Perimenopausal symptoms adversely impacting women's sexual health are often unacknowledged or even dismissed by healthcare professionals (HCP). Using semi-structured interviews with 23 women experiencing perimenopause, we aimed to understand how HCPs address women's sexual health needs and concerns during this transition. Thematic data analysis was employed with the reduction of data into themes. Several major themes were identified concerning participants' experiences with a HCP and sexual health, including insufficient sexual health information and preventative education; the lack of open discussion about sexual health; the need for enhanced support and treatment options; feeling dismissed by a healthcare professional; and sexual pleasure not addressed by healthcare professionals. Additionally, women expressed concerns regarding medical education and training in healthcare professionals on approaching sexual health conversations. There appears to be a need for more formal training, both within medical school and continuing medical education (CME), to address these important aspects of women's sexual and reproductive health.

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction.

Infection following prepectoral tissue expander (TE) reconstruction is challenging with reported rates between 10–15%. Traditional conservative management of significant TE infections includes intravenous antibiotics and explantation leading to delayed final breast reconstruction. Local delivery of antibiotics with absorbable calcium-sulfate cement following TE reconstruction recently has been shown to reduce TE infection rates. However, the cost-effectiveness of absorbable antibiotic cement routinely during breast reconstruction remains unclear. The purpose of this study is to evaluate and model infection rates, treatment costs, and pricing to determine the break-even threshold for prophylactic antibiotic cement placement. Absolute risk reduction (ARR) and number needed to break even (NNBE) were calculated across a range of infection rates and treatment costs. Using institutional cement cost of $1075 (USD), an estimated 10% infection rate, and an average $13,000 (USD) for an infection event, prophylactic cement becomes cost-effective when baseline infection rates exceed 8.3%, when infection management costs surpass $10,750, or when calcium-sulfate pricing remains below $1300. Non-selective prophylactic use may be economically justified under commonly reported clinical conditions and become even more favorable in settings with higher treatment costs or modestly lower cement pricing. A break-even analysis may be a valuable tool for evaluating infection-prevention strategies in TE-based reconstruction.

Background and Hypothesis: Human Papillomavirus (HPV) is the causative agent in nearly all cervical cancer cases. It has been shown that the HPV type 16 E6 protein interacts directly with the host protein NFX1-123. Short-term studies have shown that NFX1-123 remains in the cytoplasm; however, it has not been investigated whether NFX1-123 actually translocates to the nucleus in the long term. We hypothesize that over time, NFX1-123 translocates to the nucleus of the cell in long term cultures with 16E6. This present study seeks to optimize the AmbionTM Paris system to allow for pure, proper separation of the cytoplasmic and nuclear cellular compartments. Project Methods: Three biologically unique backgrounds of human foreskin keratinocytes (HFKs) were cultured in a monolayer tissue culture dish. Using the AmbionTM Paris system, proteins were isolated as whole cell extracts, or nuclear and cytoplasmic fractions. For one lysis method, only protease inhibitors were added to the lysis buffers of the AmbionTM Paris system. For another lysis method, 1% NP-40 and protease inhibitors were added to the lysis buffers of the Paris system. Protein lysate concentrations were quantified, then purity of subcellular lysates was determined by western blotting. Histone H3 and GAPDH were used to identify nuclear and cytoplasmic compartments, respectfully. Results: Western blotting confirmed that adding 1% NP-40 to the lysis reagents of the AmbionTM Paris kit optimized subcellular fractionization. Potential Impact: Being able to efficiently separate the cytoplasmic and nuclear compartments will allow for accurate identification of NFX1-123 localization during long-term HPV16 infection. If NFX1-123 is found to move into the nucleus under the influence of HPV 16E6, then this could indicate potential transcriptional regulatory functions of the NFX1-123 protein during HPV infection, which is unique from its function in non-infected cells.