Disparities in Liver Transplantation for Nonalcoholic Steatohepatitis in Women.

BACKGROUND: Nonalcoholic steatohepatitis (NASH) is the fastest-growing indication for liver transplantation (LT). Sex disparities among patients with cirrhosis on the LT waitlist are well known. We wanted to understand these disparities further in women with end-stage liver disease patients listed for NASH cirrhosis in a contemporary cohort. METHODS: We used data from the Scientific Registry of Transplant Recipients to assess sex racial, and ethnic differences in NASH patients listed for LT. Adults transplanted from August 1997 to June 2021 were included. Inferential statistics were used to evaluate differences with univariate and multivariate comparisons, including competitive risk analysis. RESULTS: During the study time period, we evaluated 12 844 LT for NASH cirrhosis. Women were transplanted at a lower rate (46.5% versus 53.5%; P < 0.001) and higher model for end-stage liver disease (MELD) (23.8 versus 22.6; P < 0.001) than men. Non-White women were transplanted at a higher MELD (26.1 versus 23.1; P < 0.001) than White women and non-White male patients (26.1 versus 24.8; P < 0.001). Graft and patient survivals were significantly different (P < 0.001) between non-White women and White women and men (White and non-White). CONCLUSIONS: Evaluation of LT candidates in the United States demonstrates women with NASH cirrhosis have a higher MELD than men at LT. Additional disparities exist among non-White women with NASH as they have higher MELD and creatinine at LT compared with White women. After LT, non-White women have worse graft and patient survival compared with men or White women. These data indicate that non-White women with NASH are the most vulnerable on the LT waitlist.

Non-Lung Solid Organ Transplantation From SARS-CoV-2-Positive Donors to Uninfected Recipients.

BACKGROUND: There is a paucity of evidence on the risk of donor-recipient transmission of the SARS-CoV-2 in solid organ transplant recipients. Initial impressions suggest non-lung solid organs may be safely transplanted from SARS-CoV-2-positive donors without risk of viral transmission. METHODS: We reviewed clinical results of transplants in which SARS-CoV-2-negative recipients received non-lung solid organs from SARS-CoV-2-positive donors at a single transplant center. No prisoners were used in this study, and participants were neither coerced nor paid. The manuscript was created in compliance with the Helsinki Congress and the Declaration of Istanbul. RESULTS: Between June 2021 and January 2023, we transplanted 26 solid organs, including 13 kidneys, 8 livers, 3 hearts, and 1 simultaneous heart and kidney, from 23 SARS-CoV-2-positive donors into 25 SARS-CoV-2 negative recipients. Two of the recipients had a positive SARS-CoV-2 real-time polymerase chain reaction after transplantation, but otherwise, patients had no SARS-CoV-2-related complications, and all patients to date are alive with excellent allograft function. CONCLUSION: Transplantation of non-lung solid organs from SARS-CoV-2-positive donors into uninfected recipients can be safely performed without adverse effects from SARS-CoV-2.

Decline of seagrass (Posidonia oceanica) production over two decades in the face of warming of the Eastern Mediterranean Sea.

The response of Posidonia oceanica meadows to global warming of the Eastern Mediterranean Sea, where the increase in sea surface temperature (SST) is particularly severe, is poorly investigated. Here, we reconstructed the long-term P. oceanica production in 60 meadows along the Greek Seas over two decades (1997-2018), using lepidochronology. We determined the effect of warming on production by reconstructing the annual and maximum (i.e. August) SST, considering the role of other production drivers related to water quality (i.e. Chla, suspended particulate matter, Secchi depth). Grand mean (±SE) production across all sites and the study period was 48 ± 1.1 mg DW per shoot yr-1 . Production over the last two decades followed a trajectory of decrease, which was related to the concurrent increase in annual SST and SSTaug . Annual SST > 20°C and SSTaug > 26.5°C was related to production decline (GAMM, P < 0.05), while the rest of the tested factors did not help explain the production pattern. Our results indicate a persistent and increasing threat for Eastern Mediterranean meadows, drawing attention to management authorities, highlighting the necessity of reducing local impacts to enhance the resilience of seagrass meadows to global change threats.

Potential role of seaweeds in climate change mitigation.

