Smartphone-based procedure to determine content of single synthetic dyes in food using the Arata-Possetto extraction method.

The use of additives, including dyes, is common in the preparation of food products. The analytical control of artificial food dye content is relevant since some, such as azo dyes, may produce cancer and attention deficit disorders and hyperactivity in children. Consequently, the maximum permitted concentration of azo dyes in food is regulated by current legislation. Therefore, it is of interest to find simple and fast procedures for the control of these compounds. The aim of this study is to determine the concentration of azo dyes in food samples by the Arata-Possetto method - based on the extraction of azo dyes employing natural wool -, followed by the analysis of an image captured by a smartphone camera. After experimentally determining the optimal extraction conditions, the calibration curves for standard solutions of different food dyes and the color of the dyed wool were obtained. Results from dyed wool image processing were compared with the absorbance spectra of the solutions before extraction as measured by a diode array spectrophotometer. The spectrophotometric and the image processing procedures were employed to obtain the calibration curves for different food dyes, which were subsequently employed to analyze food samples. Statistical treatment shows that the results of both methods are comparable.

Discovering the mechanism of action of drugs with a sparse explainable network.

BACKGROUND: Although Deep Neural Networks (DDNs) have been successful in predicting the efficacy of cancer drugs, the lack of explainability in their decision-making process is a significant challenge. Previous research proposed mimicking the Gene Ontology structure to allow for interpretation of each neuron in the network. However, these previous approaches require huge amount of GPU resources and hinder its extension to genome-wide models. METHODS: We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA). To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes. Its efficiency allows it to be used with gene expression. In addition, SparseGO integrates an eXplainable Artificial Intelligence (XAI) technique, DeepLIFT, with Support Vector Machines to computationally discover the MoA of drugs. FINDINGS: SparseGO's sparse implementation significantly reduced GPU memory usage and training speed compared to other methods, allowing it to process gene expression instead of mutations as input data. SparseGO using expression improved the accuracy and enabled its use on drug repositioning. Furthermore, gene expression allows the prediction of MoA using 265 drugs to train it. It was validated on understudied drugs such as parbendazole and PD153035. INTERPRETATION: SparseGO is an effective XAI method for predicting, but more importantly, understanding drug response. FUNDING: The Accelerator Award Programme funded by Cancer Research UK [C355/A26819], Fundación Científica de la AECC and Fondazione AIRC, Project PIBA_2020_1_0055 funded by the Basque Government and the Synlethal Project (RETOS Investigacion, Spanish Government).

Precision oncology: a review to assess interpretability in several explainable methods.

Great efforts have been made to develop precision medicine-based treatments using machine learning. In this field, where the goal is to provide the optimal treatment for each patient based on his/her medical history and genomic characteristics, it is not sufficient to make excellent predictions. The challenge is to understand and trust the model's decisions while also being able to easily implement it. However, one of the issues with machine learning algorithms-particularly deep learning-is their lack of interpretability. This review compares six different machine learning methods to provide guidance for defining interpretability by focusing on accuracy, multi-omics capability, explainability and implementability. Our selection of algorithms includes tree-, regression- and kernel-based methods, which we selected for their ease of interpretation for the clinician. We also included two novel explainable methods in the comparison. No significant differences in accuracy were observed when comparing the methods, but an improvement was observed when using gene expression instead of mutational status as input for these methods. We concentrated on the current intriguing challenge: model comprehension and ease of use. Our comparison suggests that the tree-based methods are the most interpretable of those tested.

Explainable artificial intelligence for precision medicine in acute myeloid leukemia.

Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of "black box" in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 ex-vivo tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the FLT3, CBFß-MYH11, and NRAS status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.

EventPointer 3.0: flexible and accurate splicing analysis that includes studying the differential usage of protein-domains.

Alternative splicing (AS) plays a key role in cancer: all its hallmarks have been associated with different mechanisms of abnormal AS. The improvement of the human transcriptome annotation and the availability of fast and accurate software to estimate isoform concentrations has boosted the analysis of transcriptome profiling from RNA-seq. The statistical analysis of AS is a challenging problem not yet fully solved. We have included in EventPointer (EP), a Bioconductor package, a novel statistical method that can use the bootstrap of the pseudoaligners. We compared it with other state-of-the-art algorithms to analyze AS. Its performance is outstanding for shallow sequencing conditions. The statistical framework is very flexible since it is based on design and contrast matrices. EP now includes a convenient tool to find the primers to validate the discoveries using PCR. We also added a statistical module to study alteration in protein domain related to AS. Applying it to 9514 patients from TCGA and TARGET in 19 different tumor types resulted in two conclusions: i) aberrant alternative splicing alters the relative presence of Protein domains and, ii) the number of enriched domains is strongly correlated with the age of the patients.

Identifying Lethal Dependencies with HUGE Predictive Power.

Recent functional genomic screens­such as CRISPR-Cas9 or RNAi screening­have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem is related to a large number of gene knockouts limiting the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the "HUb effect in Genetic Essentiality" (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens­Project Score, CERES score and DEMETER score­identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. Using acute myeloid leukemia, breast cancer, lung adenocarcinoma and colon adenocarcinoma as disease models, we validate that our predictions are enriched in a recent harmonized knowledge base of clinical interpretations of somatic genomic variants in cancer (AUROC > 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer. All the graphs showing lethal dependencies for the 19 tumor types analyzed can be visualized in an interactive tool.

BOSO: A novel feature selection algorithm for linear regression with high-dimensional data.

With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior accuracy for feature selection in high-dimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism.

SOX9 Triggers Different Epithelial to Mesenchymal Transition States to Promote Pancreatic Cancer Progression.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers mainly due to spatial obstacles to complete resection, early metastasis and therapy resistance. The molecular events accompanying PDAC progression remain poorly understood. SOX9 is required for maintaining the pancreatic ductal identity and it is involved in the initiation of pancreatic cancer. In addition, SOX9 is a transcription factor linked to stem cell activity and is commonly overexpressed in solid cancers. It cooperates with Snail/Slug to induce epithelial-mesenchymal transition (EMT) during neural development and in diseases such as organ fibrosis or different types of cancer. METHODS: We investigated the roles of SOX9 in pancreatic tumor cell plasticity, metastatic dissemination and chemoresistance using pancreatic cancer cell lines as well as mouse embryo fibroblasts. In addition, we characterized the clinical relevance of SOX9 in pancreatic cancer using human biopsies. RESULTS: Gain- and loss-of-function of SOX9 in PDAC cells revealed that high levels of SOX9 increased migration and invasion, and promoted EMT and metastatic dissemination, whilst SOX9 silencing resulted in metastasis inhibition, along with a phenotypic reversion to epithelial features and loss of stemness potential. In both contexts, EMT factors were not altered. Moreover, high levels of SOX9 promoted resistance to gemcitabine. In contrast, overexpression of SOX9 was sufficient to promote metastatic potential in K-Ras transformed MEFs, triggering EMT associated with Snail/Slug activity. In clinical samples, SOX9 expression was analyzed in 198 PDAC cases by immunohistochemistry and in 53 patient derived xenografts (PDXs). SOX9 was overexpressed in primary adenocarcinomas and particularly in metastases. Notably, SOX9 expression correlated with high vimentin and low E-cadherin expression. CONCLUSIONS: Our results indicate that SOX9 facilitates PDAC progression and metastasis by triggering stemness and EMT.

Molecular characterization of HEPCIDIN-1 (HAMP1) gene in red-bellied pacu (Piaractus brachypomus).

Hepcidins are cysteine-rich peptides, which participate in iron metabolism regulation, the inflammatory and antimicrobial response. This study characterizes the hepcidin-1 (HAMP1) gene, its transcript expression in different tissues, as well as its regulation in a model of brain injury in Piaractus brachypomus. Bioinformatic analysis was carried out to determine conserved domains, glycosylation sites and protein structure of HAMP1, and probability that HAMP1 corresponds to an antimicrobial peptide (AMP). Relative gene expression of the P. brachypomus HAMP1 gene was determined by qPCR from cDNA of several tissues, a brain injury model, an organophosphate sublethal toxicity model and anesthetic experiment using the 2-ΔΔCt method. HAMP1 ORF encodes for a 91 aa pre-prohepcidin conformed for a prodomain with 42 aa and mature peptide of 25 aa. Mature domain was determined as an AMP. HAMP1 transcript is expressed in all the tissues, being higher in the spleen and liver. HAMP1 mRNA level was upregulated in the brain injury group, as well as in the olfactory bulb, optic chiasm and telencephalon of red-bellied pacu brain exposed to an organophosphate. In anesthetic experiment, HAMP1 mRNA level was upregulated in the liver and gills. HAMP1 gene of P. brachypomus may be involved in the inflammatory, antimicrobial, hypoxia and stress oxidative response.

L2pB1 Cells Contribute to Tumor Growth Inhibition.

Natural IgM (nIgM) antibodies play critical roles in cancer immunosurveillance. However, the role of B-1 B cells, the lymphocytes that produce nIgM, remains to be elucidated. L2pB1 cells, a subpopulation of B-1 B cells, have a unique poly-self-reactive nIgM repertoire and are capable of phagocytosis, potent antigen presentation, and immunomodulation. Using an inducible knock-in and knockout mouse model, we investigated the effect of the loss of L2pB1 cells in a B16F10 melanoma model. Our results show active tumor infiltration of L2pB1 cells in wild type mice, and conversely, depletion of L2pB1 cells results in larger tumor mass and increased angiogenesis. In vitro analysis revealed that L2pB1 cells contribute to the growth inhibition of melanoma cells in both 2D cell culture and 3D tumor spheroids. Similar effects were observed in an MC38 murine colon cancer model. Moreover, our data suggest that one of the ways that L2pB1 cells can induce tumor cell death is via lipoptosis. Lastly, we tested whether L2pB1 cell-derived monoclonal nIgM antibodies can specifically recognize tumor spheroids. Nine of the 28 nIgM-secreting L2pB1 clones demonstrated specific binding to tumor spheroids but did not bind control murine embryonic fibroblasts. Our study provides evidence that L2pB1 cells contribute to cancer immunity through their unique nIgM repertoire, tumor recognition, and lipoptosis. Taken together, because of their ability to recognize common features of tumors that are independent of genetic mutations, L2pB1 cells and their nIgM could be potential candidates for cancer treatment that can overcome tumor heterogeneity-associated drug resistance.

Light-Induced Charge Transfer from Transition-Metal-Doped Aluminum Clusters to Carbon Dioxide.

Charge transfer between molecules and catalysts plays a critical role in determining the efficiency and yield of photochemical catalytic processes. In this paper, we study light-induced electron transfer between transition-metal-doped aluminum clusters and CO2 molecules using first-principles time-dependent density-functional theory. Specifically, we carry out calculations for a range of dopants (Zr, Mn, Fe, Ru, Co, Ni, and Cu) and find that the resulting systems fall into two categories: Cu- and Fe-doped clusters exhibit no ground-state charge transfer, weak CO2 adsorption, and light-induced electron transfer into the CO2. In all other systems, we observe ground-state electron transfer into the CO2 resulting in strong adsorption and predominantly light-induced electron back-transfer from the CO2 into the cluster. These findings pave the way toward a rational design of atomically precise aluminum photocatalysts.

Duality of B Cell-CXCL13 Axis in Tumor Immunology.

Tumor immunity is a rapidly evolving area of research consisting of many possible permutations of immune cell tumor interactions that are dependent upon cell type, tumor type, and stage in tumor progression. At the same time, the majority of cancer immunotherapies have been focused on modulating the T cell-mediated antitumor immune response and have largely ignored the potential utility that B cells possess with respect to tumor immunity. Therefore, this motivated an exploration into the role that B cells and their accompanying chemokine, CXCL13, play in tumor immunity across multiple tumor types. Both B cells and CXCL13 possess dualistic impacts on tumor progression and tumor immunity which is furthered detail in this review. Specifically, various B cells subtypes are able to suppress or enhance several important immunological functions. Paradoxically, CXCL13 has been shown to drive several pro-growth and invasive signaling pathways across multiple tumor types, while also, correlating with improved survival and immune cell tumor localization in other tumor types. Potential tools for better elucidating the mechanisms by which B cells and CXCL13 impact the antitumor immune response are also discussed. In addition, multiples strategies are proposed for modulating the B cell-CXCL13 axis for cancer immunotherapies.

DrugSniper, a Tool to Exploit Loss-Of-Function Screens, Identifies CREBBP as a Predictive Biomarker of VOLASERTIB in Small Cell Lung Carcinoma (SCLC).

The development of predictive biomarkers of response to targeted therapies is an unmet clinical need for many antitumoral agents. Recent genome-wide loss-of-function screens, such as RNA interference (RNAi) and CRISPR-Cas9 libraries, are an unprecedented resource to identify novel drug targets, reposition drugs and associate predictive biomarkers in the context of precision oncology. In this work, we have developed and validated a large-scale bioinformatics tool named DrugSniper, which exploits loss-of-function experiments to model the sensitivity of 6237 inhibitors and predict their corresponding biomarkers of sensitivity in 30 tumor types. Applying DrugSniper to small cell lung cancer (SCLC), we identified genes extensively explored in SCLC, such as Aurora kinases or epigenetic agents. Interestingly, the analysis suggested a remarkable vulnerability to polo-like kinase 1 (PLK1) inhibition in CREBBP-mutant SCLC cells. We validated this association in vitro using four mutated and four wild-type SCLC cell lines and two PLK1 inhibitors (Volasertib and BI2536), confirming that the effect of PLK1 inhibitors depended on the mutational status of CREBBP. Besides, DrugSniper was validated in-silico with several known clinically-used treatments, including the sensitivity of Tyrosine Kinase Inhibitors (TKIs) and Vemurafenib to FLT3 and BRAF mutant cells, respectively. These findings show the potential of genome-wide loss-of-function screens to identify new personalized therapeutic hypotheses in SCLC and potentially in other tumors, which is a valuable starting point for further drug development and drug repositioning projects.

ISOGO: Functional annotation of protein-coding splice variants.

The advent of RNA-seq technologies has switched the paradigm of genetic analysis from a genome to a transcriptome-based perspective. Alternative splicing generates functional diversity in genes, but the precise functions of many individual isoforms are yet to be elucidated. Gene Ontology was developed to annotate gene products according to their biological processes, molecular functions and cellular components. Despite a single gene may have several gene products, most annotations are not isoform-specific and do not distinguish the functions of the different proteins originated from a single gene. Several approaches have tried to automatically annotate ontologies at the isoform level, but this has shown to be a daunting task. We have developed ISOGO (ISOform + GO function imputation), a novel algorithm to predict the function of coding isoforms based on their protein domains and their correlation of expression along 11,373 cancer patients. Combining these two sources of information outperforms previous approaches: it provides an area under precision-recall curve (AUPRC) five times larger than previous attempts and the median AUROC of assigned functions to genes is 0.82. We tested ISOGO predictions on some genes with isoform-specific functions (BRCA1, MADD,VAMP7 and ITSN1) and they were coherent with the literature. Besides, we examined whether the main isoform of each gene -as predicted by APPRIS- was the most likely to have the annotated gene functions and it occurs in 99.4% of the genes. We also evaluated the predictions for isoform-specific functions provided by the CAFA3 challenge and results were also convincing. To make these results available to the scientific community, we have deployed a web application to consult ISOGO predictions (https://biotecnun.unav.es/app/isogo). Initial data, website link, isoform-specific GO function predictions and R code is available at https://gitlab.com/icassol/isogo.

Integration of CLIP experiments of RNA-binding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data.

BACKGROUND: Splicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein. RESULTS: In this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four CLIP databases: POSTAR2, CLIPdb, DoRiNA and StarBase. The analysis of these experiments shows the strong coherence between the binding sites of RBPs of similar families. Augmenting this information with expression changes, we are able to correctly predict the splicing factors that regulate splicing in two gold-standard experiments in which specific splicing factors are knocked-down. CONCLUSIONS: The methodology presented in this study allows the prediction of active splicing factors in either cancer or any other condition by only using the information of transcript expression. This approach opens a wide range of possible studies to understand the splicing regulation of different conditions. A tutorial with the source code and databases is available at https://gitlab.com/fcarazo.m/sfprediction .

TranscriptAchilles: a genome-wide platform to predict isoform biomarkers of gene essentiality in cancer.

BACKGROUND: Aberrant alternative splicing plays a key role in cancer development. In recent years, alternative splicing has been used as a prognosis biomarker, a therapy response biomarker, and even as a therapeutic target. Next-generation RNA sequencing has an unprecedented potential to measure the transcriptome. However, due to the complexity of dealing with isoforms, the scientific community has not sufficiently exploited this valuable resource in precision medicine. FINDINGS: We present TranscriptAchilles, the first large-scale tool to predict transcript biomarkers associated with gene essentiality in cancer. This application integrates 412 loss-of-function RNA interference screens of >17,000 genes, together with their corresponding whole-transcriptome expression profiling. Using this tool, we have studied which are the cancer subtypes for which alternative splicing plays a significant role to state gene essentiality. In addition, we include a case study of renal cell carcinoma that shows the biological soundness of the results. The databases, the source code, and a guide to build the platform within a Docker container are available at GitLab. The application is also available online. CONCLUSIONS: TranscriptAchilles provides a user-friendly web interface to identify transcript or gene biomarkers of gene essentiality, which could be used as a starting point for a drug development project. This approach opens a wide range of translational applications in cancer.

Upstream analysis of alternative splicing: a review of computational approaches to predict context-dependent splicing factors.

Alternative splicing (AS) has shown to play a pivotal role in the development of diseases, including cancer. Specifically, all the hallmarks of cancer (angiogenesis, cell immortality, avoiding immune system response, etc.) are found to have a counterpart in aberrant splicing of key genes. Identifying the context-specific regulators of splicing provides valuable information to find new biomarkers, as well as to define alternative therapeutic strategies. The computational models to identify these regulators are not trivial and require three conceptual steps: the detection of AS events, the identification of splicing factors that potentially regulate these events and the contextualization of these pieces of information for a specific experiment. In this work, we review the different algorithmic methodologies developed for each of these tasks. Main weaknesses and strengths of the different steps of the pipeline are discussed. Finally, a case study is detailed to help the reader be aware of the potential and limitations of this computational approach.

In vitro characterization of chemokine-loaded liposomes.

The recent emergence of immunotherapies is transforming cancer treatments. Although many cancer immunotherapies are finding enormous success for treating hematologic tumors, a major obstacle for the treatment of solid tumors is localizing immune cells to the tumor site. Therefore, we have developed a technology that is capable of directing immune cell migration. Specifically, we have packaged chemokines, signaling molecules that promote immune cell migration, inside polyethylene glycol decorated-liposomes. The release profiles of chemokines and other large molecules from the liposomes have been examined in serum-containing media. We have demonstrated that the liposomes are able to release chemokines to induce immune cell migration. Additionally, these liposomes have been shown in vitro to limit cancer cell growth through increased immune cell recruitment. This strategy of encapsulating chemokines within liposomes paves the way for additional cancer immunotherapies and chemokine-based therapies.

Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm.

BACKGROUND: RNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem. We compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments. RESULTS: As might be expected, RNA-seq appears superior in cases where the technologies disagree and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads. CONCLUSIONS: Our results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions.

Biochemical events related to glial response in spinal cord injury / Eventos bioquímicos de respuesta glial en la fisiopatología de la lesión de médula espinal

Abstract Introduction: Spinal cord injury (SCI) is a devastating event with physical, psychological and socioeconomic implications. Morphophysiological changes are observed in the tissue close to the injury, which allow determining the functional recovery of the medullary segment and the effector organs that depend on the injured axonal tracts. Objective: To describe the most relevant sequential biochemical events of glial cells response after SCI. Materials and methods: A search of scientific publications released in the past 18 years was carried out in PubMed and Science Direct databases, with the terms spinal cord injury (SCI), SCI pathophysiology, SCI inflammation, microglia in SCI, glial scar and chondroitin sulfate proteoglycans (CSPG). Results: The pathophysiological processes resulting from SCI are determinant for the neurological recovery of patients. Activation of glial cells plays an important role in promoting bioactive molecules and the formation of physical barriers that inhibit neural regeneration. Conclusion: Knowledge of neurobiological changes after SCI allows a greater understanding of the pathophysiology and favors the search for new therapeutic alternatives that limit the progression of the primary injury and minimize secondary damage, responsible for neurological dysfunction.

Resumen Introducción. La lesión de la médula espinal (LME) es un evento devastador con implicaciones físicas, psicológicas y socioeconómicas. En el tejido cercano a la lesión se instauran cambios morfofisiológicos que determinan la recuperación funcional del segmento medular y de los órganos efectores dependientes de los tractos axonales lesionados. Objetivo. Describir los eventos bioquímicos secuenciales más relevantes de la respuesta de las células gliales posterior a la LME. Materiales y métodos. Se realizó una búsqueda de publicaciones científicas de los últimos 18 años en las bases de datos PubMed y ScienceDirect, bajo los términos en inglés spinal cord injury (SCI), SCI pathophysiology, SCI inflammation, microglia in SCI, glial scar y chondroitin sulfate proteoglycans (CSPG). Resultados. Los procesos fisiopatológicos que se producen después de la LME determinan la recuperación neurológica de los pacientes. La activación de las células gliales juega un papel importante, ya que promueve la producción de moléculas bioactivas y la formación de barreras físicas que inhiben la regeneración neural. Conclusión. El conocimiento de los cambios neurobiológicos ocurridos tras la LME permite una mayor comprensión de la fisiopatología y favorece la búsqueda de nuevas alternativas terapéuticas que limiten la progresión de la lesión primaria y que minimicen el daño secundario responsable de la disfunción neurológica.