BACKGROUND: In light of the burden of traumatic brain injury (TBI) in children and the excessive number of unnecessary CT scans still being performed, new strategies are needed to limit their use while minimising the risk of delayed diagnosis of intracranial lesions (ICLs). Identifying children at higher risk of poor outcomes would enable them to be better monitored. The use of the blood-based brain biomarkers glial fibrillar acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase-L1 (UCH-L1) could help clinicians in this decision. The overall aim of this study is to provide new knowledge regarding GFAP and UCH-L1 in order to improve TBI management in the paediatric population.
METHODS: We will conduct a European, prospective, multicentre study, the BRAINI-2 paediatric study, in 20 centres in France, Spain and Switzerland with an inclusion period of 30 months for a total of 2880 children and adolescents included. To assess the performance of GFAP and UCH-L1 used separately and in combination to predict ICLs on CT scans (primary objective), 630 children less than 18 years of age with mild TBI, defined by a Glasgow Coma Scale score of 13-15 and with a CT scan will be recruited. To evaluate the potential of GFAP and UCH-L1 in predicting the prognosis after TBI (secondary objective), a further 1720 children with mild TBI but no CT scan as well as 130 children with moderate or severe TBI will be recruited. Finally, to establish age-specific reference values for GFAP and UCH-L1 (secondary objective), we will include 400 children and adolescents with no history of TBI.
BACKGROUND: This study has received ethics approval in all participating countries. Results from our study will be disseminated in international peer-reviewed journals. All procedures were developed in order to assure data protection and confidentiality.
BACKGROUND: NCT05413499.

OBJECTIVE: Somatic variants in the ubiquitin-specific protease 8 (USP8) gene are the most common genetic cause of Cushing disease. We aimed to explore the relationship between clinical outcomes and USP8 status in a single centre.
METHODS: We investigated the USP8 status in 48 patients with pituitary corticotroph tumours. A median of 62 months of follow-up was conducted after surgery from November 2013 to January 2015. The clinical, biochemical and imaging features were collected and analysed.
RESULTS: Seven USP8 variants (p.Ser718Pro, p.Ser719del, p.Pro720Arg, p.Pro720Gln, p.Ser718del, p.Ser718Phe, p.Lys713Arg) were identified in 24 patients (50%). USP8 variants showed a female predominance (100% vs. 75% in wild type [WT], p = .022). Patients with p.Ser719del showed an older age at surgery compared to patients with the p.Pro720Arg variant (47- vs. 24-year-olds, p = .033). Patients with p.Pro720Arg showed a higher rate of macroadenoma compared to patients harbouring the p.Ser718Pro variant (60% vs. 0%, p = .037). No significant differences were observed in serum and urinary cortisol and adrenocorticotropin hormone (ACTH) levels. Immediate surgical remission (79% vs. 75%) and long-term hormone remission (79% vs. 67%) were not significantly different between the two groups. The recurrence rate was 21% (4/19) in patients harbouring USP8 variants and 13% (2/16) in WT patients. Recurrence-free survival presented a tendency to be shorter in USP8-mutated individuals (76.7 vs. 109.2 months, p = .068).
CONCLUSIONS: Somatic USP8 variants accounted for 50% of the genetic causes in this cohort with a significant female frequency. A long-term follow-up revealed a tendency toward shorter recurrence-free survival in USP8-mutant patients.

