Fibrillar extracellular matrix produced by pericyte-like cells facilitates glioma cell dissemination.

Gliomagenesis induces profound changes in the composition of the extracellular matrix (ECM) of the brain. In this study, we identified a cellular population responsible for the increased deposition of collagen I and fibronectin in glioblastoma. Elevated levels of the fibrillar proteins collagen I and fibronectin were associated with the expression of fibroblast activation protein (FAP), which is predominantly found in pericyte-like cells in glioblastoma. FAP+ pericyte-like cells were present in regions rich in collagen I and fibronectin in biopsy material and produced substantially more collagen I and fibronectin in vitro compared to other cell types found in the GBM microenvironment. Using mass spectrometry, we demonstrated that 3D matrices produced by FAP+ pericyte-like cells are rich in collagen I and fibronectin and contain several basement membrane proteins. This expression pattern differed markedly from glioma cells. Finally, we have shown that ECM produced by FAP+ pericyte-like cells enhances the migration of glioma cells including glioma stem-like cells, promotes their adhesion, and activates focal adhesion kinase (FAK) signaling. Taken together, our findings establish FAP+ pericyte-like cells as crucial producers of a complex ECM rich in collagen I and fibronectin, facilitating the dissemination of glioma cells through FAK activation.

Molecularly targeted protease-activated probes for visualization of glioblastoma: a comparison with 5-ALA.

OBJECTIVE: The highly infiltrative growth of glioblastoma (GBM) makes distinction between the tumor and normal brain tissue challenging. Therefore, fluorescence-guided surgery is often used to improve visual identification of radiological tumor margins. The aim of this study was to evaluate the ability of recently developed molecularly targeted near-infrared (NIR) protease-activated probes to visualize GBM tissue and to compare the most promising candidate with the gold standard, 5-aminolevulinic acid (5-ALA). METHODS: Single-substrate probes 6QC-ICG and 6QC-Cy5 (cysteine cathepsin cleavable), double-substrate probes AG2-FNIR and AG2-Cy5 (cysteine cathepsin and caspase 3 cleavable), and 5-ALA were administered intravenously to mice with orthotopic tumors. Activation of the probes was also evaluated in cell cultures in vitro and in biopsy material from patients with GBM ex vivo. The tumor to normal brain tissue fluorescence ratio (TNR) was quantified in brain sections using preclinical and clinical visualization platforms, and in tissue homogenates and cell suspensions using spectrofluorimetry. Subcellular localization of the fluorophores was visualized by confocal microscopy. RESULTS: In vitro, the single-substrate probe 6QC-ICG was cleaved in glioma cells and macrophages, and the resulting fluorophore accumulated intracellularly. In experimental GBMs, both single- and double-substrate probes visualized tumor tissue, while in healthy brain tissue the signal was minimal. TNR was highest for 6QC-ICG and AG2-FNIR, but the signal intensity was higher for 6QC-ICG. Using xenograft and syngeneic mouse models, as well as human GBM biopsy material ex vivo, the authors confirmed the ability of 6QC-ICG to specifically visualize the glioma tissue using preclinical and clinical visualization platforms. Finally, a comparison with 5-ALA in animals coadministered with both compounds revealed a higher TNR for 6QC-ICG in experimental GBMs. CONCLUSIONS: The cysteine cathepsin-cleavable probe 6QC-ICG is activated by glioma cells and tumor-associated macrophages, leading to a high contrast between tumor and nontumorous brain tissue that is superior to that of the current standard, 5-ALA. In addition to a well-defined mechanism of action, protease-activated probes that use NIR fluorophores (e.g., indocyanine green) have the advantage of low absorption and scattering of the NIR light and lower tissue autofluorescence. These results suggest that 6QC-ICG has the potential to become the targeted agent in intraoperative detection of GBM tissue using fluorescence imaging.

Clinical practice guideline on the management of vestibular schwannoma.

