Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas.

Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels. Virtual brain grafting, combined with Freesurfer, was used to compute morphologic features including cortical thickness, LGI, and subcortical volume in glioma patient. Radiomics features were extracted from multiregional tumor. Pycaret was used to construct the machine learning pipeline. Among the radiomics models, the whole tumor model achieved the best performance (accuracy 0.80, Area Under the Curve 0.86), while, after incorporating whole brain morphologic features, the model had a superior predictive performance (accuracy 0.82, Area Under the Curve 0.88). The features contributed most in predicting model including the right caudate volume, left middle temporal cortical thickness, first-order statistics, shape, and gray-level cooccurrence matrix. Pycaret, based on morphologic features, combined with radiomics, yielded highest accuracy in predicting isocitrate dehydrogenase mutation and vascular endothelial growth factor levels, indicating that morphologic abnormalities induced by glioma were associated with tumor biology.

A millimeter water-in-oil droplet as an alternative back exchange prevention strategy for hydrogen/deuterium exchange mass spectrometry of peptides/proteins.

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) is a versatile bioanalytical technique for protein analysis. Since the reliability of HDX-MS analysis considerably depends on the retention of deuterium labels in the post-labeling workflow, deuterium/hydrogen (D/H) back exchange prevention strategies, including decreasing the pH, temperature, and exposure time to protic sources of the deuterated samples, are widely adopted in the conventional HDX-MS protocol. Herein, an alternative and effective back exchange prevention strategy based on the encapsulation of a millimeter droplet of a labeled peptide solution in a water-immiscible organic solvent (cyclohexane) is proposed. Cyclohexane was used to prevent the undesirable uptake of water by the droplet from the atmospheric vapor through the air-water interface. Using the pepsin digest of deuterated myoglobin, our results show that back exchange kinetics of deuterated peptides is retarded in a millimeter droplet as compared to that in the bulk solution. Performing pepsin digestion directly in a water-in-oil droplet at room temperature (18-21 °C) was found to preserve more deuterium labels than that in the bulk digestion with an ice-water bath. Based on the present findings, it is proposed that keeping deuterated peptides in the form of water-in-oil droplets during the post-labelling workflow will facilitate the preservation of deuterium labels on the peptide backbone and thereby enhance the reliability of the H/D exchange data.

Machine learning-based identification and characterization of mast cells in eosinophilic esophagitis.

BACKGROUND: Eosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, understudied esophageal mastocytosis. OBJECTIVES: Using machine learning, we localized and characterized esophageal mast cells (MCs) to decipher their potential role in disease pathology. METHODS: Esophageal biopsy samples (EoE, control) were stained for MCs by anti-tryptase and imaged using immunofluorescence; high-resolution whole tissue images were digitally assembled. Machine learning software was trained to identify, enumerate, and characterize MCs, designated Mast Cell-Artificial Intelligence (MC-AI). RESULTS: MC-AI enumerated cell counts with high accuracy. During active EoE, epithelial MCs increased and lamina propria (LP) MCs decreased. In controls and EoE remission patients, papillae had the highest MC density and negatively correlated with epithelial MC density. MC density in the epithelium and papillae correlated with the degree of epithelial eosinophilic inflammation, basal zone hyperplasia, and LP fibrosis. MC-AI detected greater MC degranulation in the epithelium, papillae, and LP in patients with EoE compared with control individuals. MCs were localized further from the basement membrane in active EoE than EoE remission and control individuals but were closer than eosinophils to the basement membrane in active EoE. CONCLUSIONS: Using MC-AI, we identified a distinct population of homeostatic esophageal papillae MCs; during active EoE, this population decreases, undergoes degranulation, negatively correlates with epithelial MC levels, and significantly correlates with distinct histologic features. Overall, MC-AI provides a means to understand the potential involvement of MCs in EoE and other disorders.

Melanoma Derived Exosomes Amplify Radiotherapy Induced Abscopal Effect via IRF7/I-IFN Axis in Macrophages.

