Aluminum action on mouse bone cell metabolism and response to PTH and 1,25(OH)2D3

Aluminum action on mouse bone cell metabolism and response to PTH and 1,25(OH)2D3. Aluminum (Al) accumulation in bone is associated with low bone formation and mineralization rates; resorption may also be reduced. The mechanism of these Al-induced changes was investigated using cultured mouse osteoblast–like (OB) and osteoclast–like (OC) cells. The Al effect on bone resorption was measured by the in vitro release of 45Ca and β-glucuronidase from mouse fetal limb–bones. Al had a biphasic effect. High concentrations (>1.5 × 10-6 M) of Al inhibited collagen and DNA synthesis, ornithine decarboxylase and alkaline phosphatase activity in OB, and depressed tartrate-resistant acid phosphatase activity in OC. Lower Al concentrations stimulated these cellular activities and 45Ca and β-glucuronidase release from fetal bones. Al had no effect on basal cAMP levels in OB but inhibited the stimulating effect of bPTH on cAMP content. Al also altered the 1,25(OH)2D3 effects on the ornithine decarboxylase activity of OB cells. These data suggest that: (i) the low bone formation observed in vivo during Al intoxication may be due to the inhibition of collagen synthesis and to depressed cell proliferation; and (ii) Al may indirectly influence bone remodeling by interfering with the actions of bPTH and 1,25(OH)2D3 on bone cells

Bone pain and extremely low bone mineral density due to severe vitamin D deficiency in celiac disease

Case report A 29-year-old wheelchair-bound woman was presented to us by the gastroenterologist with suspected osteomalacia. She had lived in the Netherlands all her life and was born of Moroccan parents. Her medical history revealed iron deficiency, growth retardation, and celiac disease, for which she was put on a gluten-free diet. She had progressive bone pain since 2 years, difficulty with walking, and about 15 kg weight loss. She had a short stature, scoliosis, and pronounced kyphosis of the spine and poor condition of her teeth. Laboratory results showed hypocalcemia, an immeasurable serum25-hydroxyvitamin D level, and elevated parathyroid hormone and alkaline phosphatase levels. Spinal radiographs showed unsharp, low contrast vertebrae. Bone mineral density measurement at the lumbar spine and hip showed a T-score of -6.0 and -6.5, respectively. A bone scintigraphy showed multiple hotspots in ribs, sternum, mandible, and long bones. A duodenal biopsy revealed villous atrophy (Marsh 3C) and positive antibodies against endomysium, transglutaminase, and gliadin, compatible with active celiac disease. A bone biopsy showed severe osteomalacia but normal bone volume. She was treated with calcium intravenously and later orally. Furthermore, she was treated with high oral doses of vitamin D and a gluten-free diet. After a few weeks of treatment, her bone pain decreased, and her muscle strength improved. Discussion In this article, the pathophysiology and occurrence of osteomalacia as a complication of celiac disease are discussed. Low bone mineral density can point to osteomalacia as well as osteoporosis. © International Osteoporosis Foundation and National Osteoporosis Foundation 2011

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants

BACKGROUND: Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. METHODS: A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. RESULTS: CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0–1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. CONCLUSION: State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide