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PubMed2026

Context matters: coordinated transcriptional regulation and root plasticity under multinutrient conditions.

Plants often encounter simultaneous imbalances in multiple nutrients, but the regulatory logic coordinating their responses remains poorly understood. We aimed to uncover shared transcriptional programs and regulatory nodes underpinning multinutrient adaptation in Arabidopsis thaliana roots. We analyzed publicly available RNA-seq datasets spanning 15 nutrient and beneficial element conditions using differential expression, co-expression network (WGCNA), and gene regulatory network analysis. Selected transcription factors (TFs) were validated via root phenotyping, suberin staining, and ionomic profiling under two-nutrient stress conditions. We identified a core set of 2050 genes responsive to multiple nutrient treatments, enriched for suberin biosynthesis, and structured into modular co-expression clusters. Eight prioritized candidate TFs (ARR10, GBF3, HHO5, NAC32, NF-YA3, NF-YB2, SARD1, and WRKY33) were shown to modulate root system architecture under specific nutrient combinations. WRKY33 and NF-YB2, in particular, regulated nutrient-responsive suberin deposition and ionomic plasticity. These findings reveal suberin remodeling as a shared downstream process in multinutrient responses and suggest that plasticity is not a fixed trait but a modular, polygenic, and context-dependent outcome. Repurposed TFs with pleiotropic functions coordinate structural and physiological traits, providing regulatory entry points for improving nutrient resilience.

Plant Roots↗
PubMed1994

Artificial intelligence within the chemical laboratory.

Various techniques within the area of artificial intelligence such as expert systems and neural networks may play a role during the problem-solving processes within the clinical biochemical laboratory. Neural network analysis provides a non-algorithmic approach to information processing, which results in the ability of the computer to form associations and to recognize patterns or classes among data. It belongs to the machine learning techniques which also include probabilistic techniques such as discriminant function analysis and logistic regression and information theoretical techniques. These techniques may be used to extract knowledge from example patients to optimize decision limits and identify clinically important laboratory quantities. An expert system may be defined as a computer program that can give advice in a well-defined area of expertise and is able to explain its reasoning. Declarative knowledge consists of statements about logical or empirical relationships between things. Expert systems typically separate declarative knowledge residing in a knowledge base from the inference engine: an algorithm that dynamically directs and controls the system when it searches its knowledge base. A tool is an expert system without a knowledge base. The developer of an expert system uses a tool by entering knowledge into the system. Many, if not the majority of problems encountered at the laboratory level are procedural. A problem is procedural if it is possible to write up a step-by-step description of the expert's work or if it can be represented by a decision tree. To solve problems of this type only small expert system tools and/or conventional programming are required.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗
PubMed1998

FTIR microspectroscopic study of cell types and potential confounding variables in screening for cervical malignancies.

FTIR microscopy was applied to the analysis of cell types and other variables present in Pap smears to ascertain the limitations of infrared spectroscopy in the diagnosis of cervical cancer and dysplasia. It was found that leukocytes, and in particular lymphocytes, have spectral features in the phosphodiester region (1300-900 cm[-1]) suggestive of what has previously been described as changes indicative of malignancy. Endocervical cells and fibroblasts have similar spectral features to HeLa cells and consequently could also confound diagnosis. The use of ethanol as a fixative and dehydrating agent results in retention of glycogen in cervical cell types and thus minimizes spectral changes in the glycogen region due to sampling technique. Spectra of seminal fluids exhibit strong bands in the phosphodiester/carbohydrate region; however, sperm contamination should be easily detectable by the presence of a distinctive doublet at 981/968 cm(-1). Erythrocyte spectra exhibit a reduction in glycogen band intensity, but can be discerned by a relatively low-intensity nu(s) PO2- band. Endocervical mucin spectra exhibit a reduction in glycogen bands and a very pronounced nu(s) PO2- band, which is similar in intensity to the corresponding band in HeLa cells. Thrombocytes have strong bands in the phosphodiester region, but thrombocytes can be discerned from other cell types by the presence of two small broad bands at 980 and 935 cm(-1). Candida albicans is characterized by strong bands in the polysaccharide region which could potentially obscure diagnostic bands if C. albicans is present in large numbers. Spectra of bacteria common to the female genital tract, in general, also have strong absorptions in the polysaccharide region; however, bacterial contamination is usually minimal and would not be expected to obscure cervical cell spectra. Nylon threads and bristles from cervical sampling implements produce characteristic IR profiles which allow for easy identification. Given the number of potential confounding variables associated with cervical cytology, a multivariate statistical or neural network analysis would appear to be necessary before the implementation of FTIR technology in clinical laboratories.

