Next Article in Journal
High-Value Plant Species Used for the Treatment of “Fever” by the Karen Hill Tribe People
Previous Article in Journal
Efficacy of A Poly(MeOEGMA) Brush on the Prevention of Escherichia coli Biofilm Formation and Susceptibility
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Antibiotics: A Bibliometric Analysis of Top 100 Classics

Anas Imran Arshad
Paras Ahmad
Mohmed Isaqali Karobari
Jawaad Ahmed Asif
Mohammad Khursheed Alam
Zuliani Mahmood
Normastura Abd Rahman
Noraida Mamat
1 and
Mohammad Amjad Kamal
Paediatric Dentistry Unit, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
Paedodontics Department, Rashid Latif Dental College, Lahore 54600, Pakistan
Oral Medicine Unit, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
Conservative Unit, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
Oral and Maxillofacial Surgery Unit, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
Orthodontic Department, College of Dentistry, Jouf University, Sakaka 72721, Kingdom of Saudi Arabia
Dental Public Health Unit, School of Dental Sciences, Universiti Sains Malaysia, Kota Bharu 16150, Malaysia
King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Enzymoics, 7 Peterlee Place, Hebersham, NSW 2770, Australia
Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
Author to whom correspondence should be addressed.
Antibiotics 2020, 9(5), 219;
Submission received: 15 April 2020 / Revised: 22 April 2020 / Accepted: 23 April 2020 / Published: 29 April 2020


Citation frequencies represent the most significant contributions in any respective field. This bibliometric analysis aimed to identify and analyze the 100 most-cited publications in the field of antibiotics and to highlight the trends of research in this field. “All databases” of Clarivate Analytics’ Web of Science was used to identify and analyze the 100 publications. The articles were then cross-matched with Scopus and Google Scholar. The frequency of citation ranged from 940 to 11,051 for the Web of Science, 1053 to 10,740 for Scopus, and 1162 to 20,041 for Google Scholar. A total of 513 authors made contributions to the ranked list, and Robert E.W. Hancock contributed in six articles, which made it to the ranked list. Sixty-six scientific contributions originated from the United States of America. Five publications were linked to the University of Manitoba, Canada, that was identified as the educational organization, made the most contributions (n = 5). According to the methodological design, 26 of the most cited works were review-type closely followed by 23 expert opinions/perspectives. Eight articles were published in Nature journal, making it the journal with the most scientific contribution in this field. Correlation analysis between the publication age and citation frequency was found statistically significant (p = 0.012).

1. Introduction

The bibliometric analysis provides a quantitative review of literature in any field of research based on the citation frequency of the conducted research. This type of analysis identifies the countries, organizations, and authors who were affiliated with the most prominent scientific contributions [1,2]. The thrust areas of the past research in a specialty can be identified by analyzing the most-cited work currently, which information can then be used to channel the future research.
The bibliometric concept of “citation classics” was described by the founder of the Institute for Scientific Information (ISI), Dr Eugene Garfield, in 1977. Its purpose was identification as well as acknowledgment of frequently cited research of authors and their peers that would consequently encourage the respective work and its impact on the specialty [3]. The eligibility of a scientific contribution to be counted as a “classic” depends on the specialty being analyzed. While some analysts believe that 100 or more citations of a publication are sufficient [4,5,6,7], others believe that a publication must be cited more than 400 times to be counted in the list [8]. Leading scientific databases like Web of Science (WoS), Elsevier’s Scopus (ES), and Google Scholar (GS) and influential publishers like BioMed Central, Nature, Wiley, Frontiers, Elsevier, and PLOS are developing and embedding options to perform on-site citation analysis [4,8,9,10,11].
Several bibliometric analyses have been conducted in other fields of health sciences, which include the specialty of dentistry [4,8,9,12,13] and medicine [14,15,16,17,18]. However, the “classics” in the field of antibiotics has not been identified. The aim is to identify and analyze the top 100 classics in the specialty of antibiotics to highlight the notable advancements made on this very topic over the recent decades.

2. Materials and Methods

2.1. Search Strategy

Two independent reviewers (A.I.A) and (P.A) conducted a literature search on 21st March 2020 using ‘All-Databases’ collection of WoS. The search terms were identified after consulting field experts from different institutions, and a final search string was developed and agreed upon unanimously. No language restrictions, publication year range, or methodology selections were applied.

2.2. Eligibility Criteria

Titles of the articles published in peer-reviewed journals were selected when either of the following search terms identified: “antibiotics” OR “antibiotic” OR “anti-bacterial” OR “antibacterial” OR “anti-infective” OR “anti-infectious” OR “anti-microbial” OR “antimicrobial”.
Articles having less than 400 citations according to the WoS and ES databases were excluded. Articles published in low or no impact factor journals were not included in the marked list.

2.3. Data Extraction and Bibliometric Parameters

A total of 124,122 publications were initially identified using the search string described above. The publications were sorted based on the frequency of citations in a descending manner. The list of top 100 classics was marked based on the citation frequency. The marked list was then cross-matched with GS and ES databases. The marked lists from the A.I.A and P.A was then shared with the field experts, and all authors unanimously agreed upon the final list. Bibliometric parameters for the articles available in “All Databases” were recorded from the WoS database, which includes the title of the article, journal title, citation count, current citation index (CCI) 2019 (total citations received in 2019), publication year, names of authors along with their affiliated organizations, and country of origin. Each publication was then hand-searched to identify evidence level, keywords, and the methodology of the study. The missing data was then cross-matched with the ES database to ensure the accuracy and correctness of collected information.

2.4. Methodological Design

The publications were then categorized according to the methodology of the study as review articles, expert opinion, clinical practice guidelines, cross-sectional study, new material or technique, clinical studies, and laboratory studies.

2.5. Institution and Country of Origin

The author’s affiliation and origin country of publication were retrieved from the ES database as complete information for the marked list was not available from the WoS database. The retrieved information was then hand-searched and compared with the original text for each manuscript. Although corresponding addresses are considered a reliable source to identify the country of origin of publication; however, upon searching manually, it was seldom recorded. Each institution contributing to the publication was recorded as a single entry.

2.6. Data Analysis

The “Visualization of Similarities (VOS) viewer software” is widely used to graphically illustrate the bibliometric parameters in mapping networks, which allow easy visualization of critical elements [2,19,20,21]. The current study used VOS to represent a graphical mapping of keywords as identified bibliometric analysis to identify the focus of research in recent decades.

2.7. Statistical Analysis

The descriptive data and associations of citation frequency, citation density, publication age, and CCI were analyzed using IBM SPSS Statistics®, version 22, using the Spearman rank test. The normality of data was checked using the Shapiro–Wilk test. To explore the difference between two or more independent groups, the Kruskal–Wallis test was performed. Post-hoc testing was performed to confirm the difference between variables. Mann–Kendall trend test was performed to determine increasing and decreasing time trends. A p-value of < 0.05 was considered statistically significant.

