The patterning of electrospun fibers is a key technology applicable to various fields. This study reports a novel focused patterning method for electrospun nanofibers that uses a cylindrical dielectric guide. The finite elements method (FEM) was used to analyze the electric field focusing phenomenon and ground its explanation in established theory. The horizontal and vertical electric field strengths in the simulation are shown to be key factors in determining the spatial distribution of nanofibers. The experimental results demonstrate a relationship between the size of the cylindrical dielectric guide and that of the electrospun area accumulated in the collector. By concentrating the electric field, we were able to fabricate a pattern of less than 6 mm. The demonstration of continuous line and square patterning shows that the electrospun area can be well controlled. This novel patterning method can be used in a variety of applications, such as sensors, biomedical devices, batteries, and composites. Electrospinning is one of the simplest and most effective ways to fabricate nanofibers. These nanofibers have potential applications in many fields, such as nanocatalysis, filtration, tissue scaffolding, textile fabric, fibrous porous media, drug delivery, and composites ,7,8,9,10Because of the chaotic whipping nature of electrospinning, electrospun fibers are typically deposited as a randomly oriented mat on a conductive collector. Many efforts have been undertaken to control the resulting fiber pattern by either controlling the electric field or changing the architecture of the collector. Various methods can be used to control the electric field under which nanofibers are electrospun; parallel electrodes ,20,21,22Here, we report a new patterning method for nanofibers using a cylindrical dielectric guide. We controlled and focused the electric field of the electrospinning process using a dielectric structure without any additional electrode configuration. The new patterning method developed can miniaturize the electrospinning setup, which is a limitation of previous studies, and is not dependent on prepatterned collectors and masks. In this study we analyzed the electric field focusing phenomenon using the finite elements method (FEM), which is a numerical technique used to discover solutions of incomplete differential condition equations and ground its explanation in established theory. Finally, we successfully patterned nanofibers using a cylindrical dielectric guide into the desired shape using a motorized stage. We believe that the advantages of the method described herein can be extended to tissue engineering, drug delivery, sensor technology, and surface modification. We expect that this approach can be used not only for filtration and fabrication of reinforcing composites but also with other functional materials.v ≈ 300,000) in distilled water (DI water) was synthesized at room temperature for 24 h. A solution of PAN  in ethanol was synthesized at room temperature for 24 h. These three solutions were blended at a ratio of 14 wt%, 8 wt%, and 12 wt%, respectively. All materials other than the PU were purchased from Sigma-Aldrich .We fabricated nanofibers using various polymers  to confirm the patterning effect of cylindrical dielectric guides. PU, PAN, and PEO are widely known as polymers that are generally used for electrospinning, and they use different solvents such as water and organic solvent. A solution of PU  in tetrahydrofuran (THF) and dimethylformamide (DMF) was synthesized at a DMF:THF ratio of 60:40 at room temperature (20 °C–25 °C) for 24 h. A solution of PEO  using a fuse deposition modeling method 3D printer . By using a lamination method 3D printing is made economical and it has a high degree of freedom in manufacturing shapes. In this study, cylinder structures of various sizes were fabricated with a 3D printer to concentrate the electric field. The cylinder structure is symmetrical in the axial direction and can generate a uniform electric field.guideD) ranging from 30 to 120 mm. Each mixed solution was loaded into a 3-mL syringe with a 21-gage needle for electrospinning. The syringe was placed in a syringe pump , and the solution was pulled out at a rate of 1 mL/h from the needle tip. The tip of the syringe was connected to the positive electrode of a high-voltage power supply . Each mixed solution was electrospun onto an aluminum plate located 75 mm from the needle tip. The cylindrical dielectric guide was located at the center of the syringe tip.In addition, PLA was selected as the material for producing the cylindrical dielectric guide. PLA is widely used as a material for 3D printing due to its good formability and low shrinkage of the product. Cylindrical dielectric guides were manufactured with diameters . An SPT-20 unit  operating at 500 mA for 120 s was used to sputter-coat nanofibers with platinum. An optical camera  and ImageJ software  were used to analyze the electrospun nanofibers. r = 3.645) was used for the cylindrical dielectric guide. Aluminum was used as the collector in the building of the model, and the analysis was conducted with a very fine mesh size (minimum element size: 3.75 × 10−5 m) under air. As for the electrical conditions, a voltage of 8.5 kV was applied to the needle, and the collector was grounded. The red line in Electrostatic forces are the dominant forces applied to the jet, so the distribution and strength of the electric field greatly affect the trajectory of the jet. Analyzing the electric field provides a better understanding of the effects of jet path control modifications. The distribution and strength of the electric field in the plane of the electrospinning setup were analyzed using the finite elements method (FEM). FEM is a numerical technique which is utilized to discover solutions of incomplete differential condition equations (PDE). It gives an option approach to see high-electric field. Since, the practical dimensions and material properties can be used for the FEM calculation, it allows us to envision the electric field profile and to comprehend the impacts of material characteristics, dimension and physical geometry . The disguideD between 30 and 120 mm was placed between the needle and the collector. Its placement 1 mm from the collector ensured that the cylindrical dielectric guide would experience no interference due to movement of the collector. The positive voltage generated by the high-voltage power supply was applied to the needle, and the negative voltage was applied to the collector. The setup was grounded in order to prevent charge from accumulating in the nanofibers. The stage was driven by computer control software (PiMikroMove and LabVIEW), and the solution was electrospun on the moving collector. Computer control software was coded in hold, line, and square patterns and operated in a variety of shapes.For continuous patterning of nanofibers, a setup differing from a conventional electrospinning setup was required. A 1-axis stage  and rotating structure was placed at the bottom of the collector. For patterning, the collector was fixed on the 1-axis stage. A cylindrical dielectric guide with a r = 3.645 F/m) with an average diameter (guideD) of 30–120 mm and a height of 75 mm (guideH) located between a needle and the collector. The needle tip is placed in the center of the cylindrical dielectric guide. For the PU, PEO, and PAN solutions, voltages of 8.5, 10.0, and 9.7 kV were respectively applied to the needle tip through the high-voltage power supply. The negative electrode was connected to the collector, where nanofibers were deposited. The grounding of the collector prevented the accumulation of electric charge on the nanofibers electrospun on the collector. The stage was controlled at a rate of 1 mm/s, moving in a straight line; the movement range was 50 mm  decreased by approximately 80%, from 50 to 13 mm. In addition, the density of the electrospun nanofibers increased as guideD decreased. This result shares an explanation with the electric field simulation result: xE repels nanofibers increasingly strongly from the inner wall of the cylindrical dielectric guide as guideD decreases. We provide a more detailed quantitative analysis of this result in the next paragraph. guideD of 90 and 40 mm, respectively. The “analyze particles” tool of Image J was used to calculate the nanofiber density. In the case of a nanofiber with a guideD of 90 mm, pores comprised 27.9% of the total area, whereas in the case of a nanofiber with a guideD of 40 mm, pores comprised only 11.0%. Thus, the smaller the guideD, the higher the fiber density.To confirm the patterning effect of the dielectric guide, we fabricated PU nanofibers using various guide diameters. When a high voltage was applied to the needle tip, the jet erupted from the tip of the spinneret and moved to the collection plate. A cylindrical dielectric guide was placed between the needle tip and the collector. guideD for nanofibers electrospun from each of the three solutions as functions of guideD. Focused patterning fibers exhibited fiberD of 6–60 mm. At a guideD of 120 mm, a nanofiber with a fiberD of 60.1 mm was fabricated, i.e., approximately half of guideD. At a guideD of 30 mm, a nanofiber with a fiberD of 6 mm was fabricated, i.e., approximately 1/5 of guideD. fiberD decreased exponentially as guideD decreased, because the smaller the guideD, the stronger the xE was, as shown in guideD, the more focused the electric field became, causing a nonlinear decrease in fiberD. However, as guideD decreased, yE decreased, leading to the adverse effect of bead formation.guideD. The collector used in the experiment had an RGB value close to 0; as nanofibers accumulated, the RGB value approached the maximum value of 255. The relative density of nanofibers was estimated accordingly. The brightness intensity of nanofibers was measured at five points: at the center of the fiber fiberD and at four cardinal points located at a radius of one-quarter of the nanofiber diameter. Brightness intensity reached its minimum of 163.1 at a guideD of 120 mm; brightness intensity was 239.6 at a guideD of 30 mm, i.e., ~1.5 times the minimum value. This means that as guideD decreased, the nanofiber density increased. The standard deviation of brightness intensity among the five points measured was 1.5 at a guideD of 120 mm but 9.4 at a guideD of 30 mm, i.e., a factor of 6.3 difference. This means that the smaller the guideD, the greater the even density distribution of nanofiber.guideD, parabola-shaped mats of nanofibers were formed, with large differences in thickness between the center and edge of the nanofiber collection area. As guideD increased, nanofiber mats of more uniform thickness were formed. Thus, it was possible to selectively apply fibers to achieve either uniform application surfaces or thickly matted centers. guideD of 60 mm and a 1-axis stage. Nanofibers were electrospun into the cylindrical dielectric guide from a needle tip to which a voltage of 8 kV was applied, and the collector fixed on the stage moved at a rate of 1 mm/s. fiberD of 10 mm and was continuously electrospun along a stage length of 50 mm. This method has the advantageous capability to pattern nanofibers in a desired shape through computer control software.In this study, we verified that it is possible to pattern nanofibers by using a cylindrical dielectric guide. The cylindrical dielectric guide focused the electric field in the space in which electrospinning occurs, and the electrospun nanofibers were able to be patterned without an additional power source. Electric field analysis in COMSOL Multiphysics confirmed that nanofiber electrospinning inside the cylindrical dielectric guide experienced an electrical force applied in the direction from the inner wall of the cylindrical dielectric guide toward the central line. Experimental results compared well with simulation results. We also demonstrated the use of a cylindrical dielectric guide to produce continuous line and square patterns and to control nanofiber mat diameter in the range of 6–60 mm. Cylindrical dielectric guides for patterning electrospun nanofibers are expected to be utilized as a new, simpler diameter control method in sensor packaging, gas sensing, tissue scaffolding, and the composites industry.


