ABSTRACT. The goal of this paper is to introduce a tree-based model that combines aspects of CART (Classi- ses. An alternative specification strategy based on a 10-fold cross-validation is also discussed and a detailed Monte Carlo experiment is carried out to evaluate the performance of the proposed methodology in comparison with standard techniques. The STR-Tree model outperforms CART when the correct selection of the architecture of simulated trees is considered. Furthermore, the LM test seems to be a promising alternative to 10-fold cross-validation. When put into proof with real datasets, the STR-Tree model has a superior predictive ability than CART.

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Background The incidence of adult‐onset atopic dermatitis (AOAD) is increasing. However, the unique characteristics of AOAD compared to pediatric‐onset AD persisting into adulthood (POAD) are underexplored, hampering the development of targeted‐therapeutics for this growing population. We thus assessed the profile of AOAD in skin and blood compared to that of POAD. Methods We collected skin biopsies and blood from adults with AOAD, POAD, and healthy controls (n = 15 in each group). Skin samples were analyzed by RNA sequencing, qRT‐PCR, and immunohistochemistry, and Olink Proseek multiplex assay was used to identify the serum proteomic profile. Results Compared to healthy controls, both AOAD and POAD showed cutaneous immune and barrier dysregulations with a shared Th2/Th22 hyperactivation. Overall, POAD showed greater inflammation in lesional skin, with more prominent expression of Th2/Th17/Th22 markers (CCL17/22, S100A8/9, IL‐36A, PI3/Elafin, DEFB4) in POAD compared to AOAD (p‐value < .05). In contrast, higher Th1‐(IFN‐γ, IL‐2, IL‐15, CCL5) upregulation and Th1‐skewing were seen in AOAD. The epidermal barrier was also more compromised in POAD, with greater epidermal hyperplasia and lower expression of markers related to terminal differentiation, lipids, and cell adhesion. In parallel with increased rates of cardiovascular comorbidities, AOAD demonstrated many more significantly dysregulated proteins in serum (n = 148) compared to POAD (n = 86), including pro‐inflammatory and cardiovascular‐risk markers. Th1‐related products showed significant correlations between their skin and blood expressions only in AOAD subjects. Conclusion Age‐of‐onset delineates two distinct endophenotypes in adult AD potentially suggesting the need for broader (beyond Th2) therapeutic targeting in AOAD.

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BackgroundA delay in diagnosis and treatment of Psoriatic Arthritis (PsA) is associated with increased disability and damage in the long term. There is currently scarce data available about diagnosis delay, referrals delays, and time to first treatment in patients with PsA in developing countries.ObjectivesTo describe the journey of patients with psoriatic arthritis, with special emphasis on diagnosis and treatment delays.MethodsAll patients with PsA registered in the Rheumatology Unit data base (between 2000–2016), with complete data, were included. Electronic medical records were manually revised, and the following data were obtained: date of first visit to a Dermatologists due to Psoriasis (PsO) symptoms, date of PsO diagnosis, date and type of first musculoskeletal symptom, specialty of physician seen at first visit for musculoskeletal symptoms, date of PsA diagnosis, date and reason for prescription of first Disease Modifying anti-Rheumatic Drug (DMARD). Primary outcome variable was mean lag time between first musculoskeletal symptom and diagnosis of PsA. Other variables calculated were: mean lag time between first musculoskeletal symptom and first physician encounter because of those symptoms, mean lag time to first DMARD and mean lag time between PsO diagnosis and PsA diagnosis. Variables associated with a delay in PsA diagnosis (more than one year delay) were analyzed in multivariable analysis (logistic regression).Results93 patients were included, mean age 60.8 years (SD: 15.3), 61% males. Mean age at time of PsA diagnosis was 52 years (SD: 14.8). The most common musculoskeletal symptom was arthralgia (46%), followed by arthritis (37%), enthesitis (6%), low back pain (6%), and dactylitis (4%). Mean lag time between first musculoskeletal symptoms and visit to a physician because of those symptoms was 16,8 months (SD: 44,4) (median: 1,92 (IQR: 0,35–11,6). In Only 33% of the cases the first specialist seen was a Rheumatologist. Mean lag time between first musculoskeletal symptom and diagnosis of PsA was 19,2 (SD: 28,8) months (Median: 7,2 (IQR: 2,4–21,6 months). In 90 patients (97%), the diagnosis of PsO preceded the diagnosis of PsA, a mean time of 15.1 years (SD: 14.4). 83 patients (89%) received traditional DMARDs, 82% because of the musculoskeletal symptoms, with a mean lag time between PsA diagnosis and initiation of DMARDs of 11,4 months (SD: 31,2) (Median: 0,48 (IQR: 0–4,3) months). Forty-three patients (46.2%) had a delay on PsA diagnosis equal or greater than 1 year. In logistic regression analysis, including age, sex, first specialist seen and type of musculoskeletal symptom, none was independently associated with a delay equal or greater than 1 year in PsA diagnosis.ConclusionsMean time between symptoms' onset and PsA diagnosis was relatively short. However, a delay greater that one year was observed in almost half of patients. As none of the variables studied was associated with a delay in diagnosis, more studies are needed to identify potential actions that would help reducing this delay.Referen...

What was the research about?Staphylococcus aureus, or staph, is a type of bacteria found on people's skin. MRSA and MSSA are types of staph infections that are hard to treat. They can lead to serious infections or even death. These infections can come back or spread to other people.