not only the training subset) in order to obtain a model that takes advantage of all the samples available. These processes are performed by steps 5.A and 5.B of In the anomaly detection model case (step 5.A), an automatic retraining process can be triggered if the configuration of the ICE changes significantly . On the other hand, the supervised classifier needs a human-driven dataset generation process to be updated.Decision & Reaction module which will interpret the results and will react in consequence to mitigate the potential ransomware attack.The Analyser is the module that evaluates in real time each incoming feature vector from the medical devices and systems of ICE by means of previously trained models . The output of the anomaly detection  allows the system administrator to predefine rules that control in real time and on-demand the mitigation reactions. These rules consist of two lists of predicates: the antecedent and the consequent. If all the predicates of the antecedent part take the Boolean value true, all predicates in the consequent part are evaluated. The antecedent considers conditions related to the Analyser outputs and the consequent indicates the action taken over the datasets, NFV or SDN paradigms to mitigate the ransomware.In the context of our hospital rooms of the future equipped with ICE, we define three types of mitigation rules: Control plane, Data plane and Hybrids.v is the feature vector evaluated by the two models; Control plane rules are used to manage the different components and modules making up the control plane of the ICE++ architecture. Among the potential actions taken by these policies, we highlight the retraining of ML models when unseen ransomware strains are detected, the set-up of new time windows, or the management of the architecture hardware resources. As an example, the next rule retrains the Anomaly Detector ML model when a new medical device is integrated into ICE. In this particular situation, the rule indicates that the anomaly detector needs to be retrained if it has detected an anomaly and the classifier considers that it is more likely that the vector belongs to normal traffic:Data plane rules are oriented to control the medical devices, systems, EHR, and medical applications of ICE. Potential reactions could be the isolation of infected medical devices or systems, the replacement of medical controllers, or the communication management. As an example, the next rule indicates that a medical device must be isolated and replaced if an anomaly has been detected and the classifier certainly believes that the vector belongs to one of the known ransomware. In other words, the ICE is experiencing a propagation stage of a ransomware sufficiently similar to one of the known samples:Hybrid rules are a combination of the two previous families. The consequence of these rules implies changes in both control (ICE++ Architecture) and data hospital rooms of the future devices and systems. The next rule shows an example where it is required to isolate and replace a medical device as well as retrain the classifier model. In this case, the Anomaly Detector says that there is an anomaly and the Classifier is not able to establish a classification with enough guarantees. This situation is provoked by an attack conducted by an unseen ransomware, which behaviour is not similar to any of the well-known ones:The Reaction and Notification component (step 8) is in charge of interacting with two different modules. On the one hand, it interacts with the Offline Model Generation module when it is required to retrain a given ML module. On the other hand, it interacts with the Orchestrator module see  to schedThis section describes the ICE++ architecture, and how it integrates the modules making up the proposed solution to detect, classify, and mitigate ransomware in ICE  and the ICE System Management. The OSS deals with the logic of the ICE system. This element provides the system administrator with an interface to define the rules controlling the architecture behaviour. On the other hand, the rules are provided to the ICE system management in order to identify concrete actions and orchestrate their enforcement.Monitoring, Analyser, Decision and Reaction, and Orchestrator. They are in charge of detecting and mitigating ransomware attacks in ICE. ME applications: ICE components  to replace infected systems of the hospital room of the future.ME services: For example, an ME service could be in charge of assuring the level of security of the ICE elements. In particular, it could establish specific authentication mechanisms to ICE applications or determine a particular security protocol  between the ICE elements.Network infrastructure: For example, virtual SDN-based networks to isolate infected ICE components. Another alternative could be the automatic management of the network communication to block ransomware attacks.The ICE system management is composed of the next elements: Finally, the Orchestrator is responsible for scheduling and triggering the previous countermeasures as well as maintaining an overall view of ICE status .Mobile edge host and the Mobile edge host level management.This level focuses on running the mitigation countermeasures  in ICE. This level is composed of two elements: the Mobile edge applications, a Mobile edge platform, and a Virtualization infrastructure. ME applications could be instantiated as the ICE element defined by the ICE framework as well as some applications oriented to improve the security of the clinical scenario. Applications run as virtual machines (VM) and containers on top of the virtualization infrastructure allocated at the edge of the network. This fact provides our solution with the flexibility, efficiency, and low latency required by ICE. The virtualization infrastructure can use the hardware resources of computers, BeagleBones Black, or even medical devices, depending on the scenario configuration. ME applications interact with the ME platform to consume and provide services. Specifically, the ME platform is a set of essential services required to run ME applications on a particular virtualization infrastructure. These services can be specific for given applications or even shared among some of them. Examples of services could be secure communication protocols (like Transport Layer Security) or traffic rules control.On the one hand, the ME host provides ICE with compute, storage, and services for running ME applications. To reach it, this entity contains Mobile edge platform manager and the Virtualization infrastructure manager. The ME platform manager is responsible for managing the life cycle of applications, including informing the ME orchestrator of relevant application related events. The Virtualization Infrastructure Manager is responsible for allocating, managing and releasing virtualized  resources of the virtualization infrastructure, as well as collecting and reporting performance and fault information about the virtualized resources.On the other hand, the ME host level management is composed of two elements: the Networks level is the lowest one and it contains two elements: the Networks and the Networks management. Networks contain the physical infrastructure required to provide connectivity between the different ME applications. The Networks management contains the SDN Controller, which is able to monitor and manage in real time and on-demand the communications of ME applications.Finally, the ICE Administrator, Clinicians & Operators, and Patients. The ICE Administrator is responsible for defining the logic of the system in a high level. The rules defined in The actors composing the proposed architecture are: Orchestrator enforces the mitigation actions taken by the second rule defined in Once the main components of the architecture has been explained, this section shows how the Regarding the enforcement of the rule in charge of replacing and isolating the infected medical device, the Orchestrator receives the notification and interacts with the virtualization infrastructure manager (VIM) to create the new ICE Equipment Interface in the available hardware located in our hospital room of the future . In this point, the Orchestrator interacts with the SDN Controller to add a new rule in the flow tables of the network switch and drop