3. Results

3.1. Bibliometric Parameters

The marked list of top 100 classics received a sum of 167,320 citations based on WoS, 165,947 citations based on ES, and 262,727 based on the GS database. The frequency of citations ranged from 940 to 11,051 (WoS), 1053 to 10,740 (ES), and 1162 to 20,041 (GS). Citation density is defined as the average number of citations/annum; it was calculated as 2742 (WoS), 2720 (ES), and 4307 (GS) for the 100 classics. “Antibiotic susceptibility testing by a standardized single disk method” was identified as the most cited “classic” with 11,051, 10,740, and 20,041 citations according to WoS, ES, and GS databases, respectively, with a citation density of 205 [22]. “Antimicrobial peptides of multicellular organisms” was ranked second with 5685, 5668, 7994 citations according to WoS, ES, and GS databases, respectively, with a citation density of 316 [23]. “Transformation of mammalian cells to antibiotic resistance with a bacterial gene under the control of the SV40 early region promoter” was ranked third with 3891, 2319, 3875 citations according to WoS, ES, and GS databases, respectively, with a citation density of 102 [24]. The marked list of top 100 classics along with their citation frequency from WoS, ES, and GS databases, publication age, citation density, and CCI 2019 is presented in Table 1. Shapiro–Wilk test revealed non-normal data on the citation frequency, citation density, and age of publication (years). Figure 1a shows a statistically significant upward trend of citation frequency was noted with the increase in publication age (R2 = 0.044, p = −0.012). Figure 1b shows a downward trend of citation density was noted with an increase in the age of publication (R2 = 0.304, p = −0.551), which was not statistically significant. The Supplementary Figure S1 illustrates the distribution of citation frequency over the last six decades.

3.2. Year of Publication

Chronologically, the oldest classic with 60 years of publication age was published in 1959 [60], and three articles with four years of publication age were published in 2015 [92,109,119] made it to the “classics” list. Fifty articles were published during 2000–2009, followed by 22 published during 1990–1999, 13 published during 2010–2019, seven published during 1980–1989, five published during 1959–1969, and three published during 1970–1979. Nine articles were published in 1999, marking it the year of most publications. Interestingly, 63% of the articles were published within the last two decades. The highest number of “classics” were published between 2000 and 2009 (n = 50).

3.3. Methodological Design and Evidence Level (EL)

The distribution of the list based on methodological design is illustrated in Figure 2. Based on the level of evidence, 71 publications were graded as level-V, two were graded as level-IV, one belonged to level-III, four publications were graded as level-II, and 17 were graded as level-I. The evidence level and methodological design of five publications [24,52,60,77,111] were not identified as full-text of the articles were not accessible through different electronic sources.

3.4. Contributing Authors, Institutions, and Countries

Robert E.W. Hancock was identified as the most contributing, authoring six classics, followed by Tomas Ganz, who contributed in four classics. A total of 513 authors contributed to the top 100 classics, among them 26 authors were contributed in two “classics” each. Complete texts for 95 publications were obtained, and five publications were not accessible through different institutions [24,52,60,77,111]. Based on the institutional address of the corresponding author as retrieved from the ES database, individuals from 26 countries contributed to the “classic” articles. Among these, 69 scientific contributions were from the United States of America. Followed by 18 publications from Canada, 11 from Germany, and four from Sweden. Three publications originated from Belgium, China, and Israel. Two publications originated from Egypt, Denmark, and India. One publication originated from, Argentina, Croatia, Ecuador, France, Kenya, Korea, Netherlands, New Zealand, Pakistan, South Africa, South Korea, Spain, Tanzania, Thailand, United Kingdom, and Australia.
Among 246 international institutions, the greatest contribution to the “classic” articles was made by the University of Manitoba, Canada, in six classics followed by the Stanford University School of Medicine, USA, in five classics. “University of Washington, USA”, “University of British Colombia, Canada”, “The University of California at Los Angeles, USA”, and “Harvard University, USA” contributed in four classics. “University of Kiel, Germany” and “University of California at San Diego, USA” contributed in three classics. “Robert Wood Johnson Medical School, USA”, “Weizmann Institute of Science, Israel”, “Emory University, Atlanta, Georgia, USA”, “Laurentian University, Ontario, Canada”, “Rush-Presbyterian-St. Luke’s Medical Center, USA”, “St. Agnes Medical Center, USA”, “the Centers for Disease Control and Prevention, Atlanta, Georgia, USA”, and “Veterans Affairs Palo Alto Health Care System, California, USA” contributed to two classics each.

3.5. Journal of Publication

The 100 classics were published across 63 different journals. Figure 3 presents the list of journals in which the highest number of classics were published. The list of the remaining journals is available as Supplementary Table S1.

3.6. Keywords

The most frequently occurring keywords in the top 100 classics were “anti-bacterial agents” and “antibiotic agent”, followed by “antibiotic resistance”, “anti-infective agent”, and “antimicrobial”. Figure 4 is a graphical presentation of keywords arranged in a network of clusters. Colorful nodes represent the linkage of specific keywords to each cluster. Table S2 enlists the total number of index keywords and their frequency of occurrence based on the Elsevier Scopus database.

4. Discussion

The current study identified and analyzed the top 100 classics on antibiotics, antimicrobials, or antibacterial agents. Identification of any scientific contribution and inclusion in classics warrants the excellence and acclaimed acknowledgment by the relevant field experts, researchers, and scientists [12]. Theoretically, a higher citation frequency of a publication indicates the quality of the research conducted as identified by the scientific community [122]. Identification is imperative to study whether the classics have elaborated or explored the understanding of a problem and/or provided a comprehensive approach towards its solution, or whether the publication introduced a research trend or provided an expert opinion/summary on a topic of interest. The results of this study present the research perspective in the field of antibiotics, antimicrobials, or antibacterial agents for the last six decades. Additionlly, it illustrates key trends of research as well as clinical practice [2,8].
The definition of “classics” largely depends on the research field/specialty to which the publication belongs. In some fields, 100 or more citations of a publication are considered enough to classify it as a “classic” [6]. In perspective, the article ranked as 100th in the current study received 940 citations in comparison with the article ranked as 1st in the field of physics research in Korea that received 302 citations [123] or with the article ranked as 1st in the dental caries research that received 2003 citations [19]. For the current study, the publications receiving more than 400 citations can be considered classics. However, these publications will not make it to the top 100 due to the immense availability of the highly cited publications.
Web of Science was used as a benchmark database because it has citation metrics from 1945 to the present [124]. A significant variance was observed when the citation metrics were cross-matched with other databases. The Elsevier Scopus database reports the citations dated back to 1996, which is a severe flaw while figuring out the most-cited papers. In contrast, the Google Scholar database counts the citations based on published articles, books, conference proceedings, thesis/dissertations, technical reports, and preprints, which explains the higher citation counts reported in the current study [2].
The current study found a statistically significant correlation of the citation frequency with the age of publication, which is similar to the findings of a previous bibliometric analysis report [2]. Although there was an upward trend of citations received by the classics to the age of publication [125], the trend analysis of the influence of age of publication on the citation density revealed that certain topics after reaching maturity show a decrease in citation density. This change in trend can be also be noticed from the current citation index 2019.
It has been reported that the actual impact of a publication can only be assessed at least two decades after it has been published [2,4,17]. Interestingly, this phenomenon has been observed in the current study as the most number of classics were published in 1999. However, it is noteworthy that with the changing trends of how published work is reviewed, the accessibility of literature has increased multifold, and research from around the world can be remotely reviewed without needing access to archives, libraries, and published paper journals. This debate is backed up by the current study, which observed that 63 classics were published during the last two decades. This finding indicates that in the current era of digital technology, classics might require lesser years to reach their maturity stage.
With the evolution of research, several guidelines have been introduced to fulfill the ever-growing need for organized reporting of observational studies [126], laboratory studies [127], clinical studies [128], or reviews [129]. These guidelines allow the scrutinization of scientific information and improve the quality and transparency of reports. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement is used to report systematic review and meta-analysis mainly focusing on evaluating randomized trials to provide the highest level of evidence. Surprisingly, the current study did not identify any systematic review of literature or meta-analysis, which made it to the list. The title of the study report is another key element which is stressed upon in various guidelines. It is entirely possible that some classics were not identified in the current study owing to how their titles were designed. A title should explicitly describe the methodology of study and key elements which identify the study to allow proper indexing of the article.
Keywords play an essential role in the discoverability of any published article [130]. While searching any specific type of literature, scholars tend to methodically utilize search terms which are generally used in a specific field [131]. In this study, prime examples of such terms are antibiotics, antibacterials, or antimicrobials. However, it was noted that keywords only appeared in articles published after 1995 and more so not mandatorily in every publication. It was noted that even though keywords might have been submitted in the journal database during submission of manuscripts, the published articles did not display the keywords [55,63,109]. These incoherencies make the network analysis of keywords somewhat misleading and inconsistent with the actual data if we only rely on hand-searching. Therefore, the ES database was utilized to retrieve the relevant data to allow a presentable and fair network analysis.

