Developing a Clinically Representative Model of Periprosthetic Joint Infection

Carli, Alberto V. MD, MSc, FRCSC; Ross, F. Patrick PhD; Bhimani, Samrath J. BS; Nodzo, Scott R. MD; Bostrom, Mathias P.G. MD

Journal of Bone & Joint Surgery - American Volume:
doi: 10.2106/JBJS.15.01432
Current Concepts Review

The poor treatment outcomes for periprosthetic joint infection (PJI) reflect the limited understanding that currently exists regarding the pathogenesis of this devastating clinical problem.

Current animal models of PJI are limited in their translational nature primarily because of their inability to recreate the periprosthetic environment.

A greater mechanistic understanding of the musculoskeletal and immune systems of small animals, such as mice and rats, provides a more robust platform for modeling and examining the pathogenesis of PJI.

A clinically representative PJI model must involve an implant that recreates the periprosthetic space and be amenable to methodologies that identify implant biofilm as well as quantify the peri-implant bacterial load.

Author Information

1Hospital for Special Surgery, New York, NY

E-mail address for A.V. Carli:

E-mail address for F.P. Ross:

E-mail address for S.J. Bhimani:

E-mail address for S.R. Nodzo:

E-mail address for M.P.G. Bostrom:

Article Outline

Periprosthetic joint infection (PJI) following total joint arthroplasty of the hip or knee is one of the most devastating complications that can affect this otherwise successful surgical procedure. Although the risk of PJI following primary total joint arthroplasty has been reported to be as low as 1%1,2, more recent analyses have suggested that the incidence of PJI is increasing3,4, culminating in a predicted 4 million cases annually in the United States by 20305. This rising incidence, coupled with increasing costs and an increasing number of total joint arthroplasties, leads to a projected financial burden for PJI in excess of $1.6 billion in the United States by 20205. Furthermore, the effect of PJI on patient morbidity and mortality cannot be overemphasized. Patients with PJI report higher levels of dissatisfaction and substantially poorer health-related quality-of-life measures compared with patients without an infection after total joint arthroplasty6. Furthermore, PJI has been associated with a dramatically increased risk of overall mortality, with 5-year rates ranging from 25.9%7 to 45%8.

The current treatment regimens for PJI result in poor to moderate outcomes. Treatment of acute PJI through irrigation and debridement has a failure rate of 29% to 92%9-12, while the gold standard treatment for chronic PJI, the 2-stage revision involving an antibiotic-impregnated polymethylmethacrylate spacer, exposes the patient to 2 surgical procedures with a failure rate of up to 23%13-15. In light of limited treatment successes, persistence of poor patient outcomes, and an increasing number of infections16, we need to develop clinically relevant animal models to enhance our fundamental understanding and treatment of PJI.

Once established, such models are likely to transform our understanding of the pathogenesis of PJI and lead to novel treatments. In this review, we break down the process of developing an animal model of PJI through extensively reviewing pertinent topics, including animal physiology, principles of implant design, and characteristics of both bacterial activity and virulence. Current models in the literature were identified using PubMed, Ovid, Web of Science, and Orthopaedic Research Society conference proceedings through the following keyword searches: (1) “model” AND “joint” AND “infection,” and (2) “periprosthetic” AND “infection.”

Furthermore, we propose 4 standardized criteria that future models should meet to be considered as clinically representative of PJI:

1. The modeling should be performed in an animal that has musculoskeletal and immunological properties comparable to those in humans.

2. The implant utilized should be made of clinically relevant materials, bear load, and effectively reproduce the periprosthetic environment.

3. The model should utilize clinically relatable bacteria and demonstrate biofilm formation on the implant surface.

4. The model methodology should include quantitative measurements of bacteria, biofilm, and host immune response.

Back to Top | Article Outline

Criteria for Animal Selection: Species-Specific Effects

A variety of animals, including dogs, rabbits, and rodents (rats and mice), have been utilized in PJI models. Although all are mammals, specific technical and biological variables must be considered when determining which species to utilize.

Back to Top | Article Outline
Animal Size

Animal size is the most obvious variable to consider since it directly affects cost and surgical methodology. Small-sized rodents require relatively low housing costs and are easy to handle. However, rodents are fragile physiologically, cannot tolerate multiple surgeries, and cannot tolerate frequent blood sampling. Furthermore, their small size necessitates technically demanding surgical procedures. Conversely, models involving dogs and sheep can utilize commercially available implants and can involve multiple surgical insults and blood draws. However, large animals cost substantially more to maintain and can carry additional ethical challenges. In addition to these logistical and technical issues, the biological similarity of the animal’s skeletal system to that of humans must be considered.

Back to Top | Article Outline
Bone Structure

The macroscopic bone structure and rate of development vary considerably across mammals, with varying degrees of similarity to human bone. Larger animals demonstrate a secondary osteonal structure containing haversian systems17, but their bone has lower porosity and higher bone mineral density than human bone18. This limitation, along with a variable bone turnover rate19, makes it difficult to interpret load-bearing implant effects in these animals.

Although rabbit bone has mineral density similar to human bone and conveniently reaches maturity at 6 months20, it contains parallel vascular canals of osteons instead of distinct cortical and trabecular architecture21. Furthermore, bone turnover is faster22,23, and rabbit extremities often remain in hyperflexion, making it difficult to extrapolate peri-implant bone changes.

Rats and mice lack haversian systems and have dissimilar skeletal proportions secondary to their substantially smaller stature24. However, remodeling in rats and mice is similar to that observed in larger animals and humans25. Furthermore, genetic manipulation in mouse models26,27 offers an opportunity to study variations in bone structure and specific bone-signaling pathways not yet observed in larger animals and humans.

Back to Top | Article Outline
Immune System

The overall organization of the immune system remains consistent across mammals, but variations in cell proportions, signaling pathways, and the time after birth by which the animals become immunocompetent complicate translational interpretations. In humans, B and T-lymphocyte populations form within the first to second trimester28, immunocompetence is acquired in early childhood, and immune development culminates into an adult circulation rich in neutrophils (50% to 70% of white blood cells). Rodents, however, produce immune cells only after birth and develop an adult circulation that is rich in lymphocytes (75% to 90%)29. Rabbits and dogs share a similar immune delay to rodent development28, but the details of their postnatal immunocompetence remain underexplored30.

Comparative evaluation of neutrophil function across species also reveals distinct responses31,32, a result that could explain the differences in infection susceptibility. A recent review of animal models of Staphylococcus aureus osteomyelitis revealed a 10,000-fold difference in the amount of bacteria required to induce infection in 2 individual rat strains33. While dissimilarities in immune function and infection susceptibility among species do not invalidate their translational potential, they highlight the difficulty in extrapolating experimental results in animals whose immune pathways are less understood. For all of the above reasons, use of a better biologically defined animal such as the mouse is the optimal approach to develop the best model for PJI studies in vivo.

Back to Top | Article Outline

Criteria for Implant Selection: Reproducing the Periprosthetic Environment

A key requirement for any candidate PJI model is that it must reproduce the periprosthetic environment, a complex milieu consisting of the hypovascular, immune-privileged (tolerant to antigen exposure) articular space34,35; the marrow-filled, hypercellular intramedullary space; and the arthroplasty implant that separates the 2 environments. Therefore, it is critical to utilize an implant that can separate the 2 spaces while correctly interacting with host bone and invading bacteria in a clinically representative manner.

Two aspects of an implant that are critical to defining the periprosthetic space include the achievement of stable fixation in bone following insertion and load-bearing. Implant stability and load-bearing are not only critical for limb function but also greatly affect bacterial biofilm activity and immune response. Bioreactor investigations have demonstrated that bacteria respond to loading by increasing biofilm density and cellular activity in a nearly linear fashion36. Shear forces resulting from implant instability can interfere with bacterial adhesion and affect biofilm density and strength37. The immune response is similarly affected. In a noninfected environment, the immune system and associated anabolic pathways must successfully tolerate compression, tension, shear, bending, and torsion to complete osseointegration and permit load transfer. However, studies of arthroplasty implants have demonstrated how poor interference fit38, insufficient surface pore size39, or excessive stress transfer due to constraint40 can prevent osseointegration and instead lead to fibrous fixation41, chronic neutrophil and lymphocyte-induced inflammation42,43, and thus clinical failure. Therefore, the immunological environments surrounding loose and stable implants are drastically different. The combined effects of implant stability and loading on both the immune response and bacterial growth illustrate the substantial translational shortcomings of utilizing a nonloaded, loose implant to recreate the periprosthetic environment.

Although current models of clinical PJI have provided insights into the acute peri-implant infectious process, they are ultimately limited in translational appeal because of inadequately recreating the periprosthetic environment (Table I). Initial animal models of PJI used loose, cylindrical intramedullary implants placed in an antegrade fashion following intramedullary bacterial inoculation44,45. This method has recently enjoyed a resurgence in mice, using cement46 or a smooth, stainless-steel pin inserted after coating with biofilm (Fig. 1-A)47 or followed by intra-articular inoculation48-50. These models are suboptimal since their implants do not bear weight, are made of a clinically irrelevant material, are loose, and do not separate the intra-articular and intramedullary spaces.

A more attractive model inserts transcortical, extra-articular screws into the distal part of the femur, followed by intra-articular bacterial inoculation (Fig. 1-B)51. This model is easy to reproduce, employs a loaded implant, can be scaled up or down in animal size, and can use materials made of metal alloys, polyethylene, or polymethylmethacrylate. The model is limited since the implant encounters compressive loads without torsion or tension, and it does not enter the medullary canal. An older model of PJI that does separate intra-articular and intramedullary spaces places a stemmed silicone-elastomer implant in the rabbit tibia (Fig. 1-C)52. Unfortunately, the unrepresentative nature of the material used decreases enthusiasm for this approach.

A recent development utilizes a 3-dimensional (3-D) printed Ti-6Al-4V mouse implant (Fig. 1-D) that bears load in a manner similar to that of a human tibial component, osseointegrates with and without anabolic stimulus53, successfully recreates the periprosthetic space, and provides clinically relatable end points for PJI54. This model serves as an example of how novel engineering techniques and careful design considerations can lead to more accurate preclinical orthopaedic models. A decisive advantage of this approach is the capability to properly simulate any clinically based PJI treatment. This development represents an important breakthrough since no current PJI model possesses the versatility to include clinically relatable treatments.

Implant variables, including alloy composition, surface characteristics, loading profile, porosity, and stability, must be carefully considered since they all affect bacterial activity, biofilm formation, and the immune response.

Bacterial adhesion and biofilm formation on a surface is a complex process that is directly affected by the composition and charge of the implant. Bacteria use a combination of 2 to 8-nm-long lipopolysaccharide fibers and proteinaceous adhesins55,56 to interact with implant surfaces, via a balance of attractive van der Waals forces and repelling negative charges57. On the basis of this theory, many investigators have examined how surface properties modulate the adhesive process58,59. A negatively charged implant could theoretically resist bacterial adhesion through repelling the negatively charged bacterial cell60. However, this effect is mitigated by adhesion of positively charged proteins, dead cells, and modifications developed by the bacteria themselves61. Hydrophobic and hydrophilic surfaces also affect bacterial adhesion. Such effects, specifically on polished surfaces of arthroplasty implant materials, have been investigated62. Cobalt-chromium-molybdenum, with high hydrophobicity and low surface free energy, exhibits the least biofilm coverage following a static culture. After consideration of the material properties of an implant, the next variable to focus on is surface topography.

The 3-D surface topography of an implant substantially affects bacterial biofilm formation. Surface roughness improves bacterial adhesion because of increased surface area and protection from shear forces63-65. However, the effect of roughness varies across different materials: increasing roughness increases bacterial adhesion on TiZr implants66, but not on ceramic surfaces67. Moreover, the distribution of surface peaks and valleys, but not their amplitude, is more important than roughness in affecting bacterial adhesion68,69. Exciting advances in material engineering have led to the development of micrometer and nanometer-scale surface topographies. Nanostructures of various shapes prevent bacterial adhesion in materials such as silicone and polydimethylsiloxane70,71. The ability to modify the shape and volume of these nanostructures could reveal what surface features bacteria evaluate prior to adhering72. Implant surface stiffness may also affect bacterial adhesion73.

Back to Top | Article Outline

Establishing an Infection: Choosing a Bacterium

Embarking on PJI model development requires a fundamental understanding of common mechanisms that bacteria utilize to adhere to surfaces and form biofilm. Most bacteria utilize a complex array of MSCRAMMs (microbial surface components recognizing adhesive matrix molecules) to attach to both nonorganic surfaces and human matrix proteins including fibrinogen and fibronectin74. Within MSCRAMMs are enzymes called autolysins, which facilitate attachment to plastic-like surfaces and serve as anchors that adhere bacteria to implants75,76. Following adhesion, bacterial RNA transcription changes dramatically, orienting metabolism toward anaerobic processes and production of polysaccharide intercellular adhesin (PIA), a hallmark polymer of biofilm that forms the majority of the so-called slime on medical devices77,78. Bacteria then undertake species-specific processes to identify their surroundings. In gram-positive bacteria, both S. aureus and S. epidermidis utilize the same gene, accessory gene regulator (agr) locus, to produce an autoinducing peptide that permits them to identify (via a complex process referred to as “quorum sensing”) surrounding bacteria and determine if further biofilm proliferation or colony separation and spreading is necessary79-81.

Back to Top | Article Outline
Selecting the Pathogen

Investigators developing PJI models should utilize common clinical pathogens and be aware of the similarities and differences with regard to virulence and biofilm formation. Methicillin-sensitive and methicillin-resistant S. aureus and coagulase-negative Staphylococcus species (S. epidermidis and most other Staphylococcus species) represent the most common pathogens in clinical PJI82-84. Although these species can reside on skin, the prevalence of each is remarkably different, with healthy adults carrying up to 10 strains of S. epidermidis at a time85, while some never get colonized with S. aureus86. Furthermore, S. aureus expresses multiple virulent features, including hemolysins and leukotoxins that lyse immune cells, protein A and coagulase that provide immunological disguises, and exotoxins that damage nonimmune-based host tissues87,88. Conversely, the sole virulence factor for S. epidermidis is its ability to produce biofilm77. In addition to gram-positive bacteria, gram-negative bacteria, including Escherichia coli, Enterococcus faecalis, and Pseudomonas aeruginosa, are responsible for 10% of clinical PJI82 and can produce aggressive biofilms.

Back to Top | Article Outline
Selecting the Clinical Isolate

After selecting a pathogen, the next step is to select an appropriate clinical isolate. This step is critical since both genetic and molecular differences among strains of the same pathogen can substantially influence experimental results and generalizability of conclusions. While the full description of every clinical isolate is beyond the scope of this review, a summary of the most commonly utilized isolates and their relevant differences are described in the Appendix.

Back to Top | Article Outline
Selecting the Route of Infection

The method of bacterial inoculation must be carefully considered. Previous PJI models either have directly administered bacteria into the operative site or have pregrown biofilm on an implant surface prior to implantation (Table I). The first method (intraoperative contamination) is preferred since the precise number of bacteria administered is known, the method is clinically representative, and it permits investigation into how a biofilm develops on an orthopaedic implant in vivo. With regard to bacterial dose, inocula administered in current animal models have ranged from 10 to 100,000-fold higher than the estimated bacterial exposure occurring clinically (<100 colony-forming units [CFU]/m3)89. Although this discrepancy exists, it should be emphasized that the minimum bacterial exposure needed to cause clinical PJI remains unknown and is dependent on host factors. Therefore, a logical approach when developing a PJI model is to first establish PJI through a supraphysiological inoculum. On verifying that an infection does occur, subsequent experimentation while decreasing the bacteria on a logarithmic scale should be performed until the minimum inoculum needed to establish PJI is found.

Back to Top | Article Outline

Metrics: Choosing What to Measure

A critical step in establishing a PJI model is measuring quantitative variables that can describe local and systemic host response, as well as the presence and progression of infection.

Back to Top | Article Outline
Systemic Measures

Quantitative measures of systemic response to infection can be collected through indirect and direct means. Indirect means include regular measurements of weight and body temperature. Gradual, subtle weight loss is the earliest and most reliable sign of systemic deterioration90. Normal body temperature varies among species and by sex91, and is most accurately measured in small animals either rectally or through surgically implanted transmitters92. Direct measures of systemic response mostly consist of measurements of circulating inflammatory markers (interleukin [IL]-1, IL-6, and tumor necrosis factor-α) or acute phase reactants, a process familiar to clinicians for diagnosis of PJI. Inflammatory acute reactants vary across species. C-reactive protein is strongly upregulated in humans, primates, and dogs, but only minimally increased in other species93. In the rat, alpha-1 acid glycoprotein is a suitable acute phase reactant94,95. Serum amyloid A is elevated acutely in sheep, rabbits, horses, mice, dogs, and humans, making it an attractive interspecies marker93,96-99. As in humans, acute markers can be elevated following surgery or other systemic stresses and therefore must be compared with noninfected controls.

Back to Top | Article Outline
Local Measures

Metrics of local host response include weight-bearing assessments, imaging evaluation, and histological examination. Radiographs are useful for establishing the clinical diagnosis of PJI100 and evaluating PJI models. A popular composite score based on radiographic findings in osteomyelitis101 can be used for PJI animal models (Fig. 2-A). Use of serial, in vivo modern imaging modalities such as densitometric analysis, computed tomography, and magnetic resonance imaging can provide a more detailed analysis of bone and soft-tissue changes. However, such methods require expensive imaging equipment and may be unable to evaluate the bone-implant interface in small-animal models because of implant artifact. The local tissue response can also be evaluated at the time of implant-bone harvest using a 4-point visual score102,103. Although simple, this scale represents the intraoperative findings found in clinical PJI, including bone loss, soft-tissue involvement, and purulent exudates (Fig. 2-B).

Back to Top | Article Outline
Measuring Bacterial Activity and Biofilm Formation

Quantitative measurements of in vivo bacterial activity are critical components of PJI model methodology, including confirmation of the experimental bacteria within the periprosthetic space. CFUs are the fundamental unit utilized to estimate the number of viable bacteria and are determined through placing periprosthetic samples onto a plate containing bacterial agar and counting colonies that grow overnight (Fig. 3-A). The most direct, widely accepted method for assessing bacterial load within periprosthetic tissues involves homogenizing bone and soft tissue into a fine consistency that can then be plated to count CFUs48,49,104,105. A novel, indirect method to identify periprosthetic bacteria involves bioluminescent strains that produce the light-emitting pigment luciferin (Fig. 3-B). Using sensitive light detectors, luciferin-mediated light signals can be identified through animal soft tissue to indirectly demonstrate infection49,50,106. Although promising for bacterial identification, a correlation between luminescence intensity and in vivo bacterial load is unlikely to be possible because of soft-tissue density, bacterial location, and varying bacterial metabolism, which affect how much light reaches the detector. Furthermore, luminescence cannot penetrate through cortical bone, limiting the use of this technology for intramedullary infections.

Confirmation of implant biofilm both qualitatively and quantitatively is another critical metric to achieve when validating a PJI model. A traditional quantitative method involves staining the implant with crystal violet and measuring its concentration spectrophotometrically107,108 (Fig. 4-A). This method is cheap and reproducible, but it does not distinguish bacteria from biomass. The most convincing method for demonstrating viable bacteria involves agitating109-111 the implant, plating the supernatant, and counting bacterial colonies. Distinguishing live and dead bacteria can be accomplished using confocal laser scanning microscopy (CLSM)112-114 (Fig. 4-B), in which a specimen surface is treated with a fluorescent probe and then illuminated. The main drawback to CLSM is short depth of imaging, making it difficult to visualize biofilm on rough surfaces or complex 3-D shapes. The most detailed imaging of implant biofilm is provided by scanning electron microscopy (Fig. 4-C), which permits direct observation of biofilm morphology and bacteria115,116. Scanning electron microscopy can be used quantitatively, measuring the percent coverage of an implant with biofilm47. This modality is limited by its cost, sampling issues, and inability to see beneath the external layer.

Retrieving pathogens from an animal model of PJI provides an opportunity to study how exposure to the in vivo environment affects bacterial activity. Proliferation can be compared between stock bacteria and retrieved bacteria through serial measures of bacterial turbidity in culture medium117. Similar comparisons can be made with regard to antimicrobial resistance, an aspect of bacterial virulence that can develop in vivo following exposure to antibiotics118. One simple, accurate method to quantify antibiotic sensitivity is the E-test (Fig. 4-D), which simultaneously diffuses 30 different concentrations of an antibiotic onto a plate to determine the minimum concentration needed to inhibit bacterial growth119,120. Sophisticated bacterial genome and RNA analysis can be performed on retrieved samples, with techniques available to identify gene dosage effects, essential genes for virulence, sites that could produce mutations, and potential sites for experimental plasmid insertion121-125. A drawback of repetitive sampling of the same retrieved bacterial culture is that this process gradually selects for bacteria that are defective in biofilm formation126,127. One way to compensate for this “bottle effect”128 is to separately examine bacteria from the implant surface versus bacteria from the periprosthetic tissues.

Back to Top | Article Outline


Clinically representative animal models offer an appealing opportunity to improve our fundamental understanding of PJI. Creating and appraising these models requires careful consideration of how animal choice, implant design, specific bacterial strain, and appropriate outcome measurements all relate to the clinical condition. Current PJI models possess serious limitations with regard to their ability to adequately recreate the periprosthetic environment. They also fail to regularly utilize pertinent quantitative metrics when bacterial infection is described. Moreover, current models do not exhibit sufficient versatility to serve as models for PJI treatment regimens.

In response to the shortcomings of the current literature and the urgent need for translational innovation, we have provided the first set of published criteria for establishing a pertinent model of PJI and urge interested investigators to consider following the question-based thought process outlined within this review. With increasing sophistication in animal and bacteriological genetic manipulation, 3-D printing, and quantitative detection methods, investigators have no shortage of tools necessary to design compelling animal models that could yield translational benefits.

Back to Top | Article Outline

Appendix Cited Here...

A more detailed description of common pathogens and how to choose the right clinical isolate (including references 129 through 146, which are cited only in the Appendix) is available with the online version of this article as a data supplement at

Investigation performed at the Hospital for Special Surgery, New York, NY

Disclosure: The authors indicated that no external funding was received for any aspect of this work. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article.

Back to Top | Article Outline


1. Jämsen E, Huhtala H, Puolakka T, Moilanen T. Risk factors for infection after knee arthroplasty. A register-based analysis of 43,149 cases. J Bone Joint Surg Am. 2009 ;91(1):38–47.
2. Phillips JE, Crane TP, Noy M, Elliott TS, Grimer RJ. The incidence of deep prosthetic infections in a specialist orthopaedic hospital: a 15-year prospective survey. J Bone Joint Surg Br. 2006 ;88(7):943–8.
3. Bozic KJ, Kurtz SM, Lau E, Ong K, Vail TP, Berry DJ. The epidemiology of revision total hip arthroplasty in the United States. J Bone Joint Surg Am. 2009 ;91(1):128–33.
4. Bozic KJ, Kurtz SM, Lau E, Ong K, Chiu V, Vail TP, Rubash HE, Berry DJ. The epidemiology of revision total knee arthroplasty in the United States. Clin Orthop Relat Res. 2010 ;468(1):45–51. Epub 2009 Jun 25.
5. Kurtz SM, Lau E, Watson H, Schmier JK, Parvizi J. Economic burden of periprosthetic joint infection in the United States. J Arthroplasty. 2012 ;27(8)(Suppl):61–5.e1. Epub 2012 May 2.
6. Kapadia BH, Berg RA, Daley JA, Fritz J, Bhave A, Mont MA. Periprosthetic joint infection. Lancet. 2016 ;387(10016):386–94. Epub 2015 Jun 28.
7. Toulson C, Walcott-Sapp S, Hur J, Salvati E, Bostrom M, Brause B, Westrich GH. Treatment of infected total hip arthroplasty with a 2-stage reimplantation protocol: update on “our institution’s” experience from 1989 to 2003. J Arthroplasty. 2009 ;24(7):1051–60. Epub 2008 Oct 9.
8. Berend KR, Lombardi AV Jr, Morris MJ, Bergeson AG, Adams JB, Sneller MA. Two-stage treatment of hip periprosthetic joint infection is associated with a high rate of infection control but high mortality. Clin Orthop Relat Res. 2013 ;471(2):510–8.
9. Deirmengian C, Greenbaum J, Lotke PA, Booth RE Jr, Lonner JH. Limited success with open debridement and retention of components in the treatment of acute Staphylococcus aureus infections after total knee arthroplasty. J Arthroplasty. 2003 ;18(7)(Suppl 1):22–6.
10. Tsukayama DT, Estrada R, Gustilo RB. Infection after total hip arthroplasty. A study of the treatment of one hundred and six infections. J Bone Joint Surg Am. 1996 ;78(4):512–23.
11. Hanssen AD, Rand JA. Evaluation and treatment of infection at the site of a total hip or knee arthroplasty. Instr Course Lect. 1999;48:111–22.
12. Crockarell JR, Hanssen AD, Osmon DR, Morrey BF. Treatment of infection with débridement and retention of the components following hip arthroplasty. J Bone Joint Surg Am. 1998 ;80(9):1306–13.
13. Azzam K, McHale K, Austin M, Purtill JJ, Parvizi J. Outcome of a second two-stage reimplantation for periprosthetic knee infection. Clin Orthop Relat Res. 2009 ;467(7):1706–14. Epub 2009 Feb 18.
14. Haleem AA, Berry DJ, Hanssen AD. Mid-term to long-term followup of two-stage reimplantation for infected total knee arthroplasty. Clin Orthop Relat Res. 2004 ;428:35–9.
15. Fehring TK, Calton TF, Griffin WL. Cementless fixation in 2-stage reimplantation for periprosthetic sepsis. J Arthroplasty. 1999 ;14(2):175–81.
16. Lehil MS, Bozic KJ. Trends in total hip arthroplasty implant utilization in the United States. J Arthroplasty. 2014 ;29(10):1915–8. Epub 2014 May 28.
17. Egermann M, Goldhahn J, Schneider E. Animal models for fracture treatment in osteoporosis. Osteoporos Int. 2005 ;16(Suppl 2):S129–38. Epub 2005 Mar 5.
18. Wang X, Mabrey JD, Agrawal CM. An interspecies comparison of bone fracture properties. Biomed Mater Eng. 1998;8(1):1–9.
19. Kimmel DB, Jee WS. A quantitative histologic study of bone turnover in young adult beagles. Anat Rec. 1982 ;203(1):31–45.
20. Gilsanz V, Roe TF, Gibbens DT, Schulz EE, Carlson ME, Gonzalez O, Boechat MI. Effect of sex steroids on peak bone density of growing rabbits. Am J Physiol. 1988 ;255(4 Pt 1):E416–21.
21. Martiniaková M, Omelka R, Chrenek P, Ryban L, Parkányi V, Grosskopf B, Vondráková M, Bauerová M. Changes of femoral bone tissue microstructure in transgenic rabbits. Folia Biol (Praha). 2005;51(5):140–4.
22. Castañeda S, Largo R, Calvo E, Rodríguez-Salvanés F, Marcos ME, Díaz-Curiel M, Herrero-Beaumont G. Bone mineral measurements of subchondral and trabecular bone in healthy and osteoporotic rabbits. Skeletal Radiol. 2006 ;35(1):34–41. Epub 2005 Oct 25.
23. Newman E, Turner AS, Wark JD. The potential of sheep for the study of osteopenia: current status and comparison with other animal models. Bone. 1995 ;16(4)(Suppl):277S–84S.
24. Nunamaker DM. Experimental models of fracture repair. Clin Orthop Relat Res. 1998 ;355(Suppl):S56–65.
25. Holstein JH, Garcia P, Histing T, Klein M, Becker SC, Menger MD, Pohleman T. Mouse models for the study of fracture-healing and bone regeneration. In: Duque G, Watanabe K, editors. Osteoporosis research: animal models. London: Springer; 2011.
26. Ke HZ, Brown TA, Qi H, Crawford DT, Simmons HA, Petersen DN, Allen MR, McNeish JD, Thompson DD. The role of estrogen receptor-beta, in the early age-related bone gain and later age-related bone loss in female mice. J Musculoskelet Neuronal Interact. 2002 ;2(5):479–88.
27. Seidlova-Wuttke D, Nguyen BT, Wuttke W. Long-term effects of ovariectomy on osteoporosis and obesity in estrogen-receptor-β-deleted mice. Comp Med. 2012 ;62(1):8–13.
28. Holsapple MP, West LJ, Landreth KS. Species comparison of anatomical and functional immune system development. Birth Defects Res B Dev Reprod Toxicol. 2003 ;68(4):321–34.
29. Doeing DC, Borowicz JL, Crockett ET. Gender dimorphism in differential peripheral blood leukocyte counts in mice using cardiac, tail, foot, and saphenous vein puncture methods. BMC Clin Pathol. 2003 ;3(1):3. Epub 2003 Sep 12.
