Genetic Identification of Vagal Sensory Neurons That Control Feeding.

Energy homeostasis requires precise measurement of the quantity and quality of ingested food. The vagus nerve innervates the gut and can detect diverse interoceptive cues, but the identity of the key sensory neurons and corresponding signals that regulate food intake remains unknown. Here, we use an approach for target-specific, single-cell RNA sequencing to generate a map of the vagal cell types that innervate the gastrointestinal tract. We show that unique molecular markers identify vagal neurons with distinct innervation patterns, sensory endings, and function. Surprisingly, we find that food intake is most sensitive to stimulation of mechanoreceptors in the intestine, whereas nutrient-activated mucosal afferents have no effect. Peripheral manipulations combined with central recordings reveal that intestinal mechanoreceptors, but not other cell types, potently and durably inhibit hunger-promoting AgRP neurons in the hypothalamus. These findings identify a key role for intestinal mechanoreceptors in the regulation of feeding.

Pleiotropic consequences of metabolic stress for the major histocompatibility complex class II molecule antigen processing and presentation machinery.

Hyperglycemia and hyperlipidemia are often observed in individuals with type II diabetes (T2D) and related mouse models. One dysmetabolic biochemical consequence is the non-enzymatic reaction between sugars, lipids, and proteins, favoring protein glycation, glycoxidation, and lipoxidation. Here, we identified oxidative alterations in key components of the major histocompatibility complex (MHC) class II molecule antigen processing and presentation machinery in vivo under conditions of hyperglycemia-induced metabolic stress. These modifications were linked to epitope-specific changes in endosomal processing efficiency, MHC class II-peptide binding, and DM editing activity. Moreover, we observed some quantitative and qualitative changes in the MHC class II immunopeptidome of Ob/Ob mice on a high-fat diet compared with controls, including changes in the presentation of an apolipoprotein B100 peptide associated previously with T2D and metabolic syndrome-related clinical complications. These findings highlight a link between glycation reactions and altered MHC class II antigen presentation that may contribute to T2D complications.

Disparities in Emergency Medical Services Care Delivery in the United States: A Scoping Review.

BACKGROUND: Emergency medical services (EMS) often serve as the first medical contact for ill or injured patients, representing a critical access point to the health care delivery continuum. While a growing body of literature suggests inequities in care within hospitals and emergency departments, limited research has comprehensively explored disparities related to patient demographic characteristics in prehospital care. OBJECTIVE: We aimed to summarize the existing literature on disparities in prehospital care delivery for patients identifying as members of an underrepresented race, ethnicity, sex, gender, or sexual orientation group. METHODS: We conducted a scoping review of peer-reviewed and non-peer-reviewed (gray) literature. We searched PubMed, CINAHL, Web of Science, Proquest Dissertations, Scopus, Google, and professional websites for studies set in the U.S. between 1960 and 2021. Each abstract and full-text article was screened by two reviewers. Studies written in English that addressed the underrepresented groups of interest and investigated EMS-related encounters were included. Studies were excluded if a disparity was noted incidentally but was not a stated objective or discussed. Data extraction was conducted using a standardized electronic form. Results were summarized qualitatively using an inductive approach. RESULTS: One hundred forty-five full-text articles from the peer-reviewed literature and two articles from the gray literature met inclusion criteria: 25 studies investigated sex/gender, 61 studies investigated race/ethnicity, and 58 studies investigated both. One study investigated sexual orientation. The most common health conditions evaluated were out-of-hospital cardiac arrest (n = 50), acute coronary syndrome (n = 36), and stroke (n = 31). The phases of EMS care investigated included access (n = 55), pre-arrival care (n = 46), diagnosis/treatment (n = 42), and response/transport (n = 40), with several studies covering multiple phases. Disparities were identified related to all phases of EMS care for underrepresented groups, including symptom recognition, pain management, and stroke identification. The gray literature identified public perceptions of EMS clinicians' cultural competency and the ability to appropriately care for transgender patients in the prehospital setting. CONCLUSIONS: Existing research highlights health disparities in EMS care delivery throughout multiple health outcomes and phases of EMS care. Future research is needed to identify structured mechanisms to eliminate disparities, address clinician bias, and provide high-quality equitable care for all patient populations.

Structure of an allosteric modulator bound to the CB1 cannabinoid receptor.