5. Limitations

Firstly, a large amount of “classic” articles had to be excluded from the list as it was not considered possible to perform the bibliometric analysis of 500 or more articles in the current study. Therefore, the top 100 classics which achieved the maximum citations were selected for the present study. Secondly, the most recently published research papers are at a disadvantage irrespective of their content and quality, since they were outside the time window considered. Under this spectrum, it would not be wrong to say that the real impact of a research article cannot be accurately determined for at least five years post-publication.

6. Conclusions

This bibliometric analysis of the top 100 classics on antibiotics revealed that the increase in the age of publication positively influenced the citation frequency. Unlike times before 1996, the explosion of access to scientific articles in the current era of digital technology means that classics written more recently might require fewer years to reach their mature stage. In spite of substantial developments and advancements in this field/specialty in recent decades, there is a dearth of systematic reviews and meta-analyses among the top 100 publications. Keywords are the cornerstones of the discoverability of any manuscript and therefore, quality journals and publishers should mandate the inclusion of keywords in every publication to ensure maximum visibility of the publication across all databases.

Supplementary Materials

The following are available online at, Figure S1: Distribution of citations frequency over last six decades, Table S1: List of journals which published top 100 classics, Table S2: List of keywords identified from the Elsevier Scopus database.

Author Contributions

Conceptualization, M.A.K., M.K.A., and N.A.R.; methodology, Z.M. and N.M.; software, M.I.K.; validation, Z.M. and N.M.; formal analysis, A.I.A. and P.A.; resources, J.A.A. and N.A.R.; data curation, M.I.K.; writing—original draft preparation, A.I.A. and P.A.; writing—review and editing, J.A.A., M.A.K., M.K.A., N.A.R., Z.M. and N.M. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.