30. Felsburg PJ. Overview of immune system development in the dog: comparison with humans. Hum Exp Toxicol. 2002 ;21(9-10):487–92.
31. Holladay SD, Smialowicz RJ. Development of the murine and human immune system: differential effects of immunotoxicants depend on time of exposure. Environ Health Perspect. 2000 ;108(Suppl 3):463–73.
32. Styrt B. Species variation in neutrophil biochemistry and function. J Leukoc Biol. 1989 ;46(1):63–74.
33. Reizner W, Hunter JG, O’Malley NT, Southgate RD, Schwarz EM, Kates SL. A systematic review of animal models for Staphylococcus aureus osteomyelitis. Eur Cell Mater. 2014;27:196–212. Epub 2014 Mar 25.
34. Moser SA, Gilbert SR. Osteomyelitis. In: McManus LM, Mitchell RN, editors. Pathobiology of human disease: a dynamic encyclopedia of disease mechanisms. San Diego: Academic Press; 2014. p 799–814.
35. Fujihara Y, Takato T, Hoshi K. Macrophage-inducing FasL on chondrocytes forms immune privilege in cartilage tissue engineering, enhancing in vivo regeneration. Stem Cells. 2014 ;32(5):1208–19.
36. Wijeyekoon S, Mino T, Satoh H, Matsuo T. Effects of substrate loading rate on biofilm structure. Water Res. 2004 ;38(10):2479–88.
37. Liu Y, Tay JH. Metabolic response of biofilm to shear stress in fixed-film culture. J Appl Microbiol. 2001 ;90(3):337–42.
38. Fears KP, Gonzalez-Begne M, Love CT, Day DE, Koo H. Surface-induced changes in the conformation and glucan production of glucosyltransferase adsorbed on saliva-coated hydroxyapatite. Langmuir. 2015 ;31(16):4654–62. Epub 2015 Apr 13.
39. Gristina AG. Biomaterial-centered infection: microbial adhesion versus tissue integration. Science. 1987 ;237(4822):1588–95.
40. Lorite GS, Rodrigues CM, de Souza AA, Kranz C, Mizaikoff B, Cotta MA. The role of conditioning film formation and surface chemical changes on Xylella fastidiosa adhesion and biofilm evolution. J Colloid Interface Sci. 2011 ;359(1):289–95. Epub 2011 Mar 24.
41. Søballe K, Hansen ES, B-Rasmussen H, Jørgensen PH, Bünger C. Tissue ingrowth into titanium and hydroxyapatite-coated implants during stable and unstable mechanical conditions. J Orthop Res. 1992 ;10(2):285–99.
42. Abu-Amer Y, Darwech I, Clohisy JC. Aseptic loosening of total joint replacements: mechanisms underlying osteolysis and potential therapies. Arthritis Res Ther. 2007;9(Suppl 1):S6.
43. Athanasou NA, Quinn J, Bulstrode CJ. Resorption of bone by inflammatory cells derived from the joint capsule of hip arthroplasties. J Bone Joint Surg Br. 1992 ;74(1):57–62.
44. Petty W, Spanier S, Shuster JJ, Silverthorne C. The influence of skeletal implants on incidence of infection. Experiments in a canine model. J Bone Joint Surg Am. 1985 ;67(8):1236–44.
45. Petty W, Spanier S, Shuster JJ. Prevention of infection after total joint replacement. Experiments with a canine model. J Bone Joint Surg Am. 1988 ;70(4):536–9.
46. Nijhof MW, Dhert WJ, Fleer A, Vogely HC, Verbout AJ. Prophylaxis of implant-related staphylococcal infections using tobramycin-containing bone cement. J Biomed Mater Res. 2000 ;52(4):754–61.
47. Nishitani K, Sutipornpalangkul W, de Mesy Bentley KL, Varrone JJ, Bello-Irizarry SN, Ito H, Matsuda S, Kates SL, Daiss JL, Schwarz EM. Quantifying the natural history of biofilm formation in vivo during the establishment of chronic implant-associated Staphylococcus aureus osteomyelitis in mice to identify critical pathogen and host factors. J Orthop Res. 2015 ;33(9):1311–9. Epub 2015 May 18.
48. Bernthal NM, Stavrakis AI, Billi F, Cho JS, Kremen TJ, Simon SI, Cheung AL, Finerman GA, Lieberman JR, Adams JS, Miller LS. A mouse model of post-arthroplasty Staphylococcus aureus joint infection to evaluate in vivo the efficacy of antimicrobial implant coatings. PLoS One. 2010;5(9):e12580. Epub 2010 Sep 7.
49. Niska JA, Meganck JA, Pribaz JR, Shahbazian JH, Lim E, Zhang N, Rice BW, Akin A, Ramos RI, Bernthal NM, Francis KP, Miller LS. Monitoring bacterial burden, inflammation and bone damage longitudinally using optical and μCT imaging in an orthopaedic implant infection in mice. PLoS One. 2012;7(10):e47397. Epub 2012 Oct 17.
50. Pribaz JR, Bernthal NM, Billi F, Cho JS, Ramos RI, Guo Y, Cheung AL, Francis KP, Miller LS. Mouse model of chronic post-arthroplasty infection: noninvasive in vivo bioluminescence imaging to monitor bacterial burden for long-term study. J Orthop Res. 2012 ;30(3):335–40. Epub 2011 Aug 11.
51. Craig MR, Poelstra KA, Sherrell JC, Kwon MS, Belzile EL, Brown TE. A novel total knee arthroplasty infection model in rabbits. J Orthop Res. 2005 ;23(5):1100–4.
52. Belmatoug N, Crémieux AC, Bleton R, Volk A, Saleh-Mghir A, Grossin M, Garry L, Carbon C. A new model of experimental prosthetic joint infection due to methicillin-resistant Staphylococcus aureus: a microbiologic, histopathologic, and magnetic resonance imaging characterization. J Infect Dis. 1996 ;174(2):414–7.
53. Yang X, Ricciardi BF, Dvorzhinskiy A, Brial C, Lane Z, Bhimani S, Burket JC, Hu B, Sarkisian AM, Ross FP, van der Meulen MC, Bostrom MP. Intermittent parathyroid hormone enhances cancellous osseointegration of a novel murine tibial implant. J Bone Joint Surg Am. 2015 ;97(13):1074–83.
54. Carli A, Yang X, Craveiro VL, Shirley MB, de Mesy Bentley K, Ross FP, Bostrom M. Quantification of peri-implant bacterial load and in vivo biofilm formation in an innovative, clinically-representative mouse model of periprosthetic joint infection. Read at the Annual Meeting of the Orthopaedic Research Society; 2016 Mar 5-8; Orlando, FL.
55. Flemming HC, Wingender J. Relevance of microbial extracellular polymeric substances (EPSs)—part I: structural and ecological aspects. Water Sci Technol. 2001;43(6):1–8.
56. Soto GE, Hultgren SJ. Bacterial adhesins: common themes and variations in architecture and assembly. J Bacteriol. 1999 ;181(4):1059–71.
57. Hermansson M. The DLVO theory in microbial adhesion. Colloids Surf B Biointerfaces. 1999;14:105–19.
58. Fletcher M. How do bacteria attach to solid surfaces? Microbiol Sci. 1987 ;4(5):133–6.
59. Costerton JW. The biofilm primer. New York: Springer; 2007. p 3–83.
60. Song F, Koo H, Ren D. Effects of material properties on bacterial adhesion and biofilm formation. J Dent Res. 2015 ;94(8):1027–34. Epub 2015 May 22.
61. Rzhepishevska O, Hakobyan S, Ruhal R, Gautrot J, Barbero D, Ramstedt M. The surface charge of anti-bacterial coatings alters motility and biofilm architecture. Biomater Sci. 2013;1(6):589–602.
62. Mabboux F, Ponsonnet L, Morrier JJ, Jaffrezic N, Barsotti O. Surface free energy and bacterial retention to saliva-coated dental implant materials—an in vitro study. Colloids Surf B Biointerfaces. 2004 ;39(4):199–205.
63. Quirynen M, Bollen CM. The influence of surface roughness and surface-free energy on supra- and subgingival plaque formation in man. A review of the literature. J Clin Periodontol. 1995 ;22(1):1–14.
64. Koseki H, Yonekura A, Shida T, Yoda I, Horiuchi H, Morinaga Y, Yanagihara K, Sakoda H, Osaki M, Tomita M. Early staphylococcal biofilm formation on solid orthopaedic implant materials: in vitro study. PLoS One. 2014;9(10):e107588. Epub 2014 Oct 9.
65. Teughels W, Van Assche N, Sliepen I, Quirynen M. Effect of material characteristics and/or surface topography on biofilm development. Clin Oral Implants Res. 2006 ;17(Suppl 2):68–81.
66. Xing R, Lyngstadaas SP, Ellingsen JE, Taxt-Lamolle S, Haugen HJ. The influence of surface nanoroughness, texture and chemistry of TiZr implant abutment on oral biofilm accumulation. Clin Oral Implants Res. 2015 ;26(6):649–56. Epub 2014 Feb 20.
67. Lin HY, Liu Y, Wismeijer D, Crielaard W, Deng DM. Effects of oral implant surface roughness on bacterial biofilm formation and treatment efficacy. Int J Oral Maxillofac Implants. 2013 ;28(5):1226–31.
68. Poncin-Epaillard F, Herry JM, Marmey P, Legeay G, Debarnot D, Bellon-Fontaine MN. Elaboration of highly hydrophobic polymeric surface—a potential strategy to reduce the adhesion of pathogenic bacteria? Mater Sci Eng C Mater Biol Appl. 2013 ;33(3):1152–61. Epub 2012 Dec 11.
69. Siegismund D, Undisz A, Germerodt S, Schuster S, Rettenmayr M. Quantification of the interaction between biomaterial surfaces and bacteria by 3-D modeling. Acta Biomater. 2014 ;10(1):267–75. Epub 2013 Sep 23.
70. Perera-Costa D, Bruque JM, González-Martín ML, Gómez-García AC, Vadillo-Rodríguez V. Studying the influence of surface topography on bacterial adhesion using spatially organized microtopographic surface patterns. Langmuir. 2014 ;30(16):4633–41. Epub 2014 Apr 17.
71. Manabe K, Nishizawa S, Shiratori S. Porous surface structure fabricated by breath figures that suppresses Pseudomonas aeruginosa biofilm formation. ACS Appl Mater Interfaces. 2013 ;5(22):11900–5. Epub 2013 Nov 13.
72. Hou S, Gu H, Smith C, Ren D. Microtopographic patterns affect Escherichia coli biofilm formation on poly(dimethylsiloxane) surfaces. Langmuir. 2011 ;27(6):2686–91. Epub 2011 Feb 14.
73. Song F, Ren D. Stiffness of cross-linked poly(dimethylsiloxane) affects bacterial adhesion and antibiotic susceptibility of attached cells. Langmuir. 2014 ;30(34):10354–62. Epub 2014 Aug 20.
74. Patti JM, Allen BL, McGavin MJ, Höök M. MSCRAMM-mediated adherence of microorganisms to host tissues. Annu Rev Microbiol. 1994;48:585–617.
75. Heilmann C, Hussain M, Peters G, Götz F. Evidence for autolysin-mediated primary attachment of Staphylococcus epidermidis to a polystyrene surface. Mol Microbiol. 1997 ;24(5):1013–24.
76. Heilmann C, Thumm G, Chhatwal GS, Hartleib J, Uekötter A, Peters G. Identification and characterization of a novel autolysin (Aae) with adhesive properties from Staphylococcus epidermidis. Microbiology. 2003 ;149(Pt 10):2769–78.
77. Otto M. Staphylococcal biofilms. Curr Top Microbiol Immunol. 2008;322:207–28.
78. Mack D, Fischer W, Krokotsch A, Leopold K, Hartmann R, Egge H, Laufs R. The intercellular adhesin involved in biofilm accumulation of Staphylococcus epidermidis is a linear beta-1,6-linked glucosaminoglycan: purification and structural analysis. J Bacteriol. 1996 ;178(1):175–83.
79. Davies DG, Parsek MR, Pearson JP, Iglewski BH, Costerton JW, Greenberg EP. The involvement of cell-to-cell signals in the development of a bacterial biofilm. Science. 1998 ;280(5361):295–8.
80. Vuong C, Saenz HL, Götz F, Otto M. Impact of the agr quorum-sensing system on adherence to polystyrene in Staphylococcus aureus. J Infect Dis. 2000 ;182(6):1688–93. Epub 2000 Oct 13.
81. Beenken KE, Blevins JS, Smeltzer MS. Mutation of sarA in Staphylococcus aureus limits biofilm formation. Infect Immun. 2003 ;71(7):4206–11.
82. Pulido L, Ghanem E, Joshi A, Purtill JJ, Parvizi J. Periprosthetic joint infection: the incidence, timing, and predisposing factors. Clin Orthop Relat Res. 2008 ;466(7):1710–5. Epub 2008 Apr 18.
83. Aggarwal VK, Bakhshi H, Ecker NU, Parvizi J, Gehrke T, Kendoff D. Organism profile in periprosthetic joint infection: pathogens differ at two arthroplasty infection referral centers in Europe and in the United States. J Knee Surg. 2014 ;27(5):399–406. Epub 2014 Jan 10.
84. New York State Department of Health. Hospital-Acquired Infections. New York State; 2014. Accessed 2015 Dec 1.
85. Rath PM, Knippschild M, Ansorg R. Diversity and persistence of Staphylococcus epidermidis strains that colonize the skin of healthy individuals. Eur J Clin Microbiol Infect Dis. 2001 ;20(7):517–9.
86. van Belkum A. Staphylococcal colonization and infection: homeostasis versus disbalance of human (innate) immunity and bacterial virulence. Curr Opin Infect Dis. 2006 ;19(4):339–44.
87. Novick RP, Ross HF, Projan SJ, Kornblum J, Kreiswirth B, Moghazeh S. Synthesis of staphylococcal virulence factors is controlled by a regulatory RNA molecule. EMBO J. 1993 ;12(10):3967–75.
88. Holden MT, Feil EJ, Lindsay JA, Peacock SJ, Day NP, Enright MC, Foster TJ, Moore CE, Hurst L, Atkin R, Barron A, Bason N, Bentley SD, Chillingworth C, Chillingworth T, Churcher C, Clark L, Corton C, Cronin A, Doggett J, Dowd L, Feltwell T, Hance Z, Harris B, Hauser H, Holroyd S, Jagels K, James KD, Lennard N, Line A, Mayes R, Moule S, Mungall K, Ormond D, Quail MA, Rabbinowitsch E, Rutherford K, Sanders M, Sharp S, Simmonds M, Stevens K, Whitehead S, Barrell BG, Spratt BG, Parkhill J. Complete genomes of two clinical Staphylococcus aureus strains: evidence for the rapid evolution of virulence and drug resistance. Proc Natl Acad Sci USA. 2004 ;101(26):9786–91. Epub 2004 Jun 22.
