PURA関連神経発達異常症

Purine-rich element-binding protein A (PURA) syndrome is a rare neurodevelopmental disorder caused by pathogenic variants in the PURA gene and is characterized by neonatal hypotonia, feeding difficulty, respiratory dysregulation, and severe developmental impairment. The PURA gene has recently been identified as a cause of congenital myasthenic syndrome. We report two unrelated infants with genetically confirmed PURA syndrome who were treated with neuromuscular junction-directed therapy and reviewed three additional published cases. All five patients had neonatal hypotonia and apnea or respiratory failure requiring respiratory support. Pyridostigmine was used in four of the five infants included with heterogeneous response, while salbutamol, used in three patients, was associated with clinical benefit in all. These findings suggest that neuromuscular junction dysfunction may contribute to the respiratory and motor phenotype in patients with PURA syndrome. Early consideration of salbutamol, with careful monitoring and objective outcome measurement, may help stabilize severe infantile respiratory failure and improve motor function.

Studying neural-related questions is inherently challenging due to the limited number of suitable cell models. Here, we characterize a previously reported immortalized human neural stem cell line, HNSC.100, serving as a robust model for a wide range of neurobiological research questions. The cell line expresses key neural stem cell markers, including SOX2, vimentin, nestin, and allows for efficient genetic manipulation. Furthermore, HNSC.100 cells can be differentiated into neurons, astrocytes, and oligodendrocytes, thereby covering a wide spectrum of major neural cell types. We adapted corresponding differentiation protocols and established a comprehensive panel of molecular markers to validate successful differentiation, enabling precise characterization of the resulting cell population. In addition, we provide a complete dataset of RNA expression levels for all detectable genes in HNSC.100 cells. Based on this dataset, we assembled a list of expressed genes implicated in neural disorders that can be studied with this cell line. Together, we present a detailed characterization of the HNSC.100 cell line and provide new tools and reference data to facilitate its use. This resource enables researchers to evaluate the line's suitability for specific applications and to rapidly integrate HNSC.100 cells into their experimental workflows.

BACKGROUND AND OBJECTIVES: Developmental and epileptic encephalopathies (DEEs) with early burst-suppression EEG (EIDEE-BS) are among the most severe neonatal epileptic syndromes, typically presenting in the first months of life with refractory seizures and profound neurodevelopmental impairment. Although variants in the KCNQ2, STXBP1, and SCN2A genes are recognized as major causes, the full genetic spectrum remains uncertain. We aimed to delineate the electroclinical characteristics, genetic etiologies, and long-term outcomes in a large MRI-negative EIDEE-BS cohort. METHODS: We retrospectively analyzed 110 patients with BS EEG enrolled from a database of 1,540 individuals with suspected genetic epilepsies (2008-2023). Clinical, EEG, and genetic data were systematically collected. Patients were stratified into 4 groups: KCNQ2, STXBP1, "other pathogenic variants," and "without a genetic diagnosis." EEG traces were reviewed independently, and outcomes were assessed through long-term follow-up. RESULTS: Pathogenic or likely pathogenic variants were identified in 62.7% of patients and involved 23 genes, including 2 copy number variants. KCNQ2 (n = 24) and STXBP1 (n = 16) accounted for one-third of diagnoses, whereas SCN2A (n = 3) and KCNT1 (n = 2) were less frequent. In KCNQ2 cases, seizures and BS onset occurred earlier than in STXBP1 cases: mean 2 days vs 6 weeks for seizures and 3 days vs 2 months for BS, respectively. A typical BS pattern (bursts longer than suppressions) strongly correlated with KCNQ2 and STXBP1 variants. Novel associations were found with DPM1, GRIN2A, KCNT2, PIGO, PURA, WWOX, and candidate genes (KMT2E, SNAP25, and SYT1). Most variants were de novo heterozygous; however, recessive and X-linked inheritance patterns were also observed. Mortality was high (25%), primarily from status epilepticus and complications of severe disability. Most patients (72.5%) had persistent seizures at follow-up (a mean of 6.5 years), as well as profound intellectual disabilities, irrespective of genotype. DISCUSSION: This large series highlights the strong monogenic basis of EIDEE-BS. KCNQ2, STXBP1, and SCN2A were the most commonly affected genes. Early EEG features, particularly BS timing and morphology, can help anticipate the underlying genotype and guide precision therapy, including the early use of sodium channel blockers in selected cases. These findings support recent ILAE reclassification efforts and underscore the importance of comprehensive genomic testing for improved diagnosis and counseling.

Surveillance of severe acute respiratory illness (SARI) in Brazil provides an early warning system for respiratory outbreaks, including COVID-19, and statistical nowcasting methods are vital for correcting long and variable reporting delays that would otherwise hinder timely decision-making. Coherent joint prediction of SARI and COVID-positive SARI in Brazil could improve outbreak response planning and hospital resource management, but most existing nowcasting methods only target one outcome at a time. Furthermore, existing approaches usually focus solely on reconstructing recent incidence rather than forecasting future trends, which could support more proactive risk mitigation. Here, we propose a Bayesian hierarchical framework for joint nowcasting and short-term forecasting of two outcomes, where one is a strict subset of the other. Building on the generalized-Dirichlet-multinomial (GDM) method for correcting delayed reporting, a new beta-binomial component links SARI and COVID-positive counts, while separate conditional GDM components capture their distinct delay patterns. To allow for changes over time in the level of disease and delay distributions, flexible latent effects are included in all model components. Using national surveillance data from 2021 to 2024, we conduct a 20-date rolling prediction experiment across Brazil's 27 federative units. Compared to a well-established Bayesian nowcasting approach, our joint model achieves about one-third lower mean absolute error and continuous ranked probability score for contemporaneous nowcasts, with the largest gains in high-incidence regions. Meanwhile, energy scores indicate improved calibration for joint forecasts of total and COVID-positive SARI relative to comparable independent models.