This study shows an autonomous android robot that can have a natural daily dialogue with humans. The dialogue system for daily dialogue is different from a task-oriented dialogue system in that it is not given a clear purpose or the necessary information. That is, it needs to generate an utterance in a situation where there is no clear request from humans. Therefore, to continue a dialogue with a consistent content, it is necessary to essentially change the design policy of dialogue management compared with the existing dialogue system. The purpose of our study is to constructively find out the dialogue system architecture for realizing daily dialogue through implementing an autonomous dialogue robot capable of daily natural dialogue. We defined the android’s desire necessary for daily dialogue and the dialogue management system in which the android changes its internal  states in accordance to the desire and partner’s behavior and chooses a dialogue topic suitable for the current situation. The developed android could continue daily dialogue for about 10 min in the scene where the robot and partner met for the first time in the experiment. Moreover, a multimodal Turing test has shown that half of the participants had felt that the android was remotely controlled to some degree, that is, the android’s behavior was humanlike. This result suggests that the system construction method assumed in this study is an effective approach to realize daily dialogue, and the study discusses the system architecture for daily dialogue. Until now, the dialogue  function of smart speakers , robots e.g., , and comThe existing non–task-oriented dialogue systems such as chat systems have generated appropriate responses to users’ utterances based on a huge dialogue database , so therSo far, our research group has developed a system that enables daily dialogue using an android robot that closely resembles a human. The purpose of this study is to constructively find out the dialogue system architecture for realizing daily dialogue. Therefore, we implemented an autonomous dialogue robot capable of daily humanlike dialogue (though it works in a limited situation) and investigated the developed architecture.1) This study has implemented various interactions that occur in daily dialogue with various modalities, although in a limited situation .2) This study constitutively confirms the dialogue system architecture for realizing daily dialogue, which is different from the existing dialogue systems.The significance of this study is as follows:Task-oriented dialogues such as voice guidance  and discDuring discourse analysis, we found a study investigating the desires that appear in daily human dialogues in domestic situations . In thisThe daily dialogue is non–task oriented, but it is not without patterns in context development. For example, it has been shown that a certain routine can be seen in the development of topics such as the first-meeting dialogue . In addiEach daily dialogue is composed of task-oriented interactions. As aforementioned, the utterances are generated while maintaining the context within the weak constraint of the topic. In task-oriented dialogue systems, the constraints of the topic are often not defined, therefore the context could break down in a few turns. The daily dialogue system requires an utterance generation mechanism based on topical constraints. In addition, it is known that there is a fixed speech pattern for a dialogue according to the topic. Studies examining daily dialogues have shown that there is a fixed speech chain pattern (utterance sequence organizations) . The exiThis topic-based utterance generation is helpful for the robustness of the dialogue system. It has been shown that the domain knowledge modeled with dialogue topics is important for maintaining robustness of the system and improving recognition accuracy of spoken utterances .In task-oriented dialogues, the objective information exchange is the main purpose for task achievement, but in daily dialogues, an exchange of subjective information rather than objective information mainly occurs. It has been shown that the subjective opinion exchange is the main activity, especially in situations where the dialogue is lively . In ordeIn the dialogue with an agent who responds to questions, it is desirable that the user who asks the question takes the initiative in the turn. That is, the agent takes a turn after the user’s question is finished, and if the user’s question barges in during the agent’s utterance, the agent should stop speaking and listen to the user’s utterance. However, in daily dialogue, it is not always possible to clearly determine when the dialogue participants take turns, as in the question–answering protocol. Speech overlap may occur in situations where it is unclear who should take the turn. At the time of silence or utterance overlap, who takes the turn is adjusted according to the situation .In addition, in daily dialogue, a dialogue breakdown may occur due to misunderstanding of the other’s utterance or misunderstanding of meaning . In a diOn the other hand, interrupting utterances from the system side may be required, that is, nodding and backchannel utterances. In daily dialogue, while listening to the other person’s story, we can see backchannels and behaviors that indicate conformity, empathy, and nonconformity . This haAn important interaction in daily dialogue is to exchange subjective impressions and opinions about experiences  and exchIn daily dialogue, relationships with the other party are built through alignment of each other’s values . The purThe study of Many dialogue systems have been developed, such as text-based chat systems, multimodal dialogue systems using CG agents, and dialogue robots. Here, this study describes the dialogue systems that relate to the functions necessary for daily dialogues.A daily dialogue is a mixed initiative, in which both participants take the initiative. In task-oriented dialogues, it is sufficient to build a user-initiative system that provides answers to user’s questions and requests, whereas in daily dialogues, the system needs to speak with initiative. Furthermore, since daily dialogues do not have a specific task, it is necessary to develop a system that proactively behaves according to its own motives rather than reacting to something that changes in the environment. As a desire-based dialogue system, the BDI-based model was developed based on the Bratman’s theory of human practical reasoning . AccordiScript-based dialogue management systems are adopted in mainly task-oriented dialogue systems e.g., , where tIn many spoken dialogue systems, utterances and actions are regulated by chunks for each turn of the speaker. In other words, it is assumed that the turn-taking will be smooth. In the text dialogue system, the system is designed so that speech overlap does not occur. However, in daily dialogues, utterances and actions are decided and executed in real time, and if there is an utterance of the other party during the speaker’s speech, the dialogue is interrupted and the topic is changed, or a new topic is made before the other party’s speech ends.Understanding the meaning of sentences has been studied in the field of natural language understanding (NLU), but unlike task-oriented dialogue systems, it is difficult to understand the meaning and generate utterances in open-domain dialogues such as daily dialogues. Therefore, many systems based on end-to-end learning using a huge database of interpersonal dialogue have been developed . A huge There are many studies on the expression of emotions of robots such as Kismet . They buThis study assumes the situation where the robot talks with a stranger for about 10–20 min . We assume this certain situation since it is difficult to design the robot’s desire in an open context. More concretely, the robot is in a resting space in a laboratory and talks with a person who visits the laboratory (Japanese conversation is assumed) as shown in The developed system in this study automatically selects a short dialogue script designed by human designers based on the robot’s internal state or observed information (sensor input) to realize daily dialogue. The system consists of a dialogue script of a certain length in a topic, the robot’s internal state, the internal state of the dialogue partner (estimated), and the memory about the dialogue.The script is described as the utterances of the robot and the expected responses of the person  in the manner of a decision tree. The robot waits for the utterance of the partner and selects its utterance by following the decision tree along with the partner’s utterance. The contents to be spoken are defined in advance in the script. Although the scripts are predefined, the robot can refer to the memory of the past dialogue and repeat the partner’s information such as name and age which were spoken by the partner. In the developed system, a script is designed in the form of decision tree as shown in The script is designed to have a topic . The topic has the two-dimensional feature to express privacy [degree of self-disclosure ] of contTo design the robot’s desire and intention in a daily dialogue of a first meeting, we referred to the studies of human–human conversations. The politeness theory  says thaSuch a balancing behavior, or reciprocal behavior, has been investigated from the perspective of evolutionary psychology and neuroscience. From an evolutionary psychological perspective, reciprocal behavior in humans is a necessary selection for adaptation to the environment . It has To follow the social norms in the first-meeting dialogue, the robot should behave by keeping up reciprocity, that is, the robot should behave for not only itself but also the partner. Psychological studies have shown that people have a good impression of the conversation partner when the amount of speech exchange is balanced .Based on the aforementioned knowledge, we designed the desire–intention–behavior structure as shown in To develop a robot that behaves according to the desires designed as mentioned above, we define the internal state of the robot involved in the behavior that satisfies these desires. For personal desires, the robot controls its behavior such that the relationship with the dialogue partner becomes the desired one. To be more specific, the robot behaves in such a way that the higher the robot’s favorability toward the partner, the deeper the relationship with the partner. The relationship refers to the degree of self-disclosure. In other words, it is judged that the relationship has deepened when the degree of self-disclosure is high not only for one partner but also for each other. Therefore, we define the internal states of the robot’s favorability and self-disclosure. As will be described later, the favorability of the partner in the dialogue depends on whether or not the interests in the topic match each other.For social desires, the robot recognizes the partner’s favorability toward the robot and controls the balance of the initiative of the dialogue such that it increases. In addition, as aforementioned, the emotional expression of the robot is important in daily dialogue. It is assumed that the robot has emotions related to the degree of interest in the topic and the favorability of the partner. We also assume that the emotion influences the decision-making of the robot.Summarizing the above, the relationship between the internal states and desires is as shown in -emotion and the latter as mood. Each of immediate-emotion and mood has three dimensions . Each dimension ranges from −1.0 to 1.0. The immediate-emotion change is defined according to the branching in the decision tree of the script. The mood gradually changes over time, owing to the facial expressions of the partner. The update rule of pleasure dimension in the mood is as follows: 1) decrease in attenuation coefficient by 0.8 every minute. 2) Synchronize with the pleasure level of the partner. 