the packages belonging to the spreading phase of the ransomware (step 8). Finally, the SDN controller confirms the Orchestrator that the flow tables have been modified with the new rule (step 9). The time required to perform the deployment of a new ICE Equipment interface in different hardware resources has been calculated in This section presents a pool of experiments focused on demonstrating the viability of our intelligent and automatic solution in charge of detecting, classifying and mitigating ransomware attacks in hospital rooms of the future.There are two main aspects that should be considered to measure the viability of our proposal: how good our system is at detecting ransomware threats and how effective our system is at mitigating a ransomware propagation. The detection aspect is tightly related to the discriminative power of the trained ML algorithms. On the other hand, the mitigation aspect is related to both the time needed to detect the propagation and the ability to mitigate the ransomware before it manages to infect other medical devices and systems. To measure both aspects, firstly we have designed and deployed a realistic scenario where a hospital room of the future equipped with ICE is infected with different ransomware. After that, we performed some experiments to measure the detection and classification precision as well as the time required to mitigate the ransomware. The experiments were carried out on OpenICE, the open source implementation of the ASTM F2761 (ICE) , proposeFive medical devices able to acquire different vital signs of patients and apply treatments. The medical devices are simulated using ICE Equipment Interfaces of the OpenICE v1.0.0  softwareOne clinical database storing medical data such as EHR. The database is distributed across the machines with MS Windows. Additionally, there is a clinical information system in charge of reading and writing the database content through the SMBv1 protocol of Microsoft.One closed-loop application  implemenThe Monitoring module of the ICE++ architecture. This module has been deployed on a dedicated machine with Ubuntu 16.04 that receives all the ICE network traffic. To implement the flow exporter, we have used Argus v3.0.8.1 , which iThe Analyser module of the ICE++ architecture has been deployed in a dedicated machine with Ubuntu 16.04. The Anomaly Detection module uses Scikit-learn v0.20.0, which provides efficient implementations of the three evaluated ML methods. Similarly, the Ransomware Classification module, based on Naive Bayes, has been implemented also in Scikit-learn.The Decision and Reaction components of ICE++ runs on another Ubuntu 16.04 machine and we have used SWRL (Semantic Web Rule Language)  to definOne SDN-enabled router in charge of managing the communications of the previous elements through a private network. In addition, the router also enables some external communication through the Internet to allow doctors and caregivers to control remotely specific components of the clinical scenario.This section describes the design, deployment and initial set-up of our hospital room of the future to obtain a suitable dataset that allows us to perform some experiments. These experiments are aimed to measure the performance of the detection/classification methods as well as the time required to detect and mitigate different ransomware attacks. The designed scenario has the following elements:The previous elements were fully virtualized in a physical server where OpenStack  and OpenOnce our scenario was designed and deployed, the next step was to test our detection and mitigation solution in this scenario. To this end, we decided to select four of the most dangerous and recent malware affecting clinical environments. Specifically, three ransomware strains  and one cryptomining malware (PowerGhost). Once selected, we used two of them (WannaCry and Petya) to train our solution, and the other two to test its generalization power. WannaCry  is a welOnce the ransomware selection was performed, the next step was to create suitable datasets to later measure the performance of our detection and classification solution. For that, we captured six hours of clean network traffic from our hospital room of the future equipped with ICE. Then, we cloned four times our scenario. The ICE Supervisor machine of each scenario was infected with a different ransomware . The traffic generated in each ICE scenario during the ransomware spreading phase was captured in a different dataset. In the end, we had five datasets, one with the clean traffic dataset and four with the ransomware traffic. These datasets are one of the relevant contributions of this paper and they can be downloaded in . The amoFinally, it is well-known that some anomaly detection techniques do not work efficiently with large amounts of samples. Therefore, to avoid this restriction from affecting the technique selection process, we made up each dataset by taking all the anomalous samples  plus 100,000 randomly extracted clean samples , keeping the original order. In this way, we also reduce the imbalance of the dataset significantly. Each dataset was then partitioned into three contiguous subsets: training (80%), validation (10%) and test (10%). Usually, each subset of this partitioning is created by a random selection from the original dataset; however, in our case, the high correlation between close vectors made the contiguous split more appropriate. The resulting datasets were subsequently cleaned, and an unsupervised feature selection, consisting of dropping highly correlated features, was carried out as explained in This section performs an experiment to select the most appropriate anomaly detection method. Considering the five datasets acquired and explained in the previous section and the three potential anomaly detection methods listed in The first step was to select a set of representative anomaly detection techniques: OC-SVM, LOF and IF. Each one of these algorithms detects anomalies by attempting a different strategy. OC-SVM is a boundary-based technique and its description can be found in Every ML training procedure requires a feature engineering stage. In semi-supervised anomaly detection methods, only unsupervised feature selection procedures can be used . Otherwise, the feature selection process would be biased by the set of known ransomware, and it might discard features useful in future scenarios. This is the main reason why we did not select a subset of features by using our labelled dataset.True Positive (TP): Number of anomalies correctly detected.True Negative (TN): Number of normal samples correctly classified as normal.False Positive (FP): Number of normal samples incorrectly detected as anomalies.False Negative (FN): Number of anomalies incorrectly classified as normal.In order to calculate which of the previous three algorithms has the best detection performance, a metric of such performance should be defined. Given that anomalies are supposed to be rare, the accuracy is not considered a representative performance metric. In anomaly contexts, precision, recall and F1-score give more information about the detection performance of a model. To define these metrics, some concepts should be previously defined:Precision, indicates what percent of the predicted anomalies were actual anomalies,Recall or sensitivity, defines what percent of actual anomalies where predicted,F1-score, shows the trade-off between the precision and recall regarding the anomaly class,False Positive Rate (FPR),Below, we show the metrics proposed to measure the anomaly detection technique performance. F1-score is the harmonic average of precision and recall. Its result is closer to the lowest of the two values; therefore, it includes information about how balanced precision and recall are. In anomaly detection problems, both precision and recall are important. A low precision score tells us that we are generating a large number of false positives, and a low recall indicates that we are missing many anomalies:A key aspect of the training of every ML algorithm is the hyper-parameters optimization. Specifically, in our clinical environment, there are two sets of hyper-parameters: one of them associated with the anomaly detection model, and the other related to the classification model. In both