The CB1 receptor mediates the central nervous system response to cannabinoids, and is a drug target for pain, anxiety and seizures. CB1 also responds to allosteric modulators, which influence cannabinoid binding and efficacy. To understand the mechanism of these compounds, we solved the crystal structure of CB1 with the negative allosteric modulator (NAM) ORG27569 and the agonist CP55940. The structure reveals that the NAM binds to an extrahelical site within the inner leaflet of the membrane, which overlaps with a conserved site of cholesterol interaction in many G protein-coupled receptors (GPCRs). The ternary structure with ORG27569 and CP55940 captures an intermediate state of the receptor, in which aromatic residues at the base of the agonist-binding pocket adopt an inactive conformation despite the large contraction of the orthosteric pocket. The structure illustrates a potential strategy for drug modulation of CB1 and other class A GPCRs.

3D whole-brain metabolite imaging to improve characterization of low-to-intermediate grade gliomas.

PURPOSE: MRI is the standard imaging modality used for diagnosis, treatment planning, and post-treatment management of gliomas. Contrast-enhanced T1-weighted (CE-T1w) MRI is used to plan biopsy and radiation for grade IV gliomas but is less effective for grade II and III gliomas (i.e., low-to-intermediate grade gliomas) which may have minimal or no enhancement. Magnetic resonance spectroscopic imaging (MRSI) is an advanced MRI technique that has been shown, to improve diagnostic yield of biopsy and target delineation for grade IV glioma. The purpose of this study is to determine if MRSI can improve characterization and tissue sampling of low-to-intermediate grade gliomas. METHODS: Prospective grade II and grade III glioma patients were enrolled to undergo whole brain high-resolution MRSI prior to tissue sampling. Choline/N-acetyl-aspartate (Cho/NAA) maps were overlaid on anatomic imaging and imported into stereotactic biopsy software. Patients were treated with standard-of-care surgery and radiation. Volumes of spectroscopically abnormal tissue were generated and compared with anatomic imaging and areas of enhancing recurrence on follow-up imaging. RESULTS: Ten patients had pathologic diagnosis of grade II (n = 4) or grade III (n = 6) with a median follow-up of 27.3 months. Five patients had recurrence, and regions of recurrence were found to overlap with metabolically abnormal regions on MRSI at the time of diagnosis. CONCLUSION: MRSI in low-to-intermediate grade glioma patients is predictive of areas of subsequent recurrence. Larger studies are needed to determine if MRSI can be used to guide surgical and radiation treatment planning in these patients.

Exonic mutations associated with atopic dermatitis disrupt lympho-epithelial Kazal-type related inhibitor action and enhance its degradation.

BACKGROUND: Skin desquamation is facilitated by serine proteases KLK5 and KLK7, which are tightly regulated by lympho-epithelial Kazal-type related inhibitor (LEKTI). LEKTI itself is controlled through degraded by mesotrypsin. Here, we sought to determine whether LEKTI exonic mutations associated with atopic dermatitis (AD) affect the protease inhibitory activity of LEKTI or its susceptibility to mesotrypsin degradation. METHODS: The inhibitory activities of the LEKTI domain 4 (D4) and D6 WT and AD-associated mutants on the enzyme activities of KLK5 and KLK7 were compared using fluorogenic substrates. A keratinocyte cell culture system using HaCat cells was established to assess the role of D6 WT and D386N on triggering inflammation via the induction of thymic stromal lymphopoietin (TSLP). A degradation assay was used to assess the susceptibility of D4 and D6 mutants to mesotrypsin degradation. RESULTS: Enzymatic assays revealed that the D6 D386N mutation affected the inhibitory activity of LEKTI on KLK5 but not KLK7. Other exonic mutations on D6 (N368S, V395M, and E420K) and D4 (R267Q) did not alter LEKTI inhibition. The D386N mutation disrupted the role of D6 in suppressing TSLP induction by KLK5 in HaCat cells. Although WT D4 is more susceptible to mesotrypsin degradation than WT D6, the D4 R267Q mutant was more resistant to mesotrypsin degradation, whereas the D6 E420K mutant showed enhanced mesotrypsin-mediated degradation. CONCLUSION: Exonic mutations in D6, which previously have been associated with AD, may cause a disruption of inhibitory activity on KLK5 or enhance the degradation by mesotrypsin.

