Diabetic foot ulcers and infections are common complications of diabetic foot disease. Additionally, these complications are a common cause of morbidity and impose a substantial burden to the patient and society. It is imperative to understand the major contributing factors, namely, diabetic neuropathy, peripheral arterial disease, and immune system dysfunction in order to guide treatment. Management of diabetic foot disease begins with a detailed history and thorough physical examination. This examination should focus on the manifestations of diabetic neuropathy and peripheral arterial disease, and, in particular, any evidence of diabetic foot ulcers or infection. Prevention strategies should include a multi-disciplinary approach centered on patient education.

Background We provide evidence to revise the Infectious Diseases Society of America (IDSA) diabetic foot infection classification by adding a separate tier for osteomyelitis and evaluating if moderate and severe infection criteria improve the classification’s ability to direct therapy and determine outcomes. Methods We retrospectively evaluated 294 patients with moderate and severe infections. Osteomyelitis was confirmed by bone culture or histopathology. Soft tissue infection (STI) was based on negative bone culture, magnetic resonance imaging, or single-photon emission computed tomography. We stratified STI and osteomyelitis using IDSA criteria for moderate and severe infections and compared outcomes and complications. Results Osteomyelitis patients had greater antibiotic duration (32.5 ± 46.8 vs 63.8 ± 55.1 days; P < .01), surgery frequency (55.5% vs 99.4%; P < .01), number of surgeries (2.1 ± 1.3 vs 3.3 ± 2.3; P < .01), amputations (26.3% vs 83.4%; P < .01), reinfection (38.0% vs 56.7%; P < .01), and length of stay (14.5 ± 14.9 vs 22.6 ± 19.0 days; P < .01). There were no differences in moderate and severe STI outcomes except for infection readmissions (46.2% vs 25.0%; P = .02), and acute kidney injury (31.2% vs 50.0%; P = .03). There were no differences in moderate and severe osteomyelitis except the number of surgeries (2.8 ± 2.1 vs 4.1 ± 2.5; P < .01) and length of stay (18.6 ± 17.5 vs 28.2 ± 17.7; P < .01). Conclusions The IDSA classification better reflects outcomes if risk categories are stratified by STI or osteomyelitis and moderate and severe infections are not categorized separately.

Background Distinguishing osteomyelitis from soft-tissue infection of the foot is important because osteomyelitis is associated with more operations, amputation, and prolonged antibiotic exposure. Both erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are routinely ordered inflammatory biomarkers for evaluating foot infection. When initial evaluation is inconclusive, advanced imaging is indicated, and high clinical or radiographic suspicion of osteomyelitis may indicate bone biopsy to identify organisms and antibiotic sensitivity. Although ESR and CRP levels are helpful for distinguishing osteomyelitis from soft-tissue infections in patients with diabetes-related foot infections, parameters regarding optimal cutoff values for those tests have not, to our knowledge, been defined. Questions/purposes (1) What are the optimal cutoff values for ESR and CRP to differentiate osteomyelitis from softtissue infection in patients with diabetes-related foot infection? (2) Can a diagnostic algorithm be derived to guide interpretation of ESR and CRP to improve recognition of osteomyelitis in the setting of diabetic foot infection? Methods The medical records of 1842 patients between 18 and 89 years of age treated at our institution between January 1, 2010 and February 6, 2017 for foot infection were reviewed. For inclusion, patients must have had a diagnosis of diabetes mellitus, moderate or severe infection, ESR and CRP values within 72 hours of admission, either advanced imaging (MRI or single-positron emission computed tomography/computed tomography [SPECT/ CT]) or bone biopsy during admission and must not have had comorbidities that could affect ESR and CRP, such as autoimmune disorders. As such, 1489 patients were excluded, and 353 patients were included in the study. Osteomyelitis was diagnosed by positive bone culture or histopathology. Osteomyelitis was considered to be absent if there was a negative MRI or SPECT/CT result, or negative bone culture and histology findings if imaging was inconclusive. We identified 176 patients with osteomyelitis and 177 with soft-tissue infection. A blinded investigator Each author certifies that he or she has no commercial associations (e.g. consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article. Clinical Orthopaedics and Related Research® neither advocates nor endorses the use of any treatment, drug, or device. Readers are encouraged to always seek additional information, including FDA approval status, of any drug or device before clinical use. Each author certifies that his or her institution approved the human protocol for this investigation and that all investigations were conducted in conformity with ethical principles of research.

Alternative cell sources, such as three‐dimensional organoids and induced pluripotent stem cell–derived cells, might provide a potentially effective approach for both drug development applications and clinical transplantation. For example, the development of cell sources for liver cell–based therapy has been increasingly needed, and liver transplantation is performed for the treatment for patients with severe end‐stage liver disease. Differentiated liver cells and three‐dimensional organoids are expected to provide new cell sources for tissue models and revolutionary clinical therapies. However, conventional experimental methods confirming the expression levels of liver‐specific lineage markers cannot provide complete information regarding the differentiation status or degree of similarity between liver and differentiated cell sources. Therefore, in this study, to overcome several issues associated with the assessment of differentiated liver cells and organoids, we developed a liver‐specific gene expression panel (LiGEP) algorithm that presents the degree of liver similarity as a “percentage.” We demonstrated that the percentage calculated using the LiGEP algorithm was correlated with the developmental stages of in vivo liver tissues in mice, suggesting that LiGEP can correctly predict developmental stages. Moreover, three‐dimensional cultured HepaRG cells and human pluripotent stem cell–derived hepatocyte‐like cells showed liver similarity scores of 59.14% and 32%, respectively, although general liver‐specific markers were detected. Conclusion: Our study describes a quantitative and predictive model for differentiated samples, particularly liver‐specific cells or organoids; and this model can be further expanded to various tissue‐specific organoids; our LiGEP can provide useful information and insights regarding the differentiation status of in vitro liver models. (Hepatology 2017;66:1662–1674).