Background: This study analyzes the current and evolving physical rehabilitation needs of BRICS nations , a coalition of large emergent economies increasingly important for global health. Methods: Secondary, cross-national analyses of data on Years Lived with Disability (YLDs) were extracted from the Global Burden of Disease Study 2017. Total physical rehabilitation needs, and those stratified per major condition groups are analyzed for the year 2017 (current needs), and for every year since 1990 (evolution over time). ANOVAs are used to detect significant yearly changes. Results: Total physical rehabilitation needs have increased significantly from 1990 to 2017 in each of the BRICS nations, in every metric analyzed . Musculoskeletal & pain conditions were leading cause of physical rehabilitation needs across the BRICS nations but to varying degrees: from 36% in South Africa to 60% in Brazil. Country-specific trends include: 25% of South African needs were from HIV-related conditions (no other BRICS nation had more than 1%); India had both absolute and relative growths of pediatric rehabilitation needs (p < 0.01); China had an exponential growth in the per-capita needs from neurological and neoplastic conditions ; Brazil had a both absolute and relative growth of needs coming from musculoskeletal & pain conditions (p < 0.01); and the Russian Federation had the highest neurological rehabilitation needs per capita in 2017 . Conclusions: total physical rehabilitation needs have been increasing in each of the BRICS nations, both in absolute and relative values. Apart from the common growing trend, each of the BRICS nations had own patterns for the amount, typology, and evolution of their physical rehabilitation needs, which must be taken into account while planning for health and physical rehabilitation programs, policies and resources. The BRICS countries  are increasingly recognized as important players in global health and development ,6,7,8,9.The BRICS countries, which formalized their coalition in 2006, generate 25% of the world’s gross national income, have approximately 40% of the world’s population, approximately 50% of the world’s poor, and represent 40% of the global burden of disease ,10,11. TDespite their economic developments and coalition, the BRICS countries face themselves a shortage of key health resources  for their growing and complex health needs, in the context of many other societal demands ,22. ThesWorldwide and especially in emerging economies, increasing numbers of people now live with functional limitations ,12,26. TIn line with increasing disability prevalence, a recent study using data from Global Burden of Disease Study 2017 found a 17% increase in the world’s physical rehabilitation needs per capita since 1990, and an almost twofold greater increase (29.9%) in upper-middle-income countries (UMICs), which include four of the BRICS countries (except India) . The Wor(1)How large are the physical rehabilitation needs in 2017 for each BRICS country , and how have those values evolved since 1990?(2)Which condition groups  account for the highest rates of physical rehabilitation need for each of the BRICS countries in 2017, and how have those values evolved since 1990?This paper aims to analyze the current and evolving physical rehabilitation needs of the BRICS countries. The specific study questions are:This paper refers to a secondary, cross-national comparative analysis of global epidemiological data in the public domain. To estimate the physical rehabilitation needs for each of the BRICS nations, we use data from the 2017 Global Burden of Disease Study (GBD) . Specifihttp://ghdx.healthdata.org/gbd-results-tool).To determine the physical rehabilitation needs for the BRICS nations, we apply and combine the abovementioned standard methods as follows: In April 2019, public-domain data from the GBD 2017 were systematically extracted from a freely-available web platform: the Global Health Data Exchange tool , due its exclusive focus on non-fatal health losses. YLDs consist of the years lived with any short-term or long-term health loss weighted for severity by disability weights. For stroke, for example, disability weights vary from 0.019 for mild consequences to 0.588 for severe consequences plus cognition problems. Details on how YLDs and disability weights are determined, and the disability weights for all conditions, are available elsewhere ,50.For “years”, data were extracted for every year between 1990 and 2017, for a more precise determination of the evolving trend. For “location”, YLDs were extracted at the national level for each of the BRICS. No sub-national data were extracted, as we focused on nation-wide and cross-national analyses.number , rate , and percentage .As for “metrics”, we extracted YLDs data for prevalent all ages and age-standardized rates, the latter used to determine age-standardized YLD Rates .Regarding “age”, we extracted YLDs both for All the selected data were imported from the webtool to Excel spreadsheets for data storage, management, and analysis.r2 values for that. Given negligible differences in r2 values (<0.02 between the models), we retained the linear regression option.In the Excel spreadsheets, we summed YLDs within each of the five “locations”, four “metrics”, and 28 “years”, computed any percent changes from 1990 to 2017, plotted the entire time series [1990–2017] of the combined YLD values, and then determined which type of simple regression model  best fit the plotted data. We used visualization and p = 0.05 and p = 0.01, the latter accounts for a Bonferroni correction (0.05/5 = 0.01) considering the analyses are made for five countries within each item/metric under study. The respective confidence intervals (CIs) in turn were used to analyze whether yearly changes for each BRICS nation significantly differed  from those of the 4 other BRICS countries or from the global benchmarks that we extracted from the literature–as the same methods were used [To assess yearly changes of YLDs between 1990 and 2017, ANOVA was applied. This test considers the data on every year between 1990 and 2017, which increases preciseness. The significance level for the analysis was set at two subsequent levels: ere used ,49.YLD Rates as a metric, either through actual YLD Rates or through those transformed into percent values, e.g., percentage of the YLD Rates related to physical rehabilitation that came specifically from neurological conditions.Finally, using the analytical procedures above, we performed a subgroup analysis on the physical rehabilitation needs, stratified per six major groups of conditions–detailed in the We provide below the results for the two study questions:p < 0.01); the exception being Age-Standardized YLD Rates for both India and Russia, with no significant changes since 1990 (p > 0.05). Per metric, In YLD Counts , South Africa more than doubled their physical rehabilitation needs from 1990 to 2017 , similarly to low-income countries . India, Brazil, and China had a 91.6%, 82.6%, and 68.1% growth, respectively. Russia, in turn, had the lowest percentage growth in YLD counts (7.6%), substantially lower than any global benchmark: i.e., the lowest being 37.4% for high-income countries.In YLD Rates , Russia had the highest value in 2017 (6393), but South Africa and China had the highest yearly growths , each of them significantly higher  than those of the 3 other BRICS nations or any global benchmark.In Age-standardized YLD Rates, , South Africa had the highest value in 2017 (5131), and significantly greater yearly increases  than all comparators; for example high-income countries had a significant decrease. Russia and India had non-significant yearly changes in this metric.Finally, in the percentage of YLDs likely benefiting from physical rehabilitation among total YLDs, Brazil stands out with nearly two-thirds of their YLDs coming from rehabilitation-sensitive conditions in 2017 (66.2%). For the other BRICS countries or global