Treatment of Scars with Laser-Assisted Delivery of Growth Factors and Vitamin C: A Comparative, Randomised, Double-blind, Early Clinical Trial

Application of integrated skincare in medical aesthetics: A literature review

Integrated skincare combines clinically proven skincare products with professional medical aesthetics to provide a comprehensive solution for beauty pursuers. Studies have demonstrated that a combination of medical aesthetic procedures and maintenance therapies is more effective than either treatment alone. This review outlines the current applications of integrated skincare, including different regimens of energy-based aesthetic devices and active ingredients in cosmeceuticals or chemical peels. Additionally, the benefits and limitations of integrated skincare are discussed. Lastly, this review highlights the potential for improved satisfaction and long-term maintenance of the desired outcomes through appropriate integrated skincare procedures.

Laser-Assisted Drug Delivery in the Treatment of Scars, Rhytids, and Melasma: A Comprehensive Review of the Literature

Although broad reviews on laser-assisted drug delivery (LADD) have been published in the past, an updated focused examination of its utility in the context of common, treatment-resistant, dermatologic conditions has not been published. This article reports a comprehensive scoping review of the potential benefits of LADD compared to laser or drug monotherapy for the treatment of 3 such conditions: scars, rhytids, and melasma. A PubMed (National Institutes of Health; Bethesda, MD) search was conducted for keywords including "laser-assisted drug delivery," "scar," "rhytid," and "melasma." Out-of-scope studies were excluded. To evaluate the efficacy of LADD for the treatment of scars, relevant articles were categorized by scar type: hypertrophic/keloid, atrophic, and hypopigmented. LADD, with both ablative and nonablative laser types, was studied in combination with corticosteroids, botulinum toxin-A (BTX-A), 5-fluorouracil, 5-aminolevulinic acid photodynamic therapy, stem cells, platelet-rich plasma, and prostaglandin analogs for the treatment of scars. Some randomized controlled trials demonstrated the efficacy of LADD, whereas others showed no significant differences in clinical outcomes but demonstrated reduced adverse effects. Regarding rhytids, laser treatment has been combined with various cosmeceuticals, including poly-L-lactic acid, topical retinaldehyde, and topical BTX-A. The studies reviewed supported the use of LADD with these drugs over monotherapy. Some studies showed that LADD was effective for the absorption of drugs such as poly-L-lactic acid and BTX-A which are often not effective topically. For melasma treatment, LADD with tranexamic acid and hydroquinone was superior in some studies, but not significantly different than monotherapy in other studies. LADD with certain drugs could be considered to treat scars, rhytids, and melasma.

LEVEL OF EVIDENCE: 5

Laser-Assisted Drug Delivery: A Systematic Review of Safety and Adverse Events

Laser-assisted drug delivery (LADD) is an increasingly studied and applied methodology for drug delivery. It has been used in a wide variety of clinical applications. Given the relatively low barrier to entry for clinicians as well as ongoing research in this area, the authors aimed to review outcomes relating to safety in laser-assisted drug delivery. A systematic review was conducted, with the databases PubMed, Medline and Embase searched in September 2022. Included articles were those that mentioned laser-assisted drug delivery in human subjects that also reported adverse effects or safety outcomes. There were no language-based exclusions. Conference abstracts and literature reviews were excluded. The results were then tabulated and categorized according to the application of LADD. In total, 501 articles were obtained. Following deduplication, screening, and full text review 70 articles of various study designs were included. Common findings were erythema, oedema, pain, and crusting following LADD. Several notably more severe adverse effects such as generalized urticaria, infection, scarring and dyspigmentation were noted. However, these events were varied depending on the clinical use of LADD. Relevant negatives were also noted whereby no studies reported life-threatening adverse effects. Limitations included limited details regarding the adverse effects within the full texts, lack of follow-up, and risk of bias. In conclusion, there were multiple adverse effects that clinicians should consider prior to carrying out LADD, where treatment goals and patient tolerability should be considered. Further evidence is needed to quantitatively determine these risks.

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan.