Actas dermo-sifiliograficas最新文献

Graying is a natural process, characterized by the loss of pigment in the hair, and is considered a sign of aging in society; its onset and progression is closely correlated with age and occurs to varying degrees in all individuals, regardless of gender or race. However, premature graying has a psychological and social impact, affecting patients' self-esteem. The exact etiopathogenesis is still unknown, and it has been associated with multiple factors such as: oxidative stress, ultraviolet radiation, genetic factors, and lifestyle factors such as smoking, nutritional and emotional factors. All of this causes a decrease in the production of melanin by the melanocytes of the hair bulb. Despite the extensive molecular research being carried out to understand the pathogenesis of graying, there is a shortage of effective, evidence-based treatment options. Today, graying has become a cultural phenomenon, where many people opt for cosmetic treatments to cover gray hair.

Foundation models (FMs) are deep learning models distinguished by their extensive training data and adaptability to a variety of medical tasks including disease classification, monitoring, risk stratification and treatment planning. They are categorized into large language models (LLMs) and vision-centric FMs, which process only text or only images as inputs, respectively. Alternatively, some models integrate multiple modalities, such as images and texts, in vision language models (VLMs), or may expand this multimodality by integrating audio, video, genomics and patient metadata in large multimodal models (LMMs). FMs are expected to help dermatologists in the clinical setting by leveraging advanced AI capabilities to standardize diagnosis and more accurately quantify disease severity, personalize treatment planning and ultimately improve patient outcomes. In this narrative review, we present an overview of the main milestones in generative AI that have driven the evolution of dermatology-focused FMs, a field still under active research. Additionally, we summarize the current landscape and the principal medical FMs that have been developed for image-based medical specialties. Finally, we analyze potential risks and future directions in this field, offering insights from both clinical and technical perspectives.

Background and objective: Coronavirus disease 2019 (COVID-19) associated telogen effluvium (CATE) has been observed in patients after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although it is self-limiting, hair loss can be very distressing, and some patients may seek medical treatment to help promote hair regrowth. Low-dose oral minoxidil (LDOM) has emerged as a novel and very efficient therapy for different types of alopecia. However, information on its safety and efficacy profile in the management of CATE remains scarce. This study aims to evaluate the treatment response to LDOM in CATE patients.

Methods: We conducted a retrospective study at a single dermatology center. Adult patients diagnosed with telogen effluvium from December 2020 through October 2022, with a prior history of SARS-CoV-2 infection, and treated with LDOM were included. The efficacy of LDOM was evaluated with the hair-shedding score (HSS).

Results: In all, 69 patients, 50 (72.5%) women and 19 (27.5%) men were included. A total of 55 patients (79.7%) exhibited mild and moderate symptoms; 8 (11.5%), severe disease; and 6 (8.7%) remained asymptomatic. The time elapsed between COVID-19 and telogen effluvium diagnosis was 117 days (80-181). The HSS continuously decreased during the 2nd, 3rd, and 4th visits after treatment initiation. Compared with baseline, the median (interquartile range) HSS was significantly lower at the 2nd visit (5 [5-6]; P < .001), the 3rd visit (4 [3-5]; P < .001), and the 4th visit (2 [1-2]; P < .001).The time for telogen effluvium resolution was 93 days (55-148).

Conclusions: Our findings suggest that LDOM is a safe and effective therapy for patients with CATE.