Biopsy↗
PubMed2005

Overlap in the functional neural systems involved in semantic and episodic memory retrieval.

Neuroimaging and neuropsychological data suggest that episodic and semantic memory may be mediated by distinct neural systems. However, an alternative perspective is that episodic and semantic memory represent different modes of processing within a single declarative memory system. To examine whether the multiple or the unitary system view better represents the data we conducted a network analysis using multivariate partial least squares (PLS ) activation analysis followed by covariance structural equation modeling (SEM) of positron emission tomography data obtained while healthy adults performed episodic and semantic verbal retrieval tasks. It is argued that if performance of episodic and semantic retrieval tasks are mediated by different memory systems, then there should differences in both regional activations and interregional correlations related to each type of retrieval task, respectively. The PLS results identified brain regions that were differentially active during episodic retrieval versus semantic retrieval. Regions that showed maximal differences in regional activity between episodic retrieval tasks were used to construct separate functional models for episodic and semantic retrieval. Omnibus tests of these functional models failed to find a significant difference across tasks for both functional models. The pattern of path coefficients for the episodic retrieval model were not different across tasks, nor were the path coefficients for the semantic retrieval model. The SEM results suggest that the same memory network/system was engaged across tasks, given the similarities in path coefficients. Therefore, activation differences between episodic and semantic retrieval may ref lect variation along a continuum of processing during task performance within the context of a single memory system.

Adult↗
PubMed2025

A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. METHODS: We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. RESULTS: DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. CONCLUSION: Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.

Pulmonary Disease, Chronic Obstructive↗
PubMed2025

Developing a disease-specific accessible transcriptional signature as a biomarker for ataxia with oculomotor apraxia type 2.

BACKGROUND: Genetic ataxias are clinically heterogenous neurodegenerative conditions often involving rare or private mutations and it is often difficult to assign pathogenicity to rare gene variants solely based on DNA sequencing. An effective functional assay from an easy-to-obtain biospecimen would aid this assessment and be of high clinical value. SETX encodes a ubiquitous DNA/RNA helicase crucial for resolving R-loops and maintaining genome stability. Loss-of-function mutations cause a recessive disorder, Ataxia with Oculomotor Apraxia Type 2 (AOA2). METHODS: Here we utilize Weighted Gene Co-expression Network Analysis (WGCNA) from patient blood to construct an AOA2-specific transcriptomic signature as a biomarker to evaluate SETX variants in patients clinically suspected of having AOA2. RESULTS: WGCNA from peripheral blood RNA of 11 AOA2 patients from 7 families initially identified a single gene module that was modestly effective in distinguishing individuals with AOA2 from controls (sensitivity 73%, specificity 97%) and was able to robustly differentiate AOA2 patients from those with genetically distinct, yet phenotypically similar, neurological disorders (sensitivity 100%, specificity 100%). An independent derivation of the transcriptional biomarker identified a dual module model that was able to better distinguish individuals with AOA2 from controls (sensitivity 100%, specificity 97%). As validation, we examined a second cohort of 21 patients from 13 families and demonstrate that this dual module transcriptional biomarker could discriminate patients clinically suspected of AOA2 from controls (57%, 95%CI: 34%-78%). Overall, the transcriptional biomarker was able to separate AOA2 subjects (n = 32) from controls (n = 35) with 72% sensitivity and 97% specificity. Notably, this transcriptomic biomarker enabled verification of the first pathogenic SETX mutation found in a non-canonical transcript, expanding the spectrum of mutations that contribute to AOA2. CONCLUSIONS: Our study identified a transcriptional biomarker that was able to differentiate AOA2 from controls and from other related neurological disorders, consequently expanding the spectrum of known pathogenic mutations. This proof-of-concept study illustrates that transcriptional biomarkers may be used to validate variants of uncertain significance in known genetic diseases.

Humans↗
PubMed2004

Preoperative neural network using combined magnetic resonance imaging variables, prostate-specific antigen, and Gleason score to predict positive surgical margins.

OBJECTIVES: To assess whether artificial neural network analysis (ANNA) predicts for positive surgical margins (PSMs) more effectively than logistic regression analysis (LRA) according to the combined use of the findings of pelvic coil magnetic resonance imaging (pMRI) and other preoperatively available tumor variables in patients with clinically organ-confined prostate cancer after radical prostatectomy. METHODS: A total of 205 patients with clinically localized prostate cancer, who underwent retropubic radical prostatectomy were evaluated. The predictive variables included clinical TNM stage, prostate-specific antigen (PSA) level, PSA density, biopsy Gleason score, percentage of cancer in biopsy specimens, and pMRI findings. The predicted outcome was PSMs. The patient data were randomly split into four cross-validation sets and used to develop and validate the ANNA and LRA models. For comparison, the area under the receiver operating characteristic curve was used. RESULTS: The overall PSM rate was 22% (n = 45). Using all input parameters, the accuracy of the ANNA and LRA was 84% and 75% for the prediction of PSMs, respectively. The area under the receiver operating characteristic curve of the ANNA (0.872 +/- 0.014) was significantly greater statistically (P <0.001) than that for LRA (0.791 +/- 0.006). The simplified ANNA models that used the pMRI findings in addition to PSA and Gleason score were as accurate as the model that used all the variables (P = 0.89). A high percentage of cancer in the biopsy specimens, pMRI findings, and high PSA density were equally the most influential predictors (relative weight 1.881, 1.964, and 1.493, respectively). CONCLUSIONS: All the ANNA models in this study were superior to LRA in the prediction of PSMs. The ANNA using pMRI findings, PSA level, and Gleason score as input variables performed as well as the ANNA using all the input parameters. Additional studies seem warranted.

Adenocarcinoma↗
PubMed2005

Synchronized clusters in coupled map networks. II. Stability analysis.

We study self-organized and driven synchronization in some simple coupled map networks, namely globally coupled networks and complete bipartite networks, using both linear stability analysis and Lyapunov function approach and determine stability conditions for synchronization. The phase diagrams for the networks studied here have features very similar to the different kinds of structurally similar networks studied in Part I. Lyapunov function approach shows that when any two nodes are in driven synchronization, all the coupling terms in the difference between the variables of these two nodes cancel out, whereas when they are in self-organized synchronization, the direct coupling term between the two nodes adds an extra term while the other couplings cancel out. We also discuss the conditions for the occurrence of a floating node and suggest that the fluctuations of the conditional Lyapunov exponent about zero can be a criterion for its occurrence.

Journal Article↗
PubMed1997

Beyond single indicators of social networks: a LISREL analysis of social ties among the elderly.

While the health promoting influences of social networks have been shown in a number of studies, little attention has been paid to measurement issues within the field of epidemiology. The purpose of this paper is to propose a new set of measures of social networks for use in epidemiological research on the elderly. We use confirmatory factor analysis to test a multidimensional model of social networks using data from a large epidemiologic study of community-dwelling adults age 65 and over (U.S.A.). Confirmatory factor analysis conducted using LISREL showed that our model provides a good fit to the data after several adjustments for correlated measurement error were introduced. Based on this analysis, we developed new measures of four dimensions and a summary index of social networks. Bivariate relationships between our new measures and several sociodemographic variables of interest are also presented.

