In the past few years, data privacy legislation has hampered the ability of WiFi network operators to count and map client activity for commercial and security purposes. Indeed, since client device MAC devices are now randomized at each transmission, aggregating client activity using management frames such as Probe Requests, as has been common practice in the past, becomes problematic. Recently, researchers have demonstrated that, statistically, client counts are roughly proportional to raw Probe Request counts, thus somewhat alleviating the client counting problem, even if, in most cases, ground truth measurements from alternate sensors such as cameras are necessary to establish this proportionality. Nevertheless, localizing randomized MAC clients at a network site is currently an unsolved problem. In this work, we propose a set of nine tools for extending the proportionality between client counts and Probe Requests to the mapping of client densities in real-world outdoor WiFi networks without the need for ground truth measurements. The purpose of the proposed toolkit is to transform raw, randomized MAC Probe Request counts into a density map calibrated to an estimated number of clients at each position. Since the introduction of commercial WiFi networks over twenty years ago, there has been considerable interest in determining the locations of users of the service for commercial purposes such as client counting and monitoring, as well as for safety and security concerns. Localization is typically performed by leveraging the physical properties of a client’s WiFi signals, such as the Received Signal Strength (RSS), at several network Access Points (APs). Most systems make use of the abundant Probe Request (PR) frames emitted regularly by client devices even when not connected to a network ,2,3,4,5.Footfall, also known as client counting or simply traffic, is a measure of the number of visitors entering a commercial site, such as a retail shop, commercial center, museum, and so on. Footfall counts have been used for decades to help retailers and site managers gauge the attractiveness of their offers and improve the overall customer experience. Initially an affair of simple hand clickers, turnstiles, or weight-sensitive mats placed in doorways, footfall has evolved into what is today considered a key business metric that is usually obtained using more sophisticated techniques such as still and video surveillance cameras, Passive InfraRed (PIR) thermal detectors, active infrared Time of Flight (ToF) imagers, WiFi, and others. Data from the sensors is typically transferred via an internet protocol to be stored and analyzed on a server. Realtime footfall statistics have now become a valuable business analytics input, useful not only for retail and marketing analysis, but also in so-called Smart Building and Smart City applications, including energy use monitoring, people flows, and safety and security concerns.While radio-based client-held devices such as audio-guides have been in use for a number of years, when cellphones became popular in the 1990s, interest in exploiting the connectivity of these devices for footfall applications began to grow. With the arrival of smartphones and their nearly unanimous adoption by consumers, however, the prospect of an active WiFi/Bluetooth Network Interface Card (NIC) in the pocket of every single customer led to a paradigm shift in mobile client monitoring. Indeed, while many of the footfall tools mentioned in the previous section are still alive and well, there has been a veritable explosion in the use of wireless technologies for collecting client activity data. Beyond simple client counting, wireless also allows a means of localizing clients via an analysis of the strength of their radio signals. Thus, by leveraging ubiquitous internet-enabled client devices and an existing wireless network, wireless client monitoring can be much less dependent on specialized hardware, novel expertise, and labor-intensive deployment, calibration, operation, and maintenance, when compared to surveillance cameras, infrared sensors, or legacy footfall solutions.Driven by a desire for ubiquitous connectivity, Wireless Local Area Networks (WLAN) using WiFi, or in some cases, Bluetooth technologies, are today found in almost all public commercial areas, with city-wide WiFi coverage rapidly becoming the norm. Many of the well-known personal localization services, such as Google Maps and others, exploit existing WiFi and Bluetooth networks, together with GPS signals, for navigation and orientation in indoor and outdoor spaces. From the standpoint of client monitoring, however, it is generally the WiFi signals generated by the smartphones themselves, captured at the network APs, that are of principal interest for counting and localization purposes.Indeed, in order to assure rapid access to the ambient wireless networks, the WiFi NICs found in smartphones emit on a regular basis certain Control Frames that establish a list of available networks and announce the device’s presence and its capabilities. Of particular interest is the Probe Request (PR) frame, mentioned earlier, emitted on the order of 500 times in a 24 h period by a typical NIC ,2, even Since 2016, however, with the advent of the European General Data Privacy Regulation, or GDPR, and similar legislation elsewhere in the world, many of the opportunities for WiFi client monitoring and localization have been jeopardized. To preserve clients’ identities, Medium Access Control addresses are nowadays considered private information and must be anonymized using a non-reversible hash function. As a given MAC address nevertheless always hashes to the same token value, for further protection, all user WiFi devices being manufactured today also attribute a new, randomized MAC address with each transmission. WiFi service providers, consequently, are now faced with a plethora of PR signals without any possibility of correlating them with the different clients, thus rendering invalid the de-facto industry-standard wireless client monitoring model just described.Workarounds to the problem do exist. One is to use clues in the digital content of received frames to effectively de-randomize PRs. A second is to concentrate on connected clients who, by subscribing to the WiFi service, have consented to the use of their private fixed MAC address. Both have important drawbacks, as will be discussed in Beyond the workarounds, though, in the past few years, researchers have developed methods of estimating genuine client counts from MAC-randomized PR using statistical methods. It is found that for sufficiently large client populations, the number of PRs is linearly proportional to the true number of clients. The problem remaining then amounts to determining the appropriate proportionality constant, which can be achieved either by comparison to a ground truth obtained from other sensors, such as cameras, entry monitors, etc., as in ,7,8, or counting MAC-randomized clients is perhaps in the process of being solved, following the observations in location to a group of anonymized clients presents a number of additional difficulties. Localization in wireless networks has been an active field for a number of years, with a wide variety of techniques proposed [If the problem of proposed ,11,12,13Indeed, while positioning is by nature sub-optimal in networks where localization was not an initial design priority, under GDPR, standard solutions become a priori impossible for any but connected subscribers having fixed MAC addresses. As a result, tools for performing localization despite these challenges, are today in substantial demand. The goal of the present article is to propose a set of tools intended as a first step towards re-establishing client monitoring for commercial and security purposes in an era when many traditional approaches are no longer available.-Preprocessing Tool for selecting client populations for study;-“Bowl” Tool for displaying localization probability densities;-Renormalization Tool for condensing distributed probabilities into decision regions;-Counting Tool for estimating the proportionality between PRs and clients;-Filtering Tool for removing peaks in the density that obscure smaller scale structures;-Localization Tool for device localization and calibration of the propagation model;-Fiducial Tool for delineating client density regions;-Drill-Down Tool for the study of client densities on different scales;-Re-assembly Tool to create the final map calibrated for clients rather than PR.The article presents a set of nine tools designed to facilitate the preprocessing, display, interpretation, localization, and counting of outdoor WiFi network clients based on raw randomized MAC PR counts. The objective of the toolkit is to transform randomized MAC Probe Request counts into a density map calibrated to the number of clients at each position. In many instances, the mathematical machinery deployed in the tools is well established. The contribution of the proposed work lies in assembling the necessary elements into a coherent package to make sense of a complicated problem that many network operators have dismissed as intractable. The toolkit contains the following:Some of these are straightforward procedures, where the word “tool” may seem excessive; for others, more involved, the label indeed applies. The “Bowl,” Filtering, and Drilldown tools, for instance, employ relatively standard graphical methods, whereas the Fiducial, Localization, Counting, and Renormalization tools are innovative and embody much of the originality of the present work.The remainder of the article is structured as follows. The next section gives an overview of state-of-the-art WiFi networks in the era of GDPR and introduces the use of PR for client monitoring. The problem of IoT in WiFi networks and the definition of the client classes used in the article are provided in As presented in As mentioned in A second workaround amounts to monitoring only connected clients or others with fixed MAC addresses. This choice, however, comes at a heavy price. In outdoor WiFi networks, only a small percentage of clients nowadays choose to subscribe to the service, preferring to rely on cellular telephone connectivity to access the internet. Indeed, the authors of  report aA variant of this second workaround is to assume that the number of randomized MAC clients amounts to some fixed fraction of the total number of clients. The authors of , for exaAgain, as introduced in Though site-dependent, X for a time window of one day turns out to be of the order of 500, as alluded to in A second technique for obtaining X, albeit with reduced precision, is by estimating it statistically from periodicities observed in the distribution of PR counts versus time, as was reported in  for seveThe proposed toolbox is presented first using the data sets used and some necessary definitions.The datasets studied include detailed, time-stamped PR records from public outdoor WiFi networks at an outdoor tourist site, Site 1, and a campground, Site 2, both in France. They are part of a larger data set, including the French city data introduced in [Client Randomized, CRThe MAC string is seen from 1 to “threshold” times.Client Fixed, CFThe MAC string is seen more than “threshold” times.Client Connected, CCThe MAC string appears in the connection details file.By counting the number of times in a selected time window that a distinct anonymized MAC is seen and comparing it to a threshold, and by also taking into account the connection details file, PRs are identified as belonging to one of three classes:Connected clients are separated out first, so that the classes are mutually exclusive. These client class definitions are the same as those introduced in . An impoDespite randomization of the MAC, its first 3 bytes, called the Organizationally Unique Identifier, or OUI, are retained and become part of a PR entry in the data record. The OUI is intended to provide information on the manufacturer of the WiFi network card producing the PR, available by interrogating a public OUI web repository. In practice, OUI responses include a baffling array of manufacturers that are sometimes difficult to clearly identify. A detailed investigation  suggests3, after verifying