Disorders of Facial Hyperpigmentation

A Bayesian network meta-analysis of 14 molecules inhibiting UV daylight-induced pigmentation

INTRODUCTION

Hyperpigmentation disorders are very frequent, affect the quality of life and may become a psychological burden for afflicted patients. Many anti-pigmenting or depigmenting agents are available with various efficacy and almost no comparative data. 2-mercaptonicotinoyl glycine (2-MNG) was recently proposed as a viable candidate showing safe and effective results on hyperpigmentation control in vitro and in vivo.

OBJECTIVES

A Bayesian network meta-analysis (BNMA) was conducted to map and rank the anti-pigmenting and depigmenting efficacy of 2-MNG 0.5% on UV daylight (UVDL)-induced pigmentation together with 13 other reference molecules. A comparison in the kinetics of 2-MNG 0.5% was also performed.

METHODOLOGY

Fourteen studies were conducted, for each, on 15-30 women of skin phototype III in Shanghai, China and Paris, France. The products were applied on mini zone, in randomized and blinded protocol, on the back, 5 days a week during 6 weeks, at a dose of 4 mg/cm2. During the second week, volunteers were exposed under to varying minimum erythemal dose of UVDL during 4 consecutive days-adapted to obtain a similar induction of skin pigmentation regardless of the population. Assessments were performed instrumentally using Chromameter®. Ascorbic acid 7% was used as a positive control for all experiments. A Bayesian network meta-analysis was then established to map and follow the kinetics of 2-MNG 0.5% performance with 13 reference molecules (glutathione 2%, kojic acid 1%, hydroquinone 4%, ascorbyl glucoside 2%, niacinamide 4%, etc.).

RESULTS

2-MNG 0.5% dominated the ranking at all time points with a significant high probability of strong efficacy against UVDL-induced pigmentation. Ascorbic acid 7% ranks second after 4 days of irradiations (D12) whereas hydroquinone 4% ranks second 1 month after irradiations (D40). In the kinetics, 2-MNG at 0.5% was effective as from the end of irradiations (D12) to the study endpoint (D40). This suggested an immediate and persistent efficacy across all timepoints evaluated.

CONCLUSION

The BNMA revealed a rapid and lasting efficacy of 2-MNG 0.5% on the anti-pigmenting and depigmenting phases of the clinical protocol. 2-MNG 0.5% ranked first, with immediate and lasting effect compared to 13 other references. This study is the first allowing comparison between reference anti-pigmenting and depigmenting agents and will help clinicians for proposing the most effective approach for their patients.

Development and evaluation of berberine-loaded bigel for the treatment of hyperpigmentation on B16F10 melanoma cell line

Aim: The aim of this study was to optimize, develop, characterize and evaluate a topical nanobigel (BG) formulation containing Berberine (BRB) that exhibits anti-melanogenic properties.Materials & methods: The Berberine-loaded bigel (BRB@BG) formulation was prepared by homogenously mixing the optimized hydrogel and oleogel. BRB@BG was characterized in vitro and cytotoxicity study was conducted to evaluate its effects on murine skin melanoma B16F10 cell lines.Results: The optimized BRB@BG exhibited uniform texture with nanometric size, desirable spreadability and extrudability, suitable for topical applications. Cytotoxicity studies revealed that BRB@BG had a lower IC50 value (4.84 μg/ml) on B16F10 cell lines compared with drug alone.Conclusion: In conclusion, the developed BRB@BG formulation showed good potential as safe and effective topical treatment for hyperpigmentation.

Review of Fractional Nonablative Lasers for the Treatment of Dermatologic Conditions in Darker Skin Phototypes

BACKGROUND

Fractional nonablative lasers (NAFLs) have demonstrated efficacy and safety for treating dermatologic conditions in patients with darker skin phototypes. Nonablative lasers are preferred in darker skin tones due to lower risk of postinflammatory hyperpigmentation.

OBJECTIVE

This review aims to identify the ideal laser options and parameters for treating common dermatologic conditions in patients with skin types IV-VI.

MATERIALS AND METHODS

A comprehensive literature search was conducted on PubMed in May 2023. Of 1,065 articles were identified, and 40 articles met the inclusion criteria. The studies were classified based on design, dermatologic condition, and skin phototype of patients, and assigned levels of evidence according to the Modified Criteria of the Oxford Center of Evidence Based Medicine.

RESULTS

Strong level 1 evidence supports the treatment of melasma and atrophic scars using NAFL. Moderate level 2 evidence was found for using NAFL in acne vulgaris, striae, and skin rejuvenation; 45% of the studies examined skin types III-IV, 20% III-V, 7.5% II-IV, 5% II-V, 5% IV alone, and 2.5% I-IV.

CONCLUSION

Further research is needed to determine the optimal treatment modalities and parameters for skin types V and VI. Appropriate device selection and conservative treatment settings are crucial for optimizing outcomes and minimizing adverse events.

Skin dark spot mapping and evaluation of brightening product efficacy using Line-field Confocal Optical Coherence Tomography (LC-OCT)

BACKGROUND

Facial dark spots remain a significant challenge for the cosmetic industry, in terms of providing effective treatment. Using Line-field Confocal Optical Coherence Tomography (LC-OCT), we investigated the internal structural features of photo-aging spot areas and evaluated the efficacy of a skin-brightening cosmetic product.

MATERIALS AND METHODS

Twenty-six Asian female volunteers, aged between 29 and 65 years, applied a cosmetic product on their entire face twice a day for 2 months. LC-OCT was used to evaluate the dermal-epidermal junction (DEJ) undulation and the volume density of melanin in the epidermis at D0 and D56. Skin brightening and redness were also assessed by photography (SkinCam).

RESULTS

Using LC-OCT technology, various microscopic dark spot morphologies, spanning from minimally deformed DEJ to complex DEJ patterns, were identified. Dark spots characterized by slight deformities in the DEJ were predominantly observed in the youngest age group, while older volunteers displayed a wavier pattern. Furthermore, a total of 44 spots were monitored to evaluate the brightening product efficacy. A statistically significant reduction in melanin volumetric density of 7.3% in the spots and 12.3% in their surrounding area was observed after 56 days of product application. In line with these results, an analysis of color parameters using SkinCam reveals a significant increase in brightening and decrease in redness in both pigmented spots and the surrounding skin following application.

CONCLUSIONS

LC-OCT proves to be a valuable tool for in-depth dark spots characterization and assessment of skin brightening products, enabling various applications in the field of dermatological sciences.

Image classification and auxiliary diagnosis system for hyperpigmented skin diseases based on deep learning

Background and aim

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.

Methods

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.

Results

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.

Conclusions

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.