Geoffrion D, Melki S, Harissi-Dagher M.
Correspondence. Retina 2023;43(5):e30-e31.
Gluckstein J.
Diversity in Academic Ophthalmology: Disparities and Opportunities from Medical School to Practice. Semin Ophthalmol 2023;38(4):338-343.
AbstractINTRODUCTION: Compared to the United States population as a whole, physicians are more likely to identify as men, identify as Asian or non-hispanic White, and be raised in wealthier households. Racial, ethnic, gender, and socioeconomic representation in ophthalmology is often blamed on the pipeline of matriculants. METHODS: This review collects recent data from the US census, AAMC, and primary literature on gender, racial, ethnic, and socioeconomic diversity from medical school to ophthalmology practice. RESULTS: Data from the medical and ophthalmology literature shows that medical students are less diverse than medical school applicants, ophthalmology residencies are less diverse than graduating medical students, and ophthalmology departments are less diverse than those of most other specialties. DISCUSSION: At each level, there are limitations in representation beyond the pipeline of medical school applicants or medical students applying to ophthalmology. There are many practical steps the field can take at each level of training to move the specialty toward more equitable representation.
Goenka A, Khan F, Verma B, Sinha P, Dmello CC, Jogalekar MP, Gangadaran P, Ahn B-C.
Tumor microenvironment signaling and therapeutics in cancer progression. Cancer Commun (Lond) 2023;43(5):525-561.
AbstractTumor development and metastasis are facilitated by the complex interactions between cancer cells and their microenvironment, which comprises stromal cells and extracellular matrix (ECM) components, among other factors. Stromal cells can adopt new phenotypes to promote tumor cell invasion. A deep understanding of the signaling pathways involved in cell-to-cell and cell-to-ECM interactions is needed to design effective intervention strategies that might interrupt these interactions. In this review, we describe the tumor microenvironment (TME) components and associated therapeutics. We discuss the clinical advances in the prevalent and newly discovered signaling pathways in the TME, the immune checkpoints and immunosuppressive chemokines, and currently used inhibitors targeting these pathways. These include both intrinsic and non-autonomous tumor cell signaling pathways in the TME: protein kinase C (PKC) signaling, Notch, and transforming growth factor (TGF-β) signaling, Endoplasmic Reticulum (ER) stress response, lactate signaling, Metabolic reprogramming, cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) and Siglec signaling pathways. We also discuss the recent advances in Programmed Cell Death Protein 1 (PD-1), Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4), T-cell immunoglobulin mucin-3 (TIM-3) and Lymphocyte Activating Gene 3 (LAG3) immune checkpoint inhibitors along with the C-C chemokine receptor 4 (CCR4)- C-C class chemokines 22 (CCL22)/ and 17 (CCL17), C-C chemokine receptor type 2 (CCR2)- chemokine (C-C motif) ligand 2 (CCL2), C-C chemokine receptor type 5 (CCR5)- chemokine (C-C motif) ligand 3 (CCL3) chemokine signaling axis in the TME. In addition, this review provides a holistic understanding of the TME as we discuss the three-dimensional and microfluidic models of the TME, which are believed to recapitulate the original characteristics of the patient tumor and hence may be used as a platform to study new mechanisms and screen for various anti-cancer therapies. We further discuss the systemic influences of gut microbiota in TME reprogramming and treatment response. Overall, this review provides a comprehensive analysis of the diverse and most critical signaling pathways in the TME, highlighting the associated newest and critical preclinical and clinical studies along with their underlying biology. We highlight the importance of the most recent technologies of microfluidics and lab-on-chip models for TME research and also present an overview of extrinsic factors, such as the inhabitant human microbiome, which have the potential to modulate TME biology and drug responses.
Gold NB, Adelson SM, Shah N, Williams S, Bick SL, Zoltick ES, Gold JI, Strong A, Ganetzky R, Roberts AE, Walker M, Holtz AM, Sankaran VG, Delmonte O, Tan W, Holm IA, Thiagarajah JR, Kamihara J, Comander J, Place E, Wiggs J, Green RC.
