Small cell lung cancer (SCLC), characterized by high aggressiveness, early metastasis, and poor prognosis, has long been a major challenge in lung cancer treatment. In recent years, the advent of immune checkpoint inhibitors (ICIs) has significantly improved overall survival in patients with SCLC and has become a new standard of care; however, substantial room for further improvement in clinical outcomes remains. As an emerging T cell-targeted immunotherapy, delta-like ligand 3-T cell engager (DLL3-TCE) precisely directs T cells to attack tumor cells and have demonstrated breakthrough potential in the treatment of SCLC. Tarlatamab, a representative agent in this class, has been approved for clinical use, and multiple TCE agents are currently under clinical development. Although DLL3-TCE shows promising prospects in the clinical management of SCLC, as a novel therapeutic modality, many clinicians still lack an in-depth understanding of their mechanisms of action, appropriate patient populations, and clinical management. To standardize the clinical application of DLL3-TCE in China, the Small Cell Lung Cancer Expert Committee of the Chinese Society of Clinical Oncology convened a consensus panel to develop the Chinese Expert Consensus on Delta-like Ligand 3-T Cell Engager Therapy for SCLC based on evidence-based medicine and expert practical experience, with the aim of providing standardized guidance for the clinical use of DLL3-TCE agents. .

We analyze a robust dynamic investment problem with ambiguous interest rate risk. After deriving the optimal terminal wealth and investment policy, we expand our framework into an equilibrium model, and calibrate it to data. We confirm the bond premium puzzle, i.e. in a non-robust version of our model an unreasonably high risk-aversion parameter is needed in order to match market data. Our proposed model with robust investors and a novel formulation of the representative agent provides a solution to this puzzle. As a technical contribution, which we consider to be of interest in its own right, we develop a novel formulation of robust dynamic investment problems, and we show that a robust CRRA agent exhibits features of an Uzawa-type agent with a stochastic subjective discount rate.

Efficient catalytic detoxification of nerve agents under atmospheric conditions is severely hindered by the lack of water and poor accessibility of catalytic sites. Herein, a polyamide 56 (PA56)-based nanonet membrane with spontaneous hygroscopicity and self-buffering capacity is designed for efficient catalytic detoxification of dimethyl-4-nitrophenyl phosphate (DMNP), a representative nerve agent simulant, which is fabricated by the in situ growth of metal-organic frameworks (UiO-66-NH2) on a spider-web-like electrospun PA56 nanofibrous membrane, followed by the integration of hygroscopic LiCl and alkaline polyethylenimine (PEI). The unique 2D nanonet structure within the interfiber pores significantly increases the nucleation sites of UiO-66-NH2 on the membrane, leading to a high MOF loading of 42.1 wt %, and thus a significantly shortened hydrolysis half-life of 2.8 min against DMNP in an aqueous solution. The synergistic hygroscopic effect of PEI and LiCl enables the membrane to achieve an atmospheric moisture uptake of up to 1.70 g g-1, which in turn facilitates the diffusion of DMNP. As a result, in an atmospheric environment, DMNP can be eventually converted into the nontoxic products of dimethyl phosphate (72%) and methyl phosphate (28%), with a hydrolysis half-life of 1.9 h. Additionally, the lightweight catalytic membrane exhibits effective barrier protection against DMNP aerosol/droplets. This work provides a methodology for fabricating self-detoxifying wearable devices against chemical warfare agents under atmospheric conditions.

Abstract We propose a heuristic switching model of an asset market where the agents’ choice of heuristic is consistent with their individual risk aversion. They choose between a fundamentalist and a trend-following rule to form expectations about the price of a risky asset. Given their risk aversion, agents make a deterministic trade-off between mean and variance both in choosing a forecasting heuristic and determining the number of risky assets to buy. Heterogeneous risk preferences can lead to diverse choices of heuristic. Using empirical estimates for the distribution of risk aversion, simulations show that the resulting time-varying heterogeneity of expectations can give rise to chaotic dynamics: irregular booms and busts in the asset price without exogenous shocks. Small, stochastic price shocks lead to larger asset price bubbles and can make stable solutions explosive. We prove that a representative agent cannot capture our model.

The paradigm of obesity treatment is rapidly evolving with the introduction and widespread adoption of glucagon-like peptide-1 receptor agonists (GLP-1 RAs). Semaglutide, the representative agent, has demonstrated substantial weight reduction and cardiometabolic benefits across multiple pivotal clinical trials, reshaping societal perceptions of anti-obesity pharmacotherapy. In parallel, sleeve gastrectomy (SG) has become the most commonly performed metabolic and bariatric surgical procedure worldwide due to its technical simplicity, strong safety profile, and durable outcomes. The use of anti-obesity medication in the preoperative setting of metabolic and bariatric surgery has been previously explored; however, its clinical benefit has long remained controversial and inconsistent. Recent evidence suggests that GLP-1 RAs can safely achieve greater preoperative weight loss than earlier pharmacologic agents. Furthermore, GLP-1 RAs have emerged as an effective treatment option for postoperative weight regain, demonstrating significant weight-loss benefits in both SG and gastric bypass patients compared with conventional therapies. As GLP-1-based therapies continue to advance, future strategies should shift toward a multimodal treatment model analogous to oncology, integrating pharmacologic and surgical interventions throughout all phases of obesity care. Further research is needed to establish the ideal timing, duration, safety, and long-term weight loss and metabolic effects of GLP-1 RA administration in both pre- and postoperative phases.

ABSTRACT The paper questions the thesis that Keynes's theoretical contributions lacked ‘solid microfoundations'. First, it contributes to the perception that Keynes's theory does not equal Keynesian theory by emphasising the role of money and arguing that: (i) (Neo)Classical economics is not a special case of the General Theory and (ii) when dealing with Keynes's theory, we should use the term Monetary Theory of Production. The article then provides a brief historical overview of what Denis (2016, ‘Microfoundations.' Review of Political Economy 28 (1): 134-152) called the ‘microfoundation project'. It seeks its definition in the New Classical Macroeconomics literature of the 1970s and, more importantly, presents its core components: optimisation, market clearing, the representative agent and the rational expectations hypothesis. In light of the overall inadequacy of this project, the article proposes new (and tentative) criteria based on which we ought to identify new microfoundations: agents' abilities and behaviours, as well as the institutional environment in which they act. Based on the new criteria advanced and a critique of the core components of the microfoundation project, the article proceeds to investigate whether Keynes's MTP lacks solid microfoundations and explores the role played by money and time in Keynes’s conceptions of micro-behaviour.

Abstract We introduce a framework for developing efficient and interpretable climate emulators (CEs) for economic models of climate change. The paper makes two main contributions. First, we propose a general framework for constructing carbon-cycle emulators (CCEs) for macroeconomic models. The framework is implemented as a generalized linear multi-reservoir (box) model that conserves key physical quantities and can be customized for specific applications. We consider three versions of the CCE, which we evaluate within a simple representative agent economic model: (i) a three-box setting comparable to DICE-2016, (ii) a four-box extension, and (iii) a four-box version that explicitly captures land-use change. While the three-box model reproduces benchmark results well and the fourth reservoir adds little, incorporating the impact of land-use change on the carbon storage capacity of the terrestrial biosphere substantially alters atmospheric carbon stocks, temperature trajectories, and the optimal mitigation path. Second, we investigate pattern-scaling techniques that transform global-mean temperature projections from CEs into spatially heterogeneous warming fields. We show how regional baseline climates, non-uniform warming, and the associated uncertainties propagate into economic damages.

Phosphatidylserine blockade by dipicolylamine-zinc enhances chemoimmunotherapy of B16F10 melanoma.

Regarding recently increasing governance phenomena, this article examines the modern conception of ‘fiscal decentralization,’ drawing on previous major theoretical discussions over centuries especially from a perspective of political economy. As the financial and economic component of ‘autonomy,’ fiscal decentralization may embrace both the technocratic and systematic notion of ‘management’ and governmental and legitimate notion of ‘political power.’ In this regard, the essence of this political economic component of autonomy lies local governments’ endogenous growth in their economy and finance from internalizing external economies of scale under monopolistic competition among themselves through multilayered and pluralist ‘economic’ governance whose dynamics are nested constestingly within the central government. The representative political economic agent to determine the eventual allocation of resources in this dynamics is an innovated Tieboutian consumer-voter revealing preferences for local public goods according to their increasing ‘regional ability to pay’ (RAP) when external economies of scale are internalised to expand the ‘scale’ of regional economies or finances under monopolistic competition. This simplified new ‘monopolistic competition’ model of local public goods distinguishes the (long-run) fixed cost of local public fixed asset or (social overhead) capital from the scale-variant variable cost of local public service although its basic aggregate structure of linear function of output and consumption originates from the canonical functions of the ‘new economic geography.’ Our model of local public goods newly embraces externalities, and scale economies under monopolistic competition, although its welfare analysis, whose in-depth analytics and theoretical and policy implications are to be further investigated in subsequent research, have not presented.

ABSTRACT This study employs a Heterogeneous Agent New Keynesian (HANK) model to analyze the impact of price stickiness on macroeconomic dynamics, policy responses, and distributional effects in South Korea. Using Impulse Response Functions (IRFs), it estimates the responses of output, real interest rates, and household consumption to monetary policy shocks and variations in the price stickiness parameter over an extended period. Our findings reveal that higher price stickiness amplifies initial deviations in economic variables and slows their return to equilibrium, while reduced stickiness leads to quicker convergence. Additionally, we explore the influence of price stickiness on household income and labor supply decomposition and compare the results to a Representative Agent New Keynesian (RANK) model. Finally, the study assesses the role of borrowing constraints in the HANK model, concluding that relaxing such constraints can lead to a more equitable distribution of assets and an increase in aggregate spending.

Abstract The low and (partly) negative economic growth rates in Germany since the outbreak of the energy crises and growing danger of deindustrialization sparked debates about measures to avoid growing numbers of unemployed, for example by changing the generosity of unemployment benefits (UB) or extending short-time work. This paper aims to contribute to the theoretical literature of policy responses to recessions by analyzing three prominent instruments: (a) a permanent reduction of unemployment benefits that is accompanied by increasing search efforts, (b) a fiscal stimulus package and (c) short-time work. To analyze these measures, researchers typically use DSGE models or search and matching models. In contrast, I develop an agent-based macroeconomic model (ABM). Thus, I can analyze the interplay between macro- and microeconomic effects of policies, as well as how interactions among agents shape economic outcomes. I find three main results: First, (a) has almost no effect on unemployment in the short run and negative effects in the long run. This is contrary to the canonical search and matching models, even though the simulations reveal the same "desired" effects, e.g., shorter unemployment spells in the short run. Second, in comparison with (a), policies (b) and (c) can dampen unemployment in the short run and bring employment back to the normal rate more rapidly. Third, in contrast to representative agent models, I can show that short-time work supports the economic recovery through demand stabilization and a distributive effect among heterogeneous firms. In particular, the distributive effect has not been shown in other papers so far.

Following the Great Financial Crisis of 2008–2009, there has been a shift in mainstream economic policy modelling towards ‘realism’, with DSGE models partly diverging from the representative agent framework, and large-scale New Keynesian structural models addressing real–financial interactions in greater detail. Still, the need for tractability of the former and the lack of theoretical structure of the latter prevented the complete introduction of modern – and complex – multi-sector/multi-asset financial system in policy models in use at Central Banks and Treasuries. However, empirical models adopting the stock–flow consistent (SFC) approach resolved most of these complications, with a surge in the number of country models over the past few years. The present work presents the main out-of-sample features of a quarterly SFC model of the Italian economy (MITA). Section 2 reviews the existing SEM models of the Italian economy and places SFC models along the suite of policy models in use around the world, discussing the main pros and cons of adopting the SFC approach over others. Section 3 briefly presents the model structure and main behavioural equations, as well as discuss the main differences and similarities with the other large-scale SFC model of the Italian economy. Section 4 shows the out-of-sample properties of the model, implementing different monetary and fiscal policy shocks, and assessing their effects in terms of growth, distributional dynamics and sectoral debt sustainability. Section 5 concludes.

This study models a representative agent with generalized disappointment aversion preferences in an endowment economy. This model addresses the average upward slope in U.S. real bond yields, equity premium puzzle, and equity volatility puzzle. We integrate a two-state Markov switching process for economic cycles coupled with an independent rare disaster risk. During economic expansions, disaster risk increases the probability of disappointment, thereby reinforcing precautionary saving and reducing the risk-free rate. During recessions, diminished concern about disappointment encourages borrowing and increases the risk-free rate. This pattern engenders countercyclical fluctuations in risk-free rates and accounts for the upward-sloping average yield curve.

The anticancer drug dacarbazine, known to act as an alkylating agent, is shown to inhibit the nitrite-induced autoxidation process in hemoglobin (where nitrite acts as an initiator in a nitrosative stress cascade that also involves free radical reactions). In the absence of nitrite, Hb autooxidation is not affected. Dacarbazine also influences the amount of free radicals produced when Hb reacts with a representative oxidative stress agent, hydrogen peroxide. This behavior mirrors that observed with four other anticancer agents — irinotecan, docetaxel, paclitaxel, and vinorelbine — but not with the more prooxidant reactivity induced by cisplatin.

Heterogeneous agent models have become central to modern macroeconomic research, often replacing the representative agent framework. However, what is core for these frameworks is the use of microfoundations that involve optimizing behavior. The strength of heterogeneous agent models lies in their ability to address questions where heterogeneity is essential--for instance, examining renters versus homeowners, the old versus the young, or the rich versus the poor. While advances in computational power have allowed these models to replicate observed wealth and income distributions effectively, much remains to be understood about the bottom of the income and wealth distributions.

2-acrylamido-2-methylpropane sulfonic acid (AMPS) based copolymer fluid loss agent is a kind of widely utilized additive in oil-well cement. However, when applied in ultra-high temperature (UHT) formation environment, its fluid loss control efficiency is significantly declined due to the thermal degradation behavior, and corresponding mechanism study is still lacking. Regarding the above issue, this work synthesized one representative copolymer fluid loss agent PADIM and investigated its thermal degradation mechanism in UHT aqueous environment, which was polymerized by AMPS, N, N-dimethylacrylamide (DMAA), itaconic acid (IA) and methacryloxyethyltrimethyl ammonium chloride (MTC). The aim of this paper was to provide a theoretical guidance for the futural structural design of the fluid loss agents for oil well cement slurry at UHTs. The copolymer solution was subjected to isothermal aging at 180-240 °C for 1.5 h or 6.0 h (to simulate short-period and long-period aging, respectively), and the aged products were further analyzed. It was found that the thermal decomposition onset temperature of the copolymer solid was 294.6 °C. However, its thermal stability in aqueous solution was significantly lower, with substantial main chain breakage and functional group transformations occurring below 240 °C. As a result, the apparent viscosity and average molecular weight were significantly reduced from 4216 mPa·s and 31,666 Da before aging to 107.4 mPa·s and 8590 Da after aging at 240 °C for 6.0 h. Meanwhile, the side groups (-SO3- and -COO-) were removed and the unsaturated alkenes were produced due to main chain degradation. In terms of application performance, the fluid loss control ability of the aged product diminished gradually from 22 mL to 196 mL as the aging temperature increased from room temperature to 210 °C. This decline was attributed to a reduction in molecular weight and a decrease in product adsorption capacity caused by the removal of side groups.

A positive relation between the conditional mean and conditional volatility of aggregate stock returns, although viewed as a fundamental law of finance, has been challenging to find empirically. We consider a representative agent asset pricing model with Knightian uncertainty and demonstrate that this risk-return trade-off depends on the agent’s ambiguity attitude (reflecting the agent’s degree of optimism or pessimism). The model predicts that the conditional equity premium is increasing in market volatility, but its slope flattens as market optimism rises. We develop a methodology to extract the representative agent’s ambiguity attitude from our asset pricing model. Results validate our model predictions. We document the significant in-sample and out-of-sample explanatory power of ambiguity attitude in explaining the risk-return trade-off. In our sample, market volatility is not significant in forecasting returns. However, including the market ambiguity attitude leads to a significant positive relationship between volatility and future returns. Hence, our model and results identify market ambiguity attitude as a missing state variable that can explain why the literature has found it difficult to empirically validate the risk-return trade-off. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03595 .

