Glioblastoma (GBM) is a primary central nervous system malignancy with a median survival of 15–20 months. The presence of both intra- and intertumoral heterogeneity limits understanding of biological mechanisms leading to tumor resistance, including immune escape. An attractive field of research to examine treatment resistance are immune signatures composed of cluster of differentiation (CD) markers and cytokines. CD markers are surface markers expressed on various cells throughout the body, often associated with immune cells. Cytokines are the effector molecules of the immune system. Together, CD markers and cytokines can serve as useful biomarkers to reflect immune status in patients with GBM. However, there are gaps in the understanding of the intricate interactions between GBM and the peripheral immune system and how these interactions change with standard and immune-modulating treatments. The key to understanding the true nature of these interactions is through multi-omic analysis of tumor progression and treatment response. This review aims to identify potential non-invasive blood-based biomarkers that can contribute to an immune signature through multi-omic approaches, leading to a better understanding of immune involvement in GBM.

Chronic inflammatory diseases such as autoimmune disorders, cancer, cardiovascular diseases and neurodegenerative disorders are a significant cause of morbidity and mortality in the industrialized world. Socioeconomically disadvantaged communities bear a disproportionately high burden of these inflammatory diseases. This review synthesizes evidence linking various domains of the Social Determinants of Health (SDoH)—economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, and social and community context—to inflammatory pathways and mechanisms. Across domains, biological mechanisms such as cytokine dysregulation, toll-like receptor (TLR) activation, hypothalamic-pituitary-adrenal (HPA) axis alterations and gut microbiome disruption act together to sustain proinflammatory states that drive adverse health outcomes in marginalized communities. Although causality is obscured by interrelated determinants, identifying inflammation as a shared pathway between various determinants highlights the need for structural interventions to reduce chronic disease burden.

Despite attempts at improving survival by employing novel therapies, progression in glioma is nearly universal. Precision biomarkers are critical to advancing outcomes; however, biomarkers for glioma are currently unknown. Most data on which the field can draw for biomarker identification comprise tissue-based analysis requiring the biospecimen to be removed from the tumor. Non-invasive specimen-based precision biomarkers are needed. Mucins are captured in tissue and blood and are increasingly studied in cancer, with several studies exploring their role as biomarkers to detect disease and monitor disease progression. CA125, also known as MUC16, is implemented as a biomarker in the clinic for ovarian cancer. Similarly, several mucins are membrane-bound, facilitating downstream signaling associated with tumor resistance and hallmarks of cancer. Evidence supports mucin expression in glioma cells with relationships to tumor detection, progression, resistance, and patient outcomes. The differential expression of mucins across tissues and organs could also provide a means of attributing signals measured in serum or plasma. In this review, we compiled existing research on mucins as candidate precision biomarkers in glioma, focusing on promising mucins in relationship to glioma and leading to a framework for mucin analysis in biospecimens as well as avenues for validation as data evolve.

Despite growing evidence that environmental factors and microbiome dysbiosis constitute the majority of disease pathogenesis, most current therapies target inflammation, barrier dysfunction, and/or S. aureus overgrowth. However, given the emerging understanding of microbiome-mediated and environmentally driven disease mechanisms, expanding therapeutic strategies to include dysbiosis and upstream environmental contributors represents a promising direction for future research. This manuscript will review the strengths and limitations of the standard treatments for AD, including emollients, topical corticosteroids, calcineurin inhibitors, and systemic immunomodulators. The focus however will be on the emerging microbiome-based therapies, specifically products containing live microorganisms (biotherapeutics). The potential for, and challenges against, biotherapeutics to grow within the market of AD treatment will be discussed. Immune suppressive approaches will remain limited to symptomatic control. These treatments will also be limited by tradeoffs inherent to the balance between symptom control and side effects of immune suppression. To successfully aide patients, topical biotherapeutics will need to overcome pharmaceutical-centric paradigms, academic dogma, and regulatory inertia. Limitations in current therapies, patient need, along with the potential to offer prevention against AD development, will likely propel biotherapeutics forward.

Background/Objectives: Topical steroid withdrawal syndrome is an underrecognized (and at times controversial) diagnosis, predominantly seen in individuals with a history of prolonged medium- to high-potency steroid use with sudden cessation. We aim to present topical steroid withdrawal clinical cases along with a narrative review of the literature to better characterize this understudied phenomenon. Methods: A total of 16 patients with a history of topical steroid withdrawal were enrolled in an IRB-approved clinical trial (NCT04864886). Participants underwent clinical assessments at the National Institutes of Health, including a history and physical examination, photography, genome sequencing, and comprehensive blood work. A follow-up survey assessed symptom activity and functional impact. Results: All patients reported severe itch, heat and photosensitivity, erythema, skin dryness, and pain. A total of 11 patients exhibited elevated IgE levels, 9 patients noted metallic-smelling skin, and 4 had peripheral blood eosinophilia. Symptomatic relief was observed with dupilumab, berberine, naltrexone, and various home remedies including topical ointments, vitamins, and probiotics, though effectiveness varied and often required trial and error. At follow-up, most respondents reported partial but ongoing symptoms, with several describing residual itch and intermittent interference with daily activities. Some participants continued therapeutic interventions, such as berberine, over two years after their initial evaluation. Conclusions: Our findings report improvement in patient symptoms such as itch and detail emerging management strategies that have not been discussed before. Improved recognition, physician consensus, and systemic evaluation of therapeutic options are needed to guide care and enhance quality of life for affected patients.

Introduction: Primary CNS tumors impact 25,000 individuals annually in the U.S., posing significant health challenges. Research is hindered by fragmented data across electronic health record (EHR) systems and the inefficiency of manual extraction for diagnostic, molecular, and treatment data. Large Language Models (LLMs) present a solution by automating extraction, enhancing efficiency and consistency, and enabling AI-driven insights. Methods: We analyzed 4,974 EHR documents from 256 patients with confirmed glioblastoma (GBM). All patients were treated on NCI NIH IRB (IRB00011862)-approved protocols 00-C-0074, 02C0064, 04C0200, 06C0112, 16-C-0081, and 20-C-0027. Cohort 1 (GBM, n = 109) was used to develop to the pipeline, while Cohorts 2 (various CNS histologies, n = 147) and 3 (GBM lesions, n = 15) served as testing sets. Clinical features extracted with GPT-4o via the NIH NIDAP Text Extraction Program (NTEP) included date of diagnosis, KPS, extent of resection, MGMT and IDH statuses, and radiation therapy start/end dates. Prompts underwent iterations, utilized JavaScript Object Notation (JSON) formatting, and outputs were then compared to the manual ground truth, established through detailed chart review of all 256 patients across three cohorts to extract key clinical variables of interest, allowing for the analysis of clinical feature and document type accuracy. Results: Prompt refinement led to a 30-fold increase in prompt character count, achieving ≥ 95% accuracy for five of seven features in Cohort 1. MGMT accuracy increased from 26% to 99%, and radiation dates increased from 93% to 98%. KPS (82%) and extent of resection (84%) were less accurate. For Cohort 2, MGMT and IDH were extracted with the highest accuracy (87% and 90%, respectively), while Cohort 3 achieved slightly lower but still strong performance (70% and 78%). Across these cohorts, MGMT and IDH were the most accurately extracted biomarkers. Radiation therapy summaries were the most effective documents across all three cohorts based on the extraction rates of clinical features. Conclusion: LLMs can accurately extract clinical features from EHRs with careful document selection and prompt design. This method may support CNS tumor research and have broader clinical applications. Future work will expand feature sets and validate on external datasets.