International Journal of Dermatology最新文献

Gonorrhea, a common sexually transmitted infection caused by Neisseria gonorrhoeae, represents an escalating global public health threat due to antimicrobial resistance. The current review explores diverse approaches around the globe to gonorrhea management across various settings, with a focus on diagnostic strategies, treatment practices, resistance surveillance, and new trends, such as point-of-care tests or doxycycline post-exposure prophylaxis. High-resource settings benefit from proper infrastructure, advanced diagnostics, screening programs, surveillance, access to effective treatment, and test-of-cure protocols. Nonetheless, rising incidence and resistance rates persist. In contrast, limited-resource settings often depend on syndromic management, which lacks sensitivity and fails to identify asymptomatic infections. The low diagnostic capacity and poor access to resistance-guided therapy contribute to inappropriate antimicrobial use. Migrants, incarcerated individuals, and those in vulnerable contexts face additional barriers. These disparities undermine the global control efforts and enable the emergence of drug-resistant strains. Addressing these gaps requires a multifaceted, equity-focused approach: strengthening diagnostic capacity, expanding access to molecular testing and surveillance, tailored interventions for the local contexts, and promoting antibiotic stewardship in both policy and practice.

Background: Leprosy, caused by Mycobacterium leprae, leads to peripheral neuropathy and significant functional disability if its diagnosis is delayed. Infrared thermography may offer a non-invasive method for the detection of neurological complications.

Methods: A cross-sectional study was conducted with 100 leprosy patients and 20 healthy controls. Participants were assessed for cutaneous temperature using infrared thermography at defined areas corresponding to peripheral nerve innervation points (hands, feet, and face). Sensory neuropathy was analyzed using the Semmes-Weinstein (SW) monofilament test, while autonomic nerve dysfunction was evaluated through the sympathetic skin response (SSR). Data were analyzed with the aim of verifying whether changes in skin temperature clinically correlated with sensory loss, autonomic nerve dysfunction, and distinct forms of the leprosy spectrum.

Results: Leprosy patients had significantly lower skin temperature compared to controls, with a mean temperature of 32.5°C ± 2.3°C vs. 35.2°C ± 0.4°C for hands (p < 0.001) and 24.1°C ± 2.8°C vs. 34.8°C ± 0.5°C for feet (p < 0.001). Temperature differences were most pronounced in body areas showing sensory neuropathy (e.g., 30.1°C ± 2.2°C for hands with absent sensation vs. 34.1°C ± 1.6°C with normal sensation, p < 0.001). Strong linear correlations were found between cutaneous temperature and sensory loss assessed using SW filaments (p < 0.001) and between cutaneous temperature and SSR (p < 0.001). Disability grade also correlated with lower temperatures, with hands at 34.3°C for no disability, 31.3°C for Grade 1, and 31.5°C for Grade 2 disability.

Conclusion: Infrared thermography is a promising, non-invasive tool for the detection of peripheral sensory impairment in leprosy. The results obtained by infrared thermography show a strong correlation with those obtained using standard tests for sensory loss and autonomic nerve dysfunction.

Grzybowski's generalized eruptive keratoacanthoma (GEKA) is a rare variant of keratoacanthomas, characterized by hundreds to thousands of lesions, accompanied by pruritus, mucosal involvement, and comorbidities. Our aim was to analyze the clinical presentation, associated comorbidities, treatment strategies, and outcomes of GEKA. We conducted a literature review of all published cases between 1950 and 2024, following the Preferred Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. A total of 143 articles were screened; 58 were included, yielding 64 cases. Of these, 24 were associated with severe comorbidities, while 40 were not. Cases without associated conditions were more likely to exhibit a worse prognosis, greater therapeutic resistance, and distinct clinical features. In contrast, cases associated with malignancy, systemic diseases, or other comorbidities tended to show better treatment responses and fewer complications. Pruritus remained the predominant symptom in both groups, with similar lesion morphology. The rarity of GEKA leads to a paucity of literature. Reporting bias and limited cases may lead to less generalizability. Based on our review and considering the differences in clinical presentation, demographics, associated comorbidities, complications, and prognosis, GEKA may be classified as primary or secondary to a malignancy, systemic disease, or other comorbidity, encouraging clinicians to have a high index of suspicion during evaluation and treatment for an underlying trigger.

Introduction: Cutaneous scalp metastases from breast carcinoma (CMBC) represent an uncommon manifestation of metastatic disease, with heterogeneous clinical presentations, including nodular or infiltrative lesions and scarring alopecia (alopecia neoplastica). The absence of standardized diagnostic criteria, particularly for alopecic phenotypes, poses challenges to early recognition of CMBC, which may represent either the first indication of neoplastic progression or a late recurrence.

Materials and methods: We retrospectively analyzed a multicenter cohort of 15 patients with histologically confirmed CMBC. Demographic, clinical, molecular, and trichoscopic data were collected and correlated with the main clinical phenotypes: patchy alopecia (alopecia neoplastica) versus nodules/plaques. The statistical analyses we performed were the Mann-Whitney test for group comparisons and Fisher's exact test for categorical variables.

Results: The median age at CMBC diagnosis was 64 years. Alopecia neoplastica was the most frequent phenotype (53.3%). Patients with alopecia neoplastica showed a longer median interval between primary tumor diagnosis and metastasis onset compared to those with nodules/plaques (73.5 months vs. 59.5 months; p = 0.11). Trichoscopic analysis revealed significant differences in the distribution of features between the alopecia neoplastica group and the nodular/plaque group. Statistically significant differences were found among the two groups, including linear-irregular vessels (87.5% vs. 28.6%, p = 0.041), polymorphic vessels (87.5% vs. 28.6%, p = 0.041), pili torti (75% vs. 14.3%, p = 0.041), follicular hyperkeratosis and follicular plugging (87.5% vs. 14.3%, p = 0.01). Overall, the trichoscopic pattern in alopecia neoplastica appeared more variable and heterogeneous compared to that observed in the nodular/plaque phenotype.

Conclusion: Alopecia neoplastica, often underestimated in clinical practice, emerges as the predominant CMBC phenotype in our cohort and is associated with a distinct trichoscopic profile. The complexity of the alopecic phenotype may reflect intrinsic biological differences compared to nodular lesions. Larger prospective studies are needed to validate these findings and incorporate trichoscopic profiles into standard diagnostic pathways.

Artificial intelligence (AI) techniques can allow for early diagnosis and treatment of acne. Bias in AI model training remains, leading to various challenges in achieving health equity in clinical practice. We aim to assess and provide an updated overview of (1) the types of AI-based tools developed for acne, (2) the various applications of AI in acne diagnosis and management, (3) the performance of these tools, and (4) the current data reported on skin diversity in AI model training. [Correction added on 27 December 2025, after first online publication: The preceding sentence has been corrected.] We queried PubMed, Cochrane and Scopus databases using the terms: "acne", "artificial intelligence", "machine learning", "deep learning", "large language model", and "ChatGPT". 105 articles were included for analysis. Of the 105 research articles, 96.2% (N = 101) were focused on acne diagnosis only, 9.5% (N = 10) on acne management only, and 5.7% (N = 6) on both. Most manuscripts used image-based models, including deep learning (76.2%, N = 80), classical machine learning (9.5%, N = 10), and ensemble models (11.4%, N = 12). The ensemble models hold the highest mean accuracy (89.7%), followed by deep learning (88.5%), large language models (87.5%), and machine learning models (86.9%). Only 13% (N = 14) of studies reported data on patient skin color, while 4 of the 14 studies included a full spectrum of diverse skin tones. [Correction added on 27 December 2025, after first online publication: The preceding sentence has been corrected.] The application of AI algorithms in healthcare is rapidly emerging, providing significant support to providers. With ensemble models demonstrating superior performance, AI algorithm use in acne may offer a convenient method to consistently diagnose and manage patients remotely. Designing systematic guidelines that require a diverse representation of all skin colors may improve social justice in healthcare.