3) Deteriorate when the dialogue breaks down. 4) Increase or decrease according to the degree of empathy when the topic changes  model , which inges see . The dimNext, we will explain the rule of the integration of immediate-emotion and mood into the robot’s emotional state. Since immediate-emotion is a short-term expression which changes owing to the partner’s utterance, the emotion immediately follows the immediate-emotion when it changes. If the immediate-emotion is not expressed, the emotion follows the mood. When the emotion follows the immediate-emotion, the emotion changes at a rate depending on the current emotion . When the emotion follows the mood, it gradually shifts to the mood.fp, fnu, and fng be the ratios of positive, neutral, and negative facial expressions, respectively. The pleasure of the robot’s mood is reduced by 0.03fng and increased by 0.03fp to implement the attraction to the facial expression of the dialogue partner.For the update rule (2) of the pleasure dimension in mood described above, the partner’s facial expression captured by a camera is recognized every 50 msec Then, the ratio of the facial expressions during when she is speaking in a topic is calculated. The facial expressions are categorized into three groups; happy and surprise are counted as positive facial expressions, sad and angry are counted as negative facial expressions, and the others are counted as neutral facial expressions. Here, let fng and increased by 0.05fp.The robot estimates the partner’s pleasure of mood (only the pleasure dimension is estimated) from the facial expression. The estimated pleasure is reduced by 0.05We assume that the robot has different levels of interest in the topics. Here, we assume three levels: favorable, unfavorable, and neutral. The robot’s interest in each topic is manually defined in advance, for example, we define that the robot likes a topic about romance novels and does not like a topic about having a meal.fnu) is larger than the other facial expressions (fp and fng), then the interest is recognized as neutral. In other cases, if a happy or surprised face is recognized more than a sad or angry face, the interest is estimated to be favorable. Otherwise, the interest is recognized as unfavorable.The robot estimates the partner’s degree of interest in the topic on which they are talking about based on their facial expressions. If the ratio of the neutral face . The former is the ability to understand how a person feels and what they might be thinking. The latter is to share the feelings of another person, and it requires the estimation of psychological logic and emotion and adaptation to the other person. The empathy in the proposed system is related to cognitive empathy rather than emotional empathy.The robot’s favorability toward the dialogue partner affects the topic selection. When the favorability is high, it talks about a topic which seems to be favored by the partner to continue the dialogue. The favorability ranges from −1.0 to 1.0. It changes with three events. The first is when the partner gives the answer that the robot prefers in the dialogue. For example, if the robot asks for a genre of favorite book and it likes romance, the favorability is increased only when the partner's answer is romance. The second is when a dialogue breakdown (when the robot cannot recognize the partner’s speech or when speech collision occurs) is detected. The favorability is decreased since it is difficult to talk well with a person who makes the dialogue break down. The third is when each time a topic is over. At the end of the topic, the favorability is changed according to the empathy as shown in 1) Estimation based on the speechWhen the robot detects compliments each time a voice recognition result is acquired, it updates the favorability by increasing it by 0.05.Every time the voice recognition result is acquired, it is judged whether it is a question or not, and in the case of a question, 0.02 is increased. This means that the robot guesses that the partner asks it because she has an interest with it.The robot compares the amount of speech between the partner and itself every 50 s. If the partner’s speech is more than the robot’s speech, the favorability is increased by 0.02. This means that the robot guesses that the partner speaks much since she has an interest in the robot.If the robot detects a contemptuous phrase in the partner’s speech, the favorability is decreased by 0.2.When the partner ignores the robot’s question and does not answer, the favorability is decreased by 0.05.2) Estimation based on the behaviorThe robot recognizes whether the partner looks at it by the facial image every 50 msec Every 50 s the favorability is increased by 0.02 if the partner is looking at the robot longer than not looking.The favorability is decreased by 0.02 every 50 s if the partner spends more time looking in a completely different direction than looking at the robot.The robot estimates how the partner favors itself through the partner’s speech and behavior.The degree of self-disclosure indicates how much private talk is allowed with the dialogue partner. It takes a value of 0.0–1.0. This value is on the same scale as the self-disclosure of the topic. The candidates of topic selection are determined by referring to either of the robot’s and partner’s self-disclosure degree (described later).The degree of the robot’s self-disclosure basically follows the favorability toward the partner. This means that the robot tries to self-disclose more deeply and deepen the relationship with the partner with whom it has high favorability. On the other hand, if the robot has low favorability toward the partner, the degree of self-disclosure decreases, and the selectable topics are reduced. This leads to the ending of the dialogue without self-disclosure. The degree of self-disclosure is updated every time the topic is changed.SDp is the estimated partner’s self-disclosure degree, SDt is the self-disclosure degree of the current topic, and tf is the specificity of the word in the partner’s utterance measured by TF-IDF (a and b are predefined constants tuned by the system designer).The robot estimates the partner’s self-disclosure based on how much the partner talks about specific matters. For example, if the partner answers “I’m from XX street” rather than “I’m from YY city” when the robot asks the partner’s hometown, it estimates that he/she self-discloses more deeply. The specificity is measured by the term frequency–inverse document frequency TF-IDF; . The estThe robot recognizes that the relationship with the partner is strong if both self-disclosure degrees are large. The strength of the relationship is calculated as the average of both self-disclosure degrees. By referring to the estimated relationship, the robot behaves while balancing both the self-disclosure degrees. For example, when the robot’s self-disclosure degree is high, it selects a topic with a high self-disclosure degree. But this behavior might not be suitable if the partner’s self-disclosure degree is not high, especially for the situation of the first-meeting dialogue in a public space. The robot can avoid such a behavior by referring to the relationship.The robot changes its speech style in three levels according to the degree of intimacy with the partner and social status of the partner. The intimacy degree refers to the relationship strength described above. The social status  is recognized in the contents of dialogue . If the speech level is low, the robot’s speech is honorific. If it is high, the robot’s speech is broken in tone.vice versa. More concretely, if the robot mainly selects the topics in the recent dialogue, the priority is increased by 0.1, and if the partner mainly selects the topics, the priority is decreased by 0.1.The self–other priority takes from −1.0 (giving priority to the partner) to 1.0 (giving priority to self) in the robot’s internal state that determines whether the partner’s action is prioritized or the robot’s own desire is prioritized when the topic changes. The robot recognizes the ratio of the recent eight topics that are selected by the partner. If the ratio is much biased to the partner (the partner mainly took an initiative to select the topics), it increases the self–other priority such that the partner continually takes an initiative in the topic selection to maintain the dialogue context and vice versa.In addition, the robot also balances the initiative of topic selection between the robot and partner to follow the law of reciprocity. The priority is decreased by 0.1 if the robot mainly took the initiative (selected the topics) so far and In this system, the robot memorizes the partner’s personal information , topics the robot talked about, and the partner’s answer to the robot’s question. The memory information can be accessed from the script and used for decision-making. For example, when the robot talks with the partner for the second time, it does not repeat what it talked with the person in the first time.1) Silence: a state between topics (silence after a topic is over),2) Active: a state where the robot has selected a topic, and3) Passive: a state where the partner has selected a topic.The dialogue management system automatically selects the topics based on the internal state. There are two types of topic selection: when the robot voluntarily selects the topic (Active) and when the dialogue partner asks a question, and the robot selects a question-related topic (Passive)  The management system discriminates the dialogue state by how the current topic is selected . In addition, the system deals with a state where nobody is speaking. Here, the sequence state is defined by the following three states:1) Idle: a state where a dialogue has not started .2) InitialTalk: a state in which a dialogue begins and the robot and partner talk about topics with a low degree of self-disclosure .3) Dialogue: a state where the robot and partner talk about topics with a high degree of self-disclosure .Exception: an error state such as being unable to understand what the other has said. Exceptions include two types.NoMatch Exception: a state where there is no selectable topic that matches with the speech of the partner.4) NoSelectableTopic Exception: a state where there are no more selectable topics .5) Close: a state where the dialogue is completed.Basically, the dialogue control routine is to wait for a certain period when the Silence state starts, and if the partner starts to talk during the Silence state, the appropriate topic is selected, the sequence state is moved to the passive state, and the robot starts the script related to the selected topic. Alternatively, when the waiting time has expired, the robot selects a topic to continue the dialogue, moves to the active state, and starts the script related to the selected topic. The process when the partner starts a new topic while another topic is being talked (during active or passive state) is called interrupt process. To make the topic selection strategy depend on a dialogue situation, the following five interaction states are defined and the different topic strategies are implemented:Each topic is assigned to one sequence state and on interaction state. For example, the topic of “asking the partner’s job” is assigned to Active and InitialTalk state, and “answering the partner’s question to ask the robot’s hobby” is assigned to the Passive and Dialogue states. The topics assigned to the Passive state can be selected when the partner asks a question to the robot.1) From all states: when a Passive topic is selected, the interaction state transfers to the one to which the topic belongs.2) To all states: accept a control that forces a transition within the topic.3) From Idle to InitalTalk: when the robot finds a person in the Idle state .4) From InitalTalk to Dialogue: when the robot spends a certain period in InitialTalk state and there are selectable Active and Dialogue topics related to the current topic.5) From Dialogue to InitalTalk: when the robot tries to select an Active topic but there is no selectable Active topic. In addition, when there is no interaction for a certain period in Dialogue state.6) From InitialTalk to Idle: when the robot tries to select an Active topic but there are no selectable Active topics, and the partner is not looking at the robot.7) From InitialTalk or Dialogue to Exception: when there are no more topics that can be selected while the robot tries to select an Active topic, the state transfers to Exception (NoSelectableTopic Exception). The state transfers to Exception (NoMatch Exception) when no topic matches the partner’s utterance in Silence state.8) From Exception to InitialTalk or Dialogue: when the exception-handling script (described later) ends, the state returns to the previous state.9) From InitialTalk to Close: when the dialogue partner ends the dialogue  or the dialogue spends predefined time , the state transfers to Close, and the robot tries to end the dialogue.10) From Close to Idle: when the robot detects that there is nobody to talk to.Tw in the Silence state is calculated as below:As described above, the robot waits for a certain period after a script is over. To adapt to the partner’s speech rhythm , the robot calculates the average turn-taking time in the past and basically waits for that time. In addition, the robot shortens the waiting time when the robot prioritizes itself according to the self–other priority. If the robot prioritizes the partner, the robot makes the waiting time longer such that it waits for the partner’s speech. The waiting time Th is the time duration between the onset of the Silence state and the onset of the partner speech in the last Silence state, and Sr is the degree of self–other priority. Cr is a constant parameter. As shown in the equation, the higher the self–other priority is, the shorter the waiting time gets.where 1) Interruption manner:Interrupt: stop the current script and shift to the script of a new topic (the partner is prioritized in this case).Stack: suspend the current script, state that the robot continues the current script, and start a script of a new topic after the current script ends (the partner is prioritized in this case).Ignore: ignore the interruption and start a script of a new topic after the current script ends (the robot is prioritized in this case).2) Return manner:Back: return to the original script that was interrupted.Skip: transfer to the Silence state without returning to the original script.When the robot in the Active state recognizes the partner’s utterance, it checks whether there are Passive topics that are suitable to respond to the partner’s utterance. If there is such a topic , the interrupt process starts. It consists of three types of interruption manners and two types of return manners:The pattern of handling interruption is shown in The flowchart of the interrupt process is shown in To maintain the dialogue context, the topic selection is controlled by the distance on the topic space consisting of the self-disclosure degree  and closeness of the topic content  .The topic selection in the Dialogue state is to elaborate the current topic. The selectable topics' area is near the area of the topic that has been selected so far. If the interest of the partner to the current topic is low, the robot selects the topic with the median distance from the current topic. Otherwise, it selects the topic closest to the current topic. In other words, unless the partner is not interested in the current topic, the topic to elaborate the current topic is selected .In the Idle state, the robot does not actively start to talk since there is no dialogue partner. In the Exception state, one topic is prepared to handle NoMatch Exception and another topic is prepared to handle NoSelectableTopic Exception. In the Close state, only a topic for farewell greetings is selected. Therefore, a topic can be uniquely selected in the states other than the InitialTalk and Dialogue.To handle NoMatch Exception (no topic is related to the partner’s utterance), the robot extracts nouns from the partner’s utterance and searches for relevant Active topics