Perspectives of Rare Disease Experts on Newborn Genome Sequencing. JAMA Netw Open 2023;6(5):e2312231.
AbstractIMPORTANCE: Newborn genome sequencing (NBSeq) can detect infants at risk for treatable disorders currently undetected by conventional newborn screening. Despite broad stakeholder support for NBSeq, the perspectives of rare disease experts regarding which diseases should be screened have not been ascertained. OBJECTIVE: To query rare disease experts about their perspectives on NBSeq and which gene-disease pairs they consider appropriate to evaluate in apparently healthy newborns. DESIGN, SETTING, AND PARTICIPANTS: This survey study, designed between November 2, 2021, and February 11, 2022, assessed experts' perspectives on 6 statements related to NBSeq. Experts were also asked to indicate whether they would recommend including each of 649 gene-disease pairs associated with potentially treatable conditions in NBSeq. The survey was administered between February 11 and September 23, 2022, to 386 experts, including all 144 directors of accredited medical and laboratory genetics training programs in the US. EXPOSURES: Expert perspectives on newborn screening using genome sequencing. MAIN OUTCOMES AND MEASURES: The proportion of experts indicating agreement or disagreement with each survey statement and those who selected inclusion of each gene-disease pair were tabulated. Exploratory analyses of responses by gender and age were conducted using t and χ2 tests. RESULTS: Of 386 experts invited, 238 (61.7%) responded (mean [SD] age, 52.6 [12.8] years [range 27-93 years]; 126 [52.9%] women and 112 [47.1%] men). Among the experts who responded, 161 (87.9%) agreed that NBSeq for monogenic treatable disorders should be made available to all newborns; 107 (58.5%) agreed that NBSeq should include genes associated with treatable disorders, even if those conditions were low penetrance; 68 (37.2%) agreed that actionable adult-onset conditions should be sequenced in newborns to facilitate cascade testing in parents, and 51 (27.9%) agreed that NBSeq should include screening for conditions with no established therapies or management guidelines. The following 25 genes were recommended by 85% or more of the experts: OTC, G6PC, SLC37A4, CYP11B1, ARSB, F8, F9, SLC2A1, CYP17A1, RB1, IDS, GUSB, DMD, GLUD1, CYP11A1, GALNS, CPS1, PLPBP, ALDH7A1, SLC26A3, SLC25A15, SMPD1, GATM, SLC7A7, and NAGS. Including these, 42 gene-disease pairs were endorsed by at least 80% of experts, and 432 genes were endorsed by at least 50% of experts. CONCLUSIONS AND RELEVANCE: In this survey study, rare disease experts broadly supported NBSeq for treatable conditions and demonstrated substantial concordance regarding the inclusion of a specific subset of genes in NBSeq.
Gutierrez A, Chen TC.
Artificial intelligence in glaucoma: posterior segment optical coherence tomography. Curr Opin Ophthalmol 2023;34(3):245-254.
AbstractPURPOSE OF REVIEW: To summarize the recent literature on deep learning (DL) model applications in glaucoma detection and surveillance using posterior segment optical coherence tomography (OCT) imaging. RECENT FINDINGS: DL models use OCT derived parameters including retinal nerve fiber layer (RNFL) scans, macular scans, and optic nerve head (ONH) scans, as well as a combination of these parameters, to achieve high diagnostic accuracy in detecting glaucomatous optic neuropathy (GON). Although RNFL segmentation is the most widely used OCT parameter for glaucoma detection by ophthalmologists, newer DL models most commonly use a combination of parameters, which provide a more comprehensive approach. Compared to DL models for diagnosing glaucoma, DL models predicting glaucoma progression are less commonly studied but have also been developed. SUMMARY: DL models offer time-efficient, objective, and potential options in the management of glaucoma. Although artificial intelligence models have already been commercially accepted as diagnostic tools for other ophthalmic diseases, there is no commercially approved DL tool for the diagnosis of glaucoma, most likely in part due to the lack of a universal definition of glaucoma defined by OCT derived parameters alone (see Supplemental Digital Content 1 for video abstract,
http://links.lww.com/COOP/A54 ).