Clinical and Histological Characteristics of Localized Morphea, Generalized Morphea and Systemic Sclerosis: A Comparative Study Aided by Machine Learning.
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SUBMITTER: Choi S
PROVIDER: S-EPMC10522325 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
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Clinical and Histological Characteristics of Localized Morphea, Generalized Morphea and Systemic Sclerosis: A Comparative Study Aided by Machine Learning.
Choi Sooyeon S Kim Jemin J Lee Joo Hee JH Lee Yun Na YN Lee Ju Hee JH
Acta dermato-venereologica 20230920
Similar Datasets
Project description:Morphea is an inflammatory disorder of the skin and soft tissue characterized by fibrosis that has been likened to systemic sclerosis (SSc). We sought to capture the molecular heterogeneity of morphea by examining lesional skin gene expression and blood biomarkers, and compare the gene expression profiles with those from SSc lesional and site matched non lesional skin, as well as sera obtained from adult patients with untreated morphea. We found the morphea transcriptome is dominated by IFNg-mediated Th1 immune dysregulation, with relative paucity of pathways associated with fibrosis. These results were mirrored when morphea gene expression profiles were compared with SSc. Interestingly, expression profiles of morphea skin samples clustered with the SSc inflammatory subset and distinct from the SSc fibro-proliferative subset. Unaffected morphea skin also differed from unaffected SSc skin in that it did not exhibit pathological gene expression signatures. Examination of downstream IFNg-mediated chemokines, CXCL9 and CXCL10, revealed increased transcription in the skin but not in sera. While elevated serum CXCL9 proteins quantified by ELISA were associated with active disease and widespread cutaneous disease. Taken together, these results indicate that inflammatory and sclerotic morphea is a skin directed process characterized by Th1 immune mediated dysregulation, which contrasts with fibrotic signatures and systemic transcriptional changes associated with SSc. The association of circulating CXCL9 concentration with clinical activity and burden of skin disease supports its potential as a readily accessible biomarker.
2025-04-13 | GSE201405 | GEO
Project description:BackgroundTremors are frequent and disabling in people with multiple sclerosis (MS). Characteristic tremor frequencies in MS have a broad distribution range (1-10 Hz), which confounds the diagnostic from other forms of tremors. In this study, we propose a classification method for distinguishing MS tremors from other forms of cerebellar tremors.MethodsElectromyogram (EMG), accelerometer and clinical data were obtained from a total of 120 [40 MS, 41 essential tremor (ET) and 39 Parkinson's disease (PD)] subjects. The proposed method - Soft Decision Wavelet Decomposition (SDWD) - was used to compute power spectral densities and receiver operating characteristic (ROC) analysis was performed for the automatic classification of the tremors. Association between the spectral features and clinical features (FTM - Fahn-Tolosa-Marin scale, UPDRS - Unified Parkinson's Disease Rating Scale), was assessed using a support vector regression (SVR) model.FindingsOur developed analytical framework achieved an accuracy of up to 91.67% using accelerometer data and up to 91.60% using EMG signals for the differentiation of MS tremors and the tremors from ET and PD. In addition, SVR further revealed strong significant correlations between the selected discriminators and the clinical scores.InterpretationThe proposed method, with high classification accuracy and strong correlations of these features to clinical outcomes, has clearly demonstrated the potential to complement the existing tremor-diagnostic approach in MS patients.FundingThis work was supported by the German Research Foundation (DFG): SFB-TR-128 (to SG, MM), MU 4354/1-1(to MM) and the Boehringer Ingelheim Fonds BIF-03 (to SG, MM).
| S-EPMC9287478 | biostudies-literature
Project description:IntroductionThe use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task.MethodsOscillometric and spirometric exams were performed in 82 individuals, including controls (n = 30) and patients with systemic sclerosis with normal (n = 22) and abnormal (n = 30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-Nearest Neighbors (KNN), Random Forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB).Results and discussionThe first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC = 0.77) in the scenario control group versus patients with sclerosis and normal spirometry (CGvsPSNS). In the scenario control group versus patients with sclerosis and altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC = 0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC = 0.90), significantly improving the accuracy in comparison with the BOP (p < 0.01), while in CGvsPSAS, RF obtained the best results (AUC = 0.97), also significantly improving the diagnostic accuracy (p < 0.05). In the third, fourth, fifth, and sixth experiments, different feature selection techniques allowed us to spot the best oscillometric parameters. They resulted in a small increase in diagnostic accuracy in CGvsPSNS (respectively, 0.87, 0.86, 0.82, and 0.84), while in the CGvsPSAS, the best classifier's performance remained the same (AUC = 0.97).ConclusionsOscillometric principles combined with machine learning algorithms provide a new method for diagnosing respiratory changes in patients with systemic sclerosis. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.
| S-EPMC7995797 | biostudies-literature
Project description:Scleroderma refers to a group of chronic fibrotic immune-mediated diseases of unknown etiology. Characterizing epigenetic changes in childhood-onset scleroderma, systemic sclerosis or localized scleroderma, has not been previously performed. The aim of this study was to assess DNA methylation differences and similarities between juvenile systemic sclerosis (jSSc) and juvenile localized scleroderma (jLS) compared to matched healthy controls. Genome-wide DNA methylation changes in peripheral blood mononuclear cell samples were assessed using the MethylationEPIC array followed by bioinformatic analysis and limited functional assessment. We identified a total of 105 and 144 differentially methylated sites compared to healthy controls in jSSc and jLS, respectively. The majority of differentially methylated sites and genes represented were unique to either jSSc or jLS suggesting a different underlying epigenetic pattern in both diseases. Among shared differentially methylated genes, methylation levels in a CpG site in FGFR2 can distinguish between LS and healthy PBMCs with a high accuracy. Canonical pathway analysis revealed that inflammatory pathways were enriched in genes differentially methylated in jSSc, including STAT3, NF-κB, and IL-15 pathways. In contrast, the HIPPO signaling pathway was enriched in jLS. Our data also suggest a potential role for NOTCH3 in both jSSc and jLS, and revealed a number of transcription factors unique to each of the two diseases. In summary, our data revealed important insights into jSSc and jLS and suggest a potentially novel epigenetic diagnostic biomarker for LS.
