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Objective: Donor hearts procured after circulatory death (DCD) may significantly increase the number of hearts available for transplantation. The purpose of this study was to analyze current DCD and brain-dead donor (DBD) heart transplantation rates and characterize organ refusal using the most up-to-date United Network for Organ Sharing (UNOS) and Organ Procurement and Transplantation Network data. Methods: We analyzed UNOS and Organ Procurement and Transplantation Network DBD and DCD candidate, transplantation, and demographic data from 2020 through 2022 and 2022 refusal code data to characterize DCD heart use and refusal. Subanalyses were performed to characterize DCD donor demographics and regional transplantation rate variance. Results: DCD hearts were declined 3.37 times more often than DBD hearts. The most frequently used code for DCD refusal was neurologic function, related to concerns of a prolonged dying process and organ preservation. In 2022, 92% (1329/1452) of all DCD refusals were attributed to neurologic function. When compared with DBD, DCD donor hearts were more frequently declined as the result of prolonged warm ischemic time (odds ratio, 5.65; 95% confidence interval, 4.07-7.86) and other concerns over organ preservation (odds ratio, 4.06; 95% confidence interval, 3.33-4.94). Transplantation rate variation was observed between demographic groups and UNOS regions. DCD transplantation rates are currently experiencing second order polynomial growth. Conclusions: DCD donor hearts are declined more frequently than DBD. DCD heart refusals result from concerns over a prolonged dying process and organ preservation. Heart transplantation rates may be substantially improved by ex situ hemodynamic assessment, adoption of normothermic regional perfusion guidelines, and quality initiatives.

Abstract Changes in tropical deep convection with global warming are a leading source of uncertainty for future climate projections. A comparison of the responses of active sensor measurements of cloud ice to interannual variability and next-generation global storm-resolving model (also known as k-scale models) simulations to global warming shows similar changes for events with the highest column-integrated ice. The changes reveal that the ice loading decreases outside the most active convection but increases at a rate of several percent per Kelvin surface warming in the most active convection. Disentangling thermodynamic and vertical velocity changes shows that the ice signal is strongly modulated by structural changes of the vertical wind field towards an intensification of strong convective updrafts with warming, suggesting that changes in ice loading are strongly influenced by changes in convective velocities, as well as a path toward extracting information about convective velocities from observations.

Abstract Global storm‐resolving models (GSRMs) that can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation distributions relative to traditional Coupled Model Intercomparison Project 6 (CMIP6) models. In this study, we present results from two‐year‐long integrations of a GSRM developed at Geophysical Fluid Dynamics Laboratory, eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), for the response of precipitation to sea surface temperature warming and an isolated increase in CO2 and compare it to CMIP6 models. At leading order, X‐SHiELD's response is within the range of the CMIP6 models. However, a close examination of the precipitation distribution response reveals that X‐SHiELD has a different response at lower percentiles and the response of the extreme events are at the lower end of the range of CMIP6 models. A regional decomposition reveals that the difference is most pronounced for midlatitude land, where X‐SHiELD shows a lower increase at intermediate percentiles and drying at lower percentiles.