Background and Objectives: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a dense desmoplastic stroma with a poor prognosis. The aim of this study was to investigate whether resolvin (Rv) D1 could be used as a potential serum biomarker to discriminate between PDAC and acute pancreatitis (AP). Materials and Methods: In total, 67 patients were enrolled in the present study, including 21 patients with resectable PDAC, 23 patients with metastatic PDAC, 23 patients with AP, and a control group of 21 healthy individuals. RvD1 levels of PDAC patients were also analyzed through ELISA at the 6th postoperative month. Results: The mean RvD1 was 1169.24 ± 285.99 in the control group, 885.04 ± 134.25 in the AP group, 728.57 ± 140.1 in the PDAC group, and 670.09 ± 105.6 in the metastatic pancreatic cancer (PC) group. RvD1 was significantly lower in PDAC and metastatic PC groups compared to controls and patients with AP, while it was significantly lower in patients with AP compared to the control groups. Postoperative RvD1 levels of patients with PDAC were significantly higher than preoperative levels (728.57 ± 140.1 vs. 885.43 ± 275.57). In the ROC analysis, when the cut-off value for serum RvD1 level was 825 ng/L, it was found to predict PDAC from metastatic PC with 84.1% sensitivity and 81.8% specificity. Conclusions: Serum RvD1 is a new biomarker for the detection of PDAC. Serum RvD1 may provide an important diagnostic contribution in clinical practice to predict PDAC. Serum RvD1 levels were found to be predictive with high sensitivity and specificity in differentiating PDAC from metastatic PC. However, it was concluded that serum RvD1 levels cannot be used as a detection marker to differentiate PDAC from AP. RvD1 could be a representative agent of a new class of drugs to be proposed for innovative treatment of AP and PDAC. Our future study will investigate whether RvD1 can be a marker to differentiate from chronic pancreatitis.

As a representative agent of bicyclic antidepressants, venlafaxine (VEN) has become widely used worldwide and is frequently detected in surface waters with concentrations ranging from ng/L to µg/L. To evaluate the toxicological effects of such medications on aquatic species, studies on environmentally relevant concentrations are essential. Zebrafish were used as a model organism to assess growth and development in larvae and examine tissue accumulation, oxidative stress, and DNA methylation in adults. The results showed adverse effects, including an 18.5% decrease in embryo hatching rate and an increase in mortality by 18.5%. There was also a reduction in body length (4.5%) and eye area (12.2%) in the larvae, along with abnormal developmental issues, such as pericardial edema, yolk sac edema, and spinal curvature. Venlafaxine and its metabolites induced oxidative stress, leading to observable toxic effects. In adult zebrafish, VEN and O-desmethylvenlafaxine (ODV) accumulated primarily in the liver, followed by the brain and intestines, and caused a reduction in DNA methyltransferase activity, leading to DNA hypomethylation. VEN had the most significant impact on DNA methyltransferase 1 and altered its conformation more than ODV. Overall, venlafaxine was found to be more toxic than its metabolites, providing a scientific basis for evaluating the toxic effects and ecological risks of antidepressant residues on aquatic organisms.

Traditional models deliberately simplify millions of consumers into a single, homogeneous, representative agent with perfect market knowledge and rational expectations, limiting their capacity to capture real-world complexities. To address this limitation in mainstream models, this article provides global datasets to parametrise energy consumers within climate-energy-economy models considering climate-driven energy demand, socioeconomic and demographic factors. The datasets emerge from applying geospatial artificial intelligence, machine learning and big data analytics on a range of geospatial parameters at 1 km2 resolution. Twenty distinctive energy consumers are defined using three heterogeneous geospatial features, eight diverse and two evolving parameters. This parametrisation of consumers strengthens the applicability of climate-energy-economy models to guide effective, equitable and just climate policy design. This comprehensive analysis of complex interactions between climate, socioeconomic and demographic factors supports more realistic decision-making for a sustainable transition reset. This research emphasises the geospatial distribution of energy consumers to enhance technoeconomic assessment, understanding consumer dynamics for consumer-led resource allocation and informed policy implementation. These datasets can be used in climate-energy-economy models to parametrise consumers beyond traditional approaches.