Transformer guided self-adaptive network for multi-scale skin lesion image segmentation.

BACKGROUND: In recent years, skin lesion has become a major public health concern, and the diagnosis and management of skin lesions depend heavily on the correct segmentation of the lesions. Traditional convolutional neural networks (CNNs) have demonstrated promising results in skin lesion segmentation, but they are limited in their ability to capture distant connections and intricate features. In addition, current medical image segmentation algorithms rarely consider the distribution of different categories in different regions of the image and do not consider the spatial relationship between pixels. OBJECTIVES: This study proposes a self-adaptive position-aware skin lesion segmentation model SapFormer to capture global context and fine-grained detail, better capture spatial relationships, and adapt to different positional characteristics. The SapFormer is a multi-scale dynamic position-aware structure designed to provide a more flexible representation of the relationships between skin lesion characteristics and lesion distribution. Additionally, it increases skin lesion segmentation accuracy and decreases incorrect segmentation of non-lesion areas. INNOVATIONS: SapFormer designs multiple hybrid transformers for multi-scale feature encoding of skin images and multi-scale positional feature sensing of the encoded features using a transformer decoder to obtain fine-grained features of the lesion area and optimize the regional feature distribution. The self-adaptive feature framework, built upon the transformer decoder module, dynamically and automatically generates parameterizations with learnable properties at different positions. These parameterizations are derived from the multi-scale encoding characteristics of the input image. Simultaneously, this paper utilizes the cross-attention network to optimize the features of the current region according to the features of other regions, aiming to increase skin lesion segmentation accuracy. MAIN RESULTS: The ISIC-2016, ISIC-2017, and ISIC-2018 datasets for skin lesions are used as the basis for the experiment. On these datasets, the proposed model has accuracy values of 97.9 %, 94.3 %, and 95.7 %, respectively. The proposed model's IOU values are, in order, 93.2 %, 86.4 %, and 89.4 %. The proposed model's DSC values are 96.4 %, 92.6 %, and 94.3 %, respectively. All three metrics surpass the performance of the majority of state-of-the-art (SOTA) models. SapFormer's metrics on these datasets demonstrate that it can precisely segment skin lesions. Notably, our approach exhibits remarkable noise resistance in non-lesion areas, while simultaneously conducting finer-grained regional feature extraction on the skin lesion image. CONCLUSIONS: In conclusion, the integration of a transformer-guided position-aware network into semantic skin lesion segmentation results in a notable performance boost. The ability of our proposed network to capture spatial relationships and fine-grained details proves beneficial for effective skin lesion segmentation. By enhancing lesion localization, feature extraction, quantitative analysis, and classification accuracy, the proposed segmentation model improves the diagnostic efficiency of skin lesion analysis on dermoscopic images. It assists dermatologists in making more accurate and efficient diagnoses, ultimately leading to better patient care and outcomes. This research paves the way for advances in diagnosing and treating skin lesions, promoting better understanding and decision-making in the clinical setting.

Fecal microbiota transplantation affects the recovery of AD-skin lesions and enhances gut microbiota homeostasis.

BACKGROUND: Accumulating evidence has shown that gut microbiota plays a key role in the progression of atopic dermatitis (AD). Fecal microbiota transplantation (FMT), as an effective method to restore gut microbiota homeostasis, has been successfully applied for treating many inflammatory diseases. However, the therapeutic effect of FMT on AD remains unclear. The following study examined the effect and mechanism of FMT on AD-skin lesions in an AD mouse model. METHODS: In this study, we exposed the shaved back skin of BALB/c mice to calcipotriol (MC903) to induce AD model. Mice were then treated with FMT, which was performed with gut microbiota from healthy mice. The gut microbiota of treated mice was tracked by 16S rRNA gene sequencing. Mice skin tissues were examined by histopathology and inflammatory cytokines change in serum by ELISA. RESULTS: FMT had a faster trend on the reversion of the increases in skin epidermal layer thicknesses and suppressed some of the representative inflammatory cytokines. The gut microbial community in the natural recovery process varied significantly in the FMT group at day 7 (ANOSIM P = 0.0229, r = 0.2593). Notably, FMT had a long-lasting and beneficial impact on the gut microbial compositions of AD mice by increasing the ratio of Firmicutes to Bacteroidetes and the amount of butyric-producing bacteria (BPB), including Erysipelotrichaceae, Lactobacillaceae, and Eubacteriacea. Furthermore, the relative abundances of gut microbiota-mediated functional pathways involved in the cell growth and death, amino acid, energy, lipid, and carbohydrate metabolisms, and immune system increased after FMT treatment. CONCLUSION: FMT modulated the gut microbiota homeostasis and affected the recovery from AD-related inflammations, suggesting that it could be used as a treatment strategy for AD patients in the clinic.

