Skinly: A novel handheld IoT device for validating biophysical skin characteristics.

BACKGROUND: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible. To this end, we introduced the Skinly system, a handheld device capable of evaluating various personal skin characteristics noninvasively. MATERIALS AND METHODS: Equipped with a moisture sensor and a multi-light-source camera, Skinly can assess age-related skin parameters and specific skin properties. Utilizing state-of-the-art DL, Skinly processed vast amounts of images efficiently. The Skinly system's efficacy was validated both in the lab and at home, comparing its results to established "gold standard" methods. RESULTS: Our findings revealed that the Skinly device can accurately measure age-associated parameters, that is, facial age, skin evenness, and wrinkles. Furthermore, Skinly produced data consistent with established devices for parameters like glossiness, skin tone, redness, and porphyrin levels. A separate study was conducted to evaluate the effects of two moisturizing formulations on skin hydration in laboratory studies with standard instrumentation and at home with Skinly. CONCLUSION: Thanks to its capability for multi-parameter measurements, the Skinly device, combined with its smartphone application, holds the potential to replace more expensive, time-consuming diagnostic tools. Collectively, the Skinly device opens new avenues in dermatological research, offering a reliable, versatile tool for comprehensive skin analysis.

Artificial Intelligence Confirming Treatment Success: The Role of Gender- and Age-Specific Scales in Performance Evaluation.

SUMMARY: In plastic surgery and cosmetic dermatology, photographic data are an invaluable element of research and clinical practice. Additionally, the use of before and after images is a standard documentation method for procedures, and these images are particularly useful in consultations for effective communication with the patient. An artificial intelligence (AI)-based approach has been proven to have significant results in medical dermatology, plastic surgery, and antiaging procedures in recent years, with applications ranging from skin cancer screening to 3D face reconstructions, the prediction of biological age and perceived age. The increasing use of AI and computer vision methods is due to their noninvasive nature and their potential to provide remote diagnostics. This is especially helpful in instances where traveling to a physical office is complicated, as we have experienced in recent years with the global coronavirus pandemic. However, one question remains: how should the results of AI-based analysis be presented to enable personalization? In this paper, the author investigates the benefit of using gender- and age-specific scales to present skin parameter scores calculated using AI-based systems when analyzing image data.

Reduced expression of mitochondrial fumarate hydratase in progressive multiple sclerosis contributes to impaired in vitro mesenchymal stromal cell-mediated neuroprotection.

BACKGROUND: Cell-based therapies for multiple sclerosis (MS), including those employing autologous bone marrow-derived mesenchymal stromal cells (MSC) are being examined in clinical trials. However, recent studies have identified abnormalities in the MS bone marrow microenvironment. OBJECTIVE: We aimed to compare the secretome of MSC isolated from control subjects (C-MSC) and people with MS (MS-MSC) and explore the functional relevance of findings. METHODS: We employed high throughput proteomic analysis, enzyme-linked immunosorbent assays and immunoblotting, as well as in vitro assays of enzyme activity and neuroprotection. RESULTS: We demonstrated that, in progressive MS, the MSC secretome has lower levels of mitochondrial fumarate hydratase (mFH). Exogenous mFH restores the in vitro neuroprotective potential of MS-MSC. Furthermore, MS-MSC expresses reduced levels of fumarate hydratase (FH) with downstream reduction in expression of master regulators of oxidative stress. CONCLUSIONS: Our findings are further evidence of dysregulation of the bone marrow microenvironment in progressive MS with respect to anti-oxidative capacity and immunoregulatory potential. Given the clinical utility of the fumaric acid ester dimethyl fumarate in relapsing-remitting MS, our findings have potential implication for understanding MS pathophysiology and personalised therapeutic intervention.