Percorrer por autor "Pinto, Frederico G."
A mostrar 1 - 5 de 5
Resultados por página
Opções de ordenação
- Autoencoder/RandomForest-TabPFN for cross-cancer metabolomics: prostate and breast cancer diagnosis using paper spray and ion mobility-mass spectrometry techniquesPublication . Hauns, Sven; Pinto, Frederico G.; Khyriem, Costerwell; Singh, Ankita; Al-Sadi, Azzat; Yazeedi, Talal Al; Mohammad, Rasheed; Cisse, Babacar; Garrett, Timothy J.; Uddin, Mohammed; Soares, Nelson C.; Backofen, Rolf; Alkhnbashi, Omer S.Accurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved patient outcomes. Metabolomic signatures represent a robust molecular framework for elucidating cancer-associated biochemical reprogramming. The use of artificial intelligence (AI) in biology in recent years has become widespread and promising. This study introduces a novel predictive method that integrates an Autoencoder, random forest-based feature selection and Tabular Prior-data Fitted Network (TabPFN) to achieve high diagnostic accuracy from metabolomics data of prostate and BC patients. The datasets were acquired using paper spray ionization mass spectrometry and flow injection-traveling-wave ion mobility-mass spectrometry of individuals diagnosed with PC and BC. When leveraging metabolomic profiling data from two distinct sources, PC urine and serum samples, the proposed model achieved an accuracy up to 98.75% in distinguishing diseased from healthy conditions. Additionally, we employed a BC dataset containing metabolic and lipidomic signatures acquired from core needle biopsies using a miniature MS platform coupled with PSI to assess the fidelity of our implementation across distinct cancer types. Our results on a well-characterized targeted dataset show that we can effectively reduce high-dimensional data into latent feature representations. At the same time, TabPFN captures tumor progression-related changes and feature interaction, thereby enhancing the possibility that the model will be a highly potent and effective tool for stage-specific diagnostic precision. Most existing machine learning approaches for disease diagnosis primarily rely on imaging, genomics, or clinical parameters, often overlooking the critical role of metabolites in identifying disease-specific biochemical signatures. By integrating metabolite-specific data with a robust deep-learning approach, this study demonstrates the transformative potential of AI in metabolomics-based diagnostics. The proposed model offers scalability and versatility, with applications extending beyond oncology to a much broader disease profiling aspect. These findings emphasize the value of combining multi-source metabolomic data with deep learning to advance personalized medicine and enhance diagnostic efficiency in clinical practice.
- Methods of Extraction, Detection, and Identification of Different Metabolites-A Metabolomics ApproachPublication . Bennici, Giulia; Permatasari, Devi; Al-Younis, Inas; Dhahri, Manel; Pinto, Frederico G.; Uddin, Mohammed J.; Emwas, Abdul-Hamid; Jaremko, Mariusz; Soares, Nelson C.; Soliman, Sameh S.M.; Husseiny, Mohamed I.Metabolic dynamics in attack and defense mechanisms are fascinating topics that illustrate the complex ways organisms adapt to their environments. Attack mechanisms involve several functions, including energy production and metabolic adaptation, where some organisms change their appearance or behavior to balance into their environment. Defense mechanisms involve stress response, immune system activation, repair, and recovery. This chapter provides an overview of the methods applied in modern metabolomics, including single-cell metabolomics and targeted/untargeted approaches, emphasizing how metabolite production rapidly shifts in response to environmental and cellular changes. To effectively monitor these spontaneous effects, it is essential to conduct metabolomics studies with a carefully controlled experimental design. This includes meticulous sample collection, efficient metabolite extraction, and optimization of parameters for the most relevant analytical platform. This chapter outlines the most relevant and commonly used methods for metabolite extraction, aiming to maximize the number of metabolites recovered. This is followed by a summary of the advantages and limitations of the most frequently employed analytical tools, with a particular focus on nuclear magnetic resonance (NMR) and various types of mass spectrometry (MS). Finally, we emphasize the importance of integrating different omics sciences to achieve a comprehensive understanding of biological systems.