that their timestamps are consistent with near-simultaneous reception. We recall that the MAC of a PR received at multiple APs will hash to the same result at all APs concerned. The quantities P and P3 will be important later in the localization, counting, and reassembly steps.To prepare the data for the remaining steps in the toolbox, it is necessary to carry out a preprocessing step based on the points outlined in This section presents a tutorial-style introduction to some of the difficulties encountered in real-world outdoor WiFi network localization and the manner in which the proposed tools can be brought to bear upon them. Note that the Preprocessing Tool was already introduced in As outdoor WiFi networks cover extended areas, obtaining full coverage for both traffic and localization is challenging. Numerous approaches based on RSS , Time ofThe physical origin of the triangulation approach is embodied in the well-known Friis equation relating RSS with distance:n is called the fading exponent. The Friis equation can be derived from first principles for free space propagation, where power decreases as the inverse square of the distance, giving n = 2. Propagation over a flat, unencumbered surface can also be shown to produce n = 4. For intermediate situations with clutter and multipath effects, as expected in outdoor WiFi networks, values in the range 2–3 are encountered, depending on the particular environment.Here, RSS is measured in dB with respect to 1 milliwatt (dBm), while the client device-AP distance d is in meters. The constant A, also in dBm, is the emitted power at 1 m from the client device—by industry standard, normally taken as −30 dBm. The constant Concerning positioning accuracy, while in indoor WiFi networks, where AP counts are higher, a precision of ± a few meters is typical, in a calibrated outdoor network, an accuracy of ±10 m is already considered a good result. This observation will be an important consideration in evaluating the viability of some of the tools proposed in the article.Let us first consider a toy configuration with three APs in a triangular arrangement as in fp is the bowl density function, σ is a distance variance parameter derived from the 2 dBm variance in RSS, drssi is the distance obtained from inverting Friis, x and y are the coordinates of a point in the density map, and xap and yap are the coordinates of the AP. The form of the bowl function is illustrated in 2D projection and in 3D in An equivalent but somewhat more realistic simulation is shown in To illustrate the use of the Bowl Tool, one returns to the toy example, overlaying, in To reflect this decision, the Renormalization Tool is applied by integrating all probabilities outside the decision region and re-injecting them inside the region. The renormalized selected zone appears in 2D in In The probability distribution in est = 13/X = 13. As the true number of clients is C = 11, it is tempting to say that the estimate is already pretty good. In fact, there are two choices. As a first possibility, if it is certain that the probability of three AP PRs is small compared to that of single AP PRs, one may choose to simply tolerate the counting error they introduce in exchange for the ambiguity-resolution property that the three AP PRs provide. A second possibility would be to measure the fraction of three AP PRs in the data and somehow use it to make a correction to X for a more precise estimate. The Counting Tool and the possibility of introducing such a correction to X will be returned to in greater detail in Although the group of 11 clients has been localized in the example, it has not yet been attempted to count them. To do so, the Renormalization Tool may be applied to obtain Before advancing to the applications in the next section, it is important to discuss a simple, yet troublesome additional configuration frequently encountered in real outdoor WiFi datasets. In some networks, certain APs may be located inside buildings or in other areas favoring very close proximity to the AP—a few meters, for instance. Because of the enclosed geometry and/or accompanying increased distance from the other APs, triangulation is usually not possible. In these cases, the bowl associated with the AP will be very sharply peaked, because of its small radius. As long as the radius is not too large, the consequences for localization are in fact minimal, since one may simply tabulate the total number of PR received—almost exclusively single AP—and associate them with the narrow bowl surrounding the AP location see . The difn were known. Although, as mentioned earlier, A is normalized in the industry to −30 dBm, it is more prudent to actually measure A for a particular site. Furthermore, the value of n is highly dependent on the propagation environment of the site under study. In practice, using incorrect values for A or n can lead to dramatically different distance estimations. As site propagation studies are complicated and expensive to carry out, it is often the case in real-world scenarios that reliable ground truth measurements are not available.In the discussion of triangulation, it was assumed that the values of the propagation parameters A and 12, D13, and D23. A client located at some position in the vicinity of this triangle—in this case, within it—emits a PR that is received simultaneously at the three APs, with power values RSS1, RSS2, and RSS3. Inverting Equation (1) and taking d1, d2, and d3 as the distances of the client from the three APs gives:One way to reduce the dependence on propagation parameters is to make use of a simple observation arising from the Friis equation. Consider three APs, AP1, AP2, and AP3, arranged in a triangle as shown in The dependence of the distance ratios on parameter A has been cancelled out. Equation (2) tells us that the ratio of the distances from the client to any two APs is a constant. As it turns out, this implies that for each pair ij, the client position lies on a circle, called the Circle of Apollonius after the second-century Greek geometer who discovered the relation, which has its center on a line drawn through the two APs and a radius given by:not the same as the triangulation circles introduced in The circles appear in green, blue, and orange in If the Friis relation were an exact expression rather than a model, and in the absence of noise on the RSS values and for AP triangles that are not excessively obtuse, any two Apollonius circles will intersect at two candidate client locations, one of which can usually be rejected as unlikely. In more realistic cases, an exact solution may not exist, but numerical solvers can be used to find the best non-exact solution, for example, in a least-squares sense, which is often rather good. The added redundancy of using all three circles instead of just two can also be helpful in such cases. In practice, nonetheless, depending on the site under study, the rate of localizations with poor or no Apollonius solutions can be several tens of percent.n for the range 2 < n < 3. A simple calculation shows that for n in this range, the radius of an Apollonius circle varies by only about 16% of the distance Dij between two APs, which in turn is of the same order as the intrinsic resolution obtainable in outdoor WiFi networks. This relative independence of Apollonius client positions on propagation parameters allows us to envisage using them to calibrate A and n, which is a very interesting proposition in the not uncommon real-world situations where propagation ground truth is not available. n = 2.24. These values are then used to calibrate the Friis formula for the site in question.The importance of the Apollonius localization solution, apart from being independent of the parameter A, is that the position found also depends only weakly upon the fading exponent In this section, the tools introduced are applied to examples drawn from real WiFi datasets. Additionally, presented are the Fiducial and Drill-Down tools that enable a more detailed and quantitative interpretation of the data reduction process than presented thus far. For each site, a density plot of PR counts over a three-hour period is selected, sufficient to assure a large enough number of accumulated PRs while still allowing time-dependent analysis of client activity. The examples chosen are typical, yet they also illustrate certain particular configurations.The raw PR density over the three-hour afternoon period from 12:00 to 15:00 is shown in The red regions in With the fiducial region now defined, all probabilities are renormalized into the region. The result is shown in In order to minimize the effect of these peaks and better study the behavior of the density plot at smaller scales, a threshold is progressively lowered—easily controlled, for example, with the wheel of a mouse—that drills down to a level where the finer structure of the density becomes apparent. The PRs thus “decapitated” from the peaks are stored for later reincorporation (as discussed in the next section). The result, displayed in A second example, from Site 2, is presented here, illustrating a different situation.In Once again, it is desired to restrict the study to a fiducial area likely to represent the region where the bulk of clients are actually located. In To further study the relationship between the bowl-based density and the density of Apollonius localizations for this site, let us now apply the Drill-Down Tool. The result is shown in As stated earlier, the purpose of the Reassembly Tool is to put together the different pieces of the PR density map into a map calibrated for clients rather than PR. The idea that client counts are obtained from PR counts by dividing by a proportionality factor X appropriate to the time window in question has already been introduced. In order to follow client activity over time, however, the focus is placed on blocks of three hours throughout the day, which may need different values of X according to the type of activity—morning, afternoon, weekend, active, resting, etc.—prevalent in each block. Evidence is also visible, in the example of Site 2, of X values that vary according to the position, in particular regarding the percentage of PRs that are simultaneously detected at three APs and can thus be localized. In order to correctly reassemble the different pieces of the PR density map into a coherent client density map, one must therefore possess two key elements: a baseline X value for the site under study and a technique for correcting X as a function of time and position on the density map. In the next section, some extensions to the Counting Tool designed to facilitate the reassembly process are proposed.daily X values ranged from 420 to 670 , with a mean over all sites of X = 524 ± 47 [day. However, as the data in the present work were acquired at a historic tourist site (Site 1) and a campground (Site 2), their PR counts do not manifest the clear weekly periodicities that were the norm for the city data and, as such, cannot be directly used to measure X for the two new sites using the statistical method developed in [As mentioned earlier, the data used in this work are part of a larger set that includes the city data reported in . The priIt is, however, possible to demonstrate that the X values for the two current sites, after certain adjustments, are consistent with those measured in the city data. Indeed, in , the meaCC = P/(ACC<tCC>), where XCC is the estimated X value for the CC, P is the number of PR, ACC is the number of CC, and <tCC> is their average duration of stay on the site during the chosen time window. For the data in [CC values ranging from 378 to 948 are in reasonable agreement with the CR-estimated X range in [For the city data in , the CC- data in , the resrange in  mentionerange in , the CC range in . To now The first column of the table gives the percentage of CC retained after the ES-cut. For Site 1, very few clients had abnormally large PR emission rates, while for Site 2, the percentage was much higher. It is supposed that this is due to the different characters of the two sites. At a historical tourist site such as Site 1, most guests will stay only a short time and spend most of it shuttling from one point of interest to the next, and as such, they will have little incentive to