Cold static storage (CSS) for up to 6 h is the gold standard in heart preservation. Although some hearts stored over 6 h have been transplanted, longer CSS times have increased posttransplant morbimortality. Transmedics® Organ Care System (OCS™) is the only FDA-approved commercial system that provides an alternative to CSS using normothermic ex situ heart perfusion (NEHP) in resting mode with aortic perfusion (Langendorff method). However, it is also limited to 6 h and lacks an objective assessment of cardiac function. Developing a system that can perfuse hearts under NEHP conditions for >24 h can facilitate organ rehabilitation, expansion of the donor pool, and objective functional evaluation. The Extracorporeal Life Support Laboratory at the University of Michigan has worked to prolong NEHP to >24 h with an objective assessment of heart viability during NEHP. An NEHP system was developed for aortic (Langendorff) perfusion using a blood-derived perfusate (leukocyte/thrombocyte-depleted blood). Porcine hearts (n = 42) of different sizes (6–55 kg) were divided into five groups and studied during 24 h NEHP with various interventions in three piglets (small-size) heart groups: (1) Control NEHP without interventions (n = 15); (2) NEHP + plasma exchange (n = 5); (3) NEHP + hemofiltration (n = 10) and two adult-size (juvenile pigs) heart groups (to demonstrate the support of larger hearts); (4) NEHP + hemofiltration (n = 5); and (5) NEHP with intermittent left atrial (iLA) perfusion (n = 7). All hearts with NEHP + interventions (n = 27) were successfully perfused for 24 h, whereas 14 (93.3%) control hearts failed between 10 and 21 h, and 1 control heart (6.6%) lasted 24 h. Hearts in the piglet hemofiltration and plasma exchange groups performed better than those in the control group. The larger hearts in the iLA perfusion group (n = 7) allowed for real-time heart functional assessment and remained stable throughout the 24 h of NEHP. These results demonstrate that heart preservation for 24 h is feasible with our NEHP perfusion technique. Increasing the preservation period beyond 24 h, infection control, and nutritional support all need optimization. This proves the concept that NEHP has the potential to increase the organ pool by (1) considering previously discarded hearts; (2) performing an objective assessment of heart function; (3) increasing the donor/recipient distance; and (4) developing heart-specific perfusion therapies.

Abstract Coarse‐grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse‐grained cloud fields from a fine‐grid reference model are a natural target for a machine‐learned parameterization. We machine‐learn the coarsened‐fine cloud properties as a function of coarse‐grid model state in each grid cell of NOAA's FV3GFS global atmosphere model with 200 km grid spacing, trained using a 3 km fine‐grid reference simulation with a modified version of FV3GFS. The ML outputs are coarsened‐fine fractional cloud cover and liquid and ice cloud condensate mixing ratios, and the inputs are coarse model temperature, pressure, relative humidity, and ice cloud condensate. The predicted fields are skillful and unbiased, but somewhat under‐dispersed, resulting in too many partially cloudy model columns. When the predicted fields are applied diagnostically (offline) in FV3GFS's radiation scheme, they lead to small biases in global‐mean top‐of‐atmosphere (TOA) and surface radiative fluxes. An unbiased global‐mean TOA net radiative flux is obtained by setting to zero any predicted cloud with grid‐cell mean cloud fraction less than a threshold of 6.5%; this does not significantly degrade the ML prediction of cloud properties. The diagnostic, ML‐derived radiative fluxes are far more accurate than those obtained with the existing cloud parameterization in the nudged coarse‐grid model, as they leverage the accuracy of the fine‐grid reference simulation's cloud properties.

Abstract Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting.

Ex vivo programming of T cells can be efficacious but is complex and expensive; therefore, the development of methods to transfect T cells in situ is important. We developed and optimized anti-CD3-targeted lipid nanoparticles (aCD3-LNPs) to deliver tightly packed, reporter gene mRNA specifically to T cells. In vitro, targeted LNPs efficiently delivered mCherry mRNA to Jurkat T cells, and T-cell activation and depletion were associated with aCD3 antibody coating on the surface of LNPs. aCD3-LNPs, but not non-targeted LNPs, accumulated within the spleen following systemic injection, with mCherry and Fluc signals visible within 30 min after injection. At 24 h after aCD3-LNP injection, 2-4% of all splenic T cells and 2-7% of all circulating T cells expressed mCherry, and this was dependent on aCD3 coating density. Targeting and transfection were accompanied by systemic CD25+, OX40+, and CD69+ T-cell activation with temporary CD3e ligand loss and depletion of splenic and circulating subsets. Migration of splenic CD8a+ T cells from the white-pulp to red-pulp, and differentiation from naïve to memory and effector phenotypes, followed upon aCD3-LNP delivery. Additionally, aCD3-LNP injection stimulated the secretion of myeloid-derived chemokines and T-helper cytokines into plasma. Lastly, we administered aCD3-LNPs to tumor bearing mice and found that transfected T cells localized within tumors and tumor-draining lymph nodes following immunotherapy treatment. In summary, we show that CD3-targeted transfection is feasible, yet associated with complex immunological consequences that must be further studied for potential therapeutic applications.

Background: Although combination immunotherapies incorporating local and systemic components have shown promising results in treating solid tumors, varied tumor microenvironments (TMEs) can impact immunotherapeutic efficacy. Method: We designed and evaluated treatment strategies for breast and pancreatic cancer combining magnetic resonance-guided focused ultrasound (MRgFUS) ablation and antibody therapies. With a combination of single-cell sequencing, spectral flow cytometry, and histological analyses, we profiled an immune-suppressed KPC (Kras+/LSL-G12D; Trp53+/LSL-R172H; Pdx1-Cre) pancreatic adenocarcinoma (MT4) model and a dense epithelial neu deletion (NDL) HER2+ mammary adenocarcinoma model with a greater fraction of lymphocytes, natural killer cells and activated dendritic cells. We then performed gene ontology analysis, spectral and digital cytometry to assess the immune response to combination immunotherapies and correlation with survival studies. Result: Based on gene ontology analysis, adding ablation to immunotherapy enriched immune cell migration pathways in the pancreatic cancer model and extensively enriched wound healing pathways in the breast cancer model. With CIBERSORTx digital cytometry, aCD40 + aPD-1 immunotherapy combinations enhanced dendritic cell activation in both models. In the MT4 TME, adding the combination of aCD40 antibody and checkpoint inhibitors (aPD-1 and aCTLA-4) with ablation was synergistic, increasing activated natural killer cells and T cells in distant tumors. Furthermore, ablation with immunotherapy upregulated critical Ly6c myeloid remodeling phenotypes that enhance T-cell effector function and increased granzyme and protease encoding genes by as much as 100-fold. Ablation combined with immunotherapy then extended survival in the MT4 model to a greater extent than immunotherapy alone. Conclusion: In summary, TME profiling informed a successful multicomponent treatment protocol incorporating ablation and facilitated differentiation of TMEs in which ablation is most effective.

Summary: Microbubbles are currently approved for diagnostic ultrasound imaging and are under evaluation in therapeutic protocols. Here, we present a protocol for in vitro sonoporation validation using non-targeted microbubbles for gene delivery. We describe steps for computational simulation, experimental calibration, reagent preparation, ultrasound treatment, validation, and gene expression analysis. This protocol uses approved diagnostic microbubbles and parameters that are applicable for human use.For complete details on the use and execution of this protocol, please refer to Bez et al. (2017).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Cutaneous T-cell lymphoma (CTCL) is a rare hematologic malignancy that traditionally presents with cutaneous lesions, though metastases are not uncommon in progressive disease. We describe four cases of CTCL with central nervous system (CNS) involvement, detailing the history, pathological characteristics, treatment response, and progression. Median time from initial diagnosis to CNS metastasis was ∼5.4 years (range 3.4–15.5 years) and survival after metastasis was ∼160 days (range 19 days–4.4 years). No patients achieved long-term (>5 years) survival, though some displayed varying degrees of remission following CNS-directed therapy. We conclude that clinicians must be attentive to the development of CNS metastases in patients with CTCL. The growing body of literature on such cases will inform evolving therapeutic guidelines on this rare CTCL complication.

Abstract Using machine learning (ML) for the online correction of coarse‐resolution atmospheric models has proven effective in reducing biases in near‐surface temperature and precipitation rate. However, ML corrections often introduce new biases in the upper atmosphere and causes inconsistent model performance across different random seeds. Furthermore, they produce profiles that are outside the distribution of samples used in training, which can interfere with the baseline physics of the atmospheric model and reduce model reliability. This study introduces the use of a novelty detector to mask ML corrections when the atmospheric state is deemed out‐of‐sample. The novelty detector is trained on profiles of temperature and specific humidity in a semi‐supervised fashion using samples from the coarsened reference fine‐resolution simulation. The novelty detector responds to particularly biased simulations relative to the reference simulation by categorizing more columns as out‐of‐sample. Without novelty detection, corrective ML occasionally causes undesirably large climate biases. When coupled to a running year‐long coarse‐grid simulation, novelty detection deems about 21% of columns to be novelties. This identification reduces the spread in the root‐mean‐square error (RMSE) of time‐mean spatial patterns of surface temperature and precipitation rate across a random seed ensemble. In particular, the random seed with the worst RMSE is improved by up to 60% (depending on the variable) while the best seed maintains its low RMSE. By reducing the variance in quality of ML‐corrected climate models, novelty detection offers reliability without compromising prediction quality in atmospheric models.

Resiquimod is an immunopotent toll-like receptor 7/8 agonist with antitumor activity. Despite being potent against skin cancers, it is poorly tolerated systemically due to toxicity. Integrating resiquimod into nanoparticles presents an avenue to circumvent the toxicity problem. Herein, the preparation of degradable nanoparticles with covalently bound resiquimod and their systemic application in cancer immunotherapy is reported. Dispersion in water of amphiphilic constructs integrating resiquimod covalently bound via degradable amide or ester linkages yields immune-activating nanoparticles. The degradable agonist-nanoparticle bonds allow the release of resiquimod from the carrier nanoparticles. In vitro assays with antigen presenting cells demonstrate that the nanoparticles retain the immunostimulatory activity of resiquimod. Systemic administration of the nanoparticles and checkpoint blockade (aPD-1) to a breast cancer mouse model with multiple established tumors triggers antitumor activity evidenced by suppressed tumor growth and enhanced CD8+ T-cell infiltration. Nanoparticles with ester links, which hydrolyze more readily, yield a stronger immune response with 75% of tumors eliminated when combined with aPD-1. The reduced tumor growth and the presence of activated CD8+ T-cells across multiple tumors suggest the potential for treating metastatic cancer.

Resiquimod (R848) is a toll-like receptor 7 and 8 (TLR7/8) agonist with potent antitumor and immunostimulatory activity. However, systemic delivery of R848 is poorly tolerated because of its poor solubility in water and systemic immune activation. In order to address these limitations, we developed an intravenously-injectable formulation with R848 using thermosensitive liposomes (TSLs) as a delivery vehicle. R848 was remotely loaded into TSLs composed of DPPC: DSPC: DSPE-PEG2K (85:10:5, mol%) with 100 mM FeSO4 as the trapping agent inside. The final R848 to lipid ratio of the optimized R848-loaded TSLs (R848-TSLs) was 0.09 (w/w), 10-fold higher than the previously-reported values. R848-TSLs released 80% of R848 within 5 min at 42 °C. These TSLs were then combined with αPD-1, an immune checkpoint inhibitor, and ultrasound-mediated hyperthermia in a neu deletion (NDL) mouse mammary carcinoma model (Her2+, ER/PR negative). Combined with αPD-1, local injection of R848-TSLs showed superior efficacy with complete NDL tumor regression in both treated and abscopal sites achieved in 8 of 11 tumor bearing mice over 100 days. Immunohistochemistry confirmed enhanced CD8+ T cell infiltration and accumulation by R848-TSLs. Systemic delivery of R848-TSLs, combined with local hyperthermia and αPD-1, inhibited tumor growth and extended median survival from 28 days (non-treatment control) to 94 days. Upon re-challenge with reinjection of tumor cells, none of the previously cured mice developed tumors, as compared with 100% of age-matched control mice. The dose of R848 (10 μg for intra-tumoral injection or 6 mg/kg for intravenous injection delivered up to 4 times) was well-tolerated without weight loss or organ hypertrophy. In summary, we developed R848-TSLs that can be administered locally or systematically, resulting in tumor regression and enhanced survival when combined with αPD-1 in mouse models of breast cancer.

The era of anthropogenic climate change can be described by defined climate milestones. These milestones mark changes in the historic trajectory of change, and include peak greenhouse gas emissions, peak greenhouse gas concentration, deceleration of warming, net-zero emissions, and a transition to global cooling. However, given internal variability in the Earth system and measurement uncertainty, definitively saying that a milestone has passed requires rigour. Here CMIP6 simulations of peak-and-decline scenarios are used to examine the time needed to robustly detect three climate milestones: (1) the slowdown of global warming; (2) the end of global surface temperature increase; and (3) peak concentration of CO _2 . It is estimated that it will take 40 to 60 years after a simulated slowdown in warming rate, to robustly detect ( $ \gt\!95\%$ change) the signal in the global average temperature record. Detecting when warming has stopped will also be difficult and it takes until the mid 22nd century to have enough data to conclude warming has stopped. Detecting that CO _2 concentration has peaked is far easier and a drop in CO _2 concentration of 3 ppm is consistent with a greater than 99% chance that CO _2 has peaked in all scenarios examined. Thus it is likely that as the rate of CO _2 emissions is reduced, and net-zero emissions is approached, interpreting the global temperature record will become difficult—with a high potential to create confusion amongst policy makers and the general public.

