Multiple Agents Research Articles

3-Factor prothrombin complex concentrate versus 4-factor prothrombin complex concentrate for the reversal of oral factor Xa inhibitors.

Multiple agents exist for the reversal of oral Factor Xa inhibitor (FXa) associated bleeding, including Coagulation FXa Recombinant, Inactivated zhzo (andexanet alfa) and 4-factor prothrombin complex concentrate (4F-PCC). While classified as a 3F-PCC product, Profilnine contains up to 35IU of Factor VII (per 100IU of Factor IX) in addition to therapeutic levels of Factors II, IX, and X, and has demonstrated a similar impact on prothrombin time and blood product usage in non-warfarin related bleeding. This was a retrospective, multicenter study at four medical centers of adult patients who presented with major bleeding associated with oral FXa inhibitors and received either 4F-PCC (n = 64) or 3F-PCC (n = 61). The primary outcome was hemostatic effectiveness. Secondary outcomes included the incidence of thromboembolism, in-hospital mortality, and length of stay. The most common indication for reversal was intracranial bleeding. For the primary outcome, 84% of all patients were rated as effective with no difference noted between the groups (p = 0.81). No significant difference between groups was found in the multivariable analysis adjusting for baseline differences between groups including race, total body weight, type of bleeding, and the use of antiplatelet therapy. There was no difference in the length of stay, in-hospital mortality, or the incidence of thromboembolism between the groups. Overall, no significant differences were found in the effectiveness or safety of 4F-PCC and 3F-PCC use in the management of oral FXa inhibitor-associated bleeding. Further investigations are warranted to explore the use of 3F-PCC for this indication and its safety and effectiveness.

GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model Agents

Existing gesture interfaces only work with a fixed set of gestures defined either by interface designers or by users themselves, which introduces learning or demonstration efforts that diminish their naturalness. Humans, on the other hand, understand free-form gestures by synthesizing the gesture, context, experience, and common sense. In this way, the user does not need to learn, demonstrate, or associate gestures. We introduce GestureGPT, a free-form hand gesture understanding framework that mimics human gesture understanding procedures to enable a natural free-form gestural interface. Our framework leverages multiple Large Language Model agents to manage and synthesize gesture and context information, then infers the interaction intent by associating the gesture with an interface function. More specifically, our triple-agent framework includes a Gesture Description Agent that automatically segments and formulates natural language descriptions of hand poses and movements based on hand landmark coordinates. The description is deciphered by a Gesture Inference Agent through self-reasoning and querying about the interaction context (e.g., interaction history, gaze data), which is managed by a Context Management Agent. Following iterative exchanges, the Gesture Inference Agent discerns the user’s intent by grounding it to an interactive function. We validated our framework offline under two real-world scenarios: smart home control and online video streaming. The average zero-shot Top-1/Top-5 grounding accuracies are 44.79%/83.59% for smart home tasks and 37.50%/73.44% for video streaming tasks. We also provide an extensive discussion that includes rationale for model selection, generalizability, and future research directions for a practical system etc.

A Review on Secure Outsourcing and Privacy-Preserving Traffic Monitoring in Fog-Enabled VANETs

New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing, but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity, the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs, such as data confidentiality and message access control are still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs, better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs, including the protection of data storage, user authentication, access control, system availability across multiple network connections, and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.

Perceived parental prejudice and a tolerant class context in ethnic bullying: The buffering role of teachers.

Despite recent efforts to understand the possible impact of contextual factors on adolescents' involvement in ethnic bullying, most existing studies have focused on the effects of one context at a time. As adolescents are simultaneously exposed to the influence of multiple socialization agents, the aim of this study was to investigate whether teachers' and classmates' tolerance towards ethnic minorities could buffer the effect of perceived parental prejudice on adolescents' involvement in ethnic bullying. Data were collected between January and February 2020 from 9th grade adolescents (N = 582; Mage = 15.23; SD = 0.65; 50.9% female; 30.7% with an immigrant background), and their teachers (N = 72; aged between 27 and 65 years; 79% female), belonging to 37 classrooms located in Italy. A cross-sectional multilevel analysis showed that teachers' tolerance moderated the effect of perceived parental prejudice on adolescents' involvement in ethnic bullying. Specifically, we found that in classrooms with low levels of teachers' tolerance, perceived parental prejudice was significantly associated with students' involvement in ethnic bullying. Conversely, in classrooms with high levels of teachers' tolerance, parental prejudice was no longer associated with ethnic bullying. Furthermore, classmates' tolerance was not significantly associated with students' involvement in ethnic bullying and did not moderate the association between perceived parental prejudice and adolescents' engagement in ethnic bullying. Findings are discussed highlighting the important role of school as a context to promote positive multicultural relations and the unique role played by teachers in affecting adolescents' behaviors.

Revolutionizing Influenza Treatment: A Deep Dive into Targeted Drug Delivery Systems.

Influenza, a highly transmissible respiratory infection caused by influenza viruses A and B, poses a persistent threat to global public health due to its high mutation rate, ability to develop resistance to existing antiviral drugs, and capacity for rapid spread. Current treatment options, including four main classes of antiviral agents-adamantanes, neuraminidase inhibitors, RNA-dependent RNA polymerase inhibitors, and polymerase acidic endonuclease inhibitors- are limited by the emergence of drug-resistant viral strains, non-specific drug distribution, and adverse side effects. Moreover, the effectiveness of traditional vaccines is often compromised by antigenic drift and shift, necessitating the development of alternative therapeutic strategies. This review comprehensively explores the potential of novel targeted drug delivery systems to address these limitations and improve influenza management. Nanotechnology-based platforms, including lipid-based, polymer-based, inorganic, and hybrid nanoparticles, offer enhanced drug delivery through improved bioavailability, targeted action, and controlled release, thus minimizing systemic toxicity and optimizing therapeutic outcomes. Inhalation delivery systems such as dry powder inhalers (DPIs), nebulizers, and nanotechnology-based inhalation formulations provide direct delivery of antiviral agents to the respiratory tract, ensuring rapid onset of action with reduced systemic side effects. Transdermal delivery methods, including microneedle patches and hydrogel-based systems, offer non-invasive alternatives that enhance patient compliance and allow for sustained drug release. Furthermore, this review discusses recent innovations, such as responsive drug delivery systems and multifunctional nanoparticles capable of simultaneous delivery of multiple therapeutic agents, representing a significant advancement in the fight against influenza. These novel approaches promise improved targeting and efficacy and enable personalized treatment strategies, enhancing patient outcomes in both seasonal flu and pandemic scenarios. Integrating these advanced drug delivery systems into clinical practice could revolutionize the management of influenza, offering a promising pathway toward more effective and safer therapies.

Conceptualising meso translation policy

Abstract Like language policy, translation policy is a complex adaptive phenomenon where multiple agents interact dynamically at diverse levels and affect how principles such as equality, inclusion, and non-discrimination are implemented at concrete, structural levels. While a fair amount of work has been done on top-down and bottom-up translation policy, more research about meso-level translation policy is needed to better understand this topic. This study thus seeks to show how meso-level agency needs to be taken into consideration; in particular, the complex interplays between meso-level agency and structural factors in policymaking and appropriation processes. Taking two Toronto hospitals as examples, this study demonstrates how hospitals can be conceptualised at a meso-level as collective agents. It examines the complexity of their agentive roles and elucidates their exertion of agentive power in order to explore meso complexity, and to assess the subsequent impact on both policy outcomes and people’s lives. To do so, a framework of parameters influencing, if not determining, the effectiveness of public service translation and interpreting provision is proposed. It is then used in combination with an existing tripartite framework of translation policy to develop a more fine-grained and holistic understanding of translation policy in public service settings.

Systemic Sweet Syndrome Recurrence in Lung Allograft: Autopsy Findings from First Reported Case

Abstract Introduction/Objective Sweet syndrome is a non-vasculitic neutrophilic dermatosis characterized by acute, neutrophilic inflammation, most classically in the skin. Sweet syndrome can, however, manifest with systemic organ involvement including the lungs. Pulmonary involvement by Sweet syndrome is exceedingly rare, and its recurrence in a pulmonary allograft is understudied. Methods/Case Report A 43-year-old female with a history of Sweet syndrome with pulmonary involvement developed chronic bronchiolitis obliterans requiring bilateral lung transplantation. Seven years after transplantation, the patient began exhibiting shortness of breath requiring hospitalization. During the patient’s hospital stay, cytomegalovirus viremia and SARS-CoV-2 positivity were detected, and new oral lesions were concerning for a Sweet syndrome flare. Her respiratory status continued to decline requiring escalating ventilatory support. An autopsy limited to the chest and abdomen was performed after she expired. The pleural cavities had extensive adhesions, and both lungs were heavy. Microscopically, the lungs showed extensive, intralumenal acute inflammation of the bronchi and bronchioles with marked chronic inflammation of the submucosa, histologically similar to the patient’s explanted lung specimen. There was also focal endogenous lipoid pneumonia due to bronchiolar obstruction. In addition to the inflammatory findings, multiple infectious agents were identified in the patient’s respiratory tract including SARS-CoV-2, cytomegalovirus, Epstein-Barr virus, Candida glabrata, Klebsiella pneumoniae, and Stenotrophomonas maltophilia. No underlying malignancy was identified. (This patient’s earlier course was reported previously: PMC8411443) Results (if a Case Study enter NA) NA Conclusion Pulmonary involvement in Sweet syndrome can cause significant morbidity, and prior to this case, allograft recurrence of Sweet syndrome had not previously been reported. For transplant patients with a history of pulmonary Sweet syndrome, recurrence should be considered in cases of allograft dysfunction. This case also highlights the importance of thorough microbiological studies in autopsies of patients with systemic inflammatory diseases being treated with extensive immunosuppressive therapies.

Combining Human and AI Strengths in Object Counting under Information Asymmetry

With the recent development of artificial intelligence (AI), hybrid human-AI teams have gained more attention and have been employed to solve all kinds of problems. However, existing research tends to focus on the setting where the same task is given to humans and AI. This work investigates a scenario where different agents have access to different types of information regarding the same underlying problem. We propose a probabilistic framework that combines the predictions of humans and AI based on the quality of the information given by both agents. We apply this framework to a regression task in which humans and AI are given different views of a jar and aim to estimate the number of objects in it. We demonstrate that our model can outperform methods that ignore information asymmetry. Furthermore, we show that complementarity can be achieved, i.e., combining human and AI predictions leads to better performance than relying on humans or AI alone. This framework can be adapted to solve other problems in which different sources of information from multiple agents are present.

Therapeutic agents for the treatment of human mpox.

The aim of this study was to summarize the current knowledge of therapeutic options for mpox (formerly known as monkeypox) in the context of recent outbreaks and the ongoing evolution of the virus. Multiple therapeutic agents, including tecovirimat, cidofovir, brincidofovir, and vaccinia immune globulin, have been used during the multicountry outbreak of mpox caused by Clade 2b monkeypox virus that began in 2022. Tecovirimat has been most extensively used, based on efficacy against mpox lethal challenge in animal models, and human safety data. Real-world observational evidence has further supported safety with minimal adverse events in large cohorts and mixed reports of reductions in time to lesion resolution. Several prospective randomized controlled trials using tecovirimat are underway with headline results from a study in the Democratic Republic of the Congo showing no difference in lesion resolution compared to placebo. Other studies including in outpatient settings are underway in Europe and the Americas. Cidofovir and brincidofovir, limited by adverse event profiles, have been less extensively studied. Vaccinia immune globulin has been used predominantly in salvage therapy for severe mpox, with no large observational series available. The 2022 multicountry outbreak of mpox marked a public health emergency. Agents approved for smallpox management were widely used for mpox, supported by animal and in-vitro evidence, and human safety data. The large number of human cases has allowed retrospective observational study of these agents and facilitated recruitment in prospective trials. The ongoing evolution of the virus may pose challenges for therapeutic interventions, necessitating rigorous randomized controlled trials to guide clinical use.

A Novel Cognitive Behavioral Therapy-Based Generative AI Tool (Socrates 2.0) to Facilitate Socratic Dialogue: Protocol for a Mixed Methods Feasibility Study.

Digital mental health tools, designed to augment traditional mental health treatments, are becoming increasingly important due to a wide range of barriers to accessing mental health care, including a growing shortage of clinicians. Most existing tools use rule-based algorithms, often leading to interactions that feel unnatural compared with human therapists. Large language models (LLMs) offer a solution for the development of more natural, engaging digital tools. In this paper, we detail the development of Socrates 2.0, which was designed to engage users in Socratic dialogue surrounding unrealistic or unhelpful beliefs, a core technique in cognitive behavioral therapies. The multiagent LLM-based tool features an artificial intelligence (AI) therapist, Socrates, which receives automated feedback from an AI supervisor and an AI rater. The combination of multiple agents appeared to help address common LLM issues such as looping, and it improved the overall dialogue experience. Initial user feedback from individuals with lived experiences of mental health problems as well as cognitive behavioral therapists has been positive. Moreover, tests in approximately 500 scenarios showed that Socrates 2.0 engaged in harmful responses in under 1% of cases, with the AI supervisor promptly correcting the dialogue each time. However, formal feasibility studies with potential end users are needed. This mixed methods study examines the feasibility of Socrates 2.0. On the basis of the initial data, we devised a formal feasibility study of Socrates 2.0 to gather qualitative and quantitative data about users' and clinicians' experience of interacting with the tool. Using a mixed method approach, the goal is to gather feasibility and acceptability data from 100 users and 50 clinicians to inform the eventual implementation of generative AI tools, such as Socrates 2.0, in mental health treatment. We designed this study to better understand how users and clinicians interact with the tool, including the frequency, length, and time of interactions, users' satisfaction with the tool overall, quality of each dialogue and individual responses, as well as ways in which the tool should be improved before it is used in efficacy trials. Descriptive and inferential analyses will be performed on data from validated usability measures. Thematic analysis will be performed on the qualitative data. Recruitment will begin in February 2024 and is expected to conclude by February 2025. As of September 25, 2024, overall, 55 participants have been recruited. The development of Socrates 2.0 and the outlined feasibility study are important first steps in applying generative AI to mental health treatment delivery and lay the foundation for formal feasibility studies. DERR1-10.2196/58195.

Adaptive Multi-Agent Reinforcement Learning for Optimizing Dynamic Electric Vehicle Charging Networks in Thailand

The rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of Thailand’s evolving electric mobility landscape.

6636 Primary Hyperaldosteronism Leading to Aldosterone Deficiency

Abstract Disclosure: N.G. Haverstick: None. J.R. Anthony: None. Primary Hyperaldosteronism is a disease process that is often missed, and under checked due to poor screening tools. The disease presents with a common problem, uncontrolled hypertension. It also presents with hypokalemia, fatigue and headaches. It can easily be masked with blood pressure medications, CKD, and pain medications. After treatment there are some rare reports of patients developing hypoaldosteronism. Some risk factors are age >50, duration of hypertension >10 years, pre-existing renal impairment and adrenal adenoma size >2 cm. The patient will present with the opposite: hyperkalemia and hypotension. This is due to the atrophy of the previously healthy gland. Usually this resolves within a year. However in our patient she has continued to require long standing mineralocorticoid replacement therapy. This case highlights the importance of early recognition of hyperaldosteronism. A 63 year-old-female with a medical history of hypertension and diabetes mellitus is being followed for 30+ years of uncontrolled hypertension. Patient has been on multiple agents and blood pressure remained elevated after trying triamterene/hctz, metoprolol, irbesartan, amlodipine. After changing her PCP, there was further investigation into the cause. Leading differentials were Primary Hyperaldosteronism vs Renal Artery Stenosis vs Primary Hypertension. She also had multiple labs with her Potassium below 3 mEq/l. Additional labs were obtained and her Aldosterone level 209 ng/dl and Renin <0.167 ng/mL/hr. The patient was referred to Endocrinology who performed a fludrocortisone suppression test, which did not suppress the levels. A CT scan of the abdomen was performed that showed a 1.3 cm right adrenal mass typical with adrenal adenoma. The patient had an adrenalectomy surgery in February 2022. The patient recovered well, however now developed hypotension off medication, and had consistent hyperkalemia with K > 5.5 mEq/l. The patient had now developed aldosterone deficiency. This was due to the long standing hyperaldosteronism that had caused her left adrenal gland to atrophy after 30 years. The patient was started on Fludrocortisone 0.1 mg daily and a low potassium diet. She has remained off any blood pressure medications since her right adrenalectomy. She has continued to require the mineralocorticoid replacement to date. The diagnostic work-up for primary aldosteronism involves screening, confirmatory testing, and localization studies to distinguish unilateral pathology which may be amenable to adrenalectomy from bilateral adrenal hyperplasia. Through literature review, one theory is that chronic suppression of renin may cause atrophy of the zona glomerulosa cells and hypoaldosteronism. Throughout the case report we emphasize on the importance of early diagnostic screening of secondary hypertension as well as the complications from adrenalectomy. Presentation: 6/2/2024

Navigating Trust and Safety in Multi-Agent Reinforcement Learning: Attacks, Defenses, and Future Directions

Multi-Agent Reinforcement Learning (MARL) is a key framework for building intelligent systems where multiple agents operate within a shared environment, with applications spanning autonomous driving, robotics, and distributed control systems. However, real-world deployment of MARL brings significant trust and safety challenges, as these systems are susceptible to a range of attacks that can compromise their robustness and reliability. This paper provides a comprehensive review of trust and safety attacks in MARL, categorizing various types of attacks and their implications. We explore existing defense mechanisms designed to mitigate these threats, highlighting their strengths and limitations. Additionally, we identify open challenges that remain unaddressed and propose potential future research directions to enhance the robustness and security of MARL systems.

Tumor necrosis factor superfamily signaling: life and death in cancer.

Immune checkpoint inhibitors have shaped the landscape of cancer treatment. However, many patients either do not respond or suffer from later progression. Numerous proteins can control immune system activity, including multiple tumor necrosis factor (TNF) superfamily (TNFSF) and TNF receptor superfamily (TNFRSF) members; these proteins play a complex role in regulating cell survival and death, cellular differentiation, and immune system activity. Notably, TNFSF/TNFRSF molecules may display either pro-tumoral or anti-tumoral activity, or even both, depending on tumor type. Therefore, TNF is a prototype of an enigmatic two-faced mediator in oncogenesis. To date, multiple anti-TNF agents have been approved and/or included in guidelines for treating autoimmune disorders and immune-related toxicities after immune checkpoint blockade for cancer. A confirmed role for the TNFSF/TNFRSF members in treating cancer has proven more elusive. In this review, we highlight the cancer-relevant TNFSF/TNFRSF family members, focusing on the death domain-containing and co-stimulation members and their signaling pathways, as well as their complicated role in the life and death of cancer cells.

Robust dynamic contracts with multiple agents

We develop a continuous-time dynamic multi-agent contracting model in which the principal is unsure about the distributions of the project's terminal payoffs and worries about model misspecification. With model uncertainty, workers' wages depend on the outputs of other unrelated projects and the optimal contracts exhibit overdetermination. We demonstrate an inverse U-shaped relationship between the extent of overdetermination and group size. Moreover, model uncertainty induces wage compression, especially in small firms as the empirical evidence demonstrates. Finally, expanding the group size increases the average project value by mitigating the negative impacts of ambiguity.

Effect of repulsive interaction and initial velocity on collective motion process.

Self-propelled collective motion is a highly complex phenomenon, necessitating advanced practical and theoretical tools for comprehension. The significance of studying collective motion becomes apparent in its diverse applications. For instance, addressing evacuation challenges in scenarios with multiple agents can be achieved through an examination of collective motion. Research indicates that the transition of individuals (such as birds, fish, etc.) from a state of rest to equilibrium constitutes a phase transition. Our interest of the issue is to delve into the nature of this transitional phase and elucidate the parameters that shape it. Hence, the primary aim of this paper is to grasp the kinetic phase transition by examining how initial velocity and repulsive interactions impact the dynamics of the system. To gain insight into the complex behavior of multi-agent systems, we apply an extended version of the classical Vicsek model. This extension includes an additional interaction zone, the repulsive zone, where particles repel each other at close range to avoid collisions. Our study uses numerical simulations to explore the system's behavior under various conditions. The focus of this study is the impact of initial velocity on the collective movement of particles. The importance of this research lies in comprehending how velocity affects the overall movement. The conclusion we can draw from these results is that the initial velocity affects both the noise and the density. The novelty of the work is the transition phase, yet it lacks universal characteristics because the critical noise depends on the initial velocity system and the repulsion radius zone. Notably, the repulsion radius and particle density play pivotal roles in achieving a phase transition from one equilibrium state to another aligned equilibrium state.

Role of polypill in cardiovascular prevention and treatment.

Cardiovascular diseases (CVD) remain the leading cause of mortality globally and require innovative strategies for effective prevention and treatment. The polypill concept, which integrates multiple cardioprotective agents into a single dosage form, has emerged as a promising approach to improve adherence and simplify the management of cardiovascular risk factors. We review clinical trials and observational studies evaluating the impact of the polypill on reducing the incidence of major cardiovascular events (MACEs), its influence on medication adherence, and its potential to fill treatment gaps in diverse populations. Also of note are the pharmacoeconomic implications of the widespread use of the polypill, particularly in low- and middle-income countries where the burden of cardiovascular disease is increasing. Although the polypill demonstrates a favorable profile in improving therapeutic compliance and reducing cardiovascular risk factors, debates persist regarding its efficacy compared to individualized treatment regimens. This review summarizes the current evidence on the efficacy, safety, and cost-effectiveness of the polypill in CVD primary and secondary prevention. Furthermore, potential challenges in implementing the polypill strategy include tailoring the components to patient-specific risk profiles and the need for robust evidence from large-scale randomized controlled trials to substantiate its long-term benefits.

Recurrent fungal endocarditis of the aortic valve: A challenging clinical scenario.

Fungal endocarditis is a rare but potentially fatal infection with significant diagnostic and management challenges. Antifungal therapy and surgical debridement are the preferred treatments in these cases. Antimicrobial therapy with multiple antifungal agents may be required in high-risk patients presenting with prolonged fever suspected of having fungal endocarditis. Recurrent fungal endocarditis, particularly involving Candida parapsilosis, is a rare and challenging clinical entity with limited management guidelines. We present the case of a 44-year-old female with a history of intravenous drug use and treatment-resistant chronic hepatitis C who developed recurrent Candida parapsilosis endocarditis. Initially, she presented with native aortic valve fungal endocarditis, which was managed with antifungal therapy and surgical aortic valve replacement. Despite this intervention, she experienced a subsequent episode of prosthetic valve Candida parapsilosis endocarditis, further complicated by prolonged fungemia. This case underscores the complexities of managing recurrent fungal endocarditis and highlights the need for ongoing research to refine treatment strategies for this challenging condition.

Pareto-critical equilibrium condition for non-linear multiobjective optimization problems with applications

The new (necessary) Pareto-critical equilibrium condition (PCEC) discussed in this study is based on the Pareto-critical condition for non-linear optimization problems. The proposed condition is evaluated by defining a multiobjective optimization problems (MOOPs) subject to a set of constraints. Then this MOOPs is converted to a single objective optimization problem by using the available optimization techniques, for instance, the weighted sum method, and solved by the non-linear Nelder-Mead simplex method. The obtained solution points satisfy the proposed PCEC of an unconstrained multiobjective optimization problem (MOOPs) under the convexity assumption. This PCEC is evaluated in a search that either results in a point inside the feasible region or a point for which the PCEC holds. The resulting Pareto-critical equilibrium condition is globally valid for convex problems. Numerical experiments are carried out in order to clarify the proposed condition's validity. Moreover, the proposed PCEC is used to place a downlink transmit beamforming system base station in an ideal position as a first application and is also employed in situations where there are multiple agents working towards a common goal as a second application.

Novel Intraprostatic MR-Guided Implantation of Multidrug-Eluting Microdevice for Testing of Systemic Therapy Agents In Situ: Proof of Concept in Intermediate-Risk and High-Risk Prostate Cancer.

The purpose of this study was to assess safety and feasibility of percutaneous MR-guided placement of an implantable microdevice (IMD) to evaluate in situ intratumor response to multiple pharmacologic agents in men with intermediate-risk and high-risk localized prostate cancer. Biocompatible IMDs measuring 750 µm in diameter and 5 mm in length were prepared with 20 reservoirs containing candidate drug and drug combinations including second-generation androgen inhibitors, PARP inhibitors, PD-1 inhibitors, and conventional chemotherapy. Men with intermediate-risk or high-risk localized prostate cancer and MRI-visible lesions were enrolled. Up to 4 IMDs were placed using a transperineal approach into MRI-visible tumors 2 days before planned radical prostatectomy. After radical prostatectomy, the IMDs and a small segment of surrounding tumor tissue were removed and sectioned, stained, and analyzed for tissue drug response by a variety of pharmacodynamic markers. Fourteen patients were enrolled: 7 (50%) with intermediate-risk and 7 (50%) with high-risk localized prostate cancer. A total of 53 IMDs were implanted (mean 3.8 per patient), and 49 IMDs (92%) were successfully retrieved. All men underwent uncomplicated robotic-assisted radical prostatectomy and bilateral pelvic lymph node dissection 2 days after IMD placement. There were no severe adverse events. Pathological examination of the tissues adjacent to the IMDs demonstrated differential drug response within patients and between patients. Limitations include small sample size. A multidrug IMD can be safely placed percutaneously into MRI-visible lesions before radical prostatectomy, enabling assessment of tumor-specific local response to multiple agents simultaneously within the tumor's normal stromal environment to guide targeted systemic therapy.