This study investigated mogrol\'s impact on non-small cell lung cancer (NSCLC) radiosensitivity and underlying mechanisms, using various methods including assays, bioinformatics, and xenograft models. CCK-8, clonogenic, flow cytometry, TUNEL, and Western blot assays evaluated mogrol and radiation effects on NSCLC viability and apoptosis. Ubiquitin-specific protease 22 (USP22) expression in NSCLC patient tissues was determined by RT-qPCR and Western blot. A xenograft model validated mogrol\'s effects on tumor growth. Bioinformatics identified four ubiquitin-specific proteases, including USP22, in NSCLC. Kaplan-Meier analysis confirmed USP22\'s value in lung cancer survival. Human Protein Atlas (HPA) database analysis indicated higher USP22 expression in lung cancer tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis implicated ERK1/2 in NSCLC progression, and molecular docking showed stability between mogrol and ERK1/2. Further in vivo and in vitro experiments have demonstrated that mogrol enhances the inhibitory effect of radiation on NSCLC cell viability and clonogenic capacity. Cell viability and clonogenic capacity are reduced by >50%, and an increase in cellular apoptosis is observed, with apoptotic levels reaching 10%. USP22 expression was significantly elevated in NSCLC tissues, particularly in radiotherapy-resistant patients. Mogrol downregulated USP22 expression by inhibiting the ERK/CREB pathway, lowering COX2 expression. Mogrol also enhanced radiation\'s inhibition of tumor growth in mice. Mogrol enhances NSCLC radiosensitivity by downregulating USP22 via the ERK/CREB pathway, leading to reduced COX2 expression.NEW & NOTEWORTHY Mogrol enhances non-small cell lung cancer (NSCLC) cell sensitivity to radiotherapy by downregulating USP22 through the ERK/CREB pathway, reducing COX2 expression. These findings highlight mogrol\'s potential as an adjunct to improve NSCLC radiotherapy and open avenues for further research and clinical applications.

OBJECTIVE: Insomnia affects about one-third of patients with traumatic brain injury and is associated with worsened outcomes after injury. We hypothesized that higher levels of plasma neuroinflammation biomarkers at the time of TBI would be associated with worse 12-month insomnia trajectories.
METHODS: Participants were prospectively enrolled from 18 level-1 trauma centers participating in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury study from February 26, 2014, to August 8, 2018. Plasma glial fibrillary acidic protein (GFAP), high-sensitivity C-reactive protein (hsCRP), S100b, neuron-specific enolase (NSE), and ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1) were collected on days 1 (D1) and 14 (D14) after TBI. The insomnia severity index was collected at 2 weeks, 3, 6, and 12 months postinjury. Participants were classified into insomnia trajectory classes based on a latent class model. We assessed the association of biomarkers with insomnia trajectories, controlling for medical and psychological comorbidities and demographics.
RESULTS: Two thousand twenty-two individuals with TBI were studied. Elevations in D1 hsCRP were associated with persistent insomnia (severe, odds ratio [OR] = 1.33 [1.11, 1.59], p = 0.002; mild, OR = 1.10 [1.02, 1.19], p = 0.011). Similarly, D14 hsCRP elevations were associated with persistent insomnia (severe, OR = 1.27 [1.02, 1.59], p = 0.03). Of interest, D1 GFAP was lower in persistent severe insomnia (median [Q1, Q3]: 154 [19, 445] pg/mL) compared with resolving mild (491 [154, 1,423], p < 0.001) and persistent mild (344 [79, 1,287], p < 0.001). D14 GFAP was similarly lower in persistent (11.8 [6.4, 19.4], p = 0.001) and resolving (13.9 [10.3, 20.7], p = 0.011) severe insomnia compared with resolving mild (20.6 [12.4, 39.6]. Accordingly, increases in D1 GFAP were associated with reduced likelihood of having persistent severe (OR = 0.76 [95% CI 0.63-0.92], p = 0.004) and persistent mild (OR = 0.88 [0.81, 0.96], p = 0.003) compared with mild resolving insomnia. No differences were found with other biomarkers.
CONCLUSIONS: Elevated plasma hsCRP and, surprisingly, lower GFAP were associated with adverse insomnia trajectories after TBI. Results support future prospective studies to examine their utility in guiding insomnia care after TBI. Further work is needed to explore potential mechanistic connections between GFAP levels and the adverse insomnia trajectories.