INTRODUCTION: Vestibular schwannoma (VS) is the most common tumour of the cerebellopontine angle. The greater accessibility to radiological tests has increased its diagnosis. Taking into account the characteristics of the tumour, the symptoms and the age of the patient, three therapeutic strategies have been proposed: observation, surgery or radiotherapy. Choosing the most appropriate for each patient is a frequent source of controversy. MATERIAL AND METHODS: This paper includes an exhaustive literature review of issues related to VS that can serve as a clinical guide in the management of patients with these lesions. The presentation has been oriented in the form of questions that the clinician usually asks himself and the answers have been written and/or reviewed by a panel of national and international experts consulted by the Otology Commission of the SEORL-CCC. RESULTS: A list has been compiled containing the 13 most controversial thematic blocks on the management of VS in the form of 50 questions, and answers to all of them have been sought through a systematic literature review (articles published on PubMed and Cochrane Library between 1992 and 2023 related to each thematic area). Thirty-three experts, led by the Otology Committee of SEORL-CCC, have analyzed and discussed all the answers. In Annex 1, 14 additional questions divided into 4 thematic areas can be found. CONCLUSIONS: This clinical practice guideline on the management of VS offers agreed answers to the most common questions that are asked about this tumour. The absence of sufficient prospective studies means that the levels of evidence on the subject are generally medium or low. This fact increases the interest of this type of clinical practice guidelines prepared by experts.

Critical appraisal of the piRNA-PIWI axis in cancer and cancer stem cells.

Small noncoding RNAs play an important role in various disease states, including cancer. PIWI proteins, a subfamily of Argonaute proteins, and PIWI-interacting RNAs (piRNAs) were originally described as germline-specific molecules that inhibit the deleterious activity of transposable elements. However, several studies have suggested a role for the piRNA-PIWI axis in somatic cells, including somatic stem cells. Dysregulated expression of piRNAs and PIWI proteins in human tumors implies that, analogously to their roles in undifferentiated cells under physiological conditions, these molecules may be important for cancer stem cells and thus contribute to cancer progression. We provide an overview of piRNA biogenesis and critically review the evidence for the role of piRNA-PIWI axis in cancer stem cells. In addition, we examine the potential of piRNAs and PIWI proteins to become biomarkers in cancer.

Can Satellites Predict Yield? Ensemble Machine Learning and Statistical Analysis of Sentinel-2 Imagery for Processing Tomato Yield Prediction.

In this paper, we propose an innovative approach for robust prediction of processing tomato yield using open-source AutoML techniques and statistical analysis. Sentinel-2 satellite imagery was deployed to obtain values of five (5) selected vegetation indices (VIs) during the growing season of 2021 (April to September) at 5-day intervals. Actual recorded yields were collected across 108 fields, corresponding to a total area of 410.10 ha of processing tomato in central Greece, to assess the performance of Vis at different temporal scales. In addition, VIs were connected with the crop phenology to establish the annual dynamics of the crop. The highest Pearson coefficient (r) values occurred during a period of 80 to 90 days, indicating the strong relationship between the VIs and the yield. Specifically, RVI presented the highest correlation values of the growing season at 80 (r = 0.72) and 90 days (r = 0.75), while NDVI performed better at 85 days (r = 0.72). This output was confirmed by the AutoML technique, which also indicated the highest performance of the VIs during the same period, with the values of the adjusted R2 ranging from 0.60 to 0.72. The most precise results were obtained with the combination of ARD regression and SVR, which was the most successful combination for building an ensemble (adj. R2 = 0.67 ± 0.02).

Reducing ageism combining ageing education with clinical practice: A prospective cohort study in health sciences students.

AIM: To analyse the impact of an intervention combining ageing education with clinical practice in nursing homes on a nursing cohort's negative stereotypes and prejudices towards ageing. DESIGN: A prospective cohort study was conducted in September 2019-October 2020 in a population of health sciences students (n = 222). METHODS: Questionnaire of Negative Stereotypes towards Aging (CENVE) and Aging Semantic Differential (DSE) were used to examine negative stereotypes and prejudices towards ageing in the nursing cohort exposed to the ageing education and practice intervention compared to a medical cohort that received no intervention. Group-by-time interaction, controlled by sex and age, for the effect of the intervention on CENVE and DSE scores was determined by mixed-design ANOVA. RESULTS: The nursing cohort significantly reduced negative stereotypes and prejudices towards ageing when compared to the medical cohort in total (F = 26.926; p < 0.001), health factor (F = 16.812; p < 0.001), motivational and social factor (F = 11.266; p = 0.001), and character and personality factor (F = 19.202; p < 0.001) scores of CENVE scale and in DSE (F = 7.826; p = 0.006).

Reducing Negative Aging Stereotypes via Educational Intervention in Older People: A Randomized Controlled Study.

Anti-ageism interventions traditionally target younger individuals. We analyzed the effect of an educational intervention that combined an infusion of aging content with videos to reduce negative stereotypes toward aging in a randomized controlled study of 56 community-dwelling older adults. The experimental group received a single one-hour information session and video viewing on ageism; the control group viewed 1 hour of videos unrelated to ageism. Repeated-measures ANOVA indicated that the experimental group reported a significantly reduced Negative Stereotypes Toward Aging Questionnaire (CENVE) total score along with an independent measure of the character-personality factor in this questionnaire 1 week and 1 month after the intervention compared to baseline. The control group showed no changes. This work reports for the first time that a one-hour information session about aging combined with video viewing on ageism can efficiently reduce negative stereotypes of aging in older people in the short and medium term.

The "autonomy" of developing countries in the Olympic Movement: Assessing the fate of sports governance transplants in the Global South.

Introduction: The International Olympic Committee (IOC) imposes very specific ideas on sports governance, more precisely on sports autonomy, on countries joining the Olympic Movement. Given that the idea of sports autonomy originated in the Global North, this article introduces the concept of governance transplants to evaluate the impact that being part of the Olympic Movement has on domestic sports governance in Global South developing countries. The article explores the extent to which the IOC is successful in implementing the norms and regulations on sports autonomy as a governance transplant at the national level in countries from the Global South that are part of the Olympic Movement. Methods: The article employs a comparative qualitative case study research design that explores the relations of the IOC, National Olympic Committees (NOCs), and national governments in Botswana, Guatemala and Sri Lanka. Research relies on a mix of document analyses and expert semi-structured interviews conducted during field trips to those countries. Interviews were transcribed and analyzed by means of thematic analysis. The analyses focus on domestic policies and contexts, formal and institutional compliance with sports autonomy, provision of public funding, and participation in national sport policy-making. Results and discussion: Findings suggest that national structures and legacies have an impact on the way in which the autonomy of sport, as the governance transplant, is translated in those three countries. Although national governments enjoy some agency in "translating" governance transplants, results also suggest that misfits and tensions persist between governmental and sport stakeholders at the national and international level. Such misfits might force the IOC, as a private transnational regulator, to adopt a more pragmatic view on the enforcing of its governance transplants. The results are of relevance to existing discussions on global sports governance and debates as to whether the countries in the Global North might be able to impose their views and their governance transplants if the Global South gets a greater say in transnational sports governance.

Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning.

Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of laboratory samples is the most popular method for determining the quality characteristics of grapes, although it is time-consuming and expensive. In recent years, several machine learning-based methods have been developed to predict crop quality. Although these techniques require the extensive involvement of experts, automated machine learning (AutoML) offers the possibility to improve this task, saving time and resources. In this paper, we propose an innovative approach for robust prediction of grape quality attributes by combining open-source AutoML techniques and Normalized Difference Vegetation Index (NDVI) data for vineyards obtained from four different platforms-two proximal vehicle-mounted canopy reflectance sensors, orthomosaics from UAV images and Sentinel-2 remote sensing imagery-during the 2019 and 2020 growing seasons. We investigated AutoML, extending our earlier work on manually fine-tuned machine learning methods. Results of the two approaches using Ordinary Least Square (OLS), Theil-Sen and Huber regression models and tree-based methods were compared. Support Vector Machines (SVMs) and Automatic Relevance Determination (ARD) were included in the analysis and different combinations of sensors and data collected over two growing seasons were investigated. Results showed promising performance of Unmanned Aerial Vehicle (UAV) and Spectrosense+ GPS data in predicting grape sugars, especially in mid to late season with full canopy growth. Regression models with both manually fine-tuned ML (R² = 0.61) and AutoML (R² = 0.65) provided similar results, with the latter slightly improved for both 2019 and 2020. When combining multiple sensors and growth stages per year, the coefficient of determination R² improved even more averaging 0.66 for the best-fitting regressions. Also, when considering combinations of sensors and growth stages across both cropping seasons, UAV and Spectrosense+ GPS, as well as Véraison and Flowering, each had the highest average R² values. These performances are consistent with previous work on machine learning algorithms that were manually fine-tuned. These results suggest that AutoML has greater long-term performance potential. To increase the efficiency of crop quality prediction, a balance must be struck between manual expert work and AutoML.

