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Introduction: Recalcitrant dermatophytosis is an emerging phenomenon that occurs worldwide, and Trichophyton indotineae is currently the prominent cause. Materials and methods: Skin specimens from patients with tinea infection were obtained by scrubbing and then sectioned into three fragments. Two fragments were subjected to direct microscopic examination and culture, while the third portion was utilized in the PCR method. Results: Isolates were morphologically identified as Trichophyton mentagrophytes/interdigitale complex (n = 60 [83.33%]), Microsporum canis (n = 8 [11.11%]), Trichophyton rubrum (n = 3 [4.16%]), and Epidermophyton floccosum (n = 1 [1.38%]). Among 60 T. mentagrophytes complex isolates, 53 (88.33%) were classified as T. indotineae and seven as T. interdigitale genotype II. The disease duration was longer in the T. indotineae group (P = 0.035). Both Gradient PCR and skin‐sampling methods yield similar results in terms of positive and negative cases (P = 1.0000). The time patients stopped their medication did not impact the positive case numbers (P = 0.803). Gender had no effects on the frequency (P = 0.699). Familial contamination, dermatologic disorder, and other underlying conditions did not differ in the two group infections (P > 0.05). Steroid usage is strongly associated with the emergence of tinea infection (P < 0.04). The duration of antifungal administration had a substantial effect on the emergence of resistant organisms (P = 0.05). Conclusions: Steroid usage, T. indotineae involvement, and prolonged exposure to antifungals were the solid and influential factors in recalcitrant involvement. Regarding quick and suitable diagnosis and treatment, which is essential in preventing recalcitrant cases, we suggest that direct skin sample PCR can meet the demands. [ABSTRACT FROM AUTHOR]

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Summary: Tinea incognita (TI) can mimic other dermatoses, presenting a diagnostic challenge for dermatologists. In some uncertain cases, it is crucial to accurately identify the causative agent using internal transcribed spacer (ITS) sequencing. The global issue of drug‐resistant dermatophytosis is increasing, with Trichophyton (T.) indotineae being the main cause. This study presents four cases of TI (diagnosed as eczema) by terbinafine‐resistant T. indotineae strains and reviews the current global TI epidemiology based on geographical continent and related conditions. Furthermore, squalene epoxidase (SQLE)‐associated resistance mechanisms are evaluated. Lesions caused by terbinafine‐resistant T. indotineae strains do not respond to allylamine antifungals, thus allowing the infection to spread. Among T. indotineae isolates, the SQLE F397L substitution is the most prevalent mutation contributing to azole resistance. F397L and L393F replacements in SQLE were detected in all isolates that exhibited high‐level resistance. L393S was seen in isolates with low‐resistant strains. Interestingly, and for the first time, an L393F amino acid substitution in the SQLE gene product was detected in the Iranian clinical T. indotineae strain. Also, a genomics‐based update on terbinafine resistance that focuses on T. indotineae is discussed in this study. [ABSTRACT FROM AUTHOR]

Children's burns are a major public health concern due to their long‐term physical, psychological, and social impacts, as well as their high financial burden. This study aimed to evaluate the effectiveness of a smartphone‐based educational program on outcomes of children with severe burns. This study was designed as a double‐blinded, randomized controlled trial (RCT) to test the effectiveness of a developed mobile application. A total of 93 participants were included in the final analysis. The participants were randomly assigned to either the intervention or control groups. Participants in both groups received usual self‐care training at discharge, but those in the intervention group used an Android‐based application for 2 months. The primary goal of the burn application was scar recovery, and the secondary goals were to increase child caregivers' satisfaction rate and decrease unplanned hospital readmissions. Data collection was conducted using valid and reliable questionnaires. Data were analysed using SPSS software. The study showed that the mobile application significantly affected the frequency of patient visits to the burn clinic and the satisfaction of caregivers of children with burns with the treatment process (p‐value <0.05). Also, there was a significant relationship between the duration of application use and wound healing status ((p‐value <0.001). These findings suggest that smartphone‐based educational programs can be valuable for optimizing care for children with severe burns. Further research is warranted to explore the long‐term impact of this intervention and its potential application in different healthcare settings. [ABSTRACT FROM AUTHOR]

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital‐acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation–maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU. [ABSTRACT FROM AUTHOR]