The vial kit formulation for preparation of no-carrier-added 131 I-MIBG.

We have developed a new set of lyophilized kits, composed of 3 different kits, for the instant preparation of no-carrier-added 131 I-MIBG in the clinic. We here discussed the formulation of the kits, optimization of radiolabelling, quality control of radiolabeled 131 I-MIBG, and studies of animal biodistribution. The no-carrier-added (nca) 131 I-MIBG injection could be prepared within 30 minutes in the clinic with the help of the lyophilized kits. The radiochemical purity and specific activity (SA) could achieve above 98% and 6700 MBq/mg, respectively.

A convenient method for the preparation of radioiodinated meta-iodobenzylguanidine at a no-carrier-added level.

Radioiodinated meta-iodobenzylguanidine (MIBG) in high effective specific activity was prepared using 3-tributylstannylbenzylguanidine as the precursor. The labeling was carried out in aqueous solution with the insoluble and lyophilized precursor suspended in the solvent. Simply by filtration, the starting material and by-products were readily separated from the labeled solution. Less than 1.15 ppb tin has remained in the filtrate as determined by the atom fluorescence spectrometry. By this approach, high specific activity (3.4 GBq/µmol) [(131)I]MIBG was obtained in 72.3 ± 3% (n = 3) radiochemical yield and 97.3 ± 2% (n = 3) radiochemical purity. The whole preparation could be finished in less than 10 min. According to this method, a kit for the preparation of (123)I-MIBG and (131)I-MIBG is currently being developed.

Genomic prediction based on data from three layer lines using non-linear regression models.

BACKGROUND: Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. METHODS: In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. RESULTS: When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. CONCLUSIONS: Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional occurrence of large negative accuracies when the evaluated line was not included in the training dataset. Furthermore, when using a multi-line training dataset, non-linear models provided information on the genotype data that was complementary to the linear models, which indicates that the underlying data distributions of the three studied lines were indeed heterogeneous.

Genomic prediction based on data from three layer lines: a comparison between linear methods.

BACKGROUND: The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction. METHODS: Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we evaluated the following genomic prediction models: genome-enabled BLUP (GBLUP), ridge regression BLUP (RRBLUP), principal component analysis followed by ridge regression (RRPCA), BayesC and Bayesian stochastic search variable selection. Prediction accuracy was measured as the correlation between predicted breeding values and observed phenotypes divided by the square root of the heritability. The data used concerned laying hens with phenotypes for number of eggs in the first production period and known genotypes. The hens were from two closely-related brown layer lines (B1 and B2), and a third distantly-related white layer line (W1). Lines had 1004 to 1023 training animals and 238 to 240 validation animals. Training datasets consisted of animals of either single lines, or a combination of two or all three lines, and had 30 508 to 45 974 segregating single nucleotide polymorphisms. RESULTS: Genomic prediction models yielded 0.13 to 0.16 higher accuracies than pedigree-based BLUP. When excluding the line itself from the training dataset, genomic predictions were generally inaccurate. Use of multiple lines marginally improved prediction accuracy for B2 but did not affect or slightly decreased prediction accuracy for B1 and W1. Differences between models were generally small except for RRPCA which gave considerably higher accuracies for B2. Correlations between genomic predictions from different methods were higher than 0.96 for W1 and higher than 0.88 for B1 and B2. The greater differences between methods for B1 and B2 were probably due to the lower accuracy of predictions for B1 (~0.45) and B2 (~0.40) compared to W1 (~0.76). CONCLUSIONS: Multi-line genomic prediction did not affect or slightly improved prediction accuracy for closely-related lines. For distantly-related lines, multi-line genomic prediction yielded similar or slightly lower accuracies than single-line genomic prediction. Bayesian variable selection and GBLUP generally gave similar accuracies. Overall, RRPCA yielded the greatest accuracies for two lines, suggesting that using PCA helps to alleviate the "n ≪ p" problem in genomic prediction.

The synthesis of a new macromolecule for the sentinel node detection: 99mTc-gly-mannosyl-dextran.

OBJECTIVE: We aimed to design and synthesize a new macromolecule for sentinel node detection to improve the imaging quality and avoid possible adverse effect. BACKGROUND: The imaging of sentinel lymph node has been an important field in the nuclear medicine. A lot of imaging agents have been developed, including Tc-sulfer colloid, Tc-labeled dextrans and the latest Tc-DTPA-mannosyl-dextran. With the technology advanced, the imaging ability of the agents has been better and better. However, there are still some drawbacks. MATERIALS AND METHODS: The new macromolecule agent was based on the dextran macromolecule backbone. Then the gly-gly-gly and mannose molecules were conjugated onto the backbone proportionally by targeting two different reaction sites. Once the new macromolecule was labelled with Tc, its imaging ability was tested by single-photon emission computed tomography scanning with Tc-sulfur colloid as the comparison. RESULTS: The average numbers of gly-gyl-gyl and mannosyl groups on the dextran backbone are determined to be ∼1: 2 per dextran. The average molecular diameter and molecular weight are measured to be 5.4±0.7 nm and 10 324 g/mol, respectively. The macromolecule is labelled by Tc with 93.2±2.4% radiochemical yield. The lymphatic imaging by single-photon emission computed tomography with the labeled compound showed no worse imaging ability but cost less time than the commercially available Tc-sulfur colloid. CONCLUSION: A new macromolecule imaging agent for sentinel node detection has been synthesized with better imaging ability and less imaging time cost.