2021-05-24 | GSE175379 | GEO
Project description:Scleroderma refers to a group of chronic fibrotic immune-mediated diseases of unknown etiology. Characterizing epigenetic changes in childhood-onset scleroderma, systemic sclerosis or localized scleroderma, has not been previously performed. The aim of this study was to assess DNA methylation differences and similarities between juvenile systemic sclerosis (jSSc) and juvenile localized scleroderma (jLS) compared to matched healthy controls. Genome-wide DNA methylation changes in peripheral blood mononuclear cell samples were assessed using the MethylationEPIC array followed by bioinformatic analysis and limited functional assessment. We identified a total of 105 and 144 differentially methylated sites compared to healthy controls in jSSc and jLS, respectively. The majority of differentially methylated sites and genes represented were unique to either jSSc or jLS suggesting a different underlying epigenetic pattern in both diseases. Among shared differentially methylated genes, methylation levels in a CpG site in FGFR2 can distinguish between LS and healthy PBMCs with a high accuracy. Canonical pathway analysis revealed that inflammatory pathways were enriched in genes differentially methylated in jSSc, including STAT3, NF-κB, and IL-15 pathways. In contrast, the HIPPO signaling pathway was enriched in jLS. Our data also suggest a potential role for NOTCH3 in both jSSc and jLS, and revealed a number of transcription factors unique to each of the two diseases. In summary, our data revealed important insights into jSSc and jLS and suggest a potentially novel epigenetic diagnostic biomarker for LS.
| S-EPMC8206011 | biostudies-literature
Project description:BackgroundCurrent treatment for localized scleroderma (LS) has been shown to halt disease activity, but little is still known about patient experiences with these treatments, nor is there consensus about optimal measurement strategies for future clinical trials.ObjectiveConduct a scoping review of the literature for the types of outcomes and measures (i.e. clinician-, patient-, and caregiver-reported) utilized in published treatment studies of LS.MethodsOnline databases were searched for articles related to the evaluation of treatment efficacy in LS with a special focus on pediatrics.ResultsOf the 168 studies, the most common outcomes used were cutaneous disease activity and damage measured via clinician-reported assessments. The most frequently cited measure was the Localized Scleroderma Cutaneous Assessment Tool (LoSCAT). Few patient-reported outcome measures (PROMs) were used.LimitationsSome studies only vaguely reported the measures utilized, and the review yielded a low number of clinical trials.ConclusionIn addition to evaluating disease activity with clinician-reported measures, the field could obtain critical knowledge on the patient experience by including high-quality PROMs of symptoms and functioning. More clinical trials using a variety of outcomes and measures are necessary to determine the most suitable course of treatment for LS patients.
| S-EPMC11337887 | biostudies-literature
Project description:Localized scleroderma (LoS), or morphea, refers to a group of rare autoimmune connective tissue diseases. Autologous fat grafting was able to correct volume loss in patients with LoS to improve facial disfigurement.However, whether it could exert a positive effect on reversing skin sclerosis remains unclear.
2023-11-20 | GSE247804 | GEO
Project description:ObjectivesThe pathogenesis of intestinal involvement in systemic sclerosis (SSc) is thought to be a sequential process (vascular, neuronal, and consecutive muscular impairment), but understanding of the underlying histological changes and how they translate to symptoms, is still lacking. Therefore, we systematically investigated histological characteristics of SSc in the intestines, compared to controls.MethodsAutopsy material from the small bowel and colon was used for histological semiquantitative evaluation of the vasculature, enteric nervous system, interstitial cells of Cajal (ICC), and muscle layers, using a combination of histochemical and immunohistochemical stainings, according to guidelines of the Gastro 2009 International Working Group.ResultsVascular changes were most frequently encountered, represented by intima fibrosis in both arteries and small vessels, and represented by venous dilatation. Second, generalized fibrosis of the circular muscle layer was significantly more found in SSc patients than in controls. Third, reduction of submucosal nerve fibers and myenteric neurons was shown in the colon of four SSc patients, which may explain severe symptoms of intestinal dysmotility. The density of myenteric ICC network was decreased in the small bowel of SSc patients.ConclusionsThe postulated sequential processes of intestinal involvement in SSc could not be supported by our histological evaluation. The interpatient diversity suggests that parallel processes occur, explaining the variety of histological features and clinical symptoms. Key Points • Histological analysis showed vascular changes, fibrosis in the muscularis propria, and reduction of the ENS and ICC network in the intestines of SSc patients. • Pathophysiological mechanisms leading to intestinal dysmotility in SSc may be parallel rather than sequential. • The interpatient diversity suggests parallel pathophysiological processes, explaining the variety of histological features and clinical symptoms.
| S-EPMC7895795 | biostudies-literature
Project description: Not available
| S-EPMC6031588 | biostudies-literature
Project description:Gene expression signatures in inflammatory and sclerotic morphea skin and blood distinguishes morphea from systemic sclerosis
| PRJNA831565 | ENA