- Multiomics screening of red blood cells (RBCs) reveals new candidate diagnostic biomarkers for acute venous thrombosisPublication . Febra, Cláudia; Saraiva, Joana; AlKhnbashi, Omer; Pinto, Frederico G.; Vaz, Fátima; Macedo, João; Uddin, Mohammed J.; Goswami, Nandu; Penque, Deborah; Soares, Nelson C.Venous Thromboembolism (VTE) is a leading cause of cardiovascular mortality. The diagnosis of acute VTE is still based on complex imaging exams due to the lack of biomarkers. However, studies assessing the diagnostic capacity of novel biomarkers in VTE are scarce. We aimed to determine if patients with acute VTE have differences in the proteomics/metabolomic profile. This observational trial included 62 patients with clinical suspicion of acute deep vein thrombosis (DVT) or pulmonary embolism (PE) admitted to the emergency room (ER).
- Optimizing MS Parameters for Data-Independent Acquisition (DIA) to Enhance Untargeted MetabolomicsPublication . Pinto, Frederico G.; Giddey, Alexander D.; Almarri, Rouda S. B.; Alkhnbashi, Omer S.; Garrett, Timothy J.; Uddin, Mohammed J.; Soares, Nelson C.Data-Independent Acquisition (DIA) has emerged as a powerful mass spectrometry (MS) strategy for comprehensive metabolomics. This study presents a novel short gradient (13 min) nanosensitive analytical method for human plasma analysis using DIA LC-MS/MS, focusing on in-depth optimization of MS parameters to maximize data quality and metabolite coverage. Key MS parameters, including scan speed, isolation window width, resolution, automatic gain control, and collision energy, were systematically tuned to balance the sensitivity and specificity while minimizing interferences. The optimized method enabled the detection of 2,907 features with 675 annotated compounds, leveraging recent progress in nano-LC-MS/MS for multiomics applications and showcasing the possibility of combining proteomics and metabolomics within a single chromatographic system. Ultimately, a comparison was performed between the data acquired through the DIA and DDA MS approaches in the context of untargeted metabolomics. This optimized analytical method yields more robust and reproducible results, thereby expanding the potential for meaningful discoveries across diverse biological fields.
- Repurposing proteomic nanoLC-MS platforms for untargeted metabolomics: evaluating DIA and polarity switching performance in human plasmaPublication . Pinto, Frederico G.; Giddey, Alexander D.; Mohamed, Nesrin; Almarri, Rauda S. B.; Murtaza, Munazza; Nassir, Nasna; Alkhnbashi, Omer S.; Uddin, Mohammed J.; Soares, Nelson C.Background: Many of the advanced MS methods applied in proteomics such as nanoflow LC-MS with data-independent acquisition have yet to be verified and/or optimized on metabolomics applications. Research design and methods: This study evaluates the feasibility of repurposing a proteomics-optimized nanoLC-MS platform for untargeted metabolomics. Using NIST SRM 1950 reference human plasma, we compared the performance of polarity switching and separate polarity modes under DIA conditions, focusing on metabolite coverage, annotation, and response linearity. Results: We observed, in the separate polarity and switching polarity runs 669 and 353 features in (+) mode and 558 and 446 features in (-) mode, respectively. A total of 233 metabolites were annotated using the (±) separate polarities and 179 using the (±) switching polarity based on MassBank of North America (MoNA) public MS library and filtered with the Human Metabolome Database (HMDB). Both switching and separate polarity methods performed well regarding response linearities which were investigated by spiking some amino acid compounds into plasma matrix. Conclusions: The polarity switching DIA approach for metabolomics reduced sample consumption and analysis time, but led to fewer detected features and annotations compared to separate polarity runs. These findings support the use of unified nanoLC-MS platforms for integrated multi-omics analysis.