The blood-brain barrier (BBB) is an anatomical microstructural unit, with several different components playing key roles in normal brain physiological regulation. Formed by tightly connected cerebrovascular endothelial cells, its normal function depends on paracrine interactions between endothelium and closely related glia, with several recent reports stressing the need to consider the entire gliovascular unit in order to explain the underlying cellular and molecular mechanisms. Despite that, with regard to traumatic brain injury (TBI) and significant events in incidence and potential clinical consequences in pediatric and adult ages, little is known about the actual role of BBB disruption in its diverse pathological pathways. This Mini-Review addresses the current literature on possible factors affecting gliovascular units and contributing to posttraumatic BBB dysfunction, including neuroinflammation and disturbed transport mechanisms along with altered permeability and consequent posttraumatic edema. Key mechanisms and its components are described, and promising lines of basic and clinical research are identified, because further knowledge on BBB pathological interference should play a key role in understanding TBI and provide a basis for possible therapeutic targets in the near future, whether through restoration of normal BBB function after injury or delivering drugs in an increased permeability context, preventing secondary damage and improving functional outcome.

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Data mining is seen as a set of techniques and technologies allowing to extract, automatically or semi-automatically, a lot of useful information, models, and tendencies from a big set of data. Techniques like “clustering,” “classification,” “association,” and “regression”; statistics and Bayesian calculations; or intelligent artificial algorithms like neural networks will be used to extract patterns from data, and the main goal to achieve those patterns will be to explain and to predict their behavior. So, data are the source that becomes relevant information. Research data are gathered as numbers (quantitative data) as well as symbolic values (qualitative data). Useful knowledge is extracted (mined) from a huge amount of data. Such kind of knowledge will allow setting relationships among attributes or data sets, clustering similar data, classifying attribute relationships, and showing information that could be hidden or lost in a vast quantity of data when data mining is not used. Combination of quantitative and qualitative data is the essence of mixed methods: on one hand, a coherent integration of result data interpretation starting from separate analysis, and on the other hand, making data transformation from qualitative to quantitative and 1 vice versa. A study developed shows how data mining techniques can be a very interesting complement to mixed methods, because such techniques can work with qualitative and quantitative data together, obtaining numeric analysis from qualitative data based on Bayesian probability calculation or transforming quantitative into qualitative data using discretization techniques. As a study case, the Psychological Inventory of Sports Performance (IPED) has been mined and decision trees have been developed in order to check any relationships among the “Self-confidence” (AC), “Negative Coping Control” (CAN), “Attention Control” (CAT), “Visuoimaginative Control” (CVI), “Motivational Level” (NM), “Positive Coping Control” (CAP), and “Attitudinal Control” (CACT) factors against gender and age of athletes. These decision trees can also be used for future data predictions or assumptions.

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Subdural hematomas are a frequent and highly heterogeneous traumatic disorder, with significant clinical and socioeconomic consequences. In clinical and medicolegal practice, subdural hematomas are classified according to its apparent age, which significantly influences its intrinsic pathogenic behavior, forensic implications, clinical management, and outcome. Although practical, this empirical classification is somewhat arbitrary and scarcely informative, considering the remarkable heterogeneity of this entity. The current research project aims at implementing a comprehensive multifactorial classification of subdural hematomas, allowing a more standardized and coherent assessment and management of this condition. This new method of classification of subdural hematomas takes into account its intrinsic and extrinsic features, using imaging data and histopathological elements, to provide an easily apprehensible and intuitive nomenclature. The proposed classification unifies and organizes all relevant details concerning subdural hematomas, hopefully improving surgical care and forensic systematization.