by referring word2vec distance  between The total dialogue flowchart which includes the interruption process is shown in By linking the internal state not only to language but also to nonverbal expressions such as facial expressions, people can feel that they are naturally interacting with the robot, which is not felt during a text chat. The Emotion described above appears in the facial expression of the robot. In addition, since the more intimate the people are, the more they tend to turn their bodies toward the other party , the oriWe implemented the dialogue system and scripts to realize daily dialogue in the first meeting in a lounge space. Specifically, there is a table with sofas in the lounge space and the android robot is sitting there. A subject who comes to the space for resting has a dialogue with the android. We created scripts that assume the dialogue in this situation. As for the contents, we created 80 scripts for 25 topics such as where the subject came from, about whether they are interested in robots, and about their favorite books. To handle Active and Passive sequence states, we prepared two types of script for the same topic: one is the script that the android initiates the topic and the other is the script that the dialogue partner initiates the topic.1) The android and the person gradually disclosed themselves  and built a relationship in which both exchange their names.2) The android’s self-disclosure level became significantly different from the person’s level in the conversation. Then, the android reciprocally chose the scripts which fit to its level and the scripts which fit to the person’s level.3) In the beginning, the android proactively talked to the person, but he merely made short responses and started to use his smartphone. Then, it did not proactively talk, and both did not talk to each other.In this situation, it is not necessary for the partners to proactively talk to the android and keep talking with it. In other words, there is no clear objective of the conversation. The conversation process depends on the person’s personality and mental state. When we tested the developed system, we observed some patterns of the relationship shift such as those below:There still remained dialogue breakdown that could not be recovered by the developed system. But the resultant interaction flow and the developmental process of the relationship were reasonable and natural. The conversation control based on the android's desire and intention might produce natural interactions.The android ERICA  and the other way is that it behaves by remote control (teleoperation mode),” and asked them to talk with the android without telling which mode they were experiencing . Then, after the dialogue, we let them evaluate how much they thought that the android was remotely controlled . As a result of the evaluation with 25 subjects [average 46.0 (std. 12.3) years old], several subjects answered that they thought they were interacting with an android in teleoperating mode  impression of the dialogue. Nonverbal communication has been shown to account for a large proportion of communication . To passThe existing chatbot systems continue a dialogue by selecting or generating an utterance that is a semantically correct response to the partner’s utterances, but the system does not have a motivation to keep the context underlying the background of the dialogue. Therefore, in many systems, the dialogue easily breaks down in about 10 turns. If we predefine a dialogue script and the robot follows it, it is possible to keep the consistency of the context. But the predefined story is not suitable for daily dialogue and the topic in the dialogue should be freely chosen depending on the dialogue situation. The predefined script does not allow the robot to respond to the partner’s utterance that is not assumed in the script, such as switching to the side sequence. Therefore, the robot should be able to switch the current topic during the script. In addition, unlike discussions and interviews, in daily dialogue, the timing of turn-taking cannot be clearly determined. To realize smooth turn-taking, the robot needs to judge the end of the partner’s turn at every end of the partner’s voice activity. Through the implementation of multimodal daily dialogue, it turns out that it is necessary to make action decisions at three levels—that is, dialogue state decision, script decision, and turn-taking decision—to continue a natural dialogue without breaking the context, and furthermore, it is necessary to design the evaluation function for those decisions. It can also be seen that in the non–task-oriented dialogue, those evaluation functions are determined by the robot’s desires which determine the behavior policy.This study, on the other hand, has not focused much on the architecture of motion generation of the robot. The Situation, Agent, Intention, Behavior, Animation (SAIBA)  is a uniA daily dialogue does not have a clear purpose, and the content of the dialogue itself does not matter if a relationship with the other person can be built through understanding and empathy. A dialogue system based on a big database is desirable for an open domain dialogue, but it is still difficult to continue a dialogue while maintaining context in existing systems. Therefore, the developed system focuses on continuing a dialogue while maintaining the context. We think it is possible to have a daily dialogue even if the story of the dialogue is limited to some extent. A designer creates scripts consisting of a few to a dozen turns of utterance exchanges in advance and gives them to the robot as candidates of topic selection. In the experiment, the participants could continue the dialogue without dissatisfaction even if the story of the dialogue was decided in advance.  In daily dialogue, people talk about anything rather than something that they want to talk about clearly. People expect an interaction that connects short topics, even though a story is limited to some extent.However, the limited number of predefined scripts reduces the scalability of the system. The architecture that consists of a set of tree-structured sequences with fixed utterances and branches restricts the scalability. To improve it, an architecture that generates a set of utterance patterns from a predefined set of topic categories (topic abstracted to some extent) is necessary in the future.In the developed system, the robot’s desire was incorporated into the dialogue system as a meta rule that regulates the behavior of the internal state that governs the behavior of the robot, and there was no explicit variable called desire. In other words, the desire  was hard-coded in the system.The BDI architecture that deals with a system desire can logically derive purposeful actions based on variable intentions and desires. But it is not suitable to derive each utterance from desires and intentions in daily dialogues since there is no clear purpose of a dialogue. The daily dialogue often follows typical patterns of dialogue flow, as in scripts, regardless of the speaker’s desire. The desire in the daily dialogue system should be designed as a driving force that globally guides the content of the dialogue although more specific desires that may occur at a certain moment.The internal state of the developed system includes the intentions, emotions, and memories, but the self-model and the external model (other person’s model) do not clearly appear in the architecture. A daily dialogue essentially involves the exchange of values among those who are talking. The values can be modeled by comparison between the robot’s self-model and the external model that describe what others are like and what the society is like. In the developed system, the robot’s preferences and values are hard-coded, and they do not arise from the robot’s experience. This not only causes less scalability of the system but also causes a person who is talking with the robot to not feel the reality of the preferences and values of the robot. For robots those engage in daily dialogues, an architecture that constructs a self-model along with the construction of an external model is essential.As abovementioned, since a daily dialogue does not have a clear purpose, the robot needs to have the desire to initiatively continue the dialogue. In other words, unlike existing dialogue agents, it is necessary for daily dialogue that the robot should be an agent that is more independent of humans (not humans’ servant or tool). Here, we discuss the system architecture for that purpose.In this architecture, the behavior generation and internal state updates are summarized into two processes: one is performed consciously (deliberatively) and the other is subconscious (reflexively). For example, the internal state of self-disclosure is consciously controlled, but the emotion is automatically updated. In addition, in the motion decision, the utterance is consciously controlled, but the facial actions are automatically expressed. A hierarchical structure which consists of conscious and reflective behaviors has been considered in the control structure of conventional robots, but reflective behaviors have been less focused on in dialogue systems. This is because reflective behaviors during a dialogue were not assumed. In the developed system, there are conscious and subconscious processes, but there is no mediating function when the two processes conflict. Such a mediating system might be essential for a daily dialogue robot such that it has an agency and people feel its independence.Another important factor for robot’s agency is the behavior based on the prediction of an agent’s observation and the prediction error. In conventional dialogue systems, a method for improving the recognition accuracy such that dialogue failure does not occur has been the focus, and the design of the interaction when a dialogue breakdown occurs has not been sufficiently considered. In the developed system, the topic was limited and the tree structure in the script was fixed in advance, so any unexpected behavior from the partner which causes a dialogue breakdown has to occur. Therefore, the system is designed to generate an interaction to recover from breakdown with natural behavior. In fact, our study has shown that such error recovery behavior can prevent people from losing their dialogue motivation . To geneAs described in To realize humanlike, natural behavior of the robot, not only verbal interactions but also nonverbal interactions are important. In the proposed system, the android robot controls the eye gaze, facial expressions associated with emotions, interval at turn-taking, and gestures, but the model is simple compared to human behavior. The fact that the android robot could interact naturally even with nonverbal expressions in this system suggests that it could express at least a minimum level of humanlike behavior. But in MTT of this study, we asked the participants whether they comprehensively feel that a person is operating the robot or not. It could not analyze in detail whether humanlikeness was a linguistic or nonverbal factor, or conversely whether robot-likeness was a linguistic or nonverbal factor. It is necessary to evaluate the nonverbal humanlikeness, but it is difficult for the participant to answer questions such as “Do you think the robot’s gaze is controlled by a person?” In the future, it is necessary to develop an MTT evaluation method that can clarify the factors of humanness as well as develop rich nonverbal expressions of the android.The MTT in this study does not compare the autonomous android with the tele-operated android. Therefore, the evaluation is based on the participants’ beliefs about the autonomous robot’s performance. In the Turing test, the evaluations were made by comparing two conversations, one with an AI and one with a human, and it has been revealed that evaluators can find the difference between humans and AIs based on the slight differences of conversation flow . This ha1) Dialogue control based on the system’s own desires and intentionally controlled internal states are necessary.2) A relationship between the dialogue partners should be the focus of daily dialogues rather than conveyed information through dialogue.3) By controlling the topic described by the script in which the utterance is designed in advance, it is possible to continue the daily dialogue while suppressing the dialogue failure.4) In order to improve the scalability of the system, it is desirable to develop a method to generate the scripts from topic content.In task-oriented dialogues, information that the system should acquire and provide to the user are clearly defined in order to achieve the desired task. This makes it possible to determine the quality of the system’s utterances and generate the necessary utterances based on task evaluation. On the other hand, in daily dialogues, it is not possible to evaluate whether or not the goal is clearly approached by each utterance because the necessary information is not clearly defined. In this study, in comparison to human daily dialogue, we considered that it is necessary to generate utterances based on the robot’s desires and internal state in daily dialogue. Then, instead of controlling the utterance content for each turn of dialogues, we implemented a method to control the content of the topic based on the robot’s internal state. The developed system could continue daily dialogues for about 10 min in the scene where the robot and partner met for the first time in the experiment. This result suggests that the system construction method assumed in this study is an effective approach to realize daily dialogue. The suggestions for the development of the daily dialogue robot are summarized as follows:In this study, the recognition and nonverbal expression system were not mainly focused on, but they are also important in the daily dialogue system. It is important to recognize not only the voice, emotions, and attitudes of the dialogue partner but also the preferences, opinions, and the intention of the partner.