The first author is grateful to the university for providing financial assistance under fellowship scheme for 2 years of his candidature.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Hirsch, J.E. An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. USA 2005, 102, 16569–16572. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Ahmad, P.; Dummer, P.; Noorani, T.; Asif, J. The top 50 most-cited articles published in the International Endodontic Journal. Int. Endod. J. 2019, 52, 803–818. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Aslam-Pervez, N.; Lubek, J.E. Most cited publications in oral and maxillofacial surgery: A bibliometric analysis. Oral Maxillofac. Surg. 2018, 22, 25–37. [Google Scholar] [CrossRef] [PubMed]
  4. Feijoo, J.F.; Limeres, J.; Fernández-Varela, M.; Ramos, I.; Diz, P. The 100 most cited articles in dentistry. Clin. Oral Investig. 2014, 18, 699–706. [Google Scholar] [CrossRef] [PubMed]
  5. Gondivkar, S.M.; Sarode, S.C.; Gadbail, A.R.; Gondivkar, R.S.; Chole, R.; Sarode, G.S. Bibliometric analysis of 100 most cited articles on oral submucous fibrosis. J. Oral Pathol. Med. 2018, 47, 781–787. [Google Scholar] [CrossRef] [PubMed]
  6. Andersen, J.; Belmont, J.; Cho, C.T. Journal impact factor in the era of expanding literature. J. Microbiol. Immunol. Infect. 2006, 39, 436–443. [Google Scholar]
  7. Heldwein, F.L.; Rhoden, E.L.; Morgentaler, A. Classics of urology: A half century history of the most frequently cited articles (1955–2009). Urology 2010, 75, 1261–1268. [Google Scholar] [CrossRef]
  8. Fardi, A.; Kodonas, K.; Gogos, C.; Economides, N. Top-cited articles in endodontic journals. J. Endod. 2011, 37, 1183–1190. [Google Scholar] [CrossRef]
  9. Corbella, S.; Francetti, L.; Taschieri, S.; Weinstein, R.; Del Fabbro, M. Analysis of the 100 most-cited articles in periodontology. J. Investig. Clin. Dent. 2017, 8, e12222. [Google Scholar] [CrossRef]
  10. Kolahi, J.; Khazaei, S. Altmetric: Top 50 dental articles in 2014. Br. Dent. J. 2016, 220, 569–574. [Google Scholar] [CrossRef]
  11. Van Noorden, R.; Maher, B.; Nuzzo, R. The top 100 papers. Nature 2014, 514, 550–553. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Tarazona, B.; Lucas-Dominguez, R.; Paredes-Gallardo, V.; Alonso-Arroyo, A.; Vidal-Infer, A. The 100 most-cited articles in orthodontics: A bibliometric study. Angle Orthod. 2018, 88, 785–796. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Wu, Y.; Tiwana, H.; Durrani, M.; Tiwana, S.; Gong, B.; Hafeez, K.; Khosa, F. Hallmark of success: Top 50 classics in oral and maxillofacial cone-beam computed tomography. Pol. J. Radiol. 2018, 83, 11–18. [Google Scholar] [CrossRef] [PubMed]
  14. Shuaib, W.; Acevedo, J.N.; Khan, M.S.; Santiago, L.J.; Gaeta, T.J. The top 100 cited articles published in emergency medicine journals. Am. J. Emerg. Med. 2015, 33, 1066–1071. [Google Scholar] [CrossRef] [PubMed]
  15. Coats, A.J. Top of the charts: Download versus citations in the International Journal of Cardiology. Int. J. Cardiol. 2005, 105, 123–125. [Google Scholar] [CrossRef]
  16. Tam, W.W.; Wong, E.L.; Wong, F.C.; Hui, D.S. Citation classics: Top 50 cited articles in ‘respiratory system’. Respirology 2013, 18, 71–81. [Google Scholar] [CrossRef]
  17. Baltussen, A.; Kindler, C.H. Citation classics in anesthetic journals. Anesth. Analg. 2004, 98, 443–451. [Google Scholar] [CrossRef]
  18. Ponce, F.A.; Lozano, A.M. Highly cited works in neurosurgery. Part I: The 100 top-cited papers in neurosurgical journals: A review. J. Neurosurg. 2010, 112, 223–232. [Google Scholar] [CrossRef] [Green Version]
  19. Arshad, A.I.; Ahmad, P.; Dummer, P.M.; Alam, M.K.; Asif, J.A.; Mahmood, Z.; Abd Rahman, N.; Mamat, N. Citation Classics on Dental Caries: A Systematic Review. Eur. J. Dent. 2020, 14, 128–143. [Google Scholar] [CrossRef] [Green Version]
  20. Ahmad, P.; Asif, J.A.; Alam, M.K.; Slots, J. A bibliometric analysis of Periodontology 2000. Periodontol. 2000 2020, 82, 286–297. [Google Scholar] [CrossRef]
  21. Ahmad, P.; Vincent Abbott, P.; Khursheed Alam, M.; Ahmed Asif, J. A bibliometric analysis of the top 50 most cited articles published in the Dental Traumatology. Dent. Traumatol. 2020, 36, 89–99. [Google Scholar] [CrossRef] [PubMed]
  22. Bauer, A.W.; Kirby, W.M.; Sherris, J.C.; Turck, M. Antibiotic susceptibility testing by a standardized single disk method. Am. J. Clin. Pathol. 1966, 45, 493–496. [Google Scholar] [CrossRef]
  23. Zasloff, M. Antimicrobial peptides of multicellular organisms. Nature 2002, 415, 389–395. [Google Scholar] [CrossRef] [PubMed]
  24. Southern, P.J.; Berg, P. Transformation of mammalian cells to antibiotic resistance with a bacterial gene under control of the SV40 early region promoter. J. Mol. Appl. Genet. 1982, 1, 327–341. [Google Scholar] [PubMed]
  25. Cowan, M.M. Plant products as antimicrobial agents. Clin. Microbiol. Rev. 1999, 12, 564–582. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Sondi, I.; Salopek-Sondi, B. Silver nanoparticles as antimicrobial agent: A case study on E. coli as a model for Gram-negative bacteria. J. Colloid Interface Sci. 2004, 275, 177–182. [Google Scholar] [CrossRef] [PubMed]
  27. Brogden, K.A. Antimicrobial peptides: Pore formers or metabolic inhibitors in bacteria? Nat. Rev. Microbiol. 2005, 3, 238–250. [Google Scholar] [CrossRef]
  28. Kumar, A.; Roberts, D.; Wood, K.E.; Light, B.; Parrillo, J.E.; Sharma, S.; Suppes, R.; Feinstein, D.; Zanotti, S.; Taiberg, L.; et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit. Care Med. 2006, 34, 1589–1596. [Google Scholar] [CrossRef]
  29. Cohen, S.N.; Chang, A.C.; Hsu, L. Nonchromosomal antibiotic resistance in bacteria: Genetic transformation of Escherichia coli by R-factor DNA. Proc. Natl. Acad. Sci. USA 1972, 69, 2110–2114. [Google Scholar] [CrossRef] [Green Version]
  30. Kim, J.S.; Kuk, E.; Yu, K.N.; Kim, J.-H.; Park, S.J.; Lee, H.J.; Kim, S.H.; Park, Y.K.; Park, Y.H.; Hwang, C.-Y.; et al. Antimicrobial effects of silver nanoparticles. Nanomedicine 2007, 3, 95–101. [Google Scholar] [CrossRef]
  31. Stewart, P.S.; Costerton, J.W. Antibiotic resistance of bacteria in biofilms. Lancet 2001, 358, 135–138. [Google Scholar] [CrossRef]
  32. Hancock, R.E.W.; Sahl, H.-G. Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies. Nat. Biotechnol. 2006, 24, 1551–1557. [Google Scholar] [CrossRef] [PubMed]
  33. Kovach, M.E.; Elzer, P.H.; Hill, D.S.; Robertson, G.T.; Farris, M.A.; Roop, R.M., 2nd; Peterson, K.M. Four new derivatives of the broad-host-range cloning vector pBBR1MCS, carrying different antibiotic-resistance cassettes. Gene 1995, 166, 175–176. [Google Scholar] [CrossRef]
  34. Liu, P.T.; Stenger, S.; Li, H.; Wenzel, L.; Tan, B.H.; Krutzik, S.R.; Ochoa, M.T.; Schauber, J.; Wu, K.; Meinken, C.; et al. Toll-like receptor triggering of a vitamin D-mediated human antimicrobial response. Science 2006, 311, 1770–1773. [Google Scholar] [CrossRef] [PubMed]
  35. Dorman, H.J.; Deans, S.G. Antimicrobial agents from plants: Antibacterial activity of plant volatile oils. J. Appl. Microbiol. 2000, 88, 308–316. [Google Scholar] [CrossRef] [PubMed]
  36. Sharma, V.K.; Yngard, R.A.; Lin, Y. Silver nanoparticles: Green synthesis and their antimicrobial activities. Adv. Colloid Interface Sci. 2009, 145, 83–96. [Google Scholar] [CrossRef]
  37. Mah, T.F.; O’Toole, G.A. Mechanisms of biofilm resistance to antimicrobial agents. Trends Microbiol. 2001, 9, 34–39. [Google Scholar] [CrossRef]
  38. Neu, H.C. The crisis in antibiotic resistance. Science 1992, 257, 1064–1073. [Google Scholar] [CrossRef] [Green Version]
  39. Chopra, I.; Roberts, M. Tetracycline antibiotics: Mode of action, applications, molecular biology, and epidemiology of bacterial resistance. Microbiol. Mol. Biol. Rev. 2001, 65, 232–260. [Google Scholar] [CrossRef] [Green Version]
  40. Davies, J.; Davies, D. Origins and Evolution of Antibiotic Resistance. Microbiol. Mol. Biol. Rev. 2010, 74, 417–433. [Google Scholar] [CrossRef] [Green Version]
  41. Ganz, T. Defensins: Antimicrobial peptides of innate immunity. Nat. Rev. Immunol. 2003, 3, 710–720. [Google Scholar] [CrossRef] [PubMed]
  42. Zasloff, M. Magainins, a class of antimicrobial peptides from Xenopus skin: Isolation, characterization of two active forms, and partial cDNA sequence of a precursor. Proc. Natl. Acad. Sci. USA 1987, 84, 5449–5453. [Google Scholar] [CrossRef] [Green Version]
  43. Kuemmerer, K. Antibiotics in the aquatic environment—A review—Part I. Chemosphere 2009, 75, 417–434. [Google Scholar] [CrossRef]
  44. Dellit, T.H.; Owens, R.C.; McGowan, J.E., Jr.; Gerding, D.N.; Weinstein, R.A.; Burke, J.P.; Huskins, W.C.; Paterson, D.L.; Fishman, N.O.; Carpenter, C.F.; et al. Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin. Infect. Dis. 2007, 44, 159–177. [Google Scholar] [CrossRef] [PubMed]
  45. Wiegand, I.; Hilpert, K.; Hancock, R.E.W. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat. Protoc. 2008, 3, 163–175. [Google Scholar] [CrossRef] [PubMed]
  46. Yeaman, M.R.; Yount, N.Y. Mechanisms of antimicrobial peptide action and resistance. Pharmacol. Rev. 2003, 55, 27–55. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Nathan, C.F.; Murray, H.W.; Wiebe, M.E.; Rubin, B.Y. Identification of interferon-gamma as the lymphokine that activates human macrophage oxidative metabolism and antimicrobial activity. J. Exp. Med. 1983, 158, 670–689. [Google Scholar] [CrossRef] [Green Version]
  48. Cushnie, T.P.T.; Lamb, A.J. Antimicrobial activity of flavonoids. Int. J. Antimicrob. Agents 2005, 26, 343–356. [Google Scholar] [CrossRef]
  49. Goossens, H.; Ferech, M.; Vander Stichele, R.; Elseviers, M.; Group, E.P. Outpatient antibiotic use in Europe and association with resistance: A cross-national database study. Lancet 2005, 365, 579–587. [Google Scholar] [CrossRef]
  50. Sarmah, A.K.; Meyer, M.T.; Boxall, A.B.A. A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment. Chemosphere 2006, 65, 725–759. [Google Scholar] [CrossRef]
  51. Kumarasamy, K.K.; Toleman, M.A.; Walsh, T.R.; Bagaria, J.; Butt, F.; Balakrishnan, R.; Chaudhary, U.; Doumith, M.; Giske, C.G.; Irfan, S.; et al. Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: A molecular, biological, and epidemiological study. Lancet Infect. Dis. 2010, 10, 597–602. [Google Scholar] [CrossRef]
  52. Mast, E.E.; Margolis, H.S.; Fiore, A.E.; Brink, E.W.; Goldstein, S.T.; Wang, S.A.; Moyer, L.A.; Bell, B.P.; Alter, M.J.; Advisory Committee on Immunization, P. A comprehensive immunization strategy to eliminate transmission of hepatitis B virus infection in the United States: Recommendations of the Advisory Committee on Immunization Practices (ACIP) part 1: Immunization of infants, children, and adolescents. MMWR Morb. Mortal. Wkly. Rep. 2005, 54, 1–31. [Google Scholar]
  53. Rabea, E.I.; Badawy, M.E.T.; Stevens, C.V.; Smagghe, G.; Steurbaut, W. Chitosan as antimicrobial agent: Applications and mode of action. Biomacromolecules 2003, 4, 1457–1465. [Google Scholar] [CrossRef] [PubMed]
  54. Anthonisen, N.R.; Manfreda, J.; Warren, C.P.; Hershfield, E.S.; Harding, G.K.; Nelson, N.A. Antibiotic therapy in exacerbations of chronic obstructive pulmonary disease. Ann. Intern. Med. 1987, 106, 196–204. [Google Scholar] [CrossRef]
  55. Magill, S.S.; Edwards, J.R.; Bamberg, W.; Beldavs, Z.G.; Dumyati, G.; Kainer, M.A.; Lynfield, R.; Maloney, M.; McAllister-Hollod, L.; Nadle, J.; et al. Multistate Point- Prevalence Survey of Health Care- Associated Infections. N. Engl. J. Med. 2014, 370, 1198–1208. [Google Scholar] [CrossRef] [Green Version]
  56. Niederman, M.S.; Mandell, L.A.; Anzueto, A.; Bass, J.B.; Broughton, W.A.; Campbell, G.D.; Dean, N.; File, T.; Fine, M.J.; Gross, P.A.; et al. Guidelines for the management of adults with community-acquired pneumonia. Diagnosis, assessment of severity, antimicrobial therapy, and prevention. Am. J. Respir. Crit. Care Med. 2001, 163, 1730–1754. [Google Scholar] [CrossRef]
  57. Liang, S.C.; Tan, X.-Y.; Luxenberg, D.P.; Karim, R.; Dunussi-Joannopoulos, K.; Collins, M.; Fouser, L.A. Interleukin (IL)-22 and IL-17 are coexpressed by Th17 cells and cooperatively enhance expression of antimicrobial peptides. J. Exp. Med. 2006, 203, 2271–2279. [Google Scholar] [CrossRef]
  58. Zankari, E.; Hasman, H.; Cosentino, S.; Vestergaard, M.; Rasmussen, S.; Lund, O.; Aarestrup, F.M.; Larsen, M.V. Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 2012, 67, 2640–2644. [Google Scholar] [CrossRef]
  59. Gewirtz, D.A. A critical evaluation of the mechanisms of action proposed for the antitumor effects of the anthracycline antibiotics adriamycin and daunorubicin. Biochem. Pharmacol. 1999, 57, 727–741. [Google Scholar] [CrossRef]
  60. Steers, E.; Foltz, E.L.; Graves, B.S. An inocula replicating apparatus for routine testing of bacterial susceptibility to antibiotics. Antibiot. Chemother. 1959, 9, 307–311. [Google Scholar]
  61. Hirsch, R.; Ternes, T.; Haberer, K.; Kratz, K.L. Occurrence of antibiotics in the aquatic environment. Sci. Total Environ. 1999, 225, 109–118. [Google Scholar] [CrossRef]
  62. Jenssen, H.; Hamill, P.; Hancock, R.E.W. Peptide antimicrobial agents. Clin. Microbiol. Rev. 2006, 19, 491–511. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Laxminarayan, R.; Duse, A.; Wattal, C.; Zaidi, A.K.M.; Wertheim, H.F.L.; Sumpradit, N.; Vlieghe, E.; Levy Hara, G.; Gould, I.M.; Goossens, H.; et al. Antibiotic resistance-the need for global solutions. Lancet Infect. Dis. 2013, 13, 1057–1098. [Google Scholar] [CrossRef] [Green Version]
  64. Park, C.H.; Valore, E.V.; Waring, A.J.; Ganz, T. Hepcidin, a urinary antimicrobial peptide synthesized in the liver. J. Biol. Chem. 2001, 276, 7806–7810. [Google Scholar] [CrossRef] [Green Version]
  65. Kohanski, M.A.; Dwyer, D.J.; Hayete, B.; Lawrence, C.A.; Collins, J.J. A common mechanism of cellular death induced by bactericidal antibiotics. Cell 2007, 130, 797–810. [Google Scholar] [CrossRef] [Green Version]
  66. Shai, Y. Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by alpha-helical antimicrobial and cell non-selective membrane-lytic peptides. Biochim. Biophys. Acta 1999, 1462, 55–70. [Google Scholar] [CrossRef] [Green Version]