To foresee global economic trends, one needs to understand the present startup companies that soon may become new market leaders. In this paper, we explore textual descriptions of more than 250 thousand startups in the Crunchbase database. We analyze the 2009–2019 period by using topic modeling. We propose a novel classification of startup companies free from expert bias that contains 38 topics and quantifies the weight of each of these topics for all the startups. Taking the year of establishment and geographical location of the startups into account, we measure which topics were increasing or decreasing their share over time, and which of them were predominantly present in Europe, North America, or other regions. We find that the share of startups focused on data analytics, social platforms, and financial transfers, and time management has risen, while an opposite trend is observed for mobile gaming, online news, and online social networks as well as legal and professional services. We also identify strong regional differences in topic distribution, suggesting certain concentration of the startups. For example, sustainable agriculture is presented stronger in South America and Africa, while pharmaceutics, in North America and Europe. Furthermore, we explore which pairs of topics tend to co-occur more often together, quantify how multisectoral the startups are, and which startup classes attract more investments. Finally, we compare our classification to the one existing in the Crunchbase database, demonstrating how we improve it. We propose a novel classification of more than 250 thousand startups registered in the Crunchbase database based on machine learning algorithms and free from expert bias. We find that the share of startups focused on data analytics, social platforms, and financial transfers, and time management has risen, while an opposite trend is observed for mobile gaming, online news, and online social networks as well as legal and professional services. We also identify strong regional differences in class distribution, suggesting, for example, sustainable agriculture being present stronger in South America and Africa, while pharmaceutics, in North America and Europe. Our classification can improve analysis of the Crunchbase data further promoting the popularity of the platform, while the trends identified will be useful for investors and policy makers. Last not least, this paper presents the first application of topic modeling to startup companies, providing thus a new direction for academic research. According to the Global Startup Ecosystem Report  where we measure the prevalence (weight) of each of these classes for each of the startups. Considering the year of establishment of the startups, we observe the positive time trends for data analytics and artificial intelligence, time management, social platforms, financial transfers, and cryptocurrency, and negative time trends for mobile gaming, online social networks, search engine optimization and marketing services, online news and blogs, and legal and professional services. Geographic concentration of startup activity shows that depending on the region where the startup has been established, the prevailing classes are different. For instance, North American startups tend to focus on the fields related to information technology, medicine, and finance, similar to European startups where we find higher prevalence of IT, pharmaceutics, transport, and logistics. The Asian market generates proportionally more startups in science and tech services, data analytics and AI, and legal and professional services.We also explore which pairs of topics tend to occur more often together illustrating the multidisciplinary nature of the startups which is slightly reducing over time. Furthermore, we find that some topics are strongly overlapping in the same startup descriptions. One of the brightest examples is an overlap of financial, transfer, and science and technology services, also known as fintech. Block et al.  discuss Furthermore, we study how the shares of startup classes change after we take the amount of investments they collected into account. Among others, we find that classes related to sustainable agriculture and pharmaceutics considerably increase their share. The fact that the class on sustainable agriculture is so important further supports earlier argument by Van Gelderen et al.  that “enFinally, we illustrate that the STM classification compared to the one from Crunchbase provides better distinguishable and more concrete classes of more even size enabling their better comparison and helps to avoid expert bias .Our findings are valuable for several reasons. First, as we show in the paper, the Crunchbase database is actively used in academic research, but also well beyond by other stakeholders. A more accurate and reliable classification will contribute to better analysis of the data further promoting the popularity of the platform. Second, startup founders, investors, and policy makers will find our results on the trends on startup classes across time and space useful for their decisions on where to locate companies, where to invest, or whom to invite for collaboration. Third, our work contains many empirical findings that we connect to the previous literature on entrepreneurship, sometimes supporting earlier results with new evidence, and sometimes questioning the conclusions drawn earlier. Last not least, this paper presents the first application of topic modeling to startup companies, providing thus a new direction for academic research.The remainder of this paper is organized as follows. Section Concerning data sources on startup companies, Crunchbase is becoming increasingly prevailing. More than 100 scientific contributions based on its data have been published already by 2017  focus on startups in the financial sector that use blockchain. They propose seven classes with trading platforms being the largest. Chakraborty et al.  review 7Traditionally, firms are attributed to certain classes in large databases either by human coders (administrators of the database) or, as is the case for the Crunchbase database, the startup founders themselves. This has the disadvantage that the classification is incomplete  and subject to expert bias, i.e., when a person classifies a company to too many or too few classes, “industry groups” as they are called in Crunchbase  approach to elicit topics from companies’ descriptions and classify them according to these topics. TM is a clustering approach for textual data aimed to identify meaningful topics in texts, analyze trends in topics, and (re)classify and annotate documents Blei, .Earlier topic modeling has been applied to different types of textual information in social sciences and in economics in particular. Many studies focus on scientific literature published either in specific peer-reviewed journals across many themes . Crunchbase has more than 55 million yearly active users, 4 thousand venture program members, and 1.3 billion API calls yearly.We use data from Crunchbase, the leading platform for professionals to discover innovative companies , based on industry categories of companies that have been added by the Crunchbase team, investors, the companies themselves, or any of the platform contributors.We collected data from the Crunchbase website in May 2020. The dataset represents information on 366,274 startups founded in the period between 2009 and 2019 around the world. The period before 2009 is not included in our analysis, since the rapid development and geographic expansion of startups and venture capital activity have started in 2009 together with accelerated globalization processes and technological development around the world , Europe (24.7%), and Asia (16.3%). Around 12.1% of all startups have not identified headquarter locations of their businesses.In this study, we are focusing our analysis on the companies’ full descriptions, which is available for 254,055 startups in our sample. Unlike short descriptions that consist of a single sentence and are no longer than 32 words only, full descriptions have a mean (median) length of 75 (59) words with a small fraction of texts well exceeding 100 words  topics in the startup textual descriptions, we use the topic modeling (TM) method. Essentially, it is a clustering algorithm organizing startup descriptions in different topics. Topic modeling “groups” words into topics based on the co-occurrence of words in the startup descriptions and then assesses the weight of each topic in each individual description. For example, if we see the word “blockchain” in a topic labeled “Financial services,” we can understand that it appears frequently in combination with other words of this topic, meaning that blockchain technology is mostly employed in the topic of financial instruments and transfers.Formally speaking, TM makes a Bayesian inference of words related to a given topic and the topics being discussed in a given company description, based on descriptions already observed. In particular, TM assumes that each word in the descriptions is generated through a two-step process: first, each startup description has its own distribution of topics, and a topic is randomly drawn from it; second, each topic has its own word distribution, and a word is randomly drawn from this distribution for the topic selected in the first step. Essentially, each description is a result of repeating these two steps many times. Therefore, the startup descriptions may have multiple topics present in them in different proportions. Topic modeling discovers the topic distribution for each description and the word distribution of each topic iteratively, by fitting this two-step procedure to the observed descriptions until it finds the best model that describes the underlying data. Compared to simple count of keywords, TM considers words not in isolation, but accounting for their context, which can influence the meaning of the words.An important advantage of structural topic modeling (STM) over classical TM is that it can take into account additional information about the texts. In our case, we consider the location of the startups , and words shorter than three letters were removed. Numbers and special characters were excluded. After that, all words have been lemmatized, i.e., converted to their vocabulary form . To reduce noise in the data, we have also removed words that appear in less than ten textual descriptions. The last step is typical for topic modeling as rare words are hard to classify in any topic due to lack of observations. After this procedure, we are left with 250,226 startup descriptions, 16,917 unique words, and 7,073,171 words occurring with repetition.k has to be determined first. The method then assigns to each topic a vector with k weights  for each startup description. It, essentially, shows the degree to which the description relates to each topic. Those weights sum up to 1. If the description belongs to one topic exclusively, all weights, except one, will be zeros. This, however, is rarely the case. We also estimate topic prevalence for the whole dataset to measure topic weights, which represent the share of each topic (class) among all startup companies , exclusivity (the degree to which popular words from different topics overlap), and semantic coherence (how frequently words from the same topic co-occur within each response). Figure k ranges from 3 to 50To apply STM, we use the associated R package developed by Roberts et al. . As it iTable AI). T12 and T37 cover knowledge-intensive business