We selected 11 autoimmune traits enriched in Th1 H3K27Ac as shown in Fig 3 of Ref. 28 (link). We made predictions for 413 lead SNPs associated with 11 autoimmune diseases enriched in Th1 H3K27Ac regions (T1D: Type 1 Diabetes, CRO: Crohn’s Disease, MS: Multiple Sclerosis, CEL: Celiac Disease, PBC: Primary Biliary Cirrhosis, RA: Rheumatoid Arthritis, Allergy, ATD: Autoimmune Thyroid Disease, UC: Ulcerative Colitis, VIT: Vitiligo, SLE: Systemic Lupus Erythematosus)28 (link). We trained a gkm-SVM on the top 10,000 500 bp Th1 DHS regions, after excluding regions that were DHS in more than 30% of human ENCODE cell lines, or near promoters (< 2 kb from TSS), against an equal size GC and repeat matched training set. We scored the lead SNP and all flanking off-lead candidates in LD as defined by (R2 > .5 and PICS28 (link) probability > .0275), yielding 3113 total SNPs. Since the significance of the maximum deltaSVM score in a locus will depend on the number of SNPs in that locus, as a random control we scored random SNPs and equal size flanking sets. To determine the cutoff, we first determined 2nd percentile deltaSVM score from 10,000 random permutations for each number of flanking SNPs (1~30), and then calculated mean and standard deviation of the 100 repeated experiments as the final cutoff. We identified 17 high scoring deltaSVM SNPs which we predict to be expression perturbing SNPs with high confidence (P < .02), while at this threshold random sampling produced 8 SNPs (binomial test P < 0.004, Supplementary Figure 4 ). deltaSVM scores for all 3113 SNPs are provided as Supplementary Table 6 .
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Disorders
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Disease or Syndrome
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Vitiligo
Vitiligo
Vitiligo is a chronic autoimmune disorder characterized by the loss of pigment-producing cells (melanocytes) in the skin, resulting in the appearance of white patches.
This condition can have a significant impact on an individual's physical appearance and self-esteem.
PubCompare.ai, an AI-powered platform, can enhance your Vitiligo research by helping you locate the best protocols from literature, pre-prints, and patents, while providing accurate comparisons to optimize reproducibility and accuracy.
Streamline your Vitiligo research and gain deeper insights with the help of PubCompare.ai.
This condition can have a significant impact on an individual's physical appearance and self-esteem.
PubCompare.ai, an AI-powered platform, can enhance your Vitiligo research by helping you locate the best protocols from literature, pre-prints, and patents, while providing accurate comparisons to optimize reproducibility and accuracy.
Streamline your Vitiligo research and gain deeper insights with the help of PubCompare.ai.
Most cited protocols related to «Vitiligo»
Autoimmune Diseases
Celiac Disease
Cell Lines
Crohn Disease
Diabetes Mellitus, Insulin-Dependent
Homo sapiens
Hypersensitivity
Lupus Erythematosus, Systemic
Multiple Sclerosis
Primary Biliary Cholangitis
Rheumatoid Arthritis
Single Nucleotide Polymorphism
Thyroid Diseases
Thyroid Gland
Ulcerative Colitis
Vitiligo
Autoimmune Diseases
DNA Replication
Ethics Committees, Research
Europeans
Genome
Genome-Wide Association Study
Genotype
Hispanics
Latinos
Melanoma
Phenotype
Vitiligo
GFP-PMEL CD8+ T cells were isolated from the spleen of GFP-PMEL TCR transgenic mice through negative selection on microbeads (Miltenyi Biotech, Auburn, CA) according to the manufacturer’s instructions. Purified CD8+ T cells (1 × 106) were injected i.v. into sublethally irradiated (500 rads one day prior to transfer) Krt14-Kitl* hosts (12–16 wk of age). Recipient mice also received i.p. injection of 1 × 106 pfu rVV-hPMEL (N. Restifo, NCI, NIH) on the same day of transfer. IFN-γ blockade was through i.p. injection of 100–500 μg of IFN-γ neutralizing antibody (XMG-6) beginning at the indicated times and twice weekly for the duration of the observation period (5–7 wks). Mice used as controls for vitiligo were sublethally irradiated but did not receive rVV-hPMEL or PMEL CD8+ T cell transfer. Mice with vitiligo used as treatment controls for IFN-γ blockade were treated with either no antibody or with an equal quantity of Rat IgG.
