Osteomyelitis, or inflammation of bone, is most commonly caused by invasion of bacterial pathogens into the skeleton. Bacterial osteomyelitis is notoriously difficult to treat, in part because of the widespread antimicrobial resistance in the preeminent etiologic agent, the Gram-positive bacteriumStaphylococcus aureus. Bacterial osteomyelitis triggers pathological bone remodeling, which in turn leads to sequestration of infectious foci from innate immune effectors and systemically delivered antimicrobials.

➤ Artificial intelligence (AI) provides machines with the ability to perform tasks using algorithms governed by pattern recognition and self-correction on large amounts of data to narrow options in order to avoid errors. ➤ The 4 things necessary for AI in medicine include big data sets, powerful computers, cloud computing, and open source algorithmic development. ➤ The use of AI in health care continues to expand, and its impact on orthopaedic surgery can already be found in diverse areas such as image recognition, risk prediction, patient-specific payment models, and clinical decision-making. ➤ Just as the business of medicine was once considered outside the domain of the orthopaedic surgeon, emerging technologies such as AI warrant ownership, leverage, and application by the orthopaedic surgeon to improve the care that we provide to the patients we serve. ➤ AI could provide solutions to factors contributing to physician burnout and medical mistakes. However, challenges regarding the ethical deployment, regulation, and the clinical superiority of AI over traditional statistics and decision-making remain to be resolved.

Surgical infections are one of the most common types of infections encountered in a hospital. Staphylococcus aureus is the most common pathogen associated with this infection. These infections are resilient and difficult to eradicate, as the bacteria form biofilm, a community of bacteria held together by an extracellular matrix. Compared to bacteria that are planktonic, bacteria in a biofilm are more resistant to antibiotics. The mechanism behind how bacteria develop this resistance and establish a chronic infection is unknown. We demonstrate that mazEF , a toxin-antitoxin gene, inhibits biofilm formation and promotes biofilm antibiotic tolerance which allows S. aureus to transition from an acute to chronic infection that cannot be eradicated with antibiotics but is less virulent. This gene not only makes the bacteria more tolerant to antibiotics but makes the bacteria more tolerant to the host.

The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty‐eight subjects were selected from the osteoarthritis initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 3 years after baseline evaluation. Multi‐slice T₂‐weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T₂ maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for “progression to symptomatic OA” using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND‐CHRM). WND‐CHRM classified the isolated T₂ maps for the progression to symptomatic OA with 75% accuracy. Clinical significance: Machine learning algorithms applied to T₂ maps have the potential to provide important prognostic information for the development of OA. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2243–2250, 2017.

Ns/purposes (1) Do increasing doses of cefazolin result in decreased viable biofilm mass on arthroplasty materials? (2) Is cefazolin resistance phenotypic or genotypic? (3) Is biofilm viability a function of biofilm depth after treatment with cefazolin? (4) Is the toxin-antitoxin system, yoeB expression, associated with antibiotic stress? Methods Methicillin-sensitive Staphylococcus aureus biofilm was cultured on total knee arthroplasty (TKA) materials and exposed to increasing doses of cefazolin (control, 0.5, 1.0, 10.0, 100.0 μg/mL). Quantitative confocal microscopy and quantitative culture were used to measure viable biofilm cell density. To determine if cefazolin resistance was phenotypic or genotypic, we measured minimum inhibitory concentration (MIC) after exposure to different cefazolin concentrations; changes in MIC would suggest genotypic features, whereas unchanged MIC would suggest phenotypic behavior. Finally, quantitative reverse transcription-polymerase chain reaction was used to quantify expression of yoeB levels between biofilm and planktonic bacteria after exposure to 1 μg/mL cefazolin for 3 hours. Results Although live biofilm mass was reduced by exposure to cefazolin when compared with biofilm mass in controls (39.2 × 103 ± 26.4 × 103 pixels), where the level after 0.5 μg/mL exposure also showed reduced mass (20.3 × 103 ± 11.9 × 103 pixels), no further reduction was seen after higher doses (mass at 1.0 μg/mL: 5.0 × 103 pixels ± 1.1 × 103 pixels; at 10.0 μg/mL: 6.4 × 103 ± 9.6 × 103 pixels; at 100.0 μg/mL: 6.4 × 103 ± 3.9 × 103). At the highest concentration tested (100 μg/mL), residual viable biofilm was present on all three materials, and there were no differences in percent biofilm survival among cobalt-chromium (18.5% ± 15.1%), polymethylmethacrylate (22.8% ± 20.2%), and polyethylene (14.7% ± 10.4%). We found that tolerance was a phenotypic phenomenon, because increasing cefazolin exposure did not result in changes in MIC as compared with controls (MIC in controls: 0.13 ± 0.02; at 0.5 μg/mL: 0.13 ± 0.001, p = 0.96; at 1.0 μg/m: 0.14 ± 0.04, p = 0.95; at 10.0 μg/m: 0.11 ± 0.016, p = 0.47; at 100.0 μg/m: 0.94 ± 0.047, p = 0.47). Expression of yoeB after 1 μg/mL cefazolin for 3 hours in biofilm cells was greater in biofilm but not in planktonic cells (biofilm: 62.3-fold change, planktonic cells: −78.8-fold change, p < 0.001). Conclusions Antibiotics are inadequate at complete removal of the biofilm from the surface of TKA materials. Results suggest that bacterial persisters are responsible for this phenotypic behavior allowing biofilm high tolerance to antibiotics. Clinical Relevance Antibiotic-tolerant biofilm suggests a mechanism behind the poor results in irrigation and débridement for acute TKA PJI....