cases, a set of hyper-parameters was selected to be tuned. Then, a grid search in the hyper-parameter space was carried out in order to figure out which configuration achieved the best performance. With regard to our anomaly detection algorithm, a list of the considered hyper-parameters can be found in The performance results are summarized in Besides the anomaly detection method, our system also considers a ransomware classifier. This section focuses on showing the experiments focused on selecting the most appropriate classifier in terms of performance.Three probabilistic well-known supervised classifiers were selected to be evaluated: Neural Network, Naive Bayes and Random Forest. Following the same methodology and metrics followed in the anomaly detector selection, the three algorithms were trained and validated. The selection process with these models was performed using only three datasets: Clean, WannaCry and Petya. The corresponding training and validation subsets were used to train the models in the hyper-parameter tuning phase. A grid search in the hyper-parameters allowed us to find the configuration providing the best performance for each model. The list of hyper-parameters considered is showed in It should be noticed that there is a hyper-parameter called Feature Selection. Since our classification dataset is properly labelled, we can do a more advanced feature selection by means of a Random Forest (RF). Before the training procedure, an RF is trained to obtain an estimation of the importance of each feature in the classification process. This importance score allows us to drop every feature with an importance below a given threshold  and the classifier (Naive Bayes) are selected, another critical aspect is to measure how much time these techniques need in order to evaluate, in real time, a current feature vector and decide whether it is an anomaly or not. At this point, it is important to remember that both processes  are performed in parallel and their outputs are subsequently combined by the rules defined in Regarding the anomaly detection system deployed in the analyser module of our ICE++ architecture , we measThis section demonstrates the suitability of the proposed solution when a ransomware, affecting our hospital room of the future equipped with ICE, needs to be mitigated. Specifically, we have performed several experiments with the goal of measuring the time required by the ICE++ architecture to deploy, launch and configure an ME application with a new ICE Equipment Interface of OpenICE that simulates a medical device  CPU i7-3770 at 3.40 GHz. The operating system is Ubuntu 16.04 LTS Desktop; andServer with 64 GB or RAM, a 36 Core Intel(R) Xeon(R) CPU E5-2697 v4 at 2.30 GHz, and Ubuntu 16.04 LTS Server as an operating system.To deploy and configure the ICE Equipment Interface, we used the following three different configurations of hardware:Creation of three VM with 1 GB of RAM, a single-core CPU, and 10 GB of hard disk, each one of them using OpenStack.Installation of three operating systems with different capabilities , each in its own VM.Installation and configuration of the ICE Equipment Interface simulating a medical device, on top of the previous operating systems.Measurement of the time required to instantiate each previous VMs in each piece of hardware equipment.Measurement of the time required to launch and configure a simulated medical device in the ICE Equipment Interface running on top of the hardware equipment.For each one of the previous hardware configurations, we developed and launched an OpenICE instance using VM and containers. The first experiment focused on measuring the time required to deploy the ICE Equipment Interface on top of different VMs. For that purpose, we followed the next steps:At this point, it is important to clarify that, due to the limited computational resources of the Raspberry Pi 3, this device was not able to host virtual machines.Java SE Runtime Environment 8 (library required by OpenICE) to execute the version 0.7.0 of OpenICE. After implementing and configuring the Docker, we measured the time required to deploy and launch a simulated medical device of OpenICE. After performing the previous experiment with VMs, we carried out an additional experiment with containers. Specifically, we deployed the ICE Equipment Interface of OpenICE in a Docker that uses One of the main conclusions obtained after performing the previous experiments is that the time required to deploy the ICE Equipment Interface is inversely proportional to the amount of computational resources needed. However, it is important to keep a trade-off between resources and economical cost. Therefore, the Raspberry Pi 3 is not suitable for real-time medical scenarios where ransomware spreads quickly, and medical devices must be deployed considering critical time restriction. In contrast, medical scenarios with other requirements in terms of latency can be perfectly managed by the Raspberry Pi 3. Another conclusion we can extract from This section demonstrates that our solution is able to detect and mitigate the previous families of malware  in an acceptable time. We consider as acceptable any time lower than the required by the ransomware to spread across ICE and infect the second medical device or machine.As a conclusion, we have demonstrated that our intelligent and automatic solution is suitable for detecting and mitigating the spreading phase of different ransomware families in real time. In this context, the performed experiments provide promising results in terms of time and precision to avoid different ransomware from spreading across the integrated clinical environments.This paper presents an automatic, intelligent and real-time system able to detect, classify, and mitigate ransomware attacks in hospital rooms of the future. The proposed solution is fully integrated with the ICE++ architecture, our previous work, and makes use of Machine Learning techniques to detect and classify the spreading phase of ransomware attacks affecting ICE. Another relevant contribution of this paper is the proposed mitigation mechanism, which considers NFV/SDN paradigms to stop the spreading by isolating and replacing infected medical devices and systems.On the other hand, a pool of experiments have demonstrated the suitability of our solution detecting some of the most recent and dangerous malware . The selected techniques, OC-SVM and Naive Bayes, have proved able to detect and classify ransomware affecting ICE. OC-SVM obtained the 92.32% of precision and 99.97% of recall in anomaly detection, whereas Naive Bayes obtained a 99.99% of classification accuracy. Both of them were evaluated with known and unseen ransomware samples. Additional experiments demonstrated the viability of the proposed solution in terms of time. In the worst case, our solution detected and mitigated a ransomware attack in 29.7 s, which is acceptable because the ransomware required at least 63.1 s to spread and infect other devices. Finally, different labelled ransomware datasets in ICE have been created and made publicly available.As the next step, we are already working on the evaluation of our proposal with new ransomware families having more complex spreading behaviour patterns. In this context, the inclusion of new anomaly detection and classification techniques will be required to maintain the accuracy level obtained as output of this work. Additionally, we plan to extend our solution and analyse the encryption of shared folders, where the ransomware traffic patterns could be seen as time series data and classified by deep learning methods like Long Short-Term Memory neuronal networks (LSTM). We will also include ML techniques to detect the infection/encryption ransomware stages by means of particular system call patterns and internal processes executed on the medical devices. The combination of the solution proposed in this paper (focused on detecting the ransomware spreading phase) together with the analysis of the infection/encryption phase will improve the robustness and accuracy of our detection system. Another line of work that we are considering is the automatic generation of signatures and metadata to feed existing countermeasures such as IDS and anti-virus in real time and avoiding massive ransomware from spreading.


