Allergic contact dermatitis: a significant environmental and occupational skin disease

A database of human predictive patch test data for skin sensitization

Critical to the evaluation of non-animal tests are reference data with which to assess their relevance. Animal data are typically used because they are generally standardized and available. However, when regulatory agencies aim to protect human health, human reference data provide the benefit of not having to account for possible interspecies variability. To support the evaluation of non-animal approaches for skin sensitization assessment, we collected data from 2277 human predictive patch tests (HPPTs), i.e., human repeat insult patch tests and human maximization tests, for skin sensitization from 1555 publications. We recorded protocol elements and positive or negative outcomes, developed a scoring system to evaluate each test for reliability, and calculated traditional and non-traditional dose metrics. We also traced each test result back to its original report to remove duplicates. The resulting database, which contains information for 1366 unique substances, was characterized for physicochemical properties, chemical structure categories, and protein binding mechanisms. This database is publicly available on the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods website and in the Integrated Chemical Environment to serve as a resource for additional evaluation of alternative methods and development of new approach methodologies for skin sensitization assessments.

Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals

Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms using the novel quantitative Read-Across Structure-Activity Relationship (q-RASAR) approach. Based on the identified important set of structural and physicochemical features, Read-Across-based hyperparameters were optimized using the training set compounds followed by the calculation of similarity and error-based RASAR descriptors. Data fusion, further feature selection, and removal of prediction confidence outliers were performed to generate a partial least squares (PLS) q-RASAR model, followed by the application of various Machine Learning (ML) tools to check the quality of predictions. The PLS model was found to be the best among different models. A simple user-friendly Java-based software tool was developed based on the PLS model, which efficiently predicts the toxicity value(s) of query compound(s) along with their status of Applicability Domain (AD) in terms of leverage values. This model has been developed using structurally diverse compounds and is expected to predict efficiently and quantitatively the skin sensitization potential of environmental chemicals to estimate their occupational and health hazards.

Patch test results in dental staff with dermatitis

BACKGROUND

Dental workers are exposed to allergenic materials in the workplace.

OBJECTIVES

To summarize patch test results in dental staff with work-related dermatitis.

MATERIALS AND METHODS

Cross-sectional study of dental staff with dermatitis was conducted. Symptoms, clinical findings and results from patch tests with 40 dental allergens were summarized.

RESULTS

Of 329 participants, 155 (47.1%) had at least one positive patch reaction. The highest frequency of positive reactions was observed for metals (38.6%), acrylics (7.3%) and additives in acrylic resins (3.6%). One hundred and eighty-five (56.2%) participants were currently symptomatic; in this group, 79 (42.7%) had current dermatitis. Thirty-eight patients had at least one positive patch test considered occupationally related to their current dermatitis.

CONCLUSION

Almost half of the dental staff with dermatitis had at least one positive patch test and approximately 12% had current occupational relevance of the allergens. Our results underscore the importance of patch testing in the evaluation of dental staff with dermatitis.

Biogeochemical behaviour and toxicology of chromium in the soil-water-human nexus: A review

Chromium (Cr) affects human health if it accumulates in organs to elevated concentrations. The toxicity risk of Cr in the ecosphere depends upon the dominant Cr species and their bioavailability in the lithosphere, hydrosphere, and biosphere. However, the soil-water-human nexus that controls the biogeochemical behaviour of Cr and its potential toxicity is not fully understood. This paper synthesizes information on different dimensions of Cr ecotoxicological hazards in the soil and water and their subsequent effects on human health. The various routes of environmental exposure of Cr to humans and other organisms are also discussed. Human exposure to Cr(VI) causes both carcinogenic and non-carcinogenic health effects via complicated reactions that include oxidative stress, chromosomal and DNA damage, and mutagenesis. Chromium (VI) inhalation can cause lung cancer; however, incidences of other types of cancer following Cr(VI) exposure are low but probable. The non-carcinogenic health consequences of Cr(VI) exposure are primarily respiratory and cutaneous. Research on the biogeochemical behaviour of Cr and its toxicological hazards on human and other biological routes is therefore urgently needed to develop a holistic approach to understanding the soil-water-human nexus that controls the toxicological hazards of Cr and its detoxification.

Occupational Skin Dermatitis among Healthcare Workers Associated with the COVID-19 Pandemic: A Review of the Literature

The skin is the outermost layer of the human body and is continually exposed to numerous external stimuli, which can cause unwanted skin irritation. Occupational skin diseases are the most prevalent form of work-related illness and are found in a variety of sectors, particularly healthcare. During the recent COVID-19 pandemic, healthcare professionals experienced a variety of unexpected, unusual occupational skin diseases associated with COVID-19-engaged employment. Because the clinical characteristics of these types of skin inflammation are unique, this review focuses on the characteristics of a large category of occupational workers, namely COVID-19-engaged healthcare professionals. Furthermore, we examined the potential pathogeneses of occupational skin disorders associated with COVID-19-engaged labor, as well as different preventative methods.