89. Stocks GW, O’Connor DP, Self SD, Marcek GA, Thompson BL. Directed air flow to reduce airborne particulate and bacterial contamination in the surgical field during total hip arthroplasty. J Arthroplasty. 2011 ;26(5):771–6. Epub 2010 Sep 18.
90. Ray MA, Johnston NA, Verhulst S, Trammell RA, Toth LA. Identification of markers for imminent death in mice used in longevity and aging research. J Am Assoc Lab Anim Sci. 2010 ;49(3):282–8.
91. Sanchez-Alavez M, Alboni S, Conti B. Sex- and age-specific differences in core body temperature of C57Bl/6 mice. Age (Dordr). 2011 ;33(1):89–99. Epub 2010 Jul 16.
92. Kort WJ, Hekking-Weijma JM, TenKate MT, Sorm V, VanStrik R. A microchip implant system as a method to determine body temperature of terminally ill rats and mice. Lab Anim. 1998 ;32(3):260–9.
93. Cray C, Zaias J, Altman NH. Acute phase response in animals: a review. Comp Med. 2009 ;59(6):517–26.
94. Jinbo T, Motoki M, Yamamoto S. Variation of serum α2-macroglobulin concentration in healthy rats and rats inoculated with Staphylococcus aureus or subjected to surgery. Comp Med. 2001 ;51(4):332–5.
95. Castell JV, Andus T, Kunz D, Heinrich PC. Interleukin-6. The major regulator of acute-phase protein synthesis in man and rat. Ann N Y Acad Sci. 1989;557:87–99; discussion 100-1.
96. Hayashi S, Jinbo T, Iguchi K, Shimizu M, Shimada T, Nomura M, Ishida Y, Yamamoto S. A comparison of the concentrations of C-reactive protein and α1-acid glycoprotein in the serum of young and adult dogs with acute inflammation. Vet Res Commun. 2001 ;25(2):117–26.
97. Hobo S, Niwa H, Anzai T. Evaluation of serum amyloid A and surfactant protein D in sera for identification of the clinical condition of horses with bacterial pneumonia. J Vet Med Sci. 2007 ;69(8):827–30.
98. Pruett BS, Pruett SB. An explanation for the paradoxical induction and suppression of an acute phase response by ethanol. Alcohol. 2006 ;39(2):105–10. Epub 2006 Oct 2.
99. Ray A, Ray BK. Persistent expression of serum amyloid A during experimentally induced chronic inflammatory condition in rabbit involves differential activation of SAF, NF-κ B, and C/EBP transcription factors. J Immunol. 1999 ;163(4):2143–50.
100. Bauer TW, Parvizi J, Kobayashi N, Krebs V. Diagnosis of periprosthetic infection. J Bone Joint Surg Am. 2006 ;88(4):869–82.
101. An YH, Friedman RJ. Animal models of orthopedic implant infection. J Invest Surg. 1998 ;11(2):139–46.
102. Rissing JP, Buxton TB, Weinstein RS, Shockley RK. Model of experimental chronic osteomyelitis in rats. Infect Immun. 1985b ;47(3):581–6.
103. Shandley S, Matthews KP, Cox J, Romano D, Abplanalp A, Kalns J. Hyperbaric oxygen therapy in a mouse model of implant-associated osteomyelitis. J Orthop Res. 2012 ;30(2):203–8. Epub 2011 Aug 3.
104. Haenle M, Zietz C, Lindner T, Arndt K, Vetter A, Mittelmeier W, Podbielski A, Bader R. A model of implant-associated infection in the tibial metaphysis of rats. ScientificWorldJournal. 2013;2013:481975. Epub 2013 Dec 8.
105. Sinclair KD, Pham TX, Williams DL, Farnsworth RW, Loc-Carrillo CM, Bloebaum RD. Model development for determining the efficacy of a combination coating for the prevention of perioperative device related infections: a pilot study. J Biomed Mater Res B Appl Biomater. 2013 ;101(7):1143–53. Epub 2013 Apr 6.
106. Christensen GD, Simpson WA, Younger JJ, Baddour LM, Barrett FF, Melton DM, Beachey EH. Adherence of coagulase-negative staphylococci to plastic tissue culture plates: a quantitative model for the adherence of staphylococci to medical devices. J Clin Microbiol. 1985 ;22(6):996–1006.
107. O’Toole GA, Pratt LA, Watnick PI, Newman DK, Weaver VB, Kolter R. Genetic approaches to study of biofilms. Methods Enzymol. 1999;310:91–109.
108. Engelsman AF, van der Mei HC, Francis KP, Busscher HJ, Ploeg RJ, van Dam GM. Real time noninvasive monitoring of contaminating bacteria in a soft tissue implant infection model. J Biomed Mater Res B Appl Biomater. 2009 ;88(1):123–9.
109. Trampuz A, Piper KE, Jacobson MJ, Hanssen AD, Unni KK, Osmon DR, Mandrekar JN, Cockerill FR, Steckelberg JM, Greenleaf JF, Patel R. Sonication of removed hip and knee prostheses for diagnosis of infection. N Engl J Med. 2007 ;357(7):654–63.
110. Bjerkan G, Witsø E, Bergh K. Sonication is superior to scraping for retrieval of bacteria in biofilm on titanium and steel surfaces in vitro. Acta Orthop. 2009 ;80(2):245–50.
111. Portillo ME, Salvadó M, Trampuz A, Plasencia V, Rodriguez-Villasante M, Sorli L, Puig L, Horcajada JP. Sonication versus vortexing of implants for diagnosis of prosthetic joint infection. J Clin Microbiol. 2013 ;51(2):591–4. Epub 2012 Nov 7.
112. Stoodley P, Nistico L, Johnson S, Lasko LA, Baratz M, Gahlot V, Ehrlich GD, Kathju S. Direct demonstration of viable Staphylococcus aureus biofilms in an infected total joint arthroplasty. A case report. J Bone Joint Surg Am. 2008 ;90(8):1751–8.
113. Heidrich M, Kühnel MP, Kellner M, Lorbeer RA, Lange T, Winkel A, Stiesch M, Meyer H, Heisterkamp A. 3D imaging of biofilms on implants by detection of scattered light with a scanning laser optical tomograph. Biomed Opt Express. 2011 ;2(11):2982–94. Epub 2011 Oct 3.
114. Bremer F, Grade S, Kohorst P, Stiesch M. In vivo biofilm formation on different dental ceramics. Quintessence Int. 2011 ;42(7):565–74.
115. Hazer DB, Sakar M, Dere Y, Altinkanat G, Ziyal MI, Hazer B. Antimicrobial effect of polymer-based silver nanoparticle coated pedicle screws: experimental research on biofilm inhibition in rabbits. Spine. 2016 ;41(6):E323–9.
116. Ercan B, Kummer KM, Tarquinio KM, Webster TJ. Decreased Staphylococcus aureus biofilm growth on anodized nanotubular titanium and the effect of electrical stimulation. Acta Biomater. 2011 ;7(7):3003–12. Epub 2011 Apr 13.
117. Banche G, Bracco P, Allizond V, Bistolfi A, Boffano M, Cimino A, Brach del Prever EM, Cuffini AM. Do crosslinking and vitamin E stabilization influence microbial adhesions on UHMWPE-based biomaterials? Clin Orthop Relat Res. 2015 ;473(3):974–86.
