Social network is an important product of industrial society. In recent years, the research related to hot topics has focused on topic detection, topic trend prediction, and topic tracking. However, the important role of topic influence evaluation in hot topic research has not received enough attention, which leads the problem of inaccurate influence calculation. In order to solve the above problems, this paper proposes a novel model to evaluate the real-time relative influence of topics in social network. The proposed model can quantify the influence of topics, and some influential factors which determine topic hotness will be analyzed and identified. In this model, five impact indicators are defined, namely user engagement, topic coverage, topic activity, topic persistence, and topic novelty to consider the topic characteristics more finely. Moreover, the proposed model not only consider traditional simple factors of like, forward and comments, but also pay attention to the relative influence and time attenuation characteristics of the topics. Further, the experimental results show that our method could quickly aggregate the influence factors of hot topics and accurately provide the influence indicator of topics. In the society of smart industry, the Internet has increasingly become a way of information dissemination that cannot be ignored. On the one hand, as the most timely and widest media for information access, the Internet has become the main channel for the central government, relevant government departments, and authoritative platforms to release news and access information . On the The emergence of social media, especially the application of mobile communication technology, enables people to break the restrictions of time and space simultaneously, and garner, share, and exchange information from the Internet whenever and wherever possible, which led to an increasing number of Internet users and the rapid expansion of information on various platforms. In particular, Sina Weibo has become one of the most mainstream social platforms in China with its weak interactivity and sharing. Users can follow their friends and interact with interested bloggers. When people are interested in an event on the Internet, it will be liked, forwarded, or commented, and spread quickly, resulting in relevant events. At the same time, the same or similar events discussed by everyone form a topic, which has a certain social influence. A hot topic will be formed when a topic is highly concerned or participated by Internet users just like the microblog hot search list. The collective public opinions, emotional attitudes, and values in hot topics form public opinion and have a great impact on public life. Therefore, how to find influential topics from a large number of articles and opinions on microblog is meaningful for the government to grasp the current thoughts and concerns of the people. It is necessary to effectively evaluate the influence of the topic and study the influence of the topic on the society.However, the openness and inclusiveness of the Internet make it difficult for users to obtain information. Some blogs contain important information, while others contain trivial and meaningless information. People are eager for information that can describe social dynamics. How can we timely obtain the centralized and organized social hot spots and understand the focus information in the vast network? This problem attracts researchers to put forward many effective solutions in the field of topic detection, such as using topic detection method to identify emerging topics in network information flow, and find hot topics through hotness evaluation. Topic hotness evaluation, which can be regarded as a filtering process of hot topics, is used for hot topic discovery, topic recommendation, and topic trend prediction. The hotness of the topic is used to measure the influence of the topic. Traditional influence evaluation methods only consider the frequency of news reports for hotness evaluation. In their opinion, the more news reports contained in one topic, the higher the attention, and the greater the hotness. Or they think that the hotness of the topic is mainly reflected in the number of comments and clicks. Although the influence evaluation method has been improved in some follow-on evaluation algorithms, such as TF-PDF –5 hotnesOwing to the strong interactivity, fast propagation speed, and easy use of social networks, the network platform will produce a large amount of data every day, forming a lot of topics, which resulting in a rapid update of topic hotness, and the new hot topics will cover the old hot topics. When an event on the website may trigger massive relevant information, forwarding, and comments in a short time, the event will immediately become a hot topic. In addition, because the hotness of the topic varies in different periods of time, and new topics will continue to emerge in the same period of time, especially the topics related to judicial cases and political reports in microblog may last for a long time, but there are not many articles related to the topic produced during this period, so the hotness of the topic will be relatively small. However, if a topic produces extensive relevant articles in a short time, the topic will be more popular. Hot topics have a lifecycle, and they will go through the process from “generation” to “extinction”. Therefore, we can analyze that interactive behavior, topic volume, and time are all significant factors affecting topic hotness.In order to more accurately evaluate the popularity of topics, this paper proposes an evaluation model of real-time relative influence of topics in social networks (named ECANP), according to the initials of the five indicators---E (Engagement), C (Coverage), A (Activity), N (Novelty), and P (Persistence). Our model will analyze the law of increase and decline of topic hotness from five aspects: user engagement, topic coverage, topic activity, topic novelty, and topic persistence, so as to better distinguish hot topics from cold topics.We emphasize the relative influence and time attenuation of the topic. At the same time, multiple topics may appear at the same time, and the hotness ranking of topics depends on the relative influence between topics. In addition, topics have a lifecycle. With the passage of time, new topics gradually emerge and old topics are slowly replaced, so the time attenuation characteristics of topics are closely considered in the paper.A new model for topic influence evaluation, ECANP, has been proposed, which comprehensively evaluates the relative popularity of topics by integrating user engagement, topic coverage, topic activity, topic novelty, and topic persistence.Extensive experiments have been conducted on real datasets to prove the effectiveness of ECANP.The main contributions of this work are summarized as follows.The remainder of this paper is organized as follows. As an important channel of information dissemination and sharing, social networks bear the overload of information. Compared with the early era of lack of information, the biggest difference of social networks is that users can create information while browsing information. Moreover, the online social networking provided by the Internet almost imitates real life. Even if there is no contact between people, the purpose of information communication, sharing and dissemination can be achieved, and this way is more free and flexible. Due to the great participation of users, many subjective blog posts have been formed on the network, and continue to develop into topics, resulting in public opinion, which has a great impact on the public's point of view and life. In order to monitor public opinion in time, find hot topics and extract valuable public opinions from a large number of unorganized and complex data, numerous research scholars devote themselves to the research in this field.Since 1996, the Defense Advanced Research Projects Agency (DARPA) proposed the concept of Topic Detection and Trace (TDT) for automatically judging the theme of news data flow without human intervention, which has attracted extensive attention, including well-known universities such as IBM Watson Research Center, BBN Company, and Carnegie Mellon University. Companies and research institutions have participated in the evaluation of subsequent TDT. Although domestic research on TDT started relatively late, since National Taiwan University participated in the evaluation of TDT topic detection task in 1999, Peking University, Chinese Academy of Sciences, and Fudan University began to study TDT-related technologies, and put forward many researches using topic detection and tracking to solve the tasks of topic clustering and hot topic discovery, which have achieved prominent results.The emergence of TDT promotes the discovery and tracking of new events in news reports , 8. The Topic detection can undoubtedly help people quickly find out what topics people are discussing from a large number of online articles, but it cannot focus on hot topics, especially when there are many topics and time is limited, it is impossible to choose which part of the more important topics to participate in. Therefore, topic influence assessment is particularly important in hot topic detection tasks.Hot topic detection is generally divided into two tasks: topic detection and hotness evaluation. Topic discovery depends on topic detection technology, and the generation of hot topics depends on the hotness evaluation method. As for how to measure the hotness of topic, different researchers have successively analyzed the factors affecting the evaluation of topic hotness from different aspects based on the concept of “hot topic”, and all captured the characteristics of topic hotness to varying degrees.Chen et al.  construc∗PDF and aging theory, respectively. Wang et al. :Then, for the sake of eliminating the adverse effects caused by singular sample data, we employ maximum and minimum normalization to standardize the judgment matrix m represents the number of topics, n denotes the number of indicators to measure user engagement, and rji means value of the i-th indicator of the j-th topic;In equation , m reprermax and rmin represent the maximum and minimum values of the number of likes, forwards, and comments of the relevant articles on different topics under the same measurement indicator, respectively. And bji stands for maximum and minimum normalization value of rji.In equation , rmax an(2) Entropy Weight Determination of the Indicator. We define the weight of all likes, forwards, and comments of each topic as follows:fji denotes the proportion of the j-th indicator in the i-th topic.According to the definition of entropy, the weight of all measurement indicators can be determined:fji in equation  denotes the topic coverage in topic j, which is calculated based on the proportion of the number of articles related to topic j in all topic articles, that is,Then, coverage (j) denotes the topic activity in topic j. By calculating the number of articles on topic j in unit time, we can obtain the activity of the topic j, that is,Next, activity (j) denotes the topic novelty in topic j. According to the difference of time units between current time and the time when the topic j was first published, the novelty indicator of topic j is obtained:tt is current time , tp is the first release time of topic j, and ∇t(j) is the difference of time units between the current time