IBEB - Artigos em Revistas Internacionais
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- Exploring the late maturation of an intrinsic episodic memory network: A resting-state fMRI studyPublication . Andrade, Miguel Ângelo; Raposo, Ana; Andrade, AlexandrePrevious research suggests that episodic memory relies on functional neural networks,which are present even in the absence of an explicit task. The regions that integrate.these networks and the developmental changes in intrinsic functional connectivity.remain elusive. In the present study, we outlined an intrinsic episodic memory network.(iEMN) based on a systematic selection of functional connectivity studies, and.inspected network differences in resting-state fMRI between adolescents (13–17 years.old) and adults (23–27 years old) from the publicly available NKI-Rockland Sample.Through a review of brain regions commonly associated with episodic memory.networks, we identified a potential iEMN composed by 14 bilateral ROIs, distributed.across temporal, frontal and parietal lobes. Within this network, we found an increase.in resting-state connectivity from adolescents to adults between the right temporal pole.and two regions in the right lateral prefrontal cortex. We argue that the coordination of.these brain regions, connecting areas of semantic processing and areas of controlled. retrieval, arises as an important feature towards the full maturation of the episodic.memory system. The findings add to evidence suggesting that adolescence is a key.period in memory development and highlights the role of intrinsic functional.connectivity in such development.
- Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI ApproachPublication . Mota, Ana M.; Mendes, João; Matela, NunoBreast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy—a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.
- Nucleic Acid Aptamer-Based Biosensors: A ReviewPublication . Sequeira-Antunes, Beatriz; Ferreira, Hugo AlexandreAptamers, short strands of either DNA, RNA, or peptides, known for their exceptional specificity and high binding affinity to target molecules, are providing significant advancements in the field of health. When seamlessly integrated into biosensor platforms, aptamers give rise to aptasensors, unlocking a new dimension in point-of-care diagnostics with rapid response times and remarkable versatility. As such, this review aims to present an overview of the distinct advantages conferred by aptamers over traditional antibodies as the molecular recognition element in biosensors. Additionally, it delves into the realm of specific aptamers made for the detection of biomarkers associated with infectious diseases, cancer, cardiovascular diseases, and metabolomic and neurological disorders. The review further elucidates the varying binding assays and transducer techniques that support the development of aptasensors. Ultimately, this review discusses the current state of point-of-care diagnostics facilitated by aptasensors and underscores the immense potential of these technologies in advancing the landscape of healthcare delivery.
- Urinary Biomarkers and Point-of-Care Urinalysis Devices for Early Diagnosis and Management of Disease: A ReviewPublication . Sequeira-Antunes, Beatriz; Ferreira, Hugo AlexandreBiosensing and microfluidics technologies are transforming diagnostic medicine by accurately detecting biomolecules in biological samples. Urine is a promising biological fluid for diagnostics due to its noninvasive collection and wide range of diagnostic biomarkers. Point-of-care urinalysis, which integrates biosensing and microfluidics, has the potential to bring affordable and rapid diagnostics into the home to continuing monitoring, but challenges still remain. As such, this review aims to provide an overview of biomarkers that are or could be used to diagnose and monitor diseases, including cancer, cardiovascular diseases, kidney diseases, and neurodegenerative disorders, such as Alzheimer’s disease. Additionally, the different materials and techniques for the fabrication of microfluidic structures along with the biosensing technologies often used to detect and quantify biological molecules and organisms are reviewed. Ultimately, this review discusses the current state of point-of-care urinalysis devices and highlights the potential of these technologies to improve patient outcomes. Traditional point-of-care urinalysis devices require the manual collection of urine, which may be unpleasant, cumbersome, or prone to errors. To overcome this issue, the toilet itself can be used as an alternative specimen collection and urinalysis device. This review then presents several smart toilet systems and incorporated sanitary devices for this purpose.