Radiotherapy (RT) can induce tumor regression outside the irradiation field, known as the abscopal effect. However, the detailed underlying mechanisms remain largely unknown. A tumor-bearing mouse model is successfully constructed by inducing both subcutaneous tumors and lung metastases. Single-cell RNA sequencing, immunofluorescence, and flow cytometry are performed to explore the regulation of tumor microenvironment (TME) by RT. A series of in vitro assays, including luciferase reporter, RNA Pulldown, and fluorescent in situ hybridization (FISH) assays, are performed to evaluate the detailed mechanism of the abscopal effect. In addition, in vivo assays are performed to investigate combination therapy strategies for enhancing the abscopal effect. The results showed that RT significantly inhibited localized tumor and lung metastasis progression and improved the TME. Mechanistically, RT promoted the release of tumor-derived exosomes carrying circPIK3R3, which is taken up by macrophages. circPIK3R3 promoted Type I interferon (I-IFN) secretion and M1 polarization via the miR-872-3p/IRF7 axis. Secreted I-IFN activated the JAK/STAT signaling pathway in CD8+ T cells, and promoted IFN-γ and GZMB secretion. Together, the study shows that tumor-derived exosomes promote I-IFN secretion via the circPIK3R3/miR-872-3p/IRF7 axis in macrophages and enhance the anti-tumor immune response of CD8+ T cells.

Evaluation of Key Molecular Markers in Adult Diffuse Gliomas Based on a Novel Combination of Diffusion and Perfusion MRI and MR Spectroscopy.

BACKGROUND: Preoperative identification of isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status could help clinicians select the optimal therapy in patients with diffuse glioma. Although, the value of multimodal intersection was underutilized. PURPOSE: To evaluate the value of quantitative MRI biomarkers for the identification of IDH mutation and 1p/19q codeletion in adult patients with diffuse glioma. STUDY TYPE: Retrospective. POPULATION: Two hundred sixteen adult diffuse gliomas with known genetic test results, divided into training (N = 130), test (N = 43), and validation (N = 43) groups. SEQUENCE/FIELD STRENGTH: Diffusion/perfusion-weighted-imaging sequences and multivoxel MR spectroscopy (MRS), all 3.0 T using three different scanners. ASSESSMENT: The apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) of the core tumor were calculated to identify IDH-mutant and 1p/19q-codeleted statuses and to determine cut-off values. ADC models were built based on the 30th percentile and lower, CBV models were built based on the 75th centile and higher (both in five centile steps). The optimal tumor region was defined and the metabolite concentrations of MRS voxels that overlapped with the ADC/CBV optimal region were calculated and added to the best-performing diagnostic models. STATISTICAL TESTS: DeLong's test, diagnostic test, and decision curve analysis were performed. A P value <0.05 was considered to be statistically significant. RESULTS: Almost all ADC models achieved good performance in identifying IDH mutation status, among which ADC_15th was the most valuable parameter (threshold = 1.186; Youden index = 0.734; AUC_train = 0.896). The differential power of CBV histogram metrics for predicting 1p/19q codeletion outperformed ADC histogram metrics, and the CBV_80th-related model performed best (threshold = 1.435; Youden index = 0.458; AUC_train = 0.724). The AUCs of ADC_15th and CBV_80th models in the validation set were 0.857 and 0.733. These models tended to improve after incorporation of N-acetylaspartate/total_creatine and glutamate-plus-glutamine/total_creatine, respectively. DATA CONCLUSION: The intersection of ADC-, CBV-based histogram and MRS provide a reliable paradigm for identifying the key molecular markers in adult diffuse gliomas. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Clinical and genetic characteristics of CEL-MODY (MODY8): a literature review and screening in Chinese individuals diagnosed with early-onset type 2 diabetes.