connect to the local WiFi to set up special situations. Site 2, however, is a campground that includes bungalows. Guest will often stay several days in quarters that may lack the wireless environment and tools to which they were accustomed at their usual domiciles, so the motivation for piggybacking off the local WiFi—with its potential for high PR count services—will be high. Furthermore, at both sites, OUI values of the high-PR count CC, when exploitable, often pointed to IoT-type devices rather than user telephones. Finally, we stress that the overall number of CC is a small fraction of that of CR, making it less exploitable statistically for a detailed study, a point that will be important in the discussions that follow.CC = P/(ACC<tCC>), along with their Mean Absolute Deviation, MAD. It is seen that the mean value of 369 for Site 1 fits well with the range of CR-based X values (420–670) as well as the XCC values (378–948) obtained for the cities in [3, the number of PR received at three APs, in the present data as compared to the city data in [The second column of As mentioned in 3 values at Sites 1 and 2, we propose the following “strawman” procedure. As a first-order approximation, one may assume that the X reported in [12, is valid for sites where multiple AP PRs are rare and, from there, attempt to make a correction for sites having more dense AP layouts and hence, more three-AP PRs. We pose the following:3 is the number of PRs seen simultaneously by three APs, C12 is the number of clients producing one or two PRs per emission, and C3 is the number of clients whose PRs were seen at three APs. Note that in the last line of Equation (9), Xbase, the corrected site baseline X value, is expressed completely in terms of measurable quantities P and P3, and X12 from [3 = 0, we retrieve Xbase = X12, as required, while for P3 = P, the strawman predicts Xbase = 3X12, which is reasonable given the initial assumptions.In order to put the city data and Sites 1 and 2 data on an equal footing for comparison, as well as provide a means to take into account the increased P3/P for Sites 1 and 2. It is evident that the absolute numbers of CC at Sites 1 and 2 are too low to provide a statistically stable measurement of P3/P. To remedy this, in columns 3, 4, and 5 of the table, the ranges and mean values of P3/P measured for the CR are cited, which should be similar to those of the CC since P3/P is a quantity that depends predominantly on network layout. Columns 3 and 4 of the table show that the fraction P3/P is indeed significant and that it is much larger at Site 2 both in magnitude and in range. This observation is consistent with the much increased MAD value at site 2, i.e., the fraction P3/P varies significantly throughout the course of a day, leading to large variations about the mean value. In column 5 of the table, this average P3/P value is used to calculate the reciprocal of the strawman fraction, thus creating an ad hoc correction factor that allows to put the Site 1 and 2 daily X values on a common footing with those of the city data in [One may get an idea of the applicability of the strawman by referring again to instantaneous value of P3/P, not its mean, to correct X12. Column 6 of the table is intended to show the general trend of the correction. When actually used in the counting and reconstruction tools, the instantaneous values of CR-derived P3/P values are to be used. It is also for this reason that in the following, we prefer to use X12 = 524 from [As a caveat, it must be remembered that the strawman prescribes using the 3 are site-dependent quantities, so Xbase automatically adapts to each site. Also, in the proposed treatment, the time window used is not explicitly specified. Indeed, the resulting formula for Xbase is valid for P and P3 from any time window. In particular, it is applied directly to the 3-hour windows chosen for this article, so that Xbase also adapts to each individual time window. As a caveat, the constant value X12 in the above equations must be adjusted to correspond to the three-hour time window chosen for the client density plots in the present work, i.e., X12 = 524 × (8/24) = 65.It is clear that P and P2 or P4, etc.—but given the statistical uncertainty in X12 to begin with and the systematic uncertainties in applying it to new data sets, this may not be the major priority at this stage. The strawman provides an informed, first order estimation of the results that may be expected in estimating numerical client densities from raw probe requests in outdoor WiFi networks for which ground truth is not available. We shall employ it in the Reassembly Tool, where appropriate, for estimating overall client counts versus position.In creating the strawman, some simplifying approximations have been made. Clearly, a more rigorous treatment is possible—for example, by explicitly including a term for Pbase, color zone by color zone, when reassembling the different pieces of each site, as outlined in base may be applied according to:Apollo(color zone) is the number of Apollonius localizations in each color zone of the bowl density plot, and NApollo is the total number of such localizations.In color values withA difficulty with this correction as it stands is that while it adapts X to the different color regions, it will alter the overall predicted number of clients. This problem can be circumvented by normalizing the XAn example, with the values obtained for Site 1 in color obtained from 3 corrected X value (93); and a color zone by color zone correction . The estimated numbers of clients vary depending on the X method used, as expected. As an example, the prediction for the yellow region is 13.9 clients for uncorrected legacy X (column 4), 9.7 clients for an overall site-corrected X value (column 5), and 11.2 clients when Xcolor is used to correct for position (column 6). What has been done, using Xbase and Xcolor, is to correct for the artificial enhancement in bowl density, using legacy X, caused by the presence of proportionally more three-AP PRs.The seventh and last column of the table gives the values of XThe Site 1 PR density removed by filtering and drilling down, as well as the PR density retained after drilling down, are shown graphically in The resulting client density map appears in 2. The upper one, corresponding to one of the major tourist attractions of the site, is estimated to contain 48 clients in an area of 1482 m2, while the surrounding yellow zone contains 245 clients in an area of 8954 m2. The total integrated number of clients in all regions of the plot is estimated at 704.In the 2D plot, two red high-occupancy zones are visible. The estimate for the lower one, which is consistent with the site entry ticketing area, is 68 clients on a surface area of 1990 mThe same procedure is followed for Site 2, as in 2, while in the surrounding zone, there are 7 clients over 2124 m2. The client estimate for the entire plot is 33. A flowchart resuming the different steps used in applying the toolkit is given in Our technique estimates that in the red zone of As we have seen, the “Bowl,” Filtering, and Drilldown tools are graphical techniques used to make the spatial representation of client density clearer. The Reconstruction tool, in turn, in concert with the others, is a bookkeeping tool that allows one to retrieve the different pieces into which the problem has been divided and reconstruct them into a coherent and easily interpretable whole. It is the Fiducial tool, making best use of the limited localization information available in low-resource networks; the Localization tool, calibrated in a bootstrap procedure; the multi-step Counting tool allowing time and space domain adaptation of the X proportionality factor; and the Renormalization tools for redepositing the PR probabilities within the deduced fiducial region, that embody most of the originality and warrant the success of the approach presented.With the arrival of client privacy concerns as well as the rapid growth of IoT, the widespread practice of using client MAC addresses contained in PRs for monitoring and mapping client activity has become untenable, creating a need today for new tools. Here, a set of nine tools is presented to transform raw randomized MAC PR counts from real-world outdoor WiFi networks directly into density maps calibrated for clients, all without the use of any ground truth. The technique has been applied to data from two actual network sites in France, with interesting results.At present, the density predictions of the technique make use of a baseline PR emission probability, X, adopted from a similar study, augmented with a few reasonable, yet approximate, corrections. A high-priority perspective is thus to discover more precise estimation methods and use them to evaluate our technique on current data. An important route towards this goal is to encourage interactions with outdoor WiFi service providers and site managers so as to evaluate and calibrate the tools proposed here.Local network managers will probably retain records of the site IoT devices, such as cameras, lighting, loudspeakers, etc., in regular use, as well as of user equipment assigned to site personnel and potentially participating in P2P or similar services. Such information will be invaluable for separating the corresponding PR counts of these devices from those derived from bona fide clients.Site managers will also likely possess information on seating capacities of auditoriums, attractions, etc.; guest capacities of lodging and restaurant facilities; and visitor capacities of attractions, events, and the site itself. Such limits can be used to constrain estimates of the local site X factor.In cases where complementary footfall counting equipment is present at certain locations on a site, for example, head counts, presence sensors, etc., their outputs can also be used to condition estimates of the PR to client conversion factor X.Personnel familiar with site layout and maintenance will also have knowledge of land occupation distribution. Areas that are inaccessible, cordoned off, or fenced off, restricted in access, or simply situated outside the detailed official boundaries of the site, can be excluded from the fiducial area when the Renormalization tool is applied. Too, special-use areas, such as gardens, paths, parking lots, playing fields, and golf courses, will probably display client behaviors particular to the special usages of these areas and can be treated separately from more general user access areas when predicting client densities.When available, gate receipts, ticket counts, etc. may also, in certain circumstances, serve as indirect ground truth to which WiFi-derived client counts can be compared.Finally, site managers and organizers will also have access to scheduling information concerning day-to-day, holiday, and seasonal opening hours for the entire site as well as for individual facilities, including swimming pools, restaurants, golf, etc., and for special events as well as temporary, occasional, or unscheduled closures. Such timetables will be extremely valuable in establishing coherence between WiFi-derived counts and ground-truth expectations.Indeed, personnel having day-to-day familiarity with the details of monitored sites are likely to possess precious expert information not easily available to the data scientist, armed with an Excel file and a Google Maps satellite view of the site, charged with extracting the necessary metrics. Several examples are given as follows:The mapping of client activity based on randomized MACs is still a new endeavor. The simplicity and independence from ground truth of the proposed toolkit make it an interesting new contribution to the ongoing activity in the field, both for researchers and for the personnel involved in managing the sites under study.


