Effects of repetitive pain and morphine exposure on neonatal brain development

Hintergrund: Schmerzen bei Frühgeborenen sind ein wichtiges und unzureichend erforschtes Feld. Die Effekte von Schmerzreizen auf das sich entwickelnde Gehirn sind nicht geklärt. Daher wurde ein neonatales Tiermodell entwickelt, um die Auswirkungen von Schmerzen auf unterschiedliche Gehirnareale zu untersuchen. Methoden: Neugeborene Wistar-Ratten wurden dem, bei adulten Tieren etablierten, „Formalin-Test“ (Injektion von 10%igem Formalin subkutan in die Pfoten), unterzogen. Die zwei Kontrollgruppen bekamen entweder 0,9%NaCl oder keine Injektion. Eine Serie von neugeborenen Tieren wurde an Tag 1 (P1), Tag 2 (P2) und Tag 3 (P3) injiziert und an P4 transkardial perfundiert. Eine zweite Versuchsreihe erhielt tägliche Injektionen an P1 bis P5 und wurde an Tag 6 der Aufarbeitung zugeführt. Außerdem gab es eine Versuchsreihe mit Tieren im Alter von 12 Tagen. In einer letzten Behandlungsstudie wurde zudem Morphin (500 μg/kg oder 5 mg/kg), 20 min vor Behandlungsstart subkutan gespritzt. Nach transkardialer Perfusion wurden die Gehirne histologisch mittels DeOlmos Kupfer Silberfärbung sowie der TUNEL-Färbung zur Detektion apoptotischer Zellen aufgearbeitet. Aus 12 verschieden Hirnarealen wurde ein Summenscore degenerierter Neurone ermittelt. Für molekularbiologische Untersuchungen (Western blotting für Protein-Kinase-C Isoformen e und g sowie Doublecortin) wurden die Tiere an P3 injiziert und nach 2, 6, 12 und 24 Stunden dekapitiert, Thalamus und Cortex herauspräpariert und sofort bei -80 °C bis zur weiteren Analyse tiefgefroren. Ergebnisse: In vier Tage alten Tieren waren in der Formalin-Gruppe signifikant mehr degenerierte Neurone als in den Kontrollgruppen (p < 0,001). Bei den Tieren, die über 5 Tage Injektionen erhielten, zeigten sich in der Formalin-Gruppe und in der mit Kochsalz behandelten Gruppe die gleiche Dichte an degenerierten Neuronen und signifikant weniger in der Gruppe ohne Behandlung (p < 0,001). Die zwölf Tage alten Tiere zeigten keine Differenzen bei den untergegangenen Zellen. In den molekularbiologischen Untersuchungen zeigte sich eine schmerzassoziierte Hochregulation des Doublecortins im Thalamus sowie ein unterschiedliches Verhalten der PKCIsoenzyme in Cortex und Thalamus. Die Vorbehandlung mit Morphin (500 μg/kg) führte zu einer Verminderung der Neurodegeneration, sodass die Signifikanzen in beiden Versuchsreihen aufgehoben wurden. Schlussfolgerung: Unsere Ergebnisse zeigen, dass Schmerzen im unreifen Gehirn zu einem Neuronenverlust führen können. Bemerkenswerterweise, wird auch bei chronisch applizierter Kochsalzlösung ein hoher Schaden am Gehirn verursacht, was auf die Verwundbarkeit des sich entwickelnden Gehirns unter Stress hindeutet. Eine Therapie mit Morphin scheint diesen Effekt zu verringern. Außerdem ist dieser Effekt abhängig vom Alter des Tieres was auf spezielle, schützende Reifungsprozesse schließen lässt, welche erst im Laufe der ersten Lebenstage ausgebildet werden. Auf molekularer Ebene wurden ebenfalls Veränderungen im Expressionsmuster der untersuchten PKC-Isoenzyme und DCX gefunden. Dies lässt die Vermutung zu, dass durch Schmerzen bedingt, wichtige zererbrale Strukturierungsprozesse gestört werden und es so zu, erst später feststellbaren, Folgeschäden kommt.Objective: Pain experience during neonatalintensive care may cause damage to the developing brain and is ongoing matterof debate. The question whether or not to treat neonatal pain withpharmacological agents is still an important point of discussion. This studyinvestigated the effects of neonatal pain, with and without opioid treatment, onthe developing brain using a rat model. Methods: Newborn rats wererandomly assigned to treatment: formalin injections in all 4 paws (group 1) andcompared to controls: injections with saline in all 4 paws (group 2) orreceiving no injections at all (group 3). Afterwards the rat brains werestudied histologically to detect apoptotic cell death, and involved molecularmechanisms were evaluated by measuring protein expression (Western blotting) ofprotein kinase c epsilon (PKCvarepsilon) and doublecortin (DCX). The same modelwas used to detect effects of subcutaneous morphine if used separately as wellas in combination with formalin induced pain. Results: In 4 days old ratpups we counted significantly more apoptotic neurons in formalin treatedanimals compared to the two control groups. In 6 days old rats we countedcomparable amounts of degenerated neurons for the formalin and NaCl group andsignificantly less for the no treatment group. In 12 days old pups there was nosignificant difference in apoptotic neurons in all three study groups. Morphineinjections prior to painful stimuli lead to a reduction in apoptotic celldeath. The protein expression of DCX showed a pain related upregulation in thethalamus region, whereas the expression of PKCvarepsilon was upregulated in thecortex. Conclusions: Our resultsdemonstrate that pain in neonatal rats causes severe apoptoticneurodegeneration in the developing brain during the first week of life.Remarkably, even NaCl 0.9% treated animals showed high apoptotic scores,indicating the extreme vulnerability of the neonatal nervous system forstressful events. The altered expression of DCX and PKCvarepsilon indicate thatpainful events also result to abnormal neonatal neuronal structure. Because even todayvulnerable newborns are exposed to frequent painful procedures withoutprocedural analgesia, these findings imply the necessity of furtherinvestigations in neonatal pain models

