Browsing by Author "Pais, Ricardo J."
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- Predicting the evolution and control of the COVID-19 pandemic in PortugalPublication . Pais, Ricardo J.; Taveira, NunoCoronavirus disease 2019 (COVID-19) is a worldwide pandemic that has been affecting Portugal since 2 March 2020. The Portuguese government has been making efforts to contradict the exponential growth through lockdown, social distancing and the usage of masks. However, these measures have been implemented without controlling the compliance degree and how much is necessary to achieve an effective control. To address this issue, we developed a mathematical model to estimate the strength of Government-Imposed Measures (GIM) and predict the impact of the degree of compliance on the number of infected cases and peak of infection. We estimate the peak to be around 650 thousand infected cases with 53 thousand requiring hospital care by the beginning of May if no measures were taken. The model shows that the population compliance of the GIM was gradual between 30% to 75%, contributing to a significant reduction on the infection peak and mortality. Importantly, our simulations show that the infection burden could have been further reduced if the population followed the GIM immediately after their release on 18 March.
- Predictive modelling in clinical bioinformatics : key concepts for startupsPublication . Pais, Ricardo J.Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective.
- A rapid and affordable screening tool for early-stage ovarian cancer detection based on MALDI-ToF MS of blood serumPublication . Pais, Ricardo J.; Zmuidinaite, Raminta; Lacey, Jonathan C.; Jardine, Christian S.; Iles, Ray K.Ovarian cancer is a worldwide health issue that grows at a rate of almost 250,000 new cases every year. Its early detection is key for a good prognosis and even curative surgery. However, current medical examination methods and tests have been inefficient in detecting ovarian cancer at the early stage, leading to preventable death. So far, new screening tests based on molecular biomarker analysis techniques have not resulted in any substantial improvement in early-stage diagnosis and increased survival. Thus, whilst there remains clear potential to improve outcomes through early detection, novel approaches are needed. Here, we postulated that MALDI-ToF-mass-spectrometry-based tests can be a solution for effective screening of ovarian cancer. In this retrospective cohort study, we generated and analyzed the mass spectra of 181 serum samples of women with and without ovarian cancer. Using bioinformatics pipelines for analysis, including predictive modeling and machine learning, we found distinct mass spectral patterns composed of 9–20 key combinations of peak intensity or peak enrichment features for each stage of ovarian cancer. Based on a scoring algorithm and obtained patterns, the optimal sensitivity for detecting each stage of cancer was 95–97% with a specificity of 97%. Scoring all algorithms simultaneously could detect all stages of ovarian cancer at 99% sensitivity and 92% specificity. The results further demonstrate that the matrix and mass range analyzed played a key role in improving the mass spectral data quality and diagnostic power. Altogether, with the results reported here and increasing evidence of the MS assay’s diagnostic accuracy and instrument robustness, it has become imminent to consider MS in the clinical application for ovarian cancer screening.