Seaweed (macroalgae) has attracted attention globally given its potential for climate change mitigation. A topical and contentious question is: Can seaweeds' contribution to climate change mitigation be enhanced at globally meaningful scales? Here, we provide an overview of the pressing research needs surrounding the potential role of seaweed in climate change mitigation and current scientific consensus via eight key research challenges. There are four categories where seaweed has been suggested to be used for climate change mitigation: 1) protecting and restoring wild seaweed forests with potential climate change mitigation co-benefits; 2) expanding sustainable nearshore seaweed aquaculture with potential climate change mitigation co-benefits; 3) offsetting industrial CO2 emissions using seaweed products for emission abatement; and 4) sinking seaweed into the deep sea to sequester CO2. Uncertainties remain about quantification of the net impact of carbon export from seaweed restoration and seaweed farming sites on atmospheric CO2. Evidence suggests that nearshore seaweed farming contributes to carbon storage in sediments below farm sites, but how scalable is this process? Products from seaweed aquaculture, such as the livestock methane-reducing seaweed Asparagopsis or low carbon food resources show promise for climate change mitigation, yet the carbon footprint and emission abatement potential remains unquantified for most seaweed products. Similarly, purposely cultivating then sinking seaweed biomass in the open ocean raises ecological concerns and the climate change mitigation potential of this concept is poorly constrained. Improving the tracing of seaweed carbon export to ocean sinks is a critical step in seaweed carbon accounting. Despite carbon accounting uncertainties, seaweed provides many other ecosystem services that justify conservation and restoration and the uptake of seaweed aquaculture will contribute to the United Nations Sustainable Development Goals. However, we caution that verified seaweed carbon accounting and associated sustainability thresholds are needed before large-scale investment into climate change mitigation from seaweed projects.

Drivers of variability in Blue Carbon stocks and burial rates across European estuarine habitats.

The implementation of climate change mitigation strategies based on the conservation and restoration of Blue Carbon ecosystems requires a deep understanding of the magnitude and variability in organic carbon (Corg) storage across and within these ecosystems. This study explored the variability in soil Corg stocks and burial rates across and within intertidal estuarine habitats of the Atlantic European coast and its relation to biotic and abiotic drivers. A total of 136 soil cores were collected across saltmarshes located at different tidal zones (high marsh, N = 45; low marsh, N = 30), seagrass meadows (N = 17) and tidal flats (N = 44), and from the inner to the outer sections of five estuaries characterized by different basin land uses. Soil Corg stocks were higher in high-marsh communities (65 ± 3 Mg ha-1) than in low-marsh communities (38 ± 3 Mg ha-1), seagrass meadows (40 ± 5 Mg ha-1) and unvegetated tidal flats (46 ± 3 Mg ha-1) whereas Corg burial rates also tended to be higher in high marshes (62 ± 13 g m-2 y-1) compared to low marshes (43 ± 15 g m-2 y-1) and tidal flats (35 ± 9 g m-2 y-1). Soil Corg stocks and burial rates decreased from inner to outer estuarine sections in most estuaries reflecting the decrease in the river influence towards the estuary mouth. Higher soil Corg stocks were related to higher content of silt and clay and higher proportion of forest and natural land within the river basin, pointing at new opportunities for protecting coastal natural carbon sinks based on the conservation and restoration of upland ecosystems. Our study contributes to the global inventory of Blue Carbon by adding data from unexplored regions and habitats in Europe, and by identifying drivers of variability across and within estuaries.

Learning image representations for content-based image retrieval of radiotherapy treatment plans.

Objective.In this work, we propose a content-based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Retrieved dose distributions from this method can be incorporated into automated treatment planning workflows in order to streamline the iterative planning process. As CBIR has not yet been applied to treatment planning, our work seeks to understand which current machine learning models are most viable in this context.Approach.Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient's anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. All source code for this project is available on github.Main results.The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available image sets and clinical image sets from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network.Significance.Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works.

A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy.