benchmarks, values were all below 50%; in India little more than one-third (35.9%). In South Africa, the yearly growths in the YLDs percentage were significantly greater while in Russia significantly lower than in any other BRICS nations. Indeed, the 99% CIs of the Russia’s yearly growth was rather aligned  with that of high-income nations. YLD Rates from 1990 to 2017 across the BRICS countries, for all the condition groups (p < 0.01). The exceptions are the South African’s YLD rates coming from pediatric and from neoplasm conditions , although the latter had a significant increase within the 95% CI (0.03−0.53). YLD Rates came from: (1) HIV-related conditions in South Africa (b = 63.8), yet with a logarithmic growth ; (2) musculoskeletal & pain conditions in both Brazil and China ; (3) neurological conditions in China (b = 15.6), within exponential type of growth , and (4) pediatric conditions in India (b = 7.18). In each of these cases, the growths were significantly greater  than those of any comparators for the same condition group. Finally, Russia stands out with the highest YLD Rates for neurological conditions in 2017, e.g., over than 3 times that of Brazil, India, or South Africa.The highest yearly increases in p < 0.01). In China, the percentage of physical rehabilitation needs coming from both neurological conditions and neoplasms increased significantly , both with an exponential type of growth. In India, the percentage of physical rehabilitation needs coming from pediatric conditions increased significantly (from 15.7% to 17.8%: p < 0.01). In Russia, the percentage of physical rehabilitation needs that came from neoplasms has grown by 47% (p < 0.01). Finally, only in Brazil did the percentage of physical rehabilitation needs coming from musculoskeletal & pain conditions increased significantly , becoming greater than that in high-income countries (57.7% in 2017).Finally, In each of the BRICS countries, total physical rehabilitation needs have increased significantly from 1990 to 2017 in absolute values, per-capita, and in percentage of all YLDs. This means that, in each of these countries, physical rehabilitation needs have increased beyond the population growth, and that physical rehabilitation could be helpful for a greater portion of non-fatal health losses. Apart from common trends across the BRICS nations , we found important country-specific differences across the BRICS countries in the amount, typology, and evolution of their physical rehabilitation needs.For the overall age-standardized YLD Rates germane to physical rehabilitation, we did not observe significant changes for India and Russia. This means that for these countries the aging of the population  hChina stood out with the greatest amount and an exponential type of growth in the physical rehabilitation needs from neurological and neoplasm conditions. The population ageing, derived from the previous one-child policy, increased life expectancy , increasIn India, another highly populated and emerging economy in Asia, the typology of physical rehabilitation needs was different than for the other BRICS nations and, in some respects, closer to that of lower income countries. One such example is the absolute and relative growth of physical rehabilitation needs arising from pediatric conditions. This pattern may reflect different economic status from the other BRICS: i.e., although an emergent economy, India is still a lower-middle income nation per the World Bank classification while the BRICS counterparts are UMICs. Alternatively, the findings may reflect a different population ageing structure and higher fertility rates . In addiSouth Africa, a leading UMIC within the African continent, more than doubled their absolute physical rehabilitation needs, mimicking the trend in low-income countries. This increase in need for rehabilitation is partly driven by the HIV/AIDS endemic. HIV-related conditions accounted for one-quarter of South African’s physical rehabilitation needs in 2017, compared to 1% in any of the four other BRICS countries. South Africa’s successful roll-out of highly active antiretroviral therapy (HAART) has transformed HIV into a chronic disease, and people with HIV can now achieve normal life-expectancy . An incrRussia had the highest rate of physical rehabilitation needs in 2017 , but the lowest percent change since 1990; a pattern closest to that of high-income countries more than other BRICS nations. Similarly, we found that Russia had the highest rates, although not the highest growth, in physical rehabilitation needs from neurological conditions, including about the triple of those from Brazil, India and South Africa. The persistently high burden of rehabilitation-sensitive conditions in Russia may be a result of several factors. First, the incidence of major chronic non-communicable diseases remains high; national statistics have shown, for example, that the incidence of coronary heart disease has increased substantially from 495 to 701 per 100,000 inhabitants from 2010 to 2016 . That acFinally, for Brazil, we found that conditions responsive to physical rehabilitation currently account for about two-thirds of the nation’s YLD (no other BRICS nation came close to 50%). This means that physical rehabilitation can address a larger portion of the country’s non-fatal health losses when compared to the BRICS counterparts. Brazil also stood out with the highest portion of physical rehabilitation needs coming from musculoskeletal & pain conditions (60%), and with substantial growth in this percent value over time. Key explanations for that finding may include the prevalence of interpersonal violence in Brazil , and theFirst, YLDs from selected health conditions are but proxy indicators of physical rehabilitation needs, i.e., not a direct functional or impairments-based measure. Nonetheless, YLDs is the aggregative measure of non-fatal health loss from the prominent GBD study and includes variables such as the prevalence of conditions, the time lived with sequalae from the respective conditions, and weighted for the appraised severity of those sequelae.Second, the set of conditions whose YLDs likely benefit from rehabilitation were replicated from a previous study which systematically reviewed evidence linking those conditions to rehabilitation needs;  nonethelThird, YLD values (extracted from the GBD 2017) are only estimates based on the best-available evidence, not actual YLDs. The GBD 2017 is the most comprehensive epidemiological study to date, and the amount of data used to create those estimates is unprecedented ,48. EvenFourth, most data obtained for the GBD study  are from self-reports and hence many undiagnosed conditions might not be included within this data to represent the true picture. Hence results of this study could be a gross under-estimation of the problem, at least in the absolute values.Fifth, we did not extract or analyze sub-national data , although data are available for that from the GBD 2017 and some important differences exist in both economic and epidemiological profiles across regions or states of the analyzed countries ,16,39.Sixth, we do not supplement our analyses of physical rehabilitation ‘needs’ with indicators of physical rehabilitation ‘supply’ across nations, the other key element in the resources planning equation. In part, this follows the lack of available data. For example, the World Confederation of Physical Therapy reports data on the amount of practicing physical therapists per nations, as locally collected from authoritative sources or estimated by national associations  for a total of 89 countries, but unfortunately not from 3 of the analyzed countries  .This study has the following limitations:Physical rehabilitation needs have increased significantly from 1990 to 2017 in each of the BRICS nations, both in absolute and relative values. However, apart from the common trend in overall growth, each of the BRICS nations had own patterns for the amount, typology, and evolution of their physical rehabilitation needs. The BRICS nations and coalition need to address the common challenge of planning for and deploying the required resources for meeting the growing physical rehabilitation needs of their population, at the same time they look at country-specific challenges such as the physical rehabilitation needs coming from HIV/AIDs-related conditions in South Africa, pediatric conditions in India, musculoskeletal conditions in Brazil, and neurological conditions in Russia and China. This study shows that physical rehabilitation needs can be determined and compared across nations, and hence can be used to inform rehabilitation resources and service planning. Most importantly, this study makes clear that physical rehabilitation needs and growth patterns may not be assumed equal across nations, irrespective of similarities in income or emerging development.


Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results . Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts. In 2019, the biggest one was generated in the US American football league, National Football League (NFL)-$13 Billion. Other sports can also be very profitable: in 2019, Major League Baseball (MLB) earned $10 Billion, National Basketball Association (NBA) $7.4 Billion, Indian Premier League (Cricket) $6.3 Billion, and, finally, the English Premier League made $5.3 Billion.Sports have been one of the most popular kind of entertainment for ages. Practising and watching sport is exciting, healthy, unpredictable, and makes us feel alive. However, sports have recently become very lucrative business. Sports leagues and teams turned into industries, earning billions of dollars from various sources: sponsorships, ticket revenues, transfers, stadium rentals, broadcasting deals, merchandise, and many more. The revenue of sports leagues is constantly growing, according to the online reports  has recently increased rapidly. Currently, they are widely implemented in many domains of our everyday life, including sports. AI provides the opportunity to simulate human-like thinking in computer systems. However, it also enables analysing large amounts of data, which may prove to be hardly executable for humans.Sport is a perfect example of a field producing thousands bytes of data. A single football match  can generate an enormous amount of data: goals, attempts, attempts on goal, corners, yellow and red cards, time on field of each player, substitutes, ball possession, and many others. However, is every piece of information valuable? Or, on the other hand, are there any other types of data that can be useful in terms of player evaluation, transfer, and, finally, the whole sport team building?The major contribution of this paper is to present possible implementation of the machine learning techniques in order to predict a successful transfer of a professional football player. The presented solution uses realistic data and different definitions of transfer success. It is also remarkable that the algorithm pays attention to all aspects of a player: his technical, physical, and psychological state.The remainder of the paper is structured, as follows: At the very beginning, the term ’sports analytics’ needs to be defined. According to , this teTraditionally, the results of matches were predicted using some mathematical and statistical models, and they were often verified by a domain expert, as mentioned in . CurrentThe possible approach for predicting the outcomes was presented in , where tIn , the valNot only the basketball results are predicted using machine learning. In , the badEven though the tweets’ content is mostly analysed in order to uncover the fake pieces of information (as presented in ), the auSome of the researchers used machine learning in order to assess the risk or predict an injury, as an inline player’s injury can be a critical factor resulting in a victory or defeat. In , the injThe authors in  were excThe article  presentsBecause, across countries and continents, football has drawn increasingly more attention, more bookmakers are offering football bets. The scientific approaches to beating the bookmakers were presented in ,19.It is also possible to combine machine learning with computer vision in the sport domain, as presented in . The artMachine learning can also be implemented in order to predict the future performance of athletes. This kind of analysis can be especially beneficial for the coaches of young, promising players, or the scouts looking for new sport stars. The possible implementations of machine learning in performance prediction were presented in -in tennihttps://scoutastic.com/en/). Among its functionalities, the following are enumerated: team management-dynamic resource planning, task assignment, ticket status, area match search, and scouting-activity analytics; match report analysis-extracting, displaying and filtering the relevant content from match reports; and, data generation-automated generation of the relevant performance data and development indicators while using the AI analysis of videos . However, it does not support team building and transfers.However, the machine learning-based solutions could be useful not only for the professional athletes, but also for amateurs. The wearable devices have recently become increasingly popular. In , a possihttps://wyscout.com/). It is the world’s leading provider of football performance data. It provides advanced statistics that can help coaches to analyse and prepare matches, give scouts powerful tools to identify the most promising profiles, and enable player agents to better understand their players’ strengths and weaknesses. Even though this platform provides multiple and detailed data, it does not support transfers.Wyscout is the other platform dedicated to scouts (https://www.scisports.com/). It has three main functionalities: recruitment, performance analysis, and data delivery. Although it provides the recruitment, it does not support team building again. It only gives some extensive filters and criteria .Last but not least, a tool for scouts is Scisports  can be less important, when it comes to the potential success. Thus, in this work, we decided to analyse the full overview of a football player: not only technique and his physical parameters, but also his psychological state. In order to obtain this goal, we propose using the parameters that are listed in Yogi Berra, the baseball legend, said ’The metrics that are included in the physical aspect are the statistics showing the number of games played and the duels won against the opponent. We have chosen them in order to reflect the performance and motor skills of the player.The statistics that make up the technical aspect have been selected to reflect the contribution of the player to the game of the whole team. The key elements of soccer, such as scoring a goal, dribbling, or passing, can be presented in two ways: numerically (as an effect) or as a percentage (as an effectiveness). Soccer is a team sport, so choosing only one of these variants could create false results, e.g., if the team does not create shooting situations, even the best striker will not score a large number of goals, adequately, with a large number of situations a player with poor efficiency will score goals.statistics don’t play’ says that taking only basic statistics into account does not always reflect the skills of the player and his contribution to the team. In order to best reflect the football profile, we have decided to also include metrics, such as big chance created or mistake leads to a goal.The common saying ’The psychological aspect consists of the statistics of the cards received, contact with the ball, and the parameters that indicate the likely acclimatization of the player in the new club. Penalties received for fouls are indicative of the level of aggression of the player. The number of contacts with the ball may indicate the player’s participation in the team’s game.https://www.whoscored.com/) provides many match statistics, while Transfermarkt (https://www.transfermarkt.com/) focuses on transfers. The other website providing numerous pieces of information is Sofascore (https://www.sofascore.com/), which publishes detailed statistics for more than 20 different sport disciplines.Because the sport statistics are considered to be very informative for fans, journalists, and the people placing bets, they are widely accessible online. The WhoScored website . As mentioned, the application has two main tasks to perform. Firstly, it downloads the selected data .Firstly, a successful transfer is the transfer affecting high results in all of the player’s aspects. Thus, we provide the total player assessment equation expressed with Equation , where: Secondly, the success of the transfer can rely on the overall quality of the target team, e.g., it is very difficult to score frequently, when the partners from the team are weak and do not properly support the player. Thus, in the second approach, we compare the player assessment (Equation ) with thThe last successful transfer definition is based on the average team player’s assessment again, but it introduces one modification: the average is calculated taking only 18 scores (starting eleven + 7 substitutions). It is because, generally speaking, other players do not appear in the line-up.Because of the fact of using three different football player transfer definitions, the ratio between the successful and unsuccessful transfers is not constant for all experiments. For example, it was 2024:1458 for definition 1, 2378:1104 for definition 2, and 2384:1098 for the last definition.The dataset was randomly divided into the training set (70%) and testing set (30%). Each experiment was repeated three times while using the three-fold cross validation in order to prove the lack of dependency on the data.In the classification stage of the research, we used the industrial standards and state of the art ML techniques, namely the Random Forest, Naive Bayes, and AdaBoost algorithms. The Random Forest consists of a number (in this research-10) of individual decision trees that operate as an ensemble. Each individual tree in the Random Forest outputs a class prediction and the class with the most votes becomes our model’s prediction. The Naive Bayes classifier is a probabilistic machine learning model, whereas Adaboost was designed to use short decision tree models, each with a single decision point. Such short trees are often referred to as decision stumps. However, in further studies, we plan to deploy deep learning approach or artificial neural networks . However, the transfer cannot be considered successful so far. Hazard suffered two injuries, which made him miss numerous matches and, in fact, the whole season for Real Madrid. The market value of Eden Hazard has decreased significantly since 2019. In June 2019, it was €150 M, while, in April 2020, it was only €80 M.Eden Hazard played seven successful seasons scoring 110 goals in 352 games for Chelsea. He won two Premier League titles, two Europa Leagues, the FA Cup, and League Cup. In summer 2019, he signed a €150 M contract for Real Madrid. This transfer was one of the most expensive transfers involving an English club in history .Arsene Wenger, the veteran Arsenal boss, admitted that football, as well as any other sport, is mainly unpredictable Another threat to validity is the situation that can be currently observed. The 2020 season was interrupted and the training cycle was destroyed in most leagues all over the world due to the global COVID-19 pandemic. It may cause some misleading data in statistics and, consequently, it may lead to failed predictions in the future.Last but not least, the differences between leagues may be another threat to validity. It is very difficult to compare the leagues from different countries. The proposed method was tested against the data regarding the players and transfers from the best leagues.Additionally, our data from the top leagues only concerned the male players/leagues. It is the future plan to evaluate whether the same approach will work well on the data regarding female football players.As for construct validity, the used dataset is believed to be representative for the top football leagues, and it is well constructed based on the open and available data. As for external validity, we are aware that the proposed method is based on top leagues and that it can be biased and provide less promising results for weaker players and teams.In this paper, we present the possible approach to the professional team building by means of the machine learning based tool. The proposed solution provides promising results and it can support a scout or a team manager in the process of transfer planning. Nevertheless, as mentioned in the In this research, unlike some other approaches that have been recently published , we onlThe obtained results also depend on the successful transfer definition and the weights of the selected parameters. In this paper, we provide three various definitions and three different weights for each parameter. It is also remarkable that we do not only take the technical and physical parameters of a professional football player into account, but also his psychological state.In the near future, we are going to further improve the presented solution. The possible extension would include adding more classifiers  and building ensembles. Apart from that, we are going to verify the solution on other data, possibly coming from a greater number of (less popular) leagues. Last but not least, a possible extension is an attempt to move the proposed approach to other ball-sports, e.g., volleyball or basketball, which would definitely need the parameters’ lists to be redefined.


The synthesis of 1 was successfully achieved through consecutive four step reactions from substrates 2. The substrates 2, prepared through a two-step synthetic sequence, underwent three consecutive reactions of nitro reduction, intramolecular condensation, and nucleophilic 1,5-addition to provide the intermediates, 1-hydroxyindoles 8, which then were alkylated in situ with alkyl halide to afford the novel target products 1. We optimized the reaction conditions for 1 focusing on the alkylation step, along with the consideration of formation of intermediates 8. The optimized condition was SnCl2·2H2O (3.3 eq) and alcohols  for 1–2 h at 40 °C and then, base (10 eq) and alkyl halides  for 1–4 h at 25–50 °C. Notably, all four step reactions were performed in one-pot to give 1 in good to modest yields. Furthermore, the mechanistic aspects were also discussed regarding the reaction pathways and the formation of side products. The significance lies in development of efficient one-pot reactions and in generation of new 1-alkoxyindoles.Studies on a one-pot synthesis of novel multisubstituted 1-alkoxyindoles  With 2y and SnCl2·2H2O (3.3 eq), we chose benzyl alcohol  as a template nucleophile and benzyl bromide  as a template alkyl halide with base (10 eq) affording 1-benzyloxyindole 1yh, and the results were shown in 2CO3 gave poor yield. The order of basicity of organic bases (DMAP < TEA < DIEA < DBU) +; HRMS (+ESI) calcd for C10H8ClNNaO5 [M + Na]+ 279.9989, found 279.9983. Spectral data are in accordance with literature information +; HRMS (+ESI) calcd for C10H8BrNNaO5 [M + Na]+ 323.9484, found 323.9475. Spectral data are in accordance with literature information +; HRMS (+ESI) calcd for C11H8ClNNaO5 [M + Na]+ 291.9989, found 291.9983. Spectral data are in accordance with literature information +; HRMS (+ESI) calcd for C11H8BrNNaO5 [M + Na]+ 335.9484, found 335.9474. Spectral data are in accordance with literature information +; HRMS (+ESI) calcd for C13H14ClNNaO4 [M + Na]+ 306.0509, found 306.0509.Use of SnClMethyl 4-Chloro-1-ethoxy-3-(ethoxymethyl)-1H-indole-2-carboxylate (1xb)2·2H2O , ethanol  and 2x  for 1 h at 40 °C, then use of DBU  and bromoethane  for 1 h at 25 °C in general procedure afforded the title compound 1xb  as a pale-yellow solid. Mp 42 °C; Rf 0.49 (1:4 EtOAc/hexanes); HPLC tR 25.8 min; UV vis (CH3CN-H2O) λmax 235, 298nm; 1H NMR : δ 7.34 , 7.23 , 7.15 , 5.09 CH2O), 4.41 OCH2), 3.99 , 3.65 , 1.44 OCH2CH3), 1.25 ; 13C NMR : δ 160.8 (C=O), 136.2 (Ar), 128.6 (Ar), 126.1 (Ar), 125.3 (Ar), 122.8 (Ar), 119.5 (Ar), 116.4 (Ar), 108.4 (Ar), 74.9 (N(1)OCH2), 65.8 (OCH2CH3), 61.9 (C(3)CH2O), 52.3 (CO2CH3), 15.5 (N(1)OCH2CH3), 13.7 (OCH2CH3); MS m/z 311 [M]+; HRMS (+ESI) calcd for C15H18ClNNaO4 [M + Na]+ 334.0822, found 334.0820.Use of SnClMethyl 4-Chloro-1-n-propyloxy-3-[(n-propyloxy)methyl]-1H-indole-2-carboxylate (1xc)2·2H2O , n-propanol  and 2x  for 1 h at 40 °C, then use of DBU  and 1-bromopropane  for 1 h at 25 °C in general procedure afforded the title compound 1xc  as a white oil. Bp 170 °C (decomp.); Rf 0.54 (1:4 EtOAc/hexanes); HPLC tR 30.8 min; UV vis (CH3CN-H2O) λmax 230, 298nm; 1H NMR : δ 7.35 , 7.23 , 7.15 , 5.09 CH2O), 4.29 OCH2), 3.98 , 3.54 , 1.86 OCH2CH2), 1.64 , 1.11 OCH2CH2CH3), 0.93 ; 13C NMR : δ 160.9 (C=O), 136.1 (Ar), 128.6 (Ar), 126.0 (Ar), 125.3(Ar), 122.8 (Ar), 119.6 (Ar), 116.4 (Ar), 108.3 (Ar), 80.6 (N(1)OCH2), 72.4 (OCH2), 62.1 (C(3)CH2O), 52.2 (CO2CH3), 23.1 (N(1)OCH2CH2), 21.8 (OCH2CH2), 10.9 (N(1)O(CH2)2CH3), 10.6 (O(CH2)2CH3); MS m/z 339 [M]+; HRMS (+ESI) calcd for C17H22ClNO4 [M + Na]+ 362.1135, found 362.1134.Use of SnClMethyl 4-Chloro-1-n-butyloxy-3-[(n-butyloxy)methyl]-1H-indole-2-carboxylate (1xd)2·2H2O , n-butanol  and 2x  for 1 h at 40 °C, then use of DBU  and 1-bromobutane  for 1 h at 25 °C in general procedure afforded the title compound 1xd  as a white oil. Bp 198 °C (decomp.); Rf 0.54 (1:4 EtOAc/hexanes); HPLC tR 34.3 min; UV vis (CH3CN-H2O) λmax 235, 298 nm; 1H NMR : δ 7.34 , 7.23 , 7.15 , 5.08 CH2O), 4.32 OCH2), 3.98 , 3.58 , 1.82 OCH2CH2), 1.65–1.52 OCH2CH2CH2), 1.39 2CH2), 1.00 O(CH2)3CH3), 0.90 3CH3); 13C NMR : δ 160.9 (C=O), 136.1 (Ar), 128.6 (Ar), 126.0 (Ar), 125.3 (Ar), 122.8 (Ar), 119.6 (Ar), 116.4 (Ar), 108.3 (Ar), 79.0 (N(1)OCH2), 70.4 (OCH2), 62.1 (C(3)CH2O), 52.3 (CO2CH3), 32.1 (N(1)OCH2CH2), 30.5 (OCH2CH2) 19.6 (N(1)O(CH2)2CH2), 19.4 (O(CH2)2CH2) 14.2 (N(1)O(CH2)3CH3), 14.1 (O(CH2)3CH3); MS m/z 367 [M]+; HRMS (+ESI) calcd for C19H26ClNO4 [M + Na]+ 390.1448, found 390.1447.Use of SnClMethyl 4-Chloro-1-n-pentyloxy-3-[(n-pentyloxy)methyl]-1H-indole-2-carboxylate (1xe)2·2H2O , n-pentanol  and 2x  for 2 h at 40 °C, then use of DBU  and 1-bromopentane  for 1 h at 25 °C in general procedure afforded the title compound 1xe  as a white oil. Bp 184 °C (decomp.); Rf 0.58 (1:4 EtOAc/hexanes); HPLC tR 32.9 min; UV vis (CH3CN-H2O) λmax 235, 298 nm; 1H NMR : δ 7.34 , 7.23 , 7.15 , 5.08 CH2O), 4.32 OCH2), 3.98 , 3.59 , 1.84 OCH2CH2), 1.64–1.25 3CH3, N(1)OCH2CH2(CH2)2CH3), 0.95 O(CH2)4CH3), 0.87 4CH3); 13C NMR : δ 160.8 (C=O), 136.0 (Ar), 128.6 (Ar), 126.0 (Ar), 125.3 (Ar), 122.8 (Ar), 119.6 (Ar), 116.4 (Ar), 108.2 (Ar), 79.3 (N(1)OCH2), 70.7 (OCH2), 62.1 (C(3)CH2O), 52.2 (CO2CH3), 29.7, 28.6, 28.3, 28.1, 22.7, 22.6, (N(1)OCH2(CH2)3, OCH2(CH2)3), 14.2 (N(1)O(CH2)4CH3), 14.1 (O(CH2)4CH3); MS m/z 395 [M]+; HRMS (+ESI) calcd for C21H30ClNO4 [M + Na]+ 418.1761, found 418.1760.Use of SnClMethyl 4-Chloro-1-n-hexyloxy-3-[(n-hexyloxy)methyl]-1H-indole-2-carboxylate (1xf)2·2H2O , n-hexanol  and 2x  for 2 h at 40 °C, then use of DBU  and 1-bromohexane  for 1 h at 25 °C in general procedure afforded the title compound 1xf  as a pale-yellow oil. Bp 152 °C (decomp.); Rf 0.63 (1:4 EtOAc/hexanes); HPLC tR 29.7 min; UV vis (CH3CN-H2O) λmax 236, 298 nm; 1H NMR : δ 7.33 , 7.23 , 7.14 , 5.08 CH2O), 4.32 OCH2), 3.98 , 3.57 , 1.83 OCH2CH2), 1.63–1.50 O(CH2)2CH2), 1.37–1.25 3, N(1)OCH2CH2CH2(CH2)2), 0.92–0.84 O(CH2)5CH3, O(CH2)5CH3); 13C NMR : δ 160.7 (C=O), 135.9 (Ar), 128.5 (Ar), 125.8 (Ar), 125.1(Ar), 122.6 (Ar), 119.4 (Ar), 116.2 (Ar), 108.1 (Ar), 79.1 (N(1)OCH2), 70.5 (OCH2), 61.9 (C(3)CH2O), 52.0 (CO2CH3), 31.7, 31.6, 29.8, 28.2, 25.9, 25.6, 22.6, 22.5 (N(1)OCH2(CH2)4, OCH2(CH2)4), 14.1 (N(1)O(CH2)5CH3), 14.0 (O(CH2)5CH3); MS m/z 423 [M]+; HRMS (+ESI) calcd for C23H34ClNO4 [M + Na]+ 446.2074, found 446.2071.Use of SnClMethyl 4-Chloro-1-n-octyloxy-3-[(n-octyloxy)methyl]-1H-indole-2-carboxylate (1xg)2·2H2O , n-octanol  and 2x  for 2 h at 40 °C, then use of DBU  and 1-bromooctane  for 1 h at 25 °C in general procedure afforded the title compound 1xg  as a white solid. Mp 19–20 °C; Rf 0.70 (1:4 EtOAc/hexanes); HPLC tR 35.5 min; UV vis (CH3CN-H2O) λmax 237, 298 nm; 1H NMR : δ 7.33 , 7.23 , 7.14 , 5.08 CH2O), 4.32 OCH2), 3.98 , 3.56 , 1.83 OCH2CH2), 1.66–1.25 6CH3), N(1)OCH2CH2(CH2)5CH3), 0.89–0.85 O(CH2)7CH3, O(CH2)7CH3); 13C NMR : δ 160.8 (C=O), 136.1 (Ar), 128.7 (Ar), 126.0 (Ar), 125.3(Ar), 122.8 (Ar), 120.0 (Ar), 116.4 (Ar), 108.3(Ar), 79.3 (N(1)OCH2), 70.7 (OCH2), 62.1 (C(3)CH2O), 52.2 (CO2CH3), 32.0, 31.9, 30.0, 29.9, 29.6, 29.5, 29.4, 28.5, 26.4, 26.2, 22.8, 22.6 5 (N(1)CH2(CH2)6, OCH2(CH2)6), 14.3 (N(1)O(CH2)7CH3), 13.5 (O(CH2)7CH3); MS m/z 479 [M]+; HRMS (+ESI) calcd for C27H42ClNNaO4 [M + Na]+ 502.2700, found 502.2926.Use of SnClMethyl 4-Chloro-1-benzyloxy-3-[(benzyloxy)methyl]-1H-indole-2-carboxylate (1xh)2·2H2O , benzyl alcohol  and 2x  for 1 h at 40 °C, then use of DBU  and benzyl bromide  for 1 h at 25 °C in general procedure afforded the title compound 1xh  as a white solid. Mp 74 °C; Rf 0.54 (1:2 EtOAc/hexanes); HPLC tR 31.8 min; UV vis (CH3CN-H2O) λmax 212, 298 nm; 1H NMR : δ 7.50–7.10 , 5.32 CH2O), 5.17 OCH2), 4.66 CH2OCH2), 3.87 ; 13C NMR : δ 160.8 (C=O), 138.8 (Ar), 136.4 (Ar), 134.3 (Ar), 130.0 (Ar), 129.5 (Ar), 128.9 (Ar), 128.6 (Ar), 128.5 (Ar), 128.2 (Ar), 127.7 (Ar), 126.2 (Ar), 125.9 (Ar), 123.0 (Ar), 119.7 (Ar), 116.2 (Ar), 108.6 (Ar), 81.0 (N(1)OCH2), 72.6 (OCH2Ph), 61.8 (C(3)CH2O), 52.2 (CO2CH3); MS m/z 435 [M]+; HRMS (+ESI) calcd for C25H22ClNNaO4 [M + Na]+ 458.1135, found 458.1133.Use of SnClMethyl 4-Chloro-1-phenylethyloxy-3-[(phenylethyloxy)methyl]-1H-indole-2-carboxylate (1xi)2·2H2O , 2-phenylethyl alcohol  and 2x  for 1 h at 40 °C, then use of DBU  and 2-(bromoethyl)benzene  for 1 h at 25 °C in general procedure afforded the title compound 1xi  as a pale yellow solid. Mp 50 °C; Rf 0.60 (1:2 EtOAc/hexanes); HPLC tR 34.5 min; UV vis (CH3CN-H2O) λmax 212, 236, 297 nm; 1H NMR : δ 7.38–6.90 , 5.14 CH2O), 4.55 OCH2), 3.86 , 3.79 CH2OCH2), 3.14 OCH2CH2), 2.94 CH2OCH2CH2); 13C NMR : δ 160.8 (C=O), 139.2 (Ar), 137.8 (Ar), 136.2 (Ar), 129.3 (Ar), 129.1 (Ar), 128.8 (Ar), 128.5 (Ar), 128.4 (Ar), 126.9 (Ar), 126.2 (Ar), 125.9 (Ar), 125.4 (Ar), 122.9 (Ar), 119.6 (Ar), 116.4 (Ar), 108.3 (Ar), 79.6 (N(1)OCH2), 71.4 (OCH2), 62.2 (C(3)CH2O), 52.2 (CO2CH3), 36.5 (N(1)OCH2CH2), 35.0 (OCH2CH2); MS m/z 463 [M]+; HRMS (+ESI) calcd for C27H26ClNNaO4 [M + Na]+ 486.1448, found 486.1445.Use of SnClMethyl 4-Chloro-1-isopropyloxy-3-[(isopropyloxy)methyl]-1H-indole-2-carboxylate (1xj)2·2H2O , isopropanol  and 2x  for 2 h at 40 °C, then use of DBU  and 2-bromopropane  for 2 h at 25 °C in general procedure afforded the title compound 1xj  as a white oil. Bp 164 °C (decomp.); Rf 0.50 (1:4 EtOAc/hexanes); HPLC tR 29.1 min; UV vis (CH3CN-H2O) λmax 233, 298 nm; 1H NMR : δ 7.35 , 7.20 , 7.13 , 5.09 CH2O), 4.72 OCH(CH3)2), 3.80 CH2OCH(CH3)2), 1.33 OCH(CH3)2), 1.24 CH2OCH(CH3)2); 13C NMR : δ 161.1 (C=O), 137.5 (Ar), 128.4 (Ar), 126.1 (Ar), 125.8 (Ar), 122.6 (Ar), 119.5 (Ar), 116.8 (Ar), 109.4 (Ar), 82.1 (N(1)OCH), 71.5 (OCH), 60.0 (C(3)CH2O), 52.2 (CO2CH3), 22.4 (N(1)OCH(CH3)2), 21.3 (OCH(CH3)2); MS m/z 339 [M]+; HRMS (+ESI) calcd for C17H22ClNNaO4 [M + Na]+ 362.1135, found 362.1131.Use of SnClMethyl 4-Chloro-1-cyclohexyloxy-3-[(cyclohexyloxy)methyl]-1H-indole-2-carboxylate (1xk)2·2H2O , cyclohexanol  and 2x  for 2 h at 40 °C, then use of DBU  and bromocyclohexane  for 4 h at 50 °C in general procedure afforded the title compound 1xk  as a white solid. Mp 60–64 °C; Rf 0.57 (1:4 EtOAc/hexanes); HPLC tR 31.9 min; UV vis (CH3CN-H2O) λmax 237, 298 nm; 1H NMR : δ 7.37 , 7.20 , 7.12 , 5.11 CH2O), 4.35–4.28 OCH), 3.97 , 3.45–3.38 CH2OCH), 2.38–0.86 OCH(CH2)5, C(3)CH2OCH(CH2)5); 13C NMR : δ 161.1 (C=O), 137.3 (Ar) 128.4 (Ar), 126.0 (Ar), 125.7 (Ar), 122.5 (Ar), 119.2 (Ar), 116.7 (Ar), 109.4 (Ar), 87.9 (N(1)OCH), 78.3 (C(3)CH2OCH), 59.7 (C(3)CH2O), 52.2 (CO2CH3), 31.7, 29.9. 