An improved transformer network for skin cancer classification.

BACKGROUND: Use of artificial intelligence to identify dermoscopic images has brought major breakthroughs in recent years to the early diagnosis and early treatment of skin cancer, the incidence of which is increasing year by year worldwide and poses a great threat to human health. Achievements have been made in the research of skin cancer image classification by using the deep backbone of the convolutional neural network (CNN). This approach, however, only extracts the features of small objects in the image, and cannot locate the important parts. OBJECTIVES: As a result, researchers of the paper turn to vision transformers (VIT) which has demonstrated powerful performance in traditional classification tasks. The self-attention is to improve the value of important features and suppress the features that cause noise. Specifically, an improved transformer network named SkinTrans is proposed. INNOVATIONS: To verify its efficiency, a three step procedure is followed. Firstly, a VIT network is established to verify the effectiveness of SkinTrans in skin cancer classification. Then multi-scale and overlapping sliding windows are used to serialize the image and multi-scale patch embedding is carried out which pay more attention to multi-scale features. Finally, contrastive learning is used which makes the similar data of skin cancer encode similarly so that the encoding results of different data are as different as possible. MAIN RESULTS: The experiment is carried out based on two datasets, namely (1) HAM10000: a large dataset of multi-source dermatoscopic images of common skin cancers; (2)A clinical dataset of skin cancer collected by dermoscopy. The model proposed has achieved 94.3% accuracy on HAM10000 and 94.1% accuracy on our datasets, which verifies the efficiency of SkinTrans. CONCLUSIONS: The transformer network has not only achieved good results in natural language but also achieved ideal results in the field of vision, which also lays a good foundation for skin cancer classification based on multimodal data. This paper is convinced that it will be of interest to dermatologists, clinical researchers, computer scientists and researchers in other related fields, and provide greater convenience for patients.

Value of a Signature of Immune-Related Genes in Predicting the Prognosis of Melanoma and Its Responses to Immune Checkpoint Blocker Therapies.

Melanoma is becoming increasingly common worldwide, with high rates of transformation into malignancy compared to other skin lesions. The prognosis of patients with melanoma at an advanced stage is highly unsatisfying despite the development of immunotherapy, target therapy, or combinative therapy. The major barrier to exploiting immune checkpoint therapies and achieving the best benefits clinically is resistance that can easily develop if regimens are not selected appropriately. In this study, we investigated the possibility of using immune-related genes to predict patient survival and their responses to immune checkpoint blocker therapies with the expression profiles available at The Cancer Genome Atlas (TCGA) Program plus expression data from the Gene Expression Omnibus (GEO) for validation. A five gene signature that is highly correlated with the local infiltration of cytotoxic lymphocytes in the tumor microenvironment was identified, and a scoring model was developed with stepwise regression after multivariate Cox analyses. The score calculated strongly correlates with Breslow depth, and this model effectively predicts the prognosis of patients with melanoma, whether primary or metastasized. It also depicts the heterogenous immune-related nature of melanoma by revealing different predicted responses to immune checkpoint blocker therapies through its correlation to tumor immune dysfunction and exclusion (TIDE) score.

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.

Introduction: Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes. Methods: This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO. Results: To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets. Discussion: The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

BCG Vaccination in Early Childhood and Risk of Atopic Disease: A Systematic Review and Meta-Analysis.

