The Journal of dermatology最新文献

Observational studies have suggested an association between metabolic syndrome (MetS) and hidradenitis suppurativa (HS), but whether this relationship is causal remains unclear. Elucidating the causal direction could provide insights into disease mechanisms and potential interventions. We performed bidirectional two-sample Mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) of MetS and HS. For validation, we replicated the MetS analysis using data from an independent GWAS. We applied multiple MR methods, primarily inverse variance-weighted (IVW) regression, and conducted sensitivity analyses to assess heterogeneity and pleiotropy. The MR analysis demonstrated MetS causally increased HS risk (IVW odds ratio [OR], 1.428 [95% CI, 1.193-1.710]; p < 0.001), with consistent evidence from sensitivity analyses. However, HS did not appear to causally influence MetS risk (IVW OR, 1.008 [95% CI, 0.988-1.028]; p = 0.438). This study provides evidence that MetS causally increases the risk of developing HS. However, we found no evidence for a causal relationship in the reverse direction from HS to MetS. Further research is warranted to elucidate the mechanisms underlying the identified causal association between MetS and subsequent HS development.

Necrotizing fasciitis is a fatal, soft tissue infection of the skin that requires prompt treatment. Historically, most cases have been attributed to group A beta-hemolytic Streptococcus infection. However, in recent years, other bacteria have been identified as causing necrotizing fasciitis. In the current study, we analyzed cases of necrotizing fasciitis and examined the significant differences in symptoms caused by pathogenic bacteria. We included 79 patients (43 males and 36 females, mean age 65.4 years) diagnosed with necrotizing fasciitis who visited our hospital between April 2004 and July 2023. The patients were classified into five groups based on the identified pathogen: group A beta-hemolytic Streptococcus; group B beta-hemolytic streptococcus; group G beta-hemolytic Streptococcus; mixed infection, including anaerobic bacteria; and Staphylococcus (S) aureus. The clinical characteristics of patients, including treatment duration and laboratory values, were analyzed. Group G beta-hemolytic Streptococcus was more common in older patients (Bonferroni method, p < 0.05). Patients with S. aureus tended to be hyperglycemic (Bonferroni method, p < 0.05), had a higher rate of bacteremia (Fisher's direct probability test, p < 0.05), and had a longer treatment duration than the other examined groups (Bonferroni method, p = 0.0132). Although the five groups did not differ in the mortality rate, overall survival was shorter in the mixed infection group than in the other groups (log-rank test, p < 0.05). The legs were the most common site of infection in the non-mixed infection group; in the mixed infection group, the pubic area was identified as the most common site of infection, accompanied by a poor prognosis. Collectively, these findings suggest that necrotizing fasciitis can be characterized by pathogenic bacteria and that these characteristics may inversely predict the pathogen of origin.

Nail psoriasis is a chronic condition characterized by nail dystrophy affecting the nail matrix and bed. The severity of nail psoriasis is commonly assessed using the Nail Psoriasis Severity Index (NAPSI), which evaluates the characteristics and extent of nail involvement. Although the NAPSI is numeric, reproducible, and simple, the assessment process is time-consuming and often challenging to use in real-world clinical settings. To overcome the time-consuming nature of NAPSI assessment, we aimed to develop a deep learning algorithm that can rapidly and reliably evaluate NAPSI, thereby providing numerous clinical and research advantages. We developed a dataset consisting of 7054 single fingernail images cropped from images of the dorsum of the hands of 634 patients with psoriasis. We annotated the eight features of the NAPSI in a single nail using bounding boxes and trained the YOLOv7-based deep learning algorithm using this annotation. The performance of the deep learning algorithm (DLA) was evaluated by comparing the NAPSI estimated using the DLA with the ground truth of the test dataset. The NAPSI evaluated using the DLA differed by 2 points from the ground truth in 98.6% of the images. The accuracy and mean absolute error of the model were 67.6% and 0.449, respectively. The intraclass correlation coefficient was 0.876, indicating good agreement. Our results showed that the DLA can rapidly and accurately evaluate the NAPSI. The rapid and accurate NAPSI assessment by the DLA is not only applicable in clinical settings, but also provides research advantages by enabling rapid NAPSI evaluations of previously collected nail images.