Fractal Dimension Analysis in Neurological Disorders: An Overview.

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

Metabolomics unravels subtype-specific characteristics related to neoadjuvant therapy response in breast cancer patients.

INTRODUCTION: Breast cancer is the most diagnosed tumor and the leading cause of cancer death in women worldwide. Metabolomics allows the quantification of the entire set of metabolites in blood samples, making it possible to study differential metabolomics patterns related to neoadjuvant treatment in the breast cancer neoadjuvant setting. OBJECTIVES: Characterizing metabolic differences in breast cancer blood samples according to their response to neoadjuvant treatment. METHODS: One hundred and three plasma samples of breast cancer patients, before receiving neoadjuvant treatment, were analyzed through UPLC-MS/MS metabolomics. Then, metabolomics data were analyzed using probabilistic graphical models and biostatistics methods. RESULTS: Metabolomics data allowed the identification of differences between groups according to response to neoadjuvant treatment. These differences were specific to each breast cancer subtype. Patients with HER2+ tumors showed differences in metabolites related to amino acids and carbohydrates pathways between the two pathological response groups. However, patients with triple-negative tumors showed differences in metabolites related to the long-chain fatty acids pathway. Patients with Luminal B tumors showed differences in metabolites related to acylcarnitine pathways. CONCLUSIONS: It is possible to identify differential metabolomics patterns between complete and partial responses to neoadjuvant therapy, being this metabolomic profile specific for each breast cancer subtype.

TFG-ß Nuclear Staining as a Potential Relapse Risk Factor in Early-Stage Non-Small-Cell Lung Cancer.

Nowadays, the impact of the tumor-immune microenvironment (TME) in non-small-cell lung cancer (NSCLC) prognosis and treatment response remains unclear. Thus, we evaluated the expression of PD-L1, tumor-infiltrating lymphocytes (TILs), and transforming growth factor beta (TGF-ß) in NSCLC to identify differences in TME, detect possible new prognostic factors, and assess their relationship. We retrospectively analyzed 55 samples from patients who underwent NSCLC surgery and had over a 5-year follow-up. PD-L1 expression was determined by immunohistochemistry following standard techniques. The presence of TILs was evaluated at low magnification and classified into two categories, "intense" and "non-intense". Cytoplasmic TGF-ß staining visualization was divided into four categories, and unequivocal nuclear staining in >1% of viable tumor cells was defined as "present" or "absent". Our aim was to identify differences in disease-free survival (DFS) and overall survival (OS). Tumor stage was the only objective prognostic factor for OS. PD-L1 expression and the presence of TILs had no prognostic impact, neither their combination. There seems to be a lower expression of PD-L1 and a higher expression of TILs in early stages of the disease. Our TGF-ß nuclear staining analysis was promising, since it was associated with worse DFS, revealing this protein as a possible prognostic biomarker of recurrence for resectable NSCLC.

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach.

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA-simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple-negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted-based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow-up in the clinical practice.

Early use of tocilizumab in patients with severe pneumonia secondary to severe acute respiratory syndrome coronavirus 2 infection and poor prognostic criteria: Impact on mortality rate and intensive care unit admission.

ABSTRACT: The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2, keeps spreading globally. Evidence suggests that a subgroup of patients with severe symptomatology might have cytokine storms, which increases mortality. The use of interleukin-6 (IL-6) inhibitors may help in controlling the pathological immune response to the virus. Tocilizumab, a monoclonal antibody against IL-6, stands as an optional treatment for COVID-19 patients presenting this inflammatory hyper-response.We conducted a retrospective, observational, cohort study including 50 patients affected by COVID-19 with severe pneumonia and poor prognosis criteria, who have also undergone standard treatment; 36 of these patients additionally received tocilizumab in an early stage. The need for intensive care unit (ICU) admission, mortality, recovery of respiratory function, and improvement of biochemical and hematological parameters were compared between cohorts.Most patients were men, non-smokers and the most frequently reported comorbidities were hypertension and diabetes. Recurrent symptoms were fever, cough, and dyspnoea. 54.8% of patients from the tocilizumab group needed intubation, while in the control group 85.7% needed it. Treatment with tocilizumab significatively increased IL-6 levels, (554.45; CI 95% 186.69, 1032.93; P < .05) while C-reactive protein mean levels were reduced (-108.19; CI 95% -140.15, -75.33; P < .05), but no significant difference was found between cohorts. In comparison with the controls, tocilizumab reduced mortality (25.0% vs 42.9%, P = .021) and the number of ICU admissions (63.9% vs 100.0%, P = .021). 44.1% of patients treated with tocilizumab showed favorable radiological evolution, when compared with 15.4% of patients from the control group.Tocilizumab may improve clinical symptoms and mitigate deterioration observed in severe COVID-19 patients, and could be considered as an effective therapeutic option in subjects experiencing a significant inflammatory response to the disease.

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer.

PURPOSE: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. METHODS: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. RESULTS: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. CONCLUSION: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

Cross-disorder comparative analysis of comorbid conditions reveals novel autism candidate genes.

BACKGROUND: Numerous studies have highlighted the elevated degree of comorbidity associated with autism spectrum disorder (ASD). These comorbid conditions may add further impairments to individuals with autism and are substantially more prevalent compared to neurotypical populations. These high rates of comorbidity are not surprising taking into account the overlap of symptoms that ASD shares with other pathologies. From a research perspective, this suggests common molecular mechanisms involved in these conditions. Therefore, identifying crucial genes in the overlap between ASD and these comorbid disorders may help unravel the common biological processes involved and, ultimately, shed some light in the understanding of autism etiology. RESULTS: In this work, we used a two-fold systems biology approach specially focused on biological processes and gene networks to conduct a comparative analysis of autism with 31 frequently comorbid disorders in order to define a multi-disorder subcomponent of ASD and predict new genes of potential relevance to ASD etiology. We validated our predictions by determining the significance of our candidate genes in high throughput transcriptome expression profiling studies. Using prior knowledge of disease-related biological processes and the interaction networks of the disorders related to autism, we identified a set of 19 genes not previously linked to ASD that were significantly differentially regulated in individuals with autism. In addition, these genes were of potential etiologic relevance to autism, given their enriched roles in neurological processes crucial for optimal brain development and function, learning and memory, cognition and social behavior. CONCLUSIONS: Taken together, our approach represents a novel perspective of autism from the point of view of related comorbid disorders and proposes a model by which prior knowledge of interaction networks may enlighten and focus the genome-wide search for autism candidate genes to better define the genetic heterogeneity of ASD.

Comorbid Analysis of Genes Associated with Autism Spectrum Disorders Reveals Differential Evolutionary Constraints.

The burden of comorbidity in Autism Spectrum Disorder (ASD) is substantial. The symptoms of autism overlap with many other human conditions, reflecting common molecular pathologies suggesting that cross-disorder analysis will help prioritize autism gene candidates. Genes in the intersection between autism and related conditions may represent nonspecific indicators of dysregulation while genes unique to autism may play a more causal role. Thorough literature review allowed us to extract 125 ICD-9 codes comorbid to ASD that we mapped to 30 specific human disorders. In the present work, we performed an automated extraction of genes associated with ASD and its comorbid disorders, and found 1031 genes involved in ASD, among which 262 are involved in ASD only, with the remaining 779 involved in ASD and at least one comorbid disorder. A pathway analysis revealed 13 pathways not involved in any other comorbid disorders and therefore unique to ASD, all associated with basal cellular functions. These pathways differ from the pathways associated with both ASD and its comorbid conditions, with the latter being more specific to neural function. To determine whether the sequence of these genes have been subjected to differential evolutionary constraints, we studied long term constraints by looking into Genomic Evolutionary Rate Profiling, and showed that genes involved in several comorbid disorders seem to have undergone more purifying selection than the genes involved in ASD only. This result was corroborated by a higher dN/dS ratio for genes unique to ASD as compare to those that are shared between ASD and its comorbid disorders. Short-term evolutionary constraints showed the same trend as the pN/pS ratio indicates that genes unique to ASD were under significantly less evolutionary constraint than the genes associated with all other disorders.

Systems biology as a comparative approach to understand complex gene expression in neurological diseases.

Systems biology interdisciplinary approaches have become an essential analytical tool that may yield novel and powerful insights about the nature of human health and disease. Complex disorders are known to be caused by the combination of genetic, environmental, immunological or neurological factors. Thus, to understand such disorders, it becomes necessary to address the study of this complexity from a novel perspective. Here, we present a review of integrative approaches that help to understand the underlying biological processes involved in the etiopathogenesis of neurological diseases, for example, those related to autism and autism spectrum disorders (ASD) endophenotypes. Furthermore, we highlight the role of systems biology in the discovery of new biomarkers or therapeutic targets in complex disorders, a key step in the development of personalized medicine, and we demonstrate the role of systems approaches in the design of classifiers that can shorten the time for behavioral diagnosis of autism.

El Genoma en los Cordados: Introducción a la Genómica Comparada / The Genome in the Chordates: Introduction to Comparative Genomics

El objetivo del presente trabajo es describir brevemente conceptos de la genómica de los cordados así como de la genómica comparada y sus aspectos éticos. El genoma de los cordados cambió ligeramente dando lugar al genoma de los vertebrados, entre ellos el de los mamíferos, entre los que se incluye el ser humano. La genómica comparada estudia las semejanzas y diferencias entre genomas de diferentes organismos; trata de explicar la información proporcionada por la selección natural para entender la función y los procesos evolutivos que actúan sobre los genomas. La complejidad de los procesos evolutivos constituye todo un desafío a la hora de analizar e interpretar la información biológica generada; en este sentido, la Bioinformática y la Biología Computacional proporcionan un amplio abanico de técnicas estadísticas, matemáticas y algorítmicas para el análisis de datos biológicos. Un reto clave es analizar el caudal de datos de secuencias de ADN con el fin de comprender la información almacenada en términos de estructura, función y evolución proteicas. En Biología computacional BLAST y ClustalW2 son las herramientas más empleadas para el análisis de alineamientos múltiples de secuencias. La Genómica está resultando clave también en el campo de la medicina; conocer la cartografía del genoma humano proporciona una valiosa información a tener en cuenta a la hora de detectar genes implicados en ciertas enfermedades. Esto conlleva que en la actualidad nos centremos más en la predicción de patologías que en la prevención, por lo que la tendencia es que en el futuro la Medicina Genómica acabe desbancando a la Medicina Preventiva. El Proyecto Genoma Humano presenta diversas aplicaciones que, al no tener una clara cobertura legal, traen consigo un nuevo paradigma con problemas éticos, sociales y legales que la comunidad científica trata de resolver para compaginar los aspectos morales con el progreso en la investigación.

The aim of this paper is to briefly describe concepts of genomics of chordates and comparative genomics and its ethical aspects. The genome of chordates changed slightly resulting in the genome of vertebrates, including mammals. The human being belongs to the phylum chordates. Comparative genomics studies the similarities and differences between genomes of different organisms, trying to explain the information provided by natural selection to understand the function and evolutionary processes that act on genomes. Evolutive processes' complexity is considered a major challenge in terms of analyzing and interpreting all the biological information generated; in this sense, Bioinformatics and Computational Biology provide an enormous range of statistical, mathematical and algorythmical techniques for biological data analyses. A key challenge for bioinformatics is to analyze the flow of DNA sequence data to understand the information stored in terms of structure, function and protein evolution. BLAST and ClustalW2 tools are used for the analysis of multiple sequence alignments in Computational Biology. Genomics is also playing a key role in Medicine; human genome's cartography provides valuable information to detect genes involved in certain diseases. It entails that nowadays it is better to focus on prediction much more than on prevention. The current tendency is that in the future Genomic Medicine will displace Preventive Medicine. The Human Genome Project implies diverse applications that do not have clear legal coverage. This brings a new paradigm with ethical, social and legal problems that the scientific community is trying to resolve in order to combine morality and research progress.

Medicina genómica: aspectos éticos, legales y sociales del genoma humano / Genomic medicine: ethical, legal and social aspects of the human genome / Medicina genômica: aspectos éticos, legais e sociais do genoma humano

La Medicina Genómica es el uso de la información de los genomas y sus derivados (ARN, proteínas y metabolitos) que permite guiar la toma de decisiones médicas, es un componente clave de la medicina personalizada. La Medicina Genómica permite conocer la cartografía del genoma humano y proporciona una valiosa información a tener en cuenta a la hora de detectar genes implicados en ciertas enfermedades. Esto conlleva a que en la actualidad nos centremos más en la predicción de patologías que en la prevención, por lo que la tendencia es que en el futuro la Medicina Genómica acabe desbancando a la Medicina Preventiva. El Proyecto Genoma Humano presenta diversas aplicaciones que, al no tener una clara cobertura legal, traen consigo un nuevo paradigma con problemas éticos, sociales y legales que la comunidad científica trata de resolver para compaginar los aspectos morales con el progreso en la investigación. El objetivo del presente trabajo es describir brevemente los aspectos éticos, legales y sociales del Genoma Humano...

Genomic Medicine and its different forms of application (personalized or individualized medicine, which are part of pharmacogenomics, toxicogenomics and nutrigenomics, and predictive, regenerative placement, molecular and reproductive medicine), without a doubt have transformed the modern cine and are a new paradigm. This article aims to review the various forms of genomic medicine, since its benefits to human health and substantial changes in the approach to health-disease process, as well as the problems and paradoxes associated to be addressed from bioethics and Law...

A Medicina Genômica é o uso da informação dos genomas e seus derivados (ARN, proteínas e metabólitos) e permite guiar a tomada de decisões médicas; é um componente-chave da medicina personalizada. A Medicina Genômica permite conhecer a cartografia do genoma humano, proporciona uma valiosa informação a ser considerada na hora de detectar genes implicados em certas doenças. Isto leva a que atualmente nos concentremos mais na predição de patologias que na prevenção, sendo que a tendência é que no futuro a Medicina Genômica acabe desbancando a Medicina Preventiva. O Projeto Genoma Humano apresenta diversas aplicações, que se não tiverem uma clara cobertura legal trazem consigo um novo paradigma, com problemas éticos, sociais e legais que a comunidade científica tenta resolver para compaginar os aspectos morais com o progresso na pesquisa. O objetivo do presente trabalho é descrever brevemente os aspectos éticos, legais e sociais do Genoma Humano...