26.1, 25.6, 24.7, 24.6 (N(1)OCH(CH2)5, OCH(CH2)5); MS m/z 419 [M]+; HRMS (+ESI) calcd for C23H30ClNNaO4 [M + Na]+ 442.1760, found 442.1760.Use of SnClMethyl 4-Chloro-1-methoxy-3-[(phenylethyloxy)methyl]-1H-indole-2-carboxylate (1xl)2·2H2O , 2-phenylethyl alcohol  and 2x  for 2 h at 40 °C, then use of DBU  and methyl iodide  for 2 h at 25 °C in general procedure afforded the title compound 1xl  as a white solid. Mp 68 °C; Rf 0.30 (1:4 EtOAc/hexanes); HPLC tR 29.0 min; UV vis (CH3CN-H2O) λmax 215, 234, 298 nm; 1H NMR : δ 7.38 , 7.29–7.16 , 5.15 CH2O,), 4.19 OCH3), 3.96 , 3.81 , 2.95 ; 13C NMR : δ 160.8 (C=O), 139.3 (Ar), 135.9 (Ar), 129.1 (Ar), 128.7 (Ar), 128.5 (Ar), 126.3 (Ar), 126.2 (Ar), 125.3 (Ar), 123.0 (Ar), 119.7 (Ar), 116.5 (Ar), 108.2 (Ar), 71.5 (N(1)OCH3), 66.4 (OCH2), 62.2 (C(3)CH2O), 52.4 (CO2CH3), 36.5 (OCH2CH2); MS m/z 373 [M]+; HRMS (+ESI) calcd for C20H20ClNNaO4 [M + Na]+ 396.0979, found 396.0977.Use of SnClMethyl 4-Chloro-1-n-octyloxy -3-[(methoxymethyl]-1H-indole-2-carboxylate (1xm)2·2H2O , methanol  and 2x  for 2 h at 40 °C, then use of DBU  and 1-bromooctane  for 2 h at 25 °C in general procedure afforded the title compound 1xm  as a yellow oil. Bp 178 °C (decomp.); Rf 0.67 (1:2 EtOAc/hexanes); HPLC tR 26.3 min; UV vis (CH3CN-H2O) λmax 236, 298 nm; 1H NMR : δ 7.36 , 7.25 , 7.16 , 5.08 CH2O,), 4.32 OCH2), 4.00 , 3.46 , 1.83 , 1.54–1.47 2CH2), 1.34–1.25 3(CH2)4, 0.90 7CH3); 13C NMR : δ 160.8 (C=O), 136.0 (Ar), 128.5 (Ar), 126.1 (Ar), 125.2 (Ar), 122.9 (Ar), 119.5 (Ar), 116.1 (Ar), 108.4 (Ar), 79.4 (N(1)OCH2), 63.7 (OCH3), 58.1 (C(3)CH2O), 52.3 (CO2CH3), 32.0, 29.6, 29.4, 28.4, 26.2, 22.8 (N(1)OCH2(CH2)6), 14.3 N(1)O(CH2)7CH3; MS m/z 381 [M]+; HRMS (+ESI) calcd for C20H28ClNO4 [M]+ 381.1707, found 381.1707.Use of SnClMethyl 4-Chloro-1-methoxy-3-[(n-octyloxy)methyl]-1H-indole-2-carboxylate (1xn)2·2H2O , n-octanol  and 2x  for 2 h at 40 °C, then use of DBU  and methyl iodide  for 2 h at 25 °C in general procedure afforded the title compound 1xn  as a white solid. Mp 36 °C; Rf 0.74 (1:2 EtOAc/hexanes); HPLC tR 27.0 min; UV vis (CH3CN-H2O) λmax 236, 298 nm; 1H NMR : δ 7.37 , 7.26 , 7.16 , 5.08 CH2O,), 4.19 OCH3), 4.00 , 3.57 , 1.66–1.59 , 1.43–1.25 2(CH2)5), 0.87 7CH3); 13C NMR : δ 160.9 (C=O), 136.0 (Ar), 128.8 (Ar), 126.3 (Ar), 125.3 (Ar), 122.9 (Ar), 119.7 (Ar), 116.7 (Ar), 108.1 (Ar), 70.8 (N(1)OCH3), 66.4 (OCH2), 62.1 (C(3)CH2O), 52.4 (CO2CH3), 32.0, 30.0, 29.6, 29.5, 26.4, 22.9 (OCH2(CH2)6), 14.3 (O(CH2)7CH3); MS m/z 381 [M]+; HRMS (+ESI) calcd for C20H28ClNO4 [M]+ 381.1707, found 381.1707.Use of SnClMethyl 4-Bromo-1-methoxy-3-(methoxymethyl)-1H-indole-2-carboxylate (1ya)2·2H2O , methanol  and 2y  for 2 h at 40 °C, then use of DBU  and methyl iodide  for 2 h at 25 °C in general procedure afforded the title compound 1ya  as a white solid. Mp 50–52 °C; Rf 0.29 (1:4 EtOAc/hexanes); HPLC tR 34.3 min; UV vis (CH3CN-H2O) λmax 215, 298 nm; 1H NMR : δ 7.49–7.38 , 7.19 , 5.08 CH2O), 4.18 OCH3), 4.01 , 3.47 ; 13C NMR : δ 160.8 (C=O), 135.8 (Ar), 131.2 (Ar), 126.7 (Ar), 125.6 (Ar), 123.8 (Ar), 120.9 (Ar), 116.7 (Ar), 108.8 (Ar), 66.4 (N(1)OCH3), 63.0 (CH2OCH3), 58.0 (C(3)CH2O), 52.4 (CO2CH3); MS m/z 327 [M]+; HRMS (+ESI) calcd for C13H14BrNNaO4 [M + Na]+ 350.0004, found 350.0002.Use of SnClMethyl 4-Bromo-1-ethoxy-3-(ethoxymethyl)-1H-indole-2-carboxylate (1yb)2·2H2O , ethanol  and 2y  for 2 h at 40 °C, then use of DBU  and bromoethane  for 2 h at 25 °C in general procedure afforded the title compound 1yb  as a white solid. Mp 42 °C; Rf 0.51 (1:2 EtOAc/hexanes); HPLC tR 26.2 min; UV vis (CH3CN-H2O) λmax 235, 299 nm; 1H NMR : δ 7.45–7.33 , 7.16 , 5.10 CH2O), 4.41 OCH2), 3.99 , 3.66 , 1.44 OCH2CH3), 1.26 , 13C NMR : δ 160.9 (C=O), 136.2 (Ar), 126.5 (Ar), 126.3 (Ar), 120.8 (Ar), 125.5(Ar), 116.7 (Ar), 116.4 (Ar), 109.0 (Ar), 75.0 (N(1)OCH2), 65.8 (OCH2), 61.4 (C(3)CH2O), 52.3 (CO2CH3), 15.6 (N(1)OCH2CH3), 13.8 (OCH2CH3); MS m/z 355 [M]+; HRMS (+ESI) calcd for C15H19BrNNaO4 [M + Na]+ 378.0317, found 378.0315.Use of SnClMethyl 4-Bromo-1-n-propyloxy-3-[(n-propyloxy)methyl]-1H-indole-2-carboxylate (1yc)2·2H2O , n-propanol  and 2y  for 1 h at 40 °C, then use of DBU  and 1-bromopropane  for 2 h at 25 °C in general procedure afforded the title compound 1yc  as a white oil. Bp 224 °C (decomp.); Rf 0.62 (1:2 EtOAc/hexanes); HPLC tR 31.3 min; UV vis (CH3CN-H2O) λmax 236, 299 nm; 1H NMR : δ 7.40 , 7.39 , 7.15 , 5.10 CH2O), 4.29 OCH2), 3.98 , 3.55 , 1.86 OCH2CH2), 1.66 , 1.11 OCH2CH2CH3), 0.94 ; 13C NMR : δ 160.9 (C=O), 136.0 (Ar), 126.5 (Ar), 126.3 (Ar), 125.7(Ar), 120.9 (Ar), 116.8 (Ar), 116.4 (Ar), 108.9 (Ar), 80.7 (N(1)OCH2), 72.3 (OCH2), 61.5 (C(3)CH2O), 52.3 (CO2CH3), 23.2 (N(1)OCH2CH2), 21.8 (OCH2CH2), 11.0 (N(1)O(CH2)2CH3), 10.7 (O(CH2)2CH3); MS m/z 383 [M]+; HRMS (+ESI) calcd for C17H22BrNNaO4 [M + Na]+ 406.0630, found 408.0608.Use of SnClMethyl 4-Bromo-1-n-octyloxy-3-[(n-octyloxy)methyl]-1H-indole-2-carboxylate (1yg)2·2H2O , n-octanol  and 2y  for 1 h at 40 °C, then use of DBU  and 1-bromooctane  for 2 h at 25 °C in general procedure afforded the title compound 1yg  as a yellow oil. Bp 194 °C (decomp.); Rf 0.40 (1:10 EtOAc/hexanes); HPLC tR 36.1 min; UV vis (CH3CN-H2O) λmax 236, 299 nm; 1H NMR : δ 7.37 , 7.16 , 5.09 CH2O), 4.32 OCH2), 3.98 , 3.57 , 1.83 OCH2CH2), 1.66–1.15 OCH2CH2(CH2)5, OCH2(CH2)6), 1.00–0.77 O(CH2)7CH3, O(CH2)7CH3); 13C NMR : δ 160.9 (C=O), 136.0 (Ar), 126.4 (Ar), 126.3 (Ar), 125.6 (Ar), 120.9 (Ar), 116.8 (Ar), 116.4 (Ar), 108.9(Ar), 79.4 (N(1)OCH2), 70.6 (OCH2), 61.5 (C(3)CH2O), 53.3 (CO2CH3), 53.3, 52.3, 32.0, 30.1, 30.0, 29.7, 29.5, 29.4, 28.5, 26.5, 26.2, 22.9 (N(1)OCH2(CH2)6,OCH2(CH2)6), 14.3 (N(1)O(CH2)7CH3), 13.5 (O(CH2)7CH3); MS m/z 523 [M]+; HRMS (+ESI) calcd for C27H42BrNNaO4 [M + Na]+ 546.2195, found 546.2190.Use of SnClMethyl 4-Bromo-1-benzyloxy-3-[(benzyloxy)methyl]-1H-indole-2-carboxylate (1yh)2·2H2O , benzyl alcohol  and 2y  for 2 h at 40 °C, then use of DBU  and benzyl bromide  for 2 h at 25 °C in general procedure afforded the title compound 1yh  as a pale-yellow solid. Mp 84 °C; Rf 0.29 (1:2 EtOAc/hexanes); HPLC tR 33.3 min; UV vis (CH3CN-H2O) λmax 228, 299 nm; 1H NMR : δ 7.55–7.22 , 7.13 , 5.32 CH2O), 5.19 OCH2), 4.68 , 3.88 ; 13C NMR : δ 160.8 (C=O), 138.8 (Ar), 136.3 (Ar), 134.3 (Ar), 130.0 (Ar), 129.5 (Ar), 129.0 (Ar), 128.5 (Ar), 128.3 (Ar), 127.7 (Ar), 126.7 (Ar), 126.4 (Ar), 126.2 (Ar), 121.0 (Ar), 116.5 (Ar), 116.3 (Ar) 109.2 (Ar), 81.0 (N(1)OCH2), 72.6 (OCH2Ph), 61.2(C(3)CH2O), 52.3 (CO2CH3); MS m/z 479 [M]+; HRMS (+ESI) calcd for C25H22BrNNaO4 [M + Na]+ 502.0630, found 502.0626.Use of SnClMethyl 4-Bromo-1-isopropyloxy-3-[(isopropyloxy)methyl]-1H-indole-2-carboxylate (1yj)2·2H2O , isopropanol  and 2y  for 2 h at 40 °C, then use of DBU  and 2-bromopropane  for 2 h at 25 °C in general procedure afforded the title compound 1yj  as a white oil. Bp 178 °C (decomp.); Rf 0.69 (1:2 EtOAc/hexanes); HPLC tR 30.5 min; UV vis (CH3CN-H2O) λmax 235, 299 nm; 1H NMR : δ 7.40 , 7.35 , 7.13 , 5.11 CH2O), 4.72 OCH(CH3)2), 3.97 , 3.82 CH2OCH(CH3)2), 1.34 OCH(CH3)2), 1.26 2); 13C NMR : δ 161.1 (C=O), 137.3 (Ar) 126.3 (Ar), 126.1 (Ar), 120.7 (Ar), 117.1 (Ar), 116.1 (Ar), 109.9 (Ar), (one Ar peak was not detected and believed to overlap with the observed peak), 82.2 (N(1)OCH(CH3)2), 71.5 (OCH(CH3)2), 59.4 (C(3)OCH2), 52.2 (CO2CH3), 22.4 (N(1)OCH(CH3)2), 21.3 (OCH(CH3)2); MS m/z 383 [M]+; HRMS (+ESI) calcd for C17H22BrNNaO4 [M + Na]+ 406.0630, found 406.0632.Use of SnClMethyl 4-Bromo-1-methoxy-3-[(phenylethyloxy)methyl]-1H-indole-2-carboxylate (1yl)2·2H2O , 2-phenylethyl alcohol  and 2y  for 2 h at 40 °C, then use of DBU  and methyl iodide  for 2 h at 25 °C in general procedure afforded the title compound 1yl  as a white solid. Mp 59 °C; Rf 0.57 (1:2 EtOAc/hexanes); HPLC tR 29.7 min; UV vis (CH3CN-H2O) λmax 235, 299 nm; 1H NMR : δ 7.43 , 7.38 , 7.29–7.16 , 5.15 CH2O), 4.19 OCH3), 3.96 , 3.82 , 2.96 ; 13C NMR : δ 160.8 (C=O), 139.3 (Ar), 135.9 (Ar), 129.1 (Ar), 128.5 (Ar), 126.7 (Ar), 126.5 (Ar), 126.3 (Ar), 125.6 (Ar), 120.9 (Ar), 116.8 (Ar), 116.4 (Ar), 108.8 (Ar), 71.5 (N(1)OCH3), 66.4 (OCH2), 61.6 (C(3)CH2O), 52.4 (CO2CH3), 36.5 (OCH2CH2); MS m/z 419 [M]+; HRMS (+ESI) calcd for C20H20BrNNaO4 [M + Na]+ 440.0473, found 440.0471.Use of SnClMethyl 4-Bromo-1-pentyloxy-3-[(phenylethyloxy)methyl]-1H-indole-2-carboxylate (1ym)2·2H2O , 2-phenylethyl alcohol , and 2y  for 2 h at 40 °C, then use of DBU  and 1-bromopentane  for 2 h at 25 °C in general procedure afforded the title compound 1ym  as a white oil. Bp 224 °C (decomp.); Rf 0.70 (1:2 EtOAc/hexanes); HPLC tR 36.7 min; UV vis (CH3CN-H2O) λmax 236, 299 nm; 1H NMR : δ 7.38 , 7.30–7.10 , 5.16 CH2O), 4.32 OCH2), 3.95 , 3.81 , 2.96 , 1.95–1.73 OCH2CH2), 1.58–1.30 OCH2CH2(CH2)2), 0.96 O(CH2)4CH3); 13C NMR : δ 160.7 (C=O), 139.3 (Ar), 136.0 (Ar), 129.1 (Ar), 128.5 (Ar), 126.5 (Ar), 126.4 (Ar), 126.3 (Ar), 125.6 (Ar), 120.8 (Ar), 116.5 (Ar), 116.4 (Ar), 108.9 (Ar), 79.4 (N(1)OCH2), 71.4 (OCH2CH2Ph), 61.6 (C(3)CH2O), 52.3 (CO2CH3), 36.6 (OCH2CH2Ph), 28.3 (N(1)OCH2CH2), 28.2 (N(1)O(CH2)2CH2), 22.8 (N(1)O(CH2)3CH2), 14.2 (N(1)O(CH2)4CH3); MS m/z 473 [M]+; HRMS (+ESI) calcd for C24H28BrNNaO4 [M+Na]+ 496.1099, found 495.1097.Use of SnClMethyl 2-(4′-Chloro-1′-hydroxy-1′H-indol-3′-yl)-2-oxoacetate (10x) Rf 0.15 (2:1 EtOAc/hexanes); 1H NMR : δ 9.45 , 8.27 , 7.51 , 7.40 , 7.33 , 3.90 ; 13C NMR : δ 179.5, 164.3, 137.5, 127.2, 126.5, 126.4, 126.1, 117.4, 110.0, 109.5, 53.8; MS m/z 276 [M + Na]+; HRMS (+ESI) Calcd for C11H8ClNNaO4 [M + Na]+ 276.0040, found 276.0034.Brown solid. Mp 84–86 °C; Methyl 2-(4′-Chloro-1′-methoxy-1′H-indol-3′-yl)-2-oxoacetate (11xl)2·2H2O , 2-phenylethyl alcohol , and 2x  for 2 h at 40 °C, then use of DBU  and methyl iodide  for 2 h at 25 °C in general procedure afforded the compound 11xl  as a brown solid. Mp 64 °C; Rf 0.50 (2:1 EtOAc/hexanes); HPLC tR 16.6 min; UV vis (CH3CN-H2O) λmax 218, 262, 321 nm; 1H NMR : δ 8.40 H), 7.43–7.15 , 4.19 OCH3), 3.95 ; 13C NMR : δ 177.7 (C(3)C=O), 164.1 (C=O), 134.3 (Ar), 133.5 (Ar), 128.1 (Ar), 125.4 (Ar), 125.3 (Ar), 120.7 (Ar), 109.6 (Ar), 107.8 (Ar), 67.6 (N(1)OCH3), 53.2 (CO2CH3); MS m/z 267 [M]+; HRMS (+ESI) calcd for C12H10ClNNaO4 [M + Na]+ 290.0196, found 290.0193.Use of SnClMethyl 2-(4′-Chloro-1′-octyloxy-1′H-indol-3′-yl)-2-oxoacetate (11xm)2·2H2O , methanol , and 2x  for 2 h at 40 °C, then use of DBU  and 1-bromooctane  for 2 h at 25 °C in general procedure afforded the compound 11xm  as a brown solid. Mp 40 °C; Rf 0.57 (1:2 EtOAc/hexanes); HPLC tR 33.4 min; UV vis (CH3CN-H2O) λmax 219, 262, 320 nm; 1H NMR : δ 8.37 H), 7.40–7.15 , 4.31 OCH2), 3.95 , 1.82 OCH2CH2), 1.57–1.25 2(CH2)5, 0.90 7CH3); 13C NMR : δ 177.6 (C(3)C=O), 164.2 (C=O), 134.9 (Ar), 134.2 (Ar), 128.1 (Ar), 125.4 (Ar), 125.2 (Ar), 109.4 (Ar), 108.0 (Ar), (one Ar peak was not detected and believed to overlap with the observed peak), 80.5 (N(1)OCH2), 53.1 (CO2CH3), 32.1, 29.9, 29.5, 28.3, 25.9, 22.8 (N(1)OCH2(CH2)6), 14.3 (N(1)O(CH2)7CH3); MS m/z 365 [M]+; HRMS (+ESI) calcd for C19H24ClNO4 [M]+ 365.1394, found 365.1394.Use of SnCl1 through four step reactions. With substrates 2 obtained by two-step synthetic sequences, we performed the reactions using SnCl2·2H2O as a reducing agent and alcohols (R1OH) as nucleophiles, through reduction, condensation, and 1,5-addition, affording the intermediates, 1-hydroxyindoles 8. Subsequent alkylation reactions of 8 using alkyl halides (R2Y) in basic condition gave target compounds, 1-alkoxyindoles 1. The optimized condition was established as follows: 1) conjugate ketoester (1.0 eq), SnCl2·2H2O (3.3 eq), and alcohols (2.0 eq) in DME for 1–2 h at 40 °C and 2) DBU (10.0 eq) and alkyl halides (2.0 eq) for 1–4 h at 25–50 °C. Considering the yields and reaction efficiency, we chose 2.0 eq of both alcohols and alkyl halides, focusing on the optimization of the final alkylation step. All four step reactions were performed in one-pot, providing 1-alkoxyindoles 1 in modest to good yields . Mechanistic investigations on reaction pathways  were presented along with the formation of side products 11.We reported the studies on one-pot synthesis of novel multisubstituted 1-alkoxyindoles


The surface characteristics were evaluated by X-ray photoelectron spectroscopy (XPS), contact angle measurements, and atomic force microscopy (AFM). Moreover, the antibacterial activity against Streptococcus mutans (S. mutans) and the antifungal activity against Candida albicans  were examined by the adenosine-5’-triphosphate (ATP) emission luminescence method. XPS spectra of the modified specimens revealed peaks due to F in the Ar/F and the Ar/F+Ag groups, and due to Ag in the Ar+Ag and the Ar/F+Ag groups. The water contact angle increased significantly due to the implantation of Ar, F, and Ag. In the AFM observations, the surface roughness of the Ar/F and the Ar/F+Ag groups increased significantly by less than 5 nanometers. The presence of F and Ag was found to inhibit S. mutans growth in the Ar+Ag and the Ar/F+Ag groups. However, this method provided no significant antifungal activity against C. albicans.The purpose of this study was to examine the anti-oral microorganism effects of fluorine and/or silver ions implanted into acrylic resin (PMMA) using plasma-based ion implantation (PBII) with argon gas. The experimental PMMA specimens were implanted with F and Ag ions alone or simultaneously by the PBII method using Ar or Ar/F Poly (methyl methacrylate) (PMMA) is widely used for dental applications, such as removable appliances and orthodontic appliances and retainers ,2. The dStaphylococcus aureus (S. aureus), on the PMMA surface [The two major types of methods for functionalizing dental materials are classified into chemical and physical modification methods. In chemical methods, material is mixed with appropriate antibacterial agents. In this approach, care must be taken to avoid disrupting any original properties of the body material and to control the amount of agent released from the material ,12. Plas surface . HoweverStreptococcus mutans (S. mutans) are major cariogenic organisms. S. mutans coadhere or coaggregate with other microbial species, followed by proliferation and spreading into other sites in the oral mucosa modulated by the concerted action of genes and signaling molecules [S. mutans is the etiologic agent of dental caries, recent evidence indicates a high prevalence of S. mutans in dental biofilms where the fungal pathogen Candida albicans  resides, suggesting that the interaction between these diverse species may facilitate cariogenic development [Oral microbial communities are some of the most complex microbial floras in the human body, consisting of more than 700 different bacterial species . Streptoolecules . Conversolecules . Intereselopment . S. mutans and C. albicans).Furthermore, previous reports ,16 descr2 (Ar/F2 gas). For the Ag ion implantation, a 99.8% Ag mesh cover was placed 10 mm above the PMMA plates and then sputtered by Ar or Ar/F2 gas at an acceleration voltage of –5 keV for 60 min. The experimental groups and the conditions of the treatments by PBII are listed in PMMA  plates having dimensions of 10 mm × 10 mm × 1 mm were used. The PMMA plates were modified by PBII equipment at Plasma Ion Assist Co., Ltd., Kyoto, Japan. The gases used for the ion implantation process were 100% Ar, or 95% Ar and 5% FThe surface atomic changes of each specimen were characterized by XPS using an X-ray photoelectron spectrometer  with an Al-Kα radiation source . Peak decomposition was achieved using software , and the peak assignments were supported with Handbook of X-ray Photoelectron Spectroscopy .2I2, 267.84 g/mol, >97.0%; FUJIFILM Wako Pure Chemical Co., Osaka, Japan), and ethylene glycol  at room temperature. One point per specimen was measured, and the number of specimens treated in the same manner was five (n = 5). The surface free energy of each sample group was calculated from the average of the contact angles of each test liquid, the known surface tension component values of each test solution [Specimens were ultrasonically cleaned in distilled water for 10 min and then dried in a clean bench at room temperature before the contact angle measurements were taken. Static contact angle measurements were conducted via the sessile drop technique using a contact angle meter  with three test liquids: distilled water, diiodomethane  in the tapping mode in air. An AFM probe  with a tip radius of <10 nm was used. The surface topography was observed, and the surface roughness was calculated. The evaluation of the roughness parameter of the sample was based on a scanned area of 378 nmS. mutans (ATCC 25175) and C. albicans (ATCC 10261) were used in this study. An antimicrobial test was performed by the Adenosine-5’-triphosphate (ATP) luminescence method [S. mutans and C. albicans cell suspensions were spectrophotometrically standardized to concentrations of 8 × 105 and 3 × 105 CFU/ml, respectively. The suspension (50 μl) was dropped to the bottom of a sterilized 24 well plate, covered by the PMMA specimen, and then incubated at 37 °C for 4 h. After incubation, PBS solution was poured into the vial and S. mutans or C. albicans, which adhered to PMMA specimens, were washed out by shaking. The ATP luminescence intensity of the suspension, which was expressed in relative luminescence units (RLU), was measured using an ATP-luciferase reaction kit  and a luminescence-measuring instrument , according to the manufacturer’s instructions. Each specimen was measured three times. The number of specimens treated in the same manner was two (n = 5).Specimens were sterilized using a formalin gas sterilizer  before the antimicrobial test. Two reference strains from the American Type Culture Collection, e method . One loop < 0.05 indicating statistically significant results. The confidence interval was set at 95%.Data were presented in the form of mean ± standard deviation (S.D.) and analyzed via one-way ANOVA and Tukey’s test , with 2 (367.0 eV), in contrast, the peak of AgF2 was predominant internally. The Ag3d spectra on the surface were in the form of Ag oxide and exhibited AgF in the near-surface layer.Additionally, S. mutans suspension that was in contact with each specimen for 4 h, and C. albicans suspension after having been in contact with each specimen for 4 h. C. albicans colonies showed no apparent differences in colony numbers between all groups.3F8 was used for F ion implantation to PMMA. However, C3F8 is a perfluorocarbon that was designated as a greenhouse substance by the Kyoto Protocol [2 (Ar/F gas) was used instead of C3F8 gas in the present study, and XPS analysis revealed that F and Ag ions could be implanted into PMMA using these gases. Moreover, previous studies [In our previous study ,16, C3F8Protocol ,24. For  studies ,16 compa studies ,16. Howe studies . Additio studies . Therefo studies ,27. In t studies ,29. TherWhen comparing the effect of Ar gas and Ar/F gas on the surface properties and shape, the groups using Ar/F gas tended to have rougher surfaces from AFM observations and higher surface roughness values than the groups using Ar gas. The AFM images in S. aureus. Additionally, experiments in this study demonstrated that Ag ion and F/Ag dual ion implantation by PBII induced an antibacterial effect against S. mutans on the PMMA surface. However, they did not affect C. albicans. More recently, silver nanoparticles (AgNP) have been incorporated into several biomaterials, and many researchers have reported their effects against bacteria and fungus [S. aureus, S. mutans, and C. albicans. The author reported that the susceptibility of C. albicans to AgNP-tissue conditioner sample (0.5%) was less than that of the samples (0.1%) of S. aureus and S. mutans. Moreover, Lu [S. mutans and C. albicans in the present study were dependent on the Ag concentration on the surface of our PMMA specimens and the susceptibility of C. albicans to Ag. Moreover, Panáček [C. albicans may cause cytotoxicity in the human body.The antimicrobial effects of Ag occur by the following mechanism: (1) destruction of oxidation catalyzed by Ag, (2) disruption of electron transfer in the bacteria by monovalent Ag, and/or prevention of the unwinding of DNA in the viruses with the substitution of H ions by monovalent Ag, and (3) destruction of bacteria and viruses by bivalent and trivalent Ag . Therefod fungus . Nam [35over, Lu  demonstr Panáček  found thS. mutans in the Ar/F group implanted with F ions alone. Several studies have reported the use of F alone or a combination of F and Ag for dental materials to obtain an anti-cariogenic effect [S. mutans, Lactobacillus acidophilus, and Actinomyces naeslundii and identified the components that are effective against these bacteria. The conclusion was that Ag ions appear to be the principal antibacterial agent at both high and low concentrations, whereas F ions only have antibacterial effects at high concentrations. In another study [S. mutans. It concluded that NSF, which includes both Ag and F, had a better antibacterial effect than NaF, Ag excluded. These findings indicated that only Ag ions appear to possess an anti-caries effect under the conditions of low concentrations of F and Ag ions. Therefore, it may be said that only Ag ions should be implanted to PMMA for an antibacterial activity. Regarding the antibacterial activity against S. mutans, no significant difference was observed between the Ar+Ag group and the Ar/F+Ag group; however, the Ar/F+Ag group tended to suppress the growth of S. mutans more than the Ar+Ag group. Tsuji et al. [S. mutans due to a synergistic effect, which is the antibacterial effect of the Ag ions and the hydrophobicity suppressing bacterial adhesion by the F ions.In this study, there was no antibacterial activity against c effect ,39. Lou c effect  evaluatei et al.  indicatei et al. ,42. Tangi et al. . Yoda eti et al. . Based oS. mutans was stacked on the sample surface because the incubation was performed in a short time using a small amount of bacterial solution. Whether the antibacterial effect of Ag ions penetrated multiple bacterial layers that adhered to the sample surface by increasing the incubation time or increasing the amount of bacterial solution in contact with the sample was beyond the scope of this work and should be examined in future studies.However, the antimicrobial test in this study has its limitations. For instance, the incubation time was set to a reduced time of 4 h to control the evaporation of the bacterial solution, because a very small amount of the bacterial solution was brought into contact with the sample surface. This was so to minimize the amount of bacterial solution in contact with only one ion-implanted surface. It was considered that the antibacterial properties of Ag ion on the sample surface were exhibited before S. mutans, but no significant antifungal activity against C. albicans. In the present study, PMMA plates were simultaneously implanted with both F and Ag ions using Ar or Ar/F gases and Ag mesh by a PBII process. The following results were obtained. (1) Fluorine and silver ions were implanted in PMMA via PBII with Ar gas. (2) Surface contact angle was significantly increased by both ion implantations. (3) Surface roughness changed due to these ions implantations by less than 5 nanometers. (4) Fluorine and silver ions implantations inhibited the growth of Ar is an environmentally friendly gas; thus, it had no significant physical or chemical effects on our modification method, which made Ar gas suitable for the practical application of our method. F ion implantation improved the hydrophobicity of the surface of the PMMA sample which can reduce bacterial attachment and aggregation, and the antibacterial effect of the Ag ions gave the PMMA sample antibacterial properties against oral bacteria. However, no antifungal effect was obtained.