Neuropsychiatric disorders, such as depression, schizophrenia, bipolar disorder, obsessive compulsive disorder, post-traumatic stress disorder (PTSD), autism spectrum disorder and others are estimated to impair millions of individuals globally. Despite the high global prevalence, much remains unknown about the underlying molecular mechanisms that lead to these and other neuropsychiatric diseases. The likelihood of developing these disorders may depend on a variety of genetic, developmental and environmental factors. The molecular alterations produced by these factors may contribute to both disease onset or the persistence of a specific pathology. Identifying the molecular mechanisms of a disease will increase our understanding of the underlying pathophysiology and ultimately lead to the rational design of targeted treatments. The goal of this Research Topic was to explore fundamental molecular mechanisms underlying neuropsychiatric diseases, and how these mechanisms may be exploited for potential therapeutic benefit.A longstanding challenge in the elucidation of molecular mechanisms underlying neuropsychiatric disease has been the initial paucity of anatomical and/or molecular findings. Distinguished American neurologist Dr. Fred Plum once referred to schizophrenia as “the graveyard of neuropathologists.” This Topic features multiple papers that serve to tackle this and other challenges and link clinical entities to anatomical and molecular abnormalities.Ren et al. showed that schizophrenia patients with auditory verbal hallucinations exhibit a decrease in cortical thickness in orbitofrontal cortices, which was negatively correlated with severity of these symptoms. Seeking to discover molecular aberrations in a depression-relevant model, Chen et al. employed a large-scale-omics approach to achieve metabolic profiling in the olfactory bulb of a chronic mild stress mouse model. They discovered an abnormal metabolism of the tryptophan pathway in the olfactory bulb, which may mediate the occurrence of a depression-like phenotype in a chronic mild stress model. On the other way, Martinho, Oliveira et al. sought to identify specific molecular abnormalities underlying contextual fear memory relevant to PTSD, and that modulating this system can reverse fear-related phenotypic changes. In Martinho, Oliveira et al., they found that epinephrine may be involved in the persistence of traumatic memories in PTSD, possibly through enhancement of the expression of Nr4a2 and Nr4a3 genes in the hippocampus. The persistence of contextual traumatic memories may contribute to anxiety-like behavior and resistance of traumatic memory extinction in a PTSD rodent model. In a follow-up study, Martinho, Correia et al. showed that nepicastat, a highly potent central and peripheral DBH (dopamine beta-hydroxylase) inhibitor that is effective in modulating the sympathetic nervous system, decreases the persistence of traumatic memories and anxiety-like behavior in this model, which may have direct translational potential in the treatment of PTSD.Parnell et al. devised a clever strategy to reduce the genetic complexity to a single mutation in a relevant gene to better understand its specific contribution to both normal and abnormal neuronal architecture. Using the E1577K point mutation in Kalirin-7, Parnell et al. showed that in contrast to wild type Kalirin-7, the E1577K mutant failed to drive dendritic arborization, spine density, and NMDAR activity within spines, alongside a robust reduction in Kalirin-7 RAC1 guanine exchange factor activity. Similarly, Che et al. identified a splicing variant and a novel frameshift variant of the BCL11B gene, thus suggesting that an aberrant translation of this gene may lead to an intellectual development disorder. Fujii et al. used a FRET-based approach to understand the impact of CAMK2A  gene mutation in patients' phenotype. Han et al. discovered a novel missense single nucleotide polymorphism, rs61753730 (Q152E), located in the fourth exon of the frizzled class receptor 6 gene (FZD6), which they demonstrate impacts depressive-like symptoms of Fzd6-knockin mice. These findings contribute to our understanding of the genetic underpinnings of both normal and abnormal neurodevelopment, thus paving the way for future targeted therapeutics.Linking anatomical and phenotypic findings to genetic underpinnings has proved to be another challenge, yet one the field has fearlessly embraced. Although the high complexity of neuronal and cortical structure is regulated by a myriad of genes, Ariza-Salamanca et al. highlighted molecular and cellular mechanisms leading to catatonia, which had previously been described as a purely clinical syndrome. In a similar vein, Grubisha et al. reviewed post-translational modifications in human post-mortem brain tissues of neuropsychiatric diseases. Translational momentum is fueled by this bench-to-bedside viewpoint, with one constantly informing and advancing the other.Finally, it is imperative that we continue to integrate the field's existing cellular and molecular knowledge with clinical observations. In this Topic, In conclusion, through multiple innovative approaches, this Research Topic advanced our knowledge in understanding molecular mechanisms underlying several neuropsychiatric disorders. The papers herein integrate anatomical, cellular, molecular, and genetic findings in work that will ultimately advance the field toward elucidation of novel therapeutic targets and inform future rational drug design.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


E. coli and S. aureus bacteria showed that nanofibers with lower CS concentration cause stronger inhibitory effects on bacterial growth. The nanofibers do not have any toxic effect on cell culture, as revealed by in vitro wound healing test on NIH 3T3 fibroblasts.Drug loading in electrospun nanofibers has gained a lot of attention as a novel method for direct drug release in an injury site to accelerate wound healing. The present study deals with the fabrication of silk fibroin (SF)-chitosan (CS)-silver (Ag)-curcumin (CUR) nanofibers using the electrospinning method, which facilitates the pH-responsive release of CUR, accelerates wound healing, and improves mechanical properties. Response surface methodology (RSM) was used to investigate the effect of the solution parameters on the nanofiber diameter and morphology. The nanofibers were characterized via Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), zeta potential, and Dynamic Light Scattering (DLS). CS concentration plays a crucial role in the physical and mechanical properties of the nanofibers. Drug loading and entrapment efficiencies improved from 13 to 44% and 43 to 82%, respectively, after the incorporation of Ag nanoparticles. The application of CS hydrogel enabled a pH-responsive release of CUR under acid conditions. The Minimum Inhibitory Concentration (MIC) assay on  Over the last decade, the incidence of infections among people has skyrocketed alarmingly fast. The skin plays a pivotal role in protecting against diseases caused by threatening pathogens, such as bacteria and viruses. Infection associated with the secretion and persistent presence of microorganisms in the wound area is among the difficulties imputed to the wound healing process ,2. ThereDue to their promotion of cell proliferation, migration, and attachment, biopolymers have become increasingly popular for fabricating promising electrospun platforms for tissue engineering and wound dressing in recent years . NaturalAmong many natural-based polymers, silk fibroin (SF) is a protein-based polymer made from various insects and spider species . SF-baseE. coli and S. aureus bacteria [Various microbial agents including chemical antibiotics, herbal antibiotics, and metal nanoparticles have been recently studied in combination with wound dressing and tissue engineering platforms ,31,32. Dbacteria . Ag nanobacteria . Neverthbacteria . This prCurcumin (CU), categorized primarily into the flavonoid group of polyphenols, is a lipophilic drug insoluble in water and soluble in solvents such as dimethyl sulfoxide, acetone, ethanol, and chloroform . It has In this study, we determined the effect of different solution parameters  on the mean diameter of SF-CS-Ag-CUR nanofibers and their morphology using the Response Surface Methodology (RSM). For improving cell adhesion and proliferation, a facile one-step green method based on employing CS hydrogel as both a reducing and stabilizing agent without using harmful chemicals was applied for the formation of Ag nanoparticles. Chemical structure, physical and mechanical properties, degradation, antibacterial activity, as well as cytotoxicity of the electrospinning nanofibers were tested. Because of abnormalities of cells in bacteria-infected wounds, these sites have specificities like an acidic pH, which can be used as triggers for drug release. To investigate the pH-responsive release of CUR as the model drug from the fabricated nanofiber, the in vitro release study was performed. To the best of our knowledge, this is the first original investigation filling the existing gaps in wound dressing, providing the pH-responsive release of CUR and improving CUR loading and entrapment efficiencies for ameliorating the major problems of CUR, i.e., low solubility.2CO3), lithium bromide (LiBr), phosphate buffer saline (PBS), polyethylene oxide , and silver nitrate (AgNO3) were supplied by Merck. Sigma-Aldrich  provided fetal bovine serum (FBS), dimethylsulfoxide (DMSO), Dulbecco’s Modified Eagle’s Medium (DMEM) High Glucose, 3--2, 5-diphenyl-tetrazolium bromide (MTT), and curcumin (CUR). The American Type Culture Collection (ATCC) provided NIH 3T3 fibroblasts cell lines. Penicillin/streptomycin and 0.25% (w/v) trypsin–0.1% (w/v) ethylenediaminetetraacetic acid (EDTA) were purchased from Solarbio . Escherichia coli (E. coli) (ATCC 87398) and Staphylococcus aureus (S. aureus) (ATCC 25923) were provided by the Microbiological Resources Centre, Iranian Research Organization for Science and Technology.LMW chitosan , acetic acid, sodium carbonate (Na2CO3 was added to 2 L of boiling deionized (DI) water to reach