Background: Postmortem assessment of anaphylactic death is a challenge for forensic pathologists. One of the most frequent elicitors of anaphylaxis is insect venom. Here, a case of anaphylactic death due to Hymenoptera stings is reported to highlight the contribution of postmortem biochemistry and immunohistochemistry in assessing the cause of death. Case report: A 59-year-old Caucasian man working on his farm was presumably stung by a bee and died. He had a history of previous sensitization to insect venom. The autopsy revealed no signs of insect puncture, mild edema of the larynx, and foamy edema in the bronchial tree and lungs. Routine histology showed endo-alveolar edema and hemorrhage, bronchospasm, and scattered bronchial obstruction due to mucus hyperproduction. Biochemical analysis was performed, and serum tryptase was equal to 189 µg/L, total IgE was 200 kU/L, and specific IgE dosage was positive for bee and yellow jacket species. Immunohistochemistry for tryptase detection was carried out, revealing mast cells and degranulated tryptase expression in the larynx, lungs, spleen, and heart. These findings led to the diagnosis of anaphylactic death due to Hymenoptera stings. Conclusions: The case highlights that the role of biochemistry and immunohistochemistry in the postmortem assessment of anaphylactic reactions should be stressed by forensic practitioners. Anaphylaxis is commonly known as a “severe, life-threatening generalized or systemic hypersensitivity reaction” and can occur both with immunological and non-immunological mechanisms ,2.An anaphylactic reaction is an acute IgE-mediated hypersensitivity response mediated by inflammatory mediators released in systemic circulation from mast cells and basophil; an anaphylactoid reaction has non-IgE mediated mechanisms and, usually, is clinically indistinguishable from an IgE-mediated reaction .The most common triggers of anaphylaxis are drugs, food, and insect venom, and among these, Hymenoptera stings are quite represented ,5,6,7.The Hymenoptera order is classified into three families: bees (Apidae), wasps (Vespidae), and ants (Formicidae). These arthropods can sting humans, having the potential to cause anaphylactic and non-anaphylactic reactions. Honeybees and bumblebees have barbed stingers and generally sting only if provoked; they characteristically die after a single sting. Wasps, hornets, and most yellow jackets have no barbed stingers and can sting many times. They are usually more aggressive than bees and also sting without any provocation .Hymenoptera toxins contain various complexes of peptides, enzymes, proteins, and chemicals, and they cause cellular injury via several mechanisms . StudiesIt is important to underline that in the evaluation of deaths probably related to a bee sting, it is not always possible to appreciate macroscopic signs of the sting . In factTherefore, in such cases, the postmortem assessment of the cause of death needs biochemical and immunohistochemical investigations that, together with circumstantial data, clinical data, autopsy, and routine histological findings, can provide useful evidence .Here, the authors report a case of anaphylactic death due to Hymenoptera stings to highlight the contribution of several forensic investigations in assessing the cause of death.The case regards a 59-year-old Caucasian man with a history of previous sensitization to Hymenoptera sting, the result of which was that, a few years earlier, he had facial edema due to both bee and wasp stings, then confirmed by skin tests. Anamnestic data were negative for cardiovascular and respiratory diseases.On the day of his death, the man contacted an employee by phone, asking for help and reporting that he was probably stung by bees.Once arrived, the employee found the man unconscious, lying on the ground, and with a transparent liquid coming out of his mouth, and contacted an ambulance. The medical staff found the man in cardiorespiratory arrest and began resuscitation, but the man died. The autopsy was performed at 24 h after death.Body inspection showed no signs of bee puncture. The gross examination revealed mild edema of the larynx, whitish foamy liquid in the bronchial tree, and red-brownish foamy dense liquid in the lungs.The routine histology was also performed, showing subacute pulmonary emphysema, endo-alveolar edema and hemorrhage, marked congestion of the interalveolar septa, bronchospasm, and scattered bronchial obstruction due to mucus hyperproduction A–C. MyocThe toxicological investigations performed were negative for alcohol, abused substances, and psychotropic drugs.Biochemical investigations have been performed on the peripheral blood , showing an increased level of tryptase equal to 189 µg/L, troponin I 100,000 pg/mL, and proBNP 579 pg/mL. In addition, the Immuno-CAP method was applied for the determination of total IgE antibodies, which was found to be equal to 200 kU/L. ImmunoCap  was carried out for the specific IgE dosage against honey bee (i1), white-faced hornet (i2), common wasp (Yellow Jacket–i3), paper wasp (i4) and yellow hornet (i5); the analysis allowed the identification of honey bee IgE equal to 5.30 kUA/L and yellow jacket IgE of 3.00 kUA/L.For immunohistochemical procedures, 4-micron thick sections obtained from larynx, lung, heart, and spleen tissue blocks were deparaffinized, then washed in descending alcohol scale, treated with 3% hydrogen peroxide for 10 min, washed again in deionized water three times, and incubated with normal sheep serum to prevent unspecific adherence of serum proteins for 30 min at room temperature. After, slides were washed with deionized water and incubated for 30 min at 37 °C with primary anti-human antisera monoclonal mouse anti-tryptase antibody (Roche Diagnostics code 760-4276). Next, the sections were washed three times with PBS and incubated with a biotinylated goat anti-mouse IgG secondary antibody  for 20 min at room temperature, subsequently incubated with horseradish peroxidase-labeled secondary antibody for 30 min, developed with diaminobenzidine tetrahydrochloride, and counterstained with hematoxylin using the ULTRA Staining system . Negative controls were obtained by omitting the specific antisera and substituting PBS for the primary antibody. Immunohistochemical reaction revealed intense expression in the larynx and lungs, showing several immunopositive mast cells and spread immunopositivity for degranulated tryptase A–D; mildThe global incidence of anaphylaxis is reported between 50 and 112 episodes per 100.000 person years with a low mortality rate, estimated at 0.05–0.51 per million people/year for drugs, at 0.03–0.32 for food, and at 0.09–0.13 for venom . In ItalEven if Hymenoptera stings are a frequent cause of anaphylactic reactions, there is a consistent number of related deaths that cannot be correctly identified due to the difficulty of making a postmortem diagnosis ,16.To perform the diagnosis of death due to anaphylactic shock, it is necessary to integrate circumstantial and anamnestic data, autopsies, and histological findings . HoweverThe subject’s clinical history serves to collect information on both previous allergic reactions and sensitization phenomena to specific allergens; likewise, the circumstances of death play an important role in the forensic analysis of the case .Relevant findings can be provided from autopsy and routine histology. Sting signs and the evidence emerging from gross and microscopic analysis of the respiratory system  support the occurrence of anaphylaxis . HoweverMany researchers have suggested the use of biochemistry and immunohistochemistry to fill the gaps related to the poor or absent autopsy and histological data in performing the postmortem diagnosis of anaphylaxis .Particularly, blood biochemical investigations to evaluate tryptase and IgE are described as useful tests to confirm deaths related to anaphylactic reactions due to bee venom, especially when there are no evident signs of stings .Serum tryptase is a neutral protease of human mast cells, mostly used as a biomarker to better define the postmortem diagnosis of anaphylaxis ,22.Tryptase is a very stable enzyme and can be detected up to 6 days after death . NeverthForensic literature reports variable cut-offs for serum tryptase from peripheral blood. Meyer et al.  demonstrThe literature also offers evidence on serum tryptase measurement on blood taken from central vessels, such as the aorta, in which the suggested cut-off value is 110 µg/L . HoweverThe serum concentration of tryptase found in the femoral blood of the case presented here was equal to 189 µg/L and supported the occurrence of anaphylaxis.In the presented case, other useful data were obtained from the analysis of total and specific IgE. Particularly, the analysis revealed a total IgE value of 200 kU/L and the presence of specific IgE for honey bee (5.30 kUA/L) and yellow jacket species (3.00 kUA/L), demonstrating a high (Radio-Allergo-Sorbent-Test class 4) and moderate (Radio-Allergo-Sorbent-Test class 3) level of sensibilization, respectively.Evidence in the literature suggests combining results of both mast cell tryptase and allergen-specific IgE and/or total IgE assay in postmortem serum to support the assessment of IgE-mediated fatal anaphylaxis ,28. EvenImmunohistochemistry is another investigation useful for postmortem diagnosis of anaphylaxis. Many studies focused on the role of mast cell and tryptase detection in tissues, among which are bronchial, respiratory, and intestinal mucosa, and the red pulp of the spleen and connective tissue  . NeverthThe immunohistochemical analysis performed in the presented case revealed an intense positivity of mast cells and degranulated tryptase in the larynx and lungs, together with a mild marker expression in the spleen. These expression patterns are in accordance with the evidence in the literature and support an anaphylactic death. Moreover, the immunohistochemical findings observed in the heart, together with the increased level of serum troponin and pro-BNP, could suggest a coronary hypersensitivity similar to that described in Kounis syndrome. This morbidity is associated with allergic, hypersensitivity, anaphylactic, and anaphylactoid reactions, and it is classified into three types .The type I variant, known as vasospastic allergic angina, is characterized by endothelial dysfunction or microvascular angina and occurs in subjects with normal or nearly normal coronary arteries and in the absence of predisposing factors for coronary artery disease; the releasing of inflammatory mediators due to anaphylaxis can cause coronary artery spasm until myocardial injury with impaired cardiac enzymes and troponins. The type II variant, also known as allergic myocardial infarction, has been described in subjects with quiescent pre-existing atheromatous disease. In this case, the acute release of inflammatory mediators can provoke both coronary artery spasms with normal cardiac enzymes and troponins or coronary artery spasms associated with plaque erosion or rupture. The type III variant occurs in patients with coronary artery stent in whom the inflammatory reactions cause a prothrombotic response and the stent thrombosis; eosinophils and mast cells are generally detected in thrombi and coronary wall at histological examination .Therefore, in Kounis syndrome, the myocardial damage seems related to the effect of both mast-cell degranulation and the release of inflammatory mediators that affect the cardiovascular system  . Few casThe case argumentation highlights that postmortem diagnosis of anaphylactic death is based on a combination of data about the event, medical history, gross and microscopic examination, and blood serum analyses. Particularly, even if tryptase analysis by biochemistry and immunohistochemistry and IgE dosage have limits in specificity and sensitivity, their integration with the other information is fundamental to performing a differential diagnosis and, thus, to assessing anaphylaxis . MoreoveIn conclusion, data emerging from the forensic investigations lead to assessing the cause of death as an anaphylactic shock due to Hymenoptera stings affecting the respiratory system and the cardio-circulatory system, with possible vasospastic involvement of coronaries. The case described here supports the importance of circumstantial data in guiding postmortem investigations, especially if no external signs attributable to the insect bite and/or unspecific autopsy and histological evidence are found. Moreover, the important role of biochemistry and immunohistochemistry in demonstrating the anaphylactic reaction has been described, suggesting that these investigations should be routinely implemented in forensic practice when anaphylaxis is suspected.


Smilax china L. and to identify the active compounds in the ethyl acetate (EtOAc) fraction. Extraction of ethanol extracts from Smilax china L. and then ethanol extracts were concentrated, and the polyphenolic compounds were extracted with petroleum ether (PE), chloroform, EtOAc, n-butanol (n-BuOH), and residual ethanol fractions. Their effects on XO activity were then compared separately. The polyphenolic components of the EtOAc fraction were identified by HPLC and HPLC–mass spectrometry (HPLC-MS) analysis. Kinetic analysis demonstrated that all these extracts showed XO-inhibitory properties, and among them the EtOAc fraction had the strongest inhibitory effect (IC50 = 101.04 μg/mL). The inhibitory constant (Ki) of the EtOAc fraction on XO activity was 65.20 μg/mL, showing excellent inhibition on XO in the competitive mode. Sixteen compounds were identified from the EtOAc fraction. The study demonstrates that the EtOAc fraction of Smilax china L. may be a potential functional food to inhibit XO activity.The aim of this work was to investigate the xanthine oxidase (XO)-inhibitory activity of ethanol extracts from  Ki) value of 0.61 ± 0.31 μM − was located at m/z 353.085 (C16H18O9), the major fragment ion was located at m/z 191.055, and referring to the literature, it was identified as chlorogenic acid (quinic acid) − ions located at m/z 329.086 and 329.086, respectively. The MS2 spectrum showed that the characteristic vanillic acid molecular ions were at m/z 167.035 and 167.033. According to literature data from − ion at m/z 739.161. Thus, the peaks were characterized as catechin-O-digalloyl-C-rhamnoside (peak 4), and peak 6 should be identified as an isomer of catechin-O-digalloyl-C-rhamnoside by comparison with the literature [Peaks terature . 7 and 8 in the spectrum had the same molecular ion [M−H]−, which had an m/z value of 353. A major fragment at 450.116 and 450.110, respectively, was also observed for both peaks. Thus, the peaks were characterized as eriodictyol-C-hexose (peak 7) and eriodictyol-C-hexose isomers (peak 8), which are isomers. According to the literature, it was tentatively identified as eriodictyol-C-hexose [Peaks C-hexose .9 and 11 gave the same precursor ion at [M−H]− m/z 451.099 and MS2 fragments at m/z 341.063 with the molecular formula C24H20O9. These structural isomers exhibited different retention times to enable the identification of cinchonain I  and cinchonain I isomers  [12) had a molecular ion [M−H]− at m/z 613.128 and was therefore tentatively identified as cinchonain I hexose, but it presented a different fragment from peaks 9 and 11.Peaks .37 min) . Another10 with [M−H]− in the negative mode at m/z 243.064 and an MS2 fragment ion at 175.073 was identified as uridine.Peak − at m/z 227.068 and an MS2 ion at 143.048 was tentatively identified as (2)-2-(aspartylamino)-4-pentynoic acid by comparison with the reference spectral fragments.Peak 13 with [M−H]Smilax china L. polyphenols exhibit anti-obesity, anti-inflammatory, and anti-bacterial effects  were depicted to assess the reversibility.In the reaction system between the inhibitor and enzyme, the concentration of fixed substrate was 150 μM, and the reversibility of enzyme inhibition was measured by varying concentrations of XO and different amounts of inhibitor. The curves of the reaction rate , the reaction conditions remained constant, and the concentration of XO was fixed at 0.1 U/mL. The relationship between the reaction rate and substrate concentration was determined under different inhibitor concentrations. The inhibition type was analyzed according to the Lineweaver–Burk plot. For competitive inhibition, the Lineweaver–Burk equation can be written as :(1)1v=Kmm/z. Data from Agilent’s MassHunter 8.0 software were analyzed.The EtOAc extracts were subjected to HPLC apparatus separation on an Agilent 1260 HPLC system  equipped with a series of SHODEX KS-804 and KS-802 columns (8 mm × 300 mm) and a refractive index detector. The mobile phase was acetonitrile (solvent A) and 0.1% formic acid aqueous solution (solvent B). The linear gradient solvent system was as follows: 0–10 min, 15–25% A; 10–22 min, 25–31% A; 22–30 min, 31–45% A; 30–31 min, 45–100% A; and, finally, isocratic elution with 100% phase A until 35 min. The column temperature was 20 °C, the injection volume was 10 μL, the detection wavelength was 280 nm, and the flow rate was maintained at 0.8 mL/min. The MS analysis was performed using an Agilent Technologies 6538 OHD accurate-mass quadrupole time-of-flight (Q-TOF) mass spectrometer to analyze and identify the EtOAc fraction’s active compounds. The Q-TOF mass spectrometer was equipped with an electrospray ionization source (ESI) in the negative mode. The operating parameters were set as follows: spray voltage of 4000 KV, gas collision at He, nebulizer pressure at 50 psi, collision voltage of 250 V, and drying temperature of 350 °C. The ion scanning range was 100–1000 p < 0.05 was considered significant.The experimental results were expressed as mean ± standard deviation (SD) and analyzed by one-way ANOVA using the Origin 8.5 software; Smilax china L. ethanol extracts exhibited an inhibitory effect on XO, especially the EtOAc fraction. The EtOAc fraction showed excellent XO-inhibitory performance in the competitive mode. Sixteen compounds in the EtOAc fraction were identified by HPLC and HPLC-MS. Thus, the ethanol extract of Smilax china L. has great potential for inhibiting XO activity and preventing hyperuricemia. Future studies should focus on the pharmacological testing of these compounds in vivo.In this study,


Diabetes mellitus is a chronic metabolic disease relating to steady hyperglycemia resulting from the impairment of the endocrine and non-endocrine systems. Many new drugs having varied targets were discovered to treat this disease, especially type 2 diabetes. Among those, α-glucosidase inhibitors showed their effects by preventing the digestion of carbohydrates through their inhibition against α-amylase and α-glucosidase. Recently, chalcones have attracted considerable attention as they have a simple structure, are easily synthesized as well as have a variety of derivatives. Some reports suggested that chalcone and its derivates could inhibit α-amylase and α-glucosidase. This narrative review provides a comprehensive evaluation of the inhibition of chalcone and its derivatives against α-amylase and α-glucosidase that were reviewed and reported in published scientific articles. Twenty-eight articles were reviewed after screening 207 articles found in four databases, including PubMed, Google Scholar, VHL , and GHL . This review presented the inhibitory effects of varied chalcones, including chalcones with a basic structural framework, azachalcones, bis-chalcones, chalcone oximes, coumarin-chalcones, cyclohexane chalcones, dihydrochalcones, and flavanone-coupled chalcones. Many of these chalcones had significant inhibition against α-amylase as well as α-glucosidase that were comparable to or even stronger than standard inhibitors. This suggested that such compounds could be potential candidates for the discovery of new anti-diabetic remedies in the years to come. Diabetes mellitus is a chronic metabolic disorder characterized by persistent hyperglycemia resulting from endocrine and non-endocrine system impairment. Type 2 diabetes (T2D) is widely known for its serious complications on major and minor arteries, which can lead to retinal diseases, nephropathy, neuropathy, cardiovascular diseases, cerebrovascular disease, and peripheral vascular disease. According to the International Diabetes Federation's 2021 report, there were up to 6.7 million deaths due to diabetes worldwide, placing a significant burden on society. Managing diabetes and preventing its complications have become urgent concerns for health professionals . The WorSearch results of articles reporting inihibitive activity of chalcone against α-amylase and α-glucosidasein vitro studies published in English up to September 18th 2022, that provided information on the inhibition of chalcone and its derivatives against α-amylase and/or α-glucosidase. The search was conducted on the following four databases: PubMed, Virtual Health Library (VHL), Global Health Library (GHL), and Google Scholar, using the search terms given in Table We initially selected In total, 207 articles were found in the four databases, including PubMed, Google Scholar, VHL, and GHL. After the selection step, 28 articles were included to extract data. The selection of studies is illustrated in Figure https://pubchem.ncbi.nlm.nih.gov/) by the name or chemical structure. Our set of compounds could be useful for future structure-activity relationship studies.Many natural or synthetic chalcones have α-amylase and/or α-glucosidase inhibitory activities, including chalcones with a basic structural framework, azachalcones, bis-chalcones, chalcone analogs, chalcone oximes, coumarin-chalcone derivatives, cyclohexane chalcones, dihydrochalcones, and flavanone-coupled chalcones. Table Inhibitory activities against α-amylase and α-glucosidase of chalconesChalcones With Basic Structural FrameworkDracaena cinnabari include 2',4'-dihydroxychalcone (6), 4,4'-dihydroxy-2'-methoxychalcone (7) had α-glucosidase inhibitory activity with IC50 values of 389.6 and 270.2 μM, respectively. Chalcone 7 was stronger than chalcone 6, that revealed that the activity could be strenghened by the oxygenation pattern [Bauhinia pulla, a plant of the Fabaceae mainly distributed in Thailand, contains anti-diabetic ingredients with chalcone framework, namely 3,2’,4’-trihydroxy-4-methoxychalcone, and 4-methyl ether isoliquiritigenin. 3,2’,4’-trihydroxy-4-methoxychalcone was 6-fold less α-glucosidase inhibitory activity than 4-methyl ether isoliquiritigenin, despite the former possesses more hydroxyl groups. IC50 values of these two compounds showed a weaker effect than standard acarbose (124.11 µg/mL) [Many chalcone derivatives isolated from herbs have shown inhibitory activity on the enzymes α-amylase and α-glucosidase. Two chalcone derivatives isolated from chalcone , 4,4'-diHumulus lupulus, 3′-prenylchalconaringenin and 3′-geranylchalconaringenin showed its dual inhibition against α-glucosidase and α-amylase. 3′-geranylchalconaringenin demonstrated an inhibitory effect on α-glucosidase 50-fold higher than acarbose  and also exhibited moderate inhibition against α-amylase (IC50 = 20.46 μM). Meanwhile, 3′-prenylchalconaringenin and chalconaringenin showed their effect 20-fold weaker than 3′- geranylchalconaringenin, however, its inhibition against α-glucosidase was still more effective than acarbose. Nevertheless, the inhibition of α-amylase of these three compounds was weaker than the positive control [Among chalcones isolated from  control .Alpinia katsumadai were isolated. Most compounds showed evidently inhibition against α-glucosidase as their IC50 values ranged from 2.9 and 29.5 µM, which were 6 to 59-fold stronger than acarbose (IC50 of 170.9 µM) [Cleistocalyx operculatus (Roxb.) Merr. et Perry (Myrtaceae), was studied in vitro and showed significant non-competitive inhibition (IC50 = 43 μM) of pancreatic α-amylase [To discover antidiabetic natural ingredients on the α-glucosidase target, sixteen diarylheptanoidchalcones including katsumadainols A1−A16 (1-16), 13 similar known compounds 17-29) that originated from 70.9 µM) . 