[Synthesis and characterization of CO-3(2-) doping nano-hydroxyapatite].

CO3(2-) doping is an effective method to increase the biological activity of nano-hydroxyapatite (n-HA). In the present study, calcium nitrate and trisodium phosphate were chosen as raw materials, with a certain amount of Na2CO3 as a source of CO-3(2-) ions, to synthesize nano-carbonate hydroxyapatite (n-CHA) slurry by solution precipitation method. The structure and micro-morphology of n-CHA were characterized by transmission electron microscope (TEM), X-ray diffraction (XRD), Fourier transform-infrared spectroscopy (FTIR) and Raman spectroscopy (RS). The results revealed that the synthetic n-HA crystals are acicular in nanometer scale and have a crystal size of 20-30 nm in diameter and 60-80 nm in length, which are similar to natural bone apatite. And the crystallinity of n-CHA crystals decreases to the increment of CO3(2-). Samples with more CO3(2) have composition and structure more similar to the bone apatite. The value of lattice parameters a decreases, value of c increases, and c/a value increases with the increase in the amount of CO3(2-), in accordance with crystal cell parameters change rule of type B replacement. In the AB mixed type (substitution OH- and PO4(3-)) CHA, IR characteristic peak of CO3(2-) out-of-plane bending vibration appears at 872 cm(-1), meanwhile, the asymmetry flexible vibration band is split into band at 1 454 cm(-1) and band at 1 420 cm(-1), while weak CO3(2)-peak appears at 1 540 cm(-1). CO3(2-) Raman peak of symmetric stretching vibration appears at 1 122 cm(-1). CO3(2-) B-type (substitution PO4(3-)) peak appeared at 1 071 cm(-1). Through the calculation of integral area ratio of PO4(3-)/ CO3(2-), OH-/CO3(2-), and PO4(3-)/OH-, low quantity CO3(2-) is B-type and high quantity CO3(2-) is A-type (substitution OH-). The results show that the synthesized apatite crystals are AB hybrid substitued nano-carbonate hydroxyapatite, however B-type replacement is the main substitute mode. Due to similarity inthe shape, size, crystal structure and growth mode, the synthesized apatite crystals can be called a kind of bone-like apatite.

Artificial neural network to predict skeletal metastasis in patients with prostate cancer.

The application of an artificial neural network (ANN) in prediction of outcomes using clinical data is being increasingly used. The aim of this study was to assess whether an ANN model is a useful tool for predicting skeletal metastasis in patients with prostate cancer. Consecutive patients with prostate cancer who underwent the technetium-99m methylene diphosphate (Tc-99m MDP) whole body bone scintigraphies were retrospectively analyzed between 2001 and 2005. The predictors were the patient's age and radioimmunometric serum PSA concentration. The outcome variable was dichotomous, either skeletal metastasis or non-skeletal metastasis, based on the results of Tc-99m MDP whole body bone scintigraphy. To assess the performance for classification model in clinical study, the discrimination and calibration of an ANN model was calculated. The enrolled subjects consisted of 111 consecutive male patients aged 72.41 +/- 7.69 years with prostate cancer. Sixty-seven patients (60.4%) had skeletal metastasis based on the scintigraphic diagnosis. The final best architecture of neural network model was four-layered perceptrons. The area under the receiver-operating characteristics curve (0.88 +/- 0.07) revealed excellent discriminatory power (p < 0.001) with the best simultaneous sensitivity (87.5%) and specificity (83.3%). The Hosmer-Lemeshow statistic was 6.74 (p = 0.08 > 0.05), which represented a good-fit calibration. These results suggest that an ANN, which is based on limited clinical parameters, appears to be a promising method in forecasting of the skeletal metastasis in patients with prostate cancer.