Hydrocephalus

PurposeThis study explores the application of machine learning to high-dimensional proteomics datasets for identifying Alzheimer's disease (AD) biomarkers. AD, a neurodegenerative disorder affecting millions worldwide, necessitates early and accurate diagnosis for effective management.MethodsWe leverage Tandem Mass Tag (TMT) proteomics data from the cerebrospinal fluid (CSF) samples from the frontal cortex of patients with idiopathic normal pressure hydrocephalus (iNPH), a condition often comorbid with AD, with rare access to both lumbar and ventricular samples. Our methodology includes extensive data preprocessing to address batch effects and missing values, followed by the use of the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation to overcome the small sample size. We apply linear, and non-linear machine learning models, and ensemble methods, to compare iNPH patients with and without biomarker evidence of AD pathology ( Aβ-T- or Aβ+T+ ) in a classification task.ResultsWe present a machine learning workflow for working with high-dimensional TMT proteomics data that addresses their inherent data characteristics. Our results demonstrate that batch effect correction has no or minor impact on the models' performance and robust feature selection is critical for model stability and performance, especially in the high-dimensional proteomics data setting for AD diagnostics. The results further indicated that removing features with missing values produced stronger models than imputing them, and the batch effect had minimal impact on the models Our best-performing disease-progression detection model, a random forest, achieves an AUC of 0.84 (± 0.03).ConclusionWe identify several novel protein biomarkers candidates, such as FABP3 and GOT1, with potential diagnostic value for AD pathology detection, suggesting the necessity of different biomarkers for AD diagnoses for patients with iNPH, and considering different biomarkers for ventricular and lumbar CSF samples. This work underscores the importance of a meticulous machine learning process in enhancing biomarker discovery. Our study also provides insights in translating biomarkers from other central nervous system diseases like iNPH, and both ventricular and lumbar CSF samples for biomarker discovery, providing a foundation for future research and clinical applications.

The Dandy-Walker syndrome (DWS) encompasses a group of anatomical midline cerebellar disorders with potential shared embryological origins, including the classic Dandy-Walker malformation, Blake's pouch cyst, and mega cisterna magna. Genetic factors, chromosomal abnormalities, and environmental influences contribute to its etiology. DWS, occurring in 1 in 25,000 to 35,000 live births, often presents with hydrocephalus and other central nervous system anomalies. Clinical manifestations vary, with symptoms appearing from neonatal to adult stages. Diagnosis is performed through neuroimaging, evaluating the posterior fossa and associated anomalies. Management involves treating hydrocephalus, addressing associated anomalies, and providing neurological follow-up with a multidisciplinary team. Prognosis hinges on associated malformations and their severity, impacting long-term outcomes. An unsystematic updated review on the embryology, pathophysiology, diagnostic approach, and therapeutic management of DWS is presented.

Hydrocephalus is a prevalent neurological disorder, particularly impactful in older adults, characterized by high incidence and numerous complications that impose a significant burden on healthcare systems. This review aims to provide a comprehensive description of hydrocephalus pathogenesis, focusing on cellular and molecular insights derived from animal models. We also present the latest advances in hydrocephalus research and highlight potential therapeutic targets. Lastly, the review advocates the integration of findings from both animal and human studies to achieve better outcomes and examines the potential of emerging technologies. We wish to raise public attention about this disease in an aging society. Current animal models for hydrocephalus involve acquired hydrocephalus models and genetic/congenital hydrocephalus models. Studies from animals have shown that the main mechanisms of models can be broadly classified into nine types. A variety of drug-targeted therapy methods and non-surgical treatment methods have been used in clinical practice. But current treatment approaches primarily focus on symptomatic relief and intracranial pressure control rather than addressing the underlying pathological mechanisms. We call for the development of more accurate and representative animal models to achieve better outcomes and examine the potential of emerging technologies, such as artificial intelligence and neuroimaging. In summary, this review synthesizes recent findings in hydrocephalus research, identifies promising therapeutic targets and interventions, and critically evaluates the limitations of current research paradigms, aiming to align preclinical studies with clinical endpoints. Continued studies and multidisciplinary collaboration are essential to develop effective interventions and facilitate new treatments into bedside.

BackgroundCerebrospinal fluid (CSF) inflammation is frequently seen in patients with aneurysmal subarachnoid hemorrhage (aSAH) requiring an external ventricular drain. Its role as a physiological response to the extravasation of blood, as a driver of delayed cerebral ischemia (DCI) and chronic hydrocephalus, or as a marker of ventriculostomy-related infection remains controversial. This study explored the associations between longitudinal routine CSF parameters and the development of DCI, shunt-dependent hydrocephalus, and functional neurological outcome in a large aSAH cohort.MethodsIn this retrospective study, 702 samples from 169 patients with aSAH with at least three serial ventricular CSF samples from an external ventricular drain within the first 14 days after aSAH were included. Routine CSF parameters-that is, leukocyte count (CC) and erythrocyte count (EC), CSF lactate (LCT), and CSF/plasma glucose ratio-were considered. For bivariate analysis, values and time points of peak CC, EC, LCT, and trough CSF/plasma glucose ratio, respectively, and mean values in the early, middle, and late phase after aSAH were considered. For the analysis of longitudinal CSF parameter dynamics, generalized linear mixed-effects modeling was applied. Patients were compared according to the development of DCI, shunt-dependent hydrocephalus, and dichotomized short-term and long-term functional neurological outcome.ResultsLongitudinal CC followed a nonlinear course over the first 14 days, with a peak around day 4 to day 8 after aSAH with a temporal relationship between peak CC and the occurrence of DCI. Higher initial EC with a more rapid decline was associated with favorable functional outcome, whereas persistently elevated LCT levels were seen in patients with unfavorable outcome. Patients with microbiologically confirmed ventriculostomy-related infection did not stand out significantly regarding CC absolute values or longitudinal dynamics.ConclusionsIntrathecal inflammation during the first 14 days after aSAH with a peak around day 7 seems to be a physiological response to the extravasation of blood into the CSF space. In patients with favorable outcome, EC decay was more rapid, indicating a more efficient clearance of blood from the subarachnoid space.