Skin lesions are an important health concern, exposing the body to infection risks. Utilizing natural products containing chamomile (Chamomilla recutita L.) holds promise for curative purposes. Additionally, hyaluronic acid (HA), an active ingredient known for its tissue regeneration capacity, can expedite healing. In this study, we prepared and characterized an extract of C. recutita and integrated it into a nanoemulsion system stabilized with HA, aiming at harnessing its healing potential. We assessed the impact of alcoholic strength on flavonoid extraction and chemically characterized the extract using UHPLC/MS while quantifying its antioxidant and antimicrobial capacity. We developed a nanoemulsion loaded with C. recutita extract and evaluated the effect of HA stabilization on pH, droplet size, polydispersity index (PDI), zeta potential, and viscosity. Results indicated that 70% hydroalcoholic extraction yielded a higher flavonoid content. The extract exhibited antioxidant capacity in vitro, a desirable trait for skin regeneration, and demonstrated efficacy against key microbial strains (Staphylococcus aureus, Streptococcus pyogenes, Escherichia coli, and Pseudomonas aeruginosa) associated with skin colonization and infections. Flavonoids spireoside and apiin emerged as the most abundant bioactives. The addition of HA led to increased viscosity while maintaining a suitable pH for topical application. Zeta potential, droplet size, and PDI met acceptable criteria. Moreover, incorporating C. recutita extract into the nanoemulsion enhanced its antimicrobial effect. Hence, the nanoemulsion system loaded with C. recutita and HA stabilization exhibits favorable characteristics for topical application, showing promise in aiding the healing processes.

BACKGROUND: Dermoscopy is a growing field that uses microscopy to allow dermatologists and primary care physicians to identify skin lesions. For a given skin lesion, a wide variety of differential diagnoses exist, which may be challenging for inexperienced users to name and understand.
OBJECTIVE: In this study, we describe the creation of the dermoscopy differential diagnosis explorer (D3X), an ontology linking dermoscopic patterns to differential diagnoses.
METHODS: Existing ontologies that were incorporated into D3X include the elements of visuals ontology and dermoscopy elements of visuals ontology, which connect visual features to dermoscopic patterns. A list of differential diagnoses for each pattern was generated from the literature and in consultation with domain experts. Open-source images were incorporated from DermNet, Dermoscopedia, and open-access research papers.
RESULTS: D3X was encoded in the OWL 2 web ontology language and includes 3041 logical axioms, 1519 classes, 103 object properties, and 20 data properties. We compared D3X with publicly available ontologies in the dermatology domain using a semiotic theory-driven metric to measure the innate qualities of D3X with others. The results indicate that D3X is adequately comparable with other ontologies of the dermatology domain.
CONCLUSIONS: The D3X ontology is a resource that can link and integrate dermoscopic differential diagnoses and supplementary information with existing ontology-based resources. Future directions include developing a web application based on D3X for dermoscopy education and clinical practice.

BACKGROUND: Successful usage of autologous skin cell suspension (ASCS) has been demonstrated in some clinical trials. However, its efficacy and safety have not been verified. This latest systematic review and meta-analysis aim to examine the effects of autologous epidermal cell suspensions in re-epithelialization of skin lesions.
METHODS: Relevant articles were retrieved from PubMed, Embase, Cochrane Database, Web of Science, International Clinical Trials Registry Platform, China National Knowledge Infrastructureris, VIP Database for Chinese Technical Periodicals and Wanfang database. The primary output measure was the healing time, and the secondary outputs were effective rate, size of donor site for treatment, size of study treatment area, operation time, pain scores, repigmentation, complications, scar scale scores and satisfaction scores. Data were pooled and expressed as relative risk (RR), mean difference (MD) and standardized mean difference (SMD) with a 95% confidence interval (CI).
RESULTS: Thirty-one studies were included in this systematic review and meta-analysis, with 914 patients who received autologous epidermal cell suspensions (treatment group) and 883 patients who received standard care or placebo (control group). The pooled data from all included studies demonstrated that the treatment group has significantly reduced healing time (SMD = -0.86; 95% CI: -1.59-0.14; p = 0.02, I2 = 95%), size of donar site for treatment (MD = -115.41; 95% CI: -128.74-102.09; p<0.001, I2 = 89%), operation time (MD = 25.35; 95% CI: 23.42-27.29; p<0.001, I2 = 100%), pain scores (SMD = -1.88; 95% CI: -2.86-0.90; p = 0.0002, I2 = 89%) and complications (RR = 0.59; 95% CI: 0.36-0.96; p = 0.03, I2 = 66%), as well as significantly increased effective rate (RR = 1.20; 95% CI: 1.01-1.42; p = 0.04, I2 = 77%). There were no significant differences in the size of study treatment area, repigmentation, scar scale scores and satisfaction scores between the two groups.
CONCLUSIONS: Our meta-analysis showed that autologous epidermal cell suspensions is beneficial for re-epithelialization of skin lesions as they significantly reduce the healing time, size of donar site for treatment, operation time, pain scores and complications, as well as increased effective rate. However, this intervention has minimal impact on size of treatment area, repigmentation, scar scale scores and satisfaction scores.

