Advances in the Etiology, Detection, and Clinical Management of Seborrheic Keratoses.

Seborrheic keratoses (SKs) are ubiquitous, generally benign skin tumors that exhibit high clinical variability. While age is a known risk factor, the precise roles of UV exposure and immune abnormalities are currently unclear. The underlying mechanisms of this benign disorder are paradoxically driven by oncogenic mutations and may have profound implications for our understanding of the malignant state. Advances in molecular pathogenesis suggest that inhibition of Akt and APP, as well as existing treatments for skin cancer, may have therapeutic potential in SK. Dermoscopic criteria have also become increasingly important to the accurate detection of SK, and other noninvasive diagnostic methods, such as reflectance confocal microscopy and optical coherence tomography, are rapidly developing. Given their ability to mimic malignant tumors, SK cases are often used to train artificial intelligence-based algorithms in the computerized detection of skin disease. These technologies are becoming increasingly accurate and have the potential to significantly augment clinical practice. Current treatment options for SK cause discomfort and can lead to adverse post-treatment effects, especially in skin of color. In light of the discontinuation of ESKATA in late 2019, promising alternatives, such as nitric-zinc and trichloroacetic acid topicals, should be further developed. There is also a need for larger, head-to-head trials of emerging laser therapies to ensure that future treatment standards address diverse patient needs.

Digital skin imaging applications, part II: a comprehensive survey of post-acquisition image utilization features and technology standards.

BACKGROUND: Despite the increasing ubiquity and accessibility of teledermatology applications, few studies have comprehensively surveyed their features and technical standards. Importantly, features implemented after the point of capture are often intended to augment image utilization, while technical standards affect interoperability with existing healthcare systems. We aim to comprehensively survey image utilization features and technical characteristics found within publicly discoverable digital skin imaging applications. MATERIALS AND METHODS: Applications were identified and categorized as described in Part I. Included applications were then further assessed by three independent reviewers for post-imaging content, tools, and functionality. Publicly available information was used to determine the presence or absence of relevant technology standards and/or data characteristics. RESULTS: A total of 20 post-image acquisition features were identified across three general categories: (1) metadata attachment, (2) functional tools (i.e., those that utilized images or in-app content to perform a user-directed function), and (3) image processing. Over 80% of all applications implemented metadata features, with nearly half having metadata features only. Individual feature occurred and feature richness varied significantly by primary audience (p < 0.0001) and function (p < 0.0001). On average, each application included under three features. Less than half of all applications requested consent for user-uploaded photos and fewer than 10% provided clear data use and privacy policies. CONCLUSION: Post-imaging functionality in skin imaging applications varies significantly by primary audience and intended function, though nearly all applications implemented metadata labeling. Technical standards are often not implemented or reported consistently. Gaps in the provision of clear consent, data privacy, and data use policies should be urgently addressed.

Digital skin imaging applications, part I: Assessment of image acquisition technique features.

BACKGROUND: The rapid adoption of digital skin imaging applications has increased the utilization of smartphone-acquired images in dermatology. While this has enormous potential for scaling the assessment of concerning skin lesions, the insufficient quality of many consumer/patient-taken images can undermine clinical accuracy and potentially harm patients due to lack of diagnostic interpretability. We aim to characterize the current state of digital skin imaging applications and comprehensively assess how image acquisition features address image quality. MATERIALS AND METHODS: Publicly discoverable mobile, web, and desktop-based skin imaging applications, identified through keyword searches in mobile app stores, Google Search queries, previous teledermatology studies, and expert recommendations were independently assessed by three reviewers. Applications were categorized by primary audience (consumer-facing, nonhospital-based practice, or enterprise/health system), function (education, store-and-forward teledermatology, live-interactive teledermatology, electronic medical record adjunct/clinical imaging storage, or clinical triage), in-app connection to a healthcare provider (yes or no), and user type (patient, provider, or both). RESULTS: Just over half (57%) of 191 included skin imaging applications had at least one of 14 image acquisition technique features. Those that were consumer-facing, intended for educational use, and designed for both patient and physician users had significantly greater feature richness (p < 0.05). The most common feature was the inclusion of text-based imaging tips, followed by the requirement to submit multiple images and body area matching. CONCLUSION: Very few skin imaging applications included more than one image acquisition technique feature. Feature richness varied significantly by audience, function, and user categories. Users of digital dermatology tools should consider which applications have standardized features that improve image quality.

An Analysis of Skin of Color Publications in the Journal of Drugs in Dermatology from 2018 to 2020.

As the United States population becomes increasingly diverse, it is exceedingly important for dermatologists to be knowledgeable about treating patients with skin of color (SOC). The published literature is an especially valuable resource for treating SOC.

Psychosocial Stress and Mechanisms of Skin Health: A Comprehensive Update.