In vitro differentiation of human pluripotent stem cell into relevant cell types is a desirable model system that has the human biological context, is a renewable source, and is scalable. GABA interneurons and basal forebrain cholinergic neurons, derivates of the medial ganglionic eminence (MGE), are implicated in diverse neuropsychiatric diseases. Various protocols have been proposed to generate MGE progenitors: the embryoid body- (EB-) based rosette-derived (RD), the adherent (AdD), and the nonadherent (NAdD) approaches. While Wnt inhibition is frequently incorporated into the strategy, the timing varies between protocols and there is a lack of standardized outcome reporting, which precludes direct comparison. Here, we report a head-to-head comparison in three distinct experimental models to establish whether Wnt inhibition during neural stem cell, NSC (stage 1), or neural progenitor cell, NPC (stage 2), formation facilitates MGE differentiation. Wnt inhibition at both stages promotes MGE progenitor differentiation when compared to no inhibition. However, NSC (stage 1) Wnt inhibition markedly reduces the number of MGE progenitors available for downstream applications in the RD and the NAdD protocols due to early inhibition of proliferation. NPC (stage 2) Wnt inhibition in the adherent system is comparable to the EB-based methods offering a techically less challenging alternative. Using iPSC derived from mutation carriers, this model system can complement genetic association studies for functional characterization of mutations/variants and elucidate the cell type-specific mechanism in Mendelian diseases.Medial ganglionic eminence (MGE) progenitors arise from the ventral forebrain  and In vitro differentiation of hESC/iPSC into specific types of neurons involves neural induction and patterning first along the rostro-caudal (R-C) and subsequently along the dorso-ventral (D-V) neuraxes, mimicking the embryological developmental processes. R-C patterning of the CNS consists of expansion of the neural tube and demarcation of the forebrain, midbrain, hindbrain, and spinal cord, followed by patterning of the telencephalon along the D-V neuraxis. R-C and D-V patterning is driven by morphogen expression in a regulated temporal and spatial distribution. The most studied morphogens are bone morphogenic proteins (BMPs), fibroblast growth factors (FGFs), and Wnt proteins. Similar to in vivo differentiation, regional identity of neural progenitors in vitro is determined by the gradient of morphogens with opposing effects (reviewed in . NSC were fed every other day. At day 22, NPCs were characterized.Nonadherent protocol was adapted from Crompton et al. . Undiffeμg/ml) was added during either neuroepithelia/NSC formation (stage 1) or NPC formation (stage 2) as marked in the experimental protocols  was added from day 4 until day 12 in the NAdD protocol.Wnt inhibitor IWP-2  listed in Supplementary data, T method and normalized to GAPDH expression. Data are presented as the average of the replicates ± standard error of the mean.Total RNA was isolated from cell cultures using TRIzol (Invitrogen) according to the manufacturer's protocol. cDNA was synthesized from 500 ng of total RNA using ImProm-II Reverse Transcriptase (Promega) and oligo (DT) (Promega). The reaction was carried out at 42°C for one hour. For quantitative gene expression, standard RT-qPCR was performed using the primers .Cells plated on 8-well glass chambers (Thermo Fisher) were fixed with 4% paraformaldehyde  for 15 minutes, permeabilized with 0.1% Triton X100  for 10 minutes, and blocked with blocking buffer (5% BSA in PBS) for 1 hour at room temperature (RT). Cells were incubated overnight at 4°C with primary antibodies  according to the manufacturer's protocol.Values are expressed as means ± SD or ± SEM, as indicated in figure legends.t-test (two-tailed). p values less than 0.05 were deemed statistically significant.Statistical significance was determined by an unpaired Student μM). At D21, NPCs were collected for gene expression analysis (NKX2.1 and LHX8) is the highest and expression of a dorsal marker (PAX6) is the lowest in the groups either treated with high concentration of SHH  or PUR or cotreated with IWP-2 . The effect of IWP-2 is the highest in the context of 200 ng/ml SHH as compared to 500 ng/ml SHH, and it is higher than 500 ng/ml SHH alone. As the current study is aimed at demonstrating the importance of timing of Wnt inhibition for generation of MGE progenitors, the concentration of SHH 200 ng/ml was chosen for subsequent experiments.Preliminary experiments were conducted to address the following questions: which concentration of SHH is optimal for generation of MGE progenitors? Is FGF8 needed for MGE generation? Does Wnt inhibition facilitate the MGE fate? Can SHH be substituted with PUR? The AdD differentiation protocol was employed: neuroepithelia (D7) were treated for 14 days with either SHH alone at a concentration of 200 ng/ml  or 500 ng/ml  or in combination with FGF8, 100 ng/ml , or with a Wnt inhibitor, IWP-2 , midbrain (EN1), and hindbrain (HOXC6) markers. The results revealed that (1) treatment with SHH alone or with SHH and IWP-2 neither during neuroepithelium formation (stage 1) nor during NPC generation (stage 2) affected the expression of FOXG1 (FOXG1 expression was high in nontreated (NT) cells. (2) In contrast, EN1 expression declined in the cells treated with SHH alone compared to NT cells and treatment with IWP-2 (at both stage 1 and stage 2) decreased it further  ExpPAX6) and ventral (NKX2.1) forebrain markers. RT-qPCR data showed that the expression level of PAX6 was the highest in untreated cells; treatment with SHH alone and in combination with IWP-2 during both stages of Wnt inhibition significantly decreased PAX6 expression  resulted in only a 1.7-fold elevation of the ventral marker expression  and was equal to the SHH only-treated cells if Wnt inhibition occurred in stage 2 , pression . As expen, MAP-2 .