Garcia, César A.

OBJECTIVE Despite the known negative physiological impact of repeated mild head trauma events, their multiplicative impact on long-term seizure risk remains unclear. The objective of this study was to evaluate how multiple mild traumatic brain injuries (mTBIs) impact long-term seizure risk by testing 3 distinct machine learning approaches. Baseline and injury-specific characteristics were incorporated to enhance prognostication of individual seizure risk. METHODS Children with at least 1 mTBI event without prior evidence of seizure or antiepileptic drug treatment, from 2003 to 2021, were identified from a nationally sourced administrative claims database. The primary outcome of interest was a seizure event after mTBI, defined by qualifying principal diagnosis codes. Time-varying multivariable Cox regression was used to assess the impact of repeated mTBI. RESULTS A total of 156,118 children (mean age 11.7 ± 4.7 years) were included, with a median follow-up duration of 22.6 months (IQR 9.2–45.4 months). Among patients who experienced seizure after mTBI, the median time to seizure was 306 days. Seizures among those with radiographic findings and/or loss of consciousness occurred earlier (median time to seizure 112.5 days [imaging findings only, IQR 5–526.25 days], 80 days [loss of consciousness only, IQR 7–652 days], 22 days [both, IQR 5–192 days]). Both mTBI without and with short-term loss of consciousness resulted in increasing seizure risk with repeated trauma (HR 1.196, 95% CI 1.082–1.322; HR 2.025, 95% CI 1.828–2.244; respectively). The random survival forest approach achieved fixed-time areas under the receiver operating characteristic curve of 0.780 and 0.777 at 30 and 90 days after mTBI, and children predicted at high risk by the final model experienced a significantly higher burden of early seizure after mTBI (46.7% within the first 30 days vs 17.7% and 19.9% of children at low and medium risk). A simplified model using the top 12 contributing features achieved 95% of the full model’s performance in the validation set. CONCLUSIONS A novel machine learning model was developed and validated for personalized prediction of long-term seizure risk following multiple mTBIs. Model performance remained robust with a limited feature set, suggesting the feasibility of real-time incorporation into clinical workflows for individualized prognostication following each repeat mTBI event. In children predicted to be at high risk, early intervention should be considered.

AbstractMicroglia have fundamental roles in health and disease; however, effects of age, sex, and genetic factors on human microglia have not been fully explored. We applied bulk and single‐cell approaches to comprehensively characterize human microglia transcriptomes and their associations with age, sex, and APOE. We identified a novel microglial signature, characterized its expression in bulk tissue and single‐cell microglia transcriptomes. We discovered microglial co‐expression network modules associated with age, sex, and APOE‐ε4 that are enriched for lipid and carbohydrate metabolism genes. Integrated analyses of modules with single‐cell transcriptomes revealed significant overlap between age‐associated module genes and both pro‐inflammatory and disease‐associated microglial clusters. These modules and clusters harbor known neurodegenerative disease genes including APOE, PLCG2, and BIN1. Meta‐analyses with published bulk and single‐cell microglial datasets further supported our findings. Thus, these data represent a well‐characterized human microglial transcriptome resource and highlight age, sex, and APOE‐related microglial immunometabolism perturbations with potential relevance in neurodegeneration.

Abstract Glioblastoma (GBM) is the most common primary brain cancer in adults where tumor cell heterogeneity and sex differences influence clinical outcomes. Here, we functionally characterize three male and three female patient-derived GBM cell lines, identify protumorigenic BTICs, and create novel male and female preclinical models of GBM. Cell lines were evaluated on the following features: proliferation, stemness, migration, tumorigenesis, clinical characteristics, and sensitivity to radiation, TMZ, rhTNFSF10 (rhTRAIL), and rhBMP4. All cell lines were classified as GBM according to epigenetic subtyping, were heterogenous and functionally distinct from one another, and re-capitulated features of the original patient tumor. In establishing male and female preclinical models, it was found that two male-derived GBM cell lines (QNS108 and QNS120) and one female-derived GBM cell line (QNS315) grew at a faster rate in female mice brains. One male-derived GBM cell line (QNS108) decreased survival in female mice in comparison with male mice. However, no survival differences were observed for mice injected with a female-derived cell line (QNS315). In summary, a panel of six GBM patient-derived cell lines were functionally characterized, and it was shown that BTIC lines can be used to construct sex-specific models with differential phenotypes for additional studies.

Abstract Microglia have fundamental roles in health and disease, however effects of age, sex and genetic factors on human microglia have not been fully explored. We applied bulk and single cell approaches to comprehensively characterize human microglia transcriptomes and their associations with age, sex and APOE . We identified a novel microglial signature, characterized its expression in bulk data from 1,306 brain samples across 6 regions and in single cell microglia transcriptome. We discovered microglial co-expression network modules associated with age, sex and APOE -ε4 that are enriched for lipid and carbohydrate metabolism genes. Integrated analyses of modules with single cell transcriptomes revealed significant overlap between age-associated module genes and both pro-inflammatory and disease-associated microglial clusters. These modules and clusters harbor known neurodegenerative disease genes including APOE, PLCG2 and BIN1 . These data represent a well-characterized human microglial transcriptome resource; and highlight age, sex and APOE -related microglial immunometabolism perturbations with potential relevance in neurodegeneration.

AbstractBrain cancer persists as a difficult‐to‐treat condition. In particular, metastatic brain cancer and malignant primary brain tumors such as glioblastoma (GBM) are among the most deadly and still carry a dismal prognosis despite current standard of care with surgery and chemo‐radiation. Recent advances in nanotechnology have led to innovative nanomedicine strategies for the treatment of brain cancers and specifically for GBM. These strategies represent promising alternative or adjunctive therapies for conventional treatment modalities and are increasingly designed to target specific cell types such as brain tumor‐initiating cells and immune cells. This review provides an update on emerging insights into these nanomedicine strategies with a focus on nanoparticles as tools that facilitate 1) drug delivery 2) gene therapy, and 3) engineered‐cellular therapy for GBM and future directions for brain cancer treatment.