Considerable attention has recently been given to the contribution of the greenhouse gas (GHG) emissions of the healthcare sector to climate change. GHGs used in medical practice are regularly released into the atmosphere and contribute to elevations in global temperatures that produce detrimental effects on the environment and human health. Consequently, a comprehensive assessment of their global warming potential over 100 years (GWP) characteristics, and clinical uses, many of which have evaded scrutiny from policy makers due to their medical necessity, is needed. Of major interest are volatile anesthetics, analgesics, and inhalers, as well as fluorinated gases used as tamponades in retinal detachment surgery. In this review, we conducted a literature search from July to September 2024 on medical greenhouse gases and calculated estimates of these gases’ GHG emissions in metric tons CO₂ equivalent (MTCO₂e) and their relative GWP. Notably, the anesthetics desflurane and nitrous oxide contribute the most emissions out of the major medical GHGs, equivalent to driving 12 million gasoline-powered cars annually in the US. Retinal tamponade gases have markedly high GWP up to 23,500 times compared to CO₂ and long atmospheric lifetimes up to 10,000 years, thus bearing the potential to contribute to climate change in the long term. This review provides the basis for discussions on examining the environmental impacts of medical gases with high GWP, determining whether alternatives may be available, and reducing emissions while maintaining or even improving patient care.

To compare total retinal blood flow (TRBF) rates before and after retinopathy of prematurity (ROP) treatment with intravitreal bevacizumab using laser speckle contrast imaging (LSCI). A prospective cohort study. Twenty-five eyes from 14 premature infants in the neonatal intensive care unit receiving intravitreal bevacizumab for treatment-requiring ROP. Total retinal blood flow was measured using LSCI longitudinally before and after bevacizumab treatment. Subject characteristics and clinical ROP features, including the need for ROP retreatment, were included in regression analysis using generalized estimating equations to account for 2 eyes per subject and longitudinal measures over time. The main outcome measure was TRBF, which includes components of peak, mean, and dip over the cardiac cycle. Before ROP treatment, subjects had a peak TRBF of 11.1 ± 2.9 a.u. compared to 8.6 ± 1.8 a.u. after treatment (mean difference = 2.5 a.u., P < 0.0001). Among eyes that required ROP retreatment earlier (<10 weeks) after initial treatment, the posttreatment peak TRBF was 9.0 ± 1.5 a.u., compared to 7.3 ± 2.2 a.u. for eyes that did not require retreatment in the first 10 weeks after initial bevacizumab injection (mean difference = 1.7 a.u., P = 0.01). Peak TRBF decreased over time after bevacizumab treatment (β = -0.1 a.u./week, P = 0.004). We observed lower TRBF after treatment with intravitreal bevacizumab. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Considerable attention has recently been given to the contribution of greenhouse gas (GHG) emissions of the healthcare sector to climate change, which represents 4-6% of emissions worldwide. GHGs used in medical practice are regularly released into the atmosphere and contribute to elevation in global temperatures that produce detrimental effects on the environment and human health. Consequently, a comprehensive assessment of their global warming potential (GWP) characteristics and clinical uses, many of which have evaded scrutiny from policy makers due to their medical necessity, is needed. Of major interest are volatile anesthetics, analgesics, and inhalers, as well as fluorinated gases used as tamponades in retinal detachment surgery. In this review, we compare estimates of these gases’ GHG emissions in metric tons CO₂ equivalent (MTCO₂e) and their relative GWP. Notably, the anesthetics desflurane and nitrous oxide contribute the most emissions out of the major medical GHGs, equivalent to driving 12 million gasoline-powered cars annually in the US. Retinal tamponade gases have markedly high GWP up to 23,500 times compared to CO₂ and long atmospheric lifetimes up to 10,000 years, thus bearing the potential to contribute to climate change long-term. This review provides the basis for discussions on examining the environmental impacts of medical gases with high GWP, determining whether alternatives may be available, and reducing emissions while maintaining or even improving patient care.

Photoreceptor transplantation is being studied to restore visual function in retinal diseases causing blindness, including age‐related macular degeneration, hereditary eye diseases, and traumatic retinopathy. Preclinical studies often involve delivering exogenous human photoreceptor cells into animal models' retinas. A key readout in such experiments is distinguishing donor cell integration from artificial labeling secondary to material transfer of cytosolic or nuclear labels. Recognizing donor (human) versus animal photoreceptor nuclei is key, but purely immunohistology discrimination is challenging due to antigenic species overlap or intercellular antigen transfer. To address this, we sought to develop and validate a computational technique to discriminate between photoreceptor cells of different animal species based on machine learning of nuclear morphology. We aim to evaluate the feasibility of computer‐assisted nuclear detection combined with random forest classification to automate species differentiation in DAPI‐stained photoreceptors after xenotransplantation into mouse and pig retinas. Our models were trained on single‐species samples and validated with mixed‐species samples. We then transplanted human embryonic stem cell‐derived retinal organoid cells into rodent and pig retinal degeneration models. The random forest model accurately determined cell identity post‐xenotransplantation, validated by histological assessment using an antihuman nuclear antibody. Our results support the potential efficacy of employing machine learning image analysis and classification techniques that may promote experimental rigor, minimize observer bias, and enable high throughput semiautomated workflows for transplantation outcomes analysis. The methodological framework reported here may enable a more nuanced and precise analysis of the behavior of transplanted photoreceptors for the purposes of human retinal regeneration.