Non-coding RNA in Fragile X Syndrome and Converging Mechanisms Shared by Related Disorders

Fragile X syndrome (FXS) is one of the most common forms of hereditary intellectual disability. It is also a well-known monogenic cause of autism spectrum disorders (ASD). Repetitive trinucleotide expansion of CGG repeats in the 5′-UTR of FMR1 is the pathological mutation. Full mutation CGG repeats epigenetically silence FMR1 and thus lead to the absence of its product, fragile mental retardation protein (FMRP), which is an indispensable translational regulator at synapsis. Loss of FMRP causes abnormal neural morphology, dysregulated protein translation, and distorted synaptic plasticity, giving rise to FXS phenotypes. Non-coding RNAs, including siRNA, miRNA, and lncRNA, are transcribed from DNA but not meant for protein translation. They are not junk sequence but play indispensable roles in diverse cellular processes. FXS is the first neurological disorder being linked to miRNA pathway dysfunction. Since then, insightful knowledge has been gained in this field. In this review, we mainly focus on how non-coding RNAs, especially the siRNAs, miRNAs, and lncRNAs, are involved in FXS pathogenesis. We would also like to discuss several potential mechanisms mediated by non-coding RNAs that may be shared by FXS and other related disorders

Multimodal Molecular Pretraining via Modality Blending

Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery. Current studies consider leveraging both 2D and 3D molecular structures for representation learning. However, relying on straightforward alignment strategies that treat each modality separately, these methods fail to exploit the intrinsic correlation between 2D and 3D representations that reflect the underlying structural characteristics of molecules, and only perform coarse-grained molecule-level alignment. To derive fine-grained alignment and promote structural molecule understanding, we introduce an atomic-relation level "blend-then-predict" self-supervised learning approach, MoleBLEND, which first blends atom relations represented by different modalities into one unified relation matrix for joint encoding, then recovers modality-specific information for 2D and 3D structures individually. By treating atom relationships as anchors, MoleBLEND organically aligns and integrates visually dissimilar 2D and 3D modalities of the same molecule at fine-grained atomic level, painting a more comprehensive depiction of each molecule. Extensive experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D molecular benchmarks. We further provide theoretical insights from the perspective of mutual-information maximization, demonstrating that our method unifies contrastive, generative (cross-modality prediction) and mask-then-predict (single-modality prediction) objectives into one single cohesive framework

Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network

Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results

Age of onset correlates with clinical characteristics and prognostic outcomes in neuromyelitis optica spectrum disorder

ObjectiveNeuromyelitis optica spectrum disorder (NMOSD) is an inflammatory disease preferentially affects the optic nerve and the spinal cord. The first attack usually occurs in the third or fourth decade, though patients with disease onset in the fifties or later are not uncommon. This study aimed to investigate the clinical characteristics and prognosis in patients with different age of onset and to explore the correlations between age of onset and clinical characteristics and prognostic outcomes.MethodWe retrospectively reviewed the medical records of 298 NMOSD patients diagnosed according to the 2015 updated version of diagnostic criteria. Patients were divided into early-onset NMOSD (EO-NMOSD) (<50 years at disease onset) and late-onset NMOSD (LO-NMOSD) (≥50 years at disease onset) based on the age of disease onset. LO-NMOSD patients were divided into two subgroups: relative-late-onset NMOSD (RLO-NMOSD) (50~70 years at disease onset) and very-late-onset NMOSD (≥70 years at disease onset). Clinical characteristics, laboratory findings, neuroimaging features, and prognostic outcomes were investigated.ResultsCompared to EO-NMOSD patients, patients with LO-NMOSD showed more frequent transverse myelitis (TM) (58.20% vs. 36.00%, p = 0.007) while less frequent optic neuritis (ON) (23.10% vs. 34.80%, p = 0.031) and brainstem/cerebral attacks (7.50% vs. 18.30%, p = 0.006) as the first attack. Patients with LO-NMOSD showed less frequent relapses, higher Expanded Disability Status Scale (EDSS) score at the last follow-up, fewer NMOSD-typical brain lesions, and longer segments of spinal cord lesions. Patients with older onset age showed a higher proportion of increased protein levels in cerebrospinal fluid during the acute phase of attacks. Age at disease onset positively correlated with length of spinal cord lesions at first attack and at last follow-up, negatively correlated with ARR-1 (ARR excluding the first attack, calculated from disease onset to final follow-up), irrespective of AQP4-IgG serostatus. Patients with older age at disease onset progressed to severe motor disability sooner, and age of onset positively correlated with EDSS score at the last follow-up, irrespective of AQP4-IgG serostatus.ConclusionAge of disease onset affects clinical characteristics and prognosis outcomes of patients with NMOSD

Retrospective study on MGMT methylation status and its clinical significance in gliomas

