Publications by authors named "S S Brem"
: The accurate and early distinction of glioblastomas (GBMs) from single brain metastases (BMs) provides a window of opportunity for reframing treatment strategies enabling optimal and timely therapeutic interventions. We sought to leverage physiologically sensitive parameters derived from diffusion tensor imaging (DTI) and dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI) along with machine learning-based methods to distinguish GBMs from single BMs. : Patients with histopathology-confirmed GBMs ( = 62) and BMs ( = 26) and exhibiting contrast-enhancing regions (CERs) underwent 3T anatomical imaging, DTI and DSC-PWI prior to treatment.
View Article and Find Full Text PDFBackground: Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.
Methods: We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).
Twisted transition metal dichalcogenide (TMD) bilayers exhibit periodic moiré potentials, which can trap excitons at certain high-symmetry sites. At small twist angles, TMD lattices undergo an atomic reconstruction, altering the moiré potential landscape via the formation of large domains, potentially separating the charges in-plane and leading to the formation of intralayer charge-transfer (CT) excitons. Here, we employ a microscopic, material-specific theory to investigate the intralayer charge-separation in atomically reconstructed MoSe-WSe heterostructures.
View Article and Find Full Text PDFArticle Synopsis
- Immuno-oncology, particularly immune checkpoint inhibitors (ICIs), has significantly improved cancer treatment, yielding long-term responses in various cancers, including metastatic brain tumors, though primary brain tumors like glioblastomas still resist these therapies.
- The tumor microenvironment (TME) presents barriers like immune suppression and heterogeneity, suggesting that modifying this environment by targeting inflammatory cytokines, such as interleukin-6 (IL-6), could enhance treatment effectiveness.
- A proposed method involves using vagus nerve stimulation (VNS) to boost T cell immunity and reduce pro-inflammatory cytokines, potentially converting "cold" tumors into "hot" tumors, with future clinical trials needed to validate this approach's effectiveness in gliomas and other cancers
Background: It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.
Methods: GBM patients ( = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI.