The mass spectrometry technology MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization Time- Of-Flight) for a more rapid and economic workflow in the clinical microbiology laboratory

Introduction: In order to improve the outcome of patients, reduce length of stay, costs and resources engaged in diagnostics, more rapid reports are requested to the clinical microbiologists.The purpose of this study is to assess the impact on workflow of MALDI-TOF technology, recently made available for use in routine diagnostics. Methods:The work list by the management information system is sent to the instrument MALDI-TOF, where are held at least three successive analytic sessions: the first includes bacteria isolated from CSF, blood cultures, and cases already reported as serious/urgent, the second includes all other germs isolated, the third, microorganisms that require extraction with trifluoroacetic acid (TFA) or formic acid (FA) for identification.The results of each session direct to the execution of different types of susceptibility testing. Results:The times of microbial identifications are reduced by 24 or 48 hours and made available to the clinician for the rational empirical therapy.The reagent costs are reduced by 40%.The subcultures were reduced by 80%, and microscopic examinations by 50%.The antibiotic susceptibility tests were immediately performed with the most appropriate method, based on the knowledge of local epidemiology and microbial species. Conclusion:The bacteriology is the less automated discipline among the clinical laboratory activities and results of diagnostic tests are poorly well-timed. The new interpretative algorithms of MALDI-TOF spectra, now available, allow the correct identification of bacteria in near real time, completely eliminating the wait is necessary for biochemical identification and guiding the operator in selecting the most appropriate antibiotic susceptibility tests. This technology makes work more rapid, economic and efficient, eliminating errors and, together with effective computerization of data, transforms the information content of the microbiological report, making it much more effective for the therapy and the patient care

Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples.

Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitations due to availability of reagents and budget constraints. This stressed the need to develop screening procedures that require fewer resources and are suitable to be extended to larger portions of the population. RT-qPCR on pooled samples from individual NP swabs seems to be a promising technique to improve surveillance. We performed preliminary experimental analyses aimed to investigate the performance of pool testing on samples with low viral load and we evaluated through Monte Carlo (MC) simulations alternative screening protocols based on sample pooling, tailored to contexts characterized by different infection prevalence. We focused on the role of pool size and the opportunity to develop strategies that take advantage of natural clustering structures in the population, e.g. families, school classes, hospital rooms. Despite the use of a limited number of specimens, our results suggest that, while high viral load samples seem to be detectable even in a pool with 29 negative samples, positive specimens with low viral load may be masked by the negative samples, unless smaller pools are used. The results of MC simulations confirm that pool testing is useful in contexts where the infection prevalence is low. The gain of pool testing in saving resources can be very high, and can be optimized by selecting appropriate group sizes. Exploiting natural groups makes the definition of larger pools convenient and potentially overcomes the issue of low viral load samples by increasing the probability of identifying more than one positive in the same pool