Retrotope (United States)

Abstract Background: Progressive supranuclear palsy (PSP) is a progressive movement disorder associated with lipid peroxidation and intracerebral accumulation of tau. RT001 is a deuterium reinforced isotopologue of linoleic acid that prevents lipid peroxidation (LPO) through the kinetic isotope effect. Methods: The effects of RT001 pre-treatment on various oxidative and bioenergetic parameters were evaluated in mesenchymal stem cells (MSC) derived from patients with PSP compared to controls. In parallel, 3 patients with PSP were treated with RT001 and followed clinically. Results: MSCs derived from PSP patients had a significantly higher rate of LPO (161.8 ± 8.2% of control; p<0.001). A 72-hour incubation with RT001 restored the PSP MSCs to normal levels. Mitochondrial reactive oxygen species (ROS) overproduction in PSP-MSCs significantly decreased the level of GSH compared to control MSCs (to 56% and 47% of control; p<0.05). Incubation with RT001 significantly increased level of GSH in PSP MSCs. The level of mitochondrial DNA in the cells was significantly lower in PSP-MSCs (67.5%), compared to control MSCs. Changes in mitochondrial membrane potential, size, and shape were also observed.Three subjects with possible or probable PSP were treated with RT001 for a mean duration of 26 months. The slope of the PSPRS changed from the historical decline of 0.91 points/month to a mean of decline of 0.16 points/month (+/- 0.23 SEM). The UPDRS slope changed from an expected increase of 0.95 points/month to an average increase in score of 0.28 points/month (+/- 0.41 SEM).Conclusions: MSCs derived from patients with PSP have elevated basal levels of LPO, ROS, and mitochondrial dysfunction. These findings are reversed after incubation with RT001. In PSP patients, the progression of disease may be reduced by treatment with RT001.

Abstract ObjectiveTo create and validate a clinical outcome assessment for Infantile Neuroaxonal Dystrophy (INAD)BackgroundINAD is an autosomal recessive neurogenetic disorder caused by biallelic pathogenic variants in PLA2G6. The downstream enzyme, iPLA2, plays a critical role in cell membrane homeostasis by helping to regulate levels of phospholipids. The clinical presentation occurs between 6 months and 3 years with global developmental regression, hypotonia, and progressive spastic tetraparesis. Progression is often rapid, resulting in severe spasticity, visual impairment, and cognitive decline, with many children not surviving past the first decade of life. To date, no accepted tool for assessing the severity of INAD exists; other commonly used scales (e.g. CHOP-INTEND, Modified Ashworth, Hammersmith Functional Motor Scale) do not accurately gauge the current severity of INAD, nor are they sensitive/specific enough to monitor disease progression. Finally, these other scales are not appropriate, because they do not address the combination of CNS, peripheral nerve, and visual pathology that occurs in children with INADDesign/MethodsWe have developed and validated a structured neurological examination for INAD (scored out of 80). The examination includes five main categories of pediatric developmental evaluation: 1) gross motor-and-truncal-stability skills, 2) fine motor skills, 3) bulbar function, 4) ocular function, 5) temporo-frontal function, and a functional evaluation of the autonomic nervous system. A cohort of patients diagnosed with INAD were followed prospectively to validate the score against disease severity and disease progression.ResultsWe show significant correlation between the total neurological assessment score and months since symptom onset with a statistically significant (p = 6.7 × 10− 07) correlation between assessment score and disease onset. As hypothesized, the coefficient of months-since-symptom-onset is strongly negative, indicating a negative correlation between total score and months since symptom onset.ConclusionWe have developed and validated a novel neurological assessment score in INAD that demonstrates strong correlation with disease severity and disease progression.

Protein aggregation and abnormal lipid homeostasis are both implicated in neurodegeneration through unknown mechanisms. Here we demonstrate that aggregate-membrane interaction is critical to induce a form of cell death called ferroptosis. Importantly, the aggregate-membrane interaction that drives ferroptosis depends both on the conformational structure of the aggregate, as well as the oxidation state of the lipid membrane. We generated human stem cell-derived models of synucleinopathy, characterized by the intracellular formation of α-synuclein aggregates that bind to membranes. In human iPSC-derived neurons with SNCA triplication, physiological concentrations of glutamate and dopamine induce abnormal calcium signaling owing to the incorporation of excess α-synuclein oligomers into membranes, leading to altered membrane conductance and abnormal calcium influx. α-synuclein oligomers further induce lipid peroxidation. Targeted inhibition of lipid peroxidation prevents the aggregate-membrane interaction, abolishes aberrant calcium fluxes, and restores physiological calcium signaling. Inhibition of lipid peroxidation, and reduction of iron-dependent accumulation of free radicals, further prevents oligomer-induced toxicity in human neurons. In summary, we report that peroxidation of polyunsaturated fatty acids underlies the incorporation of β-sheet-rich aggregates into the membranes, and that additionally induces neuronal death. This suggests a role for ferroptosis in Parkinson’s disease, and highlights a new mechanism by which lipid peroxidation causes cell death.

Abstract Background Infantile neuroaxonal dystrophy (INAD) is a rapidly progressive neurodegenerative disorder of early onset causing premature death. It results from biallelic pathogenic variants in PLA2G6 , which encodes a calcium‐independent phospholipase A2. Objective We aim to outline the natural history of INAD and provide a comprehensive description of its clinical, radiological, laboratory, and molecular findings. Materials and Methods We comprehensively analyzed the charts of 28 patients: 16 patients from Riyadh, Saudi Arabia, 8 patients from North and South America and 4 patients from Europe with a molecularly confirmed diagnosis of PLA2G6 ‐associated neurodegeneration (PLAN) and a clinical history consistent with INAD. Results In our cohort, speech impairment and loss of gross motor milestones were the earliest signs of the disease. As the disease progressed, loss of fine motor milestones and bulbar dysfunction were observed. Temporo-frontal function was among the last of the milestones to be lost. Appendicular spastic hypertonia, axial hypotonia, and hyperreflexia were common neurological findings. Other common clinical findings include nystagmus (60.7%), seizures (42.9%), gastrointestinal disease (42.9%), skeletal deformities (35.7%), and strabismus (28.6%). Cerebellar atrophy and elevations in serum AST and LDH levels were consistent features of INAD. There was a statistically significant difference when comparing patients with non-sense/truncating variants compared with missense/in-frame deletions in the time of initial concern (p = 0.04), initial loss of language (p = 0.001), initial loss of fine motor skills (p = 0.009), and initial loss of bulbar skills (p = 0.007). Conclusion INAD is an ultra-rare neurodegenerative disorder that presents in early childhood, with a relentlessly progressive clinical course. Knowledge of the natural history of INAD may serve as a resource for healthcare providers to develop a targeted care plan and may facilitate the design of clinical trials to treat this disease.

New model architecture: DistilBERT Adding Huggingface's new transformer architecture, <strong>DistilBERT</strong> described in Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT by Victor Sanh, Lysandre Debut and Thomas Wolf. This new model architecture comes with two pretrained checkpoints: <code>distilbert-base-uncased</code>: the base DistilBert model <code>distilbert-base-uncased-distilled-squad</code>: DistilBert model fine-tuned with distillation on SQuAD. An awaited new pretrained checkpoint: GPT-2 large (774M parameters) The third OpenAI GPT-2 checkpoint (GPT-2 large) is available in the library under the shortcut name <code>gpt2-large</code>: 774M parameters, 36 layers, and 20 heads. New XLM multilingual pretrained checkpoints in 17 and 100 languages We have added two new XLM models in 17 and 100 languages which obtain better performance than multilingual BERT on the XNLI cross-lingual classification task. New dependency: <code>sacremoses</code> Support for XLM is improved by carefully reproducing the original tokenization workflow (work by @shijie-wu in #1092). We now rely on <code>sacremoses</code>, a python port of Moses tokenizer, truecaser and normalizer by @alvations, for XLM word tokenization. In a few languages (Thai, Japanese and Chinese) XLM tokenizer will require additional dependencies. These additional dependencies are optional at the library level. Using XLM tokenizer in these languages without the additional dependency will raise an error message with installation instructions. The additional optional dependencies are: pythainlp: Thai tokenizer kytea: Japanese tokenizer, wrapper of KyTea (Need external C++ compilation), used by the newly release XLM-17 &amp; XLM-100 jieba: Chinese tokenizer * * XLM used Stanford Segmenter. However, the wrapper (nltk.tokenize.stanford_segmenter) are slow due to JVM overhead, and it will be deprecated. Jieba is a lot faster and pip-installable. But there is some mismatch with the Stanford Segmenter. A workaround could be having an argument to allow users to segment the sentence by themselves and bypass the segmenter. As a reference, I also include nltk.tokenize.stanford_segmenter in this PR. Bug fixes and improvements to the library modules Bertology script has seen major improvements (@tuvuumass ) Iterative tokenization now faster and accept arbitrary numbers of added tokens (@samvelyan) Added RoBERTa to AutoModels and AutoTokenizers (@LysandreJik ) Added GPT-2 Large 774M model (@thomwolf ) Added language model fine-tuning with GPT/GPT-2 (CLM), BERT/RoBERTa (MLM) (@LysandreJik @thomwolf ) Multi-GPU training has been patched (@FeiWang96 ) Scripts are updated to reflect Pytorch 1.1.0 changes (scheduler, optimizer) (@Morizeyao, @adai183 ) Updated the in-depth BERT fine-tuning scripts to <code>pytorch-transformers</code> (@Morizeyao ) Models saved with pruned heads are now saved and reloaded correctly (implemented for GPT, GPT-2, BERT, RoBERTa, XLM) (@LysandreJik @thomwolf) Add <code>proxies</code> and <code>force_download</code> options to <code>from_pretrained()</code> method to be able to use proxies and update cached models/tokenizers (@thomwolf) Add shortcut to each special tokens with <code>_id</code> properties (e.g. <code>tokenizer.cls_token_id</code> for the id in the vocabulary of <code>tokenizer.cls_token</code>) (@thomwolf) Fix GPT2 and RoBERTa tokenizer so that sentences to be tokenized always begins with at least one space (see note by fairseq authors) (@thomwolf) Fix and clean up byte-level BPE tests (@thomwolf) Update the test classes for OpenAI GPT and GPT-2 so that these models are tested against common tests. (@LysandreJik ) Fix a warning raised when the decode method is called for a model with no <code>sep_token</code> like GPT-2 (@LysandreJik ) Updated the tokenizers saving method (@boy2000-007man) SpaCy tokenizers have been updated in the tokenizers (@GuillemGSubies ) Stable <code>EnvironmentErrors</code> have been added to utility files (@abhishekraok ) Fixed distributed barrier hang (@VictorSanh ) Encoding functions now return the input tokens instead of throwing an error when not implemented in child class (@LysandreJik ) Change layer norm code to PyTorch's native layer norm (@dhpollack) Improve tokenization of XLM for multilingual inputs (@shijie-wu) Add language input and access to language to id conversion in XLM tokenizer (@thomwolf) Add pretrained configuration properties for tokenizers with serialization logic (saving/reloading tokenizer configuration) (@thomwolf) Added new AutoModels: <code>AutoModelWithLMHead</code>, <code>AutoModelForSequenceClassification</code>, <code>AutoModelForQuestionAnswering</code> (@LysandreJik) Torch.hub is now based on AutoModels (@LysandreJik @thomwolf) Fix Transformer-XL attention mask dtype to be bool (@CrafterKolyan) Adding DistilBert model architecture and checpoints (@VictorSanh @LysandreJik @thomwolf) Fixes to DistilBert configuration and training script (@stefan-it) Fix XLNet attention mask for fp16 (@ziliwang) Documentation auto-deploy (@LysandreJik) Fix to add a tuple of tokens (@epwalsh) Update fp16 apex implmentation in scripts (@anhnt170489) Fix XLNet bias resizing when adding/removing tokens (@LysandreJik) Fix tokenizer reloading in example scripts (@rabeehk) Fix byte-level decoding error when using added tokens (@thomwolf @LysandreJik) Fix epsilon value in RoBERTa pretrained checkpoints (@julien-c)

ABSTRACTWhile primary open-angle glaucoma (POAG) is a leading cause of blindness worldwide, it still does not have a clear mechanism that can explain all clinical cases of the disease. Elevated IOP is associated with increased accumulation of extracellular matrix (ECM) proteins in the trabecular meshwork (TM) that prevents normal outflow of aqueous humor (AH) and has damaging effects on the fine mesh-like lamina cribrosa (LC) through which the optic nerve fibers pass. Applying a pathway analysis algorithm, we discovered that an elevated level of TGFβ observed in glaucoma-affected tissues could lead to pro-fibrotic pathway activation in TM and in LC. In turn, activated pro-fibrotic pathways lead to ECM remodeling in TM and LC, making TM less efficient in AH drainage and making LC more susceptible to damage from elevated IOP via ECM transformation in LC. We propose pathway targets for potential therapeutic interventions to delay or avoid fibrosis initiation in TM and LC tissues.

Age-related macular degeneration (AMD) is a major cause of blindness in older people and is caused by loss of the central region of the retinal pigment epithelium (RPE). Conventional methods of gene expression analysis have yielded important insights into AMD pathogenesis, but the precise molecular pathway alterations are still poorly understood. Therefore we developed a new software program, “AMD Medicine”, and discovered differential pathway activation profiles in samples of human RPE/choroid from AMD patients and controls. We identified 29 pathways in RPE-choroid AMD phenotypes: 27 pathways were activated in AMD compared to controls, and 2 pathways were activated in controls compared to AMD. In AMD, we identified a graded activation of pathways related to wound response, complement cascade, and cell survival. Also, there was downregulation of two pathways responsible for apoptosis. Furthermore, significant activation of pro-mitotic pathways is consistent with dedifferentiation and cell proliferation events, which occur early in the pathogenesis of AMD. Significantly, we discovered new global pathway activation signatures of AMD involved in the cell-based inflammatory response: IL-2, STAT3, and ERK. The ultimate aim of our research is to achieve a better understanding of signaling pathways involved in AMD pathology, which will eventually lead to better treatments.