Improved strategy for isoleucine 1H/13C methyl labeling in Pichia pastoris.

Site specific methyl labeling combined with methyl TROSY offers a powerful NMR approach to study structure and dynamics of proteins and protein complexes of high molecular weight. Robust and cost-effective methods have been developed for site specific protein 1H/13C methyl labeling in an otherwise deuterated background in bacteria. However, bacterial systems are not suitable for expression and isotope labeling of many eukaryotic and membrane proteins. The yeast Pichia pastoris (P. pastoris) is a commonly used host for expression of eukaryotic proteins, and site-specific methyl labeling of perdeuterated eukaryotic proteins has recently been achieved with this system. However, the practical utility of methyl labeling and deuteration in P. pastoris is limited by high costs. Here, we describe an improved method for 1H/13C-labeling of the δ-methyl group of isoleucine residues in a perdeuterated background, which reduces the cost by ≥ 50% without compromising the efficiency of isotope enrichment. We have successfully implemented this method to label actin and a G-protein coupled receptor. Our approach will facilitate studies of the structure and dynamics of eukaryotic proteins by NMR spectroscopy.

The Long Haul to Surgery: Long COVID Has Minimal Burden on Surgical Departments.

Many patients infected with the SARS-CoV-2 virus (COVID-19) continue to experience symptoms for weeks to years as sequelae of the initial infection, referred to as "Long COVID". Although many studies have described the incidence and symptomatology of Long COVID, there are little data reporting the potential burden of Long COVID on surgical departments. A previously constructed database of survey respondents who tested positive for COVID-19 was queried, identifying patients reporting experiencing symptoms consistent with Long COVID. Additional chart review determined whether respondents had a surgical or non-routine invasive procedure on or following the date of survey completion. Outcomes from surgeries on patients reporting Long COVID symptoms were compared to those from asymptomatic patients. A total of 17.4% of respondents had surgery or a non-routine invasive procedure in the study period. A total of 48.8% of these patients reported experiencing symptoms consistent with Long COVID. No statistically significant differences in surgical outcomes were found between groups. The results of this analysis demonstrate that Long COVID does not appear to have created a significant burden of surgical disease processes on the healthcare system despite the wide range of chronic symptoms and increased healthcare utilization by this population. This knowledge can help guide surgical operational resource allocation as a result of the pandemic and its longer-term sequelae.

Mechanisms of Allosteric Inhibition of Insulin-Regulated Aminopeptidase.

Inhibition of Insulin-Regulated Aminopeptidase is being actively explored for the treatment of several human diseases and several classes of inhibitors have been developed although no clinical applications have been reported yet. Here, we combine enzymological analysis with x-ray crystallography to investigate the mechanism employed by two of the most studied inhibitors of IRAP, an aryl sulfonamide and a 2-amino-4H-benzopyran named HFI-419. Although both compounds have been hypothesized to target the enzyme's active site by competitive mechanisms, we discovered that they instead target previously unidentified proximal allosteric sites and utilize non-competitive inhibition mechanisms. X-ray crystallographic analysis demonstrated that the aryl sulfonamide stabilizes the closed, more active, conformation of the enzyme whereas HFI-419 locks the enzyme in a semi-open, and likely less active, conformation. HFI-419 potency is substrate-dependent and fails to effectively block the degradation of the physiological substrate cyclic peptide oxytocin. Our findings demonstrate alternative mechanisms for inhibiting IRAP through allosteric sites and conformational restricting and suggest that the pharmacology of HFI-419 may be more complicated than initially considered. Such conformation-specific interactions between IRAP and small molecules can be exploited for the design of more effective second-generation allosteric inhibitors.

The Utility of Spectroscopic MRI in Stereotactic Biopsy and Radiotherapy Guidance in Newly Diagnosed Glioblastoma.