and the first release time of the topic, time in days. k is the attenuation factor, which controls the attenuation rate of topic j over time. The larger ∇t(j), the smaller novelty (j), and the less contribution of this indicator to hotness.Similarly, novelty (j) denotes the topic persistence in topic j. According to the duration of the topic j in the life cycle and the proportion of the number of units in the whole topic monitoring time, the persistence of the topic is obtained:nu of topic j is obtained by equation  ∈  reasonably explain the impact on ECANP?We testify our proposed model by conducting extensive experiments on real-world corpus, aiming to answer the following key questions.To demonstrate the performance of our method ECANP and compare it with the baseline methods, a corpus of microblog articles is adopted. Six topic data are used for experiments to verify the universal applicability of the model ECANP in this paper. The topic names are shown in Since there is no unified evaluation indicator for topic influence evaluation, in order to prove the effectiveness of the model, this paper consulted a large number of relevant literature. Inspired by , we finaBHEM-TOA : This isFSTCC : Such a HFTC : This isIEFE : This isd days.DMCBF : This moTo support the effectiveness of ECANP model, we compare it with the following five baseline models, in which the first four models are verified with the same and only one dataset, and the fifth is verified with the six datasets used in this paper due to the particularity of its method. In addition, some comparison models do not have a name, for ease of display, we give a name according to the naming method in this paper. The experimental settings of proposed model and baselines are introduced in the next subsection.For the three experiments in the next section, we adopt different settings and data processing methods.For the first experiment, we implemented our ECANP model in six experimental datasets, presenting the hotness results of each topic, and the results of some influencing factors.For the second experiment, the settings of comparison models are divided into two categories. To realize the comparison between our proposed method and the first four models, we selected Topic 1 in datasets for experiment. Firstly, we clustered multiple subtopics and topic names under Topic 1 through topic detection method and topic name detection, and then ran ECANP and four baseline models respectively to calculate the hotness of each detected subtopic. For comparability, the hotness of all models is normalized in the range of 0–100. The normalization formula is equation . Due to For the third experiment, to clearly see the change of topic hotness and its indicators over time, we divided the life cycle of topic into 8 time periods, obtained the articles related to the topic in each time period, and took the last time point of the time period as the coordinate label.  BHEM-TOA: we removed the part of text comments in this baseline method, because the author evaluates the hotness of Blog websites, which is composed of the hotness of multiple topics. Multiple topics under a website do not distinguish comments, so it is set as a constant in this paper, and its value is 0.  FSTCC: different hotness evaluation methods use different indicators for different scenarios. Owing to there is no report sources in our datasets used in this baseline method, such information is ignored. In addition, the time interval used by the author is the difference between the current time and the topic publishing time, but the experimental results show that the publishing time of the topic in the dataset is too long from the current time, and the time attenuation is quite large, resulting in the hotness value of 0. Therefore, this paper uses the duration of the topic instead of the current time in the paper. The time interval is set to 1 day.  HFTC: the author applies the proposed model to cross social networks. To compare with our method, only the single platform topic hotness evaluation in author's paper is used.  IEFE: in the implementation of this method, since there are no number of clicks in our datasets, this item and the publishing sources are ignored, and the number of microblog articles is used to replace the number of user participation.  DMCBF: the time used in the model is in days.The settings of each baseline model are as follows.In equation , the attBased on the statistical experimental data in Due to the long real name of the topic, for convenient representation and viewing, the topic name is listed in the form of “topic + number” in the charts in this paper. The actual correspondence between topic name mark and real topic name is shown in In In this part of the experiment, to validate the effectiveness of our topic influence evaluation model, we evaluated and analyzed the hotness values of six topics. ECANP first extracts the number of forwards, comments, related blog posts, topic duration, hotness evaluation time period, and current time of each topic, then utilizes these information to calculate the hotness of each topic through each indicator calculation method and hotness evaluation formula proposed in The ranking of hot topics given by ECANP model is the same as expected, which shows that the topic hotness evaluation model proposed by this paper is reasonable and effective.In this section, we contrast the performance of our ECANP with five baselines, compare their ability to distinguish hot topics from cold topics. Note that, for the baseline BHEM-TOAm FSTCC, HFTC and IEFE, we conduct experiments on the same topic, regard a topic as an event, use the clustering algorithm to obtain the subtopics under topic, and calculate and distinguish the hotness of subtopic. The experimental flow is shown in α of the ECANP, FSTCC, and HFTC model is set to 0.001, the BHEM-TOA model is set to 0.1, and the IEFE model is set to 0.0001. In Tables α of the ECANP is 0.00001, and α of the DMCBF is 1. As shown in Tables Tables 5Hht is the hotness of hot topic ht and Hct is the hotness of cold topic ct.Obviously, the user attention and topic attributes in different time periods are not invariable, which enable the hotness of each topic varying with time. In order to clearly show that the change of topic hotness over time is affected by relevant influencing indicators, As shown in The hotness value of topic is jointly determined by various factors such as the forwarding, comments, and publishing time of the topic-related articles, including user characteristics and topic attributes. Due to the large base of users' forwarding, comments, and other behaviors, and the topic is generated almost at the same time period, the user engagement has a great impact on the final hotness of the topic.ECANP relatively comprehensively analyzes the influencing factors of topic hotness, and makes full use of the factors such as the number of comments, the number of forwarding, the number of articles, and the release time to evaluate the topic hotness from five indicators. This method can effectively quantify the influence of topics and reflect the impact of user participation, topic coverage, topic activity, topic persistence, and topic novelty on the hotness evaluation results. It is more reasonable and practical for guiding topic ranking.In this paper, we analyzed the relative influence and time attenuation characteristics of topics and hot topics, as well as each indicator affecting the topic hotness according to the reality that the influence of a topic is relative, not absolute. The topic hotness is measured from five indicators: user engagement, topic coverage, topic activity, topic persistence, and topic novelty, which involves the number of comments, number of microblog articles, and time attenuation characteristics of blogs in the topic, and establishes a topic hotness evaluation model that could quickly aggregate hot topics and evaluate the influence of hot topic. In the experimental stage, we propose to verify the topic hotness evaluation performance from three aspects. Through effectiveness analysis, baseline method performance comparison, and indicator impact analysis, we realize the effective verification and analysis of the topic influence evaluation method proposed in this paper. The results show that ECANP model can effectively evaluate the influence of topics in a period of time, and give a reasonable topic ranking according to its hotness value. Our model involves the calculation of five indicators, but experiments show that the model is low complexity, time-consuming, and easy to understand. The computational complexity will not increase exponentially with the increase of the number of blogs, but linearly with the increase of topics.Nevertheless, the results of this study have to be seen in light of some limitations. The first is that the research results of this paper calculate the relative influence of topics. Since this paper proposes that it is more meaningful to study whether one topic is more hot than another topic for topic hotness ranking, we are studying the relative influence of topics, which requires the participation of multiple topics, considering the impact of different topics, and finally giving the topic popularity ranking, rather than just calculating the absolute influence of a specified topic. The second limitation relates to the evaluation method of topic influence specifically designed for single domain and single platform in this paper, which has not yet involved cross-domain social platforms.In view of the above limitations, we will solve them in future work. On the one hand, we will continue to explore the nature of topic and the characteristics of network public opinion. Through the analysis of the multidimensional characteristic attributes of topic, we will find more valuable information. Combined with the high-quality topic propagation influence structure, we will design a more effective topic hotness evaluation model, test the evaluation performance of different topic characteristic models, and realize the evaluation method using the law of topic propagation influence. On the other hand, because of its heterogeneity, multisource and high capacity, cross-domain social platforms have aroused widespread interest and posed many challenges, we will further study the use of multiplatform information features to beyond the evaluation of single platform topic influence.