OBJECTIVE: CEL-related maturity-onset diabetes of the young (CEL-MODY, MODY8) is a special type of monogenetic diabetes caused by mutations in the carboxyl-ester lipase (CEL) gene. This study aimed to summarize the genetic and clinical characteristics of CEL-MODY patients and to determine the prevalence of the disease among Chinese patients with early-onset type 2 diabetes (EOD). METHODS: We systematically reviewed the literature associated with CEL-MODY in PubMed, Embase, Web of Science, China National Knowledge Infrastructure and Wanfang Data to analyze the features of patients with CEL-MODY. We screened and evaluated rare variants of the CEL gene in a cohort of 679 Chinese patients with EOD to estimate the prevalence of CEL-MODY in China. RESULTS: In total, 21 individuals reported in previous studies were diagnosed with CEL-MODY based on the combination of diabetes and pancreatic exocrine dysfunction as well as frameshift mutations in exon 11 of the CEL gene. CEL-MODY patients were nonobese and presented with exocrine pancreatic affection (e.g., chronic pancreatitis, low fecal elastase levels, pancreas atrophy and lipomatosis) followed by insulin-dependent diabetes. No carriers of CEL missense mutations were reported with exocrine pancreatic dysfunction. Sequencing of CEL in Chinese EOD patients led to the identification of the variant p.Val736Cysfs*22 in two patients. However, these patients could not be diagnosed with CEL-MODY because there were no signs that the exocrine pancreas was afflicted. CONCLUSION: CEL-MODY is a very rare disease caused by frameshift mutations affecting the proximal VNTR segments of the CEL gene. Signs of exocrine pancreatic dysfunction provide diagnostic clues for CEL-MODY, and genetic testing is vital for proper diagnosis. Further research in larger cohorts is needed to investigate the characteristics and prevalence of CEL-MODY in the Chinese population.

Standardized Quantification of Mast Cells in the Gastrointestinal Tract in Adults.

BACKGROUND: Current data on the normal quantity of mast cells throughout the adult gastrointestinal tract are limited in several domains. These include microanatomic localization of mast cells, standardization of staining and counting methods, and reporting of microscope field of view. OBJECTIVE: To address this lack of reliable reference ranges to facilitate the study of and diagnosis of emerging mast cell-mediated diseases. METHODS: We examined biopsies obtained from the esophagus, stomach, duodenum, and colon from an unselected cohort. Mean and peak mast cell density were determined on slides stained for tryptase and CD117, and were expressed per high power field (hpf) and surface area (mm2), thus deriving reference ranges (average ± 2 SDs). RESULTS: For the most common hpf surface area (0.238 mm2), upper limits of the derived reference ranges for average/peak mast cells were 0.15/3.67 (esophagus, tryptase), 0.70/5.98 (esophagus, CD117), 22.56/35.30 (stomach, tryptase), 31.32/53.10 (stomach, CD117), 30.28/49.77 (duodenal crypts, tryptase), 41.96/65.26 (duodenal crypts, CD117), 4.98/11.56 (duodenal villi, tryptase), 8.38/14.17 (duodenal villi, CD117), 26.58/41.08 (colon, tryptase), and 35.57/57.92 (colon, CD117). Interobserver variability was moderate to good. There was significant correlation between average and peak mast cell counts. CONCLUSIONS: These data help standardize mast cell reference ranges throughout the gastrointestinal tract in adults, which can be used to determine whether abnormal levels of mast cells are present in patients with suspected mast cell-mediated disease. Our data show that the commonly used cutoff of 20 mast cells per hpf irrespective of the gastrointestinal tract segment is an underestimate of an appropriate cutoff in stomach, duodenum (crypt area), and colon.

Development and External Validation of an MRI-based Radiomics Nomogram to Distinguish Circumscribed Astrocytic Gliomas and Diffuse Gliomas: A Multicenter Study.

