BackgroundMTR gene encodes the cytoplasmic enzyme methionine synthase, which plays a pivotal role in the methionine cycle of one-carbon metabolism. This cycle holds a significant importance in generating S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), the respective universal methyl donor and end-product of epigenetic transmethylation reactions. cblG type of inherited disorders of vitamin B12 metabolism due to mutations in MTR gene exhibits a wide spectrum of symptoms, including a retinopathy unresponsive to conventional therapies.MethodsTo unveil the underlying epigenetic pathological mechanisms, we conducted a comprehensive study of epigenomic-wide alterations of DNA methylation by NGS of bisulfited retinal DNA in an original murine model with conditional Mtr deletion in retinal tissue. Our focus was on postnatal day 21, a critical developmental juncture for ocular structure refinement and functional maturation.ResultsWe observed delayed eye opening and impaired visual acuity and alterations in the one-carbon metabolomic profile, with a notable dramatic decline in SAM/SAH ratio predicted to impair DNA methylation. This metabolic disruption led to epigenome-wide changes in genes involved in eye development, synaptic plasticity, and retinoid metabolism, including promoter hypermethylation of Rarα, a regulator of Lrat expression. Consistently, we observed a decline in cone photoreceptor cells and reduced expression of Lrat, Rpe65, and Rdh5, three pivotal genes of eye retinoid metabolism.ConclusionWe introduced an original in vivo model for studying cblG retinopathy, which highlighted the pivotal role of altered DNA methylation in eye development, cone differentiation, and retinoid metabolism. This model can be used for preclinical studies of novel therapeutic targets.

PurposeThis paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, .) and services (reconfiguration, monitoring, .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required.Design/methodology/approachThis paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems.FindingsSemantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models.Originality/valueThis paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.

Meningiomas are the most common type of primary central nervous system tumors. In about 80% cases, these tumors are benign and grow very slowly, but the remainder 20% can unlock higher proliferation rates and become malignant. In this study we examined two miRs, miR-16 and miR-519, and evaluated their role in tumorigenesis and cell growth in human meningioma. A cohort of 60 intracranial grade 1 and grade 2 human meningioma plus 20 healthy meningeal tissues was used to quantify miR-16 and miR-519 expressions. Cell growth and dose-response assays were performed in two human meningioma cell lines, Ben-Men-1 (benign) and IOMM-Lee (aggressive). Transcriptomes of IOMM-lee cells were measured after both miR-mimics transfection, followed by integrative bioinformatics to expand on available data. In tumoral tissues, we detected decreased levels of miR-16 and miR-519 when compared with arachnoid cells of healthy patients (miR-16: P=8.7e-04; miR-519: P=3.5e-07). When individually overexpressing these miRs in Ben-Men-1 and IOMM-Lee, we observed that each showed reduced growth (P<0.001). In IOMM-Lee cell transcriptomes, downregulated genes, among which ELAVL1/HuR (miR-16: P=6.1e-06; miR-519:P=9.38e-03), were linked to biological processes such as mitotic cell cycle regulation, pre-replicative complex, and brain development (FDR<1e-05). Additionally, we uncovered a specific transcriptomic signature of miR-16/miR-519-dysregulated genes which was highly enriched in HuR targets (>6-fold; 79.6% of target genes). These results were confirmed on several public transcriptomic and microRNA datasets of human meningiomas, hinting that the putative tumor suppressor effect of these miRs is mediated, at least in part, via HuR direct or indirect inhibition.

BackgroundSeveral studies implicate brain-derived neurotrophic factor (BDNF) in the pathophysiology of epilepsy. In particular, preclinical data suggest that lower serum BDNF is a biomarker of epilepsy severity and psychiatric comorbidities. We tested this prediction in clinical epilepsy cohorts. MethodsPatients with epilepsy were recruited from 4 epilepsy centers in France and serum BDNF was quantified. Clinical characteristics including epilepsy duration, classification, localization, etiology, seizure frequency and drug resistance were documented. Presence of individual anti-seizure medications (ASM) was noted. Screening for depression and anxiety symptoms was carried out in all patients using the NDDI-E and the GAD-7 scales. In patients with positive screening for anxiety and/or depression, detailed psychiatric testing was performed including the Mini International Neuropsychiatric Interview (MINI), STAI-Y, Holmes Rahe Stressful Events Scale and Beck Depression Interview. Descriptive analysis was applied. Spearman’s test and Pearson’s co-efficient were used to assess the association between BDNF level and continuous variables. For discrete variables, comparison of means (Student’s t-test, Mann-Whitney u-test) was used to compare mean BDNF serum level between groups. Multivariate analysis was performed using a regression model. ResultsNo significant correlation was found between serum BDNF level and clinical features of epilepsy or measures of depression. The main group-level finding was that presence of any ASM at was associated with increased BDNF; this effect was particularly significant for valproate and perampanel. ConclusionPresence of ASM affects serum BDNF levels in patients with epilepsy. Future studies exploring BDNF as a possible biomarker of epilepsy severity and/or psychiatric comorbidity must control for ASM effects.

Bone microarchitecture has several clinical implications over and above estimating bone strength. Computed tomography (CT) analysis mainly uses high-resolution peripheral quantitative CT and micro-CT, research imaging techniques, most often limited to peripheral skeleton assessment. Ultra-high-resolution (UHR) CT and photon-counting detector CT, two commercially available techniques, provide images that can approach the spatial resolution of the trabeculae, bringing bone microarchitecture analysis into clinical practice and improving depiction of bone vascularization, tumor matrix, and cortical and periosteal bone. This review presents bone microarchitecture anatomy, principles of analysis, reference measurements, and an update on the performance and potential clinical applications of these new CT techniques. We also share our clinical experience and technical considerations using an UHR-CT device.

Unlike Quality by Testing approach, where products were tested only after drug manufacturing, Quality by Design (QbD) is a proactive control quality paradigm, which handles risks from the early development steps. In QbD, regression models built from experimental data are used to predict a risk mapping called Design Space in which the developers can identify values of critical input factors leading to acceptable probabilities to meet the efficacy and safety specifications for the expected product. These empirical models are often limited to quantitative responses. Moreover, in practice the smallness and incompleteness of datasets degrade the quality of predictions. In this study, a Bayesian approach including variable selection, parameter estimation and model quality assessment is proposed and assessed using a real case study devoted to the development of a Cationic Nano-Lipid Structures for siRNA Transfection. Two original model structures are also included to describe both binary and percentage response variables. The results confirm the practical relevance and applicability of the Bayesian implementation of the QbD analysis.