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Disorders of consciousness are neurological conditions characterized by impaired arousal and awareness of self and environment. Behavioural responses are absent or are present but fluctuate. Disorders of consciousness are commonly encountered as a consequence of both acute and chronic brain injuries, yet reliable epidemiological estimates would require inclusive, operational definitions of the concept, as well as wider knowledge dissemination among involved professionals. Whereas several manifestations have been described, including coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state, a comprehensive neurobiological definition for disorders of consciousness is still lacking. The scientific literature is primarily observational, and studies-specific aetiologies lead to disorders of consciousness. Despite advances in these disease-related forms, there remains uncertainty about whether disorders of consciousness are a disease-agnostic unitary entity with a common mechanism, prognosis or treatment response paradigm. Our knowledge of disorders of consciousness has also been hampered by heterogeneity of study designs, variables, and outcomes, leading to results that are not comparable for evidence synthesis. The different backgrounds of professionals caring for patients with disorders of consciousness and the different goals at different stages of care could partly explain this variability. The Prospective Studies working group of the Neurocritical Care Society Curing Coma Campaign was established to create a platform for observational studies and future clinical trials on disorders of consciousness and coma across the continuum of care. In this narrative review, the author panel presents limitations of prior observational clinical research and outlines practical considerations for future investigations. A narrative review format was selected to ensure that the full breadth of study design considerations could be addressed and to facilitate a future consensus-based statement (e.g. via a modified Delphi) and series of recommendations. The panel convened weekly online meetings from October 2021 to December 2022. Research considerations addressed the nosographic status of disorders of consciousness, case ascertainment and verification, selection of dependent variables, choice of covariates and measurement and analysis of outcomes and covariates, aiming to promote more homogeneous designs and practices in future observational studies. The goal of this review is to inform a broad community of professionals with different backgrounds and clinical interests to address the methodological challenges imposed by the transition of care from acute to chronic stages and to streamline data gathering for patients with disorders of consciousness. A coordinated effort will be a key to allow reliable observational data synthesis and epidemiological estimates and ultimately inform condition-modifying clinical trials.

Background: COVID-19 vaccines reduced mortality, hospitalizations and ICUs admissions. Conversely, the impact of vaccination on Long COVID-19 syndrome is still unclear. This study compared the prevalence of post-acute sequelae at short and long-term follow-up among hospitalized unvaccinated and vaccinated COVID-19 survivors through a multidisciplinary approach. Methods: After 2 months from discharge, unvaccinated and vaccinated COVID-19 survivors underwent a follow-up visit at a dedicated “post-COVID-19 Outpatient Clinic”. The follow-up visit included a cardiovascular evaluation, blood tests, chest computed tomography, 6-min walking test (6MWT), spirometry. A one-year telephone follow-up was performed to assess re-hospitalizations, death and long-lasting symptoms. An additional 1:1 case-control matching analysis adjusted for baseline characteristics was performed. Results: Between June 2020 and June 2022, a total of 458 unvaccinated and vaccinated patients (229 per group) underwent the follow-up visit. Vaccinated patients had lower rates of ICU admissions (1.7 % vs 9.6 %, p=

Seismic isolation is a design strategy adopted for the protection of both new and existing structures from the damaging effects of earthquakes. It is achieved through the use of elastomeric isolators, which, in case of fire, can suffer permanent damage, seriously compromising the bearing capacity of the isolation system. This study proposes a new methodology to assess the effect of high temperatures on the elastomeric isolators and the loss of efficiency of the isolation system after a fire event. Two possible interventions with seismic isolation for an existing reinforced concrete structure with multi-storey frames were considered to apply the methodology. The evaluation of the isolator thermal response for 30 and 60 min of exposure to the standard fire curve was used for quantifying the damage induced by the fire. This damage level was defined starting from the thermo-mechanical properties of the elastomeric layers damaged by fire, considering different critical temperature thresholds. The estimated degradation was used to evaluate the seismic performance of both isolators and structure in different design configurations, also in a multi-risk scenario that foresees the occurrence of a seismic event after a fire. It was found that the most critical case is the one in which the critical temperature is equal to 70 °C, since after 30 min of exposure to fire the isolator is not able to sustain the vertical loads due to the superstructure. Therefore, the design of a proper passive fire protection system was proposed, able to drastically reduce the temperature in the isolator, mitigating the fire damaging effect.

