PubMed · 10255638
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W S Burkle. 1982. Developing a quality assurance program for clinical services.. https://pubmed.ncbi.nlm.nih.gov/10255638/
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Predictive microbiology is an emerging research domain in which biological and mathematical knowledge is combined to develop models for the prediction of microbial proliferation in foods. To provide accurate predictions, models must incorporate essential factors controlling microbial growth. Current models often take into account environmental conditions such as temperature, pH and water activity. One factor which has not been included in many models is the influence of a background microflora, which brings along microbial interactions. The present research explores the potential of autonomous continuous-time/two-species models to describe mixed population growth in foods. A set of four basic requirements, which a model should satisfy to be of use for this particular application, is specified. Further, a number of models originating from research fields outside predictive microbiology, but all dealing with interacting species, are evaluated with respect to the formulated model requirements by means of both graphical and analytical techniques. The analysis reveals that of the investigated models, the classical Lotka-Volterra model for two species in competition and several extensions of this model fulfill three of the four requirements. However, none of the models is in agreement with all requirements. Moreover, from the analytical approach, it is clear that the development of a model satisfying all requirements, within a framework of two autonomous differential equations, is not straightforward. Therefore, a novel prototype model structure, extending the Lotka-Volterra model with two differential equations describing two additional state variables, is proposed to describe mixed microbial populations in foods.
Separations below 1 s of a mixture of organic compounds ranging from C5 to C8 have been performed to investigate the performance of a time-of-flight mass spectrometer in fast gas chromatography. The gaseous samples were focussed on a cold trap, and then injected after thermal desorption to obtain the required narrow input band-widths. Also, to obtain a very fast separation, a short narrow bore column was used, operated at above-optimum inlet pressures. With this system, it was possible to identify ten compounds within 500 ms, showing peak-widths (2.354sigma) as narrow as 12 ms. The spectral acquisition rate used for these analyses was 500 Hz. The quality of the recorded spectra and the comparison with library spectra was very high. Deconvolution algorithms offer the possibility of identifying overlapping peaks. It is shown that the spectral scan speed of the time-of-flight mass spectrometer is high enough for very fast separations.
Low-radiation-dose computed tomography (low-dose CT) is a new, promising technology presently used in screening for lung cancer in some medical centres in the USA and Japan. The three population-based studies of the efficacy of screening with low-dose CT published to date all show that more than 85% of the lung cancers detected by low-dose CT are in stage I, offering improved possibility for curative treatment. The number of false positive scans, was up to 20%. The radiation dose per low-dose CT scan was approximately the dose of a mammography (1 mSv). In two studies, low-dose CT failed to detect some centrally located tumours. None of the studies are capable of evaluating changes in lung cancer mortality in a screened population compared to those of a control population. There is, therefore, a need for further assessments of positive as well as negative effects of population-based screening with low-dose CT.