2',4'-d that ori50 value of 21 ± 2 μM followed by chalcone 21 (IC50 = 53 ± 1 μM) and chalcone 26 (IC50 = 87 ± 3 μM). Chalcone 4 (butein) was the most effective compound with an IC50 value 17-fold lower than that of acarbose with IC50 value of 357 ± 25 μM, which was the positive control [Chalcone 4 (butein) with C-2’-hydroxy, C-4’-hydroxy on the ring A and at C-3-hydroxy, C-4-hydroxy on the ring B gave the strongest efficiency against α-amylase. Most chalcones that had chloro-substitutent exhibited weak inhibition against α-amylase. The strongest inhibitor was chalcone 4 (butein) with IC50 of 21.3 ± 8.7µM). In contrast, the position of hydroxyl groups in the chalcone structure played a critical role in the inhibition of α-glucosidase. Ring A with 4-hydroxyl would have a stronger inhibitory effect than that with 3-hydroxyl group, proven by the IC50 value of compound 2c (13.4 ± 2.7 µM) compared to compound 2d (42.0 ± 6.0 µM). Nevertheless, a hydroxyl group added on C-3 position of the ring A of the compound 2b (IC50 = 35.2 ± 0.2 µM) enhanced the inhibition against α-glucosidase of compound 2f (IC50 =15.6 ± 2.8 µM). Besides, the chalcone derivative having the ring B with the 2’-hydroxyl group gave a stronger inhibitory effect than the 3’-hydroxyl group (compound 2b vs 2c) [In the series of synthetic chalcone derivatives of Cai et al. (2017) , compounb vs 2c) .in vitro. All compounds were found to be effectively against α-amylase compared to the positive control (acarbose) with an IC50 value of 1.04 ± 0.3 µM. Their IC50 values ranged from 1.25 ± 1.05 to 2.40 ± 0.09 µM. The dominant compounds were 4 and 10. SMe and OMe-substituents were mainly responsible for the inhibitory effect of chalcones, as examined by the structure-activity relationship [50 = 8.8 µM) as a reversible and non-competitive inhibitor. Using caco-2 cell monolayers model, the compound prevented the separation of glucose from maltose in the apical side of these cells [50 = 0.95 ± 0.28 μM). The inhibitory effect decreased when the size of substituents increased on para-position on the ring B. The compound 2a was the strongest inhibitor with IC50 of 4.2 ± 0.21 μM, then followed by compound 2b (IC50 = 5.4 ± 0.10 μM), compound 2c (IC50 = 6.1 ± 0.56 μM), and compound 2d (IC50 = 8.1 ± 0.61 μM) [50 values indicated their significant efficiency. However, among those compounds, only chalcone 34  (IC50 = 15.0 µM) and chalcone 35  (IC50 = 20.2 µM) demonstrated their promising anti-diabetic effect compared to positive control deoxynojirimycin (IC50 = 3.49 µM) [Thirteen synthesized chalcones 1-13) were primarily tested for their inhibition against α-amylase tionship . Xanthohse cells . The pre0.61 μM) . Compoun were pri3.49 µM) .50 = 60.8 μM). For the inhibition of α-amylase, only sulfonamide chalcone (17-20) showed strong inhibition [50 = 15 ± 0.14) and compound 21 (IC50 = 20 ± 1.42 µM) were the most potential candidates [Among synthesized aminochalcones and sulfonamide chalcones in Seo et al. (2005), only chalcone 15, 16 and sulfonamide chalcones (17-20) exhibited their remarkable inhibition against α-glucosidase. Particularly, the inhibitory effect of sulfonamide chalcone 20 was 150-fold stronger than acarbose (IC50 = 47.5 μM) exhibited the strongest inhibition against α-glucosidase with 8-fold more effectiveness than acarbose. Thus, the development of α-glucosidase inhibitors that had a scaffold of oleanolic acid-chalcone could bring many innovative approaches for T2D patients [Lin et al. (2020) synthesized five chalcones. Amongst these, only chalcone 1  and 2  actively inhibited against α-glucosidase at 10 mmol/L. Chalcone 3  only showed its effect merely at the higher concentration of 20 mmol/L . Five sypatients .50 value of 1.04 ± 0.19 μM. This proved that chalcone sulfonamide had a large potential as α-glucosidase inhibitor in the treatment of diabetes [50 = 0.9 μM) [Most synthesized oleanolic acid derivatives chalcones reported by Tang et al. 2014) exhibited moderate inhibitory activity against α-glucosidase . The par14 exhibi 0.9 μM) .50 = 1.03 ± 0.05 μM) was used as a positive control. The location of fluo-groups on the ring B of the chalcone scaffold resulted in different action manners of 5-styryl-2-aminochalcones (3a-h). The presence of 50-styryl in 20-amino-3-fluorochalcone scaffold markedly decreased the effect of the styryl-chalcone compound (3a) with IC50 of 10.7 ± 0.21 μM. On the other hand, 20-amino-50-styryl-4-fluorochalcone isomer (3e) showed significant inhibition against α-amylase with the IC50 value of 1.6 ± 0.52 μM. Compounds 20-amino-50-(4-fluorostyryl)-3-fluorochalcone  and 20-amino-50-(4-fluorostyryl)-4-fluorochalcone isomer  also shared this tendency. Besides, 20-amino-50-(4-chlorostyryl)-3-fluorochalcone (3c) improved the inhibition against the enzyme with IC50 = 2.5 µM. Its isomer, 20-amino-50-(4-chlorostyryl)-4-fluorochalcone (3g), also shared this pattern with IC50 = 1.7 ± 0.25 µM, respectively. 20-amino-50-(4-methoxystyryl)-3-fluorochalcone (3d) (IC50 = 7.0 ± 0.45 µM) and also its isomer (3h) (IC50 = 7.6 ± 0.20 µM) had been proved as moderate inhibitors against α-amylase [50 = 17.8 ± 0.32 µM). In addition, the compound 3b (having 50-(4-fluorostyryl) group and inhibition 20-amino-3-fluorochalcone scaffold) had its inhibition against α-glucosidase improved, as its IC50 values of 6.1 ± 0.24 µM. A derivative of compound 3c with a substituent of 50-(4-chlorostyryl) revealed a slightly improved effect (IC50 = 12.6 ± 0.31 µM). A combination of the hydrophobic 50-(4-methoxystyryl) group and the 20-amino-3-fluorochalcone group in the scaffold of compound 3 likely improved the inhibition against α-glucosidase (IC50 = 9.4 ± 0.50 µM). The compound 3e, with its structure having the 50-styryl group and the 20-amino-4-fluorochalcone group showed the highest inhibition among derivatives of 5-styryl-2-aminochalcone scaffold with IC50 of 5.1 ± 0.61 µM. The presence of 50-(4-fluorostyryl) and 20-amino-4-fluorochalcone groups increased the inhibitory effect of the compound 3f (IC50 = 6.9 ± 0.37 µM). Moreover, the 20-amino-4-fluorochalcone scaffold with an electron-withdrawing group 50-(4-chlorostyryl) significantly decreased the effect of the compound 3g (IC50 = 19.2 ± 0.47 µM). The stronger inhibition against α-glucosidase of compound 3h, which had a strong electron-donating group 4-methoxystyryl, was also observed with IC50 value of 10.5 ± 0.18 µM. Nevertheless, the isomer of the compound 3d was slightly more active [in vitro in terms of their inhibition against α-amylase from the porcine pancreas. All chalcones showed better inhibition than standard control acarbose . Among these, compounds 2-4, 6, 9, and 12-15 revealed modest to good inhibitory effect as their IC50 values ranged from 1.47 ± 0.481 to 1.89 ± 0.126 μM [Among the synthesized chalcone triazoles in Chinthala et al. (2015), compounds 4m, 4p, and 4s were promising candidates to inhibit α-glucosidase . Testing-amylase . The come active . SixteenAzachalcones50 are presented in Table Of the twenty-seven synthesized azachalcone derivatives 3-29), compound 3, compound 5, compound 6, compound 16, and compound 28 were strong α-amylase and α-glucosidase inhibitor. Their IC9, compouBis-chalcones50 = 21.3 ± 8.7 µM). Bis-chalcone 2k was the strongest inhibitor, as its IC50 value was 1.0 ± 0.1 µM. Compounds 2g, 2j, and 2l were the strongest non-competitive inhibitors, as shown by the kinetic analysis [in vitro, as its effect  was comparable to standard control acarbose (IC50 = 1.04 ± 0.3 µM) [Most bis-chalcones showed stronger inhibition against α-glucosidase than standard compound 1-deoxynojirimycin (IC 0.3 µM) .Chalcone Analogs50 of 41 ± 1 µM. The efficiency was 9-fold better than positive control acarbose (IC50 = 357 ± 25 µM) [Cinnamylidene acetophenone (41)  with two double bones that linking ring A and ring B showed α-glucosidase inhibitory activity with ICChalcone Oximes50 values of 1.61 ± 0.16 µM and 3.36 ± 0.58 µM [Fandaklı et al. (2018) reported the α-glucosidase inhibitory activity of synthesized chalcone oximes (2a-2k). Amongst tested compounds, compounds 2a and 2b showed more effective activity against α-glucosidase with IC 0.58 µM .Coumarin-Chalcone Derivatives50 values were 30.30 ± 2.53, 29.74 ± 2.68, 24.09 ± 2.36 μM, respectively, which were all stronger than acarbose [Of the 22 coumarin-chalcone derivatives (3a-3v) synthesized by Hu et al. (2022), compounds 3j, 3q, and 3t were significant α-glucosidase inhibitors, as their ICacarbose .Cyclohexane ChalconesBoesenbergia rotunda included 3 (panduratin A), 4 (isopanduratin A) and 7 (hydroxypanduratin A) showed α-glucosidase inhibition. Their IC50 value was 12.7 ± 1.3, 7.5 ± 0.6, 4.6 ± 0.4 µM, respectively. The inhibitory activity of these compounds was many-fold stronger than acarbose (1155.5 ± 23.0) [Three cyclohexenyl chalcones from  ± 23.0) .DihydrochalconesDracaena cinnabari, has a weaker α-glucosidase inhibitory activity than chalcones with basic structural framework as 2',4'-dihydroxychalcone (6) and 4,4'-dihydroxy-2'-methoxychalcone (7) [50 = 40.2 µg/ml) is a new chalcone-dihydrochalcone dimer from Dracaena cinnabari with C-linked that significantly inhibited α-glucosidase. Its effect was only a bit weaker than acarbose (IC50 = 30.5µg/ml) [Balanophora tobiracola had inhibitory activity against α-glucosidase at a lower concentration  [Boesenbergia rotunda named 2',4',6'-trihydroxydihydrochalcone had the inhibitory activity against α-glucosidase with IC50 value of 32.0 ± 2.2 µM many-fold lower than acarbose (IC50 = 1155.5 ± 23.0 µM) [50 = 23.17 μg/ml) showed the strongest activity against α-amylase, followed by compounds 3a (IC50 = 25.70 μg/ml) and compound 3f (IC50 = 48.17 μg/ml). In comparison with acarbose, which had IC50 value of 891.25 μg/ml, all these compounds were more effective [4,4'-dihydroxy-2-methoxydihydrochalcone 5), a dihydrochalcone from , a dihyd.5µg/ml) . Meanwhie IC50 = .5µg/ml [23.0 µM) . For α-affective .Flavanone-Coupled ChalconesBoesenbergia rotunda showed marked inhibition against α-glucosidase. Among those, flavone-coupled chalcones 9, 12, 13 (IC50 ranged from 1.3 to 3.4 μM) were found to be 3-10 folds more active than normal chalcone and even more effective than acarbose (IC50 = 1155.5 ± 23.0 μM) [Biflavones  from 23.0 μM) . Potential Toxicity of Chalcone Derivatives in HumansHumulus lupulus was known as xanthohumol to have anti-inflammatory effects in healthy humans if used in low doses achievable through the diet, it was safe and well tolerated by healthy adults at doses of 24 mg per day [Natural chalcones were considered relatively non-toxic compounds . The pre per day ,34. Lico per day . Hydroxy per day .However, the toxicity of new synthetic chalcones were not well known. In addition, antidiabetic drugs that inhibit α-amylase and α-glucosidase enzymes known as acarbose have been reported to have side effects such as stomach discomfort, gas, bloating and diarrhea . Some chSeveral studies on chalcones toxicity have been reported. 3--2,5-diphenyltetrazolium bromide (MTT) assay on normal monkey kidney cells (Vero cells) and adenocarcinomic human epithelial cells (A549 cells) showed that 5-styryl-2-sulfonamidochalcones (2f and 2h) had no cytotoxic effect on the Vero cells and narrowed the toxicity on A549 cells . PredictComputational methods for chemical toxicity prediction can also be used to select potential candidates for further study or to design a series of new structures with reduced toxicity.This narrative review presented chalcones and their derivatives as potential scaffolds with chemical characteristics that strongly affect α-amylase and α-glucosidase. Some compounds showed better efficacy in vitro than standard controls, suggesting approaches for developing new drugs to manage diabetes. Although the effects of chalcone derivatives were mentioned in several studies, the toxicity of new synthetic chalcones are not well known. Considerably more work will need to be done to determine the potential effects of Chalcones derivatives in humans. The results demonstrated positive findings that support the potential use of these candidates for discovering new anti-diabetic remedies in the years to come.