Current diagnostic and therapeutic approaches for gliomas have limitations hindering survival outcomes. We propose spectroscopic magnetic resonance imaging as an adjunct to standard MRI to bridge these gaps. Spectroscopic MRI is a volumetric MRI technique capable of identifying tumor infiltration based on its elevated choline (Cho) and decreased N-acetylaspartate (NAA). We present the clinical translatability of spectroscopic imaging with a Cho/NAA ≥ 5x threshold for delineating a biopsy target in a patient diagnosed with non-enhancing glioma. Then, we describe the relationship between the undertreated tumor detected with metabolite imaging and overall survival (OS) from a pilot study of newly diagnosed GBM patients treated with belinostat and chemoradiation. Each cohort (control and belinostat) were split into subgroups using the median difference between pre-radiotherapy Cho/NAA ≥ 2x and the treated T1-weighted contrast-enhanced (T1w-CE) volume. We used the Kaplan-Meier estimator to calculate median OS for each subgroup. The median OS was 14.4 months when the difference between Cho/NAA ≥ 2x and T1w-CE volumes was higher than the median compared with 34.3 months when this difference was lower than the median. The T1w-CE volumes were similar in both subgroups. We find that patients who had lower volumes of undertreated tumors detected via spectroscopy had better survival outcomes.

A machine learning framework to adjust for learning effects in medical device safety evaluation.

OBJECTIVES: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects. MATERIALS AND METHODS: A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical scenarios involving high-risk medical devices. We designed this process to detect learning effects using a risk-adjusted cumulative sum method, quantify the excess adverse event rate attributable to operator inexperience, and adjust for these alongside patient factors in evaluating device safety signals. To maintain integrity, we employed blinding between data generation and analysis teams. Synthetic data used underlying distributions and patient feature correlations based on clinical data from the Department of Veterans Affairs between 2005 and 2012. We generated 2494 synthetic datasets with widely varying characteristics including number of patient features, operators and institutions, and the operator learning form. Each dataset contained a hypothetical study device, Device B, and a reference device, Device A. We evaluated accuracy in identifying learning effects and identifying and estimating the strength of the device safety signal. Our approach also evaluated different clinically relevant thresholds for safety signal detection. RESULTS: Our framework accurately identified the presence or absence of learning effects in 93.6% of datasets and correctly determined device safety signals in 93.4% of cases. The estimated device odds ratios' 95% confidence intervals were accurately aligned with the specified ratios in 94.7% of datasets. In contrast, a comparative model excluding operator learning effects significantly underperformed in detecting device signals and in accuracy. Notably, our framework achieved 100% specificity for clinically relevant safety signal thresholds, although sensitivity varied with the threshold applied. DISCUSSION: A machine learning framework, tailored for the complexities of post-market device evaluation, may provide superior performance compared to standard parametric techniques when operator learning is present. CONCLUSION: Demonstrating the capacity of ML to overcome complex evaluative challenges, our framework addresses the limitations of traditional statistical methods in current post-market surveillance processes. By offering a reliable means to detect and adjust for learning effects, it may significantly improve medical device safety evaluation.

Linoleoyl-lysophosphatidylcholine suppresses immune-related adverse events due to immune checkpoint blockade.

Immune related adverse events (irAEs) after immune checkpoint blockade (ICB) therapy occur in a significant proportion of cancer patients. To date, the circulating mediators of ICB-irAEs remain poorly understood. Using non-targeted mass spectrometry, here we identify the circulating bio-active lipid linoleoyl-lysophosphatidylcholine (LPC 18:2) as a modulator of ICB-irAEs. In three independent human studies of ICB treatment for solid tumor, loss of circulating LPC 18:2 preceded the development of severe irAEs across multiple organ systems. In both healthy humans and severe ICB-irAE patients, low LPC 18:2 was found to correlate with high blood neutrophilia. Reduced LPC 18:2 biosynthesis was confirmed in preclinical ICB-irAE models, and LPC 18:2 supplementation in vivo suppressed neutrophilia and tissue inflammation without impacting ICB anti-tumor response. Results indicate that circulating LPC 18:2 suppresses human ICB-irAEs, and LPC 18:2 supplementation may improve ICB outcomes by preventing severe inflammation while maintaining anti-tumor immunity.

Differentiation of Fungal, Viral, and Bacterial Sepsis using Multimodal Deep Learning.

Sepsis is a major cause of morbidity and mortality worldwide, and is caused by bacterial infection in a majority of cases. However, fungal sepsis often carries a higher mortality rate both due to its prevalence in immunocompromised patients as well as delayed recognition. Using chest x-rays, associated radiology reports, and structured patient data from the MIMIC-IV clinical dataset, the authors present a machine learning methodology to differentiate between bacterial, fungal, and viral sepsis. Model performance shows AUCs of 0.81, 0.83, 0.79 for detecting bacterial, fungal, and viral sepsis respectively, with best performance achieved using embeddings from image reports and structured clinical data. By improving early detection of an often missed causative septic agent, predictive models could facilitate earlier treatment of non-bacterial sepsis with resultant associated mortality reduction.