Mapping Tree Canopy in Urban Environments Using Point Clouds from Airborne Laser Scanning and Street Level Imagery.

Resilient cities incorporate a social, ecological, and technological systems perspective through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have compared and combined two of the most common data sources for updating urban tree inventories: Airborne Laser Scanning (ALS) point clouds combined with aerial orthophotographs, and street-level imagery from Google Street View (GSV). Depending on the data source, different methodologies were used to identify the trees. In the first stage, the use of individual tree detection techniques in ALS point clouds was compared with the detection of objects (trees) on street level images using computer vision (CV) techniques. In both cases, a high success rate or recall (number of true positive with respect to all detectable trees) was obtained, where between 85.07% and 86.42% of the trees were well-identified, although many false positives (FPs) or trees that did not exist or that had been confused with other objects were always identified. In order to reduce these errors or FPs, a second stage was designed, where FP debugging was performed through two methodologies: (a) based on the automatic checking of all possible trees with street level images, and (b) through a machine learning binary classification model trained with spectral data from orthophotographs. After this second stage, the recall decreased to about 75% (between 71.43 and 78.18 depending on the procedure used) but most of the false positives were eliminated. The results obtained with both data sources were robust and accurate. We can conclude that the results obtained with the different methodologies are very similar, where the main difference resides in the access to the starting information. While the use of street-level images only allows for the detection of trees growing in trafficable streets and is a source of information that is usually paid for, the use of ALS and aerial orthophotographs allows for the location of trees anywhere in the city, including public and private parks and gardens, and in many countries, these data are freely available.

Psychological and Functional Impact of COVID-19 in Long-Term Care Facilities: The COVID-A Study.

OBJECTIVE: To analyze the psychological and functional sequelae of the COVID-19 pandemic among older adults living in long term care facilities (LTCFs). DESIGN: Cohort longitudinal study SETTING ANT PARTICIPANTS: A total of 215 residents ≥ 65 years without moderate-to-severe cognitive impairment, living in five LTCFs in Albacete (Spain). MEASUREMENTS: Baseline on-site data were collected between March - June 2020 and three-month follow-up between June to September 2020. Symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), and sleep disturbances were measured as psychological variables. Disability in basic activities of daily living (BADL), ambulation and frailty were assessed as functional variables. Differences were analyzed in relation to level of comorbidity and test positivity for COVID-19. RESULTS: At baseline, residents with COVID-19 presented worse functionality, higher frailty levels and malnutrition risk compared to non-COVID-19 residents. At three-month follow-up, higher rates of clinically significant depressive symptoms (57.7%), anxiety symptoms (29.3%), PTSD symptoms (19.1%) and sleep disturbances (93.0%) were found among residents regardless of COVID status. Thus, among 215 residents, 101 (47%) experienced a decline in BADL from baseline to the 3-month follow-up (median functional loss = 5 points in Barthel Index). In multivariate analyses, COVID-19 status did not explain either the functional or the ambulation loss. By contrast, residents with low comorbidity and COVID-19 presented higher PTSD symptoms (effect 2.58; 95% CI 0.93 to 4.23) and anxiety symptoms (effect 2.10; 95% CI 0.48 to 3.73) compared to the low comorbidity/non-COVID19 group. CONCLUSION: COVID-19 pandemic was associated, after three-months, with high psychological impact in older adults in LTCFs., specifically with higher post-traumatic stress and anxiety symptoms. Functional decline did not differ in relation to COVID-19 status but could be related to isolation strategies used for pandemic control.

Relationship between negative stereotypes toward aging and multidimensional variables in older people living in two different social environments.