Objective. In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. To assess the need for adaptation, motion models can be used to simulate dominant motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same set of deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient.Approach. We propose a deep learning probabilistic framework that generates deformation vector fields warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs with prostate, bladder, and rectum delineations from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and 'ground truth' distributions of volume and center of mass changes.Results. With a DICE score of 0.86 ± 0.05 and a distance between prostate contours of 1.09 ± 0.93 mm, DAM matches and improves upon previously published PCA-based models, using as few as 8 latent variables. The overlap between distributions further indicates that DAM's sampled movements match the range and frequency of clinically observed daily changes on repeat CTs.Significance. Conditioned only on planning CT values and organ contours of a new patient without any pre-processing, DAM can accurately deformations seen during following treatment sessions, enabling anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes.

Quantifying blue carbon stocks and the role of protected areas to conserve coastal wetlands.

Vegetated coastal ecosystems, in particular mangroves, tidal marshes and seagrasses are highly efficient at sequestering and storing carbon, making them valuable assets for climate change mitigation and adaptation. The state of Queensland, in northeastern Australia, contains almost half of the total area of these blue carbon ecosystems in the country, yet there are few detailed regional or state-wide assessments of their total sedimentary organic carbon (SOC) stocks. We compiled existing SOC data and used boosted regression tree models to evaluate the influence of environmental variables in explaining the variability in SOC stocks, and to produce spatially explicit blue carbon estimates. The final models explained 75 % (for mangroves and tidal marshes) and 65 % (for seagrasses) of the variability in SOC stocks. Total SOC stocks in the state of Queensland were estimated at 569 ± 98 Tg C (173 ± 32 Tg C, 232 ± 50 Tg C, and 164 ± 16 Tg C from mangroves, tidal marshes and seagrasses, respectively). Regional predictions for each of Queensland's eleven Natural Resource Management regions revealed that 60 % of the state's SOC stocks occurred within three regions (Cape York, Torres Strait and Southern Gulf Natural Resource Management regions) due to a combination of high values of SOC stocks and large areas of coastal wetlands. Protected areas in Queensland play an important role in conserving SOC assets in Queensland's coastal wetlands. For example, ~19 Tg C within terrestrial protected areas, ~27 Tg C within marine protected areas and ~ 40 Tg C within areas of matters of State Environmental Significance. Using multi-decadal (1987-2020) mapped distributions of mangroves in Queensland; we found that mangrove area increased by approximately 30,000 ha from 1987 to 2020, which led to temporal fluctuations in mangrove plant and SOC stocks. We estimated that plant stocks decreased from ~45 Tg C in 1987 to ~34.2 Tg C in 2020, while SOC stocks remained relatively constant from ~107.9 Tg C in 1987 to 108.0 Tg C in 2020. Considering the level of current protection, emissions from mangrove deforestation are potentially very low; therefore, representing minor opportunities for mangrove blue carbon projects in the region. Our study provides much needed information on current trends in carbon stocks and their conservation in Queensland's coastal wetlands, while also contributing to guide future management actions, including blue carbon restoration projects.

Sub-second photon dose prediction via transformer neural networks.

BACKGROUND: Fast dose calculation is critical for online and real-time adaptive therapy workflows. While modern physics-based dose algorithms must compromise accuracy to achieve low computation times, deep learning models can potentially perform dose prediction tasks with both high fidelity and speed. PURPOSE: We present a deep learning algorithm that, exploiting synergies between transformer and convolutional layers, accurately predicts broad photon beam dose distributions in few milliseconds. METHODS: The proposed improved Dose Transformer Algorithm (iDoTA) maps arbitrary patient geometries and beam information (in the form of a 3D projected shape resulting from a simple ray tracing calculation) to their corresponding 3D dose distribution. Treating the 3D CT input and dose output volumes as a sequence of 2D slices along the direction of the photon beam, iDoTA solves the dose prediction task as sequence modeling. The proposed model combines a Transformer backbone routing long-range information between all elements in the sequence, with a series of 3D convolutions extracting local features of the data. We train iDoTA on a dataset of 1700 beam dose distributions, using 11 clinical volumetric modulated arc therapy (VMAT) plans (from prostate, lung, and head and neck cancer patients with 194-354 beams per plan) to assess its accuracy and speed. RESULTS: iDoTA predicts individual photon beams in ≈50 ms with a high gamma pass rate of 97.72 ± 1.93 % $97.72\pm 1.93\%$ (2 mm, 2%). Furthermore, estimating full VMAT dose distributions in 6-12 s, iDoTA achieves state-of-the-art performance with a 99.51 ± 0.66 % $99.51\pm 0.66\%$ (2 mm, 2%) pass rate and an average relative dose error of 0.75 ± 0.36%. CONCLUSIONS: Offering the millisecond speed prediction per beam angle needed in online and real-time adaptive treatments, iDoTA represents a new state of the art in data-driven photon dose calculation. The proposed model can massively speed-up current photon workflows, reducing calculation times from few minutes to just a few seconds.

The trajectory in catalytic evolution of Rubisco in Posidonia seagrass species differs from terrestrial plants.

The CO2-fixing enzyme Ribulose bisphosphate carboxylase-oxygenase (Rubisco) links the inorganic and organic phases of the global carbon cycle. In aquatic systems, the catalytic adaptation of algae Rubiscos has been more expansive and followed an evolutionary pathway that appears distinct to terrestrial plant Rubisco. Here, we extend this survey to differing seagrass species of the genus Posidonia to reveal how their disjunctive geographical distribution and diverged phylogeny, along with their CO2 concentrating mechanisms (CCMs) effectiveness, have impacted their Rubisco kinetic properties. The Rubisco from Posidonia species showed lower carboxylation efficiencies and lower sensitivity to O2 inhibition than those measured for terrestrial C3 and C4-plant Rubiscos. Compared with the Australian Posidonia species, Rubisco from the Mediterranean Posidonia oceanica had 1.5-2-fold lower carboxylation and oxygenation efficiencies, coinciding with effective CCMs and five Rubisco large subunit amino acid substitutions. Among the Australian Posidonia species, CCM effectiveness was higher in Posidonia sinuosa and lower in the deep-living Posidonia angustifolia, likely related to the 20%-35% lower Rubisco carboxylation efficiency in P. sinuosa and the two-fold higher Rubisco content in P. angustifolia. Our results suggest that the catalytic evolution of Posidonia Rubisco has been impacted by the low CO2 availability and gas exchange properties of marine environments, but with contrasting Rubisco kinetics according to the time of diversification among the species. As a result, the relationships between maximum carboxylation rate and CO2- and O2-affinities of Posidonia Rubiscos follow an alternative path to that characteristic of terrestrial angiosperm Rubiscos.

Short- and Long-Term Outcomes for Ethnic Minorities in the United States After Liver Transplantation: Parsing the Hispanic Paradox.

BACKGROUND: Racial and ethnic minorities are disproportionally affected by end-stage liver disease. Unfortunately, disparities in referrals to liver transplantation (LT), organ allocation, and posttransplant outcomes exist in this population. METHODS: We performed a retrospective analysis of patients over the age of 18 years undergoing LT in the United States using the Scientific Registry of Transplant Recipients from 2002 to 2016. We evaluated factors associated with patient and graft outcomes and explored the effect of race and ethnicity along with social variables. RESULTS: During the study time period, 78,999 patients received LT. Of these, 60,102 were non-Hispanic White (NHW), 7988 were African American (AA), and 10,909 were Hispanic. AA had significantly lower patient survival, graft survival, and death-censored graft survival at both 1 and 5 years when compared to NHW. Conversely, at 1 and 5 years, patient survival and graft survival were significantly higher for Hispanics compared to NWH. In addition, AA had significantly lower survival outcomes compared to Hispanics. On multivariate analysis after controlling for race/ethnicity, age, AA race, diagnosis, and deceased donor were independent risk factors for patient death and graft failure. CONCLUSIONS: Despite socioeconomic disadvantages seen among Hispanics, this population appears to have improved short- and long-term survival after LT compared to NHW and AA.

Use of hepatitis B viremic donors in kidney transplant recipients: A single center experience.