Background: Several large-scale studies suggest that Bacille Calmette-Guerin (BCG) vaccination in early childhood may reduce the risk of atopic diseases, but the findings remain controversial. Here, we aimed to investigate the potential correlation between early childhood BCG vaccination and the risk of developing atopic diseases. Methods: Eligible studies published on PubMed, EMBASE, and Cochrane CENTRAL were systematically sourced from 1950 to July 2021. Studies with over 100 participants and focusing on the association between BCG vaccine and atopic diseases including eczema, asthma, and rhinitis were included. Preliminary assessment of methods, interventions, outcomes, and study quality was performed by two independent investigators. Odds ratio (OR) with 95% confidence interval (CI) was calculated. Random effects of the meta-analysis were performed to define pooled estimates of the effects. Results: Twenty studies with a total of 222,928 participants were selected. The quantitative analysis revealed that administering BCG vaccine in early childhood reduced the risk of developing asthma significantly (OR 0.77, 95% CI 0.63 to 0.93), indicating a protective efficacy of 23% against asthma development among vaccinated children. However, early administration of BCG vaccine did not significantly reduce the risk of developing eczema (OR 0.94, 95% CI 0.76 to 1.16) and rhinitis (OR 0.99, 95% CI 0.81 to 1.21). Further analysis revealed that the effect of BCG vaccination on asthma prevalence was significant especially in developed countries (OR 0.73, 95% CI 0.58 to 0.92). Conclusion: BCG vaccination in early childhood is associated with reduced risk of atopic disease, especially in developed countries.

Serum leukocyte cell-derived chemotaxin 2 level is associated with atopic dermatitis patients.

BACKGROUND: Atopic dermatitis (AD) is a chronic skin inflammation with a heterogeneous immunological profile. Leukocyte cell-derived chemotaxin 2 (LECT2) is a liver-derived multifunctional cytokine that has been studied due to its important role in inflammatory and autoimmune diseases. However, the relationship between AD and LECT2 has not been defined. This study was performed to investigate the levels of LECT2 in patients with AD and to determine whether it was associated with the severity and clinical characteristics of AD. METHODS: The study included 42 AD patients and 30 healthy controls from the Affiliated Hospital of Medical School of Ningbo University. Participants' serum levels of LECT2 were measured using enzyme-linked immunosorbent assay kits. The values in the patient group and control group were statistically compared. The relationships between the different markers were evaluated by correlation analysis. RESULTS: The serum levels of LECT2 were significantly higher in AD patients than those in the controls. In addition, LECT2 was significantly correlated with the Scoring of Atopic Dermatitis (SCORAD) index, the level of immunoglobulin E (IgE), and eosinophils (P<0.01, for all 3). CONCLUSIONS: Serum LECT2 levels were evaluated in AD patients. The results showed that LECT2 may be a useful biomarker of AD, and may participate in the occurrence and regulation of inflammation in AD progression.

Aberrant expression of the UPF1 RNA surveillance gene disturbs keratinocyte homeostasis by stabilizing AREG.

The up­frameshift suppressor 1 homolog (UPF1) RNA surveillance gene is a core element in the nonsense­mediated RNA decay (NMD) pathway, which impacts a broad spectrum of biological processes in a cell­specific manner. In the present study, the contribution of the NMD pathway to psoriasis lesions and its moderating effects on the biological processes of keratinocytes was reported. Sanger sequencing for skin scales from two patients with psoriasis identified two mRNA mutations (c.2935_2936insA and c.2030­2081del) in the UPF1 gene. The somatic mutants produced truncated UPF1 proteins and perturbed the NMD pathway in cells, leading to the upregulation of NMD substrates. As the most abundant epidermal growth factor receptor ligand in keratinocytes, it was concluded that amphiregulin (AREG) mRNA is a natural NMD substrate, that is dependent on its 3' untranslated region sequence. Perturbed NMD modulated keratinocyte homeostasis in an AREG­dependent but nonidentical manner, which highlighted the unique characteristics of NMD in keratinocytes. By targeting AREG mRNA post­transcriptionally, the UPF1­NMD pathway contributed to an imbalance between proliferation on the one hand, and apoptosis and abnormal differentiation, migration and inflammatory response on the other, in keratinocytes, which indicated a role of the NMD pathway in the full development of keratinocyte­related morbidity and skin diseases.