+/PAX6− forebrain progenitors. NKX2.1 and PAX6 expression at the protein level was assayed by ICC and quantified by PAX6+ and NKX2.1+ cell count . FOXG1), midbrain (EN1), and hindbrain (HOXC6) markers are expressed in untreated cells, NT  caused a 2.5-fold increase in NKX2.1 expression , treatment with SHH alone and in combination with IWP-2 treatment, especially during stage 2, leads to significant ventralization of neuroepithelia as shown by increased NKX2.1+ cell count , with WFOXG1 was increased around 12 times by IWP-2 treatment during neuronal initiation/rostralization (stage 1) and by about 3.6 times when inhibition of Wnt signaling occurred during stage 2 ) was contrasted between the three distinct differentiation strategies and between stage 1 and stage 2 inhibition within each protocol (p = 0.022), from 64% to 79% in the NAdD (p = 0.035), and from 55% to 71% in the AdD differentiation systems. At the same time, Wnt inhibition at stage 2 resulted in a 20% increase in purity of the MGE progenitor culture in both the RD and AdD protocols (from 56% to 76% (p = 0.027) and from 55% to 75% (p = 0.045), resp.) in comparison with the baseline. Purity of MGE progenitors was not affected by stage 2 inhibition in the NAdD differentiation system.Purity of MGE progenitors  to 78% in stage 1 and to 76% in stage 2 of Wnt inhibition. However, early treatment with the Wnt inhibitor (stage 1) results in a lower number of generated MGE progenitors. Increase in purity of MGE progenitors caused by inhibition of Wnt signaling during NPC stage is consistent with Li et al. [In the RD approach, both stage 1 and stage 2 inhibitions promote expression of MGE markers. Based on NKX2.1i et al. .Similar to the RD protocol, inhibition of the Wnt pathway during the NSC stage is associated with a lower number of cells due to early block of proliferation in comparison with treatment with a SMAD inhibitor, SB421543, alone in the NAdD protocol. On the other hand, it improves purity of MGE progenitors from 65% at baseline to 79% in stage 1. Stage 2 inhibition has no statistically significant impact on purity of MGE progenitors due to high variability in sizes between individual neurospheres. It is worthwhile to note that this is the first attempt to supplement the NAdD protocol previously used for BFCN generation [in vivo, EB formation followed by manual rosette picking is labor-intensive and time-consuming and has limited scalability. Furthermore, EBs are very sensitive to changes from experiment to experiment, which in turn leads to variability in the quality of neuroepithelia. To improve yield and consistency of NSC and at the same time reduce technical challenges, the adherent “dual SMAD inhibition protocol” was introduced in 2009 [While EB-based differentiation protocols better mirror neural differentiation  in 2009 . Since t in 2009 , 30. In  in 2009 . Similar in 2009 , 20, thi in 2009 , 20. HowLHX8 and GBX1 transcription factors or treating them with BMP9 [LHX8/LHX6 expression. While inhibition in the NSC stage (stage 1) upregulates LHX8, the transcription factor needed for BFCN differentiation [LHX6, the transcription factor required for GABA interneurons [MGE progenitors give rise to two neuronal subtypes—GABA interneurons and BFCN. The ratio of GABA interneurons to BFCN is significatly shifted in favor of GABA interneurons; 86%—GABA versus 14%—BFCN , transfeith BMP9 , or withith BMP9 . Here, wntiation , inhibitrneurons , suggestIn summary, stage 1 inhibition substantially increases the purity of MGE progenitors. Purity of the cell-based model systems determine the signal-to-noise ratio in downstream application, thus improving homogeneity in an active research field. The profound contribution of the MGE-derived cell types to human neuropsychiatric diseases and the current need of robust models make these efforts a priority. Further systematic protocol development strategies are needed to optimize the models.


Early life exposure to microbes plays an important role in immune system development. Germ-free mice, or mice colonized with a low-diversity microbiota, exhibit high serum IgE levels. An increase in microbial richness, providing it occurs in a critical developmental window early in life, leads to inhibition of this hygiene-induced IgE. However, whether this inhibition is dependent solely on certain microbial species, or is an additive effect of microbial richness, remains to be determined. Here we report that mice colonized with a combination of bacterial species with specific characteristics is required to inhibit IgE levels. These defined characteristics include the presence in early life, acetate production and immunogenicity reflected by induction of IgA. Suppression of IgE did not correlate with production of the short chain fatty acids propionate and butyrate, or induction of peripherally induced Tregs in mucosal tissues. Thus, inhibition of IgE induction can be mediated by specific microbes and their associated metabolic pathways and immunogenic properties. The prevalence of allergies has been increasing over the last 50 years. The hygiene hypothesis postulated a link between decreased microbial exposure and increased type 2 immune responses . Later oElevated total serum IgE levels are a hallmark of allergies but are also observed in multiple immunodeficiencies, including some characterized by deficiencies in regulatory T cells , 7. We hClostridia species, have been shown to induce pTreg differentiation via their production of the short chain fatty acid (SCFA) butyrate in the colon  DNA staining and 16S rRNA gene amplicon sequencing of their intestinal contents were performed to confirm their colonization status. The offspring of these gnotobiotic breeding pairs were analyzed at 10–13 weeks of age, unless stated otherwise. SPF mice were either purchased from Envigo (Switzerland) or bred and maintained at the University of Calgary. The SPF mice at the University of Calgary were fed the same