R)-(−)-1-(benzofuran-2-yl)-2-propylaminopentane) on neurotransmitter release via the TAAR1 signaling. Rat striatal slices were prepared and the resting and electrical stimulation-evoked [3H]dopamine release was measured. The releaser (±)methamphetamine evoked non-vesicular [3H]dopamine release in a TAAR1-dependent manner, whereas (−)BPAP potentiated [3H]dopamine release with vesicular origin via TAAR1 mediation. (−)BPAP did not induce non-vesicular [3H]dopamine release. N-Ethylmaleimide, which inhibits SNARE core complex disassembly, potentiated the stimulatory effect of (−)BPAP on vesicular [3H]dopamine release. Subsequent analyses indicated that the dopamine-release stimulatory effect of (−)BPAP was due to an increase in PKC-mediated phosphorylation. We have hypothesized that there are two binding sites present on TAAR1, one for the releaser and one for the enhancer compounds, and they activate different PKC-mediated phosphorylation leading to the evoking of non-vesicular and vesicular dopamine release. (−)BPAP also increased VMAT2 operation enforcing vesicular [3H]dopamine accumulation and release. Vesicular dopamine release promoted by TAAR1 evokes activation of D2 dopamine autoreceptor-mediated presynaptic feedback inhibition. In conclusion, TAAR1 possesses a triggering role in both non-vesicular and vesicular dopamine release, and the mechanism of action of (−)BPAP is linked to the activation of TAAR1 and the signal transduction attached.The trace amine-associated receptor 1 (TAAR1) is a Gs protein-coupled, intracellularly located metabotropic receptor. Trace and classic amines, amphetamines, act as agonists on TAAR1; they activate downstream signal transduction influencing neurotransmitter release via intracellular phosphorylation. Our aim was to check the effect of the catecholaminergic activity enhancer compound ((−)BPAP, ( There has been a major breakthrough in understanding the neurobiology of the central nervous system with the discovery of trace amine-associated receptors (TAARs) ,2,3. LatTAAR1 was found in several brain areas containing dopaminergic, noradrenergic, and serotonergic neuronal cell bodies or axon terminals: the caudate nucleus, putamen, substantia nigra, accumbens nucleus, ventral tegmental area, locus coeruleus, and raphe nuclei of mouse, monkey, and human brain ,6. It waTAAR1 has a cellular location in neurons and is functioning in the intracellular space . TAAR1 iIt has been shown that TAAR1 and dopamine transporter (DAT) are co-localized in substantia nigra neurons and other brain areas rich in dopaminergic axon terminals . A possiThere might also be a functional link between TAAR signaling and D2 receptor-mediated presynaptic negative feedback inhibition in dopamine neurons . It has Two members of the TAAR family, TAAR1 and TAAR4 were identified as receptors for trace amines ,16. PhenTrace amines are stored in cytoplasmic pools along with the newly synthesized biogenic amines within the presynaptic nerve endings . Their r2+-independent, and the newly synthesized biogenic amines are primarily involved in the efflux process.TAAR1 and TAAR4 were also shown to be sensitive to several other, mostly exogenous compounds, including amphetamine-like psychostimulant drugs and thyroamines ,19. Altep-hydroxyamphetamine, methvlenedioxymethamphetamine, methylphenidate, etc. dopamine release of 5.15 ± 1.26 and 10.45 ± 2.96 kBq/g/3 min measured in resting condition and in response to electrical stimulation methamphetamine was a concentration-dependent process.In agreement with previously reported data, we found here that (±)methamphetamine increased resting [ar range A. Figure3H]dopamine release from the striatum ((±)Methamphetamine also increased the electrical stimulation-induced [striatum B. This f−12 mol/L) on [3H]dopamine release determined in striatal slices is shown in 3H]dopamine release compared to that measured in control conditions, this effect was not associated with alteration of the resting [3H]dopamine release.The enhancer effect of the CAE compound (−)BPAP BPAP in increasing concentrations to striatal slices. As shown in −15 to 10−5 mol/L increased electrical stimulation-induced [3H]dopamine release, it was without effect on resting release (While (−)BPAP in concentrations of 10 release B. This f3H]dopamine from striatal slices and this effect was found to be concentration-dependent in a range of 1 to 10 µmol/L (The dopamine transporter (DAT) inhibitor nomifensine  increase0 µmol/L A,B. Thes3H]dopamine release was associated with unaffected resting [3H]dopamine release, indicating that nomifensine does not force DAT operation into the reverse mode  reversed the dopamine-releasing effect of (±)methamphetamine and this inhibitory effect proved to be concentration-dependent A,B. This3H]dopamine release was investigated in a condition when (−)BPAP (10−11 mol/L) was added in the presence of 1 µmol/L nomifensine in rat striatal slices (3H]dopamine release. This finding suggests that (−)BPAP enters dopaminergic axon terminals by interaction with DAT, and the carrier operates in a nomifensine-sensitive uptake mode during the transport.Interaction between nomifensine and (−)BPAP on [l slices . It was 3H]dopamine release and also its effects on the releaser (±)methamphetamine and the enhancer (−)BPAP-mediated [3H]dopamine release. EPPTB, added in a concentration range of 0.01 to 1.0 µmol/L, did not influence resting or electrical stimulation-induced [3H]dopamine release in rat striatum methamphetamine on electrical stimulation-induced [3H]dopamine release methamphethamine (10 µmol/L) on resting [ release A. Wherea release B. This f3H]dopamine release, an interaction study was made to determine whether EPPTB influences the enhancer effect of (−)BPAP on striatal [3H]dopamine release. EPPTB added in a concentration of 0.01 or 0.1 µmol/L suspended the stimulatory effect of (−)BPAP on electrical stimulation-induced [3H]dopamine release evoked by either 10−12 or 10−11 mol/L concentration in striatal slices (Since the TAAR1 antagonist EPPTB failed to modify the effect of (±)methamphetamine on electrical stimulation-induced dopamine release, the PKC inhibitor Ro31-8220 and activator phorbol-12-myristate-13-acetate (PMA) were used [3H]dopamine from striatal slices. Moreover, it was shown that the effects of the releaser and enhancer compounds on [3H]dopamine release were PKC phosphorylation-dependent processes.To study the involvement of protein kinase C (PKC)-mediated phosphorylation in [ere used . We have3H]dopamine (The PKC inhibitor Ro31-8220 (0.1–10 µmol/L) added to striatal slices concentration-dependently decreased the electrical stimulation-induced release of dopamine release from rat striatum  were reported to inhibit carrier-mediated amphetamine-induced dopamine release  and our striatum . This fi3H]dopamine release. Ro31-8220 (1 µmol/L) suspended the stimulatory effect of (−)BPAP (10−12 mol/L) measured on electrical stimulation-induced [3H]dopamine release from rat striatal slices BPAP with the PKC inhibitor Ro31-8220 and the PKC activator PMA were determined in striatal [l slices . This fi3H]dopamine  added to striatal slices concentration-dependently increased the electrical stimulation-induced release of [dopamine . The est3H]dopamine release in striatal slices. It was found that PMA used in a concentration specific for PKC activation (1 μmol/L) potentiated the enhancer effect of (−)BPAP on electrical stimulation-evoked [3H]dopamine release when added in a 10−12 mol/L concentration BPAP on the electrical stimulation-induced [ntration . These f3H]dopamine release but increased the electrical stimulation-induced [3H]dopamine release , an inhibitor of the ATPase N-ethylmaleimide-sensitive factor (NSF), was shown to increase the size of the readily releasable pool and the subsequent dopamine release . We foun release A,B. This−12 mol/L), a clear-cut potentiation was observed in the electrical stimulation-induced [3H]dopamine release (When NEM was added in a concentration of 30 µmol/L in combination with (−)BPAP BPAP), TBZ, and TBZ and (−)BPAP pretreated rats. TBZ pretreatment of rats reduced [3H]dopamine release and this reduction in release was reversed by concomitant injection of TBZ and (−)BPAP. Injection of (−)BPAP was without effect on [3H]dopamine release in these experimental conditions.Tetrabenazine (TBZ), a non-substrate-type, non-competitive inhibitor of vesicular dopamine transporter 2 (VMAT2), inhibits dopamine accumulation in the vesicles leading to depletion of vesicular dopamine content and decrease in exocytotic release . In acco3H]dopamine release in rat striatum  neurotransmitter release in the absence of action potential [3H]dopamine release can be stimulated by two mechanisms: reverse-mode operation of neurotransmitter transporters leads to non-vesicular release, and membrane depolarization-evoked exocytosis results in vesicular neurotransmitter release.dopamine release without altering resting release [3H]dopamine release exhibited a biphasic shape. Although the biphasic effect of (−)BPAP was found in measuring radiolabeled dopamine release, this effect occurred parallel with the release of endogenous dopamine dopamine following electrical stimulation suggests an interaction of (−)BPAP with two different dopamine release sites to which the enhancer compound may bind with high (picomolar) and low (µmolar) affinities. (−)BPAP acting on the high-affinity binding site may evoke dopamine release in a TBZ-sensitive manner [The biphasic concentration-response effect of (−)BPAP to release [e manner . Alternae manner . It is ae manner .3H]dopamine release. This finding raises the possibility that prior activation of TAAR1 is necessary for triggering the enhancer-mediated stimulation of vesicular [3H]dopamine release.The finding that (−)BPAP influenced vesicular dopamine release in the picomolar concentrations, raised the possibility of an effect on TAAR1 as this receptor is notorious in its activation by low concentrations of various endogenous and exogenous compounds ,37. Whet3H]dopamine release when tested in a broad concentration range. This finding indicates that DAT operation, which bidirectionally induces uptake and release of dopamine, may be uncoupled in the presence of the enhancer drugs dopamine release by blocking DAT operation. This finding indicates that the enhancer drugs need first to be transported into dopaminergic axon terminals by DAT and bind to TAAR1 before they trigger vesicular dopamine release.In contrast, the competitive DAT inhibitor nomifensine blocks transporter operation in both normal- and reverse-mode directions . Nomifen3H]dopamine release: the PKC inhibitor Ro31-8220 decreased and the PKC activator PMA increased the vesicular dopamine release. This finding confirms that exocytosis requires PKC-mediated phosphorylation of proteins involved in vesicular neurotransmitter release dopamine release was potentiated in the presence of PMA, whereas the addition of Ro31-8220 reduced the enhancer effect. Based on this finding, we concluded that (−)BPAP activating TAAR1 and its signaling system increases PKC-mediated phosphorylation of proteins participating in exocytosis, which then evokes enhanced dopamine release from vesicular stores.The enhancer compounds, which are putative agonist ligands of TAAR1, may increase the phosphorylation of proteins involved in exocytotic dopamine release. This was shown in interaction studies demonstrating the enhancer effect of (−)BPAP in the presence of PMA and Ro31-8220. The stimulatory effect of (−)BPAP on vesicular [3H]dopamine release, which may correspond with the size of the readily releasable pool.PKC-mediated phosphorylation upregulates docked vesicles at the active zone by inducing conversion of vesicles from docking to priming and further to fusion states . As a re3H]dopamine release was potentiated by the addition of PMA and NEM, respectively. We have hypothesized that the CAE compound (−)BPAP activates TAAR1 signaling and the increased PKC-mediated phosphorylation maintains SNARE core complexes assembled, which then turns on the enhancer effect in vesicular dopamine release.The assembly and disassembly of the SNARE core complexes may have a role in enhancer modulation of neurochemical transmission. This was indicated by the fact that the stimulatory effect of (−)BPAP on electrical stimulation-induced [The role of VMAT2 in dopaminergic axon terminals is vesicular refill and regulation of dopamine efflux from the vesicles into the cytoplasm . This bi3H]dopamine release from striatal slices and this inhibition was probably due to a decrease