TRPV1: A promising therapeutic target for skin aging and inflammatory skin diseases

TRPV1 is a non-selective channel receptor widely expressed in skin tissues, including keratinocytes, peripheral sensory nerve fibers and immune cells. It is activated by a variety of exogenous or endogenous inflammatory mediators, triggering neuropeptide release and neurogenic inflammatory response. Previous studies have shown that TRPV1 is closely related to the occurrence and/or development of skin aging and various chronic inflammatory skin diseases, such as psoriasis, atopic dermatitis, rosacea, herpes zoster, allergic contact dermatitis and prurigo nodularis. This review summarizes the structure of the TRPV1 channel and discusses the expression of TRPV1 in the skin as well as its role of TRPV1 in skin aging and inflammatory skin diseases.

Anti-Inflammatory and Antioxidant Properties of Physalis alkekengi L. Extracts In Vitro and In Vivo: Potential Application for Skin Care

OBJECTIVE

Inflammatory skin disorders are becoming major issues threatening public health with increasing prevalence. This study was to evaluate the anti-inflammatory, antioxidant, and antisenescent activities of traditional folk medicinal plant, Physalis alkekengi L. extracts to alleviate skin inflammation and its possible mechanisms.

METHODS

Lipopolysaccharides (LPS)-treated murine macrophages RAW264.7 and human skin keratinocytes HaCaT were incubated with the plant extracts, respectively. The production of nitric oxide (NO) was tested by using Griess reagents. The activity of nitric oxide synthase (NOS) was detected through a fluorescence microplate reader. Reactive oxygen species (ROS) production and cell apoptosis were quantified by flow cytometry. The proinflammatory cytokines were measured using ELISA and qRT-PCR. Human skin fibroblasts (HFF-1) were coincubated with D-galactose (D-gal) and the plant extracts. The senescence associated-galactosidase (SA-β-gal) was stained to evaluate cellular senescence. The senescence-associated secretory phenotype (SASP), IL-1β, was measured through ELISA. The mRNA of IL-1α in SLS-stimulated and PGE2 in UV-radiated 3D skin models were detected by qRT-PCR. In vivo ROS production and neutrophil recruitment in CuSO₄-treated zebrafish models were observed by fluorescence microscopy. Inflammation-related factors were measured by qRT-PCR. Results. In vitro, Physalis alkekengi L. significantly reduced NO production, NOS activity, cell apoptosis, transcription of TNF-α, IL-6, IL-1β and ROS production. These plant extracts markedly attenuated SA-β-gal and IL-1β and downregulated the production of IL-1α and PGE2. In vivo, the plant extracts dramatically dampened ROS production, the number of neutrophils, and proinflammatory cytokines.

CONCLUSIONS

Cumulatively, this work systematically demonstrated the anti-inflammatory, antioxidant, and antisenescent properties of Physalis alkekengi L. and proposed the possible roles of Physalis alkekengi L. in inflammatory signaling pathways, providing an effective natural product for the treatment of inflammatory skin disorders.

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.

Adsorption of Azo-Anionic Dyes in a Solution Using Modified Coconut (Cocos nucifera) Mesocarp: Kinetic and Equilibrium Study

The effect of adsorbent dose and initial concentration on removing the azo-anionic dyes Congo Red andtartrazine present in a synthetic aqueous solution was studied using natural cellulose (CC) and modified cationic cellulose (MCC) from coconut mesocarp. Three levels of adsorbent dosage (5, 8 and 12 mg/L) and initial concentration (40, 70 and 100 mg/L) were used. Cetyl trimethyl ammonium chloride (CTAC) was used as a modifying agent. TGA and DSC showed that the extracted cellulose was of good quality, composed mostly of cellulose with lignin and hemicellulose traces, and 8% moisture. The FTIR spectrum showed the effectiveness of the modification in the structure of the material with symmetric deformation of the C6H6-Cl group in 1472 cm−1 present in the CTAC. It was found that decreasing the adsorbent dosage and increasing the initial concentration favored the dyes’ adsorption capacity on the two bioadsorbents. Tartrazine removals of 5.67 mg/g on CC and 19.61 mg/g on MCC were achieved, and for CR of 15.52 mg/g on CC and 19.99 with MCC with removal percentages over 97% with the quaternized biomass in all cases. The kinetic and equilibrium study was carried out to identify the mechanisms involved in the adsorption process. The Freundlich model can describe the equilibrium isotherm data of tartrazine on CC and MCC. In contrast, those of CR is defined by the Langmuir and Dubinin–Radushkevic models for CC and MCC, respectively Adsorption kinetics showed that equilibrium was reached at 30 min, with rapid adsorption in the initial minutes with the removal of about 97% of the contaminant in the first 5 min; fitting to kinetic models showed that the kinetics of tartrazine on CC was fitted by Elovich (R2 = 0.756), and on MCC the Elovich (R2 = 0.887) and pseudo-second-order (R2 = 0.999) models. Removing CR on CC was fitted by pseudo-first-order, pseudo-second-order and Elovich models (R2 > 0.98), and when using MCC, all models show a good fitting with R2 = 0.99 in all cases.