Time-dependent Nature Research Articles

P-1594. Predictive models to improve antibiotic decision-making in the Emergency Department for Extended-Spectrum β-Lactamase-producing Enterobacterales infections

Abstract Background Extended-Spectrum β-Lactamase-producing Enterobacterales (ESBL-E) infections are associated with high morbidity and mortality, often due to delayed initiation of carbapenem therapy. Carbapenems must be used judiciously to prevent emergence of resistant organisms. We developed a machine learning algorithm (MLA) to identify patients at increased risk for ESBL-E infections in the emergency department (ED).Figure 1:Receiver Operating Characteristic (ROC) curve for predicting Extended-Spectrum β-Lactamase-producing Enterobacterales (ESBL-E) Receiver Operating Characteristic (ROC) curve for predicting Extended-Spectrum β-Lactamase-producing Enterobacterales (ESBL-E) infections in Emergency Department Patients using a gradient boosting machine learning model. The area under the curve (AUC) is 0.8, indicating good discrimination between patients with and without ESBL-E. Higher True Positive Rate (TPR) at a low False Positive Rate (FPR) signifies better model performance. Methods Adults (≥18) who visited an ED in the Johns Hopkins Health System between 1/2019 and 4/2024 and received Gram-negative therapies appropriate for sepsis or had documented ESBL-E infections were included. Timestamped ED vital signs, healthcare utilization, healthcare worker (HCW)-mediated patient connections, medication history, comorbidities and laboratory results populated prior to first antibiotic order were used as predictors; our primary outcome was ESBL-E infection defined by positive culture. The probability of ESBL-E infection was estimated using a gradient boosting machine learning algorithm (MLA). Because of the time-dependent nature of increasing ESBL-E infections, the MLA was derived using data from encounters prior to 7/1/2023 and evaluated in the remaining encounters using area under the operating characteristics curve (AUC) analysis.Table 1:Patient Cohort Demographics Results In total there were 129,773 patient encounters included, of which 4289 (3.3%) had a positive ESBL-E sample collected during their ED encounter. Out-of-sample predictive accuracy was high (AUC 0.80; Figure). Prior HCW-mediated connections to ESBL-positive patients and prior exposure (≤6 months) to carbapenems, cephalosporins and proton pump inhibitors were the strongest predictors of ESBL-E infection. Abnormal vital signs, increased age, and prior exposure to any antibiotic (prior 6 months) were also important factors in prediction accuracy. Conclusion An MLA using data readily available from the EHR at the time of antibiotic ordering provided good discrimination between ED patients with and without ESBL-E infection. Further refinement and implementation of MLA-driven decision-support in the EDs could drive more targeted use of broad-spectrum antibiotics and improved patient outcomes. Disclosures Jeremiah S. Hinson, MD, PhD, Beckman Coulter: Advisor/Consultant|Beckman Coulter: Grant/Research Support|Beckman Coulter: Stocks/Bonds (Private Company)

Time Dependent Effects of Cerebellar tDCS on Cerebello-cortical Connectivity Networks in Young Adults.

The cerebellum is involved in non-motor processing, supported by topographically distinct cerebellar activations and closed-loop circuits between the cerebellum and the cortex. Disruptions to cerebellar function may negatively impact prefrontal function and processing. Cerebellar resources may be important for offloading cortical processing, providing crucial scaffolding for normative performance and function. Here, we used transcranial direct current stimulation (tDCS) to temporarily alter cerebellar function and subsequently investigated resting state network connectivity. Critically, what happens to these circuits if the cerebellum is not functioning optimally, or after stimulation, remains relatively unknown. We employed a between-subjects design with 74 participants total (38 female; M = 22.0years, SD = 3.45), applying anodal (n = 25), cathodal (n = 25), or sham (n = 24) stimulation to the cerebellum to examine the effect of stimulation on cerebello-cortical resting state connectivity in young adults. We predicted increased functional connectivity following cathodal stimulation and decreased functional connectivity following anodal stimulation. We found, anodal stimulation resulted in increased connectivity in both ipsilateral and contralateral regions of the cortex, perhaps indicative of a compensatory response to degraded cerebellar output. Additionally, a window analysis also demonstrated a time dependent nature to the impacts of cerebellar tDCS on connectivity, particularly with cognitive regions of the cerebral cortex. This work suggests that when cerebellar outputs are degraded, in this case by tDCS, the cerebellum offloads its processing responsibility which encourages more cortical regions to engage to compensate for the degraded cerebellar output. This results in in differences in cortical activation patterns and performance deficits. These results might inform and update existing compensatory models, which focus primarily on the cortex, to include the cerebellum as a vital structure involved in the scaffolding of cortical processing.

A practical guide for the assay-dependent characterisation of irreversible inhibitors.

Irreversible targeted covalent inhibitors, in the past regarded as inappropriately reactive and toxic, have seen a recent resurgence in clinical interest. This paradigm shift is attributed to the exploitation of the two-step mechanism, in which a high affinity and selectivity (i.e., low K I) scaffold binds the target and only then does a pendant low intrinsic reactivity warhead react with the target (moderate k inact). This highlights the importance of evaluating inhibitors by deriving both their K I and k inact values. The development of methods to evaluate these inhibitors by accounting for their time-dependent nature has been crucial to the discovery of promising clinical candidates. Herein, we report all the practical kinetic methods available to date to derive k inact and K I values. These methods include direct observation of covalent modification, continuous assay (Kitz & Wilson) evaluation, and discontinuous incubation and pre-incubation time-dependent IC50 assays. We also provide practical guidelines and examples for performing these assays, comparison of their utility, and perspectives for their extended applications. This review aims to provide clarity about the use of these methods for reporting complete inhibitor kinetic profiles, guiding irreversible drug development towards increased target affinity and selectivity, while modulating in vivo stability and on-target reactivity.

Green pickup and delivery problem with private drivers for crowd-shipping distribution considering traffic congestion

Crowd-shipping, employing private drivers to partially replace company-owned trucks in distribution, has emerged as a prominent trend for its cost-effectiveness and sustainability. While crowd-shipping is known as a distribution pattern that combines economic efficiency and environmental benefits, however, the frequent occurrence of traffic congestion has made this pattern less effective than it should be. In this research, the problem of vehicle routing optimization under traffic congestion is investigated from the perspective of simultaneously reducing environmental pollution and costs. Considering private drivers picking up and delivering parcels on the way, this study incorporates the objective of minimizing transport as well as particulate matter (PM) and nitrogen oxides (NOx) emission costs into route optimization for crowd-shipping and proposes a Green Pickup and Delivery Problem with Private Drivers (GPDP-PD). To be more realistic, vehicle speeds depend on the level of traffic congestion, reflecting the time-dependent nature of the proposed model. An improved adaptive large neighborhood search (ALNS) algorithm is developed, and computational experiments are conducted to demonstrate the efficiency of the improved ALNS. Case studies show that there is uncertainty about the environmental benefits of crowd-shipping under traffic congestion. Our proposed model is capable of efficiently allocating private drivers and optimizing vehicle routes according to road conditions, thus identifying the crowd-shipping operational scheme with the lowest cost and emissions. Moreover, a time limit of 0.7-0.8 h and the low cost of private drivers can achieve environmental and economic benefits simultaneously. It provides useful insights into the sustainability of logistics and distribution.

Preserving Fairness in AI under Domain Shift

Existing algorithms for ensuring fairness in AI use a single-shot training strategy, where an AI model is trained on an annotated training dataset with sensitive attributes and then fielded for utilization. This training strategy is effective in problems with stationary distributions, where both the training and testing data are drawn from the same distribution. However, it is vulnerable with respect to distributional shifts in the input space that may occur after the initial training phase. As a result, the time-dependent nature of data can introduce biases and performance degradation into the model predictions, even if the model is initially fair. Model retraining from scratch using a new annotated dataset is a naive solution that is expensive and time-consuming. We develop an algorithm to adapt a fair model to remain fair and generalizable under domain shift using solely new unannotated data points. We recast this learning setting as an unsupervised domain adaptation (UDA) problem. Our algorithm is based on updating the model such that the internal representation of data remains unbiased despite distributional shifts in the input space. We provide empirical validation on three common fairness datasets to show that the challenge exists in practical setting and to demonstrate the effectiveness of our algorithm.

Treatment of VTE in the thrombocytopenic cancer patient.

Thrombocytopenia is a frequent complication in patients with cancer, mostly due to the myelosuppressive effects of antineoplastic therapies. The risk of venous thromboembolism (VTE) in patients with cancer is increased despite low platelet counts. The management of cancer-associated VTE in patients with thrombocytopenia is challenging, as the risk of both recurrent VTE and bleeding complications is high. Moreover, the time-dependent nature of thrombocytopenia over the course of antineoplastic therapies further complicates the management of patients in clinical practice. In the absence of evidence from high-quality studies, the management of anticoagulation therapy for VTE must be personalized, balancing the individual risk of VTE progression and recurrence against the risk of hemorrhage. In the present case-based review, we highlight the clinical challenges that arise upon managing cancer-associated VTE in the setting of present or anticipated thrombocytopenia, summarize the available evidence, and provide a comparative overview of available guidelines.

Deep Learning-Based Network Traffic Anomaly Detection: A Study in IoT Environments

This study presents a deep learning technique to detect vulnerabilities in the Internet of Things (IoT) environment. The proposed method combines the manual design with the learning function of autoencoders, together with the deep neural network architecture associated with the Long Short-Term Memory (LSTM) layer. Experiments performed on the IoT-23 dataset show that our method outperforms traditional machine learning and state-of-the-art deep learning methods, achieving an accuracy of 99.2%, an F1-score of 0.987, and an AUC-ROC of 0.998. The framework addresses critical issues in IoT security, including device diversity, vehicle model diversity, and real-time research needs. The ablation studies show the importance of combining manual and autoencoder-based feature extraction. Grad-CAM visualisations improve the model definition by identifying the essential features for classifying bad vehicles and not good. The model's ability to capture the time-dependent nature of network flows makes it helpful in investigating complex, time-dependent variables. Although there are limitations in computing needs and general capabilities across IoT ecosystems, the proposed system shows significant potential for practical use in IoT security systems. This research contributes to advancing IoT security by providing a powerful, efficient, and easily interpretable system to discover the system's capabilities in terms of strengths and weaknesses in the IoT network environment.

A time-course transcriptome reveals the response of watermelon to low-temperature stress

Watermelon (Citrullus lanatus) is an economically important horticultural crop. However, it is susceptible to low-temperature stress, which poses a significant challenge to its production and supply. Despite the great economic importance of watermelon, little is known about its response to low-temperature stress at the transcriptional level. In this study, we performed a time-course transcriptome analysis to systematically investigate the regulatory network of watermelon under low-temperature stress. Six low-temperature-responsive gene clusters representing six expression patterns were identified, revealing diverse regulation of metabolic pathways in watermelon under low-temperature stress. Analysis of temporally specific differentially expressed genes revealed the time-dependent nature of the watermelon response to low temperature. Moreover, ClMYB14 was found to be a negative regulator of low-temperature tolerance as ClMYB14-OE lines were more susceptible to low-temperature stress. Co-expression network analysis demonstrated that ClMYB14 participates in the low-temperature response by regulating the unsaturated fatty acid pathway and heat shock transcription factor. This study provides substantial information for understanding the regulatory network of watermelon in response to low-temperature stress, as well as identifies candidate genes for the genetic improvement of watermelon with higher low-temperature tolerance.

Assessment of healthy worker survivor bias among middle-aged manufacturing employees in Korea

Abstract Background Healthy Worker Survivor Bias (HWSB) describes a selection bias where those continuously employed tend to exhibit superior health outcomes compared to their unemployed counterparts. This bias challenges for adjustments due to its time-dependent nature. This study aims to evaluate the extent of HWSB related to changes in employment status among middle-aged manufacturing workers using comprehensive national data from Korea. Methods The study analyzed data from the National Health Insurance Service, targeting individuals aged 40-49 who maintained consistent insurance coverage-either as manufacturing employees or non-employees (self-employed or unemployed)-from 2008 to 2010. The observation period spanned from January 2011 to December 2022, with all-cause mortality as the primary outcome. We employed landmark analysis to quantify HWSB related to employment status by comparing age-standardized mortality ratios between employees and the general population from the initial assessment to the last landmark period (up to 7 years), calculating 95% confidence intervals (CI) using Poisson distribution. Results The cohort included 4,621,983 participants (726,616 manufacturing employees and 3,895,367 (84.3%) non-employees). Throughout an average follow-up of 10.9 years (43.1% male; median age of 44), there were 116,418 deaths (11,530 (1.6%) among manufacturing employees and 104,888 (2.7%) among non-employees). Analysis revealed a consistent linear increase in HWSB over time. Notably, the bias was more pronounced among females (7-year HWSB-ES [95% CI]: 0.27 [0.17-0.37]) compared to males (7-year HWSB-ES [95% CI]: 0.18 [0.14-0.22]). Conclusions This study quantifies the extent of HWSB among middle-aged manufacturing workers in Korea, highlighting its significant impact, notably in females. These findings are vital for occupational health research, emphasizing the necessity to consider HWSB to prevent the underestimation of health risks associated with hazardous exposures. Key messages • Findings suggest HWSB is more pronounced in female manufacturing workers, indicating the need for targeted occupational health interventions. • The study underscores the importance of accounting for HWSB in occupational health studies to avoid underestimating health risks associated with workplace exposures.

A general modeling framework for FO spiral-wound membrane and its fouling impact on FO-RO desalination system

Modeling fouling in forward osmosis (FO) spiral-wound membrane (SWM) is challenging due to the time-dependent nature of fouling and the complex flow patterns induced by baffle. This necessitates the development of a general modeling framework for FO SWM module that prioritizes both accuracy and ease of implementation. This framework was validated against FO SWM experiment data from previous work, demonstrating a reasonable agreement with a maximum error of 13.1 % in FO permeate flux. This validated model was used to study the impact of fouling on feed recovery, a critical factor influencing specific energy consumption (SEC) in FO-RO desalination systems. While improved operating conditions and membrane parameters (A, Ss and Cf) initially lead to increased water flux, this effect was significantly counteracted by accelerated fouling. Consequently, performance improvements in terms of flux and SEC remained minimal (<1 %) under severe fouling conditions. The results show that for foulant cake with larger pore diameter (>10 nm), the contribution of hydraulic resistance is insignificant compared to osmotic resistance. However, the contribution of hydraulic resistance becomes important for foulant cakes with pore diameter smaller than 10 nm. This paper shows that modeling have evolved to a stage that they can be used to understand membrane fouling phenomena at the SWM module scale.

A Hybrid CNN-Transformer Model for Predicting N Staging and Survival in Non-Small Cell Lung Cancer Patients Based on CT-Scan.

Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study proposes a hybrid model based on 3D convolutional neural networks (CNNs) and transformers for predicting the N-staging and survival rates of NSCLC patients within the NSCLC radiogenomics and Nsclc-radiomics datasets. The model achieved accuracies of 0.805, 0.828, and 0.819 for the training, validation, and testing sets, respectively. By leveraging the strengths of CNNs in local feature extraction and the superior performance of transformers in global information modeling, the model significantly enhances predictive accuracy and efficacy. A comparative analysis with traditional CNN and transformer architectures demonstrates that the CNN-transformer hybrid model outperforms N-staging predictions. Furthermore, this study extracts the one-year survival rate as a feature and employs the Lasso-Cox model for survival predictions at various time intervals (1, 3, 5, and 7 years), with all survival prediction p-values being less than 0.05, illustrating the time-dependent nature of survival analysis. The application of time-dependent ROC curves further validates the model's accuracy and reliability for survival predictions. Overall, this research provides innovative methodologies and new insights for the early diagnosis and prognostic evaluation of NSCLC.

Dynamic environmental payback of concrete due to carbonation over centuries

This research introduces a dynamic life cycle assessment (LCA) based carbonation impact calculator designed to enhance the environmental evaluation of cement-based construction products. The research emphasizes the limitations of static LCAs which fail to capture the time-dependent nature of carbon sequestration by carbonation.We provide an easy-to-use spreadsheet-based LCA carbonation model. The model is available in the supplementary information, and includes a suite of changeable parameters for exploring the effect of alternative environmental conditions and concrete block composition on carbonation. The tool enables use of both a static and dynamic LCA method to calculate the production emissions and carbonation sequestration of a concrete block over a 1000-year time horizon.Carbonation can partially mitigate initial production emissions and adjust radiative forcing over long periods. Using a static attributional LCA approach, carbonation sequesters 6 % of the CO2 generated from its production emissions. We describe the ratio of carbonation to production emissions as the partial “carbonation payback”, and with dynamic LCA show the variation of this ratio over time. Considering time by applying the dynamic LCA approach, we find this partial “carbonation payback” is split between uptake during the 60-year service life (0.13 kg CO2) and the 940-year end of life period (0.12 kg CO2) in our baseline case. Further scenario analyses illustrate the significant variability in carbonation payback, driven by environmental factors, cement composition, and the use of supplementary cementitious materials.The results highlight the critical role of modelling choices in estimating the carbonation payback. The carbonation calculator developed in this study offers a sophisticated yet user-friendly tool, providing both researchers and practitioners with the ability to dynamically model the sequestration potential of concrete, thereby promoting more sustainable construction practices.

Using attentive temporal GNN for dynamic trust assessment in the presence of malicious entities

Trust networks are built on the premise that there is a network of trust among entities. Precisely evaluating pairwise trust in such networks is essential for enhancing the quality of service in various applications, including online marketing, recommender systems, and IoT-based applications. However, it remains challenging to consider dynamic trust and account for malicious behavior in a scalable and effective manner.Recently, Graph Neural Networks (GNN)-based methods have emerged as powerful and scalable tools for trust estimation. Nevertheless, they lack thorough consideration for the time-dependent nature of trust, especially in the presence of malicious behaviors, leading to inaccurate trust predictions. To address this gap, we propose a novel trust assessment model called MATA, which handles the time dependency of trust and exhibits robustness against malicious behaviors. MATA leverages an extended Graph Convolutional Network (GCN) integrated with Recurrent Neural Networks (RNNs) and an attention mechanism to jointly capture time dependencies and trace entities’ behavior over time in complex networks. Additionally, by clustering the learned trust representations over time, we assign reputation values to entities. This allows us to monitor their dynamic behavior and mitigate potential adverse effects on trust assessment.In our experiments on real-world datasets, MATA outperforms the state-of-the-art methods by up to 8.4 % in accuracy. This improvement is a testament to the effectiveness of the proposed approach. Furthermore, our work enhances the robustness of trust assessment models, as evidenced by consistently higher performance metrics in the face of various trust-related attacks.

Epidemiological modeling of COVID-19 data with Advanced statistical inference based on Type-II progressive censoring

This research proposes the Kavya-Manoharan Unit Exponentiated Half Logistic (KM-UEHL) distribution as a novel tool for epidemiological modeling of COVID-19 data. Specifically designed to analyze data constrained to the unit interval, the KM-UEHL distribution builds upon the unit exponentiated half logistic model, making it suitable for various data from COVID-19. The paper emphasizes the KM-UEHL distribution's adaptability by examining its density and hazard rate functions. Its effectiveness is demonstrated in handling the diverse nature of COVID-19 data through these functions. Key characteristics like moments, quantile functions, stress-strength reliability, and entropy measures are also comprehensively investigated. Furthermore, the KM-UEHL distribution is employed for forecasting future COVID-19 data under a progressive Type-II censoring scheme, which acknowledges the time-dependent nature of data collection during outbreaks. The paper presents various methods for constructing prediction intervals for future-order statistics, including maximum likelihood estimation, Bayesian inference (both point and interval estimates), and upper-order statistics approaches. The Metropolis-Hastings and Gibbs sampling procedures are combined to create the Markov chain Monte Carlo simulations because it is mathematically difficult to acquire closed-form solutions for the posterior density function in the Bayesian framework. The theoretical developments are validated with numerical simulations, and the practical applicability of the KM-UEHL distribution is showcased using real-world COVID-19 datasets.

Self-Reported PrEP Use and Risk of Bacterial STIs Among Ontarian Men Who Are Gay or Bisexual or Have Sex With Men.

HIV pre-exposure prophylaxis (PrEP) may increase rates of bacterial sexually transmitted infections (STIs) among gay, bisexual, and other men who have sex with men (GBM) through risk compensation (eg, an increase in condomless sex or number of partners); however, longitudinal studies exploring the time-dependent nature of PrEP uptake and bacterial STIs are limited. We used marginal structural models to estimate the effect of PrEP uptake on STI incidence. We analyzed data from the iCruise study, an online longitudinal study of 535 Ontarian GBM from July 2017 to April 2018, to estimate the effects of PrEP uptake on incidence of self-reported bacterial STIs (chlamydia, gonorrhea, and syphilis) collected with 12 weekly diaries. The incidence rate was calculated as the number of infections per 100 person-months, with evaluation of the STIs overall and individually. We used marginal structural models to account for time-varying confounding and quantitative bias analysis to evaluate the sensitivity of estimates to nondifferential outcome misclassification. Participating GBM were followed up for a total of 1,623.5 person-months. Overall, 70 participants (13.1%) took PrEP during the study period. Relative to no uptake, PrEP uptake was associated with an increased incidence rate of gonorrhea (incidence rate ratio = 4.00; 95% CI, 1.67-9.58), but not of chlamydia or syphilis, and not of any bacterial STI overall. Accounting for misclassification, the median incidence rate ratio for gonorrhea was 2.36 (95% simulation interval, 1.08-5.06). We observed an increased incidence rate of gonorrhea associated with PrEP uptake among Ontarian GBM that was robust to misclassification. Although our findings support current guidelines for integrating gonorrhea screening with PrEP services, additional research should consider the long-term impact of PrEP among this population.Annals Early Access article.

Purified granulocytes in extracorporeal cell therapy: A multifaceted approach to combat sepsis-induced immunoparalysis.

Immune cell dysfunction plays a central role in sepsis-induced immunoparalysis. Targeted treatment using healthy donor immune cell transfusions, particularly granulocyte concentrates (GC) potentially induces tissue damage. Initial trials using GC in an extracorporeal immune cell perfusion system provided evidence for beneficial effects with fewer side effects, by separating patient and donor immune cell compartments. A multicenter clinical trial is exploring feasibility and effects of a 6-h treatment (NCT06143137). This ex vivo study examines technical feasibility and cellular effects of an extended treatment interval up to 24 h. Standard GC were purified to increase the potential storage time and subsequently implemented in the extracorporeal immune cell perfusion system. Parameters assessed included cell viability, phagocytosis activity, oxidative burst, cytokine release, and metabolic parameters of purified. GC during an extended circulation time of up to 24 h. After storage of 72 h granulocytes were viable throughout the study period and exhibited preserved functionality and metabolic activity. The findings highlight a time-dependent nature of cytokine release by neutrophils in the extracorporeal circuit, as cytokine secretion patterns showed IL-8 peaking within 6 h, while MCP-1, IL-6, IL-1β, and TNF-α increased after 24 h of circulation. Purified GC remain functional after 72 h of storage and additional 24 h in the circulating treatment model. Cytokine secretion patterns revealed a significant increase, especially between 10 and 24 h of treatment. Extending treatment time holds promise for enhancing immune response against sepsis-induced immunoparalysis. These findings provide valuable insights for optimizing immune-targeted therapeutic interventions.

Versatile fitting approach for operando spectroscopic imaging ellipsometry of HfS2 oxidation

Facile mapping of 2D heterostructures and resolving anisotropic formation kinetics down to the monolayer level are critical to developing scalable interfacing solutions and unlocking their application potential in emerging nano-optoelectronics. We adapt a Kramers–Kronig constrained variational fitting algorithm for spectroscopic imaging ellipsometry (SIE) to facilitate multi-scale heterostructure analysis comprising films with unknown complex dielectric functions and demonstrate how this enables non-destructive, scalable mapping and operando capability for the model system of HfS2 oxidation. This methodology proves highly accurate for assessing the thickness of buried HfS2 layers, oxide quality, and lateral and vertical uniformity. We capture dynamic stack evolution during thermal oxidation up to 400 ∘C, providing insights into the temperature and time-dependent nature of self-limiting oxide growth and reaction kinetics that involve the localised trapping and release of sulphur reaction products. Our methodology is versatile in material and device horizons, and advantageously agnostic to the underlying substrate. Combined with the various modes of SIE operation, it unlocks fast, high-throughput, large-area capability to accelerate process development at the atomic scale.

Immune-Related Adverse Events Can Predict Progression-Free and Overall Survival In Patients With Metastatic Renal Cell Carcinoma Treated With Immune Checkpoint Inhibitors

BackgroundDifferent combination therapies using anti - PD-1 / PD-L1 or CTLA-4 immune checkpoint inhibition (ICI) are widely used in patients with metastatic renal cell carcinoma (mRCC). In the absents of established biomarkers, immune-related adverse events (irAEs) have been discussed as potential predictors of response. MethodsIn this retrospective cohort study, data of 134 patients with mRCC undergoing ICI treatment (Nivolumab, Ipilimumab and Nivolumab, Pembrolizumab and Axitinib or Avelumab and Axitinib) between 2015 and 2021 were analyzed. To examine the utility of irAEs as predictors of overall survival (OS) and progression-free survival (PFS), separate Kaplan–Meier analyses and Cox proportional regression analyses were applied. Landmark analysis was conducted after 12 weeks to reduce immortal time bias. ResultirAEs were observed in 85 patients (63.4%). Cutaneous (n = 52, 38.8%), endocrine (n = 33, 24.6%) and hepatic (n = 19, 14.2%) irAEs were most commonly observed. In Kaplan–Meier analysis, patients experiencing irAEs showed favorable median PFS (15 months, 95% CI, 9.91-20.09) compared to the non-irAE group (5 months, 95% CI, 3.56-6.44, P < .001). The median OS was 25 months (95% CI, 16.79-33.21) in the non-irAE group, while it was not reached in the irAE group (P = .002). In multivariable analysis, the presence of any irAE was associated with favorable PFS (HR 0.46 [95% CI, 0.26-0.82] P = .008) and OS (HR: 0.28 [95% CI, 0.12-0.63] P = .002), respectively. Landmark analysis after 12 weeks showed mixed results depending on the classification of the irAE group at the landmark time. ConclusionThe presence of irAEs under ICI therapy in patients with mRCC is associated with better PFS and OS. Thus, manageable irAEs should not be cause for premature discontinuation of ICI therapy, as they seem to indicate favorable outcomes.Considering the time-dependent nature of irAEs is crucial estimating their value as predictive markers.

Time-dependent gas dynamic diffusion process in shale matrix: model development and numerical analysis

Gas diffusion is a pivotal process during shale gas recovery, which is determined by diffusion coefficient to a large extent. In previous studies, the gas diffusion coefficient is generally assumed as a constant. However, increasing experiments prove that the diffusion coefficient of shale gas is strongly time-dependent. Therefore, to perfect the theory of shale gas diffusion, this paper proposes a time-dependent diffusion model for shale gas, which incorporates time-dependent gas diffusion coefficient, composing of the bulk diffusion coefficient for free gas in organic and inorganic pores, as well as the surface diffusion coefficient for adsorbed gas in organic pores. To validate the accuracy of the new theory, we calibrate the theoretical results against experimental data, and the results show that they have strong correlation, and the time-dependent diffusion model is superior to classical model. Finally, the numerical analysis of gas dynamic diffusion process in shale matrix is conducted. The results show that at the end of diffusion, a large amounts of shale gas remain trapped in the matrix core due to the attenuation of gas diffusion coefficient. In addition, neglecting the time-dependent nature of gas diffusion in shale matrix leads to a significant overestimation of gas production.

Molecular isotopologue measurements toward super star clusters and the relation to their ages in NGC 253 with ALCHEMI

Context. Determining the evolution of the CNO isotopes in the interstellar medium (ISM) of starburst galaxies can yield important constraints on the ages of super star clusters (SSCs), or on other aspects and factors contributing to their evolution, such as the initial mass function (IMF). Due to the time-dependent nature of the abundances of isotopes within the ISM – as they are supplied from processes such as nucleosynthesis or chemical fractionation –, this provides the opportunity to test whether or not isotope ratios trace the ages of highly star-forming regions, such as SSCs. Aims. The goal of this study is to investigate whether the isotopic variations in SSC regions within NGC 253 are correlated with their different ages as derived from stellar population modelling. Methods. We measured abundance ratios of CO, HCN, and HCO+ isotopologues in six regions containing SSCs within NGC 253 using high-spatial-resolution (1.6″, ∼28 pc) data from the ALCHEMI (ALma Comprehensive High-resolution Extragalactic Molecular Inventory) ALMA Large program. We then analysed these ratios using RADEX radiative transfer modelling, with the parameter space sampled using the nested sampling Monte Carlo algorithm MLFriends. These abundance ratios were then compared to ages predicted in each region via the fitting of observed star-formation tracers (such as Brγ) to Starburst99 starburst stellar population evolution models. Results. We determined the isotopic column density ratios across multiple regions of SSC activity in NGC 253 using non-LTE radiative transfer modelling. We do not find any significant trend with age for the CO and HCN isotopologue ratios on timescales of the ages of the SSC* regions observed. However, HCO+ may show a correlation with age over these timescales in 12C/13C. Conclusions. The driving factors of these ratios within SSCs could be the IMF or fractionation effects. To further probe these effects in SSCs over time, a larger sample of SSCs must be observed spanning a larger age range.