services (KIBS). While T12 is about startups with high use of scientific and technological knowledge (such as R&D services and engineering), i.e., t-KIBS, T37 focuses on traditional professional services—legal, accountancy, management, and marketing services (p-KIBS). T33 in turn covers services of improving the quality and quantity of website traffic to a website or a web page from search engines, i.e., search engine optimization (SEO). Based on these 38 topics, we offer a new way to non-exclusively classify the startups from the Crunchbase database.We have identified two topics that cluster information based on location and the year of establishment of the startups without any clear link to the business focus of the company. For instance, startup “Help on the way” is only described as “A program initiated and managed by Dor LeDor. It began operation on June 1, 2018, with its headquarters in Tel Aviv in Israel.” For that reason, we labeled these two topics as “trash topics” (T9 and T30).Note: The terms shown are those that are the most frequent as well as exclusive for each topic. Labels for each topic reflect the content of the terms and associated startup description.A total of 3.2% of all startups in our dataset have missing assignment to industry classes which could bias the results on industry trends in this field.Industry category selection is not precise in many cases. For instance, company “Interstellar Inc” is assigned to “Information Technology” Crunchbase industry group. But, based on its full description “Interstellar is a fast secure crypto wallet and decentralized exchange, powered by the Stellar network. Our Mission is to be a true Decentralized Wallet and Exchange that allows cryptocurrency users to securely and instantly trade and transact in a peer to peer manner,” one can conclude that it is more about financial operations, transfers, and blockchain.Some companies reflect redundant classes in their profiles. This is because Crunchbase recommends startup founders to choose 2–5 categories but introduces no strict limit. Moreover, it encourages to select more categories to increase visibility of the company in the database by adding less relevant classes. For instance, startup “Gooroo” with the description “On a mission to reimagine education, unlock every student’s potential, and promote lifelong learning for all. Gooroo is a monthly subscription learning platform that matches you to the perfect tutor, the best learning programs, and the most helpful educational guidance—all powered by AI. Gooroo assesses needs, pinpoints learning styles, and continuously tracks progress with data-driven insights from personalized learning experts. We’re also transparent and fair about our pricing, which is surprisingly new in education. Gooroo is where you go to get an honest education.” is assigned to an extensively large number of categories including Apps, AI, Data and Analytics, Education, Information Technology, Internet Services, Messaging and Telecommunications, Mobile, Science and Engineering, and Software, whereas Messaging and Telecommunications and Science and Engineering seem to be redundant in the list.Some companies, in contrast, reflect an insufficient number of classes in their profiles. For instance, the startup “Economiza Club” with the description “Economiza Club is a collaborative platform that allows consumers to have access to actual prices in the stores in your area” is assigned only to the category “Software,” whereas “Commerce and Shopping” or “Consumer goods” are potentially missing.Startup classifications are subject to expert bias from the side of startup founders who attribute their companies to the Crunchbase classification.Crunchbase industry classes are often too narrow or too wide ranging from less than 1% for Government and Military to almost 38% for Software of all startups with the mean being 6.4%, standard deviation 6.8%, and the coefficient of variation being 1.06. This makes the classes hardly comparable. Software, for example, could be related to (cyber)security, data analysis, games, and many other areas.Before comparing the STM and Crunchbase classifications, one can mention that the Crunchbase classification exhibits several flaws:Attributes all startups to one or more topic classes  classification we provide:Finally, we examine topic co-occurrence between the two startup classifications. In particular, we measure covariance between topic prevalences for the two classifications Fig. .Fig. 4ToIn comparison to the Crunchbase classification, STM approach produces topics that are better distinguishable and more concrete. For instance, the vast Crunchbase group “Software” in our classification is split into “Data analytics and AI,” “Time management,” “Software development,” and “Cybersecurity.” Similarly, the extensive “Health Care” Crunchbase group is converted in our classification to several classes: “Wellness,” “Healthcare services,” “Medical devices,” and “Pharmaceutics.” Reversely, several classes in the Crunchbase database around financial services  are united in the new classification under a single topic “Financial transfers & Cryptocurrency.” STM classification excludes too narrow Crunchbase topics, which are not widely represented in the database, such as, “Government and military” or “Navigation and Mapping.”As the classification we propose is non-exclusive, allowing the same textual descriptions to contain fragments belonging to multiple topics, we analyze how many topics on average each startup belongs to, how this number is changing over time among established startups, and which topics tend to be recombined more (less) often. To answer the first question and accounting for the fact that the topic prevalences never equal exactly zero, we introduce a threshold of 5% that a topic should surpass to be considered as present in the description.Furthermore, we plot the average number of topics per startup description over time Fig. . While tFinally, let us look at what topics tend to more frequently co-occur within the startup descriptions. To this end, we plot the covariance matrix of topic prevalences. Here we do not use any threshold on topic prevalence considering also values less than 5% Fig. .Fig. 7ToWe find some topics strongly overlapping in the same startup descriptions. These combinations show that certain areas tend to be combined by startups into new businesses. These are usually complementary services, which often emerged and became popular thanks to widespread diffusion of ICT technologies:SEO and online marketing services (T33) and legal and other professional services (T37). Many companies such a Tokyo-based BIJIN & Co offer services related to business sales, advertising, publicity, sales promotion, and marketing research. Since clients are typically interested not just in advertising but comprehensive market research and sales strategy, companies offering these services naturally expand their specialization.Investment management (T24), financial transfers and cryptocurrency (T18), and science and technology services (T12). As Saiedi et al.  recentlySoftware development (T22), data analytics and AI (T3), science and technology services (T12), cybersecurity (T19), and time management (T5). This cluster represents a combination of activities with similar (computer science) skills required: developing software, providing cybersecurity, and data analytics. One example of such startup is Tel Aviv–based Vulcan Cyber. As its description states it is “security company that helps enterprises quickly detect and fix vulnerabilities in their software stack and code by utilizing Cloud technologies, scanning tools and Big Data as well as complex decision systems.”Fitness (T8), social platforms (T17), online education (T7), and recruitment services (T15). As outlined in the description of Story2, the startup employs “online group coaching […] teaching unemployed, under-employed, and unhappily employed millennials to connect their skills, experience, and temperament with high-impact work.” Such companies provide online education and training for large masses of people making knowledge more accessible than it was ever before. Allen et al.  confirm Online social networks (T32), graphic design (T4), online news and blogs (T34), event management (T28), and video and animation (T29). This cluster demonstrates addition of content services to social networks in the form of news, discussion groups, or video sharing. One of the projects in this field is SQUID. It offers an app for curating, reading, sharing, and discussing news on different topics that can be specified by the user.Topics related to production: manufacturing (T27), transport and logistics (T14), supply and distribution (T16), energy (T10), and sustainable agriculture (T23). Startups in these classes offer services along the entire value chain. In line with Wang and Hsu , startupHealthcare services (T6), wellness (T1), medical devices (T20), and pharmaceutics (T35). This combination is largely due to emergence of many mobile health applications offering wellness management, self-diagnosis, medication reminder, and other services. These startups aim to provide low-cost, around-the-clock access to health information for their clients. As Kao and Liebovitz  point ouMobile gaming (T25), telecommunication devices and services (T21), and augmented and virtual reality (T26). With the growing power of computers and progress in AR/VR technologies, many startups—including LyteShot—aim to create unique gaming experiences users can have with their PCs and smartphones. This trend has been recently discussed by Liao  pointingTravel and tourism (T2) and parking (T38). Sharing economy provides examples of several models in transportation sector which transform the way people travel. One of the examples is provided by Jian et al.  who propE-commerce (T31) and clothes and accessories (T11). Existing literature emphasizes increasing role of technological development changing the shopping behavior of customers: usage of connected devices and online retail platforms . In other cases, combination of services creates a synergy effect, e.g., in terms of lower cost or higher quality . Finally, sometimes the combinations may be driven by the skills available in the startup, where same people with computer science skills can develop software, offer cybersecurity and data analytics services.k) as follows:Now we analyze the role of covariates in explaining the variation of topic prevalences among the startup descriptions. We provide results from regression analysis of topic proportions over the seven explanatory variables we used in constructing our topic model across time and location (six dummy variables were used for each of the regions in the Crunchbase classification).Figure The negative trends are observed in mobile gaming (T25), online social networks (T32), SEO and marketing services (T33), online news and blogs (T34), and legal and professional services (T37). As reported by CB Insights , the digGraphical representation of geographical concentration is provided on Fig. Startups located in North America tend to develop in the fields related to information technology , medicine , and finance (T24: investment management). These findings are supported by analytical results from available market reports in the field of technology entrepreneurship. For example, European AI Landscape Research affirms that the USA is the global market leader in AI with a 40% market share Berger, .Startup industries dominating in the European market are similar to North America: startup projects tend to be launched in high technology and IT sectors , pharmaceutics (T35), and transport and logistics (T14). Pharmaceutical market tends to grow in both North America and Europe, which is confirmed in the iQvia Institute report (2019): the largest market shares refer to the US market forecasted at 625–665 billion USD by 2023 (growth of 4–7%), and European market at $195–225 billion (growth of 1–4%).Analysis by the McKinsey Global Institute  infers tConsidering the African technology market, startups tend to arise in the fintech  and agritech sectors (T23: Sustainable agriculture) which is confirmed by media reports in Business Insider Africa Oluwole, , PartechFinally, let us consider the distribution of venture capital investments among startup companies in our sample. For this, we collect data on the funding raised by the companies in our sample. It is proxied by the indicator of total funding amount that represents cumulative investment raised by the considered startup across all funding rounds . Investment raised by the startups in our sample is 1.4 trillion USD in total. Unfortunately, this data has a lot of missing observations: out of 250,252 companies in our sample, only 71,910 startups have information on the total funding amount. One could explain so many missings by the nascent nature of firms registered in Crunchbase: many of these companies are registered in Crunchbase before they receive funding . Nevertheless, it is worthwhile to see how shares of our startup classes change, if we weight them by the amount of funding they received.Figure As one can see, after taking the amount of investments received into account, we find a few classes such as Sustainable agriculture (T23), Science and technology services (T12), and Pharmaceutics (T36) considerably increasing their share in the overall classification, while classes like Data analytics and AI (T3), Time management (T5), Cybersecurity (T19), and Event management (T28) shrinking. One could explain this by higher requirements startups in the former classes should satisfy  compared to startups in the latter classes. This can cause lower competition at the stage the startups are founded in the former classes, stimulate investors to provide venture capital, and require larger investment to proceed with scaling up the production. Another reason is the unprecedented penetration of new technologies  in agricultural and health sectors . In comparison, our approach classifies all startups without exception, and produces classes that are better distinguishable and more concrete. The resulting topic-based classification exhibits more even distribution of class shares.Statistical analysis of topic proportions across time and regions allows us to report significant trends and regional differences among the identified startup classes. Upward trends are detected in the fields of data analytics and AI, time management, social platforms, and financial transfers and cryptocurrency. Downward trends are found in mobile gaming, online social networks, SEO and marketing services, online news and blogs, and legal and professional services. We also find that startups located in North America tend to appear in the fields related to information technology , medicine , and finance . European startup projects tend to be launched in high technology and IT-sectors , pharmaceutics, and transport and logistics. Asia hosts a large fraction of startup companies in the science and tech services, data analytics, and legal and professional services.Furthermore, we find groups of classes which startup companies tend to combine to provide novel products and services. Some of these combinations are demand-driven ; others create a synergy effect in terms of lower cost or higher quality . This information can be instructive for existing entrepreneurs on how they could diversify their businesses. Also, it can be useful to determine actors competing for the same customers since industry classification is very imprecise for this purpose  and as a result, lower competition for investments.Our findings can be of interest to startup founders, investors, and policy makers, as they elicit the hidden topic-based classification of startup companies around the world and dominant trends for these companies. Founders and startup executives could consider these results as useful insights for decision-making, where to establish the headquarters or additional offices to maximize knowledge spillovers and become profitable, and which industry is currently on the rise and is potentially more attractive for funding. Venture capital investors could benefit from the information on the geographical distribution of companies to anticipate future market leaders and unicorn companies. Policy makers could rely on our findings for development of local policies to support startup ecosystems in targeted industries. The fast evolving or, contrary, lagging industries require specific policy regulations based on country development and current technology trends. For example, existing literature provides evidence that fintech, one of the fastest growing industries nowadays, started to compete with traditional financial institutions and increased the complexity of competition policy in the USA Van Loo, .Our study has many directions for future research. As we described in our analysis, the information on the amount of investments received has too many missing observations in the Crunchbase database. To address this question, one could combine Crunchbase with other established sources collecting information on startup activity, i.e., PitchBook, LinkedIn, AngelList, and Startup Ranking. This could provide further insights on financial performance of startups and differences between startup classes. A similar comment applies to the operating status of startups in the Crunchbase database. While this data is available, checking the status of a small sample of companies by searching for their website showed that many “active” companies are not active anymore. Hence, before using this information, one should ideally double check the status of the companies in the other sources. Furthermore, one could collect data that has been added to Crunchbase over the last year and measure how shares of the described topics have changes, or whether new topics (for example related to COVID) have appeared.As for limitations, our study is dependent on availability and the quality of the full startup descriptions provided by their founders. These descriptions are vital for completeness and accuracy of our textual analysis. If no full description is provided, we cannot cover that startup in our analysis, which biases our sample. The more detailed and precise are the descriptions, the better are our results. Therefore, a way to improve the results is to collect further information describing each startup from external online sources including those listed above. Another limitation is the country coverage in Crunchbase. While North America, Europe, and Asia are well-presented in the database, this platform is less known in South America and Africa. Therefore, our findings on these regions must be taken with a grain of salt.


Dear Dr. Correia Filho,(Editor-in-chief of JBSTM)It was with great interest that I read the article published by Menezes et al. on pelvic inflammatory disease, in a recent publication of the JBSTM), is a challenging condition because it has mild or non-specific signs and symptomsThe Gainesville Stage, mentioned in their article, is an outdated concept suggested by Dr. Gilles Monif in 1982, which has little meaning nowadays, since its purpose was to guide treatment, either as outpatient or inpatient, to preserve fallopian tube structureThe treatment topic is another aspect that requires attention. Preconized treatments do not reflect up-to-date information. Our last Cochrane review on this topic was published in 2020In addition, the use of gentamicin divided into two or three doses per day has been replaced by one dose per day to reduce renal injury, a fact that has been consolidated in the literature. Extended-interval aminoglycoside has efficacy comparable with traditional intermittent administration; however, it offers three potential advantages: 1) the possibility of decreased nephrotoxicity , 2) ease of administration and serum concentration monitoring, and 3) reductions in administration and monitoring-related costs. Extended-interval dosing of aminoglycosides takes advantage of two pharmacodynamic properties: the post-antibiotic effect and concentration-dependent killingFinally, the recommendation for removing an intrauterine device (IUD) only after two doses of antibiotics has no scientific basis. Few trials were performed where the IUD was removed before treatment, and it was found that early removal of the IUD did not influence the course of the disease, although one trial had a longer duration of treatmentI hope this letter will help clinicians and policy makers on this topic.


Educating the public about antimicrobial resistance (AMR) is considered a key part of an optimal public health response. In both media depictions and policy discourses around health risks, how a problem is framed underpins public awareness and understanding, while also guiding opinions on what actions can and should be taken. Using a mixed methods approach we analyse newspaper content in Australia and the United Kingdom (UK) from 2011 to 2020 to track how causes, consequences and solutions to AMR are represented in countries with different policy approaches. Analyses demonstrate greater variability in the frames used in UK newspapers reflecting large hospital and community outbreaks and a sustained period of policy reform mid-decade. Newspapers in Australia focus more on AMR causes and consequences, highlighting the importance of scientific discovery, whereas UK coverage has greater discussion of the social and economic drivers of AMR and their associated solutions. Variations in the trends of different frames around AMR in UK newspapers indicate greater levels of public deliberation and debate around immediate and actionable solutions; whereas AMR has not had the same health and political impacts in Australia resulting in a media framing that potentially encourages greater public complacency about the issue. Antimicrobial resistance (AMR) is an existential threat to modern medicine and broader society ,2,3. ThePolicy approaches to the problem of AMR in the United Kingdom (UK) and Australia have overlapping histories and trajectories —with theIn concrete terms, the need to address the drivers of AMR is frequently operationalised as requiring behaviour change from clinicians, patients, and, in the case of agricultural systems, livestock producers and consumers ,32. TherPrevious studies of how news media represent AMR in Australia and the UK indicate that most articles are factually accurate. However, for editorial reasons, or to draw their audience’s attention to a particular feature of a news item, information relevant to AMR in these reports is presented inconsistently and with omissions ,41. One To our knowledge, the influence of government policies on news-media coverage of AMR has not been analysed. The UK has positioned itself as a global leader in taking action against AMR, whereas the approach in Australia has been more consensus driven and cautious ,21. In tWhich types of interactions between microbes, antibiotics and people are highlighted in print media coverage?Which species of antibiotic resistant microbes get media attention?What reasons are given for members