RATIONALE AND OBJECTIVES: The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHO CNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs). MATERIALS AND METHODS: We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions. After tumor segmentation, three volume of interest (VOI) types were obtained: VOItumor and peritumor, VOIwhole, and VOIinterface. Clinical and combined models (incorporating radiomics and clinical features) were also established. To address imbalances in training dataset, Synthetic Minority Oversampling Technique was employed. RESULTS: A total of 475 patients (DGs: n = 338, CAGs: n = 137) were analyzed. The VOIinterface model demonstrated the best performance for differentiating CAGs from DGs, achieving an area under the curve (AUC) of 0.806 and area under the precision-recall curve (PRAUC)of 0.894 in the cross-validation set. Using analysis of variance (ANOVA) feature selector and Support Vector Machine (SVM) classifier, seven features were selected. The model achieved an AUC and AUPRC of 0.912 and 0.972 in the internal validation dataset, and 0.897 and 0.930 in the external validation dataset. The combined model, incorporating interface radiomics and clinical features, showed improved performance in the external validation set, with an AUC of 0.94 and PRAUC of 0.959. CONCLUSION: Radiomics models incorporating the peritumoral area demonstrate greater potential for distinguishing CAGs from DGs compared to intratumoral models. These findings may hold promise for evaluating tumor nature before surgery and improving clinical management of glioma patients.

MRI characteristics of H3 G34-mutant diffuse hemispheric gliomas and possible differentiation from IDH-wild-type glioblastomas in adolescents and young adults.

OBJECTIVE: H3 G34-mutant diffuse hemispheric gliomas (G34m-DHGs) are rare and constitute a new infiltrating brain tumor entity whose characteristics require elucidation, and their difference from isocitrate dehydrogenase-wild-type glioblastomas (IDH-WT-GBMs) needs to be clarified. In this study, the authors report the demographic, clinical, and neuroradiological features of G34m-DHG and investigate the capability of quantitative MRI features in differentiating them. METHODS: Twenty-three patients with G34m-DHG and 30 patients with IDH-WT-GBM were included in this retrospective study. The authors reviewed the clinical, radiological, and molecular data of G34m-DHGs and compared their neuroimaging features with those of IDH-WT-GBMs in adolescents and young adults. Visually Accessible Rembrandt Images (VASARI) features were extracted, and the Kruskal-Wallis test was performed. A logistic regression model was constructed to evaluate the diagnostic performance for differentiating between G34m-DHG and IDH-WT-GBM. Subsequently, FeAture Explorer (FAE) was used to generate the machine learning pipeline and select important radiomics features that had been extracted with PyRadiomics. Estimates of the performance were supplied by metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC). RESULTS: The mean age of the 23 patients with G34m-DHG was 23.7 years (range 11-45 years), younger than the mean age of patients with IDH-WT-GBM (30.96 years, range 5-43 years). All tumors were hemispheric. Most cases were immunonegative for ATRX (95%) and Olig2 (100%), were immunopositive for p53 (95%), and exhibited MGMT promoter methylation (81%). The radiological presentations of G34m-DHG were different from those of IDH-WT-GBM. The majority of the G34m-DHGs were in the frontal, parietal, and temporal lobes and demonstrated no or only faint contrast enhancement (74%), while IDH-WT-GBMs were mostly seen in the frontal lobe and showed marked contrast enhancement in 83% of cases. The FAE-generated model, based on radiomics features (AUC 0.925) of conventional MR images, had better discriminatory performance between G34m-DHG and IDH-WT-GBM than VASARI feature analysis (AUC 0.843). CONCLUSIONS: G34m-DHGs most frequently occur in the frontal, parietal, and temporal lobes in adolescent and young adults and are associated with radiological characteristics distinct from those of IDH-WT-GBMs. Successful identification can be achieved by using either VASARI features or radiomics signatures, which may contribute to prognostic evaluation and assist in clinical settings.

Cortical myelin and thickness mapping provide insights into whole-brain tumor burden in diffuse midline glioma.