  67. Boman, H.G. Peptide antibiotics and their role in innate immunity. Annu. Rev. Immunol. 1995, 13, 61–92. [Google Scholar] [CrossRef]
  68. Hoiby, N.; Bjarnsholt, T.; Givskov, M.; Molin, S.; Ciofu, O. Antibiotic resistance of bacterial biofilms. Int. J. Antimicrob. Agents 2010, 35, 322–332. [Google Scholar] [CrossRef] [Green Version]
  69. Dethlefsen, L.; Huse, S.; Sogin, M.L.; Relman, D.A. The Pervasive Effects of an Antibiotic on the Human Gut Microbiota, as Revealed by Deep 16S rRNA Sequencing. PLoS Biol. 2008, 6, 2383–2400. [Google Scholar] [CrossRef]
  70. Hughes, W.T.; Armstrong, D.; Bodey, G.P.; Bow, E.J.; Brown, A.E.; Calandra, T.; Feld, R.; Pizzo, P.A.; Rolston, K.V.I.; Shenep, J.L.; et al. 2002 guidelines for the use of antimicrobial agents in neutropenic patients with cancer. Clin. Infect. Dis. 2002, 34, 730–751. [Google Scholar] [CrossRef] [Green Version]
  71. Nathan, C.F.; Hibbs, J.B., Jr. Role of nitric oxide synthesis in macrophage antimicrobial activity. Curr. Opin. Immunol. 1991, 3, 65–70. [Google Scholar] [CrossRef]
  72. Li, Q.; Mahendra, S.; Lyon, D.Y.; Brunet, L.; Liga, M.V.; Li, D.; Alvarez, P.J.J. Antimicrobial nanomaterials for water disinfection and microbial control: Potential applications and implications. Water Res. 2008, 42, 4591–4602. [Google Scholar] [CrossRef] [PubMed]
  73. Hidron, A.I.; Edwards, J.R.; Patel, J.; Horan, T.C.; Sievert, D.M.; Pollock, D.A.; Fridkin, S.K.; National Healthcare Safety Network, T.; Participating National Healthcare, S. Antimicrobial-Resistant Pathogens Associated With Healthcare-Associated Infections: Annual Summary of Data Reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect. Control Hosp. Epidemiol. 2008, 29, 996–1011. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Hammer, K.A.; Carson, C.F.; Riley, T.V. Antimicrobial activity of essential oils and other plant extracts. J. Appl. Microbiol. 1999, 86, 985–990. [Google Scholar] [CrossRef] [Green Version]
  75. Ong, P.Y.; Ohtake, T.; Brandt, C.; Strickland, I.; Boguniewicz, M.; Ganz, T.; Gallo, R.L.; Leung, D.Y.M. Endogenous antimicrobial peptides and skin infections in atopic dermatitis. N. Engl. J. Med. 2002, 347, 1151–1160. [Google Scholar] [CrossRef] [Green Version]
  76. Herrero, M.; de Lorenzo, V.; Timmis, K.N. Transposon vectors containing non-antibiotic resistance selection markers for cloning and stable chromosomal insertion of foreign genes in gram-negative bacteria. J. Bacteriol. 1990, 172, 6557–6567. [Google Scholar] [CrossRef] [Green Version]
  77. Burke, J.F. The effective period of preventive antibiotic action in experimental incisions and dermal lesions. Surgery 1961, 50, 161–168. [Google Scholar]
  78. Kollef, M.H.; Sherman, G.; Ward, S.; Fraser, V.J. Inadequate antimicrobial treatment of infections: A risk factor for hospital mortality among critically ill patients. Chest 1999, 115, 462–474. [Google Scholar] [CrossRef]
  79. Freifeld, A.G.; Bow, E.J.; Sepkowitz, K.A.; Boeckh, M.J.; Ito, J.I.; Mullen, C.A.; Raad, I.I.; Rolston, K.V.; Young, J.-A.H.; Wingard, J.R. Clinical Practice Guideline for the Use of Antimicrobial Agents in Neutropenic Patients with Cancer: 2010 Update by the Infectious Diseases Society of America. Clin. Infect. Dis. 2011, 52, E56–E93. [Google Scholar] [CrossRef] [Green Version]
  80. Ibrahim, E.H.; Sherman, G.; Ward, S.; Fraser, V.J.; Kollef, M.H. The influence of inadequate antimicrobial treatment of bloodstream infections on patient outcomes in the ICU setting. Chest 2000, 118, 146–155. [Google Scholar] [CrossRef] [Green Version]
  81. Pigeon, C.; Ilyin, G.; Courselaud, B.; Leroyer, P.; Turlin, B.; Brissot, P.; Loreal, O. A new mouse liver-specific gene, encoding a protein homologous to human antimicrobial peptide hepcidin, is overexpressed during iron overload. J. Biol. Chem. 2001, 276, 7811–7819. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Bennett, J.V.; Brodie, J.L.; Benner, E.J.; Kirby, W.M. Simplified, accurate method for antibiotic assay of clinical specimens. Appl. Microbiol. 1966, 14, 170–177. [Google Scholar] [CrossRef] [Green Version]
  83. Chambers, H.F.; DeLeo, F.R. Waves of resistance: Staphylococcus aureus in the antibiotic era. Nat. Rev. Microbiol. 2009, 7, 629–641. [Google Scholar] [CrossRef] [PubMed]
  84. Davies, J. Inactivation of antibiotics and the dissemination of resistance genes. Science 1994, 264, 375–382. [Google Scholar] [CrossRef] [PubMed]
  85. Cherepanov, P.P.; Wackernagel, W. Gene disruption in Escherichia coli: TcR and KmR cassettes with the option of Flp-catalyzed excision of the antibiotic-resistance determinant. Gene 1995, 158, 9–14. [Google Scholar] [CrossRef]
  86. Kong, M.; Chen, X.G.; Xing, K.; Park, H.J. Antimicrobial properties of chitosan and mode of action: A state of the art review. Int. J. Food Microbiol. 2010, 144, 51–63. [Google Scholar] [CrossRef]
  87. Hamblin, M.R.; Hasan, T. Photodynamic therapy: A new antimicrobial approach to infectious disease? Photoch. Photobio. Sci. 2004, 3, 436–450. [Google Scholar] [CrossRef] [Green Version]
  88. Carter, A.P.; Clemons, W.M.; Brodersen, D.E.; Morgan-Warren, R.J.; Wimberly, B.T.; Ramakrishnan, V. Functional insights from the structure of the 30S ribosomal subunit and its interactions with antibiotics. Nature 2000, 407, 340–348. [Google Scholar] [CrossRef]
  89. Ganz, T.; Selsted, M.E.; Szklarek, D.; Harwig, S.S.; Daher, K.; Bainton, D.F.; Lehrer, R.I. Defensins. Natural peptide antibiotics of human neutrophils. J. Clin. Investig. 1985, 76, 1427–1435. [Google Scholar] [CrossRef]
  90. Ceri, H.; Olson, M.E.; Stremick, C.; Read, R.R.; Morck, D.; Buret, A. The Calgary Biofilm Device: New technology for rapid determination of antibiotic susceptibilities of bacterial biofilms. J. Clin. Microbiol. 1999, 37, 1771–1776. [Google Scholar] [CrossRef] [Green Version]
  91. Classen, D.C.; Evans, R.S.; Pestotnik, S.L.; Horn, S.D.; Menlove, R.L.; Burke, J.P. The timing of prophylactic administration of antibiotics and the risk of surgical-wound infection. N. Engl. J. Med. 1992, 326, 281–286. [Google Scholar] [CrossRef] [PubMed]
  92. Ventola, C.L. The antibiotic resistance crisis: Part 1: Causes and threats. Pharmacol. Ther. 2015, 40, 277–283. [Google Scholar]
  93. Baddour, L.M.; Wilson, W.R.; Bayer, A.S.; Fowler, V.G., Jr.; Bolger, A.F.; Levison, M.E.; Ferrieri, P.; Gerber, M.A.; Tani, L.Y.; Gewitz, M.H.; et al. Infective endocarditis: Diagnosis, antimicrobial therapy, and management of complications: A statement for healthcare professionals from the Committee on Rheumatic Fever, Endocarditis, and Kawasaki Disease, Council on Cardiovascular Disease in the Young, and the Councils on Clinical Cardiology, Stroke, and Cardiovascular Surgery and Anesthesia, American Heart Association: Endorsed by the Infectious Diseases Society of America. Circulation 2005, 111, e394–e434. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Bartlett, J.G.; Chang, T.W.; Gurwith, M.; Gorbach, S.L.; Onderdonk, A.B. Antibiotic-associated pseudomembranous colitis due to toxin-producing clostridia. N. Engl. J. Med. 1978, 298, 531–534. [Google Scholar] [CrossRef] [PubMed]