of the general public to care about AMR?Who or what are portrayed as being at risk of AMR?Who or what is portrayed as being responsible for causing AMR?Who or what is portrayed as being responsible for addressing AMR?What solutions to the problem of AMR are described?Which stakeholders and expert groups get to speak and share their perspective on AMR in print media coverage?Drawing on the methodologies used in recent analysis of representations of AMR in North American newspapers , we usedFactiva and Proquest Central were searched independently for each setting using the following terms:  or (super bug*) or (superbug*) or (Flesheating bacteria) or (flesh eating bacteria) or (flesh-eating bacteria) or (golden staph*) or (MRSA) for the period 1 January 2010 through 31 December 2020. After limiting content to English language and the regions of Australia or the UK, once the Factiva and Proquest samples for each setting were combined, a total of 2041 items were identified in Australian newspapers and 3698 items in the UK  to test and confirm the inter-observer reliability of the coding framework and to extend the preliminary thematic analysis. Once the face validity of the coding frame was established the remaining articles in each sample were coded as independent sets by TH, C.D., JH, AP and CT  and 14 regional papers from all eight States and Territories. In what follows we describe and compare the key trends in how newspapers in Australia the UK have framed the problem of AMR between 2011 and 2020. The results are presented across seven dimensions or framing categories that capture the range of ways in which media reporting can convey information to members of the public about the significance, causes, consequences and potential solutions to AMR . These kOf the 594 articles in the Australian sample 571 (96%) included a frame that highlighted the role or importance of microbes; compared to 936 (91%) of the 1020 articles in the UK . Across Against this background it was notable that antibiotics are mentioned in 327 (55%) of the 594 articles in the Australian sample and 430 (42%) of the 1020 in the UK sample . In AustClostridium difficile (C. diff) and Escherichia coli (E. coli) in UK media sample was a consequence of the epidemiological scale of and political interest in specific impacts of outbreaks of these microbes during the sample period. As noted above, between 2000 and 2015, C. diff outbreaks in hospitals in the UK became a long-running national news story and political crises. Blame was ascribed to local failures of hospital trusts and staff to implement government legislated infection control codes of practice, and perceptions of falling levels of funding having impacts on standards of care in the National Health Service [E. coli mid-decade as foodborne outbreaks and concerns about antibiotic use in agriculture became more prominent in both public discourse and scientific discussions [C. diff and E. coli were at a much smaller scale or had been rapidly controlled, the focus of media reporting has remained on the communicability of the disease and its consequences for individuals—with less focus on and need for causal explanations  mentioned at least one type of microbe compared to 820 (80%) of the 1020 articles in the UK. The list of the nine most mentioned microbes in Australia and the UK are identical. The microbes on these lists were all mentioned more than 10 times across the 10 years in both Australian and UK newspapers. Unsurprisingly, given the prominence of the superbug MRSA/Golden staph in the international recognition of AMR as a critical public health problem , coverag Service ,59. UK mcussions ,61. Mentanations .At least one reason why readers should care about the problem of AMR is provided in 89% of articles in the Australian newspaper sample and 86% in the UK . Early iA group are identified as being at particular risk of AMR in 85% of the articles in the Australia sample, compared to 88% of articles in the UK . In bothThe most frequently described AMR-related risk within Australian and UK newspaper coverage is the loss of individual health and wellbeing, followed by the loss in effectiveness of the practice of modern medicine . Of thesAfter a peak mid-decade, representations of AMR endangering the practice of high-technology modern medicine in Australian media are declining. In contrast the trend in the UK is a steady increase in the prominence of representations of the threat posed to the practice of modern medicine. Both media samples have an overwhelming focus on the risks to the health of individuals, but greater prominence is afforded in UK newspapers to representations of the impacts of AMR on the health of populations. The difference between the prominence of population health concerns in the two settings is likely to be a consequence of the frequent use of future mortality figures and projected death rates in newspaper reporting of the UK government’s international advocacy of AMR as a critical global health issue. Despite the widespread use of antimicrobials in agricultural industries, the impacts of AMR on animal health are rarely reported in the media in either study setting. Discussion of the risks to human health from antibiotic use in agriculture have been present throughout the period sampled in the UK. Despite the importance of One Health approaches in policy making in both jurisdictions , this haIn Australian media coverage 520 of the 594 (87%) articles identify at least one key cause for the problem of AMR, which is a higher proportion than the 810 of 1020 (79%) of articles in the UK . MicrobiC. diff outbreaks at the time. Representations of the role of public in mitigating the drivers of AMR are infrequent in both settings despite being a stated priority in all Australian [In contrast to the UK, in newspaper coverage in Australia antibiotic overuse and microbial evolution are not consistently portrayed as being causally-connected. Discussion of social and economic drivers was much more prominent in the UK sample in the later part of the decade—including the influence of patient pressure on GP decision-making. Concomitantly, representations of the role of poor hygiene and iatrogenesis as being drivers of AMR have declined, after being previously identified foci for concerted action by the UK government due to the number and scale of community acquired MRSA cases and hospital based stralian ,30, UK [stralian ,29 and Wstralian  AMR actistralian ,64  of Australian articles compared to 820 (80%) of those from the UK . In both4% of Aus hygiene .In the Australian newspaper sample, 437 of the articles (73%) point to a group as being responsible for fixing the problem of AMR. In the UK 802 of articles (80%) do the same . Across Our findings indicate there is much greater variability in the frames used in newspapers in the UK than Australia, with the former showing many more clear inflections in the trends of several of the framing categories—especially in the period between 2014 and 2016. Acknowledging it can be difficult to disentangle cause and effect regarding events, policy reforms and media interest, the increasing variability and volatility in the framing of AMR in UK newspapers corresponds to a period of rapid reform in how AMR policy in this jurisdiction was determined and implemented. From early in the decade the approach taken to AMR in the UK became much more regulatory, placing a greater emphasis on developing and using an evidence base to direct related policies and practices . In contThe differences in newspaper framing of AMR in Australia and the UK points to the likely importance of events and material and local impacts to contextualise and provide impetus for practical measures to address otherwise abstract and existential risks . There iIn both media depictions and policy discourses around health risks, how a problem is framed underpins public awareness and understanding, while also guiding and influencing the quality of the strategies and actions taken to address it ,74,75. GThe increasing prominence of scientific frames and medical scientists in AMR related public discourses in both study settings may also be a product of the increasing institutional impetus for them to communicate the importance of their work to the general public , and, moThis study has several limitations including that our analysis only included materials from Australian and UK newspapers rather than broadcasts from national and local radio, television and social media. However evidence suggests that print news reporting continues to serve as the original source for much of what is reported later in other media, influencing a wider audience than just newspaper readers ,79. The AMR has not been a significant enough issue in Australia to drive the same levels of political, public and stakeholder engagement as seen in the UK. Widespread experience of the real world consequences of AMR have prompted government, health authority and stakeholder actions, which is reflected in and amplified by news media coverage. These media representations do have a possible role in changing how the public respond to specific issues such as AMR, especially where these are linked to structural support and changes in procedures ,66. Beca


Tizanidine, an alpha-adrenergic substance with antinociceptive and antihypertensive effects, is extensively metabolized via cytochrome P450 (CYP) 1A2. Therefore, coadministration with potent CYP1A2 inhibitors, such as ciprofloxacin, is contraindicated. However, both drugs are broadly utilized in various countries. Their concomitant use bears an inherent high risk for clinically significant symptoms, especially in multimorbid patients experiencing polypharmacy. This study aims to investigate the impact of coadministration of tizanidine and ciprofloxacin using real-world pharmacovigilance data and to raise awareness of this potentially underestimated safety issue.We conducted a retrospective study including Individual Case Safety Reports (ICSR) registered until March 1, 2017, in the World Health Organization (WHO) global database. Demographic data, drug administration information, the course of the adverse drug reaction (ADR), its severity, and outcomes were analyzed for cases reporting ciprofloxacin comedication.n = 59, 64.8%) with a median age of 54 years (range 13–85 years). The countries contributing most reports were the USA  and Switzerland . ADRs reported most often affected the nervous system and the cardiac function, especially with large tizanidine doses or drugs with CNS and cardiovascular depressant effects. In two cases, a fatal outcome was reported.In 91 (2.0%) of the identified 4192 worldwide ICSR on tizanidine, coadministration of ciprofloxacin was reported. Most of the patients were female (Despite the existing formal contraindication, the concomitant use of tizanidine and ciprofloxacin can be observed in real-world clinical practice. Reactions mainly affected the central nervous and the cardiovascular system resulting in potentially severe adverse effects. The concomitant use of tizanidine and ciprofloxacin should absolutely be avoided. Tizanidine is an alpha-adrenergic substance reducing spasticity , 2. It aTizanidine has a low oral bioavailability with a high first-pass effect, considerable interindividual variability, and a narrow therapeutic range , 4. It iIt was shown that ciprofloxacin increases the peak plasma concentration (Cmax) and the area under the plasma concentration-time curve (AUC) of tizanidine 6–24-fold and 4–21-fold, respectively, resulting in clinical toxicity of tizanidine , 