CD8-Positive T-Lymphocytes
Immunoglobulins
Interferon Type II
KRT14 protein, human
Mice, Transgenic
Microspheres
Mus
Passive Immunization
RRAD protein, human
SILV protein, human
Spleen
Vitiligo
CD8-Positive T-Lymphocytes
Immunoglobulins
Interferon Type II
KRT14 protein, human
Mice, Transgenic
Microspheres
Mus
Passive Immunization
RRAD protein, human
SILV protein, human
Spleen
Vitiligo
Chromosome Markers
DNA Replication
Genes
Genome-Wide Association Study
Histocompatibility Antigens Class I
Iplex
Mitochondria
Patients
Pyrin Domain
Single Nucleotide Polymorphism
Vitiligo
Most recents protocols related to «Vitiligo»
To study protein–protein interactions at crossover targets, the STRING database19 (link) (https://stringdb.org/ ) was used to construct a PPI network of crossover genes of AM sapiens for vitiligo and COVID-19. "Homo sapiens" was selected in the parameter Organism, "Medium confidence 0.400)" was selected in the "Minimum required interaction score", and the other parameters were kept at their default values. Then, we filtered out the top 5 ranked core crossover genes by cytohubba plugin in Cytoscape software.
Finally, we constructed the "active ingredient-crossover genes" network by Cytoscape software and screened the top 10 ranked core ingredients. The core crossover geness in the PPI network were matched with those in the "active ingredient-crossover geness" network and imported into the Venny 2.1 online website, which was presented as a Venn diagram. The overlap between the two crossover) is the most important core crossover target for AM for vitiligo and COVID-19.
Finally, we constructed the "active ingredient-crossover genes" network by Cytoscape software and screened the top 10 ranked core ingredients. The core crossover geness in the PPI network were matched with those in the "active ingredient-crossover geness" network and imported into the Venny 2.1 online website, which was presented as a Venn diagram. The overlap between the two crossover) is the most important core crossover target for AM for vitiligo and COVID-19.
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COVID 19
Gene Regulatory Networks
Genes
Genes, vif
Homo sapiens
Protein Targeting, Cellular
Vitiligo
This research does not involve the ethics of human and animal experiments.To identify potential targets of AM for vitiligo and COVID-19, we searched the comprehensive gene expression database GEO16 (link), (http://www.ncbi.nlm.nih.gov/geo ). The species "human" was selected as the study subject and the dataset GSE75819 with samples from healthy volunteers and vitiligo lesion skin samples. Differentially expressed genes (DEGs) between lesioned and healthy volunteers were screened by GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r ). Probe sets without corresponding gene symbols or multiple probe sets were removed or averaged, respectively. adj. P values < 0.05 and |LogFC|< 1 were considered statistically significant.
To identify the disease targets of AM for the treatment of COVID-19, we used the Genecards website (https://www.genecards.org ) to search for COVID-19 disease related targets using "COVID-19", "coronavirus disease", and "coronary pneumonia" as search terms.
Then the disease targets of vitiligo and COVID-19 were matched with the targets of the active ingredients of AM and imported into Venny 2.1 online website (https://bioinfogp.cnb.csic.es/tools/venny/index.html ), which was displayed as a Venn diagram, and the overlapping part intersection of the two was the crossover genes of AM for vitiligo and COVID-19.
To investigate the clustering of the crossover genes in the GSE75819 dataset, we finally drew the clustering heatmap of the crossover genes through the ImageGP website (http://www.ehbio.com/ImageGP/index.php/Home/Index/index.html ).
To identify the disease targets of AM for the treatment of COVID-19, we used the Genecards website (
Then the disease targets of vitiligo and COVID-19 were matched with the targets of the active ingredients of AM and imported into Venny 2.1 online website (
To investigate the clustering of the crossover genes in the GSE75819 dataset, we finally drew the clustering heatmap of the crossover genes through the ImageGP website (
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Coronavirus
COVID 19
Gene Expression
Genes
Healthy Volunteers
Heart
Homo sapiens
Pneumonia
Skin
Vitiligo
For histological evaluation of HND, a histological database at the Yonsei University Severance Hospital was utilized. A query search of AD patients from 2011 who underwent facial skin biopsy was performed, and five patients were randomly selected from 9 candidates.
Histological analysis of non-HND face specimens was performed through a query search of AD patients who underwent skin biopsy on the face from 2013 for suspected concomitant vitiligo (usually the biopsy is conducted with non-lesional normal skin and lesional skin with vitiligo to compare the melanocyte population). Crude age filtering was performed to age-match AD patients. Among the 10 candidates, five patients were randomly selected for image analysis.
At 200x magnification, the longest distance from the subcorneal level to the basal layer was chosen arbitrarily for epidermal thickness after calibrating the scale bar to pixels. The number of vessels/mm2 was counted in the dermis of each slide section within a 100 µm distance from the epidermal–dermal junction.
Immunohistochemical staining was performed using paraffin-embedded sections with antibodies against factor VIII-related antigen (1:100, ab236284, Abcam), stromal cell-derived factor-1-alpha (SDF1-α) (1:100, ab25117, Abcam, Cambridge, United Kingdom), Interleukin-1-beta (IL-1-β) (1:100, ab2105, Abcam), tumor necrosis factor-alpha (TNF-α) (1:50, ab1793, Abcam), transforming growth factor-beta (TGF-β) (1:100, ab66043, Abcam), and vascular endothelial growth factor (VEGF) (1:200, ab1316, Abcam). Staining intensity was determined at 400x magnification at a randomly chosen area of the upper dermis. Images were quantified using ImageJ analysis tools (National Institutes of Health, Bethesda, MA).
To calculate the stained area of the antibody, we converted the original image to an 8-bit grayscale image (ImageJ>Image>8-bit), applied a binary threshold, and calculated the percentage positive for the stained part in the standard image. Quantification was performed relative to the entire selected region. The threshold for each staining was set as the average threshold of multiple immunostaining analyses performed by three independent experimenters.
Histological analysis of non-HND face specimens was performed through a query search of AD patients who underwent skin biopsy on the face from 2013 for suspected concomitant vitiligo (usually the biopsy is conducted with non-lesional normal skin and lesional skin with vitiligo to compare the melanocyte population). Crude age filtering was performed to age-match AD patients. Among the 10 candidates, five patients were randomly selected for image analysis.
At 200x magnification, the longest distance from the subcorneal level to the basal layer was chosen arbitrarily for epidermal thickness after calibrating the scale bar to pixels. The number of vessels/mm2 was counted in the dermis of each slide section within a 100 µm distance from the epidermal–dermal junction.
Immunohistochemical staining was performed using paraffin-embedded sections with antibodies against factor VIII-related antigen (1:100, ab236284, Abcam), stromal cell-derived factor-1-alpha (SDF1-α) (1:100, ab25117, Abcam, Cambridge, United Kingdom), Interleukin-1-beta (IL-1-β) (1:100, ab2105, Abcam), tumor necrosis factor-alpha (TNF-α) (1:50, ab1793, Abcam), transforming growth factor-beta (TGF-β) (1:100, ab66043, Abcam), and vascular endothelial growth factor (VEGF) (1:200, ab1316, Abcam). Staining intensity was determined at 400x magnification at a randomly chosen area of the upper dermis. Images were quantified using ImageJ analysis tools (National Institutes of Health, Bethesda, MA).
To calculate the stained area of the antibody, we converted the original image to an 8-bit grayscale image (ImageJ>Image>8-bit), applied a binary threshold, and calculated the percentage positive for the stained part in the standard image. Quantification was performed relative to the entire selected region. The threshold for each staining was set as the average threshold of multiple immunostaining analyses performed by three independent experimenters.