Spectroscopic MRI-Guided Proton Therapy in Non-Enhancing Pediatric High-Grade Glioma.

Radiation therapy (RT) is a critical part of definitive therapy for pediatric high-grade glioma (pHGG). RT is designed to treat residual tumor defined on conventional MRI (cMRI), though pHGG lesions may be ill-characterized on standard imaging. Spectroscopic MRI (sMRI) measures endogenous metabolite concentrations in the brain, and Choline (Cho)/N-acetylaspartate (NAA) ratio is a highly sensitive biomarker for metabolically active tumor. We provide a preliminary report of our study introducing a novel treatment approach of whole brain sMRI-guided proton therapy for pHGG. An observational cohort (c1 = 10 patients) receives standard of care RT; a therapeutic cohort (c2 = 15 patients) receives sMRI-guided proton RT. All patients undergo cMRI and sMRI, a high-resolution 3D whole-brain echo-planar spectroscopic imaging (EPSI) sequence (interpolated resolution of 12 µL) prior to RT and at several follow-up timepoints integrated into diagnostic scans. Treatment volumes are defined by cMRI for c1 and by cMRI and Cho/NAA ≥ 2x for c2. A longitudinal imaging database is used to quantify changes in lesion and metabolite volumes. Four subjects have been enrolled (c1 = 1/c2 = 3) with sMRI imaging follow-up of 4-18 months. Preliminary data suggest sMRI improves identification of pHGG infiltration based on abnormal metabolic activity, and using proton therapy to target sMRI-defined high-risk regions is safe and feasible.

Mutant Isocitrate Dehydrogenase 1 Expression Enhances Response of Gliomas to the Histone Deacetylase Inhibitor Belinostat.

Histone deacetylase inhibitors (HDACis) are drugs that target the epigenetic state of cells by modifying the compaction of chromatin through effects on histone acetylation. Gliomas often harbor a mutation of isocitrate dehydrogenase (IDH) 1 or 2 that leads to changes in their epigenetic state presenting a hypermethylator phenotype. We postulated that glioma cells with IDH mutation, due to the presence of epigenetic changes, will show increased sensitivity to HDACis. This hypothesis was tested by expressing mutant IDH1 with a point alteration-converting arginine 132 to histidine-within glioma cell lines that contain wild-type IDH1. Glioma cells engineered to express mutant IDH1 produced D-2-hydroxyglutarate as expected. When assessed for response to the pan-HDACi drug belinostat, mutant IDH1-expressing glioma cells were subjected to more potent inhibition of growth than the corresponding control cells. Increased sensitivity to belinostat correlated with the increased induction of apoptosis. Finally, a phase I trial assessing the addition of belinostat to standard-of-care therapy for newly diagnosed glioblastoma patients included one patient with a mutant IDH1 tumor. This mutant IDH1 tumor appeared to display greater sensitivity to the addition of belinostat than the other cases with wild-type IDH tumors based on both standard magnetic resonance imaging (MRI) and advanced spectroscopic MRI criteria. These data together suggest that IDH mutation status within gliomas may serve as a biomarker of response to HDACis.

A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking.

Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.

Spectroscopic MRI-Based Biomarkers Predict Survival for Newly Diagnosed Glioblastoma in a Clinical Trial.

Despite aggressive treatment, glioblastoma has a poor prognosis due to its infiltrative nature. Spectroscopic MRI-measured brain metabolites, particularly the choline to N-acetylaspartate ratio (Cho/NAA), better characterizes the extent of tumor infiltration. In a previous pilot trial (NCT03137888), brain regions with Cho/NAA ≥ 2x normal were treated with high-dose radiation for newly diagnosed glioblastoma patients. This report is a secondary analysis of that trial where spectroscopic MRI-based biomarkers are evaluated for how they correlate with progression-free and overall survival (PFS/OS). Subgroups were created within the cohort based on pre-radiation treatment (pre-RT) median cutoff volumes of residual enhancement (2.1 cc) and metabolically abnormal volumes used for treatment (19.2 cc). We generated Kaplan-Meier PFS/OS curves and compared these curves via the log-rank test between subgroups. For the subgroups stratified by metabolic abnormality, statistically significant differences were observed for PFS (p = 0.019) and OS (p = 0.020). Stratification by residual enhancement did not lead to observable differences in the OS (p = 0.373) or PFS (p = 0.286) curves. This retrospective analysis shows that patients with lower post-surgical Cho/NAA volumes had significantly superior survival outcomes, while residual enhancement, which guides high-dose radiation in standard treatment, had little significance in PFS/OS. This suggests that the infiltrating, non-enhancing component of glioblastoma is an important factor in patient outcomes and should be treated accordingly.

