Basal cell carcinoma (BCC) is the most common malignancy of the skin. It is an epithelial neoplasm with origin in the precursor cells of the interfollicular epidermis. Even though it has low metastatic potential, delay in management may lead to local destruction and morbidity. In contrast, trichoepithelioma (TE) is a benign tumor originating from the outer root sheath of the hair follicle. Similar to BCC, TE tends to affect the head and neck region. Both neoplasms may exhibit clinical and histopathological similarities, making them prone to misdiagnosis. Multiple immunomarkers have been used to distinguish among these entities, but so far, no single agent or combination of agents appear to be neither sensitive nor specific enough to differentiate between them. This study was divided into 2 parts. First, 17 cases of BCC and 14 cases of TE were stained with androgen receptor and bcl-2. Then, 27 cases of borderline/equivocal of BCC and 13 cases of borderline/equivocal TE were stained with the same protocol. Sevity for BCC decreased (70.6%) but the specificity remained high (100%). Similarly, the sensitivity for borderline/equivocal BCC was 55.6%, whereas the specificity was 100%. Although moderately sensitive, combining both immunomarkers showed an excellent specificity to discriminate between BCC and TE.Melanomas with complete histological regression have been seen very infrequently. On the other hand, the diagnosis of metastatic melanoma is based on the histopathology and positivity of markers such as S100, Melan-A, and HMB-45 whose sensitivity is 99%, 82%, and 76%, respectively. It is very rare that metastatic melanomas and even more primary melanoma are negative for all of these markers. In these rare cases, there is usually a known primary. We present the case of a 82-year-old woman with a erythematous mass in the left groin and a 1-cm black-bluish irregular nodule on the skin of the ipsilateral foot. This lesion was clinical and dermoscopically compatible with primary melanoma. In the histological evaluation of the skin, a dermis full of melanophages and hemosiderophages were found in a background of fibrosis, scarce lymphocytic infiltrate, and neovascularization. Any cells expressing melanocytic markers were observed. It was diagnosed as tumoral melanosis. Lymph nodes showed a proliferation of atypical epithelioid cells with eosinophilic cytoplasm. Mitosis was conspicuous. Tumoral cells were vimentin and CD99 positive, and S100, CD34, HMB-45, Melan-A, SOX 10, tyrosinase, C-KIT, CD45, and CKAE1/AE3 negative, and BRAF-V600 mutated was detected. During follow-up, atypical vitiligo-like lesions were discovered, suggesting the diagnosis of metastatic melanoma totally regressed in our patient. Rapid-cycle deliberate practice (RCDP) is a learner-centered simulation instructional strategy that identifies performance gaps and targets feedback to improve individual or team deficiencies. Learners have multiple opportunities to practice observational, deductive, decision-making, psychomotor, and crisis resource management skills. As its implementation grows, simulationists need to have a shared mental model of RCDP to build high-quality RCDP-based initiatives. To compare and make general inferences from RCDP data, each training needs to follow a similar structure. This article seeks to describe the fundamentals of RCDP, including essential components and potential variants. We also summarize the current published evidence regarding RCDP's effectiveness. This article serves to create a shared understanding of RCDP, provide clear definitions and classifications for RCDP research, and provide options for future RCDP investigation.Rapid-cycle deliberate practice (RCDP) is a learner-centered simulation instructional strategy that identifies performance gaps and targets feedback to improve individual or team deficiencies. Learners have multiple opportunities to practice observational, deductive, decision-making, psychomotor, and crisis resource management skills. As its implementation grows, simulationists need to have a shared mental model of RCDP to build high-quality RCDP-based initiatives. To compare and make general inferences from RCDP data, each training needs to follow a similar structure. This article seeks to describe the fundamentals of RCDP, including essential components and potential variants. selleck inhibitor We also summarize the current published evidence regarding RCDP's effectiveness. This article serves to create a shared understanding of RCDP, provide clear definitions and classifications for RCDP research, and provide options for future RCDP investigation.BACKGROUNDControl of the tuberculosis (TB) pandemic remains hindered in part by a lack of simple and accurate measures of treatment efficacy, as current gold standard markers rely on sputum-based assays that are slow and challenging to implement. However, previous work identified urinary N1, N12-diacetylspermine (DiAcSpm), neopterin, hydroxykynurenine, N-acetylhexosamine, ureidopropionic acid, sialic acid, and mass-to-charge ratio (m/z) 241.0903 as potential biomarkers of active pulmonary TB (ATB). Here, we evaluated their ability to serve as biomarkers of TB treatment response and mycobacterial load.METHODSWe analyzed urine samples prospectively collected from 2 cohorts with ATB. A total of 34 study participants from African countries treated with first-line TB therapy rifampin, isoniazid, pyrazinamide, and ethambutol (HRZE) were followed for 1 year, and 35 participants from Haiti treated with either HRZE or an experimental drug were followed for 14 days. Blinded samples were analyzed by untargeted HPLC-coupcience Center at Weill Cornell College of Medicine (NIH/NCATS 1 UL1 TR002384-02 and KL2TR000458), the Department of Defense (PR170782), the National Institute of Allergy and Infectious Disease grants (NIAID T32AI007613-16, K24 AI098627, and K23 AI131913), the NIH Fogarty International Center grants (R24 TW007988 and TW010062), NIH grant (R01 GM135926), the Abby and Howard P. Milstein Program in Chemical Biology and Translational Medicine, and the Tuberculosis Research Units Networks (TBRU-N, AI111143).