in vesicular dopamine accumulation and contents [3H]dopamine release, further pointing to the importance of VMAT2 phosphorylation in vesicular operation.The importance of VMAT2 phosphorylation in vesicular dopamine release was also shown in our experiments. We found that the non-competitive VMAT2 inhibitor TBZ inhibited dopamine release was overacted by in vivo administration of (−)BPAP in doses corresponding with its CAE effects. Therefore, we speculated that (−)BPAP activates TAAR1 signaling and the increased PKC-mediated phosphorylation of VMAT2 regulates vesicular traffic of dopamine. Our preliminary findings indicate that the enhancer compound (−)BPAP increases vesicular storage capability and elicits more vesicular dopamine released as electrical stimulation is applied.We have found that the TBZ-evoked decrease of [3H]dopamine release, this effect was absent when the enhancers were added to striatal slices. The second difference between the two groups of compounds was that although both the enhancer and releaser drugs stimulated vesicular [3H]dopamine release, this effect of the enhancers was linked to TAAR1 activation, whereas TAAR1 was apparently not involved in (±)methamphetamine-induced vesicular dopamine release.The first major difference between the enhancer and releaser compounds was found in influencing resting dopamine release: whereas the releasers evoked an increase of resting [3H]dopamine release from rat striatal slices. The involvement of TAAR1 activation in (±)methamphetamine-induced [3H]dopamine release was shown by the finding that EPPTB reduced the (±)methamphetamine-induced non-vesicular [3H]dopamine release. In contrast, the stimulatory effect of (±)methamphetamine on electrically induced [3H]dopamine release was not altered by the addition of EPPTB. We concluded, therefore, that other than TAAR1 signaling may be responsible for the (±)methamphetamine-evoked vesicular dopamine release. Accordingly, TAAR1 regulates dopamine efflux both from the (±)methamphetamine-sensitive cytoplasmic pool and the enhancer-sensitive vesicular pool of dopamine in presynaptic nerve endings.A number of publications indicate that amphetamines are exogenous agonists of TAAR1 ,3. (±)Me3H]dopamine release. These data indicate that the releaser and enhancer drugs need first to be transported into dopaminergic axon terminals by DAT before they trigger non-vesicular and vesicular dopamine release, respectively.Amphetamines compete with dopamine for transport at the uptake sites of DAT and their transport is preferred instead of the endogenous ligand dopamine . This su3H]dopamine release evoked by the releaser compounds, we used the PKC inhibitor Ro31-8220 in drug combination studies. It was found that Ro31-820 decreased (±)methamphetamine-induced non-vesicular [3H]dopamine release from rat striatum. In contrast, neither Ro31-8220, nor PMA affected resting dopamine release per se, suggesting that resting dopamine release, which may be due to leakage of dopamine-containing vesicles, does not require phosphorylation to occur.It has been shown that dopamine uptake and efflux, mediated by normal- and reverse-mode operation of DAT, are phosphorylation-dependent processes ,69. ThisIt has been shown that two different PKC pools are present in the axon terminals: an amphetamine-sensitive pool is located in the synaptic plasma membrane and a reserpine-sensitive PKC pool is connected to the vesicles. The former is linked to DAT operation, whereas the latter is connected to exocytotic dopamine release . Thus, a3Hdopamine release also [3H]dopamine release was not altered by EPPTB, we concluded that other than TAAR1 signaling may be responsible for the (±)methamphetamine-induced vesicular dopamine release.In agreement with reported data, we also found that (±)methamphetamine increased not only the resting but the electrical stimulation-induced dopamine release. (−)BPAP, which was used as a model compound of the enhancers, apparently binds to the latter hypothetical binding site on TAAR1, regulating vesicular [3H]dopamine release. We have also speculated that the two binding sites activate different PKC-mediated phosphorylations linked to TAAR1 resulting in that one binding site evokes non-vesicular and the other binding site evokes vesicular dopamine release dopamine (10 µCi) for 30 min in 1.5 mL aerated  and preheated (37 °C) Krebs-bicarbonate buffer [3H]dopamine, the brain slices were transferred into low volume (0.3 mL) superfusion chambers  and superfused with aerated and preheated Krebs-bicarbonate buffer. The flow rate was kept at 1 mL/min by a Gilson multichannel peristaltic pump . The superfusate was discarded for the first 60 min period of the experiments, then twenty-five 3-min fractions were collected by a Gilson multichannel fraction collector . When used, biphasic electrical field stimuli  were delivered by a Grass S88 Electrostimulator  to evoke [3H]dopamine release. Drugs were added to brain slices, as is indicated in the Figure legends and were maintained throughout the experiments.Rat brain slices were loaded with [e buffer . After l3H]dopamine was expressed as kBq/g tissue.At the end of superfusion, tissues were collected from the superfusion chambers, weighed, and solubilized in 0.4 mL Soluene-350. An aliquot (50 µL) was mixed with 5 mL of liquid scintillation reagent (Ultra Gold XR) and subjected to liquid scintillation spectrometry for the determination of tissue content of radioactivity. The tissue content of [3H]dopamine was expressed in kBq/g/3 min fraction or as a fractional rate, i.e., a percentage of the amount of radioactivity in the tissue at the time of the release was determined. To estimate the electrically induced [3H]dopamine overflow, the mean of the basal outflow determined before and after stimulation was subtracted from each sample and summed dopamine release was determined in the presence and absence of drugs and was expressed by the ratio of [3H]dopamine efflux in fraction 17  and fraction 3 , i.e., B2/B1 ratio. The effects of drugs on electrically stimulated [3H]dopamine release were expressed by the ratio of [3H]dopamine efflux determined in response to the 2nd  and 1st  stimulations, i.e., S2/S1 ratio  less than 5% was considered significant.The Student paired 3H]Dopamine , Soluene-350 tissue solubilizer, and Ultima Gold XR liquid scintillation reagent were obtained from PerkinElmer Life and Analytical Sciences, Boston, MA, USA). (−)BPAP (R-(−)-1-(benzofuran-2-yl)-2-propyl-aminopentane HCl) was obtained from Fujimoto Pharmaceutical Co., Osaka, Japan. Other enhancer drugs (−)PPAP (R-(−)-1-(phenyl-2-yl)-2-propylaminopentane HCl), (−)IPAP (R-(−)-1-(indol-3-yl)-2-propylaminopentane)) were also donated by the Fujimoto Pharmaceutical Co., EPPTB -4-pyrrolidin-1-yl-3-trifluoromethylbenzamide), tetrabenazine, nomifensine maleate, and β-phenylethylamine HCl (PEA) were purchased from Sigma-Aldrich Chemical Co, Budapest, Hungary, 3-hydroxytyramine HCl was purchased from Merck, Germany. Ro31-8220 mesylate and phorbol 12-myristate 13-acetate (PMA) were obtained from Bio-Techne R and D Kft, Budapest, Hungary. N-Ethylmaleimide (NEM) was a product of Tokyo Chemical Industry Co., Ltd., Hungary. N,α-Diethylphenethylamine HCl (DEA) was purchased from Toronto Research Chemicals, North York, ON, Canada. (±)Amphetamine HCl and (±)methamphetamine HCl were received from Semmelweis University, Budapest, Hungary. All other chemicals were of analytical grade.[Activation of TAAR1 signaling increases dopamine release by either reversal of DAT operation or facilitation of exocytotic vesicular release. Enhanced dopamine release activates D2 dopamine receptor-mediated feedback inhibition in order to protect dopaminergic neurons from overload. We also concluded that the catecholaminergic activity enhancer drug (−)BPAP acts as an agonist on TAAR1, revealing some novel aspects in the regulation of presynaptic dopaminergic neurochemical transmission. Therapeutic aspects of the enhancer compounds, however, remain to be elucidated. Evidence has been obtained about the potential use of the CAE compounds as prophylactic agents in neurodegenerative disorders or in the treatment of the early phase of Parkinson’s and Alzheimer’s diseases. In addition, depression, mood disorders, various forms of anxieties, and the favorable influence of life expectancy or tumor genesis may represent further clinical applications of the enhancer compounds.


Humans around the globe have lived through an unprecedented time. The emergence and rapid spread of coronavirus disease 2019 (COVID-19) have resulted in the loss of millions of lives, and this number is still climbing every day and hour. As a consequence, there have been drastic changes over the past 2 years in how we live, communicate, and work; most importantly, COVID-19 has raised global awareness of infectious diseases and pandemic responses to an exceptional level. Like COVID-19, highly contagious and lethal viruses, such as the human immunodeficiency virus (HIV), Ebola, and the recent addition to the list, Monkeypox, pose extraordinary challenges to modern civilizations. On the other hand, non-communicable diseases, including heart disease, stroke, and cancer, are still claiming the majority of deaths on a global level .Before the availability of synthetic drugs, civilizations worldwide relied on natural products extracted from the environment and various organisms for healthcare. Natural products and their derivatives continue to be critical components in the drug library available today. Indeed, almost a quarter of the new drugs approved by the United States Food and Drug Administration (FDA) since 1981 are natural products, botanical mixtures, or their derivatives . Recent Nanotechnology in Traditional Medicines and Natural Products” published last year  on zein/caseinate complex nanoparticles. These structures increase the stability of ISL and prevent its early release in the stomach, which facilitates the efficacy of colonic inflammation treatment in mouse models in vivo. Razavi et al. provided a comprehensive review of recent efforts to construct nanodelivery systems to overcome ocular barriers and enable effective ocular delivery of therapeutically active natural products. In this article, the authors discussed barriers in corneal and non-corneal delivery pathways. Recent studies using polymer nanoparticles, micelles, nanofibers, dendrimers, lipid nanoparticles, liposomes, and niosomes to overcome these barriers and improve the effectiveness of drug delivery to ocular target sites were reviewed. The original research article by Dang et al. reported the modification of liposomes with a polyethylene glycol-conjugated nuclear-targeted peptide to enable nuclear-targeted delivery of gambogic acid. These engineered liposomes showed evidenced targeting and treatment of tumor tissues in a mouse breast cancer model.Three articles in this Research Topic collection specifically demonstrated how the development of nanocarrier systems helps to address challenges in the delivery of natural products through different pathways. In their original research article, The chemistry involved in the synthesis, functionalization, and modification continues to be a central topic in the development of new nanoengineered drugs. We hope that this Research Topic will serve not only as a collection of studies in the past but also as a forward-looking guide to what is possible in the cross-disciplinary field of nanotechnology-enabled natural product research.


