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- Mitochondrial flagella-like extensions (MitoFLARE) dysfunction triggers STING-mediated immune dysregulation in sepsisPublication . Hong, Weilong; Ma, Ruiyan; Long, Shiyun; Song, Rui; Ren, Shuang; Ran, Xiaoping; Wan, Junfang; Liu, Yifei; Li, Xiaofeng; Chen, Qian; Ma, Daqing; Zhang, Zhaocai; Huang, He; Ashrafizadeh, Milad; Conde, João; Liu, Liangming; Duan, Chenyang; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; Nature PortfolioSepsis is an immune dysregulation syndrome triggered by infection, characterized by host self-damage due to immune imbalances. This study focuses on dynamic changes of mitochondrial symbiotic function in host cells during sepsis and systematically investigates dysregulation of mitochondrial communication modes and the intrinsic link between mitochondrial DNA (mtDNA) release and immune dysregulation. We demonstrate that during early-stage LPS treatment, mitochondria actively remodel by extruding flagella-like extensions (termed mitoFLARE). These structures, nanotubes mediating long-distance transport, form through glycosylated TRAK1 binding FHL2 to drive actin network formation, thereby shifting mitochondrial communication from direct fusion to nanotube-mediated transport. This helps maintain dynamic exchange within the inner mitochondrial membrane under LPS treatment. However, as inflammation progresses, deteriorated mitochondrial quality control disrupts the MICOS-SAM complex, abrogates inner-outer membrane anchoring, and suppresses mitoFLARE functions. All these ultimately enhance endoplasmic reticulum-mitochondrial contacts to promote outer membrane rupture and result in mtDNA release into the cytoplasm to activate cGAS-STING signaling, further triggering immune dysregulation and inflammatory storm, culminating in programmed cell death and organ dysfunction. This study elucidates the pivotal role of dysregulated mitochondrial-host symbiosis in sepsis progression and provides important insights into the underlying mechanisms of sepsis-associated immune imbalances, laying a theoretical foundation for targeted therapy development.
- Global performance of predictive models for dengue severity, hospitalization and mortalityPublication . Delpino, Felipe Mendes; Peres, Igor Tona; Gusberti, Tomoe; de Lima, Carlos José; Duque, Sara; Merson, Laura; Garcia-Gallo, Esteban; Hamacher, Silvio; Ranzani, Otavio T.; Bozza, Fernando A.; Bastos, Leonardo S.L.; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; ElsevierObjectives Predictive models are increasingly used to support the clinical management of dengue, but their performance varies widely across settings. We aimed to evaluate multivariable prediction models for dengue severity, mortality, and hospitalization, and to summarize the predictors most consistently associated with these outcomes. Methods We searched five databases for studies that developed or validated multivariable models predicting severity, hospitalization, or mortality in dengue populations. Two reviewers independently selected studies and extracted data, and risk of bias was assessed with PROBAST. We pooled diagnostic accuracy estimates using bivariate models and synthesized predictor effects with random-effects meta-analyses. Results A total of 146 studies were included: 109 addressed severity, 42 mortality, and 12 hospitalizations. For severity, pooled sensitivity was 0·85 (95% CI 0·82–0·87) and the overall AUC was 0·93 (0·91–0·94), with machine learning models slightly outperforming traditional regression (AUC 0·93 vs 0·90). PROBAST classified 112 of 146 studies as high risk of bias. Bleeding, shock, and hypoalbuminemia were the predictors most consistently associated with adverse outcomes. Conclusions Dengue prediction models, especially those based on machine learning, show good discrimination for severity, but the evidence is limited by high risk of bias and scarce external validation.
- Non-pharmacological interventions modulating immune response in Parkinson's diseasePublication . Pirovano, Elenamaria; Silva, Inês P.; Camacho, Marta; Çanak, Asuman; Aktas, Busra; Crompton, David; Gökçe, Evrim; Tan, Fu Miao; Marino, Franca; Domingos, Josefa; Almeida, Mafalda F.; Comi, Cristoforo; Dragic, Milorad; Figueira, Inês; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); NOVA Institute for Medical Systems Biology; Comprehensive Health Research Centre (CHRC) - pólo NMS; Elsevier Science B.V., Amsterdam.Parkinson's disease (PD) imposes a growing socioeconomic burden due to its increasing prevalence and lack of a cure. Existing treatment options primarily manage motor and nonmotor symptoms but do not halt or slow disease progression, underscoring the urgent need for more effective and preventative strategies. Growing evidence suggests a strong link between immune system dysfunction, chronic inflammation, and the early pathogenesis of Parkinson's disease, often occurring years before the onset of motor symptoms, thereby indicating a critical window for early intervention. In this review, we examine current evidence on non-pharmacological approaches such as dietary changes, physical activity, and gut microbiome regulation, focusing on their potential to modulate both peripheral and central immune responses, thereby influencing the progression of PD. Besides being complementary to standard pharmacological treatments, these approaches not only reduce systemic inflammation but may also help delay, prevent, or improve clinical management of PD by targeting and modulating its immunological foundations.
- Advancing genomic surveillance of respiratory syncytial virus in Portugal through an adapted amplicon-based whole-genome sequencing workflowPublication . Lança, Miguel; Borges, Vítor; Gaio, Vânia; Rodrigues, Ana Paula; Santos, Daniela; Duarte, Sílvia; Henriques, Camila; Gomes, Licínia; Dias, Daniela; de Jesus Chasqueira, Maria; Guiomar, Raquel; Melo, Aryse; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; Laboratório Associado de Translacção e Inovação para a Saúde Global - LA Real (Pólo NMS); Elsevier Science B.V., Amsterdam.Background: Respiratory syncytial virus (RSV) poses a significant global health burden, especially among young children. Whole-genome sequencing (WGS) is key for tracking RSV evolution and epidemiology, underscoring the importance of establishing efficient workflows for routine surveillance. Objectives: To optimize a streamlined, one-step, multiplex RT-PCR method for RSV WGS, enabling precise genetic characterization of circulating RSV viruses across Portugal. Study design: A one-step RT-PCR protocol was adapted from a published method and used to characterize RSV-positive samples collected by the National RSV Surveillance Network (VigiRSV) from 2021 to 2024. Of the 1167 samples received, those with real-time PCR cycle threshold values below 25 were considered eligible for sequencing. From this subset, 166 samples were randomly selected for assay development and validation, ensuring proportional geographic and seasonal representation. Results: From the 166 samples tested, genome completeness averaged 92.69% (95% CI: 90.15%-95.23%), with an interquartile range of 6.60%, reflecting a generally consistent sequencing performance. This resulted in 134 near-complete genomes (≥30 × depth of coverage across ≥90% of the reference genome). Phylogenetic analysis revealed the circulation of 13 RSV A lineages and three RSV B lineages across Portugal over the study period. Conclusions: This adapted protocol achieved high overall horizontal coverage with consistent performance across seasons and regions, demonstrating its reliability for RSV whole-genome sequencing. The strong performance observed over multiple seasons and geographic regions indicates that the method is reproducible and suitable for integration into routine surveillance workflows. These characteristics make it a practical and scalable tool for enhancing RSV epidemiology and public health monitoring.
- Clinical large language model centered on electronic medical recordsPublication . Zhuang, Yan; Wang, Bo; Yin, Chengliang; Zhang, Junyan; Meng, Fanqing; Zhao, Jianfei; Su, Qingyong; Zhao, Xuan; Li, Xiuxing; Hu, Ping; Liu, Shiyuan; Wu, Rilige; Hua, Yun; Dong, Wei; Wei, Bing; Zhang, Li; Zheng, Lei; Conde, João; Shi, Ge; Feng, Chong; He, Kunlun; Comprehensive Health Research Centre (CHRC) - pólo NMS; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Nature Publishing GroupIn the quest to enhance medical consultation, our study introduces AI4Doctor, a sophisticated large-language model (LLM) tailored for the clinical domain. At the heart of AI4Doctor is an innovative integration strategy that synergizes distilled data extracted from electronic medical records (EMR) with empirical insights gathered from practicing physicians during the supervised fine-tuning. Although existing platforms offer informative responses, they fall short of replicating the nuanced decision-making processes of medical professionals, particularly in complex, integrative diagnostic scenarios. Motivated by the need to create a realistic medical practice environment, we propose that a combination of direct knowledge transfer from seasoned doctors and the strategic use of EMR can augment the abilities of LLM, enabling it to more closely mimic the clinical acumen of healthcare practitioners. To navigate the complexities of merging diverse instructional sources, we employ a curriculum learning approach during the fine-tuning process. Moreover, we advance our model's performance by developing a reward system that incentivizes the alignment of the LLM's outputs with the valuable attributes inherent in both doctors' expertise, including diagnostic priors, risk thresholds, and heuristic saliencies accumulated from practice and EMR data. This is achieved through a novel reinforcement-learning approach. Besides, we introduce a new benchmark involving a comparative evaluation. We utilize a subjective evaluation system wherein experts critically assess the responses from a professional perspective as well. Our research underscores the potential of this hybrid model to serve as a robust tool in medical consultations, bridging the gap between artificial intelligence and real-world clinical practice.
- Exploring molecular signatures of senescence with marker, an R Toolkit for evaluating gene sets as phenotypic markersPublication . Martins-Silva, Rita; Kaizeler, Alexandre; Barbosa-Morais, Nuno L.; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; Oxford University PressMany biological processes, including cellular senescence, manifest as diverse phenotypes across cell types and conditions. Lacking definitive markers, researchers often rely on the expression of sets of genes to identify these complex states. However, multiple approaches exist to summarize gene set expression into quantitative metrics (i.e. signatures), each with distinct strengths and limitations, and we know of no consensual framework to systematically evaluate their performance across datasets. We therefore developed markeR, an open-source, modular R package that evaluates gene sets as phenotypic markers using scoring and enrichment-based approaches. markeR generates interpretable metrics and intuitive visualizations for benchmarking gene signatures and exploring their associations with study variables. As a case study, we applied markeR to 9 published senescence-related gene sets across 25 RNA-seq datasets, 6 human cell types and 12 senescence-inducing conditions. Gene set performance varied widely: some signatures (e.g. SenMayo) were robust senescence markers across contexts, while others (e.g. MSigDB sets) performed poorly. We further applied markeR to 49 GTEx tissues, revealing tissue- and age-related differences in senescence-associated signals. Together, these findings emphasize the difficulty of characterizing molecular phenotypes and demonstrate markeR’s potential for the systematic evaluation of gene sets in various biological contexts.
- Building Research Competence Across a Nursing ProgramPublication . Nunes, Lucília; Cerqueira, Andreia Ferreri; Poeira, Ana; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; MDPI - Multidisciplinary Digital Publishing InstituteThe organized integration of research competencies into nursing curricula is still a global challenge and is key for preparing professionals to respond to complex clinical contexts, technological advancements, and contemporary societal demands. At the School of Health of the Polytechnic Institute of Setúbal, a longitudinal research axis was implemented across the four years of the undergraduate nursing program, involving epistemological foundations, the research process, evidence-based practice, and applied practice. Objective: The objective of this study was to describe the design and implementation of the longitudinal axis of research, analyzing institutional indicators of academic success and the progressive development of students’ scientific competencies. Methods: A descriptive documentary study based on institutional data analysis (the number of enrolled students, pass rates, and mean grades in the four research-related curricular units) was conducted, complemented by a review of pedagogical materials produced (two published course booklets: “Research I—From the origin to the dissemination of knowledge” and “Research II—(De)Constructing the Research Process: A Critical and Practical Analysis”) and evidence of scientific dissemination (conference presentations and published articles). Results: A continuous progression in academic performance was observed across the research curricular units, accompanied by increased complexity of student work and enhanced scientific literacy. The sequential structure proved essential: the articulation of epistemology, methodology, critical appraisal, and scientific production demonstrated strong coherence and pedagogical efficiency. Conclusions: The longitudinal research axis constitutes a curricular innovation that strengthens essential scientific competencies in undergraduate nursing education. Longitudinal models that reflect both conceptual and practical progression can significantly contribute to the development of nurses who are critical thinkers, reflective practitioners, and capable of integrating evidence into clinical decision-making.
- Autoencoder/RandomForest–TabPFN for cross-cancer metabolomicsPublication . 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.; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; Oxford University PressAccurate 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.