Nicotiana tabacum), previously proposed to be important for dark inactivation of the complex to avoid ATP hydrolysis at night. Here we show that there is no clear role for phosphorylation in the dark inactivation of ATP synthase. Instead, mutation of one of the two phosphorylated serine residues to aspartate to mimic constitutive phosphorylation strongly decreased ATP synthase abundance. We propose that the loss of N-terminal phosphorylation of ATPβ may be involved in proper ATP synthase accumulation during complex assembly.Understanding the regulation of photosynthetic light harvesting and electron transfer is of great importance to efforts to improve the ability of the electron transport chain to supply downstream metabolism. A central regulator of the electron transport chain is ATP synthase, the molecular motor that harnesses the chemiosmotic potential generated from proton-coupled electron transport to synthesize ATP. ATP synthase is regulated both thermodynamically and post-translationally, with proposed phosphorylation sites on multiple subunits. In this study we focused on two N-terminal serines on the catalytic subunit β in tobacco ( N-terminal phosphorylation of ATP synthase subunit β interferes with complex accumulation during assembly. Photosynthesis provides the starting material for nearly all life on earth. There is currently a drive to improve photosynthesis to meet food demands of a growing global population and make renewable biofuels to combat climate change. Understanding photosynthesis and how it is regulated is important if we want to modify these reactions to increase plant growth or increase flexibility to respond to stress conditions .2 fixation. The central regulator of the thylakoid reactions of photosynthesis is the ATP synthase (discussed in (o subcomplex and a soluble CF1 subcomplex that work together to convert protonmotive force (pmf), generated by proton-coupled electron transfer, into phosphorylation potential in the form of ATP , the largest component of non-photochemical quenching (NPQ) and lowers the rate constant for deprotonation of plastoquinol at the cytochrome 6fb (bf) complex Qo site (photosynthetic control) . ATP sym of ATP . The ΔpHcontrol) . In hightodamage . Changintodamage , and thebf complex [reviewed in (ATPC1 gene in the nuclear genome) is oxidized, and leads to a decrease in the rate constant of proton efflux  have raised particular interest, because they were shown to be phosphorylated at the end of the night . Plants that contained the desired mutation were sub-cultured further, and the second round of regenerated plants were screened again by PCR and DNA sequencing. Plants appearing to have a single peak in the Sanger sequencing chromatogram were further analyzed by Southern blotting for homoplasmic incorporation of the selection cassette. Plants that appeared to be homoplasmic for both the atpB mutation and the co-transformed selection cassette were transferred to the greenhouse for seed production.To test the hypothesis that phosphorylation of the N-terminus of ATPβ is involved in regulation of ATP synthase, we introduced mutations either abolishing the phosphorylation site (S-to-A) or mimicking a phosphorylated residue (S-to-D). To achieve this, we used co-transformation of 9 plasmids: 8 plasmids containing our mutations of interest in all combinations, and one plasmid with the dA gene; ), and thatpB via PCR amplification and Sanger sequencing. Plant lines with single peaks at the site(s) of the desired mutation(s) were considered homoplasmic  grown on non-selective medium . To ultioplasmic . DNA frosaB gene . At leasbundance , indicatpmf leading to changes in feedback regulation of electron transfer  would have a decreased rate constant for proton efflux (gH+) which is a measure of ATP synthase activity, resulting in increased pmf, increased NPQ, and decreased photosynthetic efficiency (ϕII). By taking the total extent of the dark to light changes of the ECS, we can calculate relative steady state pmf  , while the decrease in gH+ was comparable between the S8A + S13D and S8D + S13D mutants (∼35% decrease). This decrease in gH+ resulted in an increase in feedback control in the form of NPQ  and a CFo subunit  by tricine-SDS PAGE and western blot analyses (The decreased growth and photosynthetic rates in the mutants harboring the S13D mutation suggested two hypotheses: 1) the phosphorylation state of ATPβ has a regulatory function, as previously suggested , changingH+ , suggestanalyses .1 subunit accumulates to a lesser extent in mutants with the S13D substitution. ATPβ content in the S8A + S13D and S8D + S13D mutants is ∼25% of WT and control plants, while the single S13D mutation leads to a reduction in ATPβ content to ∼12.5% of wild type levels. Since the antibody target (ATPβ) is our point of mutation, the western blot results were confirmed by Coomassie blue staining  accumulates similarly, with only the S13D mutations leading to a decrease in content . Considering these quantifications, we were therefore unable to rule out that loss of ATP synthase in the S13D mutants is the cause of the altered steady state gH+. It also appears likely that the stronger phenotype in the single mutation of S13D is due to this mutation leading to lower protein accumulation than the S8A + S13D and S8D + S13D mutants.While the alanine mutations led to no apparent change in the amount of ATPβ, this CFstaining  where thECS) of the decay from a first order exponential fit.The phosphorylation of ATPβ on residues S8 or S13 has previously been reported at the end of a long dark period , which lpmf for activation (pmf for activation (pmft) increases as a function of time , and the x-intercept is proportional to pmft  for either mutation or site, thus the variation was likely not reflective of anything related to the mutations or protein content. ATP synthase dark activity, gH+d, is lower in the S13D mutants than in the wild type and the aadA control lines  (atpB mutants (i.e. mutants harboring the S13D mutation), we increased the protein loading to levels that would give us near wild type signals for ATPb  The S13D substitution leads to an increase in the degradation rates of ATP synthase, and 2) the S13D substitution leads to a decrease in the rate of ATP synthase synthesis. To test for altered turnover rate of ATP synthase, we infiltrated leaf discs with lincomycin and sampled at 24, 48, 96, and 144 h after infiltration. Total protein samples from our time course were then blotted and hybridized to an antibody against ATPb (encoded by atpF)  as a maratpF) , the S8AatpF) , and theatpF) . To see atpB mutants prompted us to analyze the assembly of the ATP synthase by BN-PAGE in thylakoid samples from the wild type, the control plants and the atpB mutants S13D, S8A + S13D and S8D + S13D. One-dimensional gels revealed similar complex distribution for photosystem II, photosystem I, the cytochrome b6f complex and the LHCII monomer and trimers. Despite similar content of ATP synthase in control plants and the wild type, we noted lower accumulation of fully assembly ATP synthase in all the S13D atpB mutants , in agreement with the observations reported by  dark intervals during our experiment to allow a relative measure of qE , which has been shown to accumulate in response to high pmf , all plants show the expected increase in NPQ and decrease in ϕecreases . In all ases . We demonstrated that this is due to loss of ATP synthase complex, and not dynamic regulation of the complex, by comparing the inactivation kinetics of an ATP synthase knock-down mutant (pmf threshold of activation (pmft) for ATP synthase, as both of these are altered on the same timescale when ATP synthase content is decreased. In general, the amount of ATP synthase in the chloroplast should not alter the Em of ATPγ. Instead, the faster oxidation of ATPγ could be explained by a decrease in electron transfer that might be expected from the decrease in ϕII , which n mutant . In facte in ϕII  we see io subunit ATPb in any of our mutants . Thus, 2 or Pi) may be a more productive first approach. It has been proposed that CK-II is a kinase for ATPβ  cultivar.All experiments were performed in the −2 s−1. For RNA and protein analyses, plants were grown in 200 μmol photons m−2 s−1 with a 16:8 light:dark photoperiod. RNA was extracted from the youngest leaf of plants at 3 weeks after sowing. Spectroscopic analysis and protein extraction was performed on fully expanded leaves (17–20 cm in length) at ∼5 weeks after sowing. For growth comparison, seeds were germinated in 200 μmol photons m−2 s−1 with a 16:8 light:dark photoperiod for approximately 3 weeks, then transferred to larger pots and grown at 350 μmol photons m−2 s−1 for approximately 8 weeks. Photos were taken when control plants (transformed with the construct containing the aadA marker but no atpB mutation) and wild type plants began producing flower buds. Due to space constraints, representative lines harboring the S-to-A mutations at either site and representative lines harboring the S-to-D mutations at either site, were grown in independent experiments.Leaf tissue for DNA analyses came from tobacco plants grown on 0.5 MS medium  sucrose) at 50 μmol photons mE. coli strain TOP10. Ampicillin-resistant colonies were sequenced for the desired mutation(s) before being combined with plastid transformation vector pDK308  between the PmeI and PacI restriction sites (NEB) using Gibson assembly  and primt-EctWT, ), contaiadA gene  as selecadA gene .aadA cassette, and lines with a single band shift of 1.2 kb were transferred to soil for seed production. Homoplasmy was confirmed for integration of the aadA cassette by plating the resulting seeds on selection medium and Southern blot analysis of DNA from ∼20 pooled seedlings. Lines homoplasmic for the aadA displayed a homogeneous population of spectinomycin-resistant progeny .Leaf tissue for DNA and RNA analyses was frozen in liquid nitrogen and pulverized using a steel ball Retsch mill. DNA was extracted according to  for SangatpB.Total RNA extraction and northern blot analysis was performed as described in  using pr2, 2.5 mM EDTA, 10 mM sodium ascorbate, pH 7.6) on ice for 10 min and thylakoids were pelleted at 3000×g for 10 min at 4°C. The pellet was resuspended in the osmotic shock buffer and used for subsequent experiments.Crude chloroplasts were extracted from mature leaves (∼15–20 cm in length) as described in  with the×g for 10 min at 4°C and resuspended in a modified Laemmli sample buffer  SDS, 8 M urea, 12% (v/v) β-mercaptoethanol, and 20% (v/v) glycerol). For blots based on total protein, leaf punches from mature leaves were ground in liquid nitrogen in a Retsch mill. Protein was then extracted in buffer  and assayed for total protein content . The protein was then precipitated in 80% (v/v) acetone at −20°C overnight, and further treated identically to the samples loaded based on chlorophyll concentration.For western blots based on chlorophyll concentration, thylakoids of the desired chlorophyll concentration were centrifuged at 3000Protein samples were gel electrophoretically separated as described in  through −1 chlorophyll in 25BTH20G buffer (25 mM BisTris/HCl (pH 7.0), 20% (w/v) glycerol and 2× cOmplete EDTA free protease inhibitor (Roche)). The samples were then solubilized by adding an equal volume of 2% (w/v) n-dodecyl-β-maltoside (β-DDM) and incubated for 2 min at 4°C in the dark. The samples were centrifuge at 15,000 g for 20 min and the solubilized fraction was mixed with 1/10 volume of loading dye , 30% (w/v) sucrose and 50 mg mL−1 Serva Blue G). Thylakoidal protein complexes were resolved in a 8%–13.9% (w/v) gradient gel. For direct immunodetection in one-dimensional BN-PAGE, the proteins were solubilized with Laemmli buffer  glycerol, 4.3% (w/v) SDS and 100 mM DTT) for 1.5 h and blotted onto a PVDF membrane. For the second dimension, the gel strips were treated with Laemmli buffer and resolved in a 12.5% (w/v) SDS-polyacrylamide gel. Immunodetection with the anti-AtpB antibody was conducted as described above.BN-PAGE was conducted according to  with the−2 s−1.Measurements were performed on a home-built spectrophotometer described in . Steady −2 s−1 prior to experimentation. After 15 min of illumination, the light was turned off. First, a weak flash was given at 0, 35, 120, 210, 300, 390, 480, 570, and 1200 s post illumination, and the absorbance change from a green LED filtered with a 520 nm bandpass filter was monitored. The decay of the flash-induced absorbance shift was fit to a first order exponential decay to determine the lifetime (τECS). Second, a sub-saturating flash was given at 5, 65, 150, 240, 330, 420, 510, 500, and 1230 s post illumination, and the absorbance change at 520 nm was monitored. The first derivative of the decay was plotted against the amplitude of the flash-induced ΔA520 nm. The slope of the linear portion was taken as the rate constant of ATP synthase in the dark (gH+d), and the x-intercept (ΔA520 nm amplitude) was taken as pmf threshold of activation (pmft). For the overnight dark adaptation, plants were measured for all FIRK parameters at least 4 h after the growth chamber lights had turned off.To measure ATP synthase inactivation kinetics, two flash-induced relaxation kinetics (FIRK) of the electrochromic shift at 520 nm were performed as described in . Tobacco520 nm) parameters were normalized to chlorophyll concentration of the leaf area measured.All amplitude-dependent ECS  for atpB, atpF, and psaB and NM_001325914 for ATPC1.Sequence data from this article can be found in the GenBank/EMBL data libraries under accession numbers NC_001879.2 (kiad013_Supplementary_DataClick here for additional data file.


