Publications by authors named "Jesus M Cortes"

Directional motility is an essential property of cells. Despite its enormous relevance in many fundamental physiological and pathological processes, how cells control their locomotion movements remains an unresolved question. Here, we have addressed the systemic processes driving the directed locomotion of cells.

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The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit.

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Background: Multiple Organ failure (MOF) is one of the main causes of admission to the Intensive Care Unit (ICU) of patients infected with COVID-19 and can cause short- and long-term neurological deficits.

Objective: To compare the cognitive functioning and functional brain connectivity at 6-12 months after discharge in two groups of individuals with MOF, one due to COVID-19 and the other due to another cause (MOF-group), with a group of Healthy Controls (HC).

Methods: Thirty-six participants, 12 from each group, underwent a neuropsychological and neuroimaging assessment at both time-points.

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Nonsense mutations generating premature termination codons (PTCs) in various genes are frequently associated with somatic cancer and hereditary human diseases since PTCs commonly generate truncated proteins with defective or altered function. Induced translational readthrough during protein biosynthesis facilitates the incorporation of an amino acid at the position of a PTC, allowing the synthesis of a complete protein. This may evade the pathological effect of the PTC mutation and provide new therapeutic opportunities.

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Introduction: BrainAge models based on neuroimaging data have diagnostic classification power but have replicability issues due to site and patient variability. BrainAge models trained on neuropsychological tests could help distinguish stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD).

Methods: A linear regressor BrainAge model was trained on healthy controls using neuropsychological tests and neuroimaging features separately.

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Background: There is little consensus and controversial evidence on anatomical alterations in the brains of people with autism spectrum disorder (ASD), due in part to the large heterogeneity present in ASD, which in turn is a major drawback for developing therapies. One strategy to characterize this heterogeneity in ASD is to cluster large-scale functional brain connectivity profiles.

Methods: A subtyping approach based on consensus clustering of functional brain connectivity patterns was applied to a population of 657 autistic individuals with quality-assured neuroimaging data.

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This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies.

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Article Synopsis
  • Different brain regions display shared statistical dependencies during tasks and rest, reflecting functional connections; however, negatively correlated networks (anticorrelated networks - ACNs) have been less studied than positively correlated ones.
  • The study utilized three neuroimaging datasets to identify and compare ACNs across healthy young adults, older participants, and among a larger sample for cognitive associations, highlighting distinct resting-state networks and ranking key brain areas involved.
  • Key findings indicate increased anticorrelation in cerebellar interactions among older subjects, as well as unique clusters in cognitive score associations, signifying the functional relevance of ACNs in understanding brain activity and potential alterations in various conditions.
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Epilepsy, characterized by recurrent unprovoked seizures resulting from a wide variety of causes, is one of the world's most prominent neurological disabilities. Seizures, which are an expression of neuronal network dysfunction, occur in a positive feedback loop of concomitant factors, including neuro-inflammatory responses, where seizures generate more seizures. Among other pathways involved in inflammatory responses, the JAK/STAT signalling pathway has been proposed to participate in epilepsy.

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Clinical evidence based on real-world data (RWD) is accumulating exponentially providing larger sample sizes available, which demand novel methods to deal with the enhanced heterogeneity of the data. Here, we used RWD to assess the prediction of cognitive decline in a large heterogeneous sample of participants being enrolled with cognitive stimulation, a phenomenon that is of great interest to clinicians but that is riddled with difficulties and limitations. More precisely, from a multitude of neuropsychological Training Materials (TMs), we asked whether was possible to accurately predict an individual's cognitive decline one year after being tested.

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Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here, we extend LNM by using a multimodal strategy, combining functional and structural networks from 1000 healthy participants in the Human Connectome Project. We apply multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests.

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The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan.

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Simultaneous mapping of multiple behavioral domains into brain networks remains a major challenge. Here, we shed some light on this problem by employing a combination of machine learning, structural and functional brain networks at different spatial resolutions (also known as scales), together with performance scores across multiple neurobehavioral domains, including sensation, motor skills, and cognition. Provided by the Human Connectome Project, we make use of three cohorts: 640 participants for model training, 160 subjects for validation, and 200 subjects for model performance testing thus enhancing prediction generalization.