Applying a Radiation Therapy Volume Analysis Pipeline to Determine the Utility of Spectroscopic MRI-Guided Adaptive Radiation Therapy for Glioblastoma.

Accurate radiation therapy (RT) targeting is crucial for glioblastoma treatment but may be challenging using clinical imaging alone due to the infiltrative nature of glioblastomas. Precise targeting by whole-brain spectroscopic MRI, which maps tumor metabolites including choline (Cho) and N-acetylaspartate (NAA), can quantify early treatment-induced molecular changes that other traditional modalities cannot measure. We developed a pipeline to determine how spectroscopic MRI changes during early RT are associated with patient outcomes to provide insight into the utility of adaptive RT planning. Data were obtained from a study (NCT03137888) where glioblastoma patients received high-dose RT guided by the pre-RT Cho/NAA twice normal (Cho/NAA ≥ 2x) volume, and received spectroscopic MRI scans pre- and mid-RT. Overlap statistics between pre- and mid-RT scans were used to quantify metabolic activity changes after two weeks of RT. Log-rank tests were used to quantify the relationship between imaging metrics and patient overall and progression-free survival (OS/PFS). Patients with lower Jaccard/Dice coefficients had longer PFS (p = 0.045 for both), and patients with lower Jaccard/Dice coefficients had higher OS trending towards significance (p = 0.060 for both). Cho/NAA ≥ 2x volumes changed significantly during early RT, putting healthy tissue at risk of irradiation, and warranting further study into using adaptive RT planning.

A Novel Approach to Determining Tumor Progression Using a Three-Site Pilot Clinical Trial of Spectroscopic MRI-Guided Radiation Dose Escalation in Glioblastoma.

Glioblastoma (GBM) is a fatal disease, with poor prognosis exacerbated by difficulty in assessing tumor extent with imaging. Spectroscopic MRI (sMRI) is a non-contrast imaging technique measuring endogenous metabolite levels of the brain that can serve as biomarkers for tumor extension. We completed a three-site study to assess survival benefits of GBM patients when treated with escalated radiation dose guided by metabolic abnormalities in sMRI. Escalated radiation led to complex post-treatment imaging, requiring unique approaches to discern tumor progression from radiation-related treatment effect through our quantitative imaging platform. The purpose of this study is to determine true tumor recurrence timepoints for patients in our dose-escalation multisite study using novel methodology and to report on median progression-free survival (PFS). Follow-up imaging for all 30 trial patients were collected, lesion volumes segmented and graphed, and imaging uploaded to our platform for visual interpretation. Eighteen months post-enrollment, the median PFS was 16.6 months with a median time to follow-up of 20.3 months. With this new treatment paradigm, incidence rate of tumor recurrence one year from treatment is 30% compared to 60-70% failure under standard care. Based on the delayed tumor progression and improved survival, a randomized phase II trial is under development (EAF211).

Molecular basis for ligand modulation of the cannabinoid CB1 receptor.

The cannabinoid CB1 receptor is the most abundant G protein coupled receptor (GPCR) in the central nervous system, which mediates the functional response to endocannabinoids and Cannabis compounds. A variety of ligands for CB1 receptors have been developed as promising drug candidates for the treatment of neurological disorders. New high-resolution structures of CB1 receptor in different functional states have significantly improved our molecular understanding of CB1 ligand interactions, selectivity, receptor activation and allosteric modulation. These advances have paved the way for development of novel ligands for different therapeutic applications. In this review, we describe the structural determinants for modulation of CB1 receptors by different types of ligands, as well as the differences between CB1 and its homologous, the CB2 receptor. LINKED ARTICLES: This article is part of a themed issue on Structure Guided Pharmacology of Membrane Proteins (BJP 75th Anniversary). To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.14/issuetoc.