The association between tumor-associated macrophages (TAMs) and the expression of immune checkpoint molecules has not been well described in cutaneous melanoma. We evaluated the correlations between the expression of markers of TAMs, cluster of differentiation 163 (CD163), and immune checkpoint molecules, programmed cell death protein-1 (PD-1), and lymphocyte activating gene-3 (LAG-3). We also determined their relationships with the clinicopathological features and disease outcomes in melanoma. Diagnostic tissues collected from melanoma patients were evaluated using immunohistochemistry for CD163, PD-1, and LAG-3 expression. CD163 expression positively correlated with PD-1 and LAG-3 expression. High expression of both CD163 and PD-1 expressions was significantly associated with negative prognostic factors and worse prognosis than high expression of the single markers. High co-expression of CD163 and LAG-3 was associated with poor clinicopathological indexes of melanoma and worse survival compared to the high expression of the single markers. The expression of immune checkpoint molecules PD-1 and LAG-3 positively correlated with the M2-TAM density in melanoma tissue. Simultaneous high M2-TAM density and immune checkpoint molecules expression acted as independent poor prognostic factors in cutaneous melanoma.

Inflammatory skin diseases, such as rosacea and acne, are major causes of facial erythema and accompanying skin barrier dysfunction. Several methods to restore the impaired skin barrier and improve facial erythema, such as medication, radiofrequency, laser, and ultrasound therapy were attempted. This study evaluated the efficacy and safety of dual-frequency ultrasound with impulse mode, for improving skin hydration and erythema in Asian subjects with rosacea and acne. Twenty-six subjects with facial erythema received an ultrasound treatment once per week, for 4 weeks, over both cheeks. The erythema index and transepidermal water loss (TEWL) were measured at each visit. Clinicians assessed the erythema improvement and patients evaluated their satisfaction level. The average decrease in TEWL and erythema index at 6 weeks was 5.37 ± 13.22 g·h⁻¹·m⁻² (p = 0.020) and 39.73 ± 44.21 (p = 0.010), respectively. The clinician’s erythema assessment and the subject satisfaction questionnaire score significantly improved at final follow-up (p < 0.001; p = 0.003, respectively). No serious adverse effects were observed during the treatment and follow-up periods. The dual-frequency ultrasound with impulse mode appears to be effective and safe for improving skin hydration and erythema in patients with rosacea and acne.

Background: Although deep neural networks have shown promising results in the diagnosis of skin cancer, a prospective evaluation in a real-world setting could confirm these results. This study aimed to evaluate whether an algorithm (http://b2019.modelderm.com) improves the accuracy of nondermatologists in diagnosing skin neoplasms. Methods: A total of 285 cases (random series) with skin neoplasms suspected of malignancy by either physicians or patients were recruited in two tertiary care centers located in South Korea. An artificial intelligence (AI) group (144 cases, mean [SD] age, 57.0 [17.7] years; 62 [43.1%] men) was diagnosed via routine examination with photographic review and assistance by the algorithm, whereas the control group (141 cases, mean [SD] age, 61.0 [15.3] years; 52 [36.9%] men) was diagnosed only via routine examination with a photographic review. The accuracy of the nondermatologists before and after the interventions was compared. Results: Among the AI group, the accuracy of the first impression (Top-1 accuracy; 58.3%) after the assistance of AI was higher than that before the assistance (46.5%, P = .008). The number of differential diagnoses of the participants increased from 1.9 ± 0.5 to 2.2 ± 0.6 after the assistance (P < .001). In the control group, the difference in the Top-1 accuracy between before and after reviewing photographs was not significant (before, 46.1%; after, 51.8%; P = .19), and the number of differential diagnoses did not significantly increase (before, 2.0 ± 0.4; after, 2.1 ± 0.5; P = .57). Conclusions: In real-world settings, AI augmented the diagnostic accuracy of trainee doctors. The limitation of this study is that the algorithm was tested only for Asians recruited from a single region. Additional international randomized controlled trials involving various ethnicities are required.