Measurements of Functional Network Connectivity Using Resting State Arterial Spin Labeling During Neurosurgery.

In neurosurgery, an exact delineation of functional areas is of great interest to spare important regions to ensure the best possible outcome for the patient (i.e., maximum removal while maintaining the highest possible quality of life). Preoperative imaging is routinely performed, including the visualization of not only structural but also functional information. During surgery, however, brain shift can occur, leading to an offset between the previously defined and the real position. Real-time imaging during the procedure is therefore desired to obtain this information while performing surgery. In this study 15 patients suffering from glioblastoma multiforme were included. These patients underwent structural and perfusion imaging using arterial spin labeling during the procedure. The latter has been used for gathering information about tumor residual perfusion. However, special postprocessing of this data allows for additional mapping of resting state networks and is intended to be used to gather deeper insights to aid the surgeon in planning the procedure. The data of each patient could be successfully postprocessed and used to map different resting state networks alongside the default mode network. On the basis of this study, it is feasible to use the information obtained from perfusion imaging to visualize not only vascular signal but also functional activation of resting state networks without acquiring any additional data besides the already available information. This may help guide the neurosurgeon in real time to adjust the surgical plan

Pre- and postoperative MRI of craniocervical-menigioma which is operated on by a suboccipital midline approach with left lateral extension.

<p>Axial and sagittal MRI showing distortion and compression of the brain stem. With increasing growth of these lesions a surgical ‘door’ is opened thus allowing to attack the tumor in a less invasive manner. In our experience far lateral approaches with resection of the condyle are rarely, if at all, necessary in meningiomas due to their slow progression accompanied by minor clinical symptoms. But positioning of the patient may be already of great danger as inclination of the head will most likely increase the bending force on the brain stem, especially in large tumors. Therefore intraoperative SSEP and MEP monitoring are mandatory in this surgical area and should run while the patient is brought into the prone position thus minimizing the overall morbidity of this procedure.</p

Initial pupil status is a strong predictor for in-hospital mortality after aneurysmal subarachnoid hemorrhage

Prognosis of patients with high-grade aneurysmal subarachnoid hemorrhage (aSAH) is only insufficiently displayed by current standard prognostic scores. This study aims to evaluate the role of pupil status for mortality prediction and provide improved prognostic models. Anonymized data of 477 aSAH patients admitted to our medical center from November 2010 to August 2018 were retrospectively analyzed. Identification of variables independently predicting in-hospital mortality was performed by multivariable logistic regression analysis. Final regression models included Hunt & Hess scale (H&H), pupil status and age or in a simplified variation only H&H and pupil status, leading to the design of novel H&H-Pupil-Age score (HHPA) and simplified H&H-Pupil score (sHHP), respectively. In an external validation cohort of 402 patients, areas under the receiver operating characteristic curves (AUROC) of HHPA (0.841) and sHHP (0.821) were significantly higher than areas of H&H (0.794; p<0.001) or World Federation of Neurosurgical Societies (WFNS) scale (0.775; p<0.01). Accordingly, including information about pupil status improves the predictive performance of prognostic scores for in-hospital mortality in patients with aSAH. HHPA and sHHP allow simple, early and detailed prognosis assessment while predictive performance remained strong in an external validation cohort suggesting adequate generalizability and low interrater variability

Inter- and Intrarater Agreement of Spot Sign and Noncontrast CT Markers for Early Intracerebral Hemorrhage Expansion

Background: The aim of this study was to assess the inter- and intrarater reliability of noncontrast CT (NCCT) markers [Black Hole Sign (BH), Blend Sign (BS), Island Sign (IS), and Hypodensities (HD)] and Spot Sign (SS) on CTA in patients with spontaneous intracerebral hemorrhage (ICH). Methods: Patients with spontaneous ICH at three German tertiary stroke centers were retrospectively included. Each CT scan was rated for four NCCT markers and SS on CTA by two radiology residents. Raters were blind to all demographic and outcome data. Inter- and intrarater agreement was determined by Cohen's kappa (κ) coefficient and percentage of agreement. Results: Interrater agreement was excellent in 473 included patients, ranging from 96% to 99%. Interrater κ ranged from 0.85 (95% CI [0.78-0.91]) to 0.97 (95% CI [0.94-0.99]) for NCCT markers and 0.93 (95% CI [0.88-0.98]) for SS, all p-values < 0.001. Intrarrater agreement ranged from 96% to 100%, with κ ranging from 0.85 (95% CI [0.78-0.91]) to 1.00 (95% CI [0.10-0.85]) for NCCT markers and 0.96 (95% CI [0.92-1.00]) for SS, all p-values < 0.001. Conclusions: NCCT imaging findings and SS on CTA have good-to-excellent inter- and intrarater reliabilities, with the highest agreement for BH and SS

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Abstract Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model’s predictive quality when applied to future patients. ML models for the prediction of the Glasgow outcome scale, modified Rankin Scale (mRS), and new transient or permanent neurological deficits (output variables) were created from all UIA patients that underwent microsurgery at the Kepler University Hospital Linz (Austria) between 2002 and 2020 (n = 466), based on 18 patient- and 10 aneurysm-specific preoperative parameters (input variables). Train-test splitting was performed with a temporal split for outcome prediction in microsurgical therapy of UIA. Moreover, an external validation was conducted on an independent external data set (n = 256) of the Department of Neurosurgery, University Medical Centre Hamburg-Eppendorf. In total, 722 aneurysms were included in this study. A postoperative mRS > 2 was best predicted by a quadratic discriminant analysis (QDA) estimator in the internal test set, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.87 ± 0.03 and a sensitivity and specificity of 0.83 ± 0.08 and 0.71 ± 0.07, respectively. A Multilayer Perceptron predicted the post- to preoperative mRS difference > 1 with a ROC-AUC of 0.70 ± 0.02 and a sensitivity and specificity of 0.74 ± 0.07 and 0.50 ± 0.04, respectively. The QDA was the best model for predicting a permanent new neurological deficit with a ROC-AUC of 0.71 ± 0.04 and a sensitivity and specificity of 0.65 ± 0.24 and 0.60 ± 0.12, respectively. Furthermore, these models performed significantly better than the classic logistic regression models (p  2, a pre- and postoperative difference in mRS > 1 point and a GOS < 5. Therefore, generalizability of the models could not be demonstrated in the external validation. A SHapley Additive exPlanations (SHAP) analysis revealed that this is due to the most important features being distributed quite differently in the internal and external data sets. The implementation of newly available data and the merging of larger databases to form more broad-based predictive models is imperative in the future

Local Intracerebral Immunomodulation Using Interleukin-Expressing Mesenchymal Stem Cells in Glioblastoma

Purpose: Mesenchymal stem cells (MSCs) show an inherent brain tumor tropism that can be exploited for targeted delivery of therapeutic genes to invasive glioma. We assessed whether a motile MSC-based local immunomodulation is able to overcome the immunosuppressive glioblastoma microenvironment and to induce an antitumor immune response. Experimental Design: We genetically modified MSCs to coexpress high levels of IL12 and IL7 (MSCIL7/12, Apceth-301). Therapeutic efficacy was assessed in two immunocompetent orthotopic C57BL/6 glioma models using GL261 and CT2A. Immunomodulatory effects were assessed by multicolor flow cytometry to profile immune activation and exhaustion of tumor-infiltrating immune cells. Diversity of the tumor-specific immune response as analyzed using T-cell receptor sequencing. Results: Intratumoral administration of MSCIL7/12 induced significant tumor growth inhibition and remission of established intracranial tumors, as demonstrated by MR imaging. Notably, up to 50% of treated mice survived long-term. Rechallenging of survivors confirmed long-lasting tumor immunity. Local treatment with MSCIL7/12 was well tolerated and led to a significant inversion of the CD4(+)/CD8(+)n T-cell ratio with an intricate, predominantly CD8(+) effector T-cell-mediated antitumor response. T-cell receptor sequencing demonstrated an increased diversity of TILs in MSCIL7/12-treated mice, indicating a broader tumor-specific immune response with subsequent oligoclonal specification during generation of long-term immunity. Conclusions: Local MSC-based immunomodulation is able to efficiently alter the immunosuppressive microenvironment in glioblastoma. The long-lasting therapeutic effects warrant a rapid clinical translation of this concept and have led to planning of a phase I/II study of apceth-301 in recurrent glioblastoma

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value &amp;lt;0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value &amp;lt;0.05). Machine learning-based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC