Skin Research and Technology最新文献

Background: The development of artificial intelligence (AI) is rapidly expanding, showing promise in the dermatological field. Skin checks are a resource-heavy challenge that could potentially benefit from AI-tool assistance, particularly if provided in widely available AI solutions. A novel smartphone application(app)-based AI system, "SCAI," was developed and trained to recognize spots in paired images of skin, pursuing identification of new skin lesions. This pilot study aimed to investigate the feasibility of the SCAI-app to identify simulated skin changes in vivo.

Materials and methods: The study was conducted in a controlled setting with healthy volunteers and standardized, simulated skin changes (test spots), consisting of customized 3-mm adhesive spots in three colors (black, brown, and red). Each volunteer had a total of eight test spots adhered to four areas on back and legs. The SCAI-app collected smartphone- and template-guided standardized images before and after test spot application, using its backend AI algorithms to identify changes between the paired images.

Results: Twenty-four volunteers were included, amounting to a total of 192 test spots. Overall, the detection algorithms identified test spots with a sensitivity of 92.0% (CI: 88.1-95.9) and a specificity of 95.5% (CI: 95.0-96.0). The SCAI-app's positive predictive value was 38.0% (CI: 31.0-44.9), while the negative predictive value was 99.7% (CI: 99.0-100).

Conclusion: This pilot study showed that SCAI-app could detect simulated skin changes in a controlled in vivo setting. The app's feasibility in a clinical setting with real-life skin lesions remains to be investigated, where the challenge with false positives in particular needs to be addressed.

Background: Observational studies have suggested a correlation between alopecia areata (AA) and thyroid dysfunction (TD). However, the causal relationship between AA and TD remains uncertain. The purpose of this study is to investigate the causal relationship between these two conditions. Understanding the potential causal relationship between AA and TD is valuable for elucidating the pathogenesis of AA and for designing innovative methods to prevent and treat AA and its related complications.

Methods: All data for this two-sample Mendelian randomization (MR) study were sourced from public databases. This study selected hypothyroidism, Hashimoto's thyroiditis, hyperthyroidism, subacute thyroiditis, and Graves' disease as exposure factors, with AA as the outcome variable. Data for hypothyroidism, Hashimoto's thyroiditis, hyperthyroidism, subacute thyroiditis, Graves' disease, and AA were obtained from related genome-wide association studies (GWAS). Various MR analysis methods such as inverse variance weighted (IVW), MR-Egger, and weighted median were utilized. Additionally, Cochrane's Q test was used to detect heterogeneity in MR results, and the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) test were used to detect horizontal pleiotropy. A leave-one-out analysis was conducted to investigate the sensitivity of this association.

Results: We found statistically significant genetic predictions of AA with hypothyroidism, Hashimoto's thyroiditis, and subacute thyroiditis (IVW OR = 1.4009815, 95% confidence interval [CI]: 1.1210399-1.750829; p = 0.003030698, OR = 1.396101, 95% CI: 1.030134-1.89208; p = 0.03144273, OR = 0.732702, 95% CI: 0.604812-0.887634; p = 0.001483368). Furthermore, tests for pleiotropy showed no evidence of pleiotropy, enhancing the credibility of the study results. Finally, the leave-one-out test demonstrated the stability and robustness of this association.

Conclusion: This study provides new evidence of a potential genetic link between thyroid issues and AA. By employing the two-sample MR method to eliminate confounding factors and reverse causation, unbiased results were obtained, confirming a causal relationship between hypothyroidism, Hashimoto's thyroiditis, subacute thyroiditis, and AA. This lays the foundation for further mechanistic studies and potential clinical applications. Future research should further explore the specific biological mechanisms between TD and the onset of AA.

Background: The objective of this study was to investigate the differences in skin blood flow regulations between the upper and lower limbs in healthy adults using wavelet analysis of skin blood oscillations. To the best of our knowledge, this is the first study investigating the dominant skin blood flow control of the upper and lower limbs in healthy adults.

Methods: Skin blood flow of the forearm and leg was simultaneously measured by laser Doppler flowmetry (LDF) in 17 healthy adults. Skin blood flow oscillations were analyzed using wavelet analysis to assess the dominant control among the metabolic endothelial (0.0095-0.02 Hz), neurogenic (0.02-0.05 Hz), myogenic (0.05-0.15 Hz), respiratory (0.15-0.4 Hz), and cardiac (0.4-2 Hz) origins.

Results: Skin blood flow in the leg (11.13 ± 4.90 perfusion unit) was significantly higher than in the forearm (6.90 ± 2.50 perfusion unit, p < 0.001). The metabolic endothelial control is more dominant in the forearm (1.19 ±0.51 au) compared to the leg (0.73 ± 0.41 au, p < 0.01). The myogenic control is more dominant in the leg (1.18 ± 0.28 au) compared to the forearm (0.96±0.18 au, p < 0.05).

Conclusion: Through wavelet analysis of skin blood flow oscillations, the results indicate that metabolic endothelial control is more dominant in the forearm (upper limbs) and myogenic control is more dominant in the leg (lower limbs).