- Digital Guardian Angel Supported by an Artificial Intelligence System to Improve Quality of Life, Well-being, and Health Outcomes of Patients With Cancer (ONCORELIEF): Protocol for a Single Arm Prospective Multicenter Pilot StudyPublication . Reis, Joaquim; Travado, Luzia; Scherrer, Alexander; Kosmidis, Thanos; Venios, Stefanos; Laras, Paris Emmanouil; Oestreicher, Gabrielle; Moehler, Markus; Parolini, Margherita; Passardi, Alessandro; Meggiolaro, Elena; Martinelli, Giovanni; Petracci, Elisabetta; Zingaretti, Chiara; Diamantopoulos, Sotiris; Plakia, Maria; Vassiliou, Charalampos; Mousa, Suheib; Zifrid, Robert; Sullo, Francesco Giulio; Gallio, ChiaraBackground: According to Europe’s Beating Cancer Plan, the number of cancer survivors is growing every year and is now estimated at over 12 million in Europe. A main objective of the European Commission is to ensure that cancer survivors can enjoy a high quality of life, underlining the role of digital technology and eHealth apps and tools to achieve this. Objective: The main objective of this study is the development of a user-centered artificial intelligence system to facilitate the input and integration of patient-related biopsychosocial data to improve posttreatment quality of life, well-being, and health outcomes and examine the feasibility of this digitally assisted workflow in a real-life setting in patients with colorectal cancer and acute myeloid leukemia. Methods: A total of 60 patients with colorectal cancer and 30 patients with acute myeloid leukemia will be recruited from 2 clinical centers: Universitätsmedizin der Johannes Gutenberg-Universität Mainz (Mainz, Germany) and IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori” (IRST, Italy). Psychosocial data (eg, emotional distress, fatigue, quality of life, subjective well-being, sleep problems, and appetite loss) will be collected by questionnaires via a smartphone app, and physiological data (eg, heart rate, skin temperature, and movement through step count) will be collected by a customizable smart wrist-worn sensor device. Each patient will be assessed every 2 weeks over their 3-month participation in the ONCORELIEF study. Inclusion criteria include patients with the diagnosis of acute myeloid leukemia or colorectal cancer, adult patients aged 18 years and older, life expectancy greater than 12 months, Eastern Cooperative Oncology Group performance status ≤2, and patients who have a smartphone and agree to use it for the purpose of the study. Exclusion criteria include patients with a reduced cognitive function (such as dementia) or technological illiteracy and other known active malignant neoplastic diseases (patients with a medical history of treated neoplastic disease are included). Results: The pilot study started on September 1, 2022. As of January 2023, we enrolled 33 patients with colorectal cancer and 7 patients with acute myeloid leukemia. As of January 2023, we have not yet started the data analysis. We expect to get all data in June 2023 and expect the results to be published in the second semester of 2023. Conclusions: Web-based and mobile apps use methods from mathematical decision support and artificial intelligence through a closed-loop workflow that connects health professionals and patients. The ONCORELIEF system has the potential of continuously identifying, collecting, and processing data from diverse patient dimensions to offer health care recommendations, support patients with cancer to address their unmet needs, and optimize survivorship care.
- Normative model detects abnormal functional connectivity in psychiatric disordersPublication . Oliveira-Saraiva, Duarte; Ferreira, Hugo AlexandreIntroduction: The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient’s signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders. Methods: We trained our autoencoder on resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls. The model was then tested on schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) patients to estimate how each patient deviated from the norm and associate it with abnormal functional brain networks’ (FBNs) connectivity. Rs-fMRI data processing was conducted within the FMRIB Software Library (FSL), which included independent component analysis and dual regression. Pearson’s correlation coefficients between the extracted blood oxygen level-dependent (BOLD) time series of all FBNs were calculated, and a correlation matrix was generated for each subject. Results and discussion: We found that the functional connectivity related to the basal ganglia network seems to play an important role in the neuropathology of BD and SCZ, whereas in ADHD, its role is less evident. Moreover, the abnormal connectivity between the basal ganglia network and the language network is more specific to BD. The connectivity between the higher visual network and the right executive control and the connectivity between the anterior salience network and the precuneus networks are the most relevant in SCZ and ADHD, respectively. The results demonstrate that the proposed model could identify functional connectivity patterns that characterize different psychiatric disorders, in agreement with the literature. The abnormal connectivity patterns from the two independent SCZ groups of patients were similar, demonstrating that the presented normative model was also generalizable. However, the group-level differences did not withstand individual-level analysis implying that psychiatric disorders are highly heterogeneous. These findings suggest that a precision-based medical approach, focusing on each patient’s specific functional network changes may be more beneficial than the traditional group-based diagnostic classification.
- Initial Study Using Electrocardiogram for Authentication and IdentificationPublication . Pereira, Teresa M. C.; Conceição, Raquel C.; Sebastião, RaquelRecently, several studies have demonstrated the potential of electrocardiogram (ECG) to be used as a physiological signature for biometric systems (BS). We investigated the potential of ECG as a biometric trait for the identification and authentication of individuals. We used data from a public database, CYBHi, containing two off-the-person records from 63 subjects, separated by 3 months. For the BS, two templates were generated: (1) cardiac cycles (CC) and (2) scalograms. The identification with CC was performed with LDA, kNN, DT, and SVM, whereas a convolutional neural network (CNN) and a distance-based algorithm were used for scalograms. The authentication was performed with a distance-based algorithm, with a leave-one-out cross validation, for impostors evaluation. The identification system yielded accuracies of 79.37% and 69.84% for CC with LDA and scalograms with CNN, respectively. The authentication yielded an accuracy of 90.48% and an impostor score of 13.06% for CC, and it had an accuracy of 98.42% and an impostor score of 14.34% for scalograms. The obtained results support the claim that ECG can be successfully used for personal recognition. To the best of our knowledge, our study is the first to thoroughly compare templates and methodologies to optimize the performance of an ECG-based biometric system.
- Proof-of-Concept Study of Multifunctional Hybrid Nanoparticle System Combined with NIR Laser Irradiation for the Treatment of MelanomaPublication . Lopes, Joana; Ferreira-Gonçalves, Tânia; Figueiredo, Isabel V.; Rodrigues, Cecília M. P.; Ferreira, Hugo; Ferreira, David; Viana, Ana S.; Faísca, Pedro; Gaspar, Maria Manuela; Coelho, João M. P.; Silva, Catarina Oliveira; Reis, Catarina PintoThe global impact of cancer emphasizes the importance of developing innovative, effective and minimally invasive therapies. In the context of superficial cancers, the development of a multifunctional nanoparticle-based system and its in vitro and in vivo safety and efficacy characterization are, herein, proposed as a proof-of-concept. This multifunctional system consists of gold nanoparticles coated with hyaluronic and oleic acids, and functionalized with epidermal growth factor for greater specificity towards cutaneous melanoma cells. This nanoparticle system is activated by a near-infrared laser. The characterization of this nanoparticle system included several phases, with in vitro assays being firstly performed to assess the safety of gold nanoparticles without laser irradiation. Then, hairless immunocompromised mice were selected for a xenograft model upon inoculation of A375 human melanoma cells. Treatment with near-infrared laser irradiation for five minutes combined with in situ administration of the nanoparticles showed a tumor volume reduction of approximately 80% and, in some cases, led to the formation of several necrotic foci, observed histologically. No significant skin erythema at the irradiation zone was verified, nor other harmful effects on the excised organs. In conclusion, these assays suggest that this system is safe and shows promising results for the treatment of superficial melanoma.
- Preliminary Assays towards Melanoma Cells Using Phototherapy with Gold-Based NanomaterialsPublication . Lopes, Joana; Coelho, João Miguel Pinto; Vieira, Pedro Manuel Cardoso; Viana, Ana Silveira; Gaspar, Maria Manuela; Reis, CatarinaCancer like melanoma is a complex disease, for which standard therapies have significant adverse side effects that in most cases are ineffective and highly unspecific. Thus, a new paradigm has come with the need of achieving alternative (less invasive) and effective therapies. In this work, biocompatible gold nanoparticles (GNPs) coated with hyaluronic acid and oleic acid were prepared and characterized in terms of size, morphology and cytotoxicity in the presence of Saccharomyces cerevisiae, and two cell lines, the keratinocytes (healthy skin cells, HaCat) and the melanoma cells (B16F10). Results showed that these GNPs absorb within the near-infrared region (750–1400 nm), in the optical therapeutic window (from 650 to 1300 nm), in contrast to other commercial gold nanoparticles, which enables light to penetrate into deep skin layers. A laser emitting in this region was applied and its effect also analyzed. The coated GNPs showed a spherical morphology with a mean size of 297 nm without cytotoxic effects towards yeast and tested cell lines. Nevertheless, after laser irradiation, a reduction of 20% in B16F10 cell line viability was observed. In summary, this work appears to be a promising strategy for the treatment of non-metastatic melanoma or other superficial tumors.
- A Novel Hybrid Nanosystem Integrating Cytotoxic and Magnetic Properties as a Tool to Potentiate Melanoma TherapyPublication . Cruz, Nuno; Pinho, Jacinta Oliveira; Soveral, Graça; Ascensão, Lia; Matela, Nuno; Reis, Catarina; Gaspar, Maria ManuelaCancer is a major health concern and the prognosis is often poor. Significant advances in nanotechnology are now driving a revolution in cancer detection and treatment. The goal of this study was to develop a novel hybrid nanosystem for melanoma treatment, integrating therapeutic and magnetic targeting modalities. Hence, we designed long circulating and pH-sensitive liposomes loading both dichloro(1,10-phenanthroline) copper (II) (Cuphen), a cytotoxic metallodrug, and iron oxide nanoparticles (IONPs). The synthetized IONPs were characterized by transmission electron microscopy and dynamic light scattering. Lipid-based nanoformulations were prepared by the dehydration rehydration method, followed by an extrusion step for reducing and homogenizing the mean size. Liposomes were characterized in terms of incorporation parameters and mean size. High Cuphen loadings were obtained and the presence of IONPs slightly reduced Cuphen incorporation parameters. Cuphen antiproliferative properties were preserved after association to liposomes and IONPs (at 2 mg/mL) did not interfere on cellular proliferation of murine and human melanoma cell lines. Moreover, the developed nanoformulations displayed magnetic properties. The absence of hemolytic activity for formulations under study demonstrated their safety for parenteral administration. In conclusion, a lipid-based nanosystem loading the cytotoxic metallodrug, Cuphen, and displaying magnetic properties was successfully designed.