D1DR) and GLP-1R expression were correlated. Our results point out a dietary and gut regulation of plasma dopamine, acting in the WAT to regulate GLP-1 action. Together with the known dopamine action in the pancreas, such results may identify new therapeutic opportunities to improve metabolic control in metabolic disorders.Dopamine directly acts in the liver and white adipose tissue (WAT) to regulate insulin signaling, glucose uptake, and catabolic activity. Given that dopamine is secreted by the gut and regulates insulin secretion in the pancreas, we aimed to determine its regulation by nutritional cues and its role in regulating glucagon-like peptide 1 (GLP-1) action in WAT. Solutions with different nutrients were administered to Wistar rats and postprandial dopamine levels showed elevations following a mixed meal and glucose intake. In high-fat diet-fed diabetic Goto-Kakizaki rats, sleeve gastrectomy upregulated dopaminergic machinery, showing the role of the gut in dopamine signaling in WAT. Bromocriptine treatment in the same model increased GLP-1R in WAT, showing the role of dopamine in regulating GLP-1R. By contrast, treatment with the GLP-1 receptor agonist Liraglutide had no impact on dopamine receptors. GLP-1 and dopamine crosstalk was shown in rat WAT explants, since dopamine upregulated GLP-1-induced AMPK activity in mesenteric WAT in the presence of the D2R and D3R inhibitor Domperidone. In human WAT, dopamine receptor 1 ( Besides its involvement in the regulation of several neuronal pathways in the CNS, dopamine has also been described to regulate several processes in the periphery, such as gut motility, kidney salt excretion, the chemosensory reflex of the carotid body, and the regulation of insulin secretion by β-pancreatic cells ,3,4,5,6.Our group has recently shown that dopamine directly modulates glucose uptake independently of insulin signaling in the skeletal muscle (through a D1R mechanism) and in the liver (through a D2R mechanism) . In WAT,p < 0.05), which was not observed for the vehicle or sham procedure (p < 0.05 vs. Vehicle and Sham). Intriguingly, high-concentration glucose solutions (red) showed less robust effects on plasma dopamine levels, with a non-significant increase with 200 mg/kg at 30′ and no effect with the higher dosage (500 mg/kg)  H. Starch0 mg/kg) E,I, with0 mg/kg) J. Regard0 mg/kg) K.p < 0.01 between baseline and 15′ and p < 0.05 between baseline and 30′), which was not observed in lean and obese GK rats  and D2R (p < 0.001 vs. GKSD and p < 0.05 vs. GKHCD and GKHCD_Sh), TH (p < 0.01 vs. GKHCD and GKHCD_Sh) and DARPP32 levels, the downstream signal of D1R activation (p < 0.05 vs. GKSD and GKHCD_Sh). No significant alterations were found in dopamine receptors nor DARPP32 levels in the liver of sleeve gastrectomy-submitted rats (p < 0.05 vs. WSD).Close to half of the dopamine levels in the organism are suspected to come from the gut since TH expression and activity are robust in mesenteric organs . To discted rats N,O,Q, whDRD1 and DRD4 were downregulated in the visceral adipose tissue of insulin-resistant obese patients, being correlated with the lower expression of the insulin receptor and lipid oxidation markers [GLP1R expression in the adipose tissue is not significantly altered with insulin resistance, prediabetes, or type 2 diabetes onset, in the human visceral adipose tissue (GLP1R expression was observed to be positively correlated with both DRD1 and DRD4 (p = 0.0012 and p = 0.0026), showing no associations with D2DR (DRD1 (p = 0.0008) and DRD4 (p = 0.0003), being absent in insulin-sensitive patients . Domperidone coupled with liraglutide abrogated this effect (p < 0.01 vs. dopamine + liraglutide + domperidone), suggesting that D2R inhibition triggers a synergic effect of dopamine and liraglutide on increasing AMPK-Thr172, that is highly dependent on dopamine action on other receptors, as the effect is lost on dopamine absence.The crosstalk between GLP-1 and dopamine on the regulation of energy metabolism was investigated by assaying AMP-activated protein kinase (AMPK)-Thr172 phosphorylation levels in mesenteric (mWAT), and pEWAT, incubated with GLP-1R agonist liraglutide and/or dopamine, combined with either haloperidol (antagonist for both D1R and D2R) or domperidone (D2R and D3R antagonist) A. In mWAp = 0.0509 vs. CTL). Haloperidol showed no effect on the stimulatory effect of liraglutide on AMPK activation , showing the relevance of dopamine action. The levels of Insulin receptor-Tyr972 (InsR-Tyr972) were unchanged under all tested conditions both in mWAT and pEWAT  and in obese type 2 diabetic animals, (p < 0.01 GKHCD and GKHCD_Vh vs. WSD). Bromocriptine treatment significantly improved GLP-1R levels in pEWAT to those similar to the healthy WSD group . Brown adipose tissue (BAT) GLP-1R levels were diminished in lean and obese type 2 diabetic animals compared to healthy WSD (p < 0.05 vs. GKSD and p < 0.01 vs. GKHCD and GKHCD_Vh) which were restored upon bromocriptine administration (p < 0.05 vs. GKHCD and vs. GKHCD_Vh). In other peripheral organs with a high impact on overall metabolic status, such as the liver and skeletal muscle, GLP-1R levels were not altered after bromocriptine treatment  was selected at the obesity surgery appointment at the Hospital Geral de Coimbra (Covões), Centro Hospitalar Universitário de Coimbra (CHUC). All subjects signed informed consent, and the study was approved by the institutional ethics committee , according to the principles outlined in the Declaration of Helsinki. Exclusion criteria were: active inflammatory and chronic diseases (neurodegenerative diseases or active tumors), previous restrictive (sleeve gastrectomy) or mal-absorptive  surgeries, and type 2 diabetes medication other than metformin  inhibitors or insulin). On the day before surgery, height and body weight were recorded, and fasting blood samples were collected for biochemical analysis. Visceral WAT samples were collected during surgery and kept in liquid nitrogen to be then stored at −80 °C.n = 17), composed by individuals that were both IS (Ox-HOMA2IR < 1) and normoglycemic ; 2–insulin resistant  and NG group (n = 29), 3–prediabetic group (n = 28), that allocated IR patients with fasting glucose levels from 100 to 125mg/dL or HbA1c between 5.7 and 6.4%; 4–T2D group (n = 18), constituted by IR subjects diagnosed with T2D (fasting blood glucose > 125 mg/dL or HbA1c > 6.4%).A total of 92 obese patients (77 women and 15 men) were divided into groups according to glycemic profile: fasting glucose levels, HbA1c, and Ox-HOMA2IR. Subject characterization resulted in four different groups: 1–insulin-sensitive group (IS)  using an RNeasy Lipid Tissue Mini Kit . RNA samples were analyzed by NanoDrop One/One spectrophotometer  at 260 nm to evaluate their concentration. RNA integrity was also analyzed through capillary electrophoresis with an Agilent RNA 6000 Nano Kit and the results were obtained with the Agilent 2100 Bioanalyser . Then samples were quantified by real-time polymerase chain reaction using the high throughput platform biomarker HD system as described before . ForwardWistar rats from our breeding colonies  were kept under standard conditions ,26. The n = 5–6): (1) gavage fed a mixed meal diet ; (2) gavage fed a glucose solution corresponding to the concentration described in the mixed meal diet ; (3) gavage fed a starch solution ; (4) gavage fed a bovine serum solution and (5) a solution with arginine ; (6) gavage fed corn oil solution . The volume of each nutrient solution administered by gavage was 2 mL. A Vehicle group was established through gavage administration of 2 mL of distilled water, and a Sham group through the insertion of only the gavage probe, to exclude the possible stress effect. Blood samples were collected before and 15, 30, and 45 min after gavage, as described before [Male Wistar rats  were used to evaluate the regulation of plasma dopamine levels by the specific nutrients. Animals were randomly allocated into the groups  were kept with ad libitum access to water and standard diet  (WSD and GKSD) (n = 18) was fed a high-caloric diet , between 1 and 6 months old. At 4-months-old, GKHCD rats were randomly assigned to three groups (N = 6): no intervention (GKHCD), sleeve gastrectomy (GKHCD_SL), and sham surgery (GKHCD_Sh). Vertical sleeve gastrectomy and sham surgery were performed as described previously [Wistar (WSD) and type 2 diabetic Goto-Kakizaki rats (GK) rats (nd GKSD) A. A groueviously ,32,33. An = 18) was fed the same high-caloric diet between 1 and 6-months-old. At 5 months old, the HCD-fed group was randomly divided into three groups (n = 6): the first without further treatment (GKHCD); HCD rats treated with bromocriptine in the last month (GKHCD_Br), and HCD rats treated with a vehicle DMSO solution during the same period (GKHCD_Vh) [®,  was diluted 1:4 DMSO/H2O and administered daily by intraperitoneal (i.p.) injection (10 mg/Kg/day) during the last month. In the vehicle group, the same volume (100 μL) of the vehicle 1:4 DMSO/H2O was administered i.p. during the same period.The experimental design for bromocriptine administration was similar to sleeve gastrectomy, namely the WSD and GKSD groups A. A grouKHCD_Vh) . Bromocrn = 6): (1) Control with saline injection (W); (2) Wistar rats injected subcutaneously with liraglutide  200 μg/Kg twice a day for 14 days (WL) (n = 6): (3) Saline-injected Control GK rats (GK); (4) GK rats injected subcutaneously with liraglutide 200 μg/Kg twice a day for 14 days (GKL) as described before [Male Wistar rats 14-week-old were randomly divided into two groups (ays (WL) A. Age-mad before .2/5% CO2-equilibrated tyrode solution . Then tissue samples were transferred to a 2 mL Eppendorf tube and incubated at 37 °C for 10 min for stabilization. Then tissue samples were incubated with the following experimental conditions (minimum of N = 4 to each condition): (1) dopamine ; (2) liraglutide ; (3) dopamine plus liraglutide; (4) dopamine plus liraglutide plus haloperidol ; (5) liraglutide plus haloperidol; (6) dopamine plus liraglutide plus domperidone  and (7) liraglutide plus domperidone. After 10 min of incubation, samples were frozen in liquid nitrogen to be quantified by Western blot. Dopamine and its receptors (D1R and D2R) antagonists concentrations were chosen as previously described [Animals were left in overnight fasting and euthanized with sodium pentobarbital  in the morning. WAT  was collected (50–1500 mg) in ice-cold 20% Oescribed ,35. Haloescribed ,36.