autoclaved diet as their germ-free and gnotobiotic counterparts. Although two different SPF colonies were used, one in Switzerland  and another in Canada (in house SPF colony) we did not observe any significant differences in our readouts between these two SPF colonies. The only data derived from Envigo SPF mice are n = 18 of a total of n = 80 mice shown in C57BL/6J mice were re-derived to germ-free status via two-cell embryo transfer. Axenic and gnotobiotic mice (germ-free and precisely colonized) were bred and maintained in flexible-film isolators at the Clean Mouse Facility (CMF), University of Bern, Switzerland or at the International Microbiome Centre (IMC), University of Calgary, Canada. The germ-free and gnotobiotic C57BL/6J strain used in Bern and Calgary were identical. For some experiments, mice were housed in individually ventilated isocages (Tecniplast) in the IMC. Germ-free status was routinely monitored by culture-dependent and—independent methods and all germ-free colonies were independently confirmed to be pathogen-free. To start gnotobiotic mouse colonies, germ-free breeding pairs were gavaged with single or mixed bacterial cultures, as indicated. Gram staining, SYTOX2, 10% CO2, and 80% N2 while in a Whitley A95 anaerobic incubator before being sealed and inoculated with specific bacterial species.Bacteria were cultured in Brain Heart Infusion (BHI) medium as previously described . BottlesAkkermansia muciniphila YL44, 0.25 g/L of autoclaved Type II hog gastric mucin (Sigma) was added to the filter-sterilized BHI media. Bacterial cultures were centrifuged for 10 min at 3,000 g, washed twice and re-suspended in sterile-filtered PBS/2%BSA/0.02% sodium azide and diluted to ~107 bacteria/ml (OD595 0.1 = 108 bacteria/ml). Serum was diluted 1:10 in PBS/2%BSA/0.02% sodium azide, heat-inactivated at 56°C for 30 min and centrifuged at 16,000 g for 5 min at 4°C to remove any bacteria sized contaminants. This serum supernatant was used to perform serial dilutions. 2.5 × 105 bacteria were added to each well and incubated with the serial dilutions of serum for 1 h at 4°C, centrifuged for 10 min at 3,000 g, washed twice and then re-suspended in monoclonal APC rat anti-mouse IgG1  (BD) and BV605 rat anti-mouse IgA  (BD) and incubated overnight at 4°C. The bacteria were washed twice in PBS/2%BSA/0.02% sodium azide and then re-suspended in PBS/2%BSA/0.02% sodium azide and acquired on a FACS Canto (BD) using FSc (forward scatter) and SSc (side scatter) in logarithmic mode. Data was analyzed using FlowJo software (Tree Star Inc.).Bacterial flow cytometry was performed as described previously , 24. Bri2O and 0.1 mg ml−1 soybean trypsin inhibitor (Sigma)] with 40 μl of 100 μM phenylmethylsulfonylfluoride  per 5 ml of intestinal wash buffer. The small intestinal wash was centrifuged at 4,000 g for 10 min at 4°C and stored at −80°C.The entire small intestine was removed and washed with 5 ml of ice-cold intestinal wash buffer +m/z 136.07, 138.08, 150.09, 153.10, 164.10, 166.11 → m/z 94.06. Electrospray ionization source conditions were as follows: spray voltage of 3,000 V, vaporizer temperature of 300°C, sheath gas of 5 (arbitrary units), sweep gas of 1 (arbitrary units), auxiliary gas of 2 (arbitrary units), capillary temperature of 275°C.MS/MS data were acquired in positive ionization mode with the mass spectrometer operating in Selected Reaction Monitoring (SRM) mode. Fragmentation parameters were optimized using the EZ Tune program with direct infusion of the derivatized analytical grade standards. Subsequently, the following transitions, corresponding to the three derivatized native SCFAs and respective derivatized 12C/13C signal intensity ratio and the respective 13C-IS concentration.Data analyses, on the converted mzXML files, were conducted in MAVEN , 35. In t-test. A p < 0.05 was considered statistically significant. P-values are indicated as follows: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, not significant (ns).Statistical analysis was performed by one-way ANOVA with Tukey's or Dunnett's post-test depending on whether the comparison was performed between all means (Tukey's) or compared to a control mean (Dunnett's) using GraphPad Prism 8 (GraphPad Software Inc.). The data for serum IgE concentrations was first log transformed before performing one-way ANOVA with Tukey's post-test. The SCFA in colon vs. cecum in  were comPseudoflavonifractor sp. ASF500 early in life correlated with suppression of IgE . Colonization with either C1 or C2 in the small intestine and cecum of the offspring was confirmed by 16S rRNA amplicon sequencing  was below the detection limit in both groups.We have previously shown that mice colonized with the Altered Schaedler Flora (ASF), harboring eight commensal intestinal bacterial species , displayn of IgE . We and n of IgE  or colon species , 39 resu species , 18. To y #1 C1,  consistiommunity . This apquencing . PseudofTotal serum IgE levels were measured in large control cohorts of GF and specific pathogen free (SPF) mice . As prevClostridium sp. ASF356 to the C2 community to form community C3 and added Clostridium sp. ASF502 to C3 to generate C4 (Clostridium sp. ASF356 in both C3- and C4-colonized mice and Clostridium sp. ASF502 only in C4-colonized mice (+ Tregs (+Helios− pTregs in the mesenteric lymph nodes (MLN), Peyer's patches (PP), small intestine (siLP) and colon lamina propria (cLP)  . C4 coloe spleen . The trae spleen , 41. Howe spleen , 43, cane spleen . We havee spleen –21. Thes12)  , 47 (Sup12) . Analysiquencing  with Bifobserved ]. More iyper-IgE , which cyper-IgE . Howeveryper-IgE , which i− pTregs .12 colonization dynamics in early life in the small intestine and colon starting from day 8 until day 43 after birth  most prominent in early life may contribute to regulation of IgE levels. In addition, since Clostridia species are known to be dominant producers of SCFA, and we found that C. clostridioforme YL32 and Clostridiales species B. coccoides