a concentration of 0.02 M. Next, 5 g of Bombyx mori cocoon was added, and the resulting solution was boiled for 1 h. The silk cocoons were then rinsed with DI water three times in order to extract water-soluble sericin. The obtained solution was dried under laminar flow overnight. After drying, LiBr (10 M) was added to the solution. Next, the solution was carried into an oven. The oven was heated and maintained at 60 °C for 4 h. Then, the solution was poured into a dialysis bag which was immersed in 2 L of water and kept for three days to complete the removal of LiBr. It is worth noting that six replacements of the bath water were needed. Finally, SF was obtained by centrifugation at 4500 rpm, 20 min, and 4 °C and washing three times with DI water.SF was prepared according to the previously reported literature . First, v/v) before adding to CS. Next, different concentrations  of CS were slowly added to the prepared solution by the use of a heater stirrer  to reach a homogeneous solution. Owing to the sensitivity of SF to the vortex, we selected the CS solution for the loading of the drug and Ag nanoparticles. Regarding the crosslinking of Ag ions, sodium borohydride (NaBH4) is the most widely used [4 were reported in previous studies [+ ions to Ag0 nanoparticles, one carbonyl group is produced, originating from the oxidation of alcohol and/or glycosidic groups in CS. In this regard, in order to reduce the prepared nanofibers’ toxicity, we only used CS as a reducing and stabilizing agent. Next, 10 mg of AgNO3 was added to 10 mL of prepared CS solution, and the mixture was stirred (~200 rpm) for about 30 min to reach a homogenous solution. The addition of Ag salts to the prepared homogeneous solution under stirring conditions resulted in the reduction of Ag ions. Prior to the loading of CUR into the fabricated CS-Ag nanohybrid, we dissolved CUR (2 mg/mL) in ethanol owing to its hydrophobic nature. For synthesizing the drug-loaded nanohybrid, dissolved CUR (0.25 mg/mL) was added to the prepared nanohybrid of CS-Ag under vigorous stirring conditions (~500 rpm for 15 min). Finally, the nanohybrids were left in liquid nitrogen for 5 min before transferring into the freeze-dryer.In the first stage, acetic acid was added to DI water and maintained at 50 °C for 15 min to reach 40% (ely used . Various studies . Patríci studies  investigv/v) PEO was added to the solution in order to increase the SF electrospin ability. Then, the SF solution containing PEO was fed into another 5 mL syringe and converted to nanofibers under the electrospinning parameters of voltage of 20 kW, injection flow rate of 2.1 mL/h, and gap distance of 20 cm. The prepared SF nanofibers were treated with 75% ethanol vapor  around 20 min. In case of crosslinking CS-Ag-CUR nanofibers, the prepared nanofibers were transferred into a sealed chamber saturated with the vapor of GA solution . The nanofibers were treated with GA vapor for 24 h and heated and maintained at 120 °C for 24 h in an oven to remove the unreacted GA.In order to fabricate a mixed fibrous structure, a dual pump electrospinning system  was employed. This machine features two syringe pumps on both sides of the rotating collector drum. The homogenous nanohybrid of CS-Ag-CUR was placed into a 5 mL syringe, with a 21 gauge cut off tip. The applied voltage for the electrospinning of CS-Ag-CUR nanohybrid was 18 kW, injection flow rate 0.8 mL/h, and gap distance 16 cm. Before pouring the prepared SF solution into another 5 mL syringe, 40% (Fourier Transform Infrared (FTIR) spectrophotometry was recorded on a Thermo AVATAR FT-IR spectrometer . The crystalline structure of the samples was analyzed using X-ray diffraction . The average size distribution and the surface charge of samples were obtained using Dynamic Light Scattering (DLS) and zeta potential measurements. Scanning Electron Microscopy (SEM) was also used to characterize the prepared samples’ morphology and diameter. The diameter of nanofibers was calculated using image analysis software . The mechanical properties were studied using the testing machine (STM-20). Water contact angle measurements were carried out using the contact angle analyzer . To perform this test, a water droplet volume of 4 µL was used. Brookfield E230 was used for measuring viscosity.0) was immersed in a measuring cylinder containing a known volume (V1) of ethanol. The sample was kept in ethanol for 10 min. The total volume of the ethanol and the immersed sample was then recorded as V2. The immersed sample was removed from the measuring cylinder, and the residual volume of ethanol was registered as V3. The porosity was then measured by applying the equation below [The porosity of samples was investigated by applying the liquid displacement procedure reported by Ju et al. . Owing ton below :(1)PorosFirstly, 20 mm × 20 mm of the prepared nanofiber was weighted. Next, the weighted nanofiber was immersed in 10 mL PBS . At specific time intervals of 15, 30, 60, 90, 120, 150, and 180 min, the nanofiber was taken out and weighted after the surface water was absorbed using filter paper. The swelling ratio and water uptake were derived using equations 2 and 3, respectively .(2)SwelFor the moisture retention test, first, the sample was saturated, taken out for centrifugation process at 500 rpm for 3 min, and then weighed precisely (Ws). Next, the sample was placed in an incubator with a constant temperature of 37 °C and relative humidity of 39 ± 1% and weighed every 30 min (Wt). The rate of water evaporation was measured employing the equation below :(4)WaterA biodegradability test was used in order to investigate the biodegradability behavior of the prepared nanofibers. First, 1 cm × 1 cm of the dry sample was weighed (W1), placed in PBS solution at pH 7.4 for 24 h, and then incubated at a physiological temperature of 37 °C. At specified time intervals of 0, 2, 4, 6, 8, 10, 12, and 14 days, the sample was brought out, washed with water several times, left in distilled water for 20 min to remove the nanofiber’s structure salts, and then dried. The proportion of mass loss or degradability was calculated based on the proportion of the remaining weight of the nanofibers employing the equation below:Before adding ethyl acetate, 1 mg of lyophilized CS-Ag-CUR was dispersed in 1 mL of PBS. The ethyl acetate phase can be easily separated after being shaken. A UV–Vis spectrophotometer at 419 nm was used to measure the quantity of unbound CUR. Using Equations (6) and (7), the entrapment efficiency (EE) and loading efficiency (LE) of the drug were measured, respectively .(6)EE (w/v) nanofiber at two pH of 5.4 and 7.4, we employed a dialysis technique. First, a piece of nanofibers (2 cm × 2 cm) was added to Spectrum-Labs dialysis bags (cut off Mw = 10–12 kDa). For the next 432 h, the dialysis bags containing the nanofibers mixture were submerged in 30 mL of two separate phosphate buffers with 20% v/v ethanol at 37 °C. We opted for phosphate buffer saline (PBS)  to dilute the lyophilized aptamer powders at pH 7.4. Regarding the adjustment of pH to 5.4, a pH meter and HCl solution were used. At specified time intervals, 300 µL of the medium was regularly separated for measuring the release of CUR within the buffer. A novel equal volume of fresh buffer was added to maintain a steady volume. In order to calculate absorption, the samples were evaluated spectrophotometrically at a 419 nm by a UV Vis spectrophotometer  [In order to identify and compare the release of CUR from SF-CS-Ag-CUR (CS 9% England) . EquatioE. coli and S. aureus bacteria, the Minimum Inhibitory Concentration (MIC) test was used. Bacterial strains were cultured in Mueller hinton broth media for one day at 37 °C. Then, 0.5 McFarland solution was obtained by adding physiological serum with a volume ratio of 0.01 to the cultured bacteria. The optical density (OD600) was adjusted to 0.11. Each of the wells was filled with 100 µL of sterilized growth medium. Subsequently, 5 µL of bacteria suspensions were added to all wells except the last one. Next, 100 µL of sterilized SF-CS-Ag-CUR (CS 7% w/v) sample was introduced to the first well. Then, 100 µL of first well was separated and added to the second well. The process was repeated until well column 10 was reached. Well column 10 containing bacteria and culture medium was regarded as the positive group, and the 12th well containing culture medium was regarded as the negative group. Exactly the same procedure was repeated for the next sample. Finally, all of the plates were incubated at 37 °C for 24 h. The MIC for each row was determined employing ELISA Reader.In order to study the antibacterial activity of the samples against w/v) and SF-CS-Ag-CUR (CS 9% w/v) on the NIH 3T3 fibroblast cell line, MTT assays were employed. After adding the NIH 3T3 fibroblast cell line into a 24-well cell culture plate, the cells were incubated for 24 h at 37 °C and 5% CO2. Then, nanofibers were added to each well with their MIC concentration and incubated over 1, 3, and 7 days at 37 °C. The cells that were cultured without any treatment in DMEM basic medium including 10% FBS and 1% penicillin/streptomycin for 24 h were considered as the control group. Next, 50 μL of 5 mg/mL MTT solution was introduced to each well, and all wells were incubated for 4 h at 37 °C. The wells were rinsed with PBS buffer, filled with 150 µL of DMSO, and then stirred vigorously for 20 min in order to completely solubilize formazan. The optical density of each well was determined using an ELISA reader at the absorbance wavelength of 570 nm. For studying cell adhesion and morphology via SEM images, cells were fixed on the nanofibers through the following steps: First, the nanofibers were carefully removed from the incubator, each well culture medium was removed, and then 200 μL of GA was added to them. Second, GA was allowed to be in contact with the nanofibers for 3 h and then was moved away from the wells, after which the wells were washed with PBS solution. Finally, 200 μL of graded ethanol solution  was added to the wells for 10 min, and the nanofibers were separated from the wells, dried at ambient temperature, and finally photographed with SEM for