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Antibodies
Biopsy
Blood Vessel
CXCL12 protein, human
Dermis
Epidermis
Face
Factor VIII-Related Antigen
Immunoglobulins
Interleukin-1 beta
Melanocyte
Paraffin Embedding
Patients
Skin
Strains
Stromal Cell-Derived Factor-1alpha
TGF-beta1
TNF protein, human
Transforming Growth Factor beta
Vascular Endothelial Growth Factors
Vitiligo
This was a prospective study where the cohort was recruited from the patients attending the outpatient pigmentary clinic of the dermatology department of Post Graduate Institute of Medical Education and Research, Chandigarh. Institute Ethics Committee approvals (Intramural) were obtained before initiation of the study and all research was performed in accordance with relevant guidelines/regulations. Thirty patients aged 12–60 years with a clinical diagnosis of unstable, non-segmental vitiligo with a body surface area of 10–50% and having both acral and non-acral lesions were included in the study. Unstable disease was defined as the occurrence of new lesion(s), progression of existing lesion(s), or Koebnerisation with a VIDA score of 4 + . Patients with segmental vitiligo and universal vitiligo, pregnant and lactating mothers, any contraindication to NBUVB therapy like xeroderma pigmentosa, systemic lupus erythematosus and other photo-induced or photo-aggravated dermatoses, patients who have received any systemic treatment in the past 4 weeks and patients with unrealistic expectations were excluded from the study. After obtaining informed consent from the included patients, baseline characteristics, history, and clinical examination findings of the patient were noted. Disease activity and extent were evaluated by Vitiligo disease activity score (VIDA) and Vitiligo Area Severity Index (VASI), respectively. Lesions distal to the elbows and knees were considered acral lesions. Lesional VASI was calculated separately for acral and non-acral lesions.
3'-O-methyl-nordihydroguaiaretic acid
Body Surface Area
Diagnosis
Disease Progression
Education, Medical, Graduate
Elbow
Ethics Committees
Knee
Lupus Erythematosus, Systemic
Mothers
Patients
Physical Examination
Pigmentation
Skin Diseases
Vitiligo
Xeroderma
We conducted a population-based study using both cohort design and nested case-control design in parallel, with the intention to preserve the advantages and complement the limitations of the other as we consider that cohort studies conventionally have a higher level of evidence, whereas case-control analysis was more appropriate for evaluating rare outcomes. The study period started in January 2014 and ended in December 2020. The cohort consisted of all incident patients aged above 10 years with diagnosis codes for depression (ICD-9-CM codes: 296.2, 296.3, 300.4, 311) between January 2014 and December 2016 without history of diagnosis for depression since 1993, when the database first became available. Patients were excluded if they had history of studied autoimmune diseases before onset of depression, or if they died immediately after cohort entry.
Throughout the study, patients were defined as treatment-resistant (exposed) if they had taken at least two antidepressant regimens of adequate duration (same antidepressant or combined therapy of at least 28 days with gaps of no longer than 14 days within regimens, whilst the 28-day duration was the minimum recommended duration to assess treatment responsiveness [26 ]) and had a third antidepressant regimen to confirm failure of the previous two trials. Patients who did not fulfil the criteria for TRD were considered as non-TRD (unexposed). Onset of outcome was confirmed on the date of the first autoimmune diagnosis in (1) organ-specific diseases including inflammatory bowel diseases, spondyloarthritis, psoriasis, insulin-dependent diabetes mellitus, Hashimoto’s thyroiditis, Graves’ disease, coeliac disease, vitiligo, alopecia areata, pemphigus vulgaris, dermatitis herpetiformis, pernicious anaemia, immune thrombocytopenic purpura, iridocyclitis and pemphigoid, and (2) systemic diseases including systemic lupus erythematosus, rheumatoid arthritis, Sjogren’s disease, systemic sclerosis, polymyositis/dermatomyositis, multiple sclerosis and juvenile arthritis, captured across all settings including outpatient, inpatient and emergency services. List of ICD-9-CM codes to identify the cohort and outcomes is presented in Supplementary Table1 . Using the comorbidity rates reported from a previously similar population-based study, the sample sizes required for data collection were 5403 and 12545 for the analyses in systemic and organ-specific autoimmune diseases, respectively, to achieve an 80% statistical power in the cohort study [17 (link), 27 ].
Throughout the study, patients were defined as treatment-resistant (exposed) if they had taken at least two antidepressant regimens of adequate duration (same antidepressant or combined therapy of at least 28 days with gaps of no longer than 14 days within regimens, whilst the 28-day duration was the minimum recommended duration to assess treatment responsiveness [26 ]) and had a third antidepressant regimen to confirm failure of the previous two trials. Patients who did not fulfil the criteria for TRD were considered as non-TRD (unexposed). Onset of outcome was confirmed on the date of the first autoimmune diagnosis in (1) organ-specific diseases including inflammatory bowel diseases, spondyloarthritis, psoriasis, insulin-dependent diabetes mellitus, Hashimoto’s thyroiditis, Graves’ disease, coeliac disease, vitiligo, alopecia areata, pemphigus vulgaris, dermatitis herpetiformis, pernicious anaemia, immune thrombocytopenic purpura, iridocyclitis and pemphigoid, and (2) systemic diseases including systemic lupus erythematosus, rheumatoid arthritis, Sjogren’s disease, systemic sclerosis, polymyositis/dermatomyositis, multiple sclerosis and juvenile arthritis, captured across all settings including outpatient, inpatient and emergency services. List of ICD-9-CM codes to identify the cohort and outcomes is presented in Supplementary Table
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Alopecia
Anemia, Pernicious
Antidepressive Agents
Autoimmune Diseases
Bullous Pemphigoid
Celiac Disease
Dermatitis Herpetiformis
Dermatomyositis
Diabetes Mellitus, Insulin-Dependent
Diagnosis
Graves Disease
Hashimoto Disease
Inflammatory Bowel Diseases
Inpatient
Iridocyclitis
Juvenile Arthritis
Lupus Erythematosus, Systemic
Multiple Sclerosis
Outpatients
Patients
Pemphigus Vulgaris
Psoriasis
Psychotherapy, Multiple
Rheumatoid Arthritis
Service, Emergency Medical
Sjogren's Syndrome
Spondylarthritis
Systemic Scleroderma
Thrombocytopenic Purpura, Immune
Treatment Protocols
Vitiligo
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Human Melanocyte Growth Supplement is a cell culture medium supplement designed to support the growth and maintenance of human melanocyte cells in vitro. The product contains a proprietary blend of essential nutrients, growth factors, and other components required for the optimal proliferation and survival of these specialized skin cells.
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Human Melanocyte Growth Supplement-2 is a specialized media supplement designed to support the growth and maintenance of human melanocyte cell cultures. It contains a carefully balanced mixture of essential nutrients, growth factors, and other components required for the optimal proliferation and survival of melanocytes in vitro.
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More about "Vitiligo"
Vitiligo is a chronic dermatological condition characterized by the loss of melanocytes, the pigment-producing cells in the skin.
This autoimmune disorder results in the appearance of white patches, often on visible areas like the face, hands, and feet.
Impacting both physical appearance and self-esteem, vitiligo can be a challenging condition to manage.
Researchers studying vitiligo may utilize a variety of tools and techniques to understand the underlying causes and explore potential treatments.
PubCompare.ai, an AI-powered platform, can streamline the research process by helping scientists locate the best protocols from scientific literature, preprints, and patents, while providing accurate comparisons to optimize reproducibility and accuracy.
Vitiligo research may involve the use of cell culture techniques, such as those employing Medium 254 and Human Melanocyte Growth Supplement, to study the behavior and characteristics of melanocytes.
The TRIzol reagent may be used for RNA extraction, and the SYBR Green kit for gene expression analysis.
Prism software, such as GraphPad Prism 5, can be utilized for data analysis and visualization.
By leveraging the insights and tools provided by PubCompare.ai, researchers can gain deeper understanding of vitiligo and accelerate the development of effective treatments, ultimately improving the quality of life for those affected by this condition.
This autoimmune disorder results in the appearance of white patches, often on visible areas like the face, hands, and feet.
Impacting both physical appearance and self-esteem, vitiligo can be a challenging condition to manage.
Researchers studying vitiligo may utilize a variety of tools and techniques to understand the underlying causes and explore potential treatments.
PubCompare.ai, an AI-powered platform, can streamline the research process by helping scientists locate the best protocols from scientific literature, preprints, and patents, while providing accurate comparisons to optimize reproducibility and accuracy.
Vitiligo research may involve the use of cell culture techniques, such as those employing Medium 254 and Human Melanocyte Growth Supplement, to study the behavior and characteristics of melanocytes.
The TRIzol reagent may be used for RNA extraction, and the SYBR Green kit for gene expression analysis.
Prism software, such as GraphPad Prism 5, can be utilized for data analysis and visualization.
By leveraging the insights and tools provided by PubCompare.ai, researchers can gain deeper understanding of vitiligo and accelerate the development of effective treatments, ultimately improving the quality of life for those affected by this condition.