Systemic infiltration is a hallmark of diffuse midline glioma pathogenesis, which can trigger distant disturbances in cortical structure. However, the existence and effects of these changes have been underexamined. This study aimed to investigate whole-brain cortical myelin and thickness alternations induced by diffuse midline glioma. High-resolution T1- and T2-weighted images were acquired from 90 patients with diffuse midline glioma with H3 K27-altered and 64 patients with wild-type and 86 healthy controls. Cortical thickness and myelin content was calculated using Human Connectome Project pipeline. Significant differences in cortical thickness and myelin content were detected among groups. Short-term survival prediction model was constructed using automated machine learning. Compared with healthy controls, diffuse midline glioma with H3 K27-altered patients showed significantly reduced cortical myelin in bilateral precentral gyrus, postcentral gyrus, insular, parahippocampal gyrus, fusiform gyrus, and cingulate gyrus, whereas diffuse midline glioma with H3 K27 wild-type patients exhibited well-preserved myelin content. Furtherly, when comparing diffuse midline glioma with H3 K27-altered and diffuse midline glioma with H3 K27 wild-type, the decreased cortical thickness in parietal and occipital regions along with demyelination in medial orbitofrontal cortex was observed in diffuse midline glioma with H3 K27-altered. Notably, a combination of cortical features and tumor radiomics allowed short-term survival prediction with accuracy 0.80 and AUC 0.84. These findings may aid clinicians in tailoring therapeutic approaches based on cortical characteristics, potentially enhancing the efficacy of current and future treatment modalities.

Metabolic heterogeneity in different subtypes of malformations of cortical development causing epilepsy: a proton magnetic resonance spectroscopy study.

Background: The most common subtypes of malformations of cortical development (MCDs) are gray matter heterotopia (GMH), focal cortical dysplasia (FCD), and polymicrogyria (PMG). This study aimed to characterize the possible neurometabolic abnormalities and heterogeneity in different MCDs subtypes using proton magnetic resonance spectroscopy (1H-MRS). Methods: In this prospective cross-sectional study, we recruited 29 patients with MCDs and epilepsy, including ten with GMH, ten with FCD, and nine with PMG, as well as 25 age- and sex-matched healthy controls (HC) from the Epilepsy Center of West China Hospital of Sichuan University between August 2018 and November 2021. Inclusion criteria for the patients were based upon typical magnetic resonance imaging (MRI) findings of MCDs and full clinical assessment for epilepsy. Single-voxel point-resolved spectroscopy was used to acquire data from both the lesion and the normal-appearing contralateral side (NACS) in patients and from the frontal lobe in HC. Metabolite measures, including N-acetyl aspartate (NAA), myoinositol (Ins), choline (Cho), creatine (Cr), and glutamate + glutamine (Glx) concentrations, were quantitatively estimated with linear combination model (LCModel) software and corrected for the partial volume effect of cerebrospinal fluid (CSF). Results: The NAA concentration was lower and the Ins concentration was higher in the MCDs lesions than in the NACS and in HC (P=0.002-0.007), and the Cho and Cr concentrations were higher in MCDs lesions than in HC (P=0.001-0.016). Moreover, the Cho concentration was higher in NACS than in HC (P=0.015). In the GMH lesions, the only metabolic alteration was an NAA reduction (GMH_lesion vs. HC: P=0.001). In the FCD lesions, there were more metabolite abnormalities than in the other two subtypes, particularly a lower NAA and a higher Ins than in HC and NACS (P=0.012-0.042). In the PMG lesions, Cr (lesion vs. HC or NACS: P=0.017-0.021) and Glx (lesion vs. NACS: P=0.043) were increased, while NAA was normal. Correlation analysis revealed that the Cr concentration in MCDs lesions was positively correlated with seizure frequency (r=0.411; P=0.027). Conclusions: Based upon 1H-MRS, our study demonstrated that different MCDs subtypes exhibited variable metabolic features, which may be associated with distinct functional and cytoarchitectural properties.

Machine Learning Based Identification and Characterization of Mast cells in Eosinophilic Esophagitis.