  95. Lande, R.; Gregorio, J.; Facchinetti, V.; Chatterjee, B.; Wang, Y.-H.; Homey, B.; Cao, W.; Wang, Y.-H.; Su, B.; Nestle, F.O.; et al. Plasmacytoid dendritic cells sense self-DNA coupled with antimicrobial peptide. Nature 2007, 449, 564–566. [Google Scholar] [CrossRef]
  96. Harder, J.; Bartels, J.; Christophers, E.; Schroder, J.M. A peptide antibiotic from human skin. Nature 1997, 387, 861. [Google Scholar] [CrossRef]
  97. Hancock, R.E.; Lehrer, R. Cationic peptides: A new source of antibiotics. Trends Biotechnol. 1998, 16, 82–88. [Google Scholar] [CrossRef]
  98. Shai, Y. Mode of action of membrane active antimicrobial peptides. Biopolymers 2002, 66, 236–248. [Google Scholar] [CrossRef]
  99. Rothstein, J.D.; Patel, S.; Regan, M.R.; Haenggeli, C.; Huang, Y.H.; Bergles, D.E.; Jin, L.; Dykes Hoberg, M.; Vidensky, S.; Chung, D.S.; et al. Beta-lactam antibiotics offer neuroprotection by increasing glutamate transporter expression. Nature 2005, 433, 73–77. [Google Scholar] [CrossRef]
  100. Steiner, H.; Hultmark, D.; Engstrom, A.; Bennich, H.; Boman, H.G. Sequence and specificity of two antibacterial proteins involved in insect immunity. Nature 1981, 292, 246–248. [Google Scholar] [CrossRef]
  101. Ruparelia, J.P.; Chatteriee, A.K.; Duttagupta, S.P.; Mukherji, S. Strain specificity in antimicrobial activity of silver and copper nanoparticles. Acta Biomater. 2008, 4, 707–716. [Google Scholar] [CrossRef] [PubMed]
  102. Dethlefsen, L.; Relman, D.A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl. Acad. Sci. USA 2011, 108, 4554–4561. [Google Scholar] [CrossRef] [Green Version]
  103. Fischbach, M.A.; Walsh, C.T. Antibiotics for Emerging Pathogens. Science 2009, 325, 1089–1093. [Google Scholar] [CrossRef] [PubMed]
  104. Vezina, C.; Kudelski, A.; Sehgal, S.N. Rapamycin (AY-22,989), a new antifungal antibiotic. I. Taxonomy of the producing streptomycete and isolation of the active principle. J. Antibiot. 1975, 28, 721–726. [Google Scholar] [CrossRef] [PubMed]
  105. Hancock, R.E.; Chapple, D.S. Peptide antibiotics. Antimicrob. Agents Chemother. 1999, 43, 1317–1323. [Google Scholar] [CrossRef] [Green Version]
  106. Andersson, D.I.; Hughes, D. Antibiotic resistance and its cost: Is it possible to reverse resistance? Nat. Rev. Microbiol. 2010, 8, 260–271. [Google Scholar] [CrossRef]
  107. Harder, J.; Bartels, J.; Christophers, E.; Schroder, J.M. Isolation and characterization of human beta -defensin-3, a novel human inducible peptide antibiotic. J. Biol. Chem. 2001, 276, 5707–5713. [Google Scholar] [CrossRef] [Green Version]
  108. Epand, R.M.; Vogel, H.J. Diversity of antimicrobial peptides and their mechanisms of action. Biochim. Biophys. Acta 1999, 1462, 11–28. [Google Scholar] [CrossRef] [Green Version]
  109. Ling, L.L.; Schneider, T.; Peoples, A.J.; Spoering, A.L.; Engels, I.; Conlon, B.P.; Mueller, A.; Schaeberle, T.F.; Hughes, D.E.; Epstein, S.; et al. A new antibiotic kills pathogens without detectable resistance. Nature 2015, 517, 455–459. [Google Scholar] [CrossRef]
  110. Cohen, M.L. Epidemiology of drug resistance: Implications for a post-antimicrobial era. Science 1992, 257, 1050–1055. [Google Scholar] [CrossRef]
  111. Umezawa, H.; Maeda, K.; Takeuchi, T.; Okami, Y. New antibiotics, bleomycin A and B. J. Antibiot. 1966, 19, 200–209. [Google Scholar] [PubMed]
  112. Cabello, F.C. Heavy use of prophylactic antibiotics in aquaculture: A growing problem for human and animal health and for the environment. Environ. Microbiol. 2006, 8, 1137–1144. [Google Scholar] [CrossRef] [PubMed]
  113. Kenawy, E.-R.; Worley, S.D.; Broughton, R. The chemistry and applications of antimicrobial polymers: A state-of-the-art review. Biomacromolecules 2007, 8, 1359–1384. [Google Scholar] [CrossRef] [PubMed]
  114. Hancock, R.E. Peptide antibiotics. Lancet 1997, 349, 418–422. [Google Scholar] [CrossRef]
  115. Moazed, D.; Noller, H.F. Interaction of antibiotics with functional sites in 16S ribosomal RNA. Nature 1987, 327, 389–394. [Google Scholar] [CrossRef]
  116. Baquero, F.; Martinez, J.-L.; Canton, R. Antibiotics and antibiotic resistance in water environments. Curr. Opin. Biotechnol. 2008, 19, 260–265. [Google Scholar] [CrossRef]
  117. Spellberg, B.; Guidos, R.; Gilbert, D.; Bradley, J.; Boucher, H.W.; Scheld, W.M.; Bartlett, J.G.; Edwards, J., Jr.; Infectious Diseases Society of America. The epidemic of antibiotic-resistant infections: A call to action for the medical community from the Infectious Diseases Society of America. Clin. Infect. Dis. 2008, 46, 155–164. [Google Scholar] [CrossRef]
  118. Wang, T.-T.; Nestel, F.P.; Bourdeau, V.; Nagai, Y.; Wang, Q.; Liao, J.; Tavera-Mendoza, L.; Lin, R.; Hanrahan, J.W.; Mader, S.; et al. Cutting edge: 1,25-dihydroxyvitamin D3 is a direct inducer of antimicrobial peptide gene expression. J. Immunol. 2004, 173, 2909–2912. [Google Scholar] [CrossRef] [Green Version]
  119. Zhang, Q.-Q.; Ying, G.-G.; Pan, C.-G.; Liu, Y.-S.; Zhao, J.-L. Comprehensive Evaluation of Antibiotics Emission and Fate in the River Basins of China: Source Analysis, Multimedia Modeling, and Linkage to Bacterial Resistance. Environ. Sci. Technol. 2015, 49, 6772–6782. [Google Scholar] [CrossRef]
  120. Krause, A.; Neitz, S.; Magert, H.J.; Schulz, A.; Forssmann, W.G.; Schulz-Knappe, P.; Adermann, K. LEAP-1, a novel highly disulfide-bonded human peptide, exhibits antimicrobial activity. FEBS Lett. 2000, 480, 147–150. [Google Scholar] [CrossRef] [Green Version]
  121. Prezant, T.R.; Agapian, J.V.; Bohlman, M.C.; Bu, X.; Oztas, S.; Qiu, W.Q.; Arnos, K.S.; Cortopassi, G.A.; Jaber, L.; Rotter, J.I. Mitochondrial ribosomal RNA mutation associated with both antibiotic-induced and non-syndromic deafness. Nat. Genet. 1993, 4, 289–294. [Google Scholar] [CrossRef] [PubMed]
  122. Fardi, A.; Kodonas, K.; Lillis, T.; Veis, A. Top-Cited Articles in Implant Dentistry. Int. J. Oral Maxillofac. Implant. 2017, 32, 555–564. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  123. Kim, M.-J. A bibliometric analysis of physics publications in Korea, 1994-1998. Scientometrics 2001, 50, 503–521. [Google Scholar] [CrossRef]
  124. Jafarzadeh, H.; Sarraf Shirazi, A.; Andersson, L. The most-cited articles in dental, oral, and maxillofacial traumatology during 64 years. Dent. Traumatol. 2015, 31, 350–360. [Google Scholar] [CrossRef]
  125. Ugolini, D.; Neri, M.; Cesario, A.; Bonassi, S.; Milazzo, D.; Bennati, L.; Lapenna, L.M.; Pasqualetti, P. Scientific production in cancer rehabilitation grows higher: A bibliometric analysis. Support. Care Cancer 2012, 20, 1629–1638. [Google Scholar] [CrossRef]
  126. Von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Ann. Intern. Med. 2007, 147, 573–577. [Google Scholar] [CrossRef] [Green Version]
  127. Kilkenny, C.; Browne, W.J.; Cuthill, I.C.; Emerson, M.; Altman, D.G. Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research. PLoS Biol. 2010, 8. [Google Scholar] [CrossRef]
  128. Schulz, K.F.; Altman, D.G.; Moher, D.; CONSORT Group. CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMC Med. 2010, 8, 18. [Google Scholar] [CrossRef] [Green Version]