6. IncrBased on clinical observations, we hypothesized that despite of the formal contraindication, concomitant administration could occur, due to overlapping therapeutic indication. However, no international analysis of real-world data on concomitant use and on severity of this drug-drug interaction is available. Therefore, we conducted a retrospective analysis of the World Health Organization (WHO) global database for Individual Case Safety Reports (ICSR) using a case-non-case approach to analyze this potentially underestimated drug safety issue.TM. Since 1968, VigiBaseTM, the WHO global database of ICSR, collects, processes, and homogenizes worldwide ICSR [TM includes mainly post-authorization unsolicited or spontaneous reports [TM include details on the demographic characteristics of the patient , reporting country, reporter qualification , medications , and the reported suspected drug reactions. The suspected ADRs are coded using the Medical Dictionary for Regulatory Activities , and all medications are coded using WHODrug. The MedDRA® terminology follows a hierarchical structure comprising terms in five ascending orders with higher orders including more generic terms. Drug-drug interactions are included in the MedDRA® terminology as low level (LLT) and preferred terms (PT) [TM.In this retrospective descriptive study, we searched for ICSR on suspected ADR in the WHO global database VigiBaseide ICSR , 11. Therms (PT) . In caseTM until March 1, 2017. On March 1, 2017, it contained more than 14.6 million ICSR from 126 participating countries. We received the coded data elements of all international tizanidine-associated ICSR from the WHO global database VigiBaseTM in the form of a Microsoft Excel file. ICSR of interest contained tizanidine (ATC: M03BX02) as a suspected or interacting drug. Combination therapy ICSR were extracted. Number of events was defined as number of unique ICSR, as identified by the report ID. All reported terms and corresponding MedDRA terms were included in the analysis as multiple ADRs can be reported in a single ICSR.We included all ICSR of tizanidine reported to VigiBaseIn a first step, we screened the data for ciprofloxacin as comedication. That data was then analyzed for demographic data (age at ADR onset and gender), completeness, reported ADRs, clinical outcome, concomitant drugs, seriousness, and seriousness criteria. Two subgroups were categorized by the daily dose of tizanidine with less than 12 mg and 12 mg or more per day, respectively. Other reported comedications were analyzed for CYP interactions and classified based on their effects as central nervous system or cardiovascular depressants according to the labelling in the respective summary of product characteristics (SmPC).For descriptive analyses, we used Microsoft Office Excel (2010), IBM SPSS Statistics (version 23), and STATA  for Windows software. Depending on the distribution of the values, we used the arithmetic mean with standard deviations or the median with ranges.Worldwide, 4192 ICSR on tizanidine were identified in the WHO database in the study period. After selection of cases reporting concomitant tizanidine and ciprofloxacin administration, 91 (2.2%) international ICSR reporting in total 444 ADRs remained for analysis  and Switzerland . The demographic characteristics of the investigated population are summarized in Table n = 22, 24.2%).In 59 cases (64.8%), the patients were female, and 28 patients (30.8%) were male (in 4 ICSR (4.4%), gender was not reported). Age was reported for 65 patients resulting in a median age of 54 years (range 13–85 years). The countries contributing most reports were the USA . Of those, most ICSR  were qualified as serious. The most frequent reason for severity was “caused/prolonged hospitalization” . Two ICSR reported a fatal outcome.A severity assessment was performed for most cases , “drug interaction” , “asthenia” , and “urinary tract infection” . All coded MedDRA terms with a frequency of ≥ 4 are displayed in Table Among the 444 reported ADRs, the most frequently observed reactions were “hypotension” n = 5, 5.6%, n = 18, 19.8%) followed by oxycodone  and fampridine and gabapentin . Among the most frequently reported comedications, no other CYP1A2 inhibitor besides ciprofloxacin was identified. In 25 ICSR (27.5%), patients received only tizanidine and ciprofloxacin without further comedication. In 66 ICSR (72.5%), further drugs were coded. In 66 ICSR (72.5%), tizanidine was reported as suspected and in 25 ICSR (27.5%) as interacting drug. Ciprofloxacin was reported 48 times (52.7%) as suspected, 21 times (23.1%) as interacting, and 22 times (24.2%) as concomitant drug. The most frequently used nine coadministered drugs  were furthermore classified as antihypertensive and/or CNS depressive acting agents. Except for fampridine, all these nine drugs could cause antihypertensive ADRs, and except for paracetamol, all of them could cause CNS depressive ADRs.The comedication was found to be very heterogenic. Among the 91 ICSR, 265 different drugs were used as comedications. The median number of administered drugs was five Table . The mosThe daily dose of tizanidine in milligram was reported in 46 ICSR (50.5%). The median administered dose was 8 mg (range 1–48 mg). Regarding the dose-dependent character of the interaction, two subgroups with available dosage information in milligram were analyzed: 26 patients received a tizanidine dose of less than 12 mg/day and 20 patients received 12 mg or more of tizanidine daily. Patient characteristics were similar in both groups . Proportions of serious ADRs were similar in both groups (55.0% vs 53.8%). In addition, the number of comedications was comparable in the subgroups (5 vs 6 concomitant drugs).The daily dose of ciprofloxacin was reported in 37 ICSR (40.7%). The median daily dose patients were receiving was 1000 mg (500–1500 mg).Of the identified 91 ICSR, 68 (74.7%) reported both tizanidine and ciprofloxacin as either suspected or interacting drugs. In only 23 ICSR (20.9%), the interaction between tizanidine and ciprofloxacin was coded as an ADR. When comparing the 68 ICSR to all 91 ICSR, the proportion of reporters’ qualification was found to be similar.For two patients receiving the combination ciprofloxacin and tizanidine, a fatal outcome was reported. The first was an elderly male patient of 71 years who received tizanidine in a dosage regimen of 24 mg together with ciprofloxacin for treatment of a urinary infection. He developed muscle weakness and a decreased muscle tone, hypotension, and organ failure that eventually led to his death. The only concomitant coded drug was clonazepam—exhibiting CNS depressive effects. The second patient was a 49-year-old male with a medical history of gastrointestinal carcinoma, HIV disease, chronic viral hepatitis, and alcohol and tobacco use. He had received 2 mg tizanidine daily together with 500 mg ciprofloxacin twice daily for the treatment of a bacterial pneumonia. The number of concomitant drugs was extensive and contained morphine, zolpidem, oxazepam, clindamycin, prednisone, thiamine, levothyroxine, gabapentin, sodium picosulfate, acetylcysteine, esomeprazole, and a vitamin B supplement. Notably, morphine, zolpidem, oxazepam, and tizanidine were reported as interacting, whereas ciprofloxacin was coded as concomitant drug. The ADR that led to his death was respiratory distress.The concomitant use of tizanidine with CYP1A2 inhibitors, such as ciprofloxacin, is formally contraindicated . NeverthAnother pharmacokinetic aspect is not related to drug dosage but to patients’ physiologic properties. Tizanidine undergoes an extensive hepatic metabolism. According to the product information, the therapy of patients with decreased liver function therefore is contraindicated. The results of a retrospective survey and case report  and a noDrugs with similar ADR profiles administered together may cause synergistically amplified reactions and higher numbers of concomitant drugs may increase the risk to develop toxicity. Common ADRs of tizanidine include antihypertensive and CNS depressive effects , 9, 20. Most reports analyzed in the current study originated from the USA and Switzerland. Different from the other reporting countries, only the US and Swiss reports included reports from consumers and non-healthcare professionals. Compared with other countries, the USA has the pharmacovigilance system in which patients are the most involved already since 1969 . This miNot only because of the pharmacodynamic aspects of the possible ADRs but also due to the relatively narrow therapeutic range of tizanidine , 20, theAs previously shown, the concomitant use of ciprofloxacin with tizanidine is frequently observed in the outpatient setting . ConsideTM, the WHO’s pharmacovigilance database, collects spontaneous reports on suspected ADRs of authorized drugs from countries all around the world with the aim to increase patient’s safety. Data are collected as ICSR representing mainly post-authorization unsolicited reports [Our study’s most important strength is the comprehensive dataset we used for the analysis. VigiBase reports . TherefoTM contain neither narrative nor clinical or laboratory data. Therefore, this data do not allow for a formal assessment of causal drug-ADR relationship, but reports are based on the suspicion of the reporter. Since ICSR can only be extracted from the database, a verification of coding or modifications, such as recoding, is not possible. Completeness of documentation is highly dependent on individual reporter. Due to this, the ICSR are heterogeneous regarding completeness. Due to potentially missing timing and duration of use of comedication, simultaneous administration could be misclassified. Given the small number of the coded term “drug interaction,” it can be hypothesized that the contraindicated combination could have remained unrecognized as such. On the one hand, due to missing data on exact timing of administration, it cannot be ruled out that the drug use did not result in a major CYP interaction based on the timing. This might lead to an overestimation of the interaction problem of tizanidine and ciprofloxacin. On the other hand, an unrecognized interaction might lead to reporting only of the suspected drug, without reporting of comedication, leading to an underestimation of the issue. Overall, incidence rates cannot be provided solely based on pharmacovigilance data. Further observational studies are needed to clarify the prevalence of this contraindicated drug combination and potential country-specific prescribing. This might lead to a bias, which cannot be accounted for due to missing data. Another, commonly described, limitation in pharmacovigilance is the underreporting of ADRs in a spontaneous reporting system, which amounts to up to 94% [However, the main limitation of this study is the data characteristics of the retrieved ICSR. The ICSR comprised in the register database VigiBasep to 94% . FrequenConditions usually treated with tizanidine can be associated with accompanying symptoms resulting in ciprofloxacin treatment. Results from this analysis of real-world pharmacovigilance data show that despite the existing formal contraindication, both drugs are concomitantly prescribed in clinical practice.Most reported adverse effects, such as hypotension, somnolence, fatigue, and asthenia, are related to the central nervous and the cardiovascular system. Concomitant use of cardio- or CNS-depressant drugs might aggravate these effects. The combination of ciprofloxacin with tizanidine should be absolutely avoided.