Background: Eosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, understudied esophageal mastocytosis. Objective: Using machine learning, we localized and characterized esophageal mast cells to decipher their potential role in disease pathology. Methods: Esophageal biopsy samples (EoE, control) were stained for mast cells by anti-tryptase and imaged using immunofluorescence; high-resolution whole tissue images were digitally assembled. Machine learning software was trained to identify, enumerate, and characterize mast cells, designated Mast Cell-Artificial Intelligence (MC-AI). Results: MC-AI enumerated cell counts with high accuracy. During active EoE, epithelial mast cells increased and lamina propria (LP) mast cells decreased. In controls and EoE remission patients, papillae had the highest mast cell density and negatively correlated with epithelial mast cell density. Mast cell density in the epithelium and papillae correlated with the degree of epithelial eosinophilic inflammation, basal zone hyperplasia, and LP fibrosis. MC-AI detected greater mast cell degranulation in the epithelium, papillae, and LP in EoE patients compared with control individuals. Mast cells were localized further from the basement membrane in active EoE than EoE remission and controls individuals but were closer than eosinophils to the basement membrane in active EoE. Conclusion: Using MC-AI, we identified a distinct population of homeostatic esophageal papillae mast cells; during active EoE, this population decreases, undergoes degranulation, negatively correlates with epithelial mast cell levels, and significantly correlates with distinct histologic features. Overall, MC-AI provides a means to understand the potential involvement of mast cells in EoE and other disorders. Clinical Implication: We have developed a methodology for identifying, enumerating, and characterizing mast cells using artificial intelligence; this has been applied to decipher eosinophilic esophagitis and provides a platform approach for other diseases. Capsule Summary: A machine learning protocol for identifying mast cells, designated Mast Cell-Artificial Intelligence, readily identified spatially distinct and dynamic populations of mast cells in EoE, providing a platform to better understand this cell type in EoE and other diseases.

Bacterial lipoteichoic acid induces capsular contracture by activating innate immune response.

BACKGROUND: Capsular contracture is attributed to an exaggerated fibrosis response within the capsule and is partly associated with bacterial contamination in situ. However, the cellular mechanisms that initiate this response are unclear. METHODS: We developed a mouse model of capsular contracture by repeated injection of 10 µg/ml lipoteichoic acid (LTA). The histological changes in the capsule tissue were measured by hematoxylin-eosin, Masson, and immunohistochemical staining. The expression of cytokines was measured by quantitative reverse-transcription polymerase chain reaction. We also used pharmacological methods to verify the roles of macrophages and Toll-like receptor 2 (TLR2) signaling in this pathological process. RESULTS: We discovered that repeated LTA injection, at a low concentration, could induce the thickening of the capsule tissue. Macrophage infiltration and TLR2/nuclear factor-kappa B (NF-κB) signaling activated in this process could be suppressed by macrophage depletion or TLR2 receptor inhibition. CONCLUSIONS: As TLR2 signal activation was found to cause capsular contracture by inducing macrophage infiltration as a consequence of trace amounts of LTA contamination in situ, this target is helpful for understanding that chronic or repeated subclinical infection could activate capsular contracture.

Competing Engagement of ß-arrestin Isoforms Balances IGF1R/p53 Signaling and Controls Melanoma Cell Chemotherapeutic Responsiveness.

Constraints on the p53 tumor suppressor pathway have long been associated with the progression, therapeutic resistance, and poor prognosis of melanoma, the most aggressive form of skin cancer. Likewise, the insulin-like growth factor type 1 receptor (IGF1R) is recognized as an essential coordinator of transformation, proliferation, survival, and migration of melanoma cells. Given that ß-arrestin (ß-arr) system critically governs the anti/pro-tumorigenic p53/IGF1R signaling pathways through their common E3 ubiquitin-protein ligase MDM2, we explore whether unbalancing this system downstream of IGF1R can enhance the response of melanoma cells to chemotherapy. Altering ß-arr expression demonstrated that both ß-arr1-silencing and ß-arr2-overexpression (-ß-arr1/+ß-arr2) facilitated nuclear-to-cytosolic MDM2 translocation accompanied by decreased IGF1R expression, while increasing p53 levels, resulting in reduced cell proliferation/survival. Imbalance towards ß-arr2 (-ß-arr1/+ß-arr2) synergizes with the chemotherapeutic agent, dacarbazine, in promoting melanoma cell toxicity. In both 3D spheroid models and in vivo in zebrafish models, this combination strategy, through dual IGF1R downregulation/p53 activation, limits melanoma cell growth, survival and metastatic spread. In clinical settings, analysis of the TCGA-SKCM patient cohort confirms ß-arr1-/ß-arr2+ imbalance as a metastatic melanoma vulnerability that may enhance therapeutic benefit. Our findings suggest that under steady-state conditions, IGF1R/p53-tumor promotion/suppression status-quo is preserved by ß-arr1/2 homeostasis. Biasing this balance towards ß-arr2 can limit the protumorigenic IGF1R activities while enhancing p53 activity, thus reducing multiple cancer-sustaining mechanisms. Combined with other therapeutics, this strategy improves patient responses and outcomes to therapies relying on p53 or IGF1R pathways. IMPLICATIONS: Altogether, ß-arrestin system bias downstream IGF1R is an important metastatic melanoma vulnerability that may be conductive for therapeutic benefit.