Genetic alterations including fusions in fibroblast growth factor receptor 2 (FGFR2) are detected in 10-20% of intrahepatic cholangiocarcinoma (iCCA), and FGFR2 inhibitors are effective for the treatment of iCCA. We examined a prevalence of FGFR2 genetic alterations and their clinicopathological significance in combined hepatocellular-cholangiocarcinoma (cHCC-CCA). FGFR2 expression, which is a surrogate marker for FGFR2 genetic alterations, was immunohistochemically assessed in the liver sections from 75 patients with cHCC-CCA, 35 with small duct-type iCCA, 30 with large duct-type iCCA, and 35 with hepatocellular carcinoma (HCC). FGFR2 genetic alterations were detected by reverse transcription-PCR and direct sequence. An association of FGFR2 expression with clinicopathological features was investigated in cHCC-CCAs. FGFR2 expression was detected in significantly more patients with cHCC-CCA (21.3%) and small duct-type iCCA (25.7%), compared to those with large duct-type iCCA (3.3%) and HCC (0%) (p < 0.05). FGFR2-positive cHCC-CCAs were significantly smaller size (p < 0.05), with more predominant cholangiolocarcinoma component (p < 0.01) and less nestin expression (p < 0.05). Genetic alterations of ARID1A and BAP1 and multiple genes were significantly more frequent in FGFR2-positive cHCC-CCAs (p < 0.05). 5\'/3\' imbalance in FGFR2 genes indicating exon18-truncated FGFR2 was significantly more frequently detected in FGFR2-positive cHCC-CCAs and small duct iCCAs, compared to FGFR2-negative ones (p < 0.05). FGFR2::BICC fusion was detected in a case of cHCC-CCAs. FGFR2 genetic alterations may be prevalent in cHCC-CCAs as well as small duct-type iCCAs, which suggest cHCC-CCAs may also be a possible therapeutic target of FGFR2 inhibitors.

BACKGROUND: Diagnostic accuracy of glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1) in identification of intracranial abnormalities detected by computed tomography (CT) in mild traumatic brain injury (mTBI), and in patients with mild neurological symptoms not caused by head trauma but suspected with a neurological disorder, was examined.
METHODS: GFAP and UCH-L1 were determined using the chemiluminescence immunoassays on the Alinity i analyzer (Abbott Laboratories).
RESULTS: Significantly higher GFAP (median 53.8 vs 25.7 ng/L, P < .001) and UCH-L1 (median 350.9 vs 153.9 ng/L, P < .001) were found in mTBI compared to non-head trauma patients. In mTBI diagnostic sensitivity (Se) and specificity (Sp) for the combination of GFAP and UCH-L1 were 100% and 30.9%, respectively, with area under the curve (AUC) 0.655. GFAP alone yielded Se 85.7%, Sp 41.8%, and AUC 0.638, while UCH-L1 yielded Se 57.1%, Sp 56.4%, and AUC 0.568. In non-head trauma patients, the combination of GFAP and UCH-L1 showed Se 100%, Sp 87.9%, and AUC 0.939, while GFAP alone demonstrated Se 100%, Sp 90.9%, and AUC 0.955.
CONCLUSIONS: If these results are reproduced on a larger sample, GFAP and UCH-L1 may reduce CT use in patients with mild neurological symptoms after systemic causes exclusion and neurologist\'s evaluation.

Blood levels of glial fibrillary acidic protein (GFAP) and ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1) within 12h of suspected traumatic brain injury (TBI) have been approved by the Food and Drug administration to aid in determining the need for a brain computed tomography (CT) scan. The current study aimed to determine whether this context of use can be expanded beyond 12h post-TBI in patients presenting with Glasgow Coma Scale (GCS) 13-15. The prospective, 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled TBI participants aged ≥17 years who presented to a United States Level 1 trauma center and received a clinically indicated brain CT scan within 24h post-injury, a blood draw within 24h and at 14 days for biomarker analysis. Data from participants with emergency department arrival GCS 13-15 and biomarker values at days 1 and 14 were extracted for the primary analysis. A subgroup of hospitalized participants with serial biomarkers at days 1, 3, 5, and 14 were analyzed, including plasma GFAP and UCH-L1, and serum neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B). The primary analysis compared biomarker values dichotomized by head CT results (CT+/CT-). Area under receiver-operating characteristic curve (AUC) was used to determine diagnostic accuracy. The overall cohort included 1142 participants with initial GCS 13-15, with mean age 39.8 years, 65% male, and 73% Caucasian. The GFAP provided good discrimination in the overall cohort at days 1 (AUC = 0.82) and 14 (AUC = 0.72), and in the hospitalized subgroup at days 1 (AUC = 0.84), 3 (AUC = 0.88), 5 (AUC = 0.82), and 14 (AUC = 0.74). The UCH-L1, NSE, and S100B did not perform well (AUC = 0.51-0.57 across time points). This study demonstrates the utility of GFAP to aid in decision-making for diagnostic brain CT imaging beyond the 12h time frame in patients with TBI who have a GCS 13-15.

OBJECTIVE: Data in literature indicate that in patients suffering a minor head injury (MHI), biomarkers serum levels could be effective to predict the absence of intracranial injury (ICI) on head CT scan. Use of these biomarkers in case of patients taking oral anticoagulants who experience MHI is very limited. We investigated biomarkers as predictors of ICI in anticoagulated patients managed in an ED.
METHODS: We conducted a single-cohort, prospective, observational study in an ED. Our structured clinical pathway included a first head CT scan, 24 h observation and a second CT scan. The outcome was delayed ICI (dICI), defined as ICI on the second CT scan after a first negative CT scan. We assessed the sensitivity (SE), specificity (SP), negative predictive value (NNV) and positive predictive value (PPV) of the biomarkers S100B, NSE, GFAP, UCH-L1 and Alinity TBI in order to identify dICI.
RESULTS: Our study population was of 234 patients with a negative first CT scan who underwent a second CT scan. The rate of dICI was 4.7 %. The NPV for the detection of dICI were respectively (IC 95 %): S100B 92.7 % (86.0-96.8 %,); ubiquitin C-terminal hydrolase-L1 (UCH-L1) 91.8 % (83.8-96.6 %); glial fibrillary protein (GFP) 100 % (83.2-100 %); TBI 100 % (66.4-100 %). The AUC for the detection of dICI was 0.407 for S100B, 0.563 for neuron-specific enolase (NSE), 0.510 for UCH-L1 and 0.720 for glial fibrillary acidic protein (GFAP), respectively.
CONCLUSIONS: The NPV of the analyzed biomarkers were high and they potentially could limit the number of head CT scan for detecting dICI in anticoagulated patients suffering MHI. GFAP and Alinity TBI seem to be effective to rule out a dCI, but future trials are needed.

BACKGROUND: Patients with traumatic brain injury are a heterogeneous population, and the most severely injured individuals are often treated in an intensive care unit (ICU). The primary injury at impact, and the harmful secondary events that can occur during the first week of the ICU stay, will affect outcome in this vulnerable group of patients. We aimed to identify clinical variables that might distinguish disease trajectories among patients with traumatic brain injury admitted to the ICU.
METHODS: We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. We included patients aged 18 years or older with traumatic brain injury who were admitted to the ICU at one of the 65 CENTER-TBI participating centres, which range from large academic hospitals to small rural hospitals. For every patient, we obtained pre-injury data and injury features, clinical characteristics on admission, demographics, physiological parameters, laboratory features, brain biomarkers (ubiquitin carboxy-terminal hydrolase L1 [UCH-L1], S100 calcium-binding protein B [S100B], tau, neurofilament light [NFL], glial fibrillary acidic protein [GFAP], and neuron-specific enolase [NSE]), and information about intracranial pressure lowering treatments during the first 7 days of ICU stay. To identify clinical variables that might distinguish disease trajectories, we applied a novel clustering method to these data, which was based on a mixture of probabilistic graph models with a Markov chain extension. The relation of clusters to the extended Glasgow Outcome Scale (GOS-E) was investigated.
RESULTS: Between Dec 19, 2014, and Dec 17, 2017, 4509 patients with traumatic brain injury were recruited into the CENTER-TBI core dataset, of whom 1728 were eligible for this analysis. Glucose variation (defined as the difference between daily maximum and minimum glucose concentrations) and brain biomarkers (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were consistently found to be the main clinical descriptors of disease trajectories (ie, the leading variables contributing to the distinguishing clusters) in patients with traumatic brain injury in the ICU. The disease trajectory cluster to which a patient was assigned in a model was analysed as a predictor together with variables from the IMPACT model, and prediction of both mortality and unfavourable outcome (dichotomised GOS-E ≤4) was improved.
CONCLUSIONS: First-day ICU admission data are not the only clinical descriptors of disease trajectories in patients with traumatic brain injury. By analysing temporal variables in our study, variation of glucose was identified as the most important clinical descriptor that might distinguish disease trajectories in the ICU, which should direct further research. Biomarkers of brain injury (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were also top clinical descriptors over time, suggesting they might be important in future clinical practice.
BACKGROUND: European Union 7th Framework program, Hannelore Kohl Stiftung, OneMind, Integra LifeSciences Corporation, and NeuroTrauma Sciences.

OBJECTIVE: We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients.
METHODS: In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables.
RESULTS: Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3\'s hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis.
CONCLUSIONS: Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.