A avaliação permanente dos Programas de Pós-Graduação (PPG) constitui importante instrumento para melhoria no cenário de conhecimento científico e no desenvolvimento tecnológico no Brasil. O objetivo do artigo foi desenvolver um modelo para análise da produção científica dos PPG, utilizando recursos de redes de interação, com recorte para as ciências ambientais, visando detectar padrões e conexões entre eles. O recorte da pesquisa abrangeu todos os 112 PPG e os cursos da Área de Ciências Ambientais da Coordenação de Aperfeiçoamento de Pessoas de Nível Superior (CAPES), no período de 2013 a 2016. A metodologia foi dividida em quatro etapas: 1. coleta de dados e construção de um banco de dados (informações públicas obtidas em consulta à Plataforma Sucupira — CAPES); 5. mineração e processamento dos dados e a produção de uma rede geral para representar os termos e os temas de maior relevância comuns a todas as produções dos PPG (teses, dissertações, projetos de pesquisas, artigos, livros e capítulos de livros); 3. análise das redes semânticas; 4. geração de produtos. Os resultados da pesquisa retornaram como produtos mapas de proximidade geográfica e de agrupamentos, que permitiram analisar a produção dos PPG das Ciências Ambientais em relação aos seus temas centrais, de forma integrada. A metodologia empregada se mostrou bastante robusta e apropriada para contribuir na avaliação dos programas de pós-graduação brasileiros, bem como para identificar lacunas na pesquisa e áreas emergentes em escala nacional, culminando em uma proposta de modelo que, a partir das redes semânticas, analisem as produções científicas nas avaliações quadrienais.

Abstract BackgroundIndividuals from minoritized racial and ethnic backgrounds experience pernicious and pervasive health disparities that have emerged, in part, from clinician bias. ObjectiveWe used a natural language processing approach to examine whether linguistic markers in electronic health record (EHR) notes differ based on the race and ethnicity of the patient. To validate this methodological approach, we also assessed the extent to which clinicians perceive linguistic markers to be indicative of bias. MethodsIn this cross-sectional study, we extracted EHR notes for patients who were aged 18 years or older; had more than 5 years of diabetes diagnosis codes; and received care between 2006 and 2014 from family physicians, general internists, or endocrinologists practicing in an urban, academic network of clinics. The race and ethnicity of patients were defined as White non-HispanicBlack non-HispanicHispanic or Latino ResultsWe examined EHR notes (n=12,905) of Black non-Hispanic, White non-Hispanic, and Hispanic or Latino patients (n=1562), who were seen by 281 physicians. A total of 27 clinicians participated in the validation study. In terms of bias, participants rated negative adjectives as 8.63 (SD 2.06), fear and disgust words as 8.11 (SD 2.15), and positive adjectives as 7.93 (SD 2.46) on a scale of 1 to 10, with 10 being extremely indicative of bias. Notes for Black non-Hispanic patients contained significantly more negative adjectives (coefficient 0.07, SE 0.02) and significantly more fear and disgust words (coefficient 0.007, SE 0.002) than those for White non-Hispanic patients. The notes for Hispanic or Latino patients included significantly fewer positive adjectives (coefficient −0.02, SE 0.007), trust verbs (coefficient −0.009, SE 0.004), and joy words (coefficient −0.03, SE 0.01) than those for White non-Hispanic patients. ConclusionsThis approach may enable physicians and researchers to identify and mitigate bias in medical interactions, with the goal of reducing health disparities stemming from bias.

Introduction Peripartum mental disorders (PPMD) are characterized by heterogeneous psychopathological symptoms related to specific personality traits, which are only taken into account by a few preventive and therapeutic strategies. Traumatic experiences during childhood could predispose to develop those disorders during adulthood, especially in more stressful conditions, such as pregnancy and postpartum. Objectives Our study aims to evaluate the correlation between mother’s childhood trauma and the development of certain psychopathological dimensions during peripartum and which of these dimensions could be indicative of mother’s childhood trauma. Methods The sample included 74 women, recruited from Sant’Andrea Hospital in Rome between 2011 and 2022, diagnosed with a psychiatric disorder during peripartum, according to criteria of DSM-5. All recruited women were administered the Childhood Trauma Questionnaire – Short Form (CTQ-SF) and the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). We performed a linear regression using the total CTQ score as a dependent variable and the MMPI-2 scale’s scores as independent variables. Results The linear regression used showed two significant models, of which the most inclusive explained 60% of the variance (R2 = 0.597), resulting significant (F = 31.141; p < 0.001). This model showed that a greater expression of childhood traumatic aspects was associated with greater expression of Pa (paranoia) (t = 4.04; p < 0.001) and Ma (hypomania) (t = 3.873; p < 0.001) in the clinical scales of the MMPI-2, which were indicative of childhood trauma. Conclusions Our study shows that paranoiac and hypomanic symptoms in PPMD, assumed by the MMPI-2 scale, are indicative of previous traumatic dimension. Thus, in the presence of a positive history of trauma, clinicians should pay attention especially to these aspects, in order to optimally set both pharmacological and psychotherapeutic treatment. Disclosure of Interest None Declared

BackgroundNeuroblastoma (NB) is characterized by both adrenergic (ADRN) and undifferentiated mesenchymal (MES) subsets. The ganglioside sialic acid-containing glycosphingolipid (GD2) is widely overexpressed on tumors of neuroectodermal origin promoting malignant phenotypes. MES cells are greatly enriched in post-therapy and relapsing tumors and are characterized by decreased expression of GD2. This event may cause failure of GD2-based immunotherapy. NK cells represent a key innate cell subset able to efficiently kill tumors. However, the tumor microenvironment (TME) that includes tumor cells and tumor-associated (TA) cells could inhibit their effector function.MethodsWe studied eight NB primary cultures that, in comparison with commercial cell lines, more faithfully reflect the tumor cell characteristics. We studied four primary NB-MES cell cultures and two pairs of MES/ADRN (691 and 717) primary cultures, derived from the same patient. In particular, in the six human NB primary cultures, we assessed their phenotype, the expression of GD2, and the enzymes that control its expression, as well as their interactions with NK cells, using flow cytometry, RT-qPCR, and cytotoxicity assays.ResultsWe identified mature (CD105+/CD133−) and undifferentiated (CD133+/CD105−) NB subsets that express high levels of the MES transcripts WWTR1 and SIX4. In addition, undifferentiated MES cells display a strong resistance to NK-mediated killing. On the contrary, mature NB-MES cells display an intermediate resistance to NK-mediated killing and exhibit some immunomodulatory capacities on NK cells but do not inhibit their cytolytic activity. Notably, independent from their undifferentiated or mature phenotype, NB-MES cells express GD2 that can be further upregulated in undifferentiated NB-MES cells upon co-culture with NK cells, leading to the generation of mature mesenchymal GD2bright neuroblasts. Concerning 691 and 717, they show high levels of GD2 and resistance to NK cell-mediated killing that can be overcome by the administration of dinutuximab beta, the anti-GD2 monoclonal antibody applied in the clinic.ConclusionsNB is a heterogeneous tumor representing a further hurdle in NB immunotherapy. However, different from what was reported with NB commercial cells and independent of their MES/ADRN phenotype, the expression of GD2 and its displayed sensitivity to anti-GD2 mAb ADCC indicated the possible effectiveness of anti-GD2 immunotherapy.

