Revolutionizing UTI Treatment: Harnessing Ant Colony Optimization for Effective E. coli Combat in the Urinary Tract

Revolutionizing UTI Treatment: Harnessing Ant Colony Optimization for Effective E. coli Combat in the Urinary Tract | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Revolutionizing UTI Treatment: Harnessing Ant Colony Optimization for Effective E. coli Combat in the Urinary Tract Gokuldas (Vedant) S. Raikar, Amisha S. Raikar, H.L. Gururaj, Pramod Kumar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5905599/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research presents a bio-inspired nanorobot swarming algorithm based on Ant Colony Optimization (ACO) for combating Escherichia coli ( E. coli ) in Urinary Tract Infections (UTIs). UTIs caused by E. coli are a major global health concern. An approach that mimics the collective behavior of ant colonies in foraging tasks, utilizing ACO principles to guide the navigation and targeting of nanobots within the urinary tract, was proposed. Using NetLogo, an agent-based modeling platform, simulation and evaluation of the nanorobot swarm's performance was carried out. The swarm consists of autonomous agents that mimic ant behavior, with E. coli bacteria as the target. The efficacy of the ACO-based algorithm in eradicating E. coli in UTIs was demonstrated through extensive experimentation. Experimental results show that the algorithm successfully guides nanobots to locate and neutralize E. coli bacteria, reducing infection levels. The investigation also investigated the impact of parameters like pheromone evaporation rates and agent communication strategies on swarm performance. This research contributes to nanorobot technology advancement and highlights the potential of bio-inspired algorithms for medical challenges. The proposed ACO-based algorithm offers a targeted and efficient approach to combat E. coli infections in UTIs. Insights gained from this study can guide further research and development of nanorobot-based therapies for urinary tract infections and other infectious diseases. nanorobot swarm algorithm ant colony optimization E. coli urinary tract infections. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Urinary tract infections are a common health issue, with over 6 million outpatient visits annually [ 1 ]. Elderly individuals, both men and women, are particularly susceptible to UTIs due to increased urinary tract abnormalities and catheter use. Most simple UTIs are attributed to uropathogenic strains of E. coli , accounting for up to 90% of cases [ 2 ]. These uropathogenic E. coli strains possess specific serotypes and adhesive organelles known as fimbriae, such as P fimbriae, which facilitate attachment to uroepithelial cells. Recent research has shown the involvement of type 1 fimbriae in UTIs, promoting bacterial persistence and enhancing the host's inflammatory response. Other factors, such as aerobactin-mediated iron uptake and hemolysin, may contribute to bacterial growth and host-cell injury. These virulence factors are often found together on "pathogenicity islands," large segments of DNA acquired through horizontal gene transfer [ 3 ]. Behavioral elements, such as sexual behavior and urination habits, can influence urethral colonization by uropathogens. Certain menstrual cycle stages and hormonal variations may also influence UTI susceptibility. An association between recurrent UTIs and specific Lewis’s blood-group phenotypes is observed. Clinical manifestations of UTIs include dysuria, increased frequency of urination, cloudy urine, and sometimes blood in the urine. Acute pyelonephritis, a more severe form of UTI, may present with high fever and costovertebral pain [ 4 ]. Elderly patients may exhibit atypical symptoms such as gastrointestinal discomfort and absent fever. Quantitative bacterial counts are used to determine significant bacteriuria, but the clinical relevance of the traditional threshold of 10 5 colony-forming units/mL has been reassessed. Low-count bacteriuria (10 2 to 10 4 colony-forming units/mL) may still indicate infection if the patient is symptomatic and pyuria is present [ 5 ]. Figure 1 . depicts the urinary tract infection process. Urinary tract infections present significant challenges in diagnosis and treatment due to their high prevalence and impact on patient well-being and healthcare costs. The rise of antibiotic-resistant bacteria, including E. coli strains with multidrug resistance, further complicates treatment and necessitates targeted interventions. Recurrent infections and limited drug delivery to the urinary tract pose additional hurdles in managing UTIs. The presence of biofilms and individual variations in immune response, urinary tract anatomy, and hormonal changes contribute to the complexity of UTI treatment [ 6 ]. Overcoming these challenges requires tailored interventions that address