® .Tissues were homogenized as previously described and samples were loaded in 8% polyacrylamide gels, separated by SDS-page, and transferred to a PVDF membrane . Membranes were incubated with the specific primary antibodies overnight at 4 °C (listed below), and then incubated for 2 h at room temperature with secondary antibodies. The secondary antibodies were anti-mouse  and anti-rabbit . Membranes were revealed using ECL substrate in a Versadoc system  and analyzed with Image QuantSalts and organic solvents used in solution preparations were purchased from Fisher Scientific , Sigma Chemicals , or Merck , with the highest grade of purity commercially available. It used antibodies to analyze D1R, D2R, DARPP32 , and tyrosine hydroxylase (TH),  at a 1:1000 dilution. Moreover, antibodies against GLP-1 and GLP-1R and against the phosphorylated form of insulin receptor were used  as well, against phosphorylated AMPK form . Calnexin was used as loading control . Plasma dopamine levels and plasma GLP-1 levels were assessed through the Dopamine ELISA Kit,  and Rat GLP1/Glucagon-like Peptide 1 ELISA Kit,  respectively.p < 0.05, and all computation analysis was performed using Graphpad Prism .Assessment of plasma dopamine excursion upon nutrient feeding through time one-way repeated measures ANOVA with Dunn’s post-hoc corrections for multiple comparisons were performed, while between groups a comparison one-way ANOVA with Tukey’s post-hoc corrections for multiple comparisons was performed. To assess sleeve surgery effect on plasma dopamine excursion, one-way repeated measures ANOVA with Dunn’s post-hoc corrections for multiple comparisons were performed to assess differences in plasma dopamine concentration throughout time after a mixed meal feeding. Comparison between groups was assessed by one-way ANOVA with Tukey’s post-hoc corrections for multiple comparisons. A one way-ANOVA test with Tukey’s post-hoc corrections for multiple comparisons was performed to analyze all protein levels in sleeve gastrectomy surgery, bromocriptine, and liraglutide-treated animal models in all tissues. TH level on ilium and explants data were analyzed using the non-parametric Kruskal-Wallis with multiple comparisons test with Dunn’s post-hoc corrections. Data from adipose tissue ex vivo incubations were analyzed by one-way ANOVA with no corrections for multiple comparisons (Fisher’s LSD test). Results were presented as mean ± SEM. Regarding human data analysis, non-parametric tests were performed (sample size < 30/group), and results were presented as median and interquartile range. The Kruskal-Wallis test was applied to compare GLP-1R gene expression between groups. The Spearman correlation test was performed to assess the correlation between GLP-1 and dopamine receptors gene expression. Differences were considered significant at


Analyses of protein structures have shown the existence of molecular channels in enzymes from Prokaryotes. Those molecular channels suggest a critical role of spatial voids in proteins, above all, in those enzymes functioning under high temperature. It is expected that these spaces within the protein structure are required to access the active site and to maximize availability and thermal stability of their substrates and cofactors. Interestingly, numerous substrates and cofactors have been reported to be highly temperature-sensitive biomolecules. Methanogens represent a singular phylogenetic group of Archaea that performs anaerobic respiration producing methane during growth. Methanogens inhabit a variety of environments including the full range of temperatures for the known living forms. Herein, we carry out a dimensional analysis of molecular tunnels in key enzymes of the methanogenic pathway from methanogenic Archaea growing optimally over a broad temperature range. We aim to determine whether the dimensions of the molecular tunnels are critical for those enzymes from thermophiles. Results showed that at increasing growth temperature the dimensions of molecular tunnels in the enzymes methyl-coenzyme M reductase and heterodisulfide reductase become increasingly restrictive and present strict limits at the highest growth temperatures, i.e., for hyperthermophilic methanogens. However, growth at lower temperature allows a wide dimensional range for the molecular spaces in these enzymes. This is in agreement with previous suggestions on a potential major role of molecular tunnels to maintain biomolecule stability and activity of some enzymes in microorganisms growing at high temperatures. These results contribute to better understand archaeal growth at high temperatures. Furthermore, an optimization of the dimensions of molecular tunnels would represent an important adaptation required to maintain the activity of key enzymes of the methanogenic pathway for those methanogens growing optimally at high temperatures. Protein channels or tunnels play critical roles for diverse enzymes such as in the mobility of ligands to buried active sites, in channeling intermediates through multiple steps of metabolic pathways, or in the transport of different molecules (substrate and product) and ions. The selective nature of each channel allows the access of specific molecules and create a microenvironment essential for these molecules ,2. It haIn spite of recent developments on deciphering the structure and properties of proteins , scarce information is available on the role of void spaces within protein structures and their influence on protein activity and stability above all in the Archaea. Molecular channels, tunnels, and pores all represent geometrically an empty space within an internal space of a molecule  with the molecular exterior or a pass through that molecule. Herein, we include all these geometries in the terms molecular channels or tunnels. Molecular tunnels or channels have been reported to be intimately related to the biological function and structural stability of proteins ,5,6. TheSeveral potential aspects of relevance for molecular channels have been suggested ,8,9. PasMultiple reports have shown that a number of cellular and molecular features can maintain the molecular stability of proteins, nucleic acids, and lipids ,14,15. HThe enzymes heterodisulfide reductase (Hdr) and methyl-coenzyme M reductase (Mmr) are involved in the last step of the three major methanogenic pathways ,22,23; tMethanogens represent a singular phylogenetic group of Archaea with the differential characteristic of producing methane as a final product of their metabolism. Methane is an important gas which can be used as biofuel ,22. In rThis study aims to infer if the molecular tunnels and their dimensions present a critical contribution to growth of methanogenic Archaea at high temperatures by analyzing key enzymes of the methanogenic pathway. A comparative analysis was performed including the broadest spectrum range of growth temperatures. The study was focused on realizing whether the dimensions of the molecular tunnels for hyperthermophilic methanogenic Archaea present relatively more or less relevance than for the low temperature methanogens. Optimized molecular tunnel dimensions should imply a restricted variability of these tunnel dimensions and that is what one should expected in high-temperature growing methanogenic Archaea when compared to low-temperature growing methanogens.Methanomicrobium mobile (37 °C optimum growth temperature) and Methanopyrus sp. KOL6 (110 °C optimum growth temperature) is shown in A comparative example of the visualization of predicted molecular tunnels in methyl-coenzyme M reductase  from Molecular tunnel length, surface, and volume estimates for the major subunits of the Mmr and Hdr proteins were plotted against the optimum growth temperature for methanogenic Archaea covering a wide range of temperatures . The numResults show a wide distribution of the tunnel dimensions in the low-temperature range (mesophilic temperature range) whereas the estimated dimensions tend to point toward a narrower range at high temperatures. This is observed for all the studied cases . Among tIf the amplitudes of the variability of molecular tunnel estimates were considered in growth temperature classes, we observed that the amplitude of molecular tunnel dimensions decreased with increasing optimum growth temperatures for the studied proteins from methanogenic Archaea . Thus, tUnderstanding how thermophiles can inhabit exclusively under high-temperature conditions is a point that requires further investigation. Although most macromolecules  have been studied in depth and their response and adaptations required to function under high temperatures are relatively well understood ,16, the The strategy of imposing restrictive limits to the molecular channels in methanogens growing at high temperatures presents various explanations. First, tighter molecular channels should result in more compact proteins, which results in facilitating the interaction among amino acid residues by reducing the length between potential interacting residues. The existence of more compact proteins in hyperthermophiles has been previously proposed ,20,26,27Numerous enzymes require cofactors and substrates  that areIn spite of our lack of understanding of the actual strategies and mechanisms that rule growth at high temperatures for the hyperthermophiles, the fact that these organisms grow optimally under high temperatures, using basically the same biomolecules as all other living beings, confirms the capability of hyperthermophiles to maintain the stability at high temperatures for universal, thermolabile low-molecular-weight biomolecules . Optimally dimensioned molecular channels can benefit hyperthermophiles through various mechanisms ,16,17. Fhttps://www.ncbi.nlm.nih.gov/ (accessed on 30 November 2022)). Optimum growth temperatures were retrieved from https://bacdive.dsmz.de/ (accessed on 30 November 2022). The 3D-structures for these proteins were predicted by Phyre2 [1 and r2 are the two radii of the truncated cone, and h represents its height. In the case of a cylinder, r1 and r2 are equal. To show some examples of molecular tunnels  and heterodisulfide reductase (Hdr). Because the methanogenic pathway represents an essential metabolic step for methanogens , these ey Phyre2 . These pls i.e., , they wehttps://www.rcsb.org (accessed on 30 November 2022)). A list of the sequences analyzed in this study and the model structures is available in the Available model structures from the studied enzymes were included in the analyses for comparative purposes as a reference for the results of the structures predicted from their amino-acid sequences . Model s


It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress–strain curves for each LCE molecular system. Regression analysis is applied using the stress–strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress–strain curves. To test the predictive performance of the surrogate model, stress–strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations. Soft elasticity can be achieved using a variety of external stimuli, such as stretching, thermal fields7, magnetic or electric fields10, and light exposure16. Furthermore, soft and light LCEs exhibit relatively fast and accurate reactions to these external stimuli. Therefore, LCEs are candidate materials for soft actuators21, and their mechanical properties have been extensively studied25. The mechanism of soft elasticity is closely related to the dynamics of mesogenic units embedded in polymer chains, and experiments have shown that a unidirectionally oriented polymer network with relatively low crosslink density is important for the realization of soft elasticity27. To clarify the coupling dynamics of mesogens and polymeric chains, theoretical studies have focused on the microscopic behavior of mesogens through molecular simulations36. These studies indicate the great potential of molecular simulations to uncover the mechanism of soft elasticity. Our previous study on the effect of LCE molecular architectures on microscopic dynamics under uniaxial elongation demonstrated that side-chain-type LCEs, in which mesogens are embedded in the side chain, have a different mesogen rotation mechanism from main-chain-type LCEs, in which mesogens are embedded in the main chain34. Furthermore, we found a systematic and robust trade-off between the stress and strain ranges in soft elasticity35, indicating that the optimal set of output power and amount of deformation for LCE can be selected by tuning the crosslink density. These are clear examples of how detailed information on the molecular architecture can be used to elucidate