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X-Linked Dystonia-Parkinsonism (XDP) is a neurodegenerative disease affecting individuals with ancestry to the island of Panay in the Philippines. In recent years there has been considerable progress at elucidating the genetic basis of XDP and candidate disease mechanisms in patient-derived cellular models, but the neural substrates that give rise to XDP in vivo are still poorly understood. Previous studies of limited XDP postmortem brain samples have reported a selective dropout of medium spiny neurons within the striatum, although neuroimaging of XDP patients has detected additional abnormalities in multiple brain regions beyond the basal ganglia. Given the need to fully define the CNS structures that are affected in this disease, we created a brain bank in Panay to serve as a tissue resource for detailed studies of XDP-related neuropathology. Here we describe this platform, from donor recruitment and consent to tissue collection, processing, and storage, that was assembled within a predominantly rural region of the Philippines with limited access to medical and laboratory facilities. Thirty-six brains from XDP individuals have been collected over an initial 4 years period. Tissue quality was assessed based on histologic staining of cortex, RNA integrity scores, detection of neuronal transcripts in situ by fluorescent hybridization chain reaction, and western blotting of neuronal and glial proteins. The results indicate that this pipeline preserves tissue integrity to an extent compatible with a range of morphologic, molecular, and biochemical analyses. Thus the algorithms that we developed for working in rural communities may serve as a guide for establishing similar brain banks for other rare diseases in indigenous populations.

Laminin α2 (LAMA2)-deficient congenital muscular dystrophy is a severe, early-onset disease caused by abnormal levels of laminin 211 in the basal lamina leading to muscle weakness, transient inflammation, muscle degeneration and impaired mobility. In a Lama2-deficient mouse model for this disease, animal survival is improved by muscle-specific expression of the apoptosis inhibitor Bcl-2, conferred by a MyoD-hBcl-2 transgene. Here we investigated early disease stages in this model to determine initial pathological events and effects of Bcl-2 on their progression. Using quantitative immunohistological and mRNA analyses we show that inflammation occurs very early in Lama2-deficient muscle, some aspects of which are reduced or delayed by the MyoD-hBcl-2 transgene. mRNAs for innate immune response regulators, including multiple Toll-like receptors (TLRs) and the inflammasome component NLRP3, are elevated in diseased muscle compared with age-matched controls expressing Lama2. MyoD-hBcl-2 inhibits induction of TLR4, TLR6, TLR7, TLR8 and TLR9 in Lama2-deficient muscle compared with non-transgenic controls, and leads to reduced infiltration of eosinophils, which are key death effector cells. This congenital disease model provides a new paradigm for investigating cell death mechanisms during early stages of pathogenesis, demonstrating that interactions exist between Bcl-2, a multifunctional regulator of cell survival, and the innate immune response.

As we all know, budgets are a necessity. But they must be figured to best serve everyone in the community and allocate funds to important functions and save money at the same time. But no one benefits from cutting certain programs -- such as the Osceola Child Development Center.

While mRNA splicing dysregulation is a well-established contributor to neurodegeneration in disorders such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), its role in Parkinson’s disease (PD) remains underexplored. Here, we analyse transcriptomic data from >500 post-mortem human brain samples from individuals with and without PD to show that splicing alterations are frequently detected. Differentially spliced genes were significantly more enriched for those causally-implicated in both PD and ALS than genes that were differentially expressed. Furthermore, we observed a strong association between these splicing alterations and dysfunction of the RNA-binding protein (RBP), TAR DNA-binding protein 43 (TDP-43). Strikingly, genes and exon junctions affected by TDP-43 knockdown overlapped significantly with those dysregulated across brain regions in PD. In brains from individuals with the LRRK2 c.6055G>A (p.G2019S) mutation, the most common genetic cause of PD, we also observed significant enrichment of TDP-43-dependent splicing changes. This finding was corroborated in human pluripotent stem cell-derived midbrain dopaminergic neurons and a LRRK2 p.G2019S knock-in mouse model, where reduced nuclear TDP-43 levels evidenced the well-recognised loss-of-function mechanism contributing to splicing dysregulation. By leveraging our RNA-based analyses we predicted TDP-43-dependent novel peptide sequences and validated their existence within human LRRK2 mutation mDNs, while also demonstrating an overall loss of protein and mRNA expression in mis-spliced genes. Collectively, our findings reveal that PD is marked by extensive splicing dysregulation dependent on TDP-43, making TDP-43 a promising new therapeutic target in PD.

The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability. Its development employed an open process that included participants from multiple fields. The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories, including violent crimes, nonviolent crimes, sex-related crimes, child sexual exploitation, indiscriminate weapons, suicide and self-harm, intellectual property, privacy, defamation, hate, sexual content, and specialized advice (election, financial, health, legal). Our method incorporates a complete assessment standard, extensive prompt datasets, a novel evaluation framework, a grading and reporting system, and the technical as well as organizational infrastructure for long-term support and evolution. In particular, the benchmark employs an understandable five-tier grading scale (Poor to Excellent) and incorporates an innovative entropy-based system-response evaluation. In addition to unveiling the benchmark, this report also identifies limitations of our method and of building safety benchmarks generally, including evaluator uncertainty and the constraints of single-turn interactions. This work represents a crucial step toward establishing global standards for AI risk and reliability evaluation while acknowledging the need for continued development in areas such as multiturn interactions, multimodal understanding, coverage of additional languages, and emerging hazard categories. Our findings provide valuable insights for model developers, system integrators, and policymakers working to promote safer AI deployment.

The group of sharp and curious high school students, ranging from 9th- to 12th-graders, included two aspiring orthodontists, a TV news reporter wannabe, a future doctor, a future veterinarian and an opera singer-in-training, as well as some who aren't sure what the future holds.