Aged↗
PubMed1998

Transcriptional profiling on all open reading frames of Saccharomyces cerevisiae.

Open reading frames (6116) of the budding yeast Saccharomyces cerevisiae were PCR-amplified from genomic DNA using 12,232 primers specific to the ends of the coding sequences; the success rate of amplification was 97%. PCR-products were made accessible to hybridization by being arrayed at very high density on solid support media using various robotic devices. Probes made from total RNA preparations were hybridized for the analysis of the transcriptional activity of yeast under various growth conditions and of different strains. Experimental factors that proved critical to the performance, such as different RNA isolation procedures and the assessment of hybridization results, for example, were investigated in detail. Various software tools were developed that permit convenient handling and sound analysis of the large data quantities obtained from transcriptional profiling studies. Comprehensive arrays are being distributed within the European Yeast Functional Analysis Network (EUROFAN) and beyond.

Blotting, Western↗
PubMed2000

Analysis of deletion phenotypes and GFP fusions of 21 novel Saccharomyces cerevisiae open reading frames.

As part of EUROFAN (European Functional Analysis Network), we investigated 21 novel yeast open reading frames (ORFs) by growth and sporulation tests of deletion mutants. Two genes (YNL026w and YNL075w) are essential for mitotic growth and three deletion strains (ynl080c, ynl081c and ynl225c) grew with reduced rates. Two genes (YNL223w and YNL225c) were identified to be required for sporulation. In addition we also performed green fluorescent protein (GFP) tagging for localization studies. GFP labelling indicated the spindle pole body (Ynl225c-GFP) and the nucleus (Ynl075w-GFP) as the sites of action of two proteins. Ynl080c-GFP and Ynl081c-GFP fluorescence was visible in dot-shaped and elongated structures, whereas the Ynl022c-GFP signal was always found as one spot per cell, usually in the vicinity of nuclear DNA. The remaining C-terminal GFP fusions did not produce a clearly identifiable fluorescence signal. For 10 ORFs we constructed 5'-GFP fusions that were expressed from the regulatable GAL1 promoter. In all cases we observed GFP fluorescence upon induction but the localization of the fusion proteins remained difficult to determine. GFP-Ynl020c and GFP-Ynl034w strains grew only poorly on galactose, indicating a toxic effect of the overexpressed fusion proteins. In summary, we obtained a discernible GFP localization pattern in five of 20 strains investigated (25%). A deletion phenotype was observed in seven of 21 (33%) and an overexpression phenotype in two of 10 (20%) cases.

Gene Deletion↗
PubMed1996

Protein structure and the sequential structure of mRNA: alpha-helix and beta-sheet signals at the nucleotide level.

A direct comparison of experimentally determined protein structures and their corresponding protein coding mRNA sequences has been performed. We examine whether real world data support the hypothesis that clusters of rare codons correlate with the location of structural units in the resulting protein. The degeneracy of the genetic code allows for a biased selection of codons which may control the translational rate of the ribosome, and may thus in vivo have a catalyzing effect on the folding of the polypeptide chain. A complete search for GenBank nucleotide sequences coding for structural entries in the Brookhaven Protein Data Bank produced 719 protein chains with matching mRNA sequence, amino acid sequence, and secondary structure assignment. By neural network analysis, we found strong signals in mRNA sequence regions surrounding helices and sheets. These signals do not originate from the clustering of rare codons, but from the similarity of codons coding for very abundant amino acid residues at the N- and C-termini of helices and sheets. No correlation between the positioning of rare codons and the location of structural units was found. The mRNA signals were also compared with conserved nucleotide features of 16S-like ribosomal RNA sequences and related to mechanisms for maintaining the correct reading frame by the ribosome.

Amino Acid Sequence↗
PubMed1999

Y-chromosome and mitochondrial DNA polymorphisms in Indian populations.