  129. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 2009, 339, b2700. [Google Scholar] [CrossRef] [Green Version]
  130. Natarajan, K.; Stein, D.; Jain, S.; Elhadad, N. An analysis of clinical queries in an electronic health record search utility. Int. J. Med. Inform. 2010, 79, 515–522. [Google Scholar] [CrossRef] [Green Version]
  131. Asghari, S.; Navimipour, N.J. Nature inspired meta-heuristic algorithms for solving the service composition problem in the cloud environments. Int. J. Commun. Syst. 2018, 31, e3708. [Google Scholar] [CrossRef]
Figure 1. (a) Association of citation frequency with the age of publication (years). (b) Changes in trends of citation density with the age of publication.
Figure 1. (a) Association of citation frequency with the age of publication (years). (b) Changes in trends of citation density with the age of publication.
Antibiotics 09 00219 g001
Figure 2. Pie chart diagram showing the distribution of classic articles based on the methodology of the study.
Figure 2. Pie chart diagram showing the distribution of classic articles based on the methodology of the study.
Antibiotics 09 00219 g002
Figure 3. Bar graph representation of the number of articles published in different journals
Figure 3. Bar graph representation of the number of articles published in different journals
Antibiotics 09 00219 g003
Figure 4. Network analysis of keywords identified from top 100 classics of antibiotics
Figure 4. Network analysis of keywords identified from top 100 classics of antibiotics
Antibiotics 09 00219 g004
Table 1. List of 100 classics of antibiotics ranked based on their citation frequency according to the Web of Science, Scopus, and Scholar databases along with citation density and current citation index (2019).
Table 1. List of 100 classics of antibiotics ranked based on their citation frequency according to the Web of Science, Scopus, and Scholar databases along with citation density and current citation index (2019).
R 1Author [Reference]YearCD 2CCI 3 2019WoS 4ES 5GS 6
1Bauer, Kirby, Sherris, and Turck [22]196620562111,05110,74020,041
2Zasloff [23]2002316398568556687994
3Southern and Berg [24]19821023389123193875
4Cowan [25]19991792923749459811203
5Sondi and Salopek-Sondi [26]2004212353339736775471
6Brogden [27]2005224302336333534941
7Kumar et al. [28]2006214291299631855039
8Cohen et al. [29]19725917280917753754
9Kim et al. [30]2007201311261528184164
10Stewart and Costerton [31]2001130217247426024113
11Hancock and Sahl [32]2006170139237323913185
12Kovach et al. [33]2006167244233723193019
13Liu et al. [34]199593166233224583571
14Dorman and Deans [35]2000110177220124074479
15Sharma et al. [36]2009188240207122693196
16Mah and O’Toole [37]2001108183204321273529
17Neu [38]2003116119197020713413
18Chopra and Roberts [39]2001104230196720263414
19Davies and Davies [40]2009178312196320373817
20Ganz [41]2010195395195219833115
21Zasloff [42]2006138257193518052643
22Kuemmerer [43]19926982193020072734
23Dellit et al. [44]19875867191519511732
24Wiegand et al. [45]2010187146187118982848
25Yeaman and Yount [46]2007144146186918462702
26Nathan et al. [47]2003108169184212462033
27Cushnie and Lamb [48]2008153384183920523983
28Goossens et al. [49]2005121230181118502904
29Sarmah et al. [50]19834731173718172638
30Kumarasamy et al. [51]2005115149172618423071
31Mast et al. [52]20051157017196451868
32Rabea et al. [53]200399171168917192523
33Anthonisen et al. [54]20018738165018983065
34Magill et al. [55]19874957163316012294
35Niederman et al. [56]2014260337156218932319
36Liang et al. [57]19997496155215252168
37Zankari et al. [58]20018081151814892039
38Gewirtz [59]2006108102151215502177
39Steers et al. [60]200610612114825961288
40Hirsch et al. [61]19997082147415492469
41Jenssen et al. [62]2012184484146814622257
42Laxminarayan et al. [63]1959241145314542387
43Park et al. [64]19955830144715112483
44Kohanski et al. [65]20027922142114312062
45Shai [66]2013201326141014031990
46Boman [67]2007108147140514202100
47Hoiby et al. [68]19996665139113952249
48Dethlefsen et al. [69]2010137201137213731981
49Hughes et al. [70]2008112124134716411790
50Nathan and Hibbs [71]19996499134612301789
51Li et al. [72]19914625133613991975
52Hidron et al. [73]200811172133414182028
53Hammer et al. [74]2008110146132314923055
54Ong et al. [75]20027170128614462058
55Herrero et al. [76]2011142165128012051714
56Burke [77]19996145127810071773
57Kollef et al. [78]20016745126914622254
58Freifeld et al. [79]20006345125914932753
59Ibrahim et al. [80]19904146124314052098
60Pigeon et al. [81]19612118123412681917
61Bennett et al. [82]200911214912327041187
62Chambers and DeLeo [83]19944729121312092058
63Davies [84]1966224119412942295
64Cherepanov and Wackernagel [85]2010117184117111571708
65Kong et al. [86]19954679115412331741
66Hamblin and Hasan [87]20005833115312261740
67Carter et al. [88]19853334115211551677
68Ganz et al. [89]200472112115210241628
69Ceri et al. [90]19974917113511591716
70Classen et al. [91]20057546112913182194
71Ventola [92]19924034112812142398
72Baddour et al. [93]19995398111911981889
73Bartlett et al. [94]1981284511109491625
74Lande et al. [95]19985035109911191597
75Harder et al. [96]20078484109611281716
76Hancock and Lehrer [97]2015218458109110551628
77Shai [98]19782621108010741500
78Rothstein et al. [99]2015215413107510871402
79Steiner et al. [100]2001563810729681615
80Ruparelia et al. [101]20057158107110801518
81Dethlefsen and Relman [102]19752365104510451533
82Fischbach and Walsh [103]19995051104410311603
83Vezina et al. [104]20025870104111181699
84Hancock and Chapple [105]20099499103410581608
85Andersson and Hughes [106]2011115161103210211625
86Harder et al. [107]200674144102910691684
87Epand and Vogel [108]200885135102210091442
88Ling et al. [109]2010101154100910101594
89Cohen [110]19994835100410671857
90Umezawa et al. [111]199235239908141214
91Cabello [112]201519819898910491691
92Kenawy et al. [113]20088113597510001303
93Hancock [114]199742309689911448
94Moazed and Noller [115]200880849639001281
95Baquero et al. [116]2007749196110151587
96Spellberg et al. [117]196618179599881598
97Wang et al. [118]200048469569851476
98Zhang et al. [119]1987292794710531162
99Krause et al. [120]199335309449501549
100Prezant et al. [121]200459599409061397
1 R = rank; 2 C.D. = citation density;3 CCI = current citation index;4 WoS = Web of Science;5 ES = Elsevier Scopus;6 GS = Google Scholar.

Share and Cite

MDPI and ACS Style

Arshad, A.I.; Ahmad, P.; Karobari, M.I.; Asif, J.A.; Alam, M.K.; Mahmood, Z.; Abd Rahman, N.; Mamat, N.; Kamal, M.A. Antibiotics: A Bibliometric Analysis of Top 100 Classics. Antibiotics 2020, 9, 219.

AMA Style

Arshad AI, Ahmad P, Karobari MI, Asif JA, Alam MK, Mahmood Z, Abd Rahman N, Mamat N, Kamal MA. Antibiotics: A Bibliometric Analysis of Top 100 Classics. Antibiotics. 2020; 9(5):219.

Chicago/Turabian Style

Arshad, Anas Imran, Paras Ahmad, Mohmed Isaqali Karobari, Jawaad Ahmed Asif, Mohammad Khursheed Alam, Zuliani Mahmood, Normastura Abd Rahman, Noraida Mamat, and Mohammad Amjad Kamal. 2020. "Antibiotics: A Bibliometric Analysis of Top 100 Classics" Antibiotics 9, no. 5: 219.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop