Post-inflammatory skin hyper- or hypo-pigmentation is a common occurrence with unclear etiology. There is currently no reliable method to predict skin pigmentation outcomes after inflammation. In ...this study, we analyzed the 5 GEO datasets to screen for inflammatory
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related genes involved in melanogenesis, and used candidate cytokines to establish different machine learning (LASSO regression, logistic regression and Random Forest) models to predict the pigmentation outcomes of post-inflammatory skin. Further, to further validate those models, we evaluated the role of these candidate cytokines in pigment cells. We found that IL-37, CXCL13, CXCL1, CXCL2 and IL-19 showed high predictive value in predictive models. All models accurately classified skin samples with different melanogenesis-related gene scores in the training and testing sets (AUC>0.7). Meanwhile, we mainly evaluated the effects of IL-37 in pigment cells, and found that it increased the melanin content and expression of melanogenesis-related genes (MITF, TYR, TYRP1 and DCT), also enhanced tyrosinase activity. In addition, CXCL13, CXCL1, CXCL2 and IL-19 could down-regulate the expression of several melanogenesis-related genes. In conclusion, evaluation models basing on machine learning may be valuable in predicting outcomes of post-inflammatory pigmentation abnormalities. IL-37, CXCL1, CXCL2, CXCL13 and IL-19 are involved in regulating post-inflammatory pigmentation abnormalities.
In China, there is a lack of data regarding the awareness and treatment preferences among patients with vitiligo and their families. To address this gap, a cross-sectional questionnaire-based study ...was conducted to investigate disease awareness and treatment preferences in Chinese patients with vitiligo. The study also evaluated willingness to pay, using 2 standardized items, and assessed quality of life, using the Dermatology Life Quality Index (DLQI) score. Data from 307 patients with vitiligo (59.3% women, mean age 28.98 years, range 2–73 years) were analysed. Of these patients, 44.7% had insufficient knowledge of vitiligo, particularly those from rural areas or with low levels of education. Mean DLQI total score was 4.86 (5.24 for women and 4.30 for men). Among the most accepted treatments were topical drugs, phototherapy, and systemic therapy. Patients were relatively conservative about the duration and cost of treatment, with only 27.7% willing to pay more than 10,000 Chinese yuan renminbi (CNY) for complete disease remission. High level of education, high income, skin lesions in specific areas, and skin transplantation therapy predicted higher willingness to pay. Insufficient knowledge was associated with a higher burden of disease. In order to reduce the disease burden and improve treatment adherence it is crucial to enhance disease awareness and take into account patient preferences.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Schematic diagram of MAFG-AS1/MAFG positive feedback loop antagonizes ferroptosis in BUC.
MAFG-AS1 stimulates MAFG transcription by promoting H3K27ac at genomic loci of MAFG through recruiting ...acetylase EP300. MAFG binds to the MAFG-AS1 gene promoter to induce MAFG-AS1 expression, forming a positive feedback loop. MAFG-AS1 could recruit deubiquitinase UCHL5 to stabilize the expression of PCBP2 protein. Then PCBP2 interacts with transferrin 1 (FPN1) and transports iron out of the cells, leading inhibition of ferroptosis and cisplatin resistance in BUC cells. These findings suggest that inhibition of MAFG-AS1 and MAFG can increase the sensitivity of BUC cells to cisplatin through promoting ferroptosis, indicating the novel chemotherapy sensitivity biomarkers and therapeutic target for BUC.
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Though promoting ferroptosis can reduce cisplatin resistance in tumor cells, ferroptosis and cisplatin resistance in bladder urothelial carcinoma (BUC) following long non-coding RNAs (lncRNAs) is largely unknown. Here, we found the highly expressed lncRNA MAF transcription factor G antisense RNA 1 (MAFG-AS1) in BUC, and its inhibition increased the sensitivity of BUC cells to cisplatin by promoting ferroptosis. Mechanically, binding to iron chaperone poly(rC)-binding protein 2 (PCBP2) facilitated the recruitments of MAFG-AS1 to deubiquitinase ubiquitin carboxyl-terminal hydrolase isozyme L5 (UCHL5), thus stabilizing PCBP2 protein itself. Then PCBP2 was confirmed to interact with ferroportin 1 (FPN1), an iron export protein, leading to inhibition of ferroptosis. Moreover, the expression of MAFG-AS1 was regulated by the transcriptional factor MAFG. Interestingly, MAFG-AS1 stimulated MAFG transcription by recruiting histone acetyltransferase p300 (EP300) to promote the histone 3 at lysine 27 (H3K27ac) at genomic locus of MAFG, forming a MAFG-AS1/MAFG positive feedback loop. In patient samples, higher expression of MAFG-AS1 and MAFG in BUC tissues was significantly correlated with T status and N status, such that MAFG-AS1, MAFG, and the combination of the two were independent prognostic indicators and chemotherapy sensitivity predictive biomarkers for BUC patients. These findings suggest that inhibition of MAFG-AS1 and MAFG can increase the sensitivity of BUC cells to cisplatin through promoting ferroptosis, indicating the novel chemotherapy sensitivity biomarkers and therapeutic target for BUC.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Melasma (ML), naevus fusco-caeruleus zygomaticus (NZ), freckles (FC), cafe-au-lait spots (CS), nevus of ota (NO), and lentigo simplex (LS), are common skin diseases causing hyperpigmentation. Deep ...learning algorithms learn the inherent laws and representation levels of sample data and can analyze the internal details of the image and classify it objectively to be used for image diagnosis. However, deep learning algorithms that can assist clinicians in diagnosing skin hyperpigmentation conditions are lacking.
The optimal deep-learning image recognition algorithm was explored for the auxiliary diagnosis of hyperpigmented skin disease. Pretrained models, such as VGG-19, GoogLeNet, InceptionV3, ResNet50V2, ResNet101V2, ResNet152V2, InceptionResNetV2, DesseNet201, MobileNet, and NASNetMobile were used to classify images of six common hyperpigmented skin diseases. The best deep learning algorithm for developing an online clinical diagnosis system was selected by using accuracy and area under curve (AUC) as evaluation indicators.
In this research, the parameters of the above-mentioned ten deep learning algorithms were 18333510, 5979702, 21815078, 23577094, 42638854, 58343942, 54345958, 18333510, 3235014, and 4276058, respectively, and their training time was 380, 162, 199, 188, 315, 511, 471, 697, 101, and 144 min respectively. The respective accuracies of the training set were 85.94%, 99.72%, 99.61%, 99.52%, 99.52%, 98.84%, 99.61%, 99.13%, 99.52%, and 99.61%. The accuracy rates of the test set data were 73.28%, 57.40%, 70.04%, 71.48%, 68.23%, 71.11%, 71.84%, 73.28%, 70.39%, and 43.68%, respectively. Finally, the areas of AUC curves were 0.93, 0.86, 0.93, 0.91, 0.91, 0.92, 0.93, 0.92, 0.93, and 0.82, respectively.
The experimental parameters, training time, accuracy, and AUC of the above models suggest that MobileNet provides a good clinical application prospect in the auxiliary diagnosis of hyperpigmented skin.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
miR-101 is considered to play an important role in hepato-cellular carcinoma (HCC), but the underlying molecular mechanism remains to be elucidated. Here, we aimed to confirm whether Girdin is a ...target gene of miR-101 and determine the tumor suppressor of miR-101 through Girdin pathway. In our previous studies, we firstly found Girdin protein was overexpressed in HCC tissues, and it closely correlated to tumor size, T stage, TNM stage and Edmondson-Steiner stage of HCC patients. After specific small interfering RNA of Girdin was transfected into HepG2 and Huh7.5.1 cells, the proliferation and invasion ability of tumor cells were significantly inhibited. In this study, we further explored the detailed molecular mechanism of Girdin in HCC. Interestingly, we found that miR-101 significantly low-expressed in HCC tissues compared with that in matched normal tissues while Girdin had a relative higher expression, and miR-101 was inversely correlated with Girdin expression. In addition, after miR-101 transfection, the proliferation, migration and invasion abilities of HepG2 cells were weakened. Furthermore, we confirmed that Girdin is a direct target gene of miR-101. Finally we confirmed Talen-mediated Girdin knockout markedly suppressed cell proliferation, migration and invasion in HCC while down-regulation of miR-101 significantly restored the inhibitory effect. Our findings suggested that miR-101/Girdin axis could be a potential application of HCC treatment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Hyperpigmentation disorders, such as melasma and freckles, are highly prevalent and draw increasing attention. Patients thus tend to seek effective and safe cosmetic whitening agents. Fraxin, a ...bioactive substance extracted from Cortex Fraxini, possesses anti-inflammation and antioxidant properties. In this study, we further explored the anti-melanogenic activities of fraxin were explored in vitro and in vivo. We found that pretreatment with fraxin decreased the melanin content of MNT1 cells and zebrafishes. In MNT1 cells, melanogenesis-related proteins, such as MITF, TYR, TYRP1, and DCT were down-regulated and tyrosinase activity was reduced under fraxin treatment. Further exploration of the mechanism revealed that fraxin could inhibit the phosphorylation of ERK, which is closely related to melanogenesis. Besides, fraxin also protected MNT1 cells from H2O2-induced apoptosis via scavenging reactive oxygen species (ROS) in cells. Further experimentation revealed that fraxin could activate NRF2 and upregulate antioxidase CAT and HO-1. In conclusion, fraxin could be an effective agent with anti-melanogenesis and antioxidant properties for hyperpigmentation disorders.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The rate of severe cutaneous adverse drug reactions is low, and these reactions can result in death or disability. An evidence-based epidemiological study of severe cutaneous adverse drug reactions ...in China has not been reported.
