A Pilot Study of Pembrolizumab in Combination With Y90 Radioembolization in Subjects With Poor Prognosis Hepatocellular Carcinoma.

BACKGROUND: Combination checkpoint inhibition therapy with yttrium-90 (Y90) radioembolization represents an emerging area of interest in the treatment of advanced hepatocellular carcinoma (HCC). HCRN GI15-225 is an open-label, single-arm multicenter, pilot study (NCT03099564). METHODS: Eligible patients had poor prognosis, localized HCC defined as having portal vein thrombus, multifocal disease, and/or diffuse disease that were not eligible for liver transplant or surgical resection. Patients received pembrolizumab 200 mg intravenously every 3 weeks in conjunction with glass yttrium-90 (Y90) radioembolization TheraSphere. Primary endpoint was 6-month progression-free survival (PFS6) per RECIST 1.1. Secondary endpoints included time to progression (TTP), objective response rate (ORR), overall survival (OS), and safety/tolerability. RESULTS: Between October 23, 2017 and November 24, 2020, 29 patients were enrolled: 2 were excluded per protocol. Fifteen of the remaining 27 patients were free of progression at 6 months (55.6%; 95% CI, 35.3-74.5) with median PFS 9.95 months (95% CI, 4.14-15.24) and OS 27.30 months (95% CI, 10.15-39.52). One patient was not evaluable for response due to death; among the remaining 26 patients, ORR was 30.8% (95% CI, 14.3-51.8) and DCR was 84.6% (95% CI, 65.1-95.6). CONCLUSION: In patients with localized, poor prognosis HCC, pembrolizumab in addition to glass Y90 radioembolization demonstrated promising efficacy and safety consistent with prior observations (ClinicalTrials.gov Identifier: NCT03099564; IRB Approved: 16-3255 approved July 12, 2016).

Short-Term Out-of-Pocket and Total Costs of Care After Ablation, Resection, or Transplant for Early-Stage Hepatocellular Carcinoma: A National SEER-Medicare Cost Comparison.

Background: Differences in survival and morbidity among treatment options (ablation, surgical resection, and transplant) for early-stage hepatocellular carcinoma (HCC) have been well-studied. Additional understanding of the costs of such care would help to identify drivers of high costs and potential barriers to care delivery. Objective: To quantify total and patient out-of-pocket costs for ablation, surgical resection, and transplant in the management of early-stage HCC and to identify factors predictive of these costs. Methods: This retrospective U.S. population-based study used the SEER-Medicare linked dataset to identify a sample of 1067 Medicare beneficiaries (mean age, 73 years; 674 men, 393 women) diagnosed with early-stage HCC (size ≤5 cm) treated with ablation (N=623), resection (N=201), or transplant (N=243) between January 2009 and December 2016. Total costs and patient out-of-pocket costs for the index procedure as well as for any care within 30 days and 90 days post-procedure were identified and stratified by treatment modality. Additional comparisons were performed among propensity-score matched subgroups of patients treated by ablation or resection (each N=172). Multivariable linear regression models were used to identify factors predictive of total costs and out-of-pocket costs for index procedures as well as for 30-day and 90-day post-procedure periods. Results: For ablation, resection, and transplant, median index-procedure total cost was $6689, $25,614, and $66,034; index-procedure out-of-pocket cost was $1235, $1650, and $1317; 30-day total cost was $9456, $29,754, and $69,856; 30-day out-of-pocket cost was $1646, $2208, and $3198; 90-day total cost was $14,572, $34,984, and $88,103; and 90-day out-of-pocket cost was $2138, $2462, and $3876, respectively (all p<.001). In propensity-matched subgroups, ablation and resection had median index-procedure, 30-day, and 90-day total costs of $6690 and $25,716, $9995 and $30,365, and $15,851 and $34,455, respectively. In multivariable analysis adjusting for socioeconomic factors, comorbidities, and liver-disease prognostic indicators, surgical treatment (resection or transplant) was predictive of significantly greater costs compared with ablation at all time points. Conclusion: Total and out-of-pocket costs for index procedures as well as for 30-day and 90-day post-procedure periods were lowest for ablation, followed by resection and then transplant. Clinical Impact: This comprehensive cost analysis could help inform future cost-effectiveness analyses.

Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation.

Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

Diffusion-Limited Kinetics in Reactive Systems.

A proper representation of chemical kinetics is vital to understanding, modeling, and optimizing many important chemical processes. In liquid and surface phases, where diffusion is slow, the rate at which the reactants diffuse together limits the overall rate of many elementary reactions. Commonly, the textbook Smoluchowski theory is utilized to estimate effective rate coefficients in the liquid phase. On surfaces, modelers commonly resort to much more complex and expensive Kinetic Monte Carlo (KMC) simulations. Here, we extend the Smoluchowski model to allow the diffusing species to undergo chemical reactions and derive analytical formulas for the diffusion-limited rate coefficients for 3D, 2D, and 2D/3D interface cases. With these equations, we are able to demonstrate that when species react faster than they diffuse they can react orders of magnitude faster than predicted by Smoluchowski theory, through what we term "the reactive transport effect". We validate the derived steady-state equations against particle Monte Carlo (PMC) simulations, KMC simulations, and non-steady-state solutions. Furthermore, using PMC and KMC simulations, we propose corrections that agree with all limits and the computed data for the 2D and 2D/3D interface steady-state equations, accounting for unique limitations in the associated derived equations. Additionally, we derive equations to handle couplings between diffusion-limited rate coefficients in reaction networks. We believe these equations should make it possible to run much more accurate mean-field simulations of liquids, surfaces, and liquid-surface interfaces accounting for diffusion limitations and the reactive transport effect.

Are Large-Scale Test Scores Comparable for At-Home Versus Test Center Testing?

The COVID-19 pandemic led to a proliferation of remote-proctored (or "at-home") assessments. The lack of standardized setting, device, or in-person proctor during at-home testing makes it markedly distinct from testing at a test center. Comparability studies of at-home and test center scores are important in understanding whether these distinctions impact test scores. This study found no significant differences in at-home versus test center test scores on a large-scale admissions test using either a randomized controlled trial or an observational study after adjusting for differences in sample composition along baseline characteristics.

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London.

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33µg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Airborne environmental DNA captures terrestrial vertebrate diversity in nature.

The current biodiversity and climate crises highlight the need for efficient tools to monitor terrestrial ecosystems. Here, we provide evidence for the use of airborne eDNA analyses as a novel method for detecting terrestrial vertebrate communities in nature. Metabarcoding of 143 airborne eDNA samples collected during 3 days in a mixed forest in Denmark yielded 64 bird, mammal, fish and amphibian taxa, of which the detected 57 'wild' taxa represent over a quarter of the around 210 terrestrial vertebrates that occur in the overall area. We provide evidence for the spatial movement and temporal patterns of airborne eDNA and for the influence of weather conditions on vertebrate detections. This study demonstrates airborne eDNA for high-resolution biomonitoring of vertebrates in terrestrial systems and elucidates its potential to guide global nature management and conservation efforts in the ongoing biodiversity crisis.

The reduced net carbon uptake over Northern Hemisphere land causes the close-to-normal CO2 growth rate in 2021 La Niña.

La Niña climate anomalies have historically been associated with substantial reductions in the atmospheric CO2 growth rate. However, the 2021 La Niña exhibited a unique near-neutral impact on the CO2 growth rate. In this study, we investigate the underlying mechanisms by using an ensemble of net CO2 fluxes constrained by CO2 observations from the Orbiting Carbon Observatory-2 in conjunction with estimates of gross primary production and fire carbon emissions. Our analysis reveals that the close-to-normal atmospheric CO2 growth rate in 2021 was the result of the compensation between increased net carbon uptake over the tropics and reduced net carbon uptake over the Northern Hemisphere mid-latitudes. Specifically, we identify that the extreme drought and warm anomalies in Europe and Asia reduced the net carbon uptake and offset 72% of the increased net carbon uptake over the tropics in 2021. This study contributes to our broader understanding of how regional processes can shape the trajectory of atmospheric CO2 concentration under climate change.