YL58, were also present in early life, albeit at lower levels, we further dissected the protective potential of these four bacterial species.We next followed the Oligo-MMer birth . Up untiuris I48 . These curis I48 , these dB. coccoides YL58 to C2 to generate community C5 and added C. clostridioforme YL32 to C5 to generate C6   and A. muciniphila YL44/B. coccoides YL58 (YL44 + YL58) double colonized cohorts. We found that while B. coccoides YL58 alone was not able to reduce serum IgE levels, we observed reduced IgE levels when B. coccoides YL58 was present in combination with E. faecalis KB1 or A. muciniphila YL44  . We did, + YL58) . To inveila YL44 . This inila YL44 , 6. In aila YL44 , and theed group . Therefodes YL58  is requi acetate , we nextA. muciniphila as a strong inducer of systemic IgA and IgG1  during this critical window is sufficient to suppress IgE. This colonization status could potentially favor IgA and IgG1 isotype class switching events while still inducing the important RORγt+Helios−pTregs population (+Helios− pTregs at weaning was critical for protection from inflammation later in life (+Helios− pTregs alone is insufficient to inhibit IgE.Al Nabhani et al. has recently characterized a vigorous immune response at weaning, termed the weaning reaction, as it is associated with changes in the microbiota at weaning . The weapulation . Althoug in life , our datTaken together, we show that both bacterial richness and the presence of key bacterial species in early life are important for regulating serum IgE. A recent publication has demonstrated that the increased IgE levels in germ-free mice are induced by food antigens . In contin vivo gnotobiotic screen. Alternatively, once appropriate genetic tools are available, A. muciniphila YL44 and E. faecalis KB1 can be genetically modified to produce acetate and tested in isolation.It is important to note that our bacterial combinations only provided partial suppression of serum IgE levels and other additional microbial characteristics are needed to fully inhibit IgE to the levels found in SPF mice. Therefore, it will be interesting to further investigate the role of other bacterial characteristics and metabolites on IgE regulation in order to develop microbial-based therapeutics for prevention of allergic diseases in humans. Furthermore, it would be interesting to screen for commensal species that are dominant in early life, induce IgA and produce acetate, to see whether these species alone can suppress hyper-IgE. Unfortunately, thus far we did not come across species with these capabilities in our extensive https://doi.org/10.6084/m9.figshare.c.4763306.The 16S rRNA amplicon sequences generated for this study can be found on the figshare repository at The animal study was reviewed and approved by The Commission for Animal Experimentation of the Veterinary Office of Canton Bern and the Health Sciences Animal Care Committee of the University of Calgary.MW contributed to the experimental design, performed and analyzed all the experiments, and contributed to writing the manuscript. KB contributed to the bioinformatics analysis. FT, KB, CT, and FR contributed to experiments assessing pTreg induction. MK and VF performed and analyzed the bacterial FACS. DB and IL established the targeted metabolomic protocol for the SCFAs. MG and KM analyzed and interpreted the data and wrote the manuscript. KM conceived and designed the project.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.


Artemisia. These bioactive flavones exhibit various antioxidant, antiinflammatory, antiallergic, and antitumor activities. The inhibitory potentials of eupatilin and jaceosidin on the activities of seven major human cytochrome P450 enzymes in human liver microsomes were investigated using a cocktail probe assay. Eupatilin and jaceosidin potently inhibited CYP1A2-catalyzed phenacetin O-deethylation with 50% inhibitory concentration (IC50) values of 9.4 μM and 5.3 μM, respectively, and CYP2C9-catalyzed diclofenac 4-hydroxylation with IC50 values of 4.1 μM and 10.2 μM, respectively. Eupatilin and jaceosidin were also found to moderately inhibit CYP2C19-catalyzed [S]-mephenytoin 4'-hydroxylation, CYP2D6-catalyzed bufuralol 1'-hydroxylation, and CYP2C8-catalyzed amodiaquine N-deethylation. Kinetic analysis of human liver microsomes showed that eupatilin is a competitive inhibitor of CYP1A2 with a iK value of 2.3 μM and a mixed-type inhibitor of CYP2C9 with a iK value of 1.6 μM. Jaceosidin was shown to be a competitive inhibitor of CYP1A2 with a iK value of 3.8 μM and a mixed-type inhibitor of CYP2C9 with iK value of 6.4 μM in human liver microsomes. These in vitro results suggest that eupatilin and jaceosidin should be further examined for potential pharmacokinetic drug interactions in vivo due to inhibition of CYP1A2 and CYP2C9.Eupatilin and jaceosidin are bioactive flavones found in the medicinal herbs of the genus  Artemisia, such as Artemisia princeps, Artemisia argyi, Artemisia iwayomogi, and Artemisia copa. Eupatilin and jaceosidin have been shown to exert antioxidant and anti-inflammatory activities indole (Trp-P-2), 3-amino-1,4-dimethyl-5H-pyridoindole, 2-amino-3-methylimidazoquinoline, 2-amino-3,4-dimethylimidazoquinoline and 2-amino-3,8-dimethylimidazo- quinoxaline in Salmonella typhimurium TA98  and jaceosidin  are pharmacologically active flavones isolated from medicinal herbs of the genus tivities ,9 and totivities ,11. Eupaium TA98  and as pium TA98 ,20,21,22ium TA98 . Eupatil in rats . Eupatil in rats . Echinacea, garlic, Ginko biloba, green tea, kava, milk thistle, and St. John’s wort have been reviewed -mephenytoin 4'-hydroxylation with IC50 values of 48.1 μM and 64.9 μM, respectively, and CYP2D6-catalyzed bufuralol 1°-hydroxylation . Eupatilin and jaceosidin were found to weakly inhibit CYP2C8-dependent amodiaquine N-deethylation with IC50 values of 104.9 μM and 106.4 μM, respectively  were determined for eupatilin and jaceosidin using Lineweaver