Reveromycin A-Induced Apoptosis in Osteoclasts Is Not Accompanied by Necrosis.

Reveromycin A (RM-A), a small natural product isolated from Streptomyces bacteria, is a potential osteoporosis therapeutic in that it specifically induces apoptosis in osteoclasts but not osteoblasts. The purpose of the study presented here was to further elucidate the intracellular mechanisms of RM-A death effects in mature osteoclasts. A specific clone of RAW264.7 murine macrophages that was previously characterized for its ability to acquire an osteoclast nature on differentiation was differentiated in the presence of receptor activator of nuclear factor kappa B ligand (RANKL). Subsequent staining was performed for tartrate-resistant acid phosphatase to confirm their osteoclast character. These osteoclasts were treated with ten micromolar RM-A for 2, 4, 6, 24, and 48 h at a pH of 5.5. Peak apoptosis induction occurred at 4-6 h as measured by caspase 3 activity. Lactate dehydrogenase release assay revealed no significant RM-A-induced necrosis. Western blot analysis of cytoplasmic extracts demonstrated activation of caspase 9 (2.3-fold at 2 h and 2.6-fold at 4 h, each P < 0.05) and no significant changes in Bcl-XL . In nuclear extracts, NFκB levels significantly increased on differentiation with RANKL but then remained constant through RM-A treatment. Over the extended time course studied, RM-A-induced apoptosis in osteoclasts was not accompanied by necrosis, suggesting that RM-A would likely have limited effects on immediate, neighboring bone cell types. This specific cell death profile is promising for potential clinical investigations of RM-A as a bone antiresorptive.

Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record-Based Computable Phenotype Derivation and Validation Study.

BACKGROUND: Deploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department. OBJECTIVE: This study aimed to derive and validate an electronic health record-based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard. METHODS: A two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative). RESULTS: Cohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978). CONCLUSIONS: This phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.