Summary While a third of the world carries the burden of tuberculosis, disease control has been hindered by the lack of tools including a rapid, point-of-care diagnostic and a protective vaccine. In many infectious diseases, antibodies (Abs) are powerful biomarkers and important immune mediators. However, in Mycobacterium tuberculosis (Mtb) infection, a discriminatory or protective role for humoral immunity remains unclear. Using an unbiased antibody profiling approach we show that individuals with latent tuberculosis infection (Ltb) and active tuberculosis disease (Atb) have distinct Mtb-specific humoral responses, such that Ltb infection is associated with unique Ab Fc functional profiles, selective binding to FcγRIII, and distinct Ab glycosylation patterns. Moreover, compared to Abs from Atb, Abs from Ltb drove enhanced phagolysosomal maturation, inflammasome activation, and most importantly, macrophage killing of intracellular Mtb. Combined, these data point to a potential role for Fc-mediated Ab effector functions, tuned via differential glycosylation, in Mtb control.
IMPORTANCEThe use of artificial intelligence (AI) is expanding throughout the field of medicine. In dermatology, researchers are evaluating the potential for direct-to-patient and clinician decision-support AI tools to classify skin lesions. Although AI is poised to change how patients engage in health care, patient perspectives remain poorly understood.OBJECTIVE To explore how patients conceptualize AI and perceive the use of AI for skin cancer screening. DESIGN, SETTING, AND PARTICIPANTSA qualitative study using a grounded theory approach to semistructured interview analysis was conducted in general dermatology clinics at the Brigham and Women's Hospital and melanoma clinics at the Dana-Farber Cancer Institute. Forty-eight patients were enrolled. Each interview was independently coded by 2 researchers with interrater reliability measurement; reconciled codes were used to assess code frequency. The study was conducted from May 6 to July 8, 2019.MAIN OUTCOMES AND MEASURES Artificial intelligence concept, perceived benefits and risks of AI, strengths and weaknesses of AI, AI implementation, response to conflict between human and AI clinical decision-making, and recommendation for or against AI.RESULTS Of 48 patients enrolled, 26 participants (54%) were women; mean (SD) age was 53.3 (21.7) years. Sixteen patients (33%) had a history of melanoma, 16 patients (33%) had a history of nonmelanoma skin cancer only, and 16 patients (33%) had no history of skin cancer. Twenty-four patients were interviewed about a direct-to-patient AI tool and 24 patients were interviewed about a clinician decision-support AI tool. Interrater reliability ratings for the 2 coding teams were κ = 0.94 and κ = 0.89. Patients primarily conceptualized AI in terms of cognition. Increased diagnostic speed (29 participants [60%]) and health care access (29 [60%]) were the most commonly perceived benefits of AI for skin cancer screening; increased patient anxiety was the most commonly perceived risk (19 [40%]). Patients perceived both more accurate diagnosis (33 [69%]) and less accurate diagnosis (41 [85%]) to be the greatest strength and weakness of AI, respectively. The dominant theme that emerged was the importance of symbiosis between humans and AI (45 [94%]). Seeking biopsy was the most common response to conflict between human and AI clinical decision-making (32 [67%]). Overall, 36 patients (75%) would recommend AI to family members and friends. CONCLUSIONS AND RELEVANCEIn this qualitative study, patients appeared to be receptive to the use of AI for skin cancer screening if implemented in a manner that preserves the integrity of the human physician-patient relationship.
Experimentally induced depolarization of resting membrane potential in "instructor cells" in Xenopus laevis embryos causes hyperpigmentation in an all-or-none fashion in some tadpoles due to excess proliferation and migration of melanocytes. We showed that this stochastic process involved serotonin signaling, adenosine 3',5'-monophosphate (cAMP), and the transcription factors cAMP response element-binding protein (CREB), Sox10, and Slug. Transcriptional microarray analysis of embryos taken at stage 15 (early neurula) and stage 45 (free-swimming tadpole) revealed changes in the abundance of 45 and 517 transcripts, respectively, between control embryos and embryos exposed to the instructor cell-depolarizing agent ivermectin. Bioinformatic analysis revealed that the human homologs of some of the differentially regulated genes were associated with cancer, consistent with the induced arborization and invasive behavior of converted melanocytes. We identified a physiological circuit that uses serotonergic signaling between instructor cells, melanotrope cells of the pituitary, and melanocytes to control the proliferation, cell shape, and migration properties of the pigment cell pool. To understand the stochasticity and properties of this multiscale signaling system, we applied a computational machine-learning method that iteratively explored network models to reverse-engineer a stochastic dynamic model that recapitulated the frequency of the all-or-none hyperpigmentation phenotype produced in response to various pharmacological and molecular genetic manipulations. This computational approach may provide insight into stochastic cellular decision-making that occurs during normal development and pathological conditions, such as cancer.
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