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Laboratory surveillance of Streptococcus pneumoniae serotypes is crucial for the successful implementation of vaccines to prevent invasive pneumococcal diseases. The reference method of serotyping is the Quellung reaction, which is labor-intensive and expensive. In the last few years, the introduction of MALDI-TOF MS into the microbiology laboratory has been revolutionary. In brief, this new technology compares protein profiles by generating spectra based on the m/z ratio. We evaluated the performance of MALDI-TOF MS for typing serotypes of S. pneumoniae isolates included in the PCV13 vaccine using a machine learning approach. We challenged our classification algorithms with a total of new 215 isolates of S. pneumoniae from Argentinian nationwide surveillance. Our work involved a thorough evaluation and comparison of multiple machine learning models. Through this evaluation, we achieved high accuracy values, indicating the effectiveness of the approach taken in solving the problem. Additionally, we attained high kappa index values, indicating that the predictions made by the models were highly accurate and reliable. We also carefully considered the strengths and limitations of each model. As such, our results are highly valuable and have important implications for future research and applications.

Laboratory surveillance of Streptococcus pneumoniae serotypes is crucial for the successful implementation of vaccines to prevent invasive pneumococcal diseases. The reference method of serotyping is the Quellung reaction, which is labor-intensive and expensive. In the last few years, the introduction of MALDI-TOF MS into the microbiology laboratory has been revolutionary. In brief, this new technology compares protein profiles by generating spectra based on the m/z ratio. We evaluated the performance of MALDI-TOF MS for typing serotypes of S. pneumoniae isolates included in the PCV13 vaccine using a machine learning approach. We challenged our classification algorithms with a total of new 215 isolates of S. pneumoniaefrom Argentinian nationwide surveillance. Our work involved a thorough evaluation and comparison of multiple machine learning models. Through this evaluation, we achieved high accuracy values, indicating the effectiveness of the approach taken in solving the problem. Additionally, we attained high kappa index values, indicating that the predictions made by the models were highly accurate and reliable. We also carefully considered the strengths and limitations of each model. As such, our results are highly valuable and have important implications for future research and applications.

This article analyzes how the circularity of resources occurs in Brazilian craft breweries supported by innovation and proposes the creation of alternative craft breweries to promote circular economy. Data were collected through 11 interviews with entrepreneurs in the craft brewery segment, checklist application, and access to the website of the cases investigated. Our results expand the relationship with innovation suggesting that: a) the circularity of resources is still focused on several long-cycle practices; b) short cycles, which are the most desired for the retention of resource value in the production chain, are already present, but demand more attention from the enterprises surveyed; c) innovation practices can become barriers to or drivers for the feasibility of circular economy in the beer context; d) technical cycles are consolidated in most of the researched cases, while biological cycles still demand greater engagement from all stakeholders; e) the nature and content of circular practices imply different adoption stages in the cases investigated; f) companies invest in strengthening the relationship with consumers and suppliers in order to obtain support for current circular strategies in their ventures.

Laboratory surveillance of Streptococcus pneumoniae serotypes is crucial for the successful implementation of vaccines to prevent invasive pneumococcal diseases. The reference method of serotyping is the Quellung reaction, which is labor-intensive and expensive. In the last few years, the introduction of MALDI-TOF MS into the microbiology laboratory has been revolutionary. In brief, this new technology compares protein profiles by generating spectra based on the m/z ratio. We evaluated the performance of MALDI-TOF MS for typing serotypes of S. pneumoniae isolates included in the PCV13 vaccine using a machine learning approach. We challenged our classification algorithms in “real time” with a total of new 100 isolates of S. pneumoniae from Argentinian nationwide surveillance. Our best approach could correctly identify the isolates with a sensitivity of 58.33% ([95%IC 40.7–71.7]); specificity of 81.48% ([95%IC 53.6–79.7]); accuracy of 63.0% ([95%IC 61.9–93.7]); PPV of 80.77% ([95%IC 64.5–90.6]) and NPV of 59.46% ([95%IC 48.9–69.2]). In this work, it was possible to demonstrate that the combination of MALDI-TOF mass spectrometry and multivariate analysis allows the development of new strategies for the identification and characterization of Spn isolates of clinical importance.