more realistic behavior in LCE molecular systems. However, vast variety of the molecular architecture often make it difficult to understand the relationship between the microscopic characteristics and macroscopic properties of materials. This is especially true in the case of functional polymeric materials such as LCEs. That is, the large number of variables that characterize the molecular architecture prevents the determination of which variables are important for the desired macroscopic properties. This difficulty arises in both experiments and simulations. In fact, despite the many possibilities for the molecular architecture of LCEs, experiments have employed only a few molecular structures that conform to some established synthetic methods37. In other words, the complexity of the material itself inhibits material development.Liquid crystal elastomers (LCEs) are a relatively new class of materials that display soft elasticity, that is, macroscopic reversible deformation has little resistance45. However, finding a universal relationship remains a challenging task, with the exception of some successful examples limited to specific categories of phenomena or materials40. Microscopic characteristics are always complex, covering the chemistry, geometry, and dynamics of atoms and molecules. By contrast, macroscopic properties are not always sensitive to microscopic characteristics48. Moreover, the sensitivity of macroscopic properties to microscopic characteristics is material-dependent. Thus, it is often more efficient to focus exclusively on the details of the relationship between microscopic characteristics and macroscopic properties for a specific material, despite the potential significance of establishing universality across materials. For complex materials such as functional polymers, the details of the relationship remain difficult to investigate even when the class of the target material is fixed. This is because these materials have a number of microscopic parameters that constitute the design variables of the molecular architectures51. Complex information with many microscopic parameters makes it difficult to detect the major factors that characterize the macroscopic physical properties. Quantitative structure–property relationship (QSPR) analysis is a promising approach for overcoming this type of complexity. QSPRs have great potential to reveal the correlations between an objective variable  and the large number of combinations of descriptors , which are almost incomprehensible to humans55. Recently, the above characteristic of QSPRs has been enhanced by machine learning (ML) techniques and applied to materials science59. Many studies have suggested the use of specific physical properties for objective variables61, and several results have been reported for polymer elasticity64.The microscopic characteristics of matter are expected to be closely related to its macroscopic physical propertiesIn this study, an ML-based QSPR approach is employed to identify the microscopic characteristics that govern the macroscopic deformation of LCEs. To identify the influential design variables, regression analysis using ML is performed on a database containing the results of coarse-grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of surrogate model is also tested using regression analysis. The LCE database contains design variables for LCE molecular architectures, system and simulation conditions, and stress–strain curves calculated for each LCE molecular system. In this study, data sets on 140 different LCE molecules are selected from the database. In addition, 12 molecules are randomly selected and excluded to test the predictive performance of surrogate model, with the remaining 128 molecules used in the regression analysis. Regression analysis is performed with the stress–strain curve as the objective variable and the other factors as explanatory variables. To test the predictive performance of the surrogate model, the stress–strain curves are predicted using ML results for the 12 LCE molecules that were excluded prior to regression.65, Lennard–Jones (LJ) spherical particles, and harmonic bonds among particles . This model makes it possible to build numerous molecular architectures for LCEs by setting the 20 design variables shown in Fig. 35. Note that the parameters for non-bonded interactions among SCGB and LJ particles, except the point charge q, are fixed to the same values as in Refs. 34 and 35, so they are not shown in Fig. The mechanical properties of LCEs are expected to be related to the details of the molecular architectures, such as the difference between the main- and side-chain types, the type and density of the crosslinking agent, and the shape and density of the mesogens. Thus, in this study, a coarse-grained LCE model that can express the details of the molecular architectures is employed. A typical molecular architecture consists of soft-core Gay–Berne (SCGB) ellipsoidal particlesMolecular dynamics (MD) uniaxial elongation simulations yielded six stress-strain curves per LCE molecule (see Methods for details). Here, we present descriptors of the molecular system and elongation simulation conditions for regression analysis, along with specific set values. The number of chains entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaD. The 33 descriptors used in this study are listed in Table 66 implemented on Scikit-learn (version 0.20.3)67. The random forest method was employed for the following four main reasons. (i) The method does not require data normalization and is relatively simple, with the only hyperparameters being the number of trees 68. Note that the hyperparameters of the random forest, k-fold cross-validation for overlearning implemented on Scikit-learn, where k (set to 10 in this study) denotes the number of times cross-validation was performed.A simple supervised ML scheme was used for the regression task, because the close relation between input microscopic descriptors and objective macroscopic properties is assumed to be obvious and there is no need to use complex ML methods that often contain implicit parameters. Figure mentclass2pt{minimmentclass2pt{minim72. The conceptual quantities 35. The results show that 34. The regression analysis seems to capture this feature.Figure Overall, the regression analysis succeeded in capturing the following three factors that are important in the soft elasticity mechanism: (i) the relationship between initial orientation direction and deformation direction, (ii) the influence of the crosslink density and polymer chain density, and (iii) the difference between main-chain and side-chain mesogenic rotation mechanisms.D. The descriptors for the 12 LCE molecules are shown in the The surrogate model constructed by the regression analysis should be able to predict the elasticity of LCE molecular systems that were not used in the regression analysis. Therefore, we applied the surrogate model to 12 LCE molecular systems that were not used in the regression analysis to test its ability to predict stress–strain curves. The predictions were formulated in two simple steps. First, descriptors for the 12 LCE molecules were extracted from the database to generate a new Figure Overall, the surrogate model has a strong ability to predict stress–strain curves for unknown LCE molecules. In particular, in the case of strong or little anisotropy of LCE deformations (panels (a)-(d) and (i)-(l)), the model predicts the curve quantitatively in 6/8 cases, and predicts the stress relationship between large and small stresses caused by differences in the elongation direction in 7/8 cases. Considering that we are mainly interested in the case of strong anisotropy of LCE deformations in the search for LCE materials, the surrogate model provides a useful tool in the selection of LCE molecules.72, the influence of the crosslink density and polymer chain density35, and the difference between the main-chain and side-chain mesogenic rotation mechanisms34. Next, to verify the predictive performance of the surrogate model obtained by regression analysis, the stress–strain curves were predicted using ML results for 12 LCE molecules that were excluded from the regression. The surrogate model was shown to have a good ability to predict stress–strain curves for unknown LCE molecules. In particular, for cases with strong or little anisotropy in their LCE deformations, the curves were quantitatively predicted in 6/8 cases and the relationship between large and small stresses caused by differences in the elongation direction was predicted in 7/8 cases. Future research will attempt to incorporate more sophisticated ML methods for the case of weak anisotropy of the LCE deformations. However, the surrogate model established in this study already has the potential to select LCE molecules for designing materials that exhibit strong anisotropy of LCE deformations. Of course, the parameters that determine the physical properties of LCEs range from the primary structure of mesogens and polymer chains to higher-order structures such as mesogen orientation, cross-linking networks, and mixing distributions. Therefore, the macroscopic properties of LCEs may not be fully explained by molecular architecture alone. This point is expected to be filled by simulations, experiments, and informatics on a more macroscopic scale, or by fusion studies of these.In this study, we attempted to identify the microscopic characteristics governing the macroscopic deformation of LCEs using a QSPR approach refined by supervised ML. From a database containing the results of coarse-grained MD simulations of LCEs with various molecular structures, the design variables of the molecular architecture, the molecular system conditions, and the simulation conditions for 140 different LCE molecules were extracted as explanatory variables, while the stress–strain curves resulting from the simulations were extracted as objective variables. First, regression analysis was performed using the explanatory and objective variables for 128 randomly selected LCE molecules to identify the design variables of the molecular architecture that govern the macroscopic deformation of the LCEs. The key descriptors revealed by the regression analysis suggest that the surrogate model obtained is capable of representing three important elements in the soft elasticity mechanism: the relationship between initial orientation direction and deformation directionS was monitored during the cooling simulations, and the temperature dependence of the order parameter was recorded to detect S=0.7 (nematic) for each LCE system35. As the initial structures for the simulation experiments, the nematic-like structures were carefully prepared so that the mesogen orientation followed one of the rectangular cells. 73. The Verlet velocity algorithm with a time step of P for the two orthogonal directions perpendicular to the elongation direction was controlled at a constant value of Uniaxial elongation simulations were performed under a single system condition and simulation condition for each of the 220 different molecular architectures of LCE. Three initial LCE molecular systems were prepared, each elongated in the two directions described below, resulting in six stress–strain curves per LCE molecule. The nematic-like initial structures of the LCE systems were carefully prepared. All LCE systems with different molecular architectures were first formed as isotropic structures and then gradually cooled. The Onsager order parameter Supplementary Figure S1.Supplementary Information.


The best cutoff value for AMR in determining IMR ≥ 25 was 2.5 mmHg*s/cm. The overall diagnostic accuracy of AMR was 87.2% (95% CI: 83.0% to 91.3%), with a sensitivity of 93.5% (95% CI: 87.0% to 97.3%), a specificity of 82.7% (95% CI: 75.6% to 88.4%), a positive predictive value of 79.4% (95% CI: 71.2% to 86.1%) and a negative predictive value of 94.7% (95% CI: 89.3% to 97.8%). No difference in terms of CMD rate was described among different clinical presentations. (4) Conclusions: AMR derived solely from a single angiographic view is a feasible computational alternative to pressure wire-based IMR, with good diagnostic accuracy in assessing CMD.(1) Background: In spite of the undeniable clinical value of the index of microvascular resistance (IMR) in assessing the status of coronary microcirculation, its use globally remains very low. The aim of this study was to validate the novel single-view, pressure-wire- and adenosine-free angiographic microvascular resistance (AMR) index, having the invasive wire-based IMR as a reference standard. (2) Methods: one hundred and sixty-three patients (257 vessels) were investigated with pressure wire-based IMR. Microvascular dysfunction (CMD) was defined by IMR ≥ 25. AMR was independently computed from the diagnostic coronary angiography in a blinded fashion. (3) Results: AMR demonstrated a good correlation (r = 0.83,  Coronary microvascular dysfunction (CMD) is a common under-diagnosed cause of myocardial ischemia, which yields an undeniable clinical and prognostic value both in acute (ACS) and chronic coronary syndromes (CCS) ,2,3,4. SIn order to facilitate the investigation of CMD, different angiography-based solutions have been proposed and validated against invasive IMR, based on computational flow analysis ,14,15,16The objective of this study was to develop and validate a novel method to assess microvascular resistance, based on μQFR algorithms and associated advantages. The angiography-derived microcirculatory resistance (AMR) is a wire-free and adenosine-free index, which aims at providing a valid alternative to invasive wire-based IMR assessment.The present study was a single-center observational study that included all the patients admitted to Zhongnan Hospital of Wuhan University between 2012 and 2020 who underwent invasive coronary microvascular assessment, according to clinical practice indication. Patients presenting with both ACS and CCS were included. ST-segment elevation myocardial infarction (STEMI) was defined as the occurrence of ongoing chest pain for at least 30 min associated with ST-segment elevation >2 mm in at least two contiguous leads or new left bundle branch block . ACS alsOverall, vessels with non-obstructive disease by angiographic visual estimation  were included pre or post percutaneous coronary intervention (PCI). In the setting of STEMI undergoing primary PCI, invasive coronary physiology assessment of the culprit vessel was performed after flow restoration with thrombus aspiration and/or balloon dilatation and/or at completion of primary PCI. In patients with non-STEMI and unstable angina, invasive coronary physiology assessment was performed at completion of PCI of the culprit vessel. The identification of the culprit vessel was based on the combination of (1) angiographic appearance compatible with plaque instability or presence of thrombus, (2) electrocardiographic and echocardiographic findings. In the setting of CCS with obstructive coronary arteries, invasive coronary physiology assessment was performed on completion of PCI, whereas in the case of non-obstructive disease  it was performed during the diagnostic angiography.2); (3) severe coagulopathy or bleeding disorders; (4) allergy to iodine contrast agents or adenosine. Angiographic exclusion criteria included poor contrast opacification and severe vascular overlap or foreshortening of the interrogated vessel.Patients were enrolled on a prospective registry, and individual patient data were collected using standardized spreadsheets. Clinical exclusion criteria were: (1) left ventricular ejection fraction ≤50%; (2) estimated glomerular filtration rate <45 mL/min/1.73 mThe retrospective study was approved by the Institutional Review Boards at Zhongnan Hospital of Wuhan University, Wuhan, China, which conforms to the declaration of Helsinki and Good Clinical Practice Guidelines of the China National Medical Products, National Medical Products Administration. All the patients provided their written consent for the anonymous collection of the data. A comprehensive study flow-chart is provided in d) is obtained and μQFR computed as Pd divided by the mean aortic pressure (Pa), while AMR is computed as Pd divided by the hyperemic flow velocity hypVelocity.A single-view μQFR analysis was computed, using the QFR software . The analysis was performed by an experienced and certified analyst at an independent academic core laboratory  who was blinded to invasive IMR data. The detailed methodology for single-view μQFR computation has been described previously . In brieIn this computation, an average aorta pressure of 86 mmHg during maximum hyperemia is assumed, as previously reported .Invasive physiology measurement was performed in a routine manner through a pressure thermistor-tipped coronary guidewire  and thermodilution according to the standard procedures suggested by the RadiAnalyzer Xpress instrument . In brief, the wire was calibrated, equalized and advanced towards the distal third of the coronary artery. One minute after intracoronary injection of 100 μg of isosorbide dinitrate, Pa, Pd and mean transit time (tTmean) were measured at baseline and during stable hyperemia induced by the intravenous infusion of adenosine (140 µg/kg/min). Thermodilution curves were obtained by the injection of 3 mL of room temperature 0.9% saline solution, and tTmean was calculated as the average of three transit time measurements during at least three separate injections.IMR was then calculated as follows:d/Pa between 0.97 and 1.03. FFR was also recorded during this procedure.Pressure drift was checked and avoided by accurate pull-back of the pressure wire to the guiding catheter tip and defined as Pt test. A p-value < 0.05 was considered significant, and all comparisons were two-sided. All statistical analyses were performed with MedCalc .Continuous variables were recorded as mean ± standard deviation (SD) or median , and categorical variables were recorded as count (percentage). Spearman’s correlation coefficient was used for the correlation analysis. Linear regression analysis was used to quantitatively define the relation between paired variables. Bland-Altman analysis was used for evaluating the analysis agreement between different analyzers. Using wire-derived IMR ≥ 25 as the reference standard , the aren = 80; 49.1%) of the patients presented with acute MI, including STEMI  and non-STEMI . The remainder had unstable , or stable angina, with obstructive  or non-obstructive coronary arteries . Among patients presenting with acute MI, 44 patients (27%) had MI ≤ 7 days prior to the index angiography, and among them 41 patients presented with STEMI and 3 presented with non-STEMI. The quantitative coronary analysis (QCA)-defined degree of diameter stenosis was 28.78 ± 9.24%, while median QFR value was 0.94 (0.91–0.97) and mean blood flow velocity 16.87 ± 5.42 cm/s (Paired pressure wire-derived IMR and AMR were successfully acquired in 163 patients (257 vessels). Baseline demographics and vessel characteristics are reported in .42 cm/s .p < 0.001) with IMR  vs. 100% , p = 0.746} but lower specificity {62.5%  vs. 94.9% , p = 0.003}.The overall sensitivity and specificity of AMR > 2.5 mmHg*s/cm to predict IMR ≥ 25 was 93.5% and 82.7%. As shown in p = 0.85) or AMR  assessment. The rates of CMD did not differ among the two populations .No significant difference of MR values was observed between patients presenting with ACS or CCS, according to IMR  or AMR  assessment, with no significant difference in CMD rates. Individual MR scatter-plots according to clinical presentations are shown in Similarly, no significant difference of MR values was observed between diabetic and non-diabetic patients, according to IMR  and absolute agreement at the Bland-Altman analysis , as shown in Inter-observer reproducibility depicted an excellent correlation  and diagnostic accuracy  in predicting wire-based IMR; (2) AMR accuracy was not influenced by the investigated vessel, but a significantly lower specificity was observed during the first week after MI, and particularly in STEMI patients; (3) A low inter-observer variability was reported after repeated blinded AMR analysis.In this study we developed and validated a new pressure wire-free and adenosine-free index that investigates CMD, based on the analysis of a single angiographic view. The major findings of our study can be summarized as follows: (1) AMR showed good correlation , or by means of transit time analysis, using thermodilution principles, to derive IMR. IMR and HMR are recommended to assess CMD in several clinical scenarios . MR asseAlthough invasive IMR assessment is easier to obtain than HMR, its use in routine clinical practice is hampered by technical  and economic issues, as well as by the operator’s reluctance to change, since decisions remain largely driven by angiography alone. This leads to an overall under-diagnosis of CMD, resulting in repeated hospitalizations, unnecessary coronary angiographies and adverse cardiovascular outcomes in the short and long term .Therefore, several efforts have been made to enable CMD computation both on-line and off-line from angiography, with avoidance of the need to induce hyperemia and the use of dedicated pressure wires. This is expected to reduce procedural duration and costs, with the promise of broadening the adoption of CMD assessment in the real-world decision-making process.According to our original analysis, AMR provides good correlation and diagnostic accuracy against invasive IMR, as the standard reference.Of note, AMR computation overcomes two major drawbacks of the previously proposed angiography-derived IMR indexes. On the one hand, AMR computation, similar to the doppler-based HMR, is based on the estimated hyperemic flow velocity instead of the mean transit time ,15,16. TOn the other hand, previously reported angiography-derived IMR indexes were based on QFR computation from two angiographic projections, which is not devoid of several limitations. Firstly, it requires two angiographic views with at least 25° separation from each other, and with minimal vessel foreshortening and overlap. Secondly, a linear tapering of the reference vessel size is assumed during QFR computation, with reduced accuracy in specific settings . Conversely, the novel μQFR, besides being derived from a single angiographic projection, provides an artificial intelligence-empowered delineation of vessel contours, frame counting, and a more accurate reconstruction of reference vessel size, with a fast and reproducible computation, all of which reduce operator-dependence of the measurement . InteresThe accuracy of AMR computation was shown to be independent from the analyzed vessel segment, while a significant impact of the clinical presentation was observed. In patients with STEMI and those presenting ≤7 days after acute MI, AMR showed a significantly lower specificity, compared with NSTEMI, MI >7 days or CCS. Similar results were previously reported, suggesting that the agreement between angiography-derived IMR and invasive IMR was very close in NSTEMI and CCS, whereas it was less accurate in the STEMI setting. The inherently higher biological variability of IMR in a STEMI setting could explain such a difference, as the absolute numerical IMR values were less well correlated with the standard of reference in the case of extreme CMD ,14,35. TTo the best of our knowledge, this is the first study reporting a temporal influence on the accuracy of angiography-derived MR in assessing CMD in the setting of MI. It should be noted that both Tebaldi et al. and Mejia-Renteria et al. included only stable CCS in their analyses, whereas Scarsini et al. included both ACS and CCS. They provided a non-hyperemic angiography-derived IMR that did not take into account the estimated hyperemic velocity in its computation ,15,16. TTo conclude, AMR is a novel angiography-derived index that aims at increasing the availability of a prompt and integrated assessment of the microvascular function. This might represent a major step forward towards precision medicine implementation, with improvement in the diagnostic and therapeutic pathway of all the patients referred for invasive angiography assessment, over a wide span of clinical settings. Interestingly, according to our analysis, this approach led to the detection of a high CMD rate, even in patients with CCS in whom high MR was not anticipated from clinical variables. Therefore, the use of angiography-based computational technologies offers the net advantage of a systematic and comprehensive physiological assessment of both epicardial and microvascular determinants of the coronary circulation, in a fast, simple, and reproducible manner. A comprehensive illustration of the integrated coronary physiological computation based on single-view coronary angiography is provided in There are some limitations that should be considered.Firstly, even if patient demographics and coronary angiograms were collected prospectively, we performed post hoc AMR assessment. However, no selection bias was introduced, since AMR computation was attempted in all available cases meeting the study inclusion criteria.Secondly, the study sample size might not allow the identification of all variables that can potentially influence AMR computation. To the best of our knowledge though, this study, including more than 250 vessels, is one of the largest available validation studies on angiography-derived MR computation methods ,15,16.The population included in the original study presented with high average QFR values and a low degree of stenosis established by quantitative coronary analysis, with many vessels devoid of significant obstruction, expanding the scope of CMD assessment by angiography-derived methods. Notably, Meja-Renteria et al. validated their angiography-derived IMR index in vessels with significant coronary artery disease  . The assIn this study cohort, AMR was validated against thermodilution-based IMR, while no direct comparison with the doppler-based HMR was available. Future dedicated studies are warranted.Lastly but importantly, the impact of AMR values on clinical outcomes in different patient subgroups deserves further investigation in prospective trials, with adequate follow-up.Microvascular resistance can be properly derived from coronary angiography, with no need for dedicated pressure wires and hyperemic agents. In this validation study, AMR yielded a clinically useful degree of diagnostic accuracy in detecting CMD in different settings, versus wire-based IMR as a reference standard. AMR provides a valid, reproducible, widely available and fast computational alternative to wire-based IMR measurement during the routine angiographic evaluation of patients with suspected or known coronary artery disease.