The aim of this study was to analyze epidemiology and characteristics of severe cutaneous adverse drug reactions of Chinese inpatients during the recent 15 years with meta-analysis.
We retrospectively reviewed Chinese literature reporting severe cutaneous adverse drug reactions and collecting data from 2000 to 2015, which were in accordance with our inclusion criteria. All included data were analyzed with the Launch Open Meta-Analyst software.
Twenty-five articles involving 928 cases with severe cutaneous adverse drug reactions were included. Men to women ratio was 1.14:1. Twenty-one per cent of the patients had drug allergy history. Antibiotics (26.0%), sedative hypnotics and anticonvulsants (21.6%), and antipyretic analgesics (17.1%) were the most common causative drugs. The most frequent clinical subtype was Stevens-Johnson syndrome (50.1%), followed by toxic epidermal necrolysis (25.4%), exfoliative dermatitis (21.0%) and drug-induced hypersensitivity syndrome (1.6%). In addition to skin rashes, patients with severe cutaneous adverse drug reactions suffered mostly from fever (73%), and blood routine abnormality (66.7%).
This meta-analysis is limited by its retrospective design and by its methodological variation.
The most common causative drugs were antibiotics and sedative hypnotics and anticonvulsants. Stevens-Johnson syndrome was the most frequent clinical subtype of severe cutaneous adverse drug reactions. In addition to skin rashes, patients with severe cutaneous adverse drug reactions suffered mostly from fever, mucosal lesion, and hematologic abnormalities.
In non‐sun exposure conditions, skin pigmentation gradually decreases with age. Sun exposure on the other hand increases skin pigmentation. In addition, the skin of the elderly is more prone to ...tanning than youthful skin.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
-GlcNAcylation is an important post-translational modification (PTM) jointly controlled by
-GlcNAc transferase (OGT) and
-GlcNAcase (OGA). Aberrant hyper-
-GlcNAcylation is reported to yield ...hepatocellular carcinoma (HCC) malignancy, but the underlying mechanisms of the OGT/OGA imbalance responsible for HCC tumorigenesis remain largely unknown. Here, we report that RAN-binding protein 2 (RANBP2), one of the small ubiquitin-like modifier (SUMO) E3 ligases, contributed to malignant phenotypes in HCC. RANBP2 was found to facilitate CCAAT/enhancer-binding protein alpha (CEBPα) SUMOylation and degradation by direct interplay with CEBPα. As a transcriptional factor, CEBPα was verified to augment OGA transcription, and further experiments demonstrated that RANBP2 enhanced the
-GlcNAc level by downregulating OGA transcription while not affecting OGT expression. Importantly, we provided in vitro and in vivo evidence of HCC malignant phenotypes that RANBP2 triggered through an imbalance of OGT/OGA and subsequent higher
-GlcNAcylation events for oncogenic proteins such as peroxisome proliferative-activated receptor gamma coactivator 1 alpha (PGC1α) in a CEBPα-dependent manner. Altogether, our results show a novel molecular mechanism whereby RANBP2 regulates its function through CEBPα-dependent OGA downregulation to induce a global change in the hyper-
-GlcNAcylation of genes, such as PGC1α, encouraging the further study of promising implications for HCC therapy.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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