In the United States, race-based disparities in cardiovascular disease care have proven to be pervasive, deadly, and expensive. African American/Black, Hispanic/Latinx, and Native/Indigenous American ...individuals are at an increased risk of cardiovascular disease and are less likely to receive high-quality, evidence-based medical care as compared with their White American counterparts. Although the United States population is diverse, the cardiovascular workforce that provides its much-needed care lacks diversity. The available data show that care provided by physicians from racially diverse backgrounds is associated with better quality, both for minoritized patients and for majority patients. Not only is cardiovascular workforce diversity associated with improvements in health care quality, but racial diversity among academic teams and research scientists is linked with research quality. We outline documented barriers to achieving workforce diversity and suggest evidence-based strategies to overcome these barriers. Key strategies to enhance racial diversity in cardiology include improving recruitment and retention of racially diverse members of the cardiology workforce and focusing on cardiovascular health equity for patients. This review draws attention to academic institutions, but the implications should be considered relevant for nonacademic and community settings as well.
Central centrifugal cicatricial alopecia (CCCA) is a scarring alopecia that primarily affects women of African descent. Although histopathological features of CCCA have been described, the ...pathophysiology of this disease remains unclear. To better understand the components of CCCA pathophysiology, we evaluated the composition of the inflammatory infiltrate, the distribution of Langerhans cells (LCs), and the relationship between fibrosis and perifollicular vessel distribution. Our data indicate that CCCA is associated with a CD4‐predominant T‐cell infiltrate with increased LCs extending into the lower hair follicle. Fibroplasia associated with follicular scarring displaces blood vessels away from the outer root sheath epithelium. These data indicate that CCCA is an inflammatory scarring alopecia with unique pathophysiologic features that differentiate it from other lymphocytic scarring processes.
People who are uninsured and live in underserved communities face several barriers to accessing dermatologic care, including financial, geographic, and racial barriers, resulting in detrimental ...effects on health outcomes and quality of life. We (1) describe and evaluate an innovative, student-faculty run dermatology free clinic that serves people in marginalized populations and (2) present action steps to strategically develop community partnerships and integrate a service-learning program into a dermatology residency training program for medical students and residents. The Student Dermatology Clinic for the Underserved (SDU) is a quarterly, student-faculty run free clinic at a community health center in Pittsburgh that serves the marginalized populations within our community. Interprofessional teams of medical students and dermatology residents evaluate patients, devise patient care plans with the dermatology attending physician, and coordinate follow-up care. In a survey of residents who voluntarily participated in SDU, 88% (n=8) report that their involvement increased their awareness of health disparities and social factors impacting dermatologic care and encouraged them to be more involved in community service throughout their career. The SDU clinic is an instrumental resource in our community that allows for patient-centered, longitudinal care, while reducing barriers to access for patients in underserved communities. In this service-learning model for dermatology residency training programs, we not only address the dermatologic needs of marginalized populations, but we also create a rewarding training environment for medical students and residents that facilitates vertical learning and interprofessional collaboration, fosters an interest in health disparities, increases skin health equity, and cultivates cultural sensitivity.
Formulation screening for biotherapeutics can cover a vast array of excipients and stress conditions. These studies consume quantities of limited material and, with higher concentrated therapeutics, ...more material is needed. Here, we evaluate the use of crystal zenith (CZ) microtiter plates in conjunction with FluoroTec-coated butyl rubber mats as a small-volume, high-throughput system for formulation stability studies. The system was studied for evaporation, edge effects, and stability with comparisons to type 1 glass and CZ vials for multiple antibodies and formulations. Evaporation was minimal at 4°C and could be reduced at elevated temperatures using sealed, mylar bags. Edge effects were not observed until 12 weeks at 40°C. The overall stability ranking as measured by the rate of change in high molecular weight and percent main peak species was comparable across both vials and plates at 4°C and 40°C out to 12 weeks. Product quality attributes as measured by the multi-attribute method were also comparable across all containers for each molecule formulation. A potential difference was measured for subvisible particle analysis, with the plates measuring lower particle counts than the vials. Overall, CZ plates are a viable alternative to traditional vials for small-volume, high-throughput formulation stability screening studies.
Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled data to achieve ...high accuracy. However, for medical applications such as dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients, and each device only has a limited amount of data. Directly learning from limited data greatly deteriorates the performance of learned models. Federated learning (FL) can train models by using data distributed on devices while keeping the data local for privacy. Existing works on FL assume all the data have ground-truth labels. However, medical data often comes without any accompanying labels since labeling requires expertise and results in prohibitively high labor costs. The recently developed self-supervised learning approach, contrastive learning (CL), can leverage the unlabeled data to pre-train a model for learning data representations, after which the learned model can be fine-tuned on limited labeled data to perform dermatological disease diagnosis. However, simply combining CL with FL as federated contrastive learning (FCL) will result in ineffective learning since CL requires diverse data for accurate learning but each device in FL only has limited data diversity. In this work, we propose an on-device FCL framework for dermatological disease diagnosis with limited labels. Features are shared among devices in the FCL pre-training process to provide diverse and accurate contrastive information without sharing raw data for privacy. After that, the pre-trained model is fine-tuned with local labeled data independently on each device or collaboratively with supervised federated learning on all devices. Experiments on dermatological disease datasets show that the proposed framework effectively improves the recall and precision of dermatological disease diagnosis compared with state-of-the-art methods.
PDF