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Heart rate variability (HRV) abnormalities are potential early biomarkers in Parkinson's disease (PD) but their relationship with central autonomic network (CAN) activity is not fully understood. We analyzed the synchronization between HRV and brain activity in 31 PD patients and 21 age-matched healthy controls using blood-oxygen-level-dependent (BOLD) signals from resting-state functional brain MRI and HRV metrics from finger plethysmography recorded for 7.40 min.

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Myotonic Dystrophy Type 1 (DM1) is a multisystemic disease that affects gray and white matter (WM) tissues. WM changes in DM1 include increased hyperintensities and altered tract integrity distributed in a widespread manner. However, the precise temporal and spatial progression of the changes are yet undetermined.

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Article Synopsis
  • The study aimed to create a device that identifies pain perception by analyzing the brain's electrical activity and blood flow while applying heat stimuli to participants.
  • A prototype was tested on 35 healthy subjects, measuring their heat pain thresholds and comparing their responses under painful and non-painful conditions through EEG and PPG signals.
  • Results showed that specific changes in spectral entropy from EEG and PPG could distinguish between pain and no pain, indicating potential applications for monitoring pain in various neurological conditions.
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Aim: To delineate the neurogenetic profiles of brain degeneration patterns in myotonic dystrophy type I (DM1).

Methods: In two cohorts of DM1 patients, brain maps of volume loss (VL) and neuropsychological deficits (NDs) were intersected to large-scale transcriptome maps provided by the Allen Human Brain Atlas (AHBA). For validation, neuropathological and RNA analyses were performed in a small series of DM1 brain samples.

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Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC).

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Brain interdependencies can be studied from either a structural/anatomical perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions.

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The PTEN tumor suppressor gene is mutated with high incidence in tumors and in the germline of patients with cancer predisposition or with macrocephaly associated with autism. PTEN nonsense mutations generating premature termination codons (PTC) and producing nonfunctional truncated PTEN proteins are frequent in association with human disease. However, there are no studies addressing the restoration of full-length PTEN proteins from the PTC-mutated PTEN gene by translational readthrough.

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Mutations in the gene cause cobalamin D disorder (cblD), an autosomal recessive inborn disease with defects in intracellular cobalamin (cbl, vitamin B12) metabolism. CblD patients present methylmalonic aciduria (MMA), homocystinuria (HC), or combined MMA/HC, and usually suffer developmental delay and cognitive deficits. The most frequent genetic alterations associated with disease generate MMADHC truncated proteins, in many cases due to mutations that create premature termination codons (PTC).

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Development and advances in our understanding of basic sciences such as anatomy, biochemistry, histology, and biomechanics have led to a better knowledge of tendon injuries. Likewise, technological advances in available therapies have conditioned the rise of new therapeutic techniques, turning both diagnosis and therapeutic indications into the foundation of treatment for patellar tendon disorders. Furthermore, we often find no correlation between patellar tendon function and structure, as studied and diagnosed from images taken and referred symptoms.

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Objective: To characterize the progression of brain structural abnormalities in adults with pediatric and adult/late onset DM1, as well as to examine the potential predictive markers of such progression.

Methods: 21 DM1 patients (pediatric onset: N = 9; adult/late onset: N = 12) and 18 healthy controls (HC) were assessed longitudinally over 9.17 years through brain MRI.

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Accumulating neuroimaging evidence shows that age estimation obtained from brain connectomics reflects the level of brain maturation along with neural development. It is well known that autism spectrum disorder (ASD) alters neurodevelopmental trajectories of brain connectomics, but the precise relationship between chronological age (ChA) and brain connectome age (BCA) during development in ASD has not been addressed. This study uses neuroimaging data collected from 50 individuals with ASD and 47 age- and gender-matched typically developing controls (TDCs; age range: 5-18 years).

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Neuroimaging studies describing brain circuits' alterations in cobalamin (vitamin B12)-deficient patients are limited and have not been carried out in patients with inborn errors of cobalamin metabolism. The objective of this study was to assess brain functionality and brain circuit alterations in a patient with an ultra-rare inborn error of cobalamin metabolism, methylmalonic aciduria, and homocystinuria due to cobalamin D disease, as compared with his twin sister as a healthy control (HC). We acquired magnetic resonance imaging (including structural, functional, and diffusion images) to calculate brain circuit abnormalities and combined these results with the scores after a comprehensive neuropsychological evaluation.

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