cell adhesion assay.In order to investigate the toxicity of SF-CS-Ag-CUR  caused an increase in the amount of the solution viscosity from 13.4 to 58.1 cp. w/v, GA volume of 30 mL, and ethanol volume of 5 mL.Among all the vital features of nanofibers employed for wound dressing, the diameter and morphological properties of nanofibers have been widely reported to be the most important ones. A central composite design (CCD) approach-based response surface methodology (RSM) was used for investigating and optimizing the effects of the concentration of CS hydrogel, ethanol volume in the crosslinking process of SF, and the concentration of GA as a cross-linker of CS hydrogel on the diameter and morphological properties of nanofibers. The experimental range and responses of SF-CS-Ag-CUR to the independent factors at three levels are presented in han 0.05 . F-valuew/v) of CS concentrations were appropriate for the formation of smooth nanodimensional fibers. In this regard, nanofibers made of both 7% and 9% (w/v) of CS concentration with fixed GA and ethanol volumes of 30 and 5 mL, respectively, were opted for the mechanical and physical characterization of SF-CS-Ag-CUR nanofibers.As wound dressing is a bioplatfrom that leads to high cell growth and proliferation, achieving a more intertwined structure can be helpful for wound healing. The SEM images of crosslinked and uncrosslinked SF-CS-Ag-CUR nanofibers at the optimized condition are shown in The morphological analysis of the reduced Ag nanoparticles in CS solutions is demonstrated in −1 is characteristic peak of C-O bonding. The band at 1150 cm−1 is attributed to the asymmetric extension of the C-O-C bridge. The band at 1580 cm−1 is due to the N–H binding of amide I, corroborating the presence of acetylglucosamine unit of CS. The absorption band at around 1370 cm−1 can be ascribed to the CH3 symmetrical deformation [−1 were detected due to the carbonyl stretching vibration of the secondary amide and C–H stretching, respectively. The wide band located in the region 3000–3560 cm−1 is related to N–H and O–H bonding, in agreement with previous studies [FTIR analysis was conducted in order to identify the presence of CS, Ag, and CUR. FTIR spectra for CS, CS-Ag, and CS-Ag-CUR are represented in ormation . Two ban studies ,53.−1, the peak is referred to the presence of Ag nanoparticles [−1, which were ascribed to the N–H binding of amide I and CH3 symmetrical deformation, respectively, moved to another frequencies in the FTIR spectrum of CS-Ag and became broader. The band at 2850 cm−1 representing C–H stretching in the FTIR spectrum of CS disappeared in the FTIR spectrum of CS-Ag.The FTIR spectrum of CS-Ag were examined to validate the presence of Ag nanoparticles by new observed peaks. At 654 cm−1 detected in the spectrum of CS was not detected in CS-Ag-CUR, signifying the complexation of CUR with other components. The band at 3363 cm−1 indicating N–H and O–H bonding in the FTIR spectrum of CS moved right and became broader. In addition, the presence of the peaks at 1603 and 1401 cm−1 might be owing to C=O stretching vibration of CUR.In the FTIR spectrum of CS-Ag-CUR, all distinguishing peaks of CS were observed. The band at 2850 cmw/v) was shown in −1 and 1219–1245, respectively, increased, demonstrating successful crosslinking of the nanofiber and the formation of β-sheets.The FTIR spectra of crosslinked and uncrosslinked SF-CS-Ag-CUR  and SF-CS-Ag-CUR (CS 9% w/v) is demonstrated in w/v) and SF-CS-Ag-CUR (CS 9% w/v) nanofibers. There was a significant difference between crosslinked and uncrosslinked samples. The crosslinked nanofibers with a higher amount of CS showed significantly higher tensile strength as compared to other groups, while uncrosslinked nanofibers with a lower amount of CS represented significantly higher strain. For both crosslinked and uncrosslinked nanofibers, a higher CS content in nanofibers leads to a higher tensile strength. Further, for both SF-CS-Ag-CUR (CS 7% w/v) and SF-CS-Ag-CUR (CS 9% w/v), uncrosslinked nanofibers show a higher strain. Considering all findings, we can conclude that interlocked structure of crosslinked nanofibers improves the durability of nanofibers against external force, which results in higher tensile strength. In contrast, in uncrosslinked nanofibers, the presence of weak and more flexible connections between components in the nanofibers leads to an increase in the tensile modulus and elongation. Increasing the concentration of the CS chain in the fabricate nanofibers will in fact lead to an increase in the viscosity and density, which then causes the nanofibers to become thicker and endure higher tensile strength while being stretched [The mechanical analysis of crosslinked and uncrosslinked SF-CS-Ag-CUR  and SF-CS-Ag-CUR (CS 9% w/v) has been summarized in The porosity of crosslinked and uncrosslinked SF-CS-Ag-CUR (CS 7% w/v) and SF-CS-Ag-CUR (CS 9% w/v) during 240 min were 75 and 76%, respectively. The occurrence of initial rapid dehydration might be ascribed to the evaporation of free water in the opening structure of CS chains. Additionally, the high concentration gradient of water could be the reason for the fast evaporation of water in the first 240 min. After initial rapid dehydration, the evaporation became slow. This sluggish evaporation can be ascribed to an interlocked structure, favoured by the presence of hydrophilic groups present in CS, i.e., amine and hydroxyl. Owing to higher crosslinking and a stronger interlocked structure, the nanofibers with less CS content caused a delay in evaporation. In fact, water molecules were entrapped effectively within the nanofibers with less CS content net structure, in line with the former results in the literature.Among other factors, nutrient transport, cell signaling, and cell growth and proliferation are highly dependent on the water-holding capacity of a wound dressing and tissue engineering platform . As Figuw/v) and SF-CS-Ag-CUR (CS 9% w/v) are shown in w/v) was 53.160, while the water contact angle for SF-CS-Ag-CUR (CS 7% w/v) was 50.889. These findings reveal the hydrophobic nature of CS hydrogel. Comparing the water contact angle of two groups, we can conclude that the fabricated nanofibers have suitable hydrophilicity, facilitating cell adhesion, growth, and proliferation, thus accelerating the wound healing process [Water contact angles of SF-CS-Ag-CUR (CS 7%  process .w/v) nanofiber for every 2 days throughout 2 weeks of incubation in PBS solution adjusted to pH 7.4 at 37 °C has been shown in w/v) nanofiber was 77% within 2 h, while the remaining weight of 64% was achieved for SF-CS-Ag-CUR (CS 9% w/v) nanofiber after 14 h. It was found that the degradation of CS occurs through the hydrogen bonding between residual amino and hydroxyl groups of CS and water molecules.The remaining weight of SF-CS-Ag-CUR . The LE was measured 13% in CS-CUR (CS 9% w/v) and increased to 44% in CS-Ag-CUR (CS 9% w/v)  . This imw/v) and CS-Ag (CS 9% w/v) was measured as 43% and 82%, respectively, similarly supporting the crucial impact of Ag nanoparticles in the increase in EE (The EE of CUR in CS (CS 9% se in EE . To overw/v) nanofiber. As stated above, the release of CUR was investigated using the dialysis technique at pH = 7.4 and pH = 5.4 at 37 °C  for 432 h (w/v) nanofiber was measured to be 39% at pH 7.4. In contrast, at pH = 5.4 (acidic conditions), the overall release of CUR from SF-CS-Ag-CUR (CS 9% w/v) nanofiber was 69% after 144 h. At pH 5.4, 95% of CUR was released after 432 h, while at pH 7.4, only 62% was released after 432 h. The pH-sensitive release from the fabricated SF-CS-Ag-CUR (CS 9% w/v) nanofiber can be attributed to the swelling characteristics of CS hydrogel. In fact, the repulsive force between the functional groups of the components in the nanofiber, i.e., the amino and hydroxyl groups in CS and hydroxyl group in CUR, can enhance the free spaces within components in acidic conditions, increasing the penetration of buffer into disintegrated nanofiber, resulting in the increased drug release from the SF-CS-Ag-CUR (CS 9% w/v) nanofiber at pH = 5.4 as compared to pH = 7.4. On the other hand, at a higher pH, the number of hydronium (H3O+) reduces. As a result, the CS chains failed to absorb water and swell, resulting in the formation of CS polymer chains accumulated around the nanofiber, functioning as a barrier to decrease the release of CUR from the nanofiber. Ahmadi Nasab et al. [w/v) nanofiber was 30% after 24 h at pH 5.4. In addition, the cumulative release of CUR from SF-CS-Ag-CUR nanofiber at pH 7.4 after 24 h was less than that of CUR reported by Ahmadi Nasab and colleagues–about 14% of CUR was discharged from the pH-responsive chitosan mesoporous silica nanoparticles after 24 h–while in the present work, only 9% of CUR was released at pH 7.4 after 24 h. Further, in research conducted by Fahimirad et al. [w/v) after 144 h at pH 7.4.We studied the release mechanism in order to confirm the pH-sensitive release of CUR from SF-CS-Ag-CUR  and SF-CS-Ag-CUR (CS 9% w/v) nanofibers against two E. coli (Gram-negative) and S. aureus (Gram-positive) bacteria was determined by employing the MIC method summarized in w/v) and SF-CS-Ag-CUR (CS 9% w/v) nanofibers against E. coli bacteria was 0.94 and 1.23 mg/mL, respectively. In the case of S. aureus bacteria, the quantity of SF-CS-Ag-CUR (CS 7% w/v) and SF-CS-Ag-CUR (CS 9% w/v) nanofibers for MIC was measured to be 1.04 and 1.36 mg/mL, respectively. The doses at which SF-CS-Ag-CUR (CS 7% w/v) nanofiber shows antibacterial activity against E. coli and S. aureus bacteria are the lowest, corroborating the excellent antibacterial property of SF-CS-Ag-CUR (CS 7% w/v) nanofiber. These findings correlate fairly well with those of Aliasghari et al. [The antibacterial activity of SF-CS-Ag-CUR  and SF-CS-Ag-CUR (CS 9% w/v) nanofibers against E. coli and S. aureus, we investigated the stage of growth by measuring the optical density at 600 (600OD). The growth curve of both E. coli and S. aureus bacteria in the presence of SF-CS-Ag-CUR (CS 7% w/v) and SF-CS-Ag-CUR (CS 9% w/v) nanofibers is represented in w/v) and SF-CS-Ag-CUR (CS 9% w/v) nanofibers over 12 h, and 