antibiotic resistance, enhance drug delivery, and target bacterial reservoirs. Innovative approaches, such as nanobots or bio-inspired algorithms, offer promising solutions to improve treatment outcomes by combating antibiotic resistance, enhancing drug delivery to the urinary tract, and effectively targeting bacterial reservoirs, as shown in Fig. 2 . Nanobots are nanoscale devices capable of sensing, actuating, and performing various tasks. They possess promising applications in healthcare, encompassing cancer therapy, surgical procedures, precision medicine, diabetes management, dental care, blood analysis, and drug administration [ 7 ]. Nanobots are capable of precise drug targeting and delivery, early cancer diagnosis, tissue engineering, gene delivery systems, minimally invasive surgery, and imaging. There are challenges to overcome, such as high costs, complexity, interface difficulties, and the unpredictable behavior of nanobots in the bloodstream. Other challenges include developing feedback sensors for autonomous control and designing nanobots at the molecular scale [ 8 ]. Nanobots must be small enough to avoid tissue damage but large enough to handle signals and be effective in their applications. The manufacturing of nanobots faces obstacles because of the absence of efficient methodologies in nanotechnology. Controlling the behavior of nanobots and coordinating large numbers of them (swarms) are also challenges. The obstacles related to sensing, navigation, power, communication, locomotion, and manipulation must be tackled [ 9 ]. Establishing valid connections between biological and non-biological components is crucial for developing nanobots. These can be powered through passive or active driving modes. The passive drive is used for body entry and control, while the active drive employs molecular motors, electric nanomotors, pumps, or membranes to power the nanorobot [ 9 ]. Energy supply is a critical factor for nanobots, and both internal (microchemical cells, fuel cells, ATP motors) and external (energy conversion) sources are explored. Implementing motor proteins for autonomous propulsion has been challenging due to their instability in different environments. Nanobots must overcome limitations in energy storage and often rely on harnessing energy from the external environment. Fuel-based and fuel-free propulsion mechanisms are used, and factors like fabrication, scale-up, and cost are important for commercial availability. Various nanobots, such as nanoscale trains, actuators using DNA components, spinning motors, and biomimetic engines, have been developed for different applications in biomedicine [ 8 ]. Microrobots have been proposed for minimally invasive procedures, targeted drug delivery, and cell manipulation in fluidic or soft environments, often drawing inspiration from natural microscale locomotion methods. Four different concepts for propulsion are as follows [ 10 ]: (a) Chemical Propulsion : Chemical propulsion uses chemical reactions to generate thrust and propel nanobots. This can be achieved by incorporating microchemical or fuel cells onboard the nanorobot, which converts chemical energy into mechanical motion. The reaction products are ejected to create a propulsive force, enabling the nanorobot to move. (b) Magnetic Propulsion : Magnetic propulsion relies on the interaction between magnetic fields and materials integrated into the nanorobot. By manipulating external magnetic fields, the nanorobot can be propelled and controlled. This approach offers precise control over the motion of nanobots and allows for navigation in complex environments. (c) Acoustic Propulsion : Acoustic propulsion exploits the generation of acoustic waves to propel nanobots. Using sound waves or ultrasonic vibrations, the nanobots can experience acoustic radiation forces that propel them through fluids or surfaces. Acoustic propulsion has the advantage of being non-invasive and compatible with biological environments. (d) Biological Propulsion : Biological propulsion takes inspiration from natural microorganisms and biological systems to achieve nanorobot locomotion. This approach integrates biological components, such as living microorganisms or biomolecular motors, and incorporates artificial elements to construct biohybrid or organism-derived nanobots. The natural propulsion mechanisms of microorganisms, such as flagella-driven movement, can be harnessed for controlled propulsion and navigation. Recent research focuses on bioinspired nanobots composed of biological elements like DNA, carbon nanotubes, and proteins [ 10 ]. These bio nanobots utilize biomolecular motors to transform chemical energy into kinetic energy, offering advantages such as biocompatibility, self-replication, and reliability. Connecting organic and inorganic components remains a challenge in their fabrication. Another approach involves combining natural microbes with synthetic elements to generate biohybrid motors that can be propelled and guided to targeted areas [ 11 ]. The use of rotating flagella in these systems aids locomotion and sensing of temperature and concentration changes. Biohybrid nanobots have shown promise in efficient therapeutic delivery with precise control and navigation. Overcoming challenges in maintaining viability and stable physiological parameters while minimizing complexity is crucial in developing these biohybrid machines. Different designs, such as helical propellers, flexible filaments, flexible magnetic composites, and catalytic nanobots, have been explored for specific requirements [ 12 ]. The rest of the paper is structured as follows. Section 2 contains the related work, reviews existing literature on nanorobot swarming and UTI treatments, and identifies research gaps. Section 3 provides the methodology, and Section 4 shows the simulation results and presents the setup and analysis of experiments. Section 5 contains the conclusion, summarises the findings, and suggests prospects for the research. Section 6 discusses the prospects of the research. 2. Related Work The exploration of bio-inspired nanorobot swarming has shown great potential in tackling various challenges, and one promising application is combating E. coli in urinary tract infections. Extensive research has been conducted in this field to unveil the capabilities of different algorithms in targeted drug delivery. A study by Tabrizi et al. [ 11 ] highlighted the potential of nanobots and artificial intelligence in revolutionizing modern medical approaches. It introduced an algorithm combining Q-learning and ACO for parallel processing of path planning, aiming to improve nanomedicine delivery and optimize path length. The algorithm enabled autonomous path planning for post-nanite injection in vessels, resulting in quicker and more accurate navigation. The experiments demonstrated the method's ability to adapt to sudden changes in patient tumor location, improve decision-making efficiency, and reduce error ratios during operations. In 2014 [ 12 ], researchers demonstrated the motion of a swarm of nanorobots in a human bloodstream environment, using red and white cells as obstacles. The problem involved predicting obstacle movement and studying the fluid flow of blood. Modified obstacle avoidance and improved PSO algorithms were combined to achieve collision avoidance and communication. A simulation of the performance and behavior of nanobots was conducted, demonstrating the effectiveness of the proposed algorithms in swarm nanorobot navigation systems. The use of communication techniques between nanobots and their movements in a human blood-streaming environment for target area tracking and arrival was studied in 2014 [ 13 ]. The nanobots were programmed to detect and avoid blood cells as obstacles using a modified PSO algorithm. The research analyses the performance and behavior of nanobots, demonstrating the significant impact of combining communication and obstacle avoidance algorithms on their movement. A research study by Bahri et al. [ 14 ] introduced a bio-inspired molecular communication algorithm that employs ant colony optimization to aid target nodes in discovering the most efficient route to a source node. The algorithm used molecules emitted and diffused by the source, similar to ant colony pheromones. Performance measures included arrival time and number of contacts, with time step size being dominant in molecular communication. Ahmed and his co-researchers [ 15 ] presented an innovative technique for swarm nanobots to navigate within the human environment, considering obstacles like blood elements, flow, and physical properties. The combined MPSO-MOA algorithm, based on swarm intelligence and obstacle avoidance, efficiently routed nanobots past blood elements while navigating within the human body. The simulation results demonstrate the effectiveness of this approach in navigating nanobots safely. Nanobots are designed to treat various ailments by circulating in the vascular system and destroying cancer cells. They require a swarm intelligence system for efficient coordination, collaboration, adaptability, and biocompatibility. The research work by Sharma and Srivastava [ 16 ] addressed existing limitations and proposed a new algorithm based on task allocation and quorum sensing. The study [ 17 ] showed the simulated behavior of cell rovers, bio nanobots injected into astronauts, using a swarm of kilobots. The simulation captured searching, movement, and protective behavior but failed to simulate body repair. The paper explored themes from particle swarm optimization, ant colony optimization, and the wolfpack algorithm. The algorithm could be used as an optimization problem, allowing for the development of assembler nanobots capable of actively repairing damaged Mars suits. Canonical particle swarm optimization was chosen to control early-stage nanobots' locomotion in a simulation system designed by Kaewkamnerdpong and his colleagues [ 18 ]. The study demonstrated nanobots acting as artificial platelets for wound healing, potentially treating platelet diseases. The standard particle swarm optimization algorithm effectively guided early-stage nanobots with fundamental attributes in Newtonian and non-Newtonian flow simulations. Ezzat et al. [ 19 ] designed nanorobots that were being used in cancer treatment by injecting them into the body, searching for cancer cells, and releasing drugs to destroy infected cells while leaving uninfected ones untreated. This approach aimed at overcoming traditional methods like chemotherapy and radiotherapy. This paper compared TLBO and Directed PSO (DPSO) algorithms for the same problem. Cancer is a deadly disease, with current treatments causing side effects on healthy tissues. A study [ 20 ] showed that DPSO enables efficient delivery of nanorobots to cancer areas without harming healthy tissues in 2014. The rise of drug-resistant pathogenic microorganisms is a pressing global public health concern. Antimicrobial resistance is causing existing agents to lose effectiveness over time. Ozaydin et al. [ 21 ] demonstrated that Micro-/nanobots offer promising treatment due to their intrinsic antimicrobial properties, oxidative stress induction, and autonomous delivery of antibiotics. Micro- and nanoscale robots are rapidly emerging in robotics research, converting energy sources into movement and force. A thorough review unveiling Advances in design, fabrication, and operation that have enhanced their power, function, and versatility was shown by Li et al. in 2017 [ 22 ]. These untethered machines have immense potential in biomedical applications, including directed drug delivery, precision surgery, medical diagnosis, and detoxification. Collaboration between robotics, medical, and nanotechnology experts is crucial for their success. DNA nanotechnology has enabled the development of nanoscale robotics that are capable of executing sophisticated tasks in a programmed and automatic manner. Designer DNA nanobots offer chemistry, biology, and medicine research potential. This review [ 23 ] summarized the latest progress in designer DNA nanorobotics, focusing on engineering principles, functional modules, sensing tasks, sensing-and-actuation, and continuous actuation. Challenges and opportunities in developing functional DNA nanorobotics for biomedical applications are discussed. The study recently carried out in 2022 [ 24 ] assessed AI and metaheuristic algorithms for designing nano vector drug delivery systems. Adaptive Neuro-Fuzzy Inference System (ANFIS) outperformed Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) in modeling nano vector data. In contrast, cuckoo search, symbiotic organism search, and firefly algorithm outperformed Genetic Algorithm (GA). The results indicate that AI and metaheuristic algorithms are not the most efficient for modeling and optimizing the physicochemical properties of nanoparticles. Implementing these algorithms and sample size calculations could yield better predictions for optimizing NV properties. In a research study [ 25 ], a comparison of the Jaya and DPSO algorithms for delivering nanorobots for cancer treatment was demonstrated. The proposed Directed Jaya (DJaya) algorithm combines these algorithms, resulting in higher efficiency. Experiments show DJaya starts delivering nanorobots earlier than DPSO, completes drug doses earlier, and groups them. The strategy also saves 40% of nanorobots, efficiently destroying cancer cells. Zhao et al. [ 26 ] presented a nanorobot control algorithm for targeted drug delivery in local blood vessels. It uses acoustic signals and blood fluid velocity to coordinate nanobots' movements. The algorithm increases the nanorobot population and reduces the cost of reaching target sites, making it a better strategy for targeting tumor tissue in blood vessels. In another seminal paper by Dorigo et al. [ 27 ] introduced the ant colony optimization algorithm, which simulates the foraging behavior of ants. It describes how individual ants deposit and follow pheromone trails to collectively find the shortest path between a nest and a food source. The paper lays the foundation for subsequent research on ACO. The particle swarm optimization (PSO) algorithm, inspired by the social behavior of bird flocking and fish schooling, was demonstrated in another research conducted [ 28 ]. While not specifically focused on foraging, PSO demonstrates the effectiveness of swarm intelligence for optimization problems. PSO's collective behavior and exploration-exploitation balance have implications for foraging scenarios. Early research by Gazi and Passino [ 29 ] focused on the stability analysis of collective decision-