118. Zwietering MH, Jongenburger I, Rombouts FM, van ’t Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990 ;56(6):1875–81.
119. Szomolay B, Klapper I, Dockery J, Stewart PS. Adaptive responses to antimicrobial agents in biofilms. Environ Microbiol. 2005 ;7(8):1186–91.
120. Joyce LF, Downes J, Stockman K, Andrew JH. Comparison of five methods, including the PDM Epsilometer test (E test), for antimicrobial susceptibility testing of Pseudomonas aeruginosa. J Clin Microbiol. 1992 ;30(10):2709–13.
121. French GL. Bactericidal agents in the treatment of MRSA infections—the potential role of daptomycin. J Antimicrob Chemother. 2006 ;58(6):1107–17. Epub 2006 Oct 13.
122. Schmid MB, Roth JR. Genetic methods for analysis and manipulation of inversion mutations in bacteria. Genetics. 1983 ;105(3):517–37.
123. Poultsides LA, Papatheodorou LK, Karachalios TS, Khaldi L, Maniatis A, Petinaki E, Malizos KN. Novel model for studying hematogenous infection in an experimental setting of implant-related infection by a community-acquired methicillin-resistant S. aureus strain. J Orthop Res. 2008 ;26(10):1355–62.
124. Lee MS, Chang WH, Chen SC, Hsieh PH, Shih HN, Ueng SW, Lee GB. Molecular diagnosis of periprosthetic joint infection by quantitative RT-PCR of bacterial 16S ribosomal RNA. ScientificWorldJournal. 2013;2013:950548. Epub 2013 Dec 17.
125. Prax M, Lee CY, Bertram R. An update on the molecular genetics toolbox for staphylococci. Microbiology. 2013 ;159(Pt 3):421–35. Epub 2013 Feb 1.
126. Fux CA, Shirtliff M, Stoodley P, Costerton JW. Can laboratory reference strains mirror “real-world” pathogenesis? Trends Microbiol. 2005 ;13(2):58–63.
127. Zobell CE. The effect of solid surfaces upon bacterial activity. J Bacteriol. 1943 ;46(1):39–56.
128. Li D, Gromov K, Søballe K, Puzas JE, O’Keefe RJ, Awad H, Drissi H, Schwarz EM. Quantitative mouse model of implant-associated osteomyelitis and the kinetics of microbial growth, osteolysis, and humoral immunity. J Orthop Res. 2008 ;26(1):96–105.
129. Zhang YQ, Ren SX, Li HL, Wang YX, Fu G, Yang J, Qin ZQ, Miao YG, Wang WY, Chen RS, Shen Y, Chen Z, Yuan ZH, Zhao GP, Qu D, Danchin A, Wen YM. Genome-based analysis of virulence genes in a non-biofilm-forming Staphylococcus epidermidis strain (ATCC 12228). Mol Microbiol. 2003 ;49(6):1577–93.
130. Rogers KL, Fey PD, Rupp ME. Coagulase-negative staphylococcal infections. Infect Dis Clin North Am. 2009 ;23(1):73–98.
131. Rosenthal ME, Dever LL, Moucha CS, Chavda KD, Otto M, Kreiswirth BN. Molecular characterization of an early invasive Staphylococcus epidermidis prosthetic joint infection. Microb Drug Resist. 2011 ;17(3):345–50. Epub 2011 Apr 21.
132. Otto M. Staphylococcus epidermidis—the ‘accidental’ pathogen. Nat Rev Microbiol. 2009 ;7(8):555–67.
133. Ghebremedhin B, Layer F, König W, König B. Genetic classification and distinguishing of Staphylococcus species based on different partial gap, 16S rRNA, hsp60, rpoB, sodA, and tuf gene sequences. J Clin Microbiol. 2008 ;46(3):1019–25. Epub 2008 Jan 3.
134. Takahashi T, Satoh I, Kikuchi N. Phylogenetic relationships of 38 taxa of the genus Staphylococcus based on 16S rRNA gene sequence analysis. Int J Syst Bacteriol. 1999 ;49(Pt 2):725–8.
135. Herbert S, Ziebandt AK, Ohlsen K, Schäfer T, Hecker M, Albrecht D, Novick R, Götz F. Repair of global regulators in Staphylococcus aureus 8325 and comparative analysis with other clinical isolates. Infect Immun. 2010 ;78(6):2877–89. Epub 2010 Mar 8.
136. Nimmo GR. USA300 abroad: global spread of a virulent strain of community-associated methicillin-resistant Staphylococcus aureus. Clin Microbiol Infect. 2012 ;18(8):725–34. Epub 2012 Mar 27.
137. Ramalhete C, Spengler G, Martins A, Martins M, Viveiros M, Mulhovo S, Ferreira MJ, Amaral L. Inhibition of efflux pumps in methicillin-resistant Staphylococcus aureus and Enterococcus faecalis resistant strains by triterpenoids from Momordica balsamina. Int J Antimicrob Agents. 2011 ;37(1):70–4. Epub 2010 Nov 13.
138. Sassi M, Sharma D, Brinsmade SR, Felden B, Augagneur Y. Genome sequence of the clinical isolate Staphylococcus aureus subsp. aureus strain UAMS-1. Genome Announc. 2015;3(1). Epub 2015 Feb 12.
139. Olson PD, Kuechenmeister LJ, Anderson KL, Daily S, Beenken KE, Roux CM, Reniere ML, Lewis TL, Weiss WJ, Pulse M, Nguyen P, Simecka JW, Morrison JM, Sayood K, Asojo OA, Smeltzer MS, Skaar EP, Dunman PM. Small molecule inhibitors of Staphylococcus aureus RnpA alter cellular mRNA turnover, exhibit antimicrobial activity, and attenuate pathogenesis. PLoS Pathog. 2011;7(2):e1001287. Epub 2011 Feb 10.
140. Watkins RR, David MZ, Salata RA. Current concepts on the virulence mechanisms of meticillin-resistant Staphylococcus aureus. J Med Microbiol. 2012 ;61(Pt 9):1179–93. Epub 2012 Jun 28.
141. Costa SS, Viveiros M, Amaral L, Couto I. Multidrug efflux pumps in Staphylococcus aureus: an update. Open Microbiol J. 2013;7:59–71. Epub 2013 Mar 22.
142. Tendolkar PM, Baghdayan AS, Gilmore MS, Shankar N. Enterococcal surface protein, Esp, enhances biofilm formation by Enterococcus faecalis. Infect Immun. 2004 ;72(10):6032–9.
143. Chalabaev S, Chauhan A, Novikov A, Iyer P, Szczesny M, Beloin C, Caroff M, Ghigo JM. Biofilms formed by gram-negative bacteria undergo increased lipid a palmitoylation, enhancing in vivo survival. MBio. 2014;5(4). Epub 2014 Aug 19.
144. Arciola CR, Baldassarri L, Campoccia D, Creti R, Pirini V, Huebner J, Montanaro L. Strong biofilm production, antibiotic multi-resistance and high gelE expression in epidemic clones of Enterococcus faecalis from orthopaedic implant infections. Biomaterials. 2008 ;29(5):580–6.
145. Jensen PO, Givskov M, Bjarnsholt T, Moser C. The immune system vs. Pseudomonas aeruginosa biofilms. FEMS Immunol Med Microbiol. 2010 ;59(3):292–305. Epub 2010 May 28.
146. Ito A, Taniuchi A, May T, Kawata K, Okabe S. Increased antibiotic resistance of Escherichia coli in mature biofilms. Appl Environ Microbiol. 2009 ;75(12):4093–100. Epub 2009 Apr 17.
Copyright 2016 by The Journal of Bone and Joint Surgery, Incorporated