BACKGROUND: Ageism is studied extensively in community-dwelling older adults, but remains poorly understood in institutionalized older adults. Here, we compared the physical, psychological, and social variables associated with self-reported age-based biases in community-dwelling older adults and in those living in nursing homes (NHs). MATERIAL AND METHODS: Participants (n = 272) in this descriptive cross-sectional study lived in NHs (n = 126) or in their homes (n = 146), were 60 years or older, and had no cognitive or serious functional impairments. Those in NHs had been institutionalized for at least six months. The dependent variable was negative stereotypes toward aging (CENVE). We developed generalized linear models (GLMs) by place of residence and sex following the Comprehensive Geriatric Assessment´s (CGA) methodology. RESULTS: There is a statistically significant relationship between negative stereotypes of aging and different multidimensional variables. These variables differ between institutionalized and non-institutionalized adults and between men and women. While some variables are non-modifiable (age and sex) others are potentially modifiable (educational level, participation in activities, length of NH stay, hand strength, autonomy, and depression). CONCLUSIONS: This study compared, for the first time, levels of ageism in older people living in two different social environments. Because most of the significant variables are modifiable in both environments, our results provide a framework to combat this type of discrimination. Promising strategies include anti-ageism policies and laws, educational interventions, and increased intergenerational contact.

Impact of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients: a nationwide study in Spain.

OBJECTIVE: To assess the effect of the first wave of the SARS-CoV-2 pandemic on the outcome of neurosurgical patients in Spain. SETTINGS: The initial flood of COVID-19 patients overwhelmed an unprepared healthcare system. Different measures were taken to deal with this overburden. The effect of these measures on neurosurgical patients, as well as the effect of COVID-19 itself, has not been thoroughly studied. PARTICIPANTS: This was a multicentre, nationwide, observational retrospective study of patients who underwent any neurosurgical operation from March to July 2020. INTERVENTIONS: An exploratory factorial analysis was performed to select the most relevant variables of the sample. PRIMARY AND SECONDARY OUTCOME MEASURES: Univariate and multivariate analyses were performed to identify independent predictors of mortality and postoperative SARS-CoV-2 infection. RESULTS: Sixteen hospitals registered 1677 operated patients. The overall mortality was 6.4%, and 2.9% (44 patients) suffered a perioperative SARS-CoV-2 infection. Of those infections, 24 were diagnosed postoperatively. Age (OR 1.05), perioperative SARS-CoV-2 infection (OR 4.7), community COVID-19 incidence (cases/105 people/week) (OR 1.006), postoperative neurological worsening (OR 5.9), postoperative need for airway support (OR 5.38), ASA grade ≥3 (OR 2.5) and preoperative GCS 3-8 (OR 2.82) were independently associated with mortality. For SARS-CoV-2 postoperative infection, screening swab test <72 hours preoperatively (OR 0.76), community COVID-19 incidence (cases/105 people/week) (OR 1.011), preoperative cognitive impairment (OR 2.784), postoperative sepsis (OR 3.807) and an absence of postoperative complications (OR 0.188) were independently associated. CONCLUSIONS: Perioperative SARS-CoV-2 infection in neurosurgical patients was associated with an increase in mortality by almost fivefold. Community COVID-19 incidence (cases/105 people/week) was a statistically independent predictor of mortality. TRIAL REGISTRATION NUMBER: CEIM 20/217.

Customer Loyalty in Recreational Long-Distance Races: Differences Between Novice and Experienced Runners.

A growing number of recreational races are being held in different locations, drawing many local and visiting runners. This study examined the relationships between quality, value, satisfaction, and loyalty among runners in a recreational race and examines potential differences in relationships between these constructs based on the runners' experience. The participants were 985 runners with a mean age of 40.74±9.41years. Validated, reliable ad hoc instruments were used. A multi-group analysis was performed to ascertain the existence of relationships between the constructs and differences in the relationships between the different study groups. The results show that quality is a direct antecedent of value and satisfaction. Value is directly related to satisfaction and indirectly related to loyalty. Satisfaction is related to the loyalty of participants in the race. Differences in the relationship between satisfaction and loyalty were dependent upon the runners' experience. Loyalty to a race depends primarily on satisfaction and is modified by the runners' experience.

Influence of the COVID-19 Pandemic on the Lifestyles of Health Sciences University Students in Spain: A Longitudinal Study.