- Rescue transcatheter tricuspid valve replacement with a 56-mm expandable prosthesis following edge-to-edge repairPublication . Teles, Rui Campante; Presume, Márcia José Oliveira; Ribeiras, Regina; Gonçalves, Pedro Araújo; de Sousa Almeida, Manuel; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; Laboratório Associado de Translacção e Inovação para a Saúde Global - LA Real (Pólo NMS); Oxford University PressBackground: Tricuspid regurgitation (TR) is a challenging condition, particularly in advanced stages. Transcatheter therapies are emerging as viable alternatives for selected patients. Case summary: We present an 85-year-old woman with longstanding right heart failure symptoms and torrential TR who underwent edge-to-edge repair (T-TEER), complicated by single leaflet partial device attachment (SLDA). Her condition deteriorated, with readmission for decompensated heart failure. Given persistent torrential TR, clinical deterioration, and unsuitable anatomy for further leaflet-based repair due to septal leaflet plastering, she underwent successful transcatheter tricuspid valve replacement (TTVR) with a newly available 56-mm prosthesis. The patient experienced symptomatic and haemodynamic improvement. Discussion: This case highlights the limitations of leaflet-based repair in anatomically complex TR and supports TTVR as an effective alternative even in SLDA cases. The availability of newer, larger valve sizes expands its feasibility, reinforcing its role in patients previously considered unsuitable for intervention.
- Atlas of interventional cardiology 2023Publication . Van Belle, Eric; Parma, Radoslaw; Teles, Rui Campante; Saia, Francesco; Hawranek, Michal; Paradies, Valeria; Rafflenbeul, Erik; Mamas, Mamas A; Cruz-Gonzalez, Ignacio; Triantis, Georgios; Kilic, Teoman; Jeger, Raban; Magdy, Ahmed; Kefer, Joelle; Linder, Rickard; Sokolov, Maksym; Tormilainen, Hanna; Kazakiewicz, Denis; Huculeci, Radu; Townsend, Nick; Petersen, Steffen E; Timmis, Adam; Vardas, Panos; Gilard, Martine; Chieffo, Alaide; Barbato, Emanuele; Dudek, Dariusz; NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM); Comprehensive Health Research Centre (CHRC) - pólo NMS; European Society of Cardiology | Oxford University PressAIMS: To provide the most comprehensive assessment to date of interventional cardiology practices across ESC national society member countries, with a focus on infrastructure, procedural volumes, temporal trends (2013-2022), regional disparities, and adherence to guideline-recommended care. METHODS: The third edition of the ESC-EAPCI Atlas presents data from 50 ESC national society member countries, collected through a dedicated 2023 survey of national cardiac societies and interventional working groups. Data were subjected to a rigorous multi-step quality control process to ensure consistency and accuracy.Key metrics include interventional resources, such as the number of hospitals with catheterization laboratories, trained personnel, and the proportion of women in the interventional workforce; procedural volumes and types, including percutaneous coronary intervention (PCI), primary PCI, transcatheter aortic valve implantation (TAVI), transcatheter mitral valve procedures (TMVP), transcatheter tricuspid valve procedures (TTVP), as well as procedural characteristics, including arterial access site, use of intracoronary imaging, physiological lesion assessment, and sex-specific data on patient care delivery. RESULTS: Despite the ongoing expansion of structural heart transcatheter interventions, PCI remains the dominant procedure, accounting for over 90% of all percutaneous cardiovascular interventions. PCI volumes showed limited variation across ESC member countries and demonstrated no significant association with gross national income per capita (GNI). In contrast, important regional disparities were observed in the use of TAVI, TMVP and TTVP with procedure rates strongly correlated with GNI (r=0.86, r=0.63 and r=0.64). Workforce data revealed that while women constitute 39% of all cardiologists, they represent only 10% of interventional cardiologists across ESC member countries. Although interventional cardiology has helped reduce female disparity in access compared with cardiac surgery, inequalities persist, e.g. less than 30% of PCI recipients are women, despite women representing more than 40% of patients with ischemic heart disease. Temporal trend analysis showed a narrowing gap in PCI and primary PCI volumes between regions, reflecting improved access across all economic strata. However, growth in structural valve interventions remained disproportionately concentrated in wealthier countries. CONCLUSIONS: The third edition of the ESC-EAPCI Atlas highlights significant progress in percutaneous cardiovascular interventions across Europe but also underscores persistent disparities. These findings reinforce the need for balanced investment strategies, harmonized training, greater sex equity, and enhanced data infrastructures to support more equitable and evidence-based cardiovascular care.