Background and purpose: Glioma is a common malignant tumor of central nervous system with poor prognosis. Postoperative concurrent chemoradiotherapy with temozolomide (TMZ) is the main treatment for glioma. The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter can predict the sensitivity of glioma patients to TMZ treatment, however its relationship with clinical pathology and how to better predict treatment and prognosis still need further research. The purpose of this study was to analyze the status of MGMT promoter methylation (MGMTmet) in gliomas and its correlation with clinical pathological features and other common molecular abnormalities, and to explore the value of combined analysis of MGMTmet and other molecular abnormalities in predicting the prognosis of glioma and the efficacy of TMZ treatment. Methods: We retrospectively collected clinical and pathological data from 205 glioma patients diagnosed by the Department of Pathology, Fudan University Shanghai Cancer Center from July 2019 to September 2022. Real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) was used to detect MGMTmet status. Sanger sequencing was used to detect the mutation of isocitrate dehydrogenase 1 and 2 (IDH1/2) and telomerase reverse transcriptase (TERT) genes. Fluorescence in situ hybridization (FISH) was used to detect the deletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p19q). Results: Among 205 patients, the incidence of MGMTmet was higher in female patients than in male patients. Compared to glioblastoma (47.3%), astrocytoma (74.1%) and oligodendroglioma (100.0%) were more prone to methylation of the MGMT gene promoter (P<0.05). In MGMTmet group, IDH1 mutation rate and 1p19q co-deletion rate were significantly increased, and methylation of MGMT promoter was correlated with IDH1 mutation and 1p19q co-deletion (P<0.05). Patients with MGMTmet, age less than 55 years, oligodendroglioma, and World Health Organization (WHO) grade 1-3 all showed longer overall survival (OS), and the difference is statistically significant (P<0.05). Compared with individual influencing factors, dual/triple gene combination analysis (MGMTmet/IDH1 mutation or MGMTmet/1p19q co-deletion or MGMTmet/IDH1 mutation/1p19q co-deletion) had better effect for predicting the patient prognosis (P<0.05), with the latter two being independent prognostic factors. Among TMZ treated patients, MGMTmet (MGMTmet/TMZ+) patients had a better prognosis than other groups. If the patients had combined IDH1 mutations, the prognosis of the patients was further improved (P<0.05). Conclusion: MGMTmet is more common in women and patients with oligodendroglioma. It is positively correlated with IDH1 mutation and 1p19q co-deletion. Patients with MGMTmet are associated with better TMZ treatment efficacy and prognosis, and MGMTmet combined with IDH mutations and 1p19q co-deletion analysis have better TMZ treatment efficacy and prognostic implications

Factors Associated With Dyskinesia in Parkinson's Disease in Mainland China

Background and Objectives: Studies examining the risk factors for dyskinesia in Parkinson's disease (PD) have been inconsistent, and racial differences exist. Since there have been no systematic studies of the characteristics of dyskinesia in the Mainland Chinese population, we sought to elucidate the risk factors for dyskinesia.Methods: A total of 1974 PD patients from Mainland China were systematically investigated by univariable and multivariable analyses. PD patients with and without dyskinesia were stratified into 4 groups according to levodopa equivalent daily dose (LEDD) and analyzed by a Cox proportional hazards model. A longitudinal study of 87 patients with dyskinesia was classified into 3 groups according to the duration from onset of PD to the initiation of levodopa, and comparisons among groups were analyzed by the Mann-Whitney test.Results: Early age of onset, long disease duration, being female, high LEDD, low UPDRS III scores (ON-state) and high Hoehn-Yahr stage (ON-state) were predictors of dyskinesia. Dyskinesia was levodopa dosage-dependent, and the incidence increased remarkably when LEDD exceeded 300 mg/d (p < 0.05). The emergence of dyskinesia had no association with the initiation time of levodopa, and if the latter was more than 4 years, the duration of time on chronic levodopa free of motor complications was significantly shortened.Conclusions: We found risk factors for the prediction of dyskinesia. Our data shows that physicians should be cautious if the LEDD exceeds 300 mg/d. The development of dyskinesia was not correlated with the time of levodopa initiation

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study

ObjectiveAlthough risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson’s disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).Materials and methodsA total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.ResultsAt the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson’s Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan–Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.ConclusionIn this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy

A Highly Hydrophilic and Biodegradable Novel Poly(amide-imide) for Biomedical Applications

A novel biodegradable poly(amide-imide) (PAI) with good hydrophilicity was synthesized by incorporation of l-glycine into the polymer chain. For comparison purposes, a pure PAI containing no l-glycine was also synthesized with a three-step method. In this study, we evaluated the novel PAI’s thermal stability, hydrophilicity, solubility, biodegradability and ability to support bone marrow mesenchymal stem cell (BMSC) adhesion and growth by comparing with the pure PAI. The hydrophilic tests demonstrated that the novel PAI has possible hydrophilicity at a 38° water contact angle on the molecule surface and is about two times more hydrophilic than the pure PAI. Due to an extra unit of l-glycine in the novel PAI, the average degradation rate was about 2.4 times greater than that of the pure PAI. The preliminary biocompatibility studies revealed that all the PAIs are cell compatible, but the pure PAI exhibited much lower cell adhesion than the l-glycine-incorporated novel PAI. The hydrophilic surface of the novel PAI was more suitable for cell adhesion, suggesting that the surface hydrophilicity plays an important role in enhancing cell adhesion and growth

the coexistence of competitions and cooperations problems ∗

Abstract. In this paper, we present a class of interactional programming model (IPM) for the coexistence of competitions and cooperations problems, which can be regarded as decision making problems, such as supply chains. We introduce the optimal solution to the IPM, which is ideal and often does not exist. Therefore, we give a new concept of s-optimal solution, which always exists under some conditions. We prove that the soptimal solution to IPM can be obtained by solving a non-linear programming problem after we obtain some optimal solutions to the sub-problem of each decision maker. Some examples are given to explain how to compute an s-optimal solution