Utilization of Hepatitis B virus (HBV)-infected kidney allografts represents an opportunity to bridge the gap between organ supply and demand. Highly efficacious vaccines and antiviral therapies allow these allografts to be transplanted with negligible risk to the recipient. The purpose of this study was to describe the prophylactic strategies and related clinical outcomes of kidney transplant recipients who received a kidney from an HBV viremic donor. Eight patients received an allograft from an HBV viremic deceased kidney donor between January 1, 2017 and December 4, 2020. All recipients were immune to hepatitis B with a surface antibody titer greater than or equal to 10 mIU/ml (range: 12 - > 1000 mIU/ml). After transplant, 62.5% demonstrated HBV core antibody seroconversion at an average of 47.4 ± 28.5 days post-transplant. Anti-viral prophylaxis was initiated in 87.5% of patients; 62.5% preemptively during the transplant admission (range 1-3 days post-transplant) and 25% following HBcAb seroconversion (range 45-304 days post-transplant). Of the four patients who were started on entecavir preemptively, two subsequently core converted. These two patients had an HBV surface antibody less than 100 mIU/ml at the time of transplant. None of the recipients converted to HBV surface antigen positivity. The average estimated glomerular filtration rate was 41 ± 19 ml/min/1.73m2 , and there were no significant elevations in liver enzymes through one year post-transplant. The use of HBV viremic kidney allografts may represent an additional source of transplant organs; however, more studies are needed to better elucidate the optimal protective strategies for these recipients.

Fingerprinting macrophyte Blue Carbon by pyrolysis-GC-compound specific isotope analysis (Py-CSIA).

There is a need for tools to determine the origin of organic matter (OM) in Blue Carbon Ecosystems (BCE) and marine sediments to (1) facilitate the implementation of Blue Carbon strategies into carbon accounting and crediting schemes and (2) decipher changes in ecosystem condition over decadal to millennial time scales and thus to understand and predict the stability of BCE in a changing world. Pyrolysis-GC-compound specific isotope analysis (Py-CSIA) is applied for the first time in marine environments and BCE research. We studied Australian mangrove, tidal marsh and seagrass sediments, in addition to potential sources of OM (Avicennia, Posidonia, Zostera, Sarcocornia, Ecklonia and Ulva species and seagrass epiphytes), to identify precursors of different biomacromolecule constituents (lignin, polysaccharides and aliphatic structures). Firstly, the link between bulk δ13C and δ13C reconstructed from compound-specific δ13C showed that the pyrolysis approach allows for the isotopic screening of a representative portion of the OM. Secondly, for all samples, the C isotope fingerprint of the carbohydrate products (plant polysaccharides) was the heaviest (13C enriched), followed by lignin and aliphatic products. The differences in δ13C among macromolecules and the overlap in δ13C among putative sources reflect the limitations of bulk δ13C analyses for deciphering OM provenance. Thirdly, phanerogams specimen had the heaviest carbohydrate and lignin, confirming that seagrass-derived lignocellulose can be traced based on δ13C. Consistent differences for individual compounds were identified between seagrasses and between Avicennia and Sarcocornia using Py-CSIA. Fourth, ecosystem shifts (colonization of seagrass habitats by mangrove) on millenary time scales, hypothesized in previous studies on the basis of bulk δ13C and Py-GC-MS, were confirmed by Py-CSIA. We conclude that Py-CSIA is useful in Blue Carbon research to decipher OM sources in marine sediments, identify ecosystem transitions in palaeoenvironmental records, and to understand the role of different OM compounds in Blue Carbon storage.

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy.

Objective.Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries.Approach.Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g. tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80 000 different head and neck, lung, and prostate geometries.Main results.Predicting beamlet doses in 5 ± 4.9 ms with a very high gamma pass rate of 99.37 ± 1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 s depending on the number of beamlets (800-2200 in our plans), achieving a 99.70 ± 0.14% (2%, 2 mm) gamma pass rate across 9 test patients.Significance.Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.

Impacts of land-use change and urban development on carbon sequestration in tropical seagrass meadow sediments.

Seagrass meadows store significant carbon stocks at a global scale, but land-use change and other anthropogenic activities can alter the natural process of organic carbon (Corg) accumulation. Here, we assessed the carbon accumulation history of two seagrass meadows in Zanzibar (Tanzania) that have experienced different degrees of disturbance. The meadow at Stone Town has been highly exposed to urban development during the 20th century, while the Mbweni meadow is located in an area with relatively low impacts but historical clearing of adjacent mangroves. The results showed that the two sites had similar sedimentary Corg accumulation rates (22-25 g m-2 yr-1) since the 1940s, while during the last two decades (∼1998 until 2018) they exhibited 24-30% higher accumulation of Corg, which was linked to shifts in Corg sources. The increase in the δ13C isotopic signature of sedimentary Corg (towards a higher seagrass contribution) at the Stone Town site since 1998 points to improved seagrass meadow conditions and Corg accumulation capacity of the meadow after the relocation of a major sewage outlet in the mid-1990s. In contrast, the decrease in the δ13C signatures of sedimentary Corg in the Mbweni meadow since the early 2010s was likely linked to increased Corg run-off of mangrove/terrestrial material following mangrove deforestation. This study exemplifies two different pathways by which land-based human activities can alter the carbon storage capacity of seagrass meadows (i.e. sewage waste management and mangrove deforestation) and showcases opportunities for management of vegetated coastal Corg sinks.

Racial Disparities in COVID-19 Outcomes Among Black and White Patients With Cancer.

Importance: Non-Hispanic Black individuals experience a higher burden of COVID-19 than the general population; hence, there is an urgent need to characterize the unique clinical course and outcomes of COVID-19 in Black patients with cancer. Objective: To investigate racial disparities in severity of COVID-19 presentation, clinical complications, and outcomes between Black patients and non-Hispanic White patients with cancer and COVID-19. Design, Setting, and Participants: This retrospective cohort study used data from the COVID-19 and Cancer Consortium registry from March 17, 2020, to November 18, 2020, to examine the clinical characteristics and outcomes of COVID-19 in Black patients with cancer. Data analysis was performed from December 2020 to February 2021. Exposures: Black and White race recorded in patient's electronic health record. Main Outcomes and Measures: An a priori 5-level ordinal scale including hospitalization intensive care unit admission, mechanical ventilation, and all-cause death. Results: Among 3506 included patients (1768 women [50%]; median [IQR] age, 67 [58-77] years), 1068 (30%) were Black and 2438 (70%) were White. Black patients had higher rates of preexisting comorbidities compared with White patients, including obesity (480 Black patients [45%] vs 925 White patients [38%]), diabetes (411 Black patients [38%] vs 574 White patients [24%]), and kidney disease (248 Black patients [23%] vs 392 White patients [16%]). Despite the similar distribution of cancer type, cancer status, and anticancer therapy at the time of COVID-19 diagnosis, Black patients presented with worse illness and had significantly worse COVID-19 severity (unweighted odds ratio, 1.34 [95% CI, 1.15-1.58]; weighted odds ratio, 1.21 [95% CI, 1.11-1.33]). Conclusions and Relevance: These findings suggest that Black patients with cancer experience worse COVID-19 outcomes compared with White patients. Understanding and addressing racial inequities within the causal framework of structural racism is essential to reduce the disproportionate burden of diseases, such as COVID-19 and cancer, in Black patients.

Wildfires enhance phytoplankton production in tropical oceans.

Wildfire magnitude and frequency have greatly escalated on a global scale. Wildfire products rich in biogenic elements can enter the ocean through atmospheric and river inputs, but their contribution to marine phytoplankton production is poorly understood. Here, using geochemical paleo-reconstructions, a century-long relationship between wildfire magnitude and marine phytoplankton production is established in a fire-prone region of Kimberley coast, Australia. A positive correlation is identified between wildfire and phytoplankton production on a decadal scale. The importance of wildfire on marine phytoplankton production is statistically higher than that of tropical cyclones and rainfall, when strong El Niño Southern Oscillation coincides with the positive phase of Indian Ocean Dipole. Interdecadal chlorophyll-a variation along the Kimberley coast validates the spatial connection of this phenomenon. Findings from this study suggest that the role of additional nutrients from wildfires has to be considered when projecting impacts of global warming on marine phytoplankton production.

Assessment of Regional Variability in COVID-19 Outcomes Among Patients With Cancer in the United States.

Importance: The COVID-19 pandemic has had a distinct spatiotemporal pattern in the United States. Patients with cancer are at higher risk of severe complications from COVID-19, but it is not well known whether COVID-19 outcomes in this patient population were associated with geography. Objective: To quantify spatiotemporal variation in COVID-19 outcomes among patients with cancer. Design, Setting, and Participants: This registry-based retrospective cohort study included patients with a historical diagnosis of invasive malignant neoplasm and laboratory-confirmed SARS-CoV-2 infection between March and November 2020. Data were collected from cancer care delivery centers in the United States. Exposures: Patient residence was categorized into 9 US census divisions. Cancer center characteristics included academic or community classification, rural-urban continuum code (RUCC), and social vulnerability index. Main Outcomes and Measures: The primary outcome was 30-day all-cause mortality. The secondary composite outcome consisted of receipt of mechanical ventilation, intensive care unit admission, and all-cause death. Multilevel mixed-effects models estimated associations of center-level and census division-level exposures with outcomes after adjustment for patient-level risk factors and quantified variation in adjusted outcomes across centers, census divisions, and calendar time. Results: Data for 4749 patients (median [IQR] age, 66 [56-76] years; 2439 [51.4%] female individuals, 1079 [22.7%] non-Hispanic Black individuals, and 690 [14.5%] Hispanic individuals) were reported from 83 centers in the Northeast (1564 patients [32.9%]), Midwest (1638 [34.5%]), South (894 [18.8%]), and West (653 [13.8%]). After adjustment for patient characteristics, including month of COVID-19 diagnosis, estimated 30-day mortality rates ranged from 5.2% to 26.6% across centers. Patients from centers located in metropolitan areas with population less than 250 000 (RUCC 3) had lower odds of 30-day mortality compared with patients from centers in metropolitan areas with population at least 1 million (RUCC 1) (adjusted odds ratio [aOR], 0.31; 95% CI, 0.11-0.84). The type of center was not significantly associated with primary or secondary outcomes. There were no statistically significant differences in outcome rates across the 9 census divisions, but adjusted mortality rates significantly improved over time (eg, September to November vs March to May: aOR, 0.32; 95% CI, 0.17-0.58). Conclusions and Relevance: In this registry-based cohort study, significant differences in COVID-19 outcomes across US census divisions were not observed. However, substantial heterogeneity in COVID-19 outcomes across cancer care delivery centers was found. Attention to implementing standardized guidelines for the care of patients with cancer and COVID-19 could improve outcomes for these vulnerable patients.

Effect of Blood Product Resuscitation on the Pharmacokinetics of Ampicillin-Sulbactam during Orthotopic Liver Transplantation.

Background: Ampicillin-sulbactam is a piperacillin-tazobactam-sparing alternative antibiotic administered as surgical prophylaxis during orthotopic liver transplant (OLT), but limited data are available describing its pharmacokinetics and impact of blood product resuscitation. The purpose of this study was to determine the intra-operative pharmacokinetics of ampicillin-sulbactam in patients during OLT and evaluate the effects of blood resuscitation on exposure. Patients and Methods: This was a pharmacokinetic study in 10 OLT patients receiving ampicillin-sulbactam surgical prophylaxis. A 5,000-patient Monte Carlo simulation was conducted to identify optimal ampicillin-sulbactam regimens. Linear regression assessed association between blood product administration and ampicillin exposures. Results: Ampicillin and sulbactam concentrations best fitted two-compartment models. Mean ampicillin pharmacokinetic parameters were central compartment volume (Vc): 6.9 ± 2.0 L, clearance (CL): 26.6 ± 18.4 L/h, and inter-compartmental rate constants (k12 and k21): 4.8 ± 2.6 and 2.3 ± 1.4 h-1. Sulbactam pharmacokinetic parameters were Vc: 8.1 ± 2.7 L, CL: 26.1 ± 7.4 L/h, k12 and k21: 4.9 ± 1.0 and 2.8 ± 1.1 h-1. Participants received between 500 and 23,642 mL of total blood product. No statistical relations were observed between blood product administration and exposures (R2 0.00-0.26). Ampicillin-sulbactam 2/1 g every two hours and 2/1 g bolus followed by 6/3 g continuous infusion provided acceptable probability of target attainment up to minimum inhibitory concentrations (MICs) of 16 and 32 mcg/mL, respectively. Conclusions: High and frequent ampicillin-sulbactam doses are required to maintain 100% fT > MIC at relevant MICs during OLT and no impact of blood product resuscitation was observed on ampicillin exposure. These are the first data available to guide ampicillin-sulbactam dosing in patients undergoing OLT.