Enhanced analysis of real-time PCR data by using a variable efficiency model: FPK-PCR.

Current methodology in real-time Polymerase chain reaction (PCR) analysis performs well provided PCR efficiency remains constant over reactions. Yet, small changes in efficiency can lead to large quantification errors. Particularly in biological samples, the possible presence of inhibitors forms a challenge. We present a new approach to single reaction efficiency calculation, called Full Process Kinetics-PCR (FPK-PCR). It combines a kinetically more realistic model with flexible adaptation to the full range of data. By reconstructing the entire chain of cycle efficiencies, rather than restricting the focus on a 'window of application', one extracts additional information and loses a level of arbitrariness. The maximal efficiency estimates returned by the model are comparable in accuracy and precision to both the golden standard of serial dilution and other single reaction efficiency methods. The cycle-to-cycle changes in efficiency, as described by the FPK-PCR procedure, stay considerably closer to the data than those from other S-shaped models. The assessment of individual cycle efficiencies returns more information than other single efficiency methods. It allows in-depth interpretation of real-time PCR data and reconstruction of the fluorescence data, providing quality control. Finally, by implementing a global efficiency model, reproducibility is improved as the selection of a window of application is avoided.

Quantification and viability assessment of isolated bovine primordial and primary ovarian follicles retrieved through a standardized biopsy pick-up procedure.

The feasibility of repeated collection and enzymatic isolation of large numbers of viable primordial and primary follicles from living donor cows were tested. Ovarian cortical biopsies were collected transvaginally by the Biopsy Pick-Up (BPU) device, a modification of an Ovum Pick-Up instrument. Follicles were enzymatically isolated from the retrieved cortical tissue samples, and follicle viability was determined by a live/dead fluorescent assay. Six cows were subjected to BPU once per week during 4 consecutive weeks, and in each BPU session 4 cortical tissue samples were collected per ovary. Over the 4-week trial period, a total of 1443 primordial and primary follicles were collected, 1358 (94%) of which were primordial and 85 (6%) were primary follicles. In each BPU session, an average 60.1 +/- 10.7 (mean +/- SEM) primordial and primary follicles were isolated per cow. The number of follicles varied considerably throughout the trial period and between cows. Statistical analysis of the data, however, did not support the presence of any distinct trends in the follicle yields over time or between cows. A total of 111 enzymatically isolated follicles were analyzed for viability with fluorescent probes. The vast majority of isolated follicles (92.8%) were totally viable. We conclude that the standardized BPU procedure generates sufficiently large numbers of vital primordial and primary follicles, thus validating BPU as a new tool for research into early bovine follicular development.

Using deepest regression method for optimization of fluidized bed granulation on semi-full scale.

This study applied the deepest regression method to estimate the granule size of unsuccessful fluidized bed granulation runs. This study uses data from a previous study [Int. J. Pharm. 220 (2001) 149] on optimization of fluidized granulation process, wherein 8 of the 30 runs did not succeeded due to overwetting of the powder bed. The "complete data" (the observed and the estimated granule size by the depth regression method) were used to develop two regression models for the granule size: an empirical model based on the process variables (inlet air temperature, inlet airflow rate, spray rate, and inlet air humidity) and a fundamental model based on the powder bed moisture content and the relative droplet size. The regression models based on the incomplete data from the previous study and the regression models of the "complete data" were comparable in the sense that the contour plots based on the respective models and the predicted granule size were comparable.