Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation.

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person's abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately 76.6% in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a 5% improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen's Kappa ( κlw = 0.733 ). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at 0.95 , we attain an impressive estimation accuracy of 88% . Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.

An in silico approach to identify early damage biomarker candidates in metachromatic leukodystrophy.

Metachromatic leukodystrophy (MLD) is a rare, autosomal recessive lysosomal storage disease. Deficient activity of arylsulfatase A causes sulfatides to accumulate in cells of different tissues, including those in the central and peripheral nervous systems, leading to progressive demyelination and neurodegeneration. Although there is some association between specific arylsulfatase A alleles and disease severity, genotype-phenotype correlations are not fully understood. We aimed to identify biomarker candidates of early tissue damage in MLD using a modeling approach based on systems biology. A review of the literature was performed in an initial disease characterization step, allowing identification of pathophysiological processes involved in MLD and proteins relating to these processes. Three mathematical models were generated to simulate different stages of MLD at the molecular level: an early pro-inflammatory stage model (including only processes considered to be active in the early stages of disease), a pre-demyelination stage model (including additional processes that are active after some disease progression), and a demyelination stage model (in which all pathophysiological processes are active). The models evaluated 3457 proteins of interest, individually and by pairs through data mining techniques, applying five filters to prioritize biomarkers that could differentiate between the models. Sixteen potential biomarkers were identified, including effectors relating to mitochondrial dysfunction, remyelination, and neurodegeneration. The findings were corroborated in a gene expression data set from T lymphocytes of patients with MLD; all candidates formed combinations that were able to distinguish patients with MLD from controls, and all but one candidate distinguished late-infantile MLD from juvenile MLD as part of a combinatorial biomarker pair. In particular, pro-neuregulin-1 appeared as differential on all comparisons (patients with MLD vs controls and within clinical subtypes); casein kinase II subunit alpha was detected as a potential individual marker within clinical subtypes. These findings provide a panel of biomarker candidates suitable for experimental validation and highlight the utility of mathematical models to identify biomarker candidates of early tissue damage in MLD with a high degree of accuracy and sensitivity.

Increased glycolipid storage produced by the inheritance of a complex intronic haplotype in the α-galactosidase A (GLA) gene.

BACKGROUND: Accumulation of galactosphingolipids is a general characteristic of Fabry disease, a lysosomal storage disorder caused by the deficient activity of α-galactosidase A encoded by the GLA gene. Although many polymorphic GLA haplotypes have been described, it is still unclear whether some of these variants are causative of disease symptoms. We report the study of an inheritance of a complex intronic haplotype (CIH) (c.-10C > T, c.369 + 990C > A, c.370-81_370-77delCAGCC, c.640-16A > G, c.1000-22C > T) within the GLA gene associated with Fabry-like symptoms and galactosphingolipid accumulation. We analysed α-Gal A activity in plasma, leukocytes and skin fibroblasts in patients, and measured accumulation of galactosphingolipids by enzymatic methods and immunofluorescence techniques. Additionally, we evaluated GLA expression using quantitative PCR, EMSA, and cDNA cloning. RESULTS: CIH carriers had an altered GLA expression pattern, although most of the carriers had high residual enzyme activity in plasma, leukocytes and in skin fibroblasts. Nonetheless, CIH carriers had significant galactosphingolipid accumulation in fibroblasts in comparison with controls, and also glycolipid deposits in renal tubules and glomeruli. EMSA assays indicated that the c.-10C > T variant in the promoter affected a nuclear protein binding site. CONCLUSIONS: Thus, inheritance of the CIH caused an mRNA deregulation altering the GLA expression pattern, producing a tissue glycolipid storage.

The influence of genetic variability and proinflammatory status on the development of bone disease in patients with Gaucher disease.

Gaucher disease, the most common lysosomal storage disorder, is caused by ß-glucocerebrosidase deficiency. Bone complications are the major cause of morbidity in patients with type 1 Gaucher disease (GD1). Genetic components strongly influence bone remodelling. In addition, chronic inflammation produced by Gaucher cells induces the production of several cytokines, which leads to direct changes in the bone remodelling process and can also affect the process indirectly through other immune cells. In this study, we analysed the association between bone mineral density (BMD), bone marrow burden score, and relevant genetic polymorphisms related to bone metabolism, as well as profiles of proinflammatory cytokines in a GD1 cohort. This study included 83 patients distributed according to bone status. BMD was measured with DXA and broadband ultrasound attenuation; bone marrow involvement was evaluated using MRI. We also analysed 26 SNPs located in 14 genes related to bone metabolism. To assess proinflammatory status, we analysed IL-4, IL-6, IL-7, IL-10, IL-13, MIP-1α, MIP-1ß, and TNFα in plasma samples from 71 control participants and GD1 patients. SNP genotype proportions and BMD differed significantly between ESRI c.453-397T>C and VDR c.1024+283G>A variants. We also observed significant associations between GD1 genotypes and bone affectation. When patients were stratified by spleen status, we observed significant correlations between non-/splenectomized groups and Spanish MRI (S-MRI) score. Across genotype proportions of non-/splenectomized patients and S-MRI, we observed significant differences in ESRI c.453-397T>C, VDR c.-83-25988G>A, and TNFRSF11B c.9C>G polymorphisms. We observed different significant proinflammatory profiles between control participants, treatment-naïve patients, and patients on enzyme replacement therapy (ERT); between non-/splenectomized patients (between untreated and ERT-treated patients) and among those with differing GBA genotypes. The data suggest that patients with GD1 have increased susceptibility to developing bone disease owing to the coexistence of genetic variants, and that genetic background in GD1 is fundamental to regulate the impact of proinflammatory status on the development of bone disease.

Increased serum interleukin-17 levels in patients with myasthenia gravis.

It has been suggested that interleukin-17 (IL-17) plays a crucial role in the development of several autoimmune diseases. However, there are no data about the relationship between myasthenia gravis and IL-17. The aim of this study was to measure the concentration of IL-17 and determine whether levels depend on the severity of MG. Serum IL-17 concentrations were measured in 25 patients. IL-17 concentrations were higher in generalized MG compared with controls and correlated with anti-acetylcholinesterase receptor antibody titers.