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Introduction: Irritant contact dermatitis (ICD) is characterized by direct injury to the epidermal cells, activating the innate immune response. Allergic contact dermatitis (ACD), in contrast, is delineated by a delayed hypersensitivity reaction of type IV. Despite the distinct etiopathogenic mechanisms underpinning each condition, the differentiation between them presents a significant diagnostic challenge. Objective: This study aims to determine whether a combination of clinical evaluation and noninvasive measurements—encompassing oxidative stress, erythema, hydration, melanin content, transepidermal water loss (TEWL), hemoglobin concentration, and skin texture and volume—can distinguish ICD from ACD. Methods: Two cohorts, each comprising 21 patients, were evaluated: one diagnosed with ICD and the other with ACD. All participants underwent biophysical and clinical assessments, along with Antera® 3D evaluations. Tape strips were utilized for skin sampling, and oxidative stress levels were measured via fluorescence assessments. Results: ICD prompts an almost immediate inflammatory reaction (peaking at 24 hours), whereas ACD incites a delayed response (72 hours). Noninvasive evaluated parameters as hemoglobin concentration, skin texture and volume, melanin content, erythema, and TEWL showed significant differences between the ICD and ACD cohorts (p < 0.05). The allergens amcinonide, nickel sulphate, cobalt chloride, budesonide, PPD, and thiuram mix were found to induce elevated levels of oxidative stress. Conclusions: The evaluation of patients with noninvasive parameters, including transepidermal water loss (TEWL), hemoglobin concentration, and skin texture and volume, could markedly aid in distinguishing irritant contact dermatitis from allergic contact dermatitis (ACD). Nevertheless, the study is constrained by a limited sample size.

Background: Allergic contact dermatitis (ACD) is a delayed hypersensitivity reaction occurring in sensitized individuals due to exposure to allergens. Polysensitization, defined as positive reactions to multiple unrelated haptens, increases the risk of ACD development and affects patients’ quality of life. The aim of this study is to apply machine learning in order to analyze the association between ACD, polysensitization, individual susceptibility, and patients’ characteristics. Methods: Patch test results and demographics from 400 ACD patients (Study protocol Nr. 3765/2022), categorized as polysensitized or monosensitized, were analyzed. Classic statistical analysis and multiple correspondence analysis (MCA) were utilized to explore relationships among variables. Results: The findings revealed significant associations between patient characteristics and ACD patterns, with hand dermatitis showing the strongest correlation. MCA provided insights into the complex interplay of demographic and clinical factors influencing ACD prevalence. Conclusion: Overall, this study highlights the potential of machine learning in unveiling hidden patterns within dermatological data, paving the way for future advancements in the field.

Epicutaneous patch testing is a well-established diagnostic method for identifying substances that may cause Allergic Contact Dermatitis (ACD), a common skin condition caused by exposure to environmental allergens. While the patch test remains the gold standard for identifying allergens, it is prone to observer bias and consumes valuable human resources. Deep learning models can be employed to address this challenge. In this study, we collected a dataset of 1579 multi-modal skin images from 200 patients using the Antera 3D® camera. We then investigated the feasibility of using a deep learning classifier for automating the identification of the allergens causing ACD. We propose a deep learning approach that utilizes a context-retaining pre-processing technique to improve the accuracy of the classifier. In addition, we find promise in the combination of the color image and false-color map of hemoglobin concentration to improve diagnostic accuracy. Our results showed that this approach can potentially achieve more than 86% recall and 94% specificity in identifying skin reactions, and contribute to faster and more accurate diagnosis while reducing clinician workload.

Keloids are skin fibroproliferative disorders, resulting from abnormal healing of deep cutaneous injuries. Cryosurgery, the most common treatment for keloids, causes skin traumas. Even though the clinical practice of cryosurgery has increased, effective wound healing therapy is still lacking. In this investigation, nonwoven nanofibrous patches composed of ulvan, a marine sulfated polysaccharide exhibiting anti-inflammatory and antioxidant activities, and polyethylene oxide (PEO) were fabricated through electrospinning and characterized. Their wound healing efficacy on skin traumas resulting from cryosurgical treatment of keloids was clinically tested and evaluated in comparison to a reference product. Twenty-four volunteer patients undergoing cryosurgery as a treatment of keloids were selected to apply either the ulvan/PEO patch or the reference product for 21 days. The ulvan/PEO patch, 21 days after cryosurgery, showed significant wound healing, elimination of skin inflammation, restoration of biophysical parameters similar to normal values and significant decrease in haemoglobin concentration, skin texture and volume, while no discomfort or adverse reaction was observed. In contrast, the reference product showed inferior performance in all evaluated parameters. The designed ulvan/PEO patch represents the first wound dressing to effectively heal skin trauma after cryosurgical treatment of keloids.