Y-chromosome polymorphism using short tandem repeat (STR) markers on 94 normal males belonging to the Brahmin and Kamma caste populations of Andhra Pradesh, India, and Siddis, a migrant population from Africa, inhabiting Hyderabad, India, revealed heterogeneity as indicated by network analysis. We have observed population-specific haplotypes and alleles. Analysis of Y-Alu polymorphism (YAP) in Siddis showed the presence of Alu insertion in 40% of the individuals. However, YAP insertion was not found in Brahmins and Kammas. The dendrogram based on hypervariable region I (HVR I) of the displacement loop (D-loop) sequence of mitochondrial DNA (mtDNA) of Siddis showed genetic relationships to African populations. YAP and mtDNA analysis of Siddis also confirmed their immigration from Africa.

Alleles↗
PubMed2001

Association of fibrillin 1 single-nucleotide polymorphism haplotypes with systemic sclerosis in Choctaw and Japanese populations.

OBJECTIVE: Previously, we demonstrated with the use of microsatellite markers that a 2-cM haplotype on chromosome 15q containing the fibrillin 1 gene (FBN1) was strongly associated with systemic sclerosis (SSc) in the Choctaw, a population with high SSc prevalence. In this study, all 69 known FBN1 exons were sequenced to ascertain the presence of changes that might show associations with SSc in the Choctaw and Japanese SSc patients and controls. METHODS: Screening of FBN1 exons was accomplished by polymerase chain reaction-based fluorescence sequencing of genomic DNA using single-nucleotide polymorphism (SNP) haplotypes, and their frequencies were determined with a new algorithm that recognizes past recombination events between sites. Haplotype phylogenies were inferred using the median-joining network analysis. RESULTS: Five SNPs were identified in FBN1. They are located in the 5'-untranslated region (SNP-1), exon 15 (SNP-2), intron 17 (SNP-3), exon 27 (SNP-4), and intron 27 (SNP-5). Only SNP-1 (T-->C) demonstrated an association with SSc in the Choctaw. Eleven FBN1 SNP haplotypes were ascertained in the Choctaw population, 2 of which (SNPs 5 and 6) were found only in the SSc patients. These same FBN1 SNP haplotypes were associated with SSc in the Japanese. CONCLUSION: A SNP in the 5'-untranslated region of FBN1 (SNP-1, C allele) was strongly associated with SSc in the Choctaw. Furthermore, this polymorphism is present on 2 unique FBN1 haplotypes found only in Choctaw SSc patients. The same 2 haplotypes demonstrate associations with SSc in the Japanese. These data extend the earlier microsatellite studies and are consistent with the hypothesis that FBN1 or a nearby gene on chromosome 15q is involved in SSc susceptibility in the Choctaw and the Japanese.

5' Untranslated Regions↗
PubMed2026

Identifying Co-Expressed lncRNAs Correlated With Traits of Interest in an Animal Model for Metabolic Diseases in Humans.

Nutrigenomics investigates how nutrients modulate gene expression. Among them, fatty acids (FA) play important roles in regulating gene transcription, while long non-coding RNAs (lncRNAs) may be associated with gene regulation and metabolic diseases. This study aimed to analyze the hepatic transcriptome of pigs, a species frequently used as a model for nutrigenomic studies, to identify novel lncRNAs and their potential target genes in response to diets containing different sources of FA. Seventy-two pigs were fed four diets supplemented with 1.5% soybean oil (control), 3% canola oil, 3% fish oil, and 3% soybean oil. RNA sequencing of liver samples was performed to identify novel lncRNAs. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify modules associated with phenotypic traits related to lipid metabolism and inflammation. Functional enrichment analyses were then conducted to annotate genes within these modules using Gene Ontology (GO) terms and to assess overlap with Quantitative Trait Loci (QTL). The results revealed 106 novel lncRNAs potentially regulating genes associated with lipid metabolism and immune responses in pigs fed diets with different FA sources. These findings enhance understanding of the regulatory role of lncRNAs in pigs and reinforce their relevance as models for human metabolic diseases.

Animals↗
PubMed2006

Genetic structure of the Aleuts and Circumpolar populations based on mitochondrial DNA sequences: a synthesis.

The mtDNA variation of 198 Aleuts, as well as North American and Asian populations drawn from the literature, were analyzed to reconstruct the Aleuts' genetic prehistory and to investigate their role in the peopling of the Circumarctic region. From median-joining network analysis, three star-like clusters were identified in the Aleuts within the following subhaplogroups: A3, A7 (an Aleut-specific subclade of A3), and D2. Mismatch analyses, neutrality test scores, and coalescent time estimates for these three components provided evidence of two expansion events, one occurring at approximately 19,900 B.P. and the other at 5,400 B.P. Based on these findings and evidence from the archaeological data, four general models for the genetic prehistory of the Aleutian Island chain are proposed: 1) biological continuity involving a kin-structured peopling of the archipelago; 2) intrusion and expansion of a non-native biface-producing population dominated by subhaplogroup D2; 3) amalgamation of Arctic Small Tool tradition peoples characterized by D2 with an older Anangula substratum; and 4) biological continuity with significant gene flow from neighboring populations of the Alaskan mainland and Kodiak Island. The Aleut mtDNAs are consistent with the Circumarctic pattern by the fixation of A3 and D2, and the exhibition of depressed diversity levels relative to Amerind and Siberian groups. The results of this study indicate a broad postglacial reexpansion of Na-Dene and Esko-Aleuts from reduced populations within northern North America, with D2 representing a later infusion of Siberian mtDNAs into the Beringian gene pool.

Arctic Regions↗
PubMed2006

Genetic diversity within a caste population of India as measured by Y-chromosome haplogroups and haplotypes: subcastes of the Golla of Andhra Pradesh.

The extent of population subdivision based on 15 Y-chromosome polymorphisms was studied in seven subcastes of the Golla (Karnam, Pokanati, Erra, Doddi, Punugu, Puja, and Kurava), who inhabit the Chittoor district of southern Andhra Pradesh, India. These Golla subcastes are traditionally pastoralists, culturally homogeneous and endogamous. DNA samples from 146 Golla males were scored for seven unique event polymorphisms (UEPs) and eight microsatellites, permitting allocation of each into haplogroups and haplotypes, respectively. Genetic diversity (D) was high (range, 0.9048-0.9921), and most of the genetic variance (>91%) was explained by intrapopulation differences. Median-joining network analysis of microsatellite haplotypes demonstrated an absence of any structure according to subcaste affiliation. Superimposition of UEPs on this phylogeny, however, did create some distinct clusters, indicating congruence between haplotype and haplogroup phylogenies. Our results suggest many male ancestors for the Golla as well as for each of the subcastes. Genetic distances among the seven subcastes, based on autosomal markers (short tandem repeats and human leukocyte antigens) as well as those on the chromosome Y, indicate that the Kurava may not be a true subcaste of the Golla. Although this finding is based on a very small Kurava sample, it is in accordance with ethnohistorical accounts related by community elders. The Punugu was the first to hive off the main Golla group, and the most recently separated subcastes (Karnam, Erra, Doddi, and Pokanati) fissioned from the Puja. This phylogeny receives support from the analysis of autosomal microsatellites as well as HLA loci in the same samples. In particular, there is a significant correlation (r = 0.8569; P = 0.0097) between Y-chromosome- and autosomal STR-based distances.

Analysis of Variance↗
PubMed2005

The metabolic pathology of dopa-responsive dystonia.

We used [(18)F]-fluorodeoxyglucose and positron emission tomography to determine a discrete cerebral pattern of abnormal glucose utilization in dopa-responsive dystonia. Network analysis demonstrated that dopa-responsive dystonia is associated with a specific pattern of regional metabolic covariation, characterized by increases in the dorsal midbrain, cerebellum, and supplementary motor area, as well as reductions in motor and lateral premotor cortex and in the basal ganglia. This pattern was not expressed in mutation carriers for primary torsion dystonia. Dopa-responsive dystonia has a unique metabolic architecture that differs from other inherited forms of dystonia.

Adult↗