Although the relationship between psychosocial stress and skin health is commonly invoked in both the scientific and popular literature, its underlying mechanisms are still not well understood. In this review, we provide a comprehensive update on the pathophysiology of stress and its clinical impact on skin homeostasis. The recent characterization of a bidirectional HPA stress axis in the skin has illuminated peripheral stress pathways, with effects spanning inflammation, atopy, barrier function, dermal thinning, wound healing, and melanogenesis. Additionally, new research into the cutaneous microbiome suggests the development of stress-induced dysbiosis through the “gut-brain-skin” axis. These new findings help contextualize how lifestyle factors such as diet, personal care practices, and sleep patterns may mediate and sometimes amplify the cutaneous impacts of psychological stress. We aim to clarify these clinically important relationships and highlight areas of future study that have widespread academic, clinical, and commercial implications. J Drugs Dermatol. 2021;20(1):62-29. doi:10.36849/JDD.5608.

Assessment of published literature regarding skin of color in Pediatric Dermatology.

We sought to analyze the existence of skin of color (SOC)-related literature in Pediatric Dermatology. To do so, we applied criteria developed by Wilson et al (Assessment of skin of color and diversity and inclusion content of dermatologic published literature: an analysis and call to action. Int J Women Dermatol. 2021;15:26) to categorize SOC articles. We found that Pediatric Dermatology published 28.4% SOC articles in the last three years, higher than the average (16.8%) found across surveyed dermatology journals. Our findings demonstrate opportunity for improvement through the implementation of keyword standardization and continued prioritization of SOC-related content.

Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.

OBJECTIVE. The objective of this study was to assess the impact of artificial intelligence (AI)-based decision support (DS) on breast ultrasound (US) lesion assessment. MATERIALS AND METHODS. A multicenter retrospective review of 900 breast lesions (470/900 [52.2%] benign; 430/900 [47.8%] malignant) on US by 15 physicians (11 radiologists, two surgeons, two obstetrician/gynecologists). An AI system (Koios DS for Breast, Koios Medical) evaluated images and assigned them to one of four categories: benign, probably benign, suspicious, and probably malignant. Each reader reviewed cases twice: 750 cases with US only or with US plus DS; 4 weeks later, cases were reviewed in the opposite format. One hundred fifty additional cases were presented identically in each session. DS and reader sensitivity, specificity, and positive likelihood ratios (PLRs) were calculated as well as reader AUCs with and without DS. The Kendall τ-b correlation coefficient was used to assess intraand interreader variability. RESULTS. Mean reader AUC for cases reviewed with US only was 0.83 (95% CI, 0.78-0.89); for cases reviewed with US plus DS, mean AUC was 0.87 (95% CI, 0.84-0.90). PLR for the DS system was 1.98 (95% CI, 1.78-2.18) and was higher than the PLR for all readers but one. Fourteen readers had better AUC with US plus DS than with US only. Mean Kendall τ-b for US-only interreader variability was 0.54 (95% CI, 0.53-0.55); for US plus DS, it was 0.68 (95% CI, 0.67-0.69). Intrareader variability improved with DS; class switching (defined as crossing from BI-RADS category 3 to BI-RADS category 4A or above) occurred in 13.6% of cases with US only versus 10.8% of cases with US plus DS (p = 0.04). CONCLUSION. AI-based DS improves accuracy of sonographic breast lesion assessment while reducing inter- and intraobserver variability.

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.

To develop a convolutional neural network (CNN) algorithm that can predict the molecular subtype of a breast cancer based on MRI features. An IRB-approved study was performed in 216 patients with available pre-treatment MRIs and immunohistochemical staining pathology data. First post-contrast MRI images were used for 3D segmentation using 3D slicer. A CNN architecture was designed with 14 layers. Residual connections were used in the earlier layers to allow stabilization of gradients during backpropagation. Inception style layers were utilized deeper in the network to allow learned segregation of more complex feature mappings. Extensive regularization was utilized including dropout, L2, feature map dropout, and transition layers. The class imbalance was addressed by doubling the input of underrepresented classes and utilizing a class sensitive cost function. Parameters were tuned based on a 20% validation group. A class balanced holdout set of 40 patients was utilized as the testing set. Software code was written in Python using the TensorFlow module on a Linux workstation with one NVidia Titan X GPU. Seventy-four luminal A, 106 luminal B, 13 HER2+, and 23 basal breast tumors were evaluated. Testing set accuracy was measured at 70%. The class normalized macro area under receiver operating curve (ROC) was measured at 0.853. Non-normalized micro-aggregated AUC was measured at 0.871, representing improved discriminatory power for the highly represented Luminal A and Luminal B subtypes. Aggregate sensitivity and specificity was measured at 0.603 and 0.958. MRI analysis of breast cancers utilizing a novel CNN can predict the molecular subtype of breast cancers. Larger data sets will likely improve our model.

Dietary-induced gestational iron deficiency inhibits postnatal tissue iron delivery and postpones the cessation of active nephrogenesis in rats.