Increased Plasma Levels of S100A8, S100A9, and S100A12 in Chronic Spontaneous Urticaria.

BACKGROUND: Chronic spontaneous urticaria (CSU) is a skin disorder with an important immunologic profile. S100A8, S100A9, and S100A12 are the members of S100 family that have been reported to play important role in autoimmune diseases, but the characteristics of these three S100 members have not been defined in CSU. AIMS: This study was performed to investigate the levels of these three S100s in patients with CSU and to study whether they were associated with the severity and clinical characteristics of CSU. MATERIALS AND METHODS: The levels of plasma S100A8, S100A9, and S100A12 were measured in 51 CSU patients and 20 healthy controls using enzyme linked immunosorbent assay kits. The values in the patient group and that of the healthy controls were statistically compared. The relationships between the different markers were evaluated by correlation analysis. RESULTS: The plasma levels of S100A8, S100A9, and S100A12 were significantly higher in CSU patients than those in controls. Interestingly, the level of S100A12 was significantly correlated with S100A8 and S100A9 in CSU patients (P < 0.05 and P < 0.001, respectively). In addition, S100A8, S100A9, and S100A12 were all significantly inversely correlated with blood basophil percentage. CONCLUSIONS: Plasma S100A8, S100A9, and S100A12 levels were elevated in CSU patients. They might be useful biomarkers of CSU, with the potential role in the pathogenesis of CSU.

Increased plasma IL-17, IL-31, and IL-33 levels in chronic spontaneous urticaria.

Chronic spontaneous urticaria (CSU) is considered in a subset of patients to be an autoimmune disorder. Interleukin(IL)-17, IL-31, and IL-33 are involved in some immune response. The aim of this study was to quantify plasma IL-17, IL-31, and IL-33 levels in CSU patients and to examine their relationships with disease severity. Plasma IL-17, IL-31, and IL-33 concentration were measured in 51 CSU patients and 20 healthy subjects (HCs). Plasma IL-17 (P < 0.001), IL-31 (P < 0.001), and IL-33 (P < 0.001) concentrations were significantly higher in CSU patients when compared with those of HCs. Concerning UAS7, severe group of CSU patients had significantly higher IL-17 levels than the moderate and mild groups (P = 0.028 and 0.007, respectively), and significantly higher IL-33 concentrations than the mild group (P = 0.026). Regarding only pruritus, severe group of patients had significantly higher IL-31 levels than the mild group (P = 0.003). The IL-33 levels in the total IgE positive group were significantly higher than that of negative group (P = 0.010). Our results showed higher plasma levels of IL-17, IL-31, and IL-33 among CSU patients which may highlight a functional role of these cytokines in the pathogenesis of CSU.

Repression of M-phase phosphoprotein 8 inhibits melanoma growth and metastasis in vitro and in vivo.

Metastatic melanoma accounts for the majority of skin cancer deaths due to its aggressiveness and high resistance to current therapies. M-phase phosphoprotein 8 (MPP8) has been shown to bind to methylated H3K9 and promote tumor cell motility and invasion. The current study aimed to investigate the role of MPP8 in melanoma growth and metastasis. Our results showed that MMP8 was up-regulated in the metastatic melanoma specimens. Knockdown of MPP8 inhibited melanoma cell growth both in vitro and in vivo. Furthermore, down-regulation of MPP8 induced S-phase cell cycle arrest as well as altered expression of cell cycle-related proteins in melanoma cells. In addition, repression of MPP8 inhibited the migration and invasion of melanoma cells both in vitro and in vivo. Taken together, these data suggest that MPP8 knockdown could inhibit the growth and metastasis of melanoma cells and provide novel therapeutic target for melanoma treatment.