Abstract The development of probabilistic maps associated with lava flow inundation is essential to assess hazard in open vent volcanoes, especially those that have highly urbanized flanks. In this study we present the new lava flow hazard map linked to the summit eruptions of Mt. Etna, which has been developed using a probabilistic approach that integrates statistical analyses of the volcanological historical data with numerical simulations of lava flows. The statistical analysis of volcanological data (including vent location, duration and lava volumes) about all summit eruptions occurred since 1998 has allowed us both to estimate the spatiotemporal probability of future vent opening and to extract the effusion rate curves for lava flow modelling. Numerical simulations were run using the GPUFLOW model on a 2022 Digital Surface Model derived from optical satellite images. The probabilistic approach has been validated through a back-analysis by calculating the fit between the expected probabilities of inundation and the lava flows actually emplaced during the 2020-2022 period. The obtained map shows a very high probability of inundation of lava flows emitted at vents linked to the South East Crater, according to the observation of the eruptive dynamics in the last decades.

Abstract Hydrothermal systems can generate phreatic and/or phreatomagmatic explosions with little warning. Understanding the temporal and spatial evolution of geophysical and geochemical signals at hydrothermal systems is crucial for detecting precursory signs to unrest and to inform on hazard. Thermal signatures of such systems are poorly defined because data records are often too short or discrete compared to activity timescales, which can be decadal. La Fossa system of Vulcano has been monitored since the 1980s and entered a period of unrest in 2021. We assessed the thermal signature of La Fossa using ground- and satellite-based data with various temporal and spatial scales. While continuously-recording stations provided continuous but point-based measurements, fumarole field vent surveys and infrared images obtained from satellite-flown sensors (ASTER and VIIRS) allowed lower temporal resolution but synoptic records to be built. By integrating this multi-resolution data set, precursory signs of unrest could retrospectively be detected from February to June 2021. Intensity of all unrest metrics increased during summer 2021, with an onset over a few days in September 2021. By September, seismic, CO2, SO2 and other geochemical metrics also indicated unrest, leading Civil Protection to raise the alert level to yellow on October 1. Heat flux, having been 4 MW in May 2019, increasing to 90 MW by September, and peaking at 120 MW in March 2022. We convolved our thermal data sets with all other monitoring data to validate a Vulcano Fossa Unrest Index (VFUI), framework of which can be potentially applied to any hydrothermal system. The VFUI highlighted four stages of unrest, none of which were clear in any single data set: background, precursory, onset and unrest. Onset was characterized by sudden release of fluids, likely caused by failure of sealed zones that had become pressurized during the precursory phase that began possibly as early as February 2021. Unrest has been ongoing for more than 18 months, and may continue for several more years. Our understanding of this system behavior has been due to hindsight, but demonstrates how multiparametric surveys can track and forecast unrest. Graphical Abstract

Background—Pregnancy represents a nutritional challenge, since macro- and micronutrients intake can affect mother’ health and influence negative outcomes. The aim of this retrospective pilot study is to evidence whether the oral supplementation with high molecular weight hyaluronic acid (HMWHA), in association with alpha lipoic acid (ALA), magnesium, vitamin B6 and vitamin D, in pregnant women, could reduce adverse effects, such as PTB, pelvic pain, contraction and hospitalization. Methods—Data were collected from n = 200 women treated daily with oral supplements of 200 mg HMWHA, 100 mg ALA, 450 mg magnesium, 2.6 mg vitamin B6 and 50 mcg vitamin D (treatment group) and from n = 50 women taking with oral supplements of 400 mg magnesium (control group). In both groups, supplementation started from the 7th gestational week until delivery. Results—Oral treatment with HMWHA, in association with ALA, magnesium, vitamin B6 and vitamin D in pregnant women, significantly reduced adverse events, such as PTB (p < 0.01), pelvic pain and contractions (p < 0.0001), miscarriages (p < 0.05) and admission to ER/hospitalization (p < 0.0001) compared with the control group. Conclusions—Despite HMWHA having been poorly used as a food supplement in pregnant women, our results open a reassuring scenario regarding its oral administration during pregnancy.

Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.

In the last years, a growing trend in different educational contexts focused on Computational Thinking (CT) skills acquisition for both in-service teachers and students. But very low attention has been paid to pre-service teachers' education in regards to CT skills. To solve this issue, an empirical experimentation has been carried out with 141 Italian pre-service teachers, that attended at a programming course, with the following aims: (1) provide them the main coding concepts by using Scratch 2.0; (2) offer practical advice on how to design educational applications (apps) to be applied into school context; (3) assess their apps by applying an already existing methodology, useful to give them feedback on their programming expertise and CT skills. Empirical findings showed that most of the participants achieved a medium-high level of CT skills, combining both design and programming skills in their school internship. Moreover, they reported a sense of greater self-esteem in teaching practice and a great emotional response from kids.

In a world in continuous evolution and in which human needs grow exponentially according to the increasing world population, the advent of new technologies plays a fundamental role in all fields of industry, especially in agriculture. Optimizing times, automating machines, and guaranteeing product quality are key objectives in the field of Agriculture 4.0, which integrates various innovative technologies to meet the needs of producers and consumers while guaranteeing respect for the environment and the planet’s resources. In this context, our research aims to propose an integrated system using data coming from an innovative experimental atmospheric and forecasting simulator (capable of predicting some characteristic climate variables subsequently validated with local sensors), combined with indices deriving from Remote Sensing and UAV images (treated with the data fusion method), that can give fundamental information related to Agriculture 4.0 with particular reference to the subsequent phases of system automation. These data, in fact, can be collected in an open-source GIS capable of displaying areas that need irrigation and fertilization and, moreover, establishing the path of an automated drone for the monitoring of the crops and the route of a self-driving tractor for the irrigation of the areas of interest.

Abstract Background Although slack is an asset to resilient hospitals, it is usually explicitly discussed only in terms of the quantity and quality of beds and staff. This paper expands this view by addressing slack in four infrastructures of intensive care units (ICUs) (physical space, electricity supply, oxygen supply, and air treatment) during the COVID pandemic. Methods The study occurred in a leading private hospital in Brazil, aiming at the identification of slack in four units originally designed as ICUs and two units adapted as ICUs. Data collection was based on 12 interviews with healthcare professionals, documents, and comparison between infrastructures and regulatory requirements. Results Twenty-seven instantiations of slack were identified, with several indications that the adapted ICUs did not provide infrastructure conditions as good as the designed ones. Findings gave rise to five propositions addressing: relationships intra and inter infrastructures; the need for adapted ICUs that match as closely as possible the designed ICUs; the consideration of both clinical and engineering perspectives in design; and the need for the revision of some requirements of the Brazilian regulations. Conclusions Results are relevant to both the designers of the infrastructures and to the designers of clinical activities as these must take place in fit-for-purpose workspaces. Top management might also benefit as they are the ultimate responsible for decision-making on whether or not to invest in slack. The pandemic dramatically demonstrated the value of investing in slack resources, creating momentum for this discussion in health services.

ABSTRACTG-protein coupled receptor 120 (GPR 120) has been implicated in anti-inflammatory functions. However, how GPR120 regulates the neutrophil function remains unknown. This study investigated the role of GPR120 in the regulation of neutrophil function against enteric bacteria. 16S rRNA sequencing was used for measuring the gut microbiota of wild-type (WT) mice and Gpr120−/− mice. Citrobacter rodentium infection and dextran sulfate sodium (DSS)-induced colitis models were performed in WT and Gpr120−/− mice. Mouse peritoneal-derived primary neutrophils were used to determine the neutrophil functions. Gpr120−/− mice showed altered microbiota composition. Gpr120−/− mice exhibited less capacity to clear intestinal Citrobacter rodentium and more severe intestinal inflammation upon infection or DSS insults. Depletion of neutrophils decreased the intestinal clearance of Citrobacter rodentium. GPR120 agonist, CpdA, enhanced WT neutrophil production of reactive oxygen species (ROS) and extracellular traps (NETs), and GPR120-deficient neutrophils demonstrated a lower level of ROS and NETs. CpdA-treated neutrophils showed an enhanced capacity to inhibit the growth of Citrobacter rodentium, which was abrogated by the inhibition of either NETs or ROS. CpdA promoted neutrophil inhibition of the growth of commensal bacteria Escherichia coli O9:H4 and pathobiont Escherichia coli O83:H1 isolated from a Crohn’s disease patient. Mechanically, mTOR activation and glycolysis mediated GPR120 induction of ROS and NETs in neutrophils. Additionally, CpdA promoted the neutrophil production of IL-17 and IL-22, and treatment with a conditioned medium of GPR120-activated neutrophils increased intestinal epithelial cell barrier functions. Our study demonstrated the critical role of GPR120 in neutrophils in protection against enteric bacterial invasion.

