: An inverse correlation between the incidence of cancer and neurodegenerative disease has been observed, with the prevalence of cancer peaking around 60 years of age, then slowly tapering off as neurodegenerative diseases increase in the elderly. Although the diseases rarely occur concurrently, the same genes are differentially expressed between the diseases, with four transcription factors found to be in common for their expression. In the brain, mature astrocytes are the origin of astrocytoma, which make up 58.2% of malignant brain tumors in patients 65 or older, while GFAP+ astrocyte-like neural stem cells from the subventricular zone give rise to glioblastoma and anaplastic astrocytoma, which make up 41.6%. Likewise, in neurodegenerative disease, a decrease in astrocyte density is observed in early disease states, and senescent astrocytes increase. Because astrocytes coordinate synaptic function, astrocyte dysfunction likely contributes to or causes initial synapse loss and cognitive decline seen in neurodegenerative disease. In non-disease states, astrocytes retain their ability to successfully re-enter the cell cycle through adult astrogenesis to maintain the neuroenvironment, and controlled astrocytic proliferation could be an important contributor to neurological function. Disruption to this astrogenic balance could account for the inverse correlation of cell cycle dysregulation resulting in malignant astrocytes and tumorigenesis, and astrocytic senescence and cell death without self-renewal in aging resulting in neurodegenerative disease. The current understanding of the astrocytic roles of the transcription factors that could be the cause of this imbalance will be discussed, as well as possible therapeutic approaches to modulate their expression in the astrocyte.
Background Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the therapeutic dose. Warfarin sensitivity has been reported to be associated with increased incidence of international normalized ratio (INR) > 5. However, whether warfarin sensitivity is a risk factor for adverse outcomes in critically ill patients remains unknown. In the present study, we aimed to evaluate the utility of different machine learning algorithms for the prediction of warfarin sensitivity and to determine the impact of warfarin sensitivity on outcomes in critically ill patients. Methods Nine different machine learning algorithms for the prediction of warfarin sensitivity were tested in the International Warfarin Pharmacogenetic Consortium cohort and Easton cohort. Furthermore, a total of 7,647 critically ill patients was analyzed for warfarin sensitivity on in-hospital mortality by multivariable regression. Covariates that potentially confound the association were further adjusted using propensity score matching or inverse probability of treatment weighting. Results We found that logistic regression (AUC = 0.879, 95% CI: 0.834–0.924) was indistinguishable from support vector machine with a linear kernel, neural network, AdaBoost and light gradient boosting trees, and significantly outperformed all the other machine learning algorithms. Furthermore, we found that warfarin sensitivity predicted by the logistic regression model was significantly associated with worse in-hospital mortality in critically ill patients with an odds ratio (OR) of 1.33 (95% CI, 1.01–1.77). Conclusions Our data suggest that the logistic regression model is the best model for the prediction of warfarin sensitivity clinically and that warfarin sensitivity is likely to be a risk factor for adverse outcomes in critically ill patients.
BACKGROUND: While both glial/glioneuronal neoplasia and ganglioneuroma have been reported as components of multiple primary neoplasms, no patient has been diagnosed with multiple primary neoplasms of cerebral glial/glioneuronal tumors with oligodendroglioma-like features and adrenal ganglioneuroma up to now. CASE: A previously healthy five-year-old girl was admitted with a two-week history of headaches and vomiting. Brain Magnetic resonance imaging (MRI) showed a massive heterogenous multi-cystic enhancing lesion in the right temporoparietal area with substantial vasogenic edema. The patient underwent craniotomy and tumor gross total resection. The intra-operative histomorphological assessment of the tumor was well-matched with a glial tumor. The patient developed systolic hypertension during postoperative care in the Intensive Care Unit. Subsequent abdominal CT scan unveiled a calcified mass of the left adrenal gland origin. Blood and urine catecholamine tests, vanillylmandelic acid (VMA), were within the normal range. The surgical excision specimen exhibited a clear cell neoplasm with diffuse infiltrative growth. A distinguishing combination of oligodendroglioma-like perinuclear haloes, clear cell appearance and vascular proliferation rendered the diffuse Glioneuronal tumor with Oligodendroglioma-like features. With the combination of oligodendroglial-like appearance, negative 1p/19q codeletion, Wild IDH, no BRAF mutation, weak GFAP, and positive synaptophysin altogether, the tumor was compatible with the novel diffuse glioneuronal tumor with oligodendroglioma-like features and nuclear clusters (DGONC). The patient underwent laparotomy and tumor resection subsequently. Morphologic histopathological examinations of the adrenal mass were in line with ganglioneuroma. After discharge, no pathological uptake was identified with iodine-131 meta-iodobenzylguanidine scan (MIBG scan). No tumor residue was apparent on postoperative brain MRI. The patient received no adjuvant therapy for brain and adrenal tumors and underwent close surveillance for both tumors. No clinical or radiologic recurrence was recognized after six months of follow-up. CONCLUSIONS: Concurrent glioneuronal tumor and ganglioneuroma can be managed safely when diagnosed timely, leading to favorable outcomes.
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