Multiparametric mapping of white matter reorganizations in patients with frontal glioma-related epilepsy.

AIMS: Epilepsy is a common symptom in diffuse lower-grade glioma (DLGG). The specific role of white matter (WM) alteration in patients with glioma-related epilepsy (GRE) is largely unknown. This study aims to investigate the reorganization of WM tracts and changes in structural networks related to GRE. METHODS: Diffusion-weighted images were collected from 70 patients with left frontal DLGG (GRE = 33, non-GRE = 37) and 41 healthy controls (HC). Tractometry with TractSeg was applied to segment tracts and quantify fractional anisotropy (FA) along each tract. Structural network was constructed using constrained spherical deconvolution and probabilistic tractography. FA and network properties were compared among three groups. RESULTS: Compared with HC, both GRE and non-GRE showed decreased FA in contralateral inferior fronto-occipital fasciculus, superior longitudinal fasciculus II and arcuate fasciculus, increased nodal efficiency in contralateral nodes of frontal-parietal and limbic networks, whereas decreased degree centrality and betweenness centrality in nodes of dorsal temporal lobe and rostral middle frontal gyrus (rMFG). Additionally, when compared GRE with non-GRE, increased FA in contralateral corticospinal tract (CST) and lower betweenness centrality in paracentral lobule (PCL) in GRE (all p < 0.05 after Bonferroni correction). CONCLUSION: This study indicates that patients with left frontal DLGG exhibit complex WM reorganization, and the altered regions mainly concentrated in the language, frontal-parietal and limbic networks. Moreover, the preserved integrity in contralateral CST and server decreased nodal betweenness in PCL may be potential neuroimaging markers underlying the occurrence of presurgical seizures of GRE.

Peptide Oxidation Induced by Liquid-Solid Contact Electrification as Revealed in Liquid Microjunction-Surface Sampling Probe Mass Spectrometry.

Reproducible peptide oxidation was observed using a homebuilt liquid microjunction-surface sampling probe (LMJ-SSP) platform for analyzing peptide standards. Although electrochemical oxidation and corona discharges have previously been associated with analyte oxidation in electrospray ionization (ESI) and ESI-related ambient ionization mass spectrometry (MS) methods, they were unlikely the causes for the peptide oxidation observed in the LMJ-SSP studies. A systematic investigation demonstrated that analyte oxidation was induced during the droplet drying on a solid surface through liquid-solid electrification processes. To minimize unwanted analyte oxidation, the water content in the sample solution should be decreased and the use of hydroxyl-functionalized substrates, such as glass slides, should be avoided. In addition, if water is an essential solvent component, adding an antioxidant, such as ascorbic acid, to the sample solution before droplet evaporation on the solid surface could lower the percentage of analyte oxidation. The present findings apply to all the MS methods that involve drying microliters of sample solution onto a suitable substrate in their sample preparation protocols.

CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study.

OBJECTIVE: Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule. METHODS: Data of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOItumor), VOIperitumor, and VOIintra-plus peritumor. Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). RESULTS: The results showed that the radiomics models based on features from VOIintra-plus peritumor achieved higher AUCs compared to models based on features from VOItumor. The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features. CONCLUSIONS: We demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA.

