Matching Patients to Clinical Trials with Large Language Models

Clinical trials are vital in advancing drug development and evidence-based medicine, but their success is often hindered by challenges in patient recruitment. In this work, we investigate the potential of large language models (LLMs) to assist individual patients and referral physicians in identifying suitable clinical trials from an extensive selection. Specifically, we introduce TrialGPT, a novel architecture employing LLMs to predict criterion-level eligibility with detailed explanations, which are then aggregated for ranking and excluding candidate clinical trials based on free-text patient notes. We evaluate TrialGPT on three publicly available cohorts of 184 patients and 18,238 annotated clinical trials. The experimental results demonstrate several key findings: First, TrialGPT achieves high criterion-level prediction accuracy with faithful explanations. Second, the aggregated trial-level TrialGPT scores are highly correlated with expert eligibility annotations. Third, these scores prove effective in ranking clinical trials and exclude ineligible candidates. Our error analysis suggests that current LLMs still make some mistakes due to limited medical knowledge and domain-specific context understanding. Nonetheless, we believe the explanatory capabilities of LLMs are highly valuable. Future research is warranted on how such AI assistants can be integrated into the routine trial matching workflow in real-world settings to improve its efficiency

Radiative Forcing Due to Major Aerosol Emitting Sectors in China and India

Understanding the radiative forcing caused by anthropogenic aerosol sources is essential for making effective emission control decisions to mitigate climate change. We examined the net direct plus indirect radiative forcing caused by carbonaceous aerosol and sulfur emissions in key sectors of China and India using the GISS-E2 chemistry-climate model. Diesel trucks and buses (67 mW/ sq. m) and residential biofuel combustion (52 mW/ sq. m) in India have the largest global mean, annual average forcings due mainly to the direct and indirect effects of BC. Emissions from these two sectors in China have near-zero net global forcings. Coal-fired power plants in both countries exert a negative forcing of about -30 mW/ sq. m from production of sulfate. Aerosol forcings are largest locally, with direct forcings due to residential biofuel combustion of 580 mW/ sq. m over India and 416 mW/ sq. m over China, but they extend as far as North America, Europe, and the Arcti

Ozone Monitoring Instrument Observations of Interannual Increases in SO2 Emissions from Indian Coal-fired Power Plants During 2005-2012

Due to the rapid growth of electricity demand and the absence of regulations, sulfur dioxide (SO2) emissions from coal-fired power plants in India have increased notably in the past decade. In this study, we present the first interannual comparison of SO2 emissions and the satellite SO2 observations from the Ozone Monitoring Instrument (OMI) for Indian coal-fired power plants during the OMI era of 2005-2012. A detailed unit-based inventory is developed for the Indian coal-fired power sector, and results show that its SO2 emissions increased dramatically by 71 percent during 2005-2012. Using the oversampling technique, yearly high-resolution OMI maps for the whole domain of India are created, and they reveal a continuous increase in SO2 columns over India. Power plant regions with annual SO2 emissions greater than 50 Gg year-1 produce statistically significant OMI signals, and a high correlation (R equals 0.93) is found between SO2 emissions and OMI-observed SO2 burdens. Contrary to the decreasing trend of national mean SO2 concentrations reported by the Indian Government, both the total OMI-observed SO2 and average SO2 concentrations in coal-fired power plant regions increased by greater than 60 percent during 2005-2012, implying the air quality monitoring network needs to be optimized to reflect the true SO2 situation in India

Estimates of Power Plant NOx Emissions and Lifetimes from OMI NO2 Satellite Retrievals

Isolated power plants with well characterized emissions serve as an ideal test case of methods to estimate emissions using satellite data. In this study we evaluate the Exponentially-Modified Gaussian (EMG) method and the box model method based on mass balance for estimating known NOx emissions from satellite retrievals made by the Ozone Monitoring Instrument (OMI). We consider 29 power plants in the USA which have large NOx plumes that do not overlap with other sources and which have emissions data from the Continuous Emission Monitoring System (CEMS). This enables us to identify constraints required by the methods, such as which wind data to use and how to calculate background values. We found that the lifetimes estimated by the methods are too short to be representative of the chemical lifetime. Instead, we introduce a separate lifetime parameter to account for the discrepancy between estimates using real data and those that theory would predict. In terms of emissions, the EMG method required averages from multiple years to give accurate results, whereas the box model method gave accurate results for individual ozone seasons

Cold Atmospheric-Pressure Plasma Caused Protein Damage in Methicillin-Resistant \u3ci\u3eStaphylococcus aureus\u3c/i\u3e Cells in Biofilms

Biofilms formed by multidrug-resistant bacteria are a major cause of hospital-acquired infections. Cold atmospheric-pressure plasma (CAP) is attractive for sterilization, especially to disrupt biofilms formed by multidrug-resistant bacteria. However, the underlying molecular mechanism is not clear. In this study, CAP effectively reduced the living cells in the biofilms formed by methicillin-resistant Staphylococcus aureus, and 6 min treatment with CAP reduced the S. aureus cells in biofilms by 3.5 log10. The treatment with CAP caused the polymerization of SaFtsZ and SaClpP proteins in the S. aureus cells of the biofilms. In vitro analysis demonstrated that recombinant SaFtsZ lost its self-assembly capability, and recombinant SaClpP lost its peptidase activity after 2 min of treatment with CAP. Mass spectrometry showed oxidative modifications of a cluster of peaks differing by 16 Da, 31 Da, 32 Da, 47 Da, 48 Da, 62 Da, and 78 Da, induced by reactive species of CAP. It is speculated that the oxidative damage to proteins in S. aureus cells was induced by CAP, which contributed to the reduction of biofilms. This study elucidates the biological effect of CAP on the proteins in bacterial cells of biofilms and provides a basis for the application of CAP in the disinfection of biofilms

The Observed Response of Ozone Monitoring Instrument (OMI) NO2 Columns to NOx Emission Controls on Power Plants in the United States: 2005-2011

We show that Aura Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO2) tropospheric column data may be used to assess changes of the emissions of nitrogen oxides (NOx) from power plants in the United States, though careful interpretation of the data is necessary. There is a clear response for OMI NO2 data to NOx emission reductions from power plants associated with the implementation of mandated emission control devices (ECDs) over the OMI record (2005e2011). This response is scalar for all intents and purposes, whether the reduction is rapid or incremental over several years. However, it is variable among the power plants, even for those with the greatest absolute decrease in emissions. We document the primary causes of this variability, presenting case examples for specific power plants

Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study

IntroductionPostoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients.MethodsPatients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort.ResultsA total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application.ConclusionWe developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients

Sources, distribution, and acidity of sulfate–ammonium aerosol in the Arctic in winter–spring

We use GEOS-Chem chemical transport model simulations of sulfate–ammonium aerosol data from the NASA ARCTAS and NOAA ARCPAC aircraft campaigns in the North American Arctic in April 2008, together with longer-term data from surface sites, to better understand aerosol sources in the Arctic in winter–spring and the implications for aerosol acidity. Arctic pollution is dominated by transport from mid-latitudes, and we test the relevant ammonia and sulfur dioxide emission inventories in the model by comparison with wet deposition flux data over the source continents. We find that a complicated mix of natural and anthropogenic sources with different vertical signatures is responsible for sulfate concentrations in the Arctic. East Asian pollution influence is weak in winter but becomes important in spring through transport in the free troposphere. European influence is important at all altitudes but never dominant. West Asia (non-Arctic Russia and Kazakhstan) is the largest contributor to Arctic sulfate in surface air in winter, reflecting a southward extension of the Arctic front over that region. Ammonium in Arctic spring mostly originates from anthropogenic sources in East Asia and Europe, with added contribution from boreal fires, resulting in a more neutralized aerosol in the free troposphere than at the surface. The ARCTAS and ARCPAC data indicate a median aerosol neutralization fraction [NH4+]/(2[SO42−] + [NO3−]) of 0.5 mol mol−1 below 2 km and 0.7 mol mol−1 above. We find that East Asian and European aerosol transported to the Arctic is mostly neutralized, whereas West Asian and North American aerosol is highly acidic. Growth of sulfur emissions in West Asia may be responsible for the observed increase in aerosol acidity at Barrow over the past decade. As global sulfur emissions decline over the next decades, increasing aerosol neutralization in the Arctic is expected, potentially accelerating Arctic warming through indirect radiative forcing and feedbacks.Chemistry and Chemical Biolog