Antimicrobial resistance (AMR) is a major global public health threat, which has been largely driven by the excessive use of antimicrobials. Control measures are urgently needed to slow the trajectory of AMR but are hampered by an incomplete understanding of the interplay between pathogens, AMR encoding genes, and mobile genetic elements at a microbial level. These factors, combined with the human, animal, and environmental interactions that underlie AMR dissemination at a population level, make for a highly complex landscape. Whole-genome sequencing (WGS) and, more recently, metagenomic analyses have greatly enhanced our understanding of these processes, and these approaches are informing mitigation strategies for how we better understand and control AMR. This review explores how WGS techniques have advanced global, national, and local AMR surveillance, and how this improved understanding is being applied to inform solutions, such as novel diagnostic methods that allow antimicrobial use to be optimised and vaccination strategies for better controlling AMR. We highlight some future opportunities for AMR control informed by genomic sequencing, along with the remaining challenges that must be overcome to fully realise the potential of WGS approaches for international AMR control. Antimicrobial resistance (AMR) is one of the greatest current threats in international public health . The rapThe first wave of antimicrobials was derived from naturally occurring compounds; therefore, the emergence of AMR is largely a natural process, and AMR genes have been detected in samples originating millions of years before the widespread use of antimicrobials –9. HowevThe detection of AMR has been traditionally reliant on culture-based antimicrobial susceptibility testing (AST), which remains the mainstay of clinical microbiology and patient management. Whilst phenotyping provides direct visual evidence of how a bacterium will interact with an antimicrobial, it generally provides little or no data regarding the resistance mechanisms, with disparate genetic clones often demonstrating identical resistance profiles . GeneticThe power of WGS is being increasingly employed to address the public health challenge of AMR, supporting surveillance, outbreak investigation, and contributing to improved diagnostics and therapeutics as highlighted in Table Surveillance is the cornerstone of public health efforts in controlling AMR. AMR surveillance has traditionally relied on phenotypic AST, but different testing methods, variation in interpretation, the extent to which thresholds are clinically validated, and changes in interpretive guidelines limit standardisation. WGS data overcomes many of these limitations, providing detailed insights that can greatly augment the value of AMR surveillance. Such data can inform an understanding of AMR evolution and spread, inform control strategies, facilitate the detection of new and emerging threats, and support new diagnostic and therapeutic approaches , 28–30.Staphylococcus aureus (MRSA), penicillin-resistant Streptococcus pneumoniae, vancomycin-resistant enterococci, and fluroquinolone-resistant Clostridium difficile [Underlying the expansion of AMR is a dynamic and complex interaction between microbes, AMR encoding genes, and mobile genetic elements that act as vehicles for AMR via horizontal gene transfer (HGT) , 11, 31.ifficile –37. The ifficile , providiE. coli ST131, which has rapidly spread to become a frequent cause of healthcare and community-acquired infection since it was first described in 2008 [E. coli frequently exhibit cephalosporin (most commonly due to a CTX-M-15 encoding gene) and fluroquinolone resistance [E. coli was initially speculated to have arisen from frequent and independent acquisitions of mobile genetic elements [E. coli was associated with a sustained clonal expansion. However, it was not until a more comprehensive global study that the true diversity of AMR in ST131 was revealed, which identified the chromosomal integrations of various resistance genes, the persistence and evolution of mobile elements within sub-lineages, and the sporadic acquisition of several different resistance elements [E. coli in long-term care facilities focusing on contact precautions have been largely ineffective [Increased accessibility to WGS has significantly enhanced our understanding of the global evolution and spread of AMR. High-throughput WGS methods have facilitated the sequencing of large, geographically representative collections of isolates, overcoming many of the biases associated with historic, small-scale studies that were often skewed by the domination of local clonal dissemination. This paradigm is illustrated by a modified understanding of AMR in  in 2008 . ST131 Esistance , 39. Theelements . Howeverelements . As wellelements . These selements –47. Infeffective , 48, leaffective . The tarffective , 47.Enterobacterales (CRE), carbapenem-resistant Acinetobacter baumannii and Pseudomonas aeruginosa, [Klebsiella pneumoniae, which can cause a wide range of infections, including pneumonia, bacteraemia, and urinary tract infections [Enterobacterales [. Carbapenem-resistant K. pneumoniae (CR-Kp) is considered to be the fastest-growing AMR threat in Europe [Global AMR surveillance has been bolstered by programmes such as the Global Antimicrobial Resistance and Use Surveillance System (GLASS) from the World Health Organization (WHO) , and theuginosa, , 51–55. fections , and havcterales –59. Carbn Europe  and has n Europe , 62. Sysn Europe . As welln Europe , 63, 64.n Europe  and HGT n Europe , 63, 64 blaNDM-1 and blaOXA-48 CR-Kp in Germany [blaNDM-1- and blaOXA-48-positive CR-Kp. It is anticipated that the planned incorporation of WGS into the European Antimicrobial Resistance Genes Surveillance Network (EURGen-Net) will facilitate the routine detection of such transmission events and inform targeted control measures [Embedding WGS into prospective, cross-national surveillance will further strengthen AMR surveillance, as highlighted by the recent outbreak genomic investigation of  Germany . In the measures .Klebsiella, with mobile genetic element (MGE) acquisition often leading to MDR [The inclusion of WGS can similarly strengthen national AMR surveillance. The introduction of WGS into AMR surveillance in the Philippines, for example, showed that HGT was key for driving carbapenem resistance in g to MDR . WGS datg to MDR . By contg to MDR .National surveillance can directly guide the need for AMR control measures that can be driven, resourced, and monitored centrally, as was demonstrated in Israel following the introduction of CR-Kp ST258 in 2005. The initial measures were unsuccessful at controlling the spread of CR-Kp ST258, and infections rapidly rose to a peak incidence of 41.9 per 100,000 patient-days . NationaThe routine inclusion of WGS into national surveillance can also be used to evaluate the risk from emerging AMR threats, such as transferable colistin resistance . PhenotyHealthcare facilities house patients with serious and complex infections in proximity to patients with compromised immunity, in settings that are characterised by high antimicrobial use , 72. In S. aureus HCAI have similarly shown the improved granularity of WGS compared to other approaches, with more conventional S. aureus-specific staphylococcal protein A (spa) typing failing to detect transmission events, as well as the false attribution of unrelated isolates as HCAIs [A key strength of WGS is that it permits the degree of relatedness between selected isolates to be determined and, in the context of HCAIs, allows the reconstruction of nosocomial outbreaks , 39. Oneas HCAIs .Acinetobacter baumannii in a UK hospital treating civilian patients and injured military personnel returning from the Middle East [A. baumannii was described, with the onward transmission of one specific clone leading to the infection of a civilian. Importantly, an additional civilian infection was not associated with the military-related infection, highlighting that other distantly related A. baumannii were also circulating. Similarly, the utility of WGS in determining the chains of transmission has also been demonstrated for MRSA and CR-Kp [By exploiting a ‘molecular clock’ (the mutation rate in the genome of a specific organism), WGS can also infer directionality in outbreaks , 19, 27.dle East . Pre-admnd CR-Kp .By generating detailed insights into HCAI, WGS surveillance can help identify the main sources of AMR, providing an opportunity for targeted infection control measures that can reduce the risk of further transmission events . A largeK. pneumoniae in ICUs [Prospectively applying WGS for local HCAI control has also been suggested for early outbreak detection, and thereby early intervention and source control, with a substantial cost saving . This ap in ICUs . WGS of  in ICUs .WGS is also contributing to community public health efforts, such as contact tracing and the detection of secondary cases of TB. Contact tracing is difficult as there is often a long interval between the initial infection and the diagnosis. Several countries, such as the UK, have established a nationwide WGS database of TB to facilitate the identification of transmission events. This system enhances contact tracing as well as informs a wider understanding of TB control. Using WGS in preference to variable number tandem repeat (MIRU-VNTR) typing has been shown to reduce the number of false case clusters and has improved the detection of cases where potential clusters are identified , 78–81.E. coli isolated from symptomatic and asymptomatic children in South Asia and sub-Saharan Africa [The paradigm that the rapid expansion in AMR is a response to selective pressure from antimicrobial use drives AMR is well accepted . WGS is n Africa . Despiten Africa .Enterobacterales are habitual colonisers of the gastrointestinal tract, where they can act as a major reservoir for mobile AMR genes [On an individual patient level, the likelihood of developing infection with extended-spectrum beta-lactamase-producing bacteria is associated with an increased length of hospital stay prior to infection, exposure to antimicrobials, and recent overseas travel . PredictMR genes . MetagenMR genes . HoweverMR genes . This efMR genes  and faciMR genes , 86. WGSE. coli isolates in livestock in China after a rapid increase of colistin resistance, for example, showed that this increase was due to the emergence of a plasmid associated colistin resistance gene in E. coli, designated MCR-1, and led to international efforts to control the dissemination of MCR-1 via food supply networks [One Health focuses on understanding the interconnectivity between ecosystems , recogninetworks .Rapid, accurate, low-cost diagnostic tests can aid in optimising and limiting antimicrobial use, thereby minimising the potential selective pressures. Culture-based microbiological diagnostics and AST are widely utilised for bacterial infections but are not rapid and not applicable to viral infections or fastidious organisms. WGS-based diagnostic approaches are being used to overcome these limitations, notably for HIV and tuberculosis, and offer the prospect of improved outcomes.The prognosis of HIV infection has been transformed by combination anti-retroviral therapy (cART), but these treatments are complicated by the emergence of viral resistance. Genomic replication of RNA viruses utilises a reverse transcriptase lacking proofreading