Bile acids are acknowledged as signaling molecules involved in metabolic syndrome. The Takeda G protein-coupled receptor 5 (TGR5) functions as a significant bile acid receptor. The accumulated evidence suggests that TGR5 involves lipid homeostasis, glucose metabolism, and inflammation regulation. In line with this, recent preclinical studies also demonstrate that TGR5 plays a significant role in the generation and progression of metabolic syndrome, encompassing type 2 diabetes mellitus, obesity, atherosclerosis, and non-alcoholic fatty liver disease (NAFLD). In this review, we discuss the role of TGR5 in metabolic syndrome, illustrating the underlying mechanisms and therapeutic targets. Metabolic syndrome (MS) refers to a group of metabolic dysregulations characterized by central obesity, hyperglycemia, insulin resistance, and hypertension. Individuals with MS have an increased risk of cardiovascular diseases and type 2 diabetes mellitus (T2DM) ,2. MS haTakeda G protein-coupled receptor 5  is a bile acid membrane receptor and a member of the G protein-coupled receptor subfamily. TGR5 can influence bile acid composition and enterohepatic circulation ,7,8,9. TBile acids (BAs) have been acknowledged as pivotal signal factors involving diverse biological functions including metabolic homeostasis and the immune response, mainly through activating two bile acid receptors: TGR5 and farnesoid X receptor (FXR). Recently, hundreds of research articles about TGR5 and MS have been collected in SCIE and PubMed up to 2023. However, the approved drugs targeting TGR5 for the treatment of MS are still limited. This review aims to map the underlying mechanisms of TGR5 and its potential as a drug target in the treatment of MS.The encoded sequence of the TGR5 gene is evolutionarily conserved in humans, rats, and other mammals and is located on human chromosome 2q35 and mouse chromosome 1c3 ,20. TGR5s, β, and γ subunits ..2+ levelTGR5 is activated by endogenous bile acids, with TCA being the most effective, followed by LCA, DCA, chenodeoxycholic acid (CDCA), and cholic acids (CA). A study revealed that individuals with atherosclerosis had significantly lower levels of LCA in their serum. This reduction level of LCA could impede TGR5 on anti-inflammatory in macrophages, leading to increasing plaque formation. Thus, LCA may be a marker for the clinic diagnosis of coronary atherosclerosis .−/− HFD mice showed lipid accumulation and increased hepatic fat energy [−/− mice showed higher levels of hepatic steatosis than female Tgr5−/− mice, highlighting the regulatory role of TGR5 in this process.Prior research has demonstrated that TGR5 protects against several liver diseases. Non-alcoholic fatty liver disease (NAFLD) is a common complication of obesity and T2DM. NAFLD has the potential to advance to non-alcoholic steatohepatitis (NASH), liver fibrosis, hepatocellular injury, cirrhosis, and hepatocellular carcinoma, because of lipid buildup in the liver . The TGRt energy . Furthert energy  on DIO mThe development of NAFLD is significantly influenced by insulin resistance in both the liver and adipose tissue . The TGRMonocytes and macrophages are important sources of inflammatory factors in the liver, playing a crucial role in the development of NASH and hepatocellular carcinoma . The TGR−/− mice show severe liver injury, hepatocyte apoptosis, and inflammation and cause elevated serum alanine and aspartate aminotransferase [Activation of TGR5 in macrophages has been shown to effectively inhibit inflammatory responses through the toll-like receptor 4 (TLR4)/NF-κB pathway . Additionsferase . The aboTGR5 agonists ameliorate liver fibrosis by inducing apoptosis, inhibiting inflammation, and improving blood circulation.LCA has been identified as the effective natural agonist of TGR5, which plays a crucial role in regulating metabolism and inhibiting inflammation. A recent study has investigated the effects of LCA on immune cells and hematopoietic stem cells (HSCs) in liver fibrosis. According to the findings, LCA could hinder the activity of transforming growth factor beta (TGF-β) and glycolysis while enhancing oxidative phosphorylation. This leads to the differentiation of macrophages towards the M2 type and prevents their differentiation into the M1 type . Meanwhi2S production. Moreover, the activation of Akt leads to the reduced transcription of forkhead box protein O1 (FoxO1), inhibiting the expression of vasoconstrictor endothelin-1 (ET-1) [The activation of TGR5 in liver sinusoidal endothelial cells (LSECs) promotes vasodilators secretion and inhibition of vasoconstrictors release . The for1 (ET-1) . These fThe interaction between gut flora and the TGR5 receptor has become a current research hotspot. Accumulating evidence shows that gut flora indirectly activates the TGR5 pathway by metabolizing the composition of the bile acid profile, facilitating the effect on a series of MS. In contrast, TGR5 deficiency causes gut microbiota imbalance and aggravates liver injury.Bacteroides vulgatus and an increase in the abundance of Ruminococcus torques. These changes result in higher TCA and DCA and TGR5 activation levels. Activation of TGR5 enhances the thermogenic capacity of white adipose tissue by increasing the expression of UCP-1 and creatine kinase mitochondrial 2 (CKMT2) [Firmicutes and increasing the abundance of Bifidobacterium and Actinobacteria. Additionally, it can increase the proportion of LCA and DCA, which can inhibit liver inflammation and protect against HFD-induced hepatic steatosis via the TGR5–NF–κB signaling pathway [Bacteroides that further induce the TGR5–GLP-1 pathway and inhibit intestinal dipeptidyl peptidase-4 (DDP4) [Studies have demonstrated that knocking out hypoxia-inducible factor 2α (HIF-2α) in mice can effectively reduce obesity by decreasing gut lactate levels . This re (CKMT2) . A recen pathway . Dietary4 (DDP4) . Meanwhi4 (DDP4) .TGR5 activation could improve insulin resistance, increase adipose tissue thermo-genesis and energy expenditure, and inhibit inflammation. However, the approved drug targeting TGR5 is limited in the treatment of MS. Bile acid analogs and non-bile acid regulators exhibit therapeutic potential for the treatment of metabolic disorders, including obesity, diabetes, and NAFLD. Ursodeoxycholic acid (UDCA) is a secondary bile acid used as a first-line drug in the treatment of primary biliary cholangitis . A cliniRDX8940 is a highly effective and selective TGR5 agonist, which could promote the secretion of GLP-1, GLP-2, and PYY in mouse intestinal L cells with minimal systemic side effects. Unlike other agonists, it does not inhibit gallbladder emptying in mice, and it is also non-toxic to the heart, liver, and pancreas. Furthermore, RDX8940 improves hepatic steatosis and insulin sensitivity in insulin-resistant mouse models, which has the potential to treat NASH and NAFLD . A-IononNevertheless, systemic TGR5 activation can cause some unpleasant adverse effects including excessive gallbladder filling and insufficient emptying ,107. TGRIn summary, TGR5 activation influences glucolipid metabolism, energy metabolism, inflammatory response, and endogenous signaling molecules expression through multiple signal transduction pathways. It has excellent potential effects in treating T2DM, obesity, atherosclerosis, and NAFLD, which is a key direction and hot area for drug research and development at home and abroad. However, the activity of TGR5 has been demonstrated in animal and cellular assays and needs to be further validated and observed in clinical trials. A comprehensive understanding of the potential mechanisms of TGR5 in metabolic syndrome can serve as a theoretical foundation and reference value for future TGR5-specific targeting in the treatment of metabolic syndrome.