Robust cytotoxic T cell infiltration has proven to be difficult to achieve in solid tumors. We set out to develop a flexible protocol to efficiently transfect tumor and stromal cells to produce immune-activating cytokines, and thus enhance T cell infiltration while debulking tumor mass. By combining ultrasound with tumor-targeted microbubbles, membrane pores are created and facilitate a controllable and local transfection. Here, we applied a substantially lower transmission frequency (250 kHz) than applied previously. The resulting microbubble oscillation was significantly enhanced, reaching an effective expansion ratio of 35 for a peak negative pressure of 500 kPa in vitro. Combining low-frequency ultrasound with tumor-targeted microbubbles and a DNA plasmid construct, 20% of tumor cells remained viable, and ∼20% of these remaining cells were transfected with a reporter gene both in vitro and in vivo. The majority of cells transfected in vivo were mucin 1+/CD45- tumor cells. Tumor and stromal cells were then transfected with plasmid DNA encoding IFN-β, producing 150 pg/106 cells in vitro, a 150-fold increase compared to no-ultrasound or no-plasmid controls and a 50-fold increase compared to treatment with targeted microbubbles and ultrasound (without IFN-β). This enhancement in secretion exceeds previously reported fourfold to fivefold increases with other in vitro treatments. Combined with intraperitoneal administration of checkpoint inhibition, a single application of IFN-β plasmid transfection reduced tumor growth in vivo and recruited efficacious immune cells at both the local and distant tumor sites.

Abstract One approach to improving the accuracy of a coarse‐grid global climate model is to add machine‐learned (ML) state‐dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine‐grid global storm‐resolving model (GSRM). Our past work demonstrating this approach was trained with short (40‐day) simulations of GFDL's X‐SHiELD GSRM with 3 km global horizontal grid spacing. Here, we extend this approach to span the full annual cycle by training and testing our ML using a new year‐long GSRM simulation. Our corrective ML models are trained by learning the state‐dependent tendencies of temperature and humidity and surface radiative fluxes needed to nudge a closely related 200 km grid coarse model, FV3GFS, to the GSRM evolution. Coarse‐grid simulations adding these learned ML corrections run stably for multiple years. Compared to a no‐ML baseline, the time‐mean spatial pattern errors with respect to the fine‐grid target are reduced by 6%–26% for land surface temperature and 9%–25% for land surface precipitation. The ML‐corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the no‐ML baseline simulation.

IgG4-related disease-associated hypertrophic pachymeningitis (IgG4RD-HP) is a fibroinflammatory autoimmune disorder in which diagnosis is difficult without biopsy. Guidance on management of disease refractory to glucocorticoids and intravenous rituximab is limited. We present the case of a 68-year-old woman with IgG4RD-HP who developed sensorineural hearing loss with associated bulky basilar pachymeningeal enhancement. Her cerebrospinal fluid was inflammatory and had an elevated IgG4 concentration, strongly suggestive of IgG4RD-HP. Biopsy of involved meninges was not possible due to surgical risk. Over years she developed bilateral optic neuropathies and hydrocephalus, requiring intravenous rituximab and ventriculoperitoneal shunt. Her disease was refractory to glucocorticoids. Despite maintenance intravenous rituximab, she developed slowly progressive symptoms of intracranial hypertension and hydrocephalus with persistently inflammatory spinal fluid. Switching to intrathecal rituximab therapy led to dramatic improvement in gait and headache and reduced pachymeningeal bulk and metabolic activity. In patients with IgG4RD-HP refractory to glucocorticoids and intravenous rituximab, intrathecal rituximab may be an efficacious therapy.