Gestational iron deficiency (ID) can alter developmental programming through impaired nephron endowment, leading to adult hypertension, but nephrogenesis is unstudied. Iron status and renal development during dietary-induced gestational ID (<6 mg Fe kg-1 diet from Gestational Day 2 to Postnatal Day (PND) 7) were compared with control rats (198 mg Fe kg-1 diet). On PND2-PND10, PND15, PND30 and PND45, blood and tissue iron status were assessed. Nephrogenic zone maturation (PND2-PND10), radial glomerular counts (RGCs), glomerular size density and total planar surface area (PND15 and PND30) were also assessed. Blood pressure (BP) was measured in offspring. ID rats were smaller, exhibiting lower erythrocyte and tissue iron than control rats (PND2-PND10), but these parameters returned to control values by PND30-PND45. Relative kidney iron (µg g-1 wet weight) at PND2-PND10 was directly related to transport iron measures. In ID rats, the maturation of the active nephrogenic zone was later than control. RGCs, glomerular size, glomerular density, and glomerular planar surface area were lower than control at PND15, but returned to control by PND30. After weaning, the kidney weight/rat weight ratio (mg g-1) was heavier in ID than control rats. BP readings at PND45 were lower in ID than control rats. Altered kidney maturation and renal adaptations may contribute to glomerular size, early hyperfiltration and long-term renal function.

Intersectional Care for Asian American and Pacific Islander Youth With Sexual or Gender Minority Identities in the Acute Psychiatric Care Setting.

Youth who hold both Asian American and Pacific Islander (AAPI) and sexual or gender minority (SGM) identities are frequently overlooked and underserved, and experience intersecting forms of discrimination, interpersonal stressors, and structural barriers.1 Amid heightened anti-AAPI and anti-SGM violence, these populations are particularly vulnerable to poor mental health outcomes. In 2023, over half of AAPI SGM reported experiences of depression, anxiety, and gender-based discrimination, and nearly half reported racial abuse.2 Despite growing need, there are few established best practices for supporting the mental health needs of AAPI SGM youth. Guidelines tailoring psychiatric care to this population's needs in outpatient settings1 have recently been complemented with considerations for SGM youth in the inpatient psychiatric setting.3 Building on this work, we identify 5 considerations that we believe to be key to the provision of high-quality mental health care to AAPI SGM youth and their families in both the acute emergency department (ED) and inpatient settings.

Ovine uterine space restriction alters placental transferrin receptor and fetal iron status during late pregnancy.

BACKGROUND: Fetal growth restriction is reported to be associated with impaired placental iron transport. Transferrin receptor (TfR) is a major placental iron transporter in humans but has not been studied in sheep. TfR is regulated by both iron and nitric oxide (NO), the molecule produced by endothelial nitric oxide synthase (eNOS). We hypothesized that limited placental development downregulates both placental TfR and eNOS expression, thereby lowering fetal tissue iron. METHODS: An ovine surgical uterine space restriction (USR) model, combined with multifetal gestation, tested the extremes of uterine and placental adaptation. Blood, tissues, and placentomes from non-space restricted (NSR) singletons were compared with USR fetuses at gestational day (GD) 120 or 130. RESULTS: When expressed proportionate to fetal weight, liver iron content did not differ, whereas renal iron was higher in USR vs. NSR fetuses. Renal TfR protein expression did not differ, but placental TfR expression was lower in USR fetuses at GD130. Placental levels of TfR correlated to eNOS. TfR was localized throughout the placentome, including the hemophagous zone, implicating a role for TfR in ovine placental iron transport. CONCLUSION: Fetal iron was regulated in an organ-specific manner. In USR fetuses, NO-mediated placental adaptations may prevent the normal upregulation of placental TfR at GD130.

Improving Artificial Intelligence-Based Diagnosis on Pediatric Skin Lesions.

Artificial intelligence algorithms to classify melanoma are dependent on their training data, which limits generalizability. The objective of this study was to compare the performance of an artificial intelligence model trained on a standard adult-predominant dermoscopic dataset before and after the addition of additional pediatric training images. The performances were compared using held-out adult and pediatric test sets of images. We trained two models: one (model A) on an adult-predominant dataset (37,662 images from the International Skin Imaging Collaboration) and the other (model A+P) on an additional 1,536 pediatric images. We compared performance between the two models on adult and pediatric held-out test images separately using the area under the receiver operating characteristic curve. We then used Gradient-weighted Class Activation Maps and background skin masking to understand the contributions of the lesion versus background skin to algorithm decision making. Adding images from a pediatric population with different epidemiological and visual patterns to current reference standard datasets improved algorithm performance on pediatric images without diminishing performance on adult images. This suggests a way that dermatologic artificial intelligence models can be made more generalizable. The presence of background skin was important to the pediatric-specific improvement seen between models. Our study highlights the importance of carefully curated and labeled data from diverse inputs to improve the generalizability of AI models for dermatology, in this case applied to dermoscopic images of adult and pediatric lesions to improve melanoma detection.