Adewole S. Adamson

Bio: Adewole S. Adamson is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Medicine & Dermatology. The author has an hindex of 12, co-authored 54 publications receiving 706 citations. Previous affiliations of Adewole S. Adamson include University of North Carolina at Chapel Hill & Rockefeller University.

The Rapid Rise in Cutaneous Melanoma Diagnoses.

Abstract: Is the Melanoma Epidemic Real? The incidence of melanoma of the skin is 6 times as high as it was 40 years ago; mortality has stayed low. UV light exposure is the strongest environmental risk facto...

Association of Delays in Surgery for Melanoma With Insurance Type.

Abstract: Importance Timely receipt of treatment for cancer is an important aspect of health care quality. It is unknown how delays of surgery for melanoma vary by insurance type. Objective To analyze factors associated with delays between diagnosis and surgery for melanoma in patients with Medicare, Medicaid, or private insurance. Design, Setting, and Participants Retrospective cohort study of patients who received a diagnosis of melanoma between 2004 and 2011 in North Carolina using data from the North Carolina Cancer Registry linked to administrative claims from Medicare, Medicaid, and private insurance. Inclusion criteria were incident patients with a diagnosis of melanoma stage 0 to III and with continuous insurance enrollment from at least 1 month prior to the month of diagnosis to 12 months after diagnosis of melanoma. Main Outcomes and Measures Surgical delay, defined as definitive surgical excision occurring more than 6 weeks after melanoma diagnosis. Generalized linear models with log link, Poisson distributions, and robust standard errors were used to estimate adjusted risk ratios (RRs) to model risk of delay in definitive surgery. Results A total of 7629 patients were included (4210 [55%] female; mean [SD] age, 64 [15] years), 48% (n = 3631) Medicare, 48% (n = 3667) privately insured, and 4% (n = 331) Medicaid patients. Privately insured patients were least likely to experience a delay in definitive surgery, followed by Medicare and Medicaid patients (519 [14%], 609 [17%], and 79 [24%], respectively; P Conclusion and Relevance Surgical treatment delays were common but were less prevalent in patients diagnosed or surgically treated by a dermatologist. Medicaid patients experienced the most surgical delays. A reduction in delays in melanoma surgery could be achieved through better access to specialty care and cross-disciplinary coordination.

Characterization of a murine Ahr null allele: Involvement of the Ah receptor in hepatic growth and development (dioxiny2,3,7,8-tetrachlorodibenzo-p-dioxinygene targetingyliver)

Abstract: The Ah receptor (AHR) is a ligand-activated transcription factor that mediates a pleiotropic response to environmental contaminants such as benzo(a)pyrene and 2,3,7,8-tetrachlorodibenzo-p-dioxin. In an effort to gain in- sight into the physiological role of the AHR and to develop models useful in risk assessment, gene targeting was used to inactivate the murine Ahr gene by homologous recombination. Ahr 2/2 mice are viable and fertile but show a spectrum of hepatic defects that indicate a role for the AHR in normal liver growth and development. The Ahr 2/2 phenotype is most severe between 0-3 weeks of age and involves slowed early growth and hepatic defects, including reduced liver weight, transient microvesicular fatty metamorphosis, prolonged extramedul- lary hematopoiesis, and portal hypercellularity with thicken- ing and fibrosis.

Dynamic regulatory network controlling Th17 cell differentiation

Abstract: Despite their importance, the molecular circuits that control the differentiation of naive T cells remain largely unknown. Recent studies that reconstructed regulatory networks in mammalian cells have focused on short-term responses and relied on perturbation-based approaches that cannot be readily applied to primary T cells. Here, we combine transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing perturbations in primary T cells to systematically derive and experimentally validate a model of the dynamic regulatory network that controls Th17 differentiation. The network consists of two self-reinforcing, but mutually antagonistic, modules, with 12 novel regulators, whose coupled action may be essential for maintaining the balance between Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors, embeds them within a comprehensive temporal network and reveals its organizational principles, and highlights novel drug targets for controlling Th17 differentiation.

Ensuring Fairness in Machine Learning to Advance Health Equity.

Abstract: Machine learning is used increasingly in clinical care to improve diagnosis, treatment selection, and health system efficiency. Because machine-learning models learn from historically collected data, populations that have experienced human and structural biases in the past-called protected groups-are vulnerable to harm by incorrect predictions or withholding of resources. This article describes how model design, biases in data, and the interactions of model predictions with clinicians and patients may exacerbate health care disparities. Rather than simply guarding against these harms passively, machine-learning systems should be used proactively to advance health equity. For that goal to be achieved, principles of distributive justice must be incorporated into model design, deployment, and evaluation. The article describes several technical implementations of distributive justice-specifically those that ensure equality in patient outcomes, performance, and resource allocation-and guides clinicians as to when they should prioritize each principle. Machine learning is providing increasingly sophisticated decision support and population-level monitoring, and it should encode principles of justice to ensure that models benefit all patients.