Leishmaniasis, caused by Leishmania parasites, is a neglected tropical disease and Cutaneous Leishmaniasis (CL) is the most common form. Despite the associated toxicity and adverse effects, Meglumine antimoniate (MA) remains the first-choice treatment for CL in Brazil, pressing the need for the development of better alternatives. Bacterial NanoCellulose (BNC), a biocompatible nanomaterial, has unique properties regarding wound healing. In a previous study, we showed that use of topical BNC + systemic MA significantly increased the cure rate of CL patients, compared to treatment with MA alone. Herein, we performed a study comparing the combination of a wound dressing (BNC or placebo) plus systemic MA versus systemic MA alone, in CL caused by Leishmania braziliensis. We show that patients treated with the combination treatment (BNC or placebo) + MA showed improved cure rates and decreased need for rescue treatment, although differences compared to controls (systemic MA alone) were not significant. However, the overall time-to-cure was significantly lower in groups treated with the combination treatment (BNC+ systemic MA or placebo + systemic MA) in comparison to controls (MA alone), indicating that the use of a wound dressing improves CL treatment outcome. Assessment of the immune response in peripheral blood showed an overall downmodulation in the inflammatory landscape and a significant decrease in the production of IL-1a (p < 0.05) in patients treated with topical BNC + systemic MA. Our results show that the application of wound dressings to CL lesions can improve chemotherapy outcome in CL caused by L. braziliensis.

Skin cancer can be detected through visual screening and skin analysis based on the biopsy and pathological state of the human body. The survival rate of cancer patients is low, and millions of people are diagnosed annually. By determining the different comparative analyses, the skin malignancy classification is evaluated. Using the Isomap with the vision transformer, we analyze the high-dimensional images with dimensionality reduction. Skin cancer can present with severe cases and life-threatening symptoms. Overall performance evaluation and classification tend to improve the accuracy of the high-dimensional skin lesion dataset when completed. In deep learning methodologies, the distinct phases of skin malignancy classification are determined by its accuracy, specificity, F1 recall, and sensitivity while implementing the classification methodology. A nonlinear dimensionality reduction technique called Isomap preserves the data\'s underlying nonlinear relationships intact. This is essential for the categorization of skin malignancies, as the features that separate malignant from benign skin lesions may not be linearly separable. Isomap decreases the data\'s complexity while maintaining its essential characteristics, making it simpler to analyze and explain the findings. High-dimensional datasets for skin lesions have been evaluated and classified more effectively when evaluated and classified using Isomap with the vision transformer.

Patagial wing tags are commonly used for identification of Red Kites (Milvus milvus) for postrelease monitoring, as they are easy to apply, affordable, permanent, and are apparently safe. The Red Kite was successfully reintroduced in the UK in the second half of the 20th century and postrelease health surveillance has been achieved through radio and satellite tracking, monitoring nest sites, and pathologic investigation of Red Kites found dead. This study reports on pathologic findings associated with the use of patagial wing tags in three of 142 (2.1%) wing-tagged Red Kites examined postmortem since the beginning of the reintroduction project in 1989. In these three Red Kites the presence of the patagial wing tags was associated with inflammatory lesions. Further surveys of the potential short- and longer-term negative effects of patagial wing tags on Red Kites and other birds are advocated; the future use of patagial wing tags in raptors should be carefully monitored.

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection.

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Nowadays, skin disease is becoming one of the most malignant diseases that threaten people\'s health. Computer aided diagnosis based on deep learning has become a widely used technology to assist medical professionals in diagnosis, and segmentation of lesion areas is one of the most important steps in it. However, traditional medical image segmentation methods rely on numerous pixel-level labels for fully supervised training, and such labeling process is time-consuming and requires professional competence. In order to reduce the costs of pixel-level labeling, we proposed a method only using image-level label to segment skin lesion areas. Due to the lack of lesion\'s spatial and intensity information in image-level labels, and the wide distribution range of irregular shape and different texture on skin lesions, the algorithm must pay great attention to the automatic lesion localization and perception of lesion boundary. In this paper, we proposed a Self-Guided Multiple Information Aggregation Network (SG-MIAN). Our backbone network MIAN utilizes the Multiple Spatial Perceptron (MSP) solely using classification information as guidance to discriminate the key classification features of lesion areas, and thereby performing more accurate localization and activation of lesion areas. Additionally, adjunct to MSP, we also proposed an Auxiliary Activation Structure (AAS) and two auxiliary loss functions to further self-guided boundary correction, achieving the goal of accurate boundary activation. To verify the effectiveness of the proposed method, we conducted extensive experiments using the HAM10000 dataset and the PH2dataset, which demonstrated superior performance compared to most existing weakly supervised segmentation methods.

Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet\'s performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet\'s effectiveness, as it consistently outperformed both benchmarks across all datasets.