The COVID-19 pandemic has significantly impacted daily activities worldwide. University students may have experienced substantial changes in daily living as a result of restrictions on university attendance. The return to normalcy may take a long time, and understanding the influence that shifts in daily routines have had on the lifestyles of university students may inform approaches to support overall well-being. We analyzed changes in the lifestyles of students enrolled at a health sciences university during the COVID-19 pandemic. This longitudinal study took place at the Faculty of Medicine and Nursing in the University of the Basque Country in Spain, and the final sample consisted of 113 nursing students, 109 medical students, and 45 physiotherapy students. Our results demonstrate changes in lifestyles of university students during the pandemic. MedDiet adherence scores and the percentage of students with high adherence increased during the pandemic. This increase was due to the increased consumption of vegetables and nuts. In terms of physical activity, the practice of moderate and intense physical activity was maintained. These results provide important information for both public health authorities and educational institutions to guide strategies to maintain the well-being of students and enhance opportunities for young adults to lead a healthy lifestyle.

Investigating a Selection of Methods for the Prediction of Total Soluble Solids Among Wine Grape Quality Characteristics Using Normalized Difference Vegetation Index Data From Proximal and Remote Sensing.

The most common method for determining wine grape quality characteristics is to perform sample-based laboratory analysis, which can be time-consuming and expensive. In this article, we investigate an alternative approach to predict wine grape quality characteristics by combining machine learning techniques and normalized difference vegetation index (NDVI) data collected at different growth stages with non-destructive methods, such as proximal and remote sensing, that are currently used in precision viticulture (PV). The study involved several sets of high-resolution multispectral data derived from four sources, including two vehicle-mounted crop reflectance sensors, unmanned aerial vehicle (UAV)-acquired data, and Sentinel-2 (S2) archived imagery to estimate grapevine canopy properties at different growth stages. Several data pre-processing techniques were employed, including data quality assessment, data interpolation onto a 100-cell grid (10 × 20 m), and data normalization. By calculating Pearson's correlation matrix between all variables, initial descriptive statistical analysis was carried out to investigate the relationships between NDVI data from all proximal and remote sensors and the grape quality characteristics in all growth stages. The transformed dataset was then ready and applied to statistical and machine learning algorithms, firstly trained on the data distribution available and then validated and tested, using linear and nonlinear regression models, including ordinary least square (OLS), Theil-Sen, and the Huber regression models and Ensemble Methods based on Decision Trees. Proximal sensors performed better in wine grapes quality parameters prediction in the early season, while remote sensors during later growth stages. The strongest correlations with the sugar content were observed for NDVI data collected with the UAV, Spectrosense+GPS (SS), and the CropCircle (CC), during Berries pea-sized and the Veraison stage, mid-late season with full canopy growth, for both years. UAV and SS data proved to be more accurate in predicting the sugars out of all wine grape quality characteristics, especially during a mid-late season with full canopy growth, in Berries pea-sized and the Veraison growth stages. The best-fitted regressions presented a maximum coefficient of determination (R 2) of 0.61.

Serum Amyloid A1/Toll-Like Receptor-4 Axis, an Important Link between Inflammation and Outcome of TBI Patients.

Traumatic brain injury (TBI) is one of the leading causes of mortality and disability worldwide without any validated biomarker or set of biomarkers to help the diagnosis and evaluation of the evolution/prognosis of TBI patients. To achieve this aim, a deeper knowledge of the biochemical and pathophysiological processes triggered after the trauma is essential. Here, we identified the serum amyloid A1 protein-Toll-like receptor 4 (SAA1-TLR4) axis as an important link between inflammation and the outcome of TBI patients. Using serum and mRNA from white blood cells (WBC) of TBI patients, we found a positive correlation between serum SAA1 levels and injury severity, as well as with the 6-month outcome of TBI patients. SAA1 levels also correlate with the presence of TLR4 mRNA in WBC. In vitro, we found that SAA1 contributes to inflammation via TLR4 activation that releases inflammatory cytokines, which in turn increases SAA1 levels, establishing a positive proinflammatory loop. In vivo, post-TBI treatment with the TLR4-antagonist TAK242 reduces SAA1 levels, improves neurobehavioral outcome, and prevents blood-brain barrier disruption. Our data support further evaluation of (i) post-TBI treatment in the presence of TLR4 inhibition for limiting TBI-induced damage and (ii) SAA1-TLR4 as a biomarker of injury progression in TBI patients.