plots, Dixon plots, and secondary reciprocal plots. The results are summarized in Ki, 1.6 μM; α, 10.3) and CYP2C19-catalyzed [S]-mephenytoin 4'-hydroxylation  , CYP2C8-dependent amodiaquine N-deethylation , and CYP2D6-catalyzed bufuralol 1'-hydroxylation  , CYP2C8-dependent amodiaquine N-deethylation , CYP2C9-catalyzed diclofenac 4-hydroxylation , and CYP2D6-catalyzed bufuralol 1'-hydroxylation . Jaceosidin was found to exhibit the mixed type of inhibition for CYP2C19-catalyzed [S]-mephenytoin 4'-hydroxylation with a Ki of 45.1 μM  , Table 294.6 μM) , Table 2 45.1 μM , Table 2Ki values of 2.3 and 3.8 μM, respectively, indicating that eupatilin and jaceosidin should be used carefully with drugs metabolized by CYP1A2 such as acetaminophen, caffeine, phenacetin, ropivacaine, tacrine, theophylline, and tizanidine to avoid drug interactions -mephenytoin, 4-hydroxydiclofenac, 4'-hydroxymephenytoin, 1'-hydroxybufuralol, N-desethylamodiaquine, 1'-hydroxymidazolam, [2H9]-1'-hydoxybufuralol maleate, [2H5]-7-hydroxycoumarin, [2H3]-4'-hydoxymephenytoin, -acetaminophen, and [13C6]-4-hydoxydiclofenac were obtained from BD Gentest Co. . Acetonitrile and methanol (HPLC grade) were obtained from Burdick & Jackson Inc.  and the other chemicals were of the highest quality available. Eupatilin and jaceosidin were donated by Dong-A Pharmaceutical Co.  and the Plant Diversity Research Center, Korea Research Institute of Bioscience and Biotechnology , respectively. NADPH, phenacetin, acetaminophen, coumarin, 7-hydroxycoumarin, diclofenac and midazolam were purchased from Sigma Chemical Co. . Pooled human liver microsomes (H161), bufuralol, -mephenytoin 4'-hydroxylase, bufuralol 1'-hydroxylase, and midazolam 1'-hydroxylase activities were determined as probe activities for CYP1A2, CYP2A6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A, respectively, using cocktail incubation and tandem mass spectrometry. The incubation mixtures were prepared in a total volume of 200 μL as follows: pooled human liver microsomes (0.25 mg/mL), 1.3 mM NADPH, 3.3 mM MgCl2, 50 mM potassium phosphate buffer (pH 7.4), and a cocktail of probe substrates and various concentrations of eupatilin or jaceosidin (0.1−100 μM). The substrates were used at concentrations approximately equal to their respective Km values: 50 μM phenacetin, 2.5 μM coumarin, 2 μM amodiaquine, 10 μM diclofenac, 100 μM [S]-mephenytoin, 5 μM bufuralol, and 2.5 μM midazolam. After a 3 min preincubation at 37 ºC, the reactions were initiated by adding NADPH and incubated for 20 min at 37 ºC with shaking in a water bath. After incubation, the reaction was stopped by placing the tubes on ice and adding 100 μL of ice-cold acetonitrile containing internal standards . The incubation mixtures were then centrifuged at 10,000× g for 5 min. All incubations were performedin triplicate. The inhibitory potencies  were preincubated for 30 min with NADPH, buffer, and microsomes before the addition of the CYP probe substrates. The reaction was started by addition of the cocktail of probe substrates. Ki values, human liver microsomes (0.2 mg/mL) were incubated with various concentrations of substrates , 1 mM NADPH, 10 mM MgCl2, and various concentrations of jaceosidin or eupatilin in 50 mM potassium phosphate buffer (pH 7.4) in a total incubation volume of 200 μL. The reactions were initiated by addition of NADPH at 37 ºC and stopped after 15 min by placing the incubation tubes on ice and adding 100 μL of ice-cold methanol containing an internal standard. The incubation mixtures were centrifuged at 10,000' g for 5 min, and aliquots (5 μL) of the supernatants were analyzed by liquid chromatography/tandem mass spectrometry (LC/MS/MS).For the determination of 18 column  using the gradient elution of a mixture of 5% methanol in 0.1% formic acid (mobile phase A) and 95% methanol in 0.1% formic acid (mobile phase B) at a flow rate of 0.25 mL/min: 10% mobile phase B for 1 min, 10% to 95% mobile phase B for 1 min, 95% mobile phase B for 5 min. The column and autosampler temperatures were 50 ºC and 4 ºC, respectively. After 1.5 min, the LC eluant was diverted from waste to the mass spectrometer that was fitted with an electrospray ionization (ESI) source and operated in the positive ion mode. The ESI source settings for the ionization of the metabolites were as follows: electrospray voltage, 5.0 kV; vaporizer temperature, 420 ºC; capillary temperature 360 ºC; sheath gas pressure, 35 psi; auxiliary gas pressure, 10 psi. Quantification was performed by selected reaction monitoring (SRM) of the [M+H]+ ion and the related product ion for each metabolite. SRM transitions for the metabolites and internal standards are summarized in ® software (ThermoFisher Scientific).All seven metabolites produced from the cocktail of CYP isoform-specific substrates were simultaneously determined by LC/MS/MS. The system consisted of a tandem quadrupole mass spectrometer  coupled with a Nanospace SI-2 LC system. The separation was performed on an Atlantis dC50 values  were calculated using WinNonlin software, a non-linear regression analysis program . The apparent kinetic parameters for inhibitory potential  were estimated from the fitted curves using Enzyme Kinetics Ver. 1.3 program .The ICArtemisia spp. as well as Eupatorium spp. These results suggest that the use of eupatilin, jaceosidin, and herbal preparations thereof may cause interactions with drugs metabolized by CYP1A2 and CYP2C9 in some individuals. However, it is notable that in vitro inhibition of CYP1A2 and CYP2C9 activities does not necessarily result in drug interactions in clinical situations.By screening the inhibitory effects of eupatilin and jaceosidin on the activities of seven CYP isoforms in human liver microsomes, eupatilin and jaceosidin were shown to be the potent inhibitors of CYP1A2 and CYP2C9 and moderate inhibitors of CYP2C8, CYP2C19, and CYP2D6. Eupatilin and jaceosidin are pharmacologically active components in medicinal herbs such as