Bre1/RNF20 promotes Rad51-mediated strand exchange and antagonizes the Srs2/FBH1 helicases.

Central to homologous recombination (HR) is the assembly of Rad51 recombinase on single-strand DNA (ssDNA), forming the Rad51-ssDNA filament. How the Rad51 filament is efficiently established and sustained remains partially understood. Here, we find that the yeast ubiquitin ligase Bre1 and its human homolog RNF20, a tumor suppressor, function as recombination mediators, promoting Rad51 filament formation and subsequent reactions via multiple mechanisms independent of their ligase activities. We show that Bre1/RNF20 interacts with Rad51, directs Rad51 to ssDNA, and facilitates Rad51-ssDNA filament assembly and strand exchange in vitro. In parallel, Bre1/RNF20 interacts with the Srs2 or FBH1 helicase to counteract their disrupting effect on the Rad51 filament. We demonstrate that the above functions of Bre1/RNF20 contribute to HR repair in cells in a manner additive to the mediator protein Rad52 in yeast or BRCA2 in human. Thus, Bre1/RNF20 provides an additional layer of mechanism to directly control Rad51 filament dynamics.

Magnetic Resonance Imaging Features of Zinc Finger Translocation Associated-RELA Fusion Ependymoma Compared to Its Wild-Type Counterpart.

OBJECTIVE: To explore the predictive value of quantitative features extracted from conventional magnetic resonance imaging (MRI) in distinguishing Zinc Finger Translocation Associated (ZFTA)-RELA fusion-positive and wild-type ependymomas. METHODS: Twenty-seven patients with pathologically confirmed ependymomas (17 patients with ZFTA-RELA fusions and 10 ZFTA-RELA fusion-negative patients) who underwent conventional MRI were enrolled in this retrospective study. Two experienced neuroradiologists who were blinded to the histopathological subtypes independently extracted imaging features using Visually Accessible Rembrandt Images annotations. The consistency between the readers was evaluated with the Kappa test. The imaging features with significant differences between the 2 groups were obtained using the least absolute shrinkage and selection operator regression model. Logistic regression analysis and receiver operating characteristic analysis were performed to analyze the diagnostic performance of the imaging features in predicting the ZFTA-RELA fusion status in ependymoma. RESULTS: There was a good interevaluator agreement on the imaging features (kappa value range 0.601-1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive performance in identifying ZFTA-RELA fusion-positive and ZFTA-RELA fusion-negative ependymomas (C-index = 0.862 and area under the curve= 0.8618). CONCLUSIONS: Quantitative features extracted from preoperative conventional MRI by Visually Accessible Rembrandt Images provide high discriminatory accuracy in predicting the ZFTA-RELA fusion status of ependymoma.

Artificial Intelligence Applications in Glioma With 1p/19q Co-Deletion: A Systematic Review.

As an important genomic marker for oligodendrogliomas, early determination of 1p/19q co-deletion status is critical for guiding therapy and predicting prognosis in patients with glioma. The purpose of this study is to systematically review the literature concerning the magnetic resonance imaging (MRI) with artificial intelligence (AI) methods for predicting 1p/19q co-deletion status in glioma. PubMed, Scopus, Embase, and IEEE Xplore were searched in accordance with the Preferred Reporting Items for systematic reviews and meta-analyses guidelines. Methodological quality of studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2. Finally, 28 studies were included in the quantitative analysis. Diagnostic test accuracy reached an area under the ROC curve of 0.71-0.98 were reported in 24 studies. The remaining four studies with no available AUC provided an accuracy of 0.75-0. 89. The included studies varied widely in terms of imaging sequences, input features, and modeling methods. The current review highlighted that integrating MRI with AI technology is a potential tool for determination 1p/19q status pre-operatively and noninvasively, which can possibly help clinical decision-making. However, the reliability and feasibility of this approach still need to be further validated and improved in a real clinical setting. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 2.