Radiodermatitis is a common complication of radiation therapy and poses particular risk in breast reconstruction, where skin barrier failure can lead to implant exposure and reconstructive loss. NeoPep-S, an AIMP1-derived peptide, has demonstrated wound healing and anti-inflammatory properties preclinically. This prospective, randomized, active-controlled trial evaluated the clinical efficacy of a NeoPep-S-containing cream in comparison with a conventional moisturizing barrier repair cream for managing radiodermatitis in breast cancer patients undergoing adjuvant radiation therapy after mastectomy and reconstruction. Patients receiving adjuvant radiation therapy after mastectomy and reconstruction were randomized to the NeoPep-S group or control group between March 2022 and March 2023. Objective and subjective assessments were performed at baseline, 1 month, and 3 months. Outcomes included transepidermal water loss (TEWL) for skin barrier function, modified Vancouver Scar Scale (VSS) for scar quality, Visual Analog Scale (VAS) for pruritus, and BREAST-Q for patient satisfaction. Of 30 randomized patients (2:1 ratio), 23 completed the study (NeoPep-S: n = 15, control: n = 8). TEWL recovery was comparable at 1 month (p = 0.877) but showed a sustained superiority trend at 3 months. VSS improved significantly in the NeoPep-S group at 3 months (p = 0.019), with pain reduction at both time points (p = 0.023; p = 0.002). Satisfaction with information (p = 0.007) and complete pruritus resolution (100 www.springer.com/00266 .
Intraoperative blood loss (BL) and subsequent hemoglobin (Hb) decline remain significant safety concerns in body contouring procedures. Also, the role of body mass index (BMI) in predicting BL and the comparative safety of excisional versus non-excisional procedures have not been fully characterized. To determine Hb changes, BL, and the role of BMI among patients underwent body contouring procedures. A prospective cohort study included 33 consecutive patients (ASA I–II, aged 21–54 years) who underwent either liposuction alone or liposuction combined with excisional body contouring (abdominoplasty, brachioplasty, thigh lift, or back lift) for cosmetic reasons at Royal Private Hospital, Sulaimaniyah, Iraq, from April 2024 to May 2025. All procedures utilized a standardized wetting solution (1 mg epinephrine, 10 cc of 1 www.springer.com/00266 .
Artificial intelligence is increasingly integrated into healthcare, demonstrating applications across surgical prediction, intraoperative precision, and patient communication. However, there is limited literature examining the role of artificial intelligence in improving patient comprehension of educational materials for breast reconstruction. 30 texts from online education resources for breast reconstruction surgery were selected. Readability of the texts was then evaluated using the Flesch–Kincaid grade level, the Gunning Fog index, and the Coleman–Liau index. ChatGPT 4.0 was instructed to translate the individual texts to the 6th-grade level of English comprehension, and the readability of the translated texts was analyzed. Patient Education Materials Assessment Tool analysis assessed the understandability and actionability of each text. Shapiro–Wilk test on the paired differences showed that the Flesch–Kincaid grade level and the Gunning Fog index followed normal distributions, whereas the Coleman–Liau index did not. The mean Flesch–Kincaid grade level, the Gunning Fog index, and the Coleman–Liau index for the original texts were 11.2, 13.9, and 11.3. The mean Flesch–Kincaid grade level, the Gunning Fog index, and the Coleman–Liau index for the AI-translated counterparts were 8.6, 10.5, and 9.7 (all p < 0.001). The mean understandability (0.70 SD = 0.06 vs. 0.58 SD = 0.08, p < 0.001) and actionability (0.40, SD = 0.09 vs. 0.37 SD = 0.11, p = 0.057) scores of the artificial intelligence-translations were higher than that of the original texts upon Patient Education Materials Assessment Tool analysis. Artificial intelligence may be a promising tool for enhancing comprehension of breast reconstruction education materials among individuals with limited health literacy. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Aesthetic assessment after breast reconstruction remains subjective and is often affected by inter-rater variability. A practical and reproducible evaluation method is needed for routine clinical use. This study aimed to develop an automated algorithm for classifying postoperative breast aesthetics into three categories: excellent, fair, and bad using Google Cloud AutoML Vision, without requiring advanced programming expertise. A total of 519 frontal two-dimensional photographs obtained 1 year after breast reconstruction were retrospectively collected. Images were annotated into three aesthetic categories based primarily on the Harris method, with additional semi-quantitative elements from the Sawai aesthetic scale. The original dataset was divided into training (n = 336), validation (n = 84), and test (n = 99) sets. Data augmentation was applied only to the training set, increasing it to 1,344 images. In total, 1,527 images were uploaded to AutoML Vision to build a single-label image-classification model. Model performance was compared with that of plastic surgeons, nurses, and laypersons using the same test set. The algorithm achieved a precision of 62.1 www.springer.com/00266 .
Aesthetic lip enhancement has traditionally been dominated by volumetric approaches, particularly hyaluronic acid fillers. However, lip aging involves both volumetric loss and structural changes, including elongation of the cutaneous upper lip (ergotrid), which cannot be adequately addressed with fillers alone. Despite the wide range of available techniques, no standardized algorithm currently guides treatment selection based on specific anatomical targets. To propose and evaluate a novel, anatomy-driven therapeutic algorithm for aesthetic lip enhancement, integrating surgical and non-surgical techniques based on the distinction between structural and volumetric components. A multicentric observational study was conducted over a 9-year period, including 168 patients undergoing aesthetic lip treatment. Patients were classified into four groups according to anatomical presentation: isolated volumetric deficiency (Type 1), isolated structural alteration (Type 2), combined structural and volumetric deficiency (Type 3), and secondary deformities (Type 4). Treatment selection was guided by the proposed algorithm, incorporating hyaluronic acid fillers, autologous fat grafting, lip implants, V-Y mucosal advancement flaps, and lip lift procedures. Outcomes were assessed through standardized photographic analysis, patient satisfaction (5-point Likert scale), and complication rates at a minimum follow-up of 12 months. A structural component was identified in 50 www.springer.com/00266 .