300 µL of samples were extracted at 2-h intervals. The SF-CS-Ag-CUR (CS 7% w/v) nanofiber caused more growth inhibition against both E. coli and S. aureus bacteria as compared to SF-CS-Ag-CUR (CS 9% w/v) nanofiber. This finding indicates the inverse effect of CS concentration on bacterial growth. All these findings are also in accordance with the results discussed in the antibacterial property section.To further make a comparison between the antibacterial activity of SF-CS-Ag-CUR (CS 7% w/v) and SF-CS-Ag-CUR (CS 9% w/v) nanofibers on NIH 3T3 fibroblast cells after the incubation of 1, 3, and 7 days and to find out if the nanofibers can be applied without showing toxicity. NIH 3T3 fibroblast cells received no treatment and were regarded as the control group. Cell viability higher than 80% is acceptable for considering a nanofiber as a biocompatible platform for wound dressing applications based on ISO standards. According to the results represented in w/v) and SF-CS-Ag-CUR (CS 9% w/v) nanofibers prolonged, the viability of cell increased subsequently. This result corroborated that the fabricated nanofibers do not have cytotoxic effects on cancer cells by themselves and only increase the growth and proliferation of fibroblast cells. The SEM images of SF-CS-Ag-CUR (CS 7% w/v) and SF-CS-Ag-CUR (CS 9% w/v) nanofibers after 3 and 7 days of incubation are represented in w/v) was much more than that of SF-CS-Ag-CUR (CS 7% w/v), suggesting the positive effect of CS on adhesion.MTT assay was used to determine the cytotoxicity effect of SF-CS-Ag-CUR  approach-based response surface methodology (RSM) was applied to investigate the effect of the solution parameters on the nanofiber size and morphology. The results obtained indicated that the physical and mechanical properties of the nanofibers are dependent on the CS concentration and that CS hydrogel plays an important role in the pH-responsive release of CUR. The addition of Ag nanoparticles to the nanofibers results in a larger surface for interaction with the drug, hence leading to significantly higher CUR loading and entrapment into the fabricated nanofibers. As shown by MIC analysis on


This paper explored the influence of long non-coding MELTF Antisense RNA 1 (lncRNA MELTF-AS1) on the prognosis of non-small cell lung cancer (NSCLC), and further deepened the understanding of NSCLC. A total of 130 patients with NSCLC participated in current study to detect and compare lncRNA MELTF-AS1 expression in cancer and normal tissues. Kaplan-Meier analysis and log-rank test were chosen to analyze the effect of MELTF-AS1 expression on the survival of patients within 5 years. The correlation between the expression of MELTF-AS1 and the clinical characteristics of NSCLC patients was analyzed, and the prognostic factors of NSCLC were analyzed by multivariate Cox regression. Subsequently, MELTF-AS1 expression in NSCLC cells were detected. The Cell Counting Kit-8 (CCK-8) and Transwell methods were selected to study the proliferation, migration capability and invasion level of NSCLC cells that silencing MELTF-AS1. Through the luciferase activity assay to explore the relationship between MELTF-AS1 and miR-1299, to further understand the effect of silencing MELTF-AS1 on NSCLC. MELTF-AS1 was increased in NSCLC tissues and cells. Silencing MELTF-AS1 suppressed the proliferation ability, migration capability and invasion level of NSCLC cells, which means that low expression of MELTF-AS1 may be more conducive to patient survival. In addition, through luciferase activity analysis and bioinformatics analysis, MELTF-AS1 has a negative effect on miR-1299, and silencing MELTF-AS1 enhanced miR-1299 expression in NSCLC cells. MELTF-AS1 is highly likely to be a promising prognostic biomarker, and associated with the progression of NSCLC.  TRB3 interacts with GRB2 in HG-triggered retinal pigment epithelial cells sTRB3 modulates inflammation and oxidative stress in DR through binding to GRB2TRB3 modulates cell damage in DR through binding to GRB2The latest research shows that deaths from lung cancer accounting for about 18% of all cancer deaths . The incIn recent years, research on long non-coding RNA (lncRNA) has continued to increase. It is understood that lncRNA is more than 200 nucleotides in length and cannot encode proteins, but it can regulate gene expression and malignant tumors . It has Therefore, based on the increasing number of NSCLC patients and the high recurrence rate within 5 years, there is an urgent need to discuss the molecular mechanism of NSCLC and monitor potential prognostic biomarkers to improve patient survival.A total of 130 Chinese NSCLC patients participated, and all patients did not receive any chemotherapy or other related tumor treatments before the study. NSCLC and normal tissues were removed, frozen with liquid nitrogen, and stored in a refrigerator . The res2 [Shanghai Cell Bank of the Chinese Academy of Sciences  provided four NSCLC cell lines  and control cell lines (BEAS-2B). The cells involved in the experiment were cultured in Dulbecco’s modified Eagle medium , supplemented with 10% fetal bovine serum  and 100 U/mL penicillin/streptomycin . The cells were washed with phosphate buffered saline  solution and placed in a 37°C humidified incubator containing 5% CO2 .GenePharma Co. Ltd.  chemically synthesized miR-1299 mimic, mimic NC, miR-1299 inhibitor, and inhibitor NC. The cells were subcultured in 6-well plates. Then, Lipofectamine 2000  was used to transfect RNA into cells.2O. Subsequently, GAPDH and U6 were used as endogenous controls, and the data was calculated and processed by 2−ΔΔCt method. The primer sequences are as follows: MELTF-AS1 forward, 5´-GCGTTCACACTCATTACCC-3´ and reverse, 5´-CTATTCAGACCCCTTCACCC-3´ GAPDH forward, 5´-GAGCCACATCGCTCAGACAC-3´ and reverse, 5´-GCCCAATACGACCAAATCC-3´ U6 forward, 5´-GCTTCGGCAGCACATATACTAAAAT-3´ and reverse, 5´-CGCTTCACGAATTTGCGTGTCAT-3´.TRIZOL reagent  was selected to obtain total RNA in tissues and cells, and the purity and concentration of total RNA were determined via a spectrophotometer (Eppendorf) . Accordi4 cells/well), and 100 μL of DMEM medium containing 10% FBS was added to each well. Then added CCK-8 solution (10 µL), incubated in a humidified incubator containing 5% CO2 at 37°C for 4 hours, and the absorbance at 450 nm in each well was measured with a microplate reader .The cell proliferation ability of SK-MES-1 and A549 cells were measured according to the Cell Counting Kit-8 . The transfected cells were cultured in a 96-well culture plate  method. One hundred mocroliter DMEM medium was placed on the upper chamber of Transwell, and DMEM medium containing 10% FBS was served into the lower chamber to induce cell migration. After incubation for 48 h, fixation with 4% paraformaldehyde for 20 min at room temperature. Then washing with PBS solution and staining with 0.1% crystal violet for 10 min. Cell invasion and migration levels were detected in similar steps, but Matrigel  was applied to the upper Transwell chamber in advance. Finally, NSCLC cells were cleaned by PBS solution again, and an inverted microscope (Olympus Corporation) was used for random image sampling. Each assay was repeated for 3 times.The luciferase reporter gene detection system (Promega Corporation) was selected to measure A549 cells. The wild-type and mutant-type lncRNA MELTF-AS1 were cloned into pmirGLO vector (Promega Corporation), and WT-MELTF-AS1 and MUT-MELTF-AS1 were constructed respectively. Subsequently, A549 cells were co-transfected with miR-1299 mimic, miR-1299 inhibitor, mimic NC or inhibitor NC and WT-MELTF-AS1 or MUT-MELTF-AS1. After transfection for 48 hours and washing with PBS solution, the luciferase activity was measured with a dual-luciferase reporter gene detection system. Argonaute 2 (Ago2) is a key component of RNA-induced silencing complex (RISC), and miRNAs regulate the expression of their target genes by binding to Ago2 . In this2 test. The prognostic significance of MELTF-AS1 was detected by Kaplan-Meier method. Multivariate Cox regression method was chosen to analyze the prognostic factors of NSCLC patients. Each assay was repeated for 3 times. P < 0.05 represents statistical significance.Data processing and statistical analysis were performed by SPSS 20.0 software and GraphPad Prism 5.0 software (USA). The measurement data were expressed as mean ± standard deviation (x ± s), and count data were expressed by the χThe prognostic and therapeutic potential of lncRNA MELTF-AS1 in NSCLC is the main thrust of this study. To understand the possibility of MELTF-AS1 as a prognostic biomarker, RT-qPCR was used to detect the expression level of MELTF-AS1 in tissues and cells. The effects of silencing MELTF-AS1 on the proliferation, migration and invasion of NSCLC cells were discussed by methods such as CCK-8 and Transwell. The targeting effect of MELTF-AS1 on miR-1299 by luciferase activity detection assay.P = 0.000).RT-qPCR was chosen to test and compare MELTF-AS1 expression in NSCLC and normal tissues, and Kaplan-Meier analysis and log-rank test were selected to analyze the effects of different expressions of MELTF-AS1 on overall survival. For the TCGA NSCLC cohort data, LUAD samples and LUSC samples were analyzed separately. The results are shown in P = 0.006) and TNM stage (P = 0.003). There was no significant correlation with patient age, gender, tumor size, smoking status and differentiation. P < 0.001), lymph node metastasis (P = 0.013) and TNM stage (P = 0.007) were all related prognostic risk factors of NSCLC. These results implied that MELTF-AS1 has the potential as a prognostic biomarker of NSCLC. P < 0.05 represents statistical significance.The correlation between MELTF-AS1 expression and clinical characteristics of Chinese NSCLC patients was studied, and the prognostic risk factors of NSCLC were analyzed by multivariate Cox regression. RT-qPCR was used to detect and compare the relative expression of MELTF-AS1 in different NSCLC cells and normal cells, and to discuss the effects of silencing MELTF-AS1 on the proliferation capacity, migration and invasion levels of NSCLC cells. The relative expression of MELTF-AS1 in A549, HCC95, SK-MES-1 and NCI-H650 cells was determined, and the content of MELTF-AS1 increased compared with the control BEAS-2B, as shown in In P < 0.0001, Luciferase activity was further explored in A549 cells and bioinformatics analysis was performed to discuss the interaction between MELTF-AS1 and miR-1299. To demonstrate that MELTF-AS1 affects the development of NSCLC by regulating the expression of miR-1299, A549 cells were co-transfected with control, si-NC, si-MELTF-AS1, si-MELTF-AS1+ inhibitor NC or si-MELTF-AS1+ miR-1299 inhibitor via Lipofectamine 2000. RT-qPCR results are shown in It is known that lung cancer incorporated small cell carcinoma (SCLC) and NSCLC, such as squamous cell carcinoma, adenocarcinoma and large cell carcinoma, which all belong to the latter ,22. TherAs the molecular level is increasingly applied to the discovery and treatment of various cancers, researchers have shown that lncRNAs as prognostic marker may regulate and play a role in colorectal cancer , lung caThis paper elaborated that MELTF-AS1 increased in NSCLC tissues and cells, which was also confirmed in the TCGA NSCLC cohort. It has been reported that some RNA transcripts, such as mRNA and lncRNA, competitively bind the same microRNA, communicated and regulated their expression levels through shared miRNAs. Thus, the upregulation mechanism of MELTF-AS1 expression in NSCLC was speculated, which was also explained by Ding et al. in the regulation of MELTF-AS1 on the treatment of osteosarcoma . Kaplan-In summary, the current paper results indicated that lncRNA MELTF-AS1 contributed to the proliferation and growth of NSCLC cells by targeting miR-1299, which was helpful to the progress of NSCLC. As expected, MELTF-AS1 has the potential as a prognostic marker of NSCLC, which may provide a certain theoretical basis for the subsequent treatment of NSCLC.


Owing to the rapid improvement of computational methodologies and high-performance computational resources, computer-aided drug design (CADD) has been validated as an efficient and powerful strategy in almost every stage of drug discovery and development.de novo drug design, QSAR analysis, as well as in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties.Generally, CADD can be divided into structure-based drug design (SBDD) and ligand-based drug design (LBDD). Due to the rapid development of crystallography and homology modeling, structure-based virtual screening has emerged a useful technique to identify potential hits during early stage of drug discovery. LBDD strategies based on available information of known bioactive molecules, such as QSAR (Quantitative Structure–Activity Relationship) analysis, scaffold hopping, pharmacophore modeling, are also widely used for hit optimization and activity prediction. In addition, computational techniques like quantum chemistry calculation, molecular dynamics (MD) simulations and elastic network models can be used to study protein catalytic mechanism, conformational transition and allosteric regulations at an atomic level of detail, which provide useful information for mechanism-based drug design. Recently, with the development of machine learning theory and the accumulation of pharmacological data, artificial intelligence (AI), a powerful data mining technology, has been widely used in various fields of drug design, including virtual screening, Khattab and Al-Karmalawy), Dengue Virus NS5 protein , α-Glucosidase , hACE2 receptor of SARS-CoV-2 , Fascin , TMPRSS2  and Alzheimer’s disease targets . Santana et al. discussed the development of computational approaches to explore the chemo-structural diversity of natural products. CADD methods are also widely used for exploring interactions between ligand and receptor, as well as inhibition mechanisms of active compounds. Tao et al. performed molecular docking and MD simulations to study the interaction between RBD and two glycopeptide antibiotics (Vancomycin and Teicoplanin). Wang et al. used network pharmacology and molecular docking to explore the mechanism of Shan Ci Gu (Cremastra appendiculata) against non-small cell lung cancer. In addition, Di Filippo et al. proposed a machine learning model to predict drug transfer across the human placenta barrier, which could be used as a filter for chemical libraries in virtual screening campaigns.In this Research Topic, we have invited some scientists worldwide to contribute original research and review articles which could enhance our understanding of some of the above issues. Several studies utilized multiple computational approaches, such as molecular docking, DFT calculations, molecular dynamics (MD) simulations, ADME/T prediction, as well as biological evaluations to identify novel compounds against a series of important targets, like Tubulin (In summary, the above works presented in this special Research Topic illustrate the applications of CADD approaches and highlight the importance of developing new methods. At last, as the Guest Editors of this Research Topic, we would like to thank all the authors for their contributed articles and all the referees for their comments on the manuscripts. We hope that the readers will find this Research Topic interesting and useful for their research. Finally, we appreciate the editorial staff of Frontiers in Chemistry for their work in publishing this Research Topic.


Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that is characterized by the degeneration of both upper motor neurons and lower motor neurons, leading to motor and extra-motor symptoms. Although ALS has a low prevalence, its high mortality makes it one of the most intractable diseases. Mean survival time from the onset is 3–5 years, but some people could live for more than 10 years while slowly losing mobility. The pathogenesis of ALS remains unknown; however, recent research suggests that genetic factors may play an important role.The objective of the Research Topic “New Insights into the Molecular Mechanism of Amyotrophic Lateral Sclerosis Pathogenesis” was to gather original research articles and reviews illustrating the recent advances concerning the molecular mechanism of ALS pathogenesis. This Research Topic consists of four original articles.Zhang et al. proposed 27 candidate loci that were most possibly linked with sALS, which were identified for further analysis employing sequenom massARRAY technology and DNA sequencing in a separate case/control cohort of 239 sALS patients and 261 control subjects of Han ancestry from mainland China (HACM) ethnicity. They discovered that the polymorphism rs2619566 located within the contactin-4 (CNTN4) gene, rs10260404 in the dipeptidyl-peptidase 6 (DPP6) gene, and rs79609816 in the inositol polyphosphate-5-phosphatase B (INPP5B) gene were strongly associated with sALS in subjects of Han ancestry of China mainland. They also demonstrated the polymorphisms of rs2619566, rs10260404, and rs79609816 may affect the splicing, transcription, and translation of CNTN4, DPP6, and INPP5B genes and may play roles in the pathogenesis of ALS.Barton et al. used human pathology material from sporadic ALS patients, genetic ALS patients (carrying the C9orf72 mutation), and age- and sex-matched non-neurological controls to explore myelination at an RNA, protein, and structural level. They used electron microscopy to achieve (i) quantitative spatial profiling of the myelin basic protein (MBP) mRNA transcript, (ii) quantification of MBP protein, and (iii) the first quantitative structural assessment of myelination in ALS post-mortem tissues. Although significant dysregulation of subcellular transport of MBP mRNA was found in ALS patients compared with controls, there were no differences in MBP protein levels or myelination ultrastructure. They confirm that whilst there is a cell-autonomous mRNA transport defect affecting oligodendrocytes in ALS, it has no effect on myelin structure.Xu et al. compared the transcriptomic profile of the anterior horns of the lumbar spinal cord (AHLSC) between SOD1G93A mice and their wild-type (WT) littermates. When comparing pre-symptomatic/symptomatic ALS mice to WT mice, they discovered that bone marrow stromal cell antigen 2 (BST2) was considerably greater in the AHLSC of pre-symptomatic/symptomatic ALS mice. Immunofluorescent staining further confirmed that BST2 is mainly expressed on microglia in the AHLSC of ALS mice. These findings support the view that immune-related neuroinflammation plays a role in the early stages of ALS pathogenesis, and that BST2 could be used to treat microglia-mediated neuroinflammation later in the disease.Bigelow et al. explored the β-sitosterol β-D-glucoside (BSSG) model with a focus on motor function, and associated immunohistochemical markers. They used BSSG modeling to detect behavioral changes and immunohistochemical markers in mice. The results revealed that no changes in behavior were observed at any time point. Animals were processed for immunohistochemistry markers of substantia nigra integrity after behavioral testing. In the substantia nigra, immunohistochemistry indicated no alterations in the microglial marker Iba1 or the dopaminergic integrity marker tyrosine hydroxylase (TH) at any assessment point. The lack of any behavioral or immunohistochemical changes across groups implies an inability to duplicate earlier findings. Before the BSSG model can be used in preclinical studies, more research into the sources of variability in the model is required.In summary, the articles presented in the Research Topic “New Insights into the Molecular Mechanism of Amyotrophic Lateral Sclerosis Pathogenesis” cover four different aspects of ALS research, expanding our understanding of the disease and provide a valuable source of information concerning the molecular mechanism of ALS pathogenesis.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.This study was supported by grants the Committee of National Natural Science Foundation of China , Education Department of Jiangxi Province (GJJ13198 and GJJ170021), Jiangxi provincial department of science and technology  and Health and Family Planning Commission of Jiangxi province (20181019) (All to RX).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.