Individual Agents Research Articles

Combination of retagliptin and henagliflozin as add-on therapy to metformin in patients with type 2 diabetes inadequately controlled with metformin: A multicentre, randomized, double-blind, active-controlled, phase 3 trial.

This study assessed the efficacy and safety of co-administering retagliptin and henagliflozin versus individual agents at corresponding doses in patients with type 2 diabetes mellitus who were inadequately controlled with metformin. This multicentre, phase 3 trial consisted of a 24-week, randomized, double-blind, active-controlled period. Patients with glycated haemoglobin (HbA1c) levels between 7.5% and 10.5% were randomized to receive once-daily retagliptin 100 mg (R100; n = 155), henagliflozin 5 mg (H5; n = 156), henagliflozin 10 mg (H10; n = 156), co-administered R100/H5 (n = 155), or R100/H10 (n = 156). The primary endpoint was the change in HbA1c from baseline to week 24. Based on the primary estimand, the least squares mean reductions in HbA1c at week 24 were significantly greater in the R100/H5 (-1.51%) and R100/H10 (-1.54%) groups compared with those receiving the corresponding doses of individual agents (-0.98% for R100, -0.86% for H5 and -0.95% for H10, respectively; p < .0001 for all pairwise comparisons). Achievement of HbA1c <7.0% at week 24 was observed in 27.1% of patients in the R100 group, 21.2% in the H5 group, 24.4% in the H10 group, 57.4% in the R100/H5 group and 56.4% in the R100/H10 group. Reductions in fasting plasma glucose and 2-h postprandial glucose were also more pronounced in the co-administration groups compared with the individual agents at corresponding doses. Decreases in body weight and systolic blood pressure were greater in the groups containing henagliflozin than in the R100 group. The incidence rates of adverse events were similar across all treatment groups, with no reported episodes of severe hypoglycaemia. For patients with type 2 diabetes mellitus inadequately controlled by metformin monotherapy, the co-administration of retagliptin and henagliflozin yielded more effective glycaemic control through 24 weeks compared with the individual agents at their corresponding doses.

Improving Farm Yield through Agent Based Modeling

This paper explores the potential of Agent-Based Modeling (ABM) using NetLogo to enhance farm yield. Traditional approaches to agricultural management often overlook the complexity and interdependencies within farming systems. ABM offers a dynamic and flexible framework to simulate the behaviours of individual agents within a farming ecosystem, enabling a more nuanced understanding of the factors influencing yield. By modeling the interactions between agents such as farmers, crops, pests, weather conditions, and market dynamics, this study aims to identify optimal strategies for improving farm yield while minimizing input costs and environmental impacts. Through experimentation and scenario analysis, various farming practices and policies can be simulated and evaluated in silico, providing valuable insights for real-world decision-making. This interdisciplinary approach integrates concepts from computer science, economics, ecology, and agronomy to develop a holistic understanding of agricultural systems. The findings of this research contribute to the development of sustainable farming practices and policy interventions to address food security challenges in a rapidly changing world.

Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem

The Nurse Rostering Problem (NRP) aims to create an efficient and fair work schedule that balances both the needs of employees and the requirements of hospital operations. Traditional local search-based metaheuristic algorithms, such as adaptive neighborhood search (ANS) and variable neighborhood descent (VND), mainly focus on optimizing the current solution without considering potential long-term consequences, which may easily get stuck in local optima and limit the overall performance. Thus, we propose a multi-agent deep Q-network-based metaheuristic algorithm (MDQN-MA) for NRP to harness the strengths of various metaheuristics. Each agent encapsulates a metaheuristic algorithm, where its available actions represent different perspectives of the problem environment. By combining their strengths and various perspectives, these agents can work collaboratively to navigate and search for a broader range of potential solutions effectively. Furthermore, to improve the performance of an individual agent, we model its neighborhood search as a Markov Decision Process model and integrate a deep Q-network to consider long-term impacts for its neighborhood sequential decision-making. The experimental results clearly show that an individual agent in MDQN-MA can outperform ANS and VND, and multiple agents in MDQN-MA even perform better, achieving the best results among metaheuristic algorithms on the Second International Nurse Rostering Competition dataset.

A data-driven agent-based model of occupants’ thermal comfort behaviors for the planning of district-scale flexible work arrangements

In a global context of increasing flexibility in the way workplaces and the districts in which they are located are used, there is a need for occupant-driven approaches to plan urban energy systems. Several authors have suggested the use of agent-based models (ABM) of building occupants in urban building energy simulations due to their ability to reproduce emergent behaviors from individual agents’ actions. However, few works in the literature take full advantage of the ABM paradigm, accounting for both occupant presence and energy-relevant behaviors at the district scale. In this work, we propose a methodology to develop a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban district. Our methodology combines the use of campus-scale Wi-Fi data to derive feasible occupant activity and location plans, along with thermal preference profiles derived from a longitudinal field study where off-the-shelf, non-intrusive sensors were used to capture the right-here-right-now thermal preference of 35 participants in the same case study district. Our model is then used to explore how different district operation strategies could affect building energy performance in the context of increased workspace flexibility. Our results show that by providing a diversity of building operation conditions, with different buildings having different set point temperatures, occupants’ thermal comfort hours could be improved by an average of about 10% with little effect on district energy performance. Meanwhile, a 6%–15% average decrease in space cooling energy use intensity was observed when implementing occupant-driven ventilation and setpoint controls, regardless of location choice scenario.

Evolution of cooperation on reinforcement-learning driven-adaptive networks.

Complex networks are widespread in real-world environments across diverse domains. Real-world networks tend to form spontaneously through interactions between individual agents. Inspired by this, we design an evolutionary game model in which agents participate in a prisoner's dilemma game (PDG) with their neighboring agents. Agents can autonomously modify their connections with neighbors using reinforcement learning to avoid unfavorable environments. Interestingly, our findings reveal some remarkable results. Exploiting reinforcement learning-based adaptive networks improves cooperation when juxtaposed with existing PDGs performed on homogeneous networks. At the same time, the network's topology evolves from homogeneous to heterogeneous states. This change occurs as players gain experience from past games and become more astute in deciding whether to join PDGs with their current neighbors or disconnect from the least profitable neighbors. Instead, they seek out more favorable environments by establishing connections with second-order neighbors with higher rewards. By calculating the degree distribution and modularity of the adaptive network in a steady state, we confirm that the adaptive network follows a power law and has a clear community structure, indicating that the adaptive network is similar to networks in the real world. Our study reports a new phenomenon in evolutionary game theory on networks. It proposes a new perspective to generate scale-free networks, which is generating scale-free networks by the evolution of homogeneous networks rather than typical ways of network growth and preferential connection. Our results provide new aspects to understanding the network structure, the emergence of cooperation, and the behavior of actors in nature and society.

Comparative analysis of two evacuation simulation tools when applied to high-rise residential buildings

Currently there are numerous emergency evacuation simulation tools with varying levels of sophistication and differing capabilities. It is expected that investigating the same scenarios using different tools might not give the same outcome. This paper illustrates how a microscopic agent-based modelling tool (Pathfinder) and a macroscopic flow-based modelling tool (Evacuationz) can be setup to investigate high-rise residential building evacuations in comparable ways by varying configuration parameters. Both tools represent individual agents with associated characteristics (e.g., walking speed and pre-evacuation delay). These are varied in the scenarios examined to stress-test the designs or explore the impact of design/procedural changes.The two tools differ in how they deal with the building geometry and therefore have different algorithms to manage evacuee movement. In this work, assumed performance parameters (e.g., travel speeds, spacing, etc.) have been calibrated to better align the conditions represented in the tools and the outcomes produced across the scenarios examined. This then allows the tools to function at their respective levels of sophistication and granularity (providing different perspectives on performance) but adopt a more equivalent performance baseline given the calibration effort.The use of two tools increased confidence in the predictions, and also allowed for an examination of a wider range of scenario conditions such as number of stairs, stair width, and building size, given the different computational expense associated with the tools.

Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

AbstractMulti‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents. However, in high‐dimensional continuous spaces, the non‐stationary environment can provide outdated experiences that hinder convergence, resulting in ineffective training performance for multi‐agent systems. To tackle this issue, a novel reinforcement learning scheme, Mutual Information Oriented Deep Skill Chaining (MioDSC), is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency. These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state. In addition, MioDSC can generate cooperative policies using the options framework, allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning. MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels. The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations

Peer learning is a novel high-level reinforcement learning framework for agents learning in groups. While standard reinforcement learning trains an individual agent in trial-and-error fashion, all on its own, peer learning addresses a related setting in which a group of agents, i.e., peers, learns to master a task simultaneously together from scratch. Peers are allowed to communicate only about their own states and actions recommended by others: "What would you do in my situation?". Our motivation is to study the learning behavior of these agents. We formalize the teacher selection process in the action advice setting as a multi-armed bandit problem and therefore highlight the need for exploration. Eventually, we analyze the learning behavior of the peers and observe their ability to rank the agents' performance within the study group and understand which agents give reliable advice. Further, we compare peer learning with single agent learning and a state-of-the-art action advice baseline. We show that peer learning is able to outperform single-agent learning and the baseline in several challenging discrete and continuous OpenAI Gym domains. Doing so, we also show that within such a framework complex policies from action recommendations beyond discrete action spaces can evolve.

Cooper: Coordinating Specialized Agents towards a Complex Dialogue Goal

In recent years, there has been a growing interest in exploring dialogues with more complex goals, such as negotiation, persuasion, and emotional support, which go beyond traditional service-focused dialogue systems. Apart from the requirement for much more sophisticated strategic reasoning and communication skills, a significant challenge of these tasks lies in the difficulty of objectively measuring the achievement of their goals in a quantifiable way, making it difficult for existing research to directly optimize the dialogue procedure towards them. In our work, we emphasize the multifaceted nature of complex dialogue goals and argue that it is more feasible to accomplish them by comprehensively considering and jointly promoting their different aspects. To this end, we propose a novel dialogue framework, Cooper, which coordinates multiple specialized agents, each dedicated to a specific dialogue goal aspect separately, to approach the complex objective. Through this divide-and-conquer manner, we make complex dialogue goals more approachable and elicit greater intelligence via the collaboration of individual agents. Experiments on persuasion and emotional support dialogues demonstrate the superiority of our method over a set of competitive baselines. Our codes are available at https://github.com/YiCheng98/Cooper.

Unravelling Expressive Delegations: Complexity and Normative Analysis

We consider binary group decision-making under a rich model of liquid democracy: agents submit ranked delegation options, where each option may be a function of multiple agents' votes; e.g., "I vote yes if a majority of my friends vote yes." Such ballots are unravelled into a profile of direct votes by selecting one entry from each ballot so as not to introduce cyclic dependencies. We study delegation via monotonic Boolean functions, and two unravelling procedures: MinSum, which minimises the sum of the ranks of the chosen entries, and its egalitarian counterpart, MinMax. We provide complete computational dichotomies: MinSum is hard to compute (and approximate) as soon as any non-trivial functions are permitted, and polynomial otherwise; for MinMax the easiness results extend to arbitrary-arity logical ORs and ANDs taken in isolation, but not beyond. For the classic model of delegating to individual agents, we give asymptotically near-tight algorithms for carrying out the two procedures and efficient algorithms for finding optimal unravellings with the highest vote count for a given alternative. These algorithms inspire novel tie-breaking rules for the setup of voting to change a status quo. We then introduce a new axiom, which can be viewed as a variant of the participation axiom, and use algorithmic techniques developed earlier in the paper to show that it is satisfied by MinSum and a lexicographic refinement of MinMax (but not MinMax itself).

Abstract 716: Depletion of stromal cells remodels tumor microenvironment and potentiates immune checkpoint blockade in pancreatic cancer

Abstract During carcinoma progression, mesenchymal stromal cells (MSCs) become recruited to tumors and contribute to the pool of cancer-associated fibroblasts (CAFs). Sub-populations of CAFs, have diverse and incompletely understood effects on disease progression and resistance to therapy. Adipose stromal cells (ASCs), the MSCs from fat tissue increasingly recruited by carcinomas in the context of obesity, have been shown to support cancer progression. Here, the roles of perivascular CAFs expressing platelet-derived growth factor receptor beta (Pdgfrb) and of CAFs expressing non-glycanated decorin (ngDCN), a marker of ASCs, were analyzed. We used mice orthotopically grafted with pancreatic ductal adenocarcinoma (KPC) cells. Ablation of cells expressing thymidine kinase under the control of Pdgfrb promoter with ganciclovir resulted in suppression of primary pancreatic tumor growth. However, ablation Pdgfrb+ cells induced spontaneous metastases to the liver. For depletion of ngDCN+ cells, we used a hunter-killer peptide D-CAN, which has been previously reported to induce apoptosis of ASCs and CAFs in mouse models. Here, D-CAN also had a negative effect on pancreatic tumor growth, however, there was no significant effect on metastatic dissemination. Single cell RNA sequencing of tumors demonstrated that targeting of Pdgfrb+ and ngDCN+ stromal cells had distinct effects on sub-populations of CAFs. Endothelial cell and cancer cell survival was decreased by depletion of either Pdgfrb+ or ngDCN+ stroma. However, pathway analysis revealed that depletion of Pdgfrb+ and ngDCN+ stromal cells has different effects on the markers of cancer cell aggressiveness. D-CAN treatment, and to a less extent Pdgfrb+ cell ablation, suppressed macrophage M2 polarization and increased infiltration of cytotoxic T-lymphocytes. This observation, confirmed in the MyCap graft model of prostate cancer, suggested that ablation of ngDCN+ stroma should synergize with immune checkpoint inhibitors. We tested the effect of D-CAN on the efficacy of anti-PDL1 antibody in the orthotopic KPC model. Compared to D-CAN and anti-PDL1 antibody alone, combination treatment had a synergistic effect on tumor growth. Importantly, liver metastases were suppressed by the D-CAN/anti-PDL1 antibody combination, but not by individual agents. We conclude that improved approaches to target mesenchymal stroma in tumors may be effective in combination with immunotherapy. Citation Format: Joseph Rupert, Alexes Daquinag, Lingyi Cai, Yulin Dai, Zhongming Zhao, Dimitris Anastassiou, Mikhail Kolonin. Depletion of stromal cells remodels tumor microenvironment and potentiates immune checkpoint blockade in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 716.

Abstract 4158: Non-invasive mapping of acidosis and extracellular lactosis for assessing tumor invasion in vivo

Abstract Tumor cells typically display a hyperglycolytic metabolic phenotype characterized by increased lactate production, known as the Warburg effect. This drift in oxidative metabolism often results in a more acidic microenvironment. This adverse microenvironment represents a unique metabolic signature in cancer cells with more invasive phenotypes. However, to date, there are no effective methods available to evaluate cancer progression and metastatic potential in vivo. To bridge this gap, we have developed a molecular imaging technique (CEST-MRI) that can track extracellular pH (pHe; acidoCEST) and extracellular lactate levels (shiftCEST) simultaneously to discern different cancer phenotypes. In this study we have generated and selected in vivo two PyMT derived murine breast cancer cell lines, a parental and a G6 metastatic. We first characterized the cell’s metabolism by analyzing key biomarkers with qPCR, Western Blot and microscopy, and confirmed their distinct metabolic profiles with Seahorse and RNA Sequencing. Notably, the metastatic PyMT exhibited a more active metabolism, aligning with the invasive phenotype observed by spheroid invasion assay. Herein, we combined the injection of iopamidol and shift reagent (SR) to perform quasi-simultaneous acidoCEST and shiftCEST. First, we assessed the approach in vitro by introducing iopamidol and SR into phantoms containing the individual contrast agents, and the combination in pure water and cells supernatants at different pHe and lactate levels. We performed LDH assays to cross-validate the extracellular concentrations and monitored the pH with a micro pH meter (Mettler-Toledo). In vivo, the cells were injected orthotopically in the 4th mammary fat pad of C57BL/6N mice. Hybrid shift/acidoCEST sequences were optimized and acquired following the injection of the iopamidol+SR solution. Preclinical shift/acidoCEST enabled the simultaneous and accurate determination of extracellular acidosis and lactosis, revealing increased acidification in the metastatic models despite lower lactate secretion. Metastatic tumors exhibited a negative correlation between tumor volume and pHe, indicating the significance of acidity in mutagenesis and invasion. In contrast, no correlation was observed for the parental non-metastatic tumor. Additionally, the metastatic tumors had lower levels of lactate than the parental tumors, which could be attributed to their higher mitochondrial activity, reflecting a diverted metabolic fitness. In summary, our study highlights the efficacy of shift/acidoCEST as a reliable method for non-invasive metabolic imaging, enabling the simultaneous detection of extracellular acidity and lactate production by MRI. This research introduces a unique platform for assessing the metabolic aggressiveness of cancer, emphasizing the significance of acidity and lactate production as crucial biomarkers in cancer invasion. Citation Format: Anais Choffart, Remy Chiaffarelli, Max Zimmermann, Laura Kuebler, André Martins. Non-invasive mapping of acidosis and extracellular lactosis for assessing tumor invasion in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4158.

Abstract 3229: IACS-16559, a CBP/P300 bromodomain inhibitor for the treatment of specific AML subsets

Abstract Block in differentiation and accumulation of undifferentiated blasts are hallmarks of Acute Myeloid Leukemia (AML) and epigenetic processes have been shown to play a critical role in hematological malignancies. Paralogs CREBBP and EP300, in association with co-factors, are hijacked during leukemogenesis by aberrant transcriptional factors, thus driving blast proliferation and maintaining the undifferentiated phenotype. Here, we introduce IACS-16559, a bromodomain inhibitor targeting CREBBP/EP300. IACS-16559 is highly selective for the bromodomains of CBP and EP300 versus BRD4, showing a dose-dependent inhibition of H3K27 acetylation residue in vivo and in vitro. In addition, IACS-16559 pharmacokinetic properties show low clearances, high oral exposures and predicted QD dosing in human. We screened AML cell lines in vitro for their response to IACS-16559 and identified specific subtypes of AML that exhibit heightened sensitivity to CBP/EP300 inhibition, specifically AML with AML1-ETO and MLL-AF9 rearrangements, and AML featuring mutations in the nucleophosmin (NPM1c) gene. IACS-16559 inhibited proliferation of AML responder cell lines in vitro, halted cell cycle progression, with an accumulation of cells in G1 and a decrease of cells in S phase. IACS-16559 did not induce apoptosis across a panel of AML cell lines, indicating that induction of apoptosis is not a major mechanism of action of CBP/EP300 inhibition. To augment the anti-tumor effects of IACS-16559, we prioritized the pharmacological combination of CBP/EP300 and MLL-Menin inhibitors. This combination synergistically impeded cell proliferation and potently triggered apoptosis in vitro and in vivo, whereas none of the individual agents alone had this effect. The changes in gene and surface marker expression produced by these treatments, as single agents and in combination with synergistic effects, were in line with an induced myeloid differentiation phenotype. Collectively, our findings provide a compelling basis for further exploring the use of a CBP/EP300 bromodomain inhibitor in conjunction with an inhibitor of the MLL-Menin interaction in a subset of AML patients with MLL rearrangements or NPM1 mutations. Citation Format: Ciro Zanca, Michael Soth, Guang Gao, Jason Gay, Ningping Feng, Christopher Bristow, Kang Le, Quanyun Xu, Phuong Nguyen, Yongying Jiang, Teresa Perry, Faika Mseeh, Paul Leonard, Jason Cross, Virginia Giuliani, Joe Marszalek, Philip Jones, Tim Heffernan. IACS-16559, a CBP/P300 bromodomain inhibitor for the treatment of specific AML subsets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3229.

Covert Subversive Agents and Consensus Disruption on Large Projects

Is it possible to dramatically affect and influence military and other projects through social engineering of the consensus processes? In this paper we explore the impact that subversive agents can have on the ability of projects to move forward by disrupting the social cohesion and decision-making abilities of the processes designed to reach consensus. A consensus simulator is used to model group social cohesion behaviour in the context of project deliverables and show what the effect can be on the effort to reach consensus (number of meetings) as well as the time to reach consensus (calendar time) when subversive agents attempt to influence the groups making up the project team in such a way that it delays the ability of the team to reach consensus on key decisions. Many military options are available to delay enemy projects, including the assassination of enemy scientists, sanctions aimed at denying key project components, or even direct military action such as bombing the enemy facilities. However, this paper focusses on aspects of soft-force projection through covert disruption of project timelines. A social simulator was constructed that models individual agent’s beliefs about various key topics within the context of a project. The effect that a small number of subversive agents can have on the time- and effort of a project is shown. In their covert actions, these subversive agents need to stay hidden, and thus their covert actions are limited, yet they can exert significant damage to the project in terms of delays. In this paper we present results showing the effects that such a small group can have, as well as pointing out that there seem to be a critical group size over which the subversive agents can not only have significant impact on project-delays but can also steer and direct certain key decisions.

Building back better: Modeling decentralized recovery in sociotechnical systems using strategic network dynamics

The ability of sociotechnical systems to recover from disruptions through decentralized, bottom-up processes is a crucial element of their overall resilience, but it is also among the most overlooked in resilience research. This paper proposes a computational framework leveraging dynamic multi-agent networks to conceptualize, model, and quantify multi-agent, decentralized recovery process building upon strategic network formation methods. This innovative approach addresses limitations in conventional strategic network formation models by accounting for the fluidity of structural shifts induced by the strategic behaviors of individual agents, and by acknowledging the inherent heterogeneity within agent populations. The central objective of this study is to highlight the critical role of bottom-up recovery and present a systematic, adaptable framework for modeling these processes in diverse real-world scenarios. We achieve this through four key elements: modeling post-disruption responses, exploring their interdependencies, accommodating multiple equilibria, and generating metrics for scenario comparison and forecasting. Our dynamic strategic network formation model offers valuable insights into crucial trade-offs in decentralized recovery processes and facilitates the integration of both bottom-up and top-down resilience strategies. Furthermore, it could serve as a predictive tool informing initial network architectural decisions by estimating likely post-disruption equilibrium states based on the initial network structure. Ultimately, this study provides a robust platform for analyzing network efficiency and resilience trade-offs, setting the stage for more informed decision-making in complex sociotechnical systems.

A Comprehensive Overview on Chemotherapy-Induced Cardiotoxicity: Insights into the Underlying Inflammatory and Oxidative Mechanisms.

While oncotherapy has made rapid progress in recent years, side effects of anti-cancer drugs and treatments havealso come to the fore. These side effects include cardiotoxicity, which can cause irreversible cardiac damages withlong-term morbidity and mortality. Despite the continuous in-depth research on anti-cancer drugs, an improvedknowledge of the underlying mechanisms of cardiotoxicity are necessary for early detection and management ofcardiac risk.Although most reviews focus on the cardiotoxic effect of a specific individual chemotherapeutic agent, the aim of our review is to provide comprehensive insight into various agents that induced cardiotoxicity and their underlying mechanisms. Characterization of these mechanisms are underpinned by research on animal models and clinical studies. In order to gain insight into these complex mechanisms, we emphasize the role of inflammatory processes and oxidative stress on chemotherapy-induced cardiac changes.A better understanding and identification of the interplay between chemotherapy and inflammatory/oxidativeprocesses hold some promise to prevent or at least mitigate cardiotoxicity-associated morbidity and mortalityamong cancer survivors.

Formulation and Evaluation of Niosomal gel using Tretinoin and Clindamycin combination

The objective of the present study was to formulate topical Niosomal gel loaded with combination of Tretinoin, also known as all-trans retinoic acid (ATRA), and Clindamycin for the beneficial of acne patients, to provide sustained release effects, to prevent their side effects. Purpose of this study was to develop a stable formulation that allows progressive follicle penetration and increased efficacy. Niosomes was prepared by ether injection method. Preformulation studies such as melting point, FTIR spectroscopy, UV spectroscopy, solubility, partition coefficient (log P) were performed for both drugs. In preformulation study, FTIR spectrum reveals that drugs were in pure state. Combination of Tretinoin and clindamycin reveal better therapeutic efficacy and patient compliance as compared to individual agent. From the research findings, it can be concluded that Tretinoin as well as Clindamycin hydrochloride was successfully integrated into niosomal gel by ether injection method for topical application in the treatment of acne.&#x0D; Keywords: Niosomes; Tretinoin (ALL TRANS RETINOIC ACID); Clindamycin; Carbopol gel; Acne; Ether injection method.

Integrating Multi-Agent Deep Deterministic Policy Gradient and Go-Explore for Enhanced Reward Optimization

The field of Multi-Agent Reinforcement Learning (MARL) continues to advance with the development of new and effective methods. This research is centered on two prominent approaches within this field: Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Go-Explore. The study explores the synergistic potential of combining these two methodologies to enhance rewards for individual agents as well as for agent groups. In the course of this research, MADDPG is introduced into the experimental environment, providing agents with both actor networks (policy networks) and critic networks (Q networks) to implement the actor-critic model. Additionally, each individual agent is equipped with a Go-Explore network, empowering them to conduct deeper explorations of the environment and accumulate rewards at an accelerated rate, often resulting in higher overall rewards. This novel approach emphasizes achieving a balance between individual and collaborative rewards, offering a promising avenue for optimizing multi-agent systems. The results of this study demonstrate that the combined method exhibits notable advantages in certain scenarios. Specifically, it showcases a higher rate of reward accumulation and improved overall performance. This research contributes to the MARL domain by highlighting the potential of combining MADDPG and Go-Explore to enhance the efficiency and effectiveness of multi-agent systems.

Innovative search and imitation heuristics: an agent-based simulation study

AbstractProminent research in strategic imitation, exploration, exploitation, and organizational learning identifies imitation as a less costly alternative to experimentation. Yet, its role in the exploration–exploitation dilemma remains underexplored in the literature. This study employs an agent-based model to examine how two distinct agent types—those who imitate and those who experiment—interact and influence each other. The model incorporates the concept of “satisficing” derived from the behavioral theory of the firm, along with insights from research on imitative heuristics. The findings reveal that overcrowding affects both agent types negatively. Imitators suffer from diminished performance due to intensified competition, which increases as more imitators join the system. Meanwhile, explorers are hindered in their attempts at radical innovation due to the presence of other explorers and clusters of imitators. This paper contributes to the field as the first to model individual agents as ‘satisficers’ within a competitive exploration–exploitation framework. By incorporating imitation, it provides novel insights into the dynamics of organizational learning and strategic decision-making.

Effect of valproic acid on histone deacetylase expression in oral cancer (Review).

Oral squamous cell carcinoma (OSCC) is a frequent human malignancy that demonstrates a range of genetic and epigenetic alterations. Histone deacetylases (HDACs) are key epigenetic regulators of cell-cycle progression, differentiation and apoptosis and their dysregulation is implicated in cancer development. HDACs are promising targets for anticancer therapy through the utilisation of HDAC inhibitors (HDACis). OSCC cells have been shown to have low levels of histone acetylation, suggesting that HDACis may produce beneficial effects in patients with OSCC. Valproic acid (VPA) is a class I and IIa HDACi and, therefore, may be useful in anticancer therapy. VPA has been reported as a chemo-preventive epigenetic agent in individuals with high-risk oral dysplasia (OD) and thus associated with a reduced risk of HNSCC. It is hypothesised that HDAC inhibition by VPA triggers a change in the expression levels of different HDAC family gene-members. The present review summarises the current literature on HDAC expression changes in response to VPA in oral cancer patients and in vitro studies in an effort to better understand the potential epigenetic impact of VPA treatment. The present review outlined the need for exploring supportive evidence of the chemo-preventive role played by VPA-based epigenetic modification in treating oral pre-cancerous lesions and, thus, providing a novel tolerable chemotherapeutic strategy for patients with oral cancer.