capacity, leading to the accumulation of mutations, with in host infection existing as a population of closely related genomes (quasispecies) . FurtherM. tuberculosis declines from 83% for susceptible isolates to 54% and 30% for MDR- and XDR-TB isolates, respectively [M. tuberculosis is slow growing, with culture-based diagnosis and AST often taking weeks and requiring a high-level biosafety laboratory. Poor reproducibility, uncertainty regarding appropriate drug concentrations for susceptibility testing [M. tuberculosis genome comprised a single chromosome with drug resistance mediated through mutations in core genes or promoters [Treatment success in ectively . M. tube testing , and a p testing –104. Theromoters , making romoters . WGS diaromoters , 107, plromoters , whilst romoters . These aHaemophilus influenzae and Streptococcus pneumoniae have been shown to not only decrease the incidence of disease, but also reduce AMR [. Increasing access to these vaccines would likely have a further impact on decreasing both disease and AMR [.Vaccination has an established role in AMR reduction. Conjugate vaccines for duce AMR , 111. In and AMR , 113. Va and AMR –114.Salmonella enterica serovar Typhi (S. Typhi) and causes an estimated 10.9 million cases and 116,800 deaths annually [S. Typhi is highly clonal [S. Typhi is heavily associated with a single haplotype (H58) [blaCTX-M-15 and a qnrS fluoroquinolone resistance gene [More recently, vaccination has been used as a direct mechanism for controlling AMR in typhoid fever, the result of many years of effort at understanding typhoid fever, in which WGS has played a significant role. Typhoid fever is caused by the bacterium annually . Whilst annually . S. Typhpe (H58) . The glope (H58) . MDR witpe (H58) , 119. WGpe (H58) . This vance gene .Following the initial reports of XDR typhoid, intensified surveillance was undertaken to monitor its spread of typhoid into Karachi and beyond . ReactivS. Typhi” [In addition to elucidating the molecular mechanisms of AMR and providing well-defined, standardised AMR data where mechanisms of resistance are known, WGS data can also provide important information regarding typhoid transmission pathways. The 2018 WHO Position Paper on typhoid vaccines indicated that TCV introduction should be prioritised “in countries with the highest burden of typhoid disease or a high burden of antimicrobial-resistant . Typhi” . Given t. Typhi” . Globall. Typhi” ; therefoM. tuberculosis, have been suggested in order to establish a threshold beyond which recent transmission is unlikely [The concept of relatedness between bacterial isolates is based on a process of continuous evolution, but prospectively identifying genetic signals of AMR evolution from the background noise of genetic variation and sequencing error is difficult. Using sequence data to infer relatedness and transmission is highly variable, and mutation rates can vary because of different lifestyles/conditions such as biofilm formation, antimicrobial exposure, disease states, and environmental pressures such as starvation –130. Theunlikely .Inferring or excluding transmission events for pathogens that accumulate mutations slowly can be difficult , which pHighly transmissible MGEs (such as plasmids) facilitate non-mutational AMR gene acquisition by HGT and are highly implicated in AMR dissemination . In compThe routine, prospective inclusion of WGS into microbiological surveillance has the potential to greatly enhance and strengthen public health efforts to combat AMR but comes with significant logistical and financial implications . UnfortuS. aureus are highly encouraging [Ensuring the reproducibility and validity of results through standardisation and quality assurance (QA) processes is an essential part of routine medical laboratory processing . Howeverouraging , 153 andThe ability to share, integrate, and compare the vast wealth of data derived from WGS across laboratories, settings, and time is key to harnessing the power of WGS for global AMR surveillance , 154. BaDespite AMR being a major problem in LMICs, there is currently limited understanding of AMR dynamics in these settings , 28 and The rapid emergence and spread of AMR in recent decades are a major threat to global public health, and current antimicrobial development efforts cannot keep pace with pathogen evolutionary dynamics. Addressing this threat will require diverse, cross-sectoral interventions at all levels of public health and political systems. Improvements in genomic technologies in recent years have made these systems more widely accessible and affordable, and they are now being implemented as front-line tools in the battle against AMR. By enhancing AMR surveillance, WGS has greatly improved understanding of how, when, where, and why AMR emerges and spreads and has helped quantify temporal and geographical variations in AMR epidemiology. Genomic insights have also been applied to facilitate outbreak detection and control, improve diagnostic tests, optimise antimicrobial use, and inform vaccination strategies. Although logistical and technological barriers to the universal implementation of genomics in public health remain (particularly in LMICs), the use of these approaches is likely to further expand in the coming years and will hopefully help restrict the adverse public health impacts of AMR.


The reduction in infectious disease morbidity and mortality may be attributed to a variety of factors; however, improved sanitation and public health, and the introduction of vaccines and antibiotics are among the most significant. The development of antimicrobial resistance (AMR) in bacterial pathogens is an expected consequence of evolutionary adaptation to these noxious agents and the widespread use of these drugs has significantly sped up this process. Infections caused by multidrug resistant pathogens are directly associated with worse clinical outcomes, longer hospital stays, excess mortality in the affected patients and an increasing burden and costs on the healthcare infrastructure. The Sustainable Development Goals (SDGs) were published in 2015 by the United Nations to serve as a global blueprint for a better, more equitable, more sustainable life on our planet. The SDGs contextualize AMR as a global public health and societal issue; in addition, the continuing emergence of AMR may limit the attainment on many SDGs. The aim of this mini-review is to provide insight on the interface between attainment of SDGs and the clinical problem of drug resistance in bacteria. Mycobacterium tuberculosis), diarrheal illness and neglected tropical diseases (NTDs) were still major causes of suffering in developing countries, for a short while, it seems that humanity have finally conquered infectious diseases [Since the occurrence of the so-called epidemiological transition, infectious diseases and pandemics—which have previously decimated the population—have started to recede, while the life-expectancy of people around the world has increased significantly ; althougdiseases . Consequdiseases . The reddiseases . The disdiseases ,6. Sincediseases . HoweverEnterococcus faecium, S: Staphylococcus aureus or recently Stenotrophomonas maltophilia, K: Klebsiella pneumoniae or recently C: Clostridioides difficile, A: Acinetobacter baumannii, P: Pseudomonas aeruginosa, E: Enterobacter spp., or recently Enterobacteriaceae) receive the most attention, both from public health authorities and from drug development agencies [P. aeruginosa and A. baumannii, C. difficile, MDR Neisseria gonorrhoeae and carbapenem- and cephalosporin-resistant Enterobacteriaceae as “urgent” threats [Enterobacteriaceae), posing as particular threats due to their pathogenic potential and transmissibility  [The development of antimicrobial resistance (AMR) in bacterial pathogens is an expected consequence of evolutionary adaptation to these noxious agents ; nonetheagencies . The Cen threats . The lat threats . In 2017g homes) .Enterobacteriaceae [K. pneumoniae carbapenemase), Class D: OXA (oxacillinase)) and metallo-β-lactamases (Class B: IMP (imipenemase), VIM  and NDM) [As previously stated, microorganisms possess a plethora of mechanisms—which are intrinsic to the bacterial genus or acquired from exogenous sources (encoded by antibiotic-resistance genes (ARGs) to enhance their survival ,24. The eriaceae . More reeriaceae . Based oand NDM) ,30. Subsand NDM) ,32.Two phenomena have been identified as the main driving forces behind the clinical problem of antibacterial resistance in human medicine: on one hand, the imprudent use of these agents (including overuse (administration of antibiotics in inappropriate indications) and misuse (administration of antibiotics in inappropriate doses and durations)), which facilitates the development of resistance in bacteria  ,34,35; oSelf-medication with antibiotics (SMA) is defined as the use of these agents on the basis of the individual’s symptoms (without any consultation with a physician or a diagnosis), with the aim of alleviating these complaints . The preInfections caused by MDR pathogens are directly associated with worse clinical outcomes, longer hospital stays, excess mortality in the affected patients and an increasing burden and cost on the healthcare infrastructure ,49; the In the recent years, there has been a fundamental understanding that the issue of AMR needs to be addressed using a One Health approach, which—apart from the use of these drugs in human medicine—also takes into consideration the considerable use of antibiotics in animal husbandry, the presence of antimicrobials in food products and in the environment  through anthropogenic contamination ,60. In lThe Sustainable Development Goals (SDGs) were published in 2015 by the UN within the program of “2030 Agenda for Sustainable Development” to serve as a global blueprint for a better, more equitable, more sustainable life on our planet . The UN Unsurprisingly, increasing levels of AMR threaten the attainment of the SDGs as this phenomenon considerably influences changes in society and healthcare: it can be said that the SDGs contextualize AMR as a global public health and societal issue . This maAmong other things, the continuing emergence of AMR may limit the attainment of Goal 1 (No Poverty) and 2 (Zero Hunger). Due to the increasing global population, economic growth and developments in consumption habits, food production is expected to grow by 50–70% in the timeframe between 2010–2030; in parallel, the use of antimicrobials in food production  is also expected to increase by a similar margin . This haClimate Action). In fact, in the report by the WEF, the similarities of climate change and AMR are shown as being insidious, inter-disciplinary and urgent problems for humanity [The relationship between AMR and worsening climate change is a discreet, but an important one, nevertheless, Goal 13 (humanity . Changeshumanity ,86. A 1 humanity . It has humanity . Antibiohumanity ,90. As rhumanity .By the beginning of the 21st century, it has become painfully clear that AMR has become one of the major issues in human medicine. Previously, we have highlighted the multilateral and inter-disciplinary interface between SDGs and AMR. To monitor the impact of antibiotic resistance and to track the progress of specific interventions—in the context of SDGs—an important issue that needs to be addressed is the development of a specific indicator for AMR, or for the hindrance AMR causes in the attainment of SDGs . As prev