As is well known, image segmentation is widely used in the fields of echocardiography and diagnostic and interventional radiology. The delineation of structural components of various organs from 2D images is a technique used in the medical field in order to identify intervention targets with increasing precision and accuracy. In recent decades, the automation of this task has been the subject of intensive research. In particular, to improve the segmentation of such images, investigations have focused on the use of neural networks, and in particular convolutional neural networks (CNNs). However, most existing CNN-based methods can produce unsatisfactory segmentation masks without precise object boundaries (Wang, Chen, Ji, Fan & Ye Li, 2022); this is mainly due to the shadows and high noise in these images. To address the problem of automated image segmentation, this work proposes a pipeline technique with two stages (applied primarily to the echocardiographic domain): the first consisting of a Self-normalising Neural Networks (SNNs) performs image denoising, while the second applies a Watershed segmentation algorithm on the cleaned image. The latter is a technique successfully applied in geomatics and land surveying. The proposed methodology may be of interest both in the medical field and in the field of Geomatics where segmentation and classification operations are required in different application areas.

Data from the Copernicus project are proving to be fundamental in so many areas of spatial governance, and in particular in the context of safeguarding the cultural and natural heritage. This is mainly due to their dissemination mode, which allows them to be used by a countless number of institutions and organizations. With regard to their use in order to create effective supervised-training AI (Artificial Intelligence) classifiers (useful for continuous land-use monitoring), their low resolution could be a problem in terms of accuracy or in any case during the training process. This process which could be very costly (large training sets and high-end hardware required). In this paper, a methodology is tested that creates an AI classifier from high-resolution training data, Remote Sensing Image Classification Benchmark (RSI-CB128) and using Self-Normalising Neural Networks (SNNs). The efficiency of the Neural Network is, however, measured with a test dataset constructed with Raster Sentinel-2. In other words, the aim is to assess the goodness of an AI classifier on Sentinel-2 images built from Rasters generated by high-resolution sensors. Therefore, there seem to be clear advantages over the classification methodologies in use today.

Abstract Introduction Altered levels of cerebrospinal fluid (CSF) glucose and lactate concentrations are associated with poor outcomes in acute brain injury patients. However, no data on changes in such metabolites consequently to therapeutic interventions are available. The aim of the study was to assess CSF glucose-to-lactate ratio (CGLR) changes related to therapies aimed at reducing intracranial pressure (ICP). Methods A multicentric prospective cohort study was conducted in 12 intensive care units (ICUs) from September 2017 to March 2022. Adult (> 18 years) patients admitted after an acute brain injury were included if an external ventricular drain (EVD) for intracranial pressure (ICP) monitoring was inserted within 24 h of admission. During the first 48–72 h from admission, CGLR was measured before and 2 h after any intervention aiming to reduce ICP (“intervention”). Patients with normal ICP were also sampled at the same time points and served as the “control” group. Results A total of 219 patients were included. In the intervention group (n = 115, 53%), ICP significantly decreased and CPP increased. After 2 h from the intervention, CGLR rose in both the intervention and control groups, although the magnitude was higher in the intervention than in the control group (20.2% vs 1.6%; p = 0.001). In a linear regression model adjusted for several confounders, therapies to manage ICP were independently associated with changes in CGLR. There was a weak inverse correlation between changes in ICP and CGRL in the intervention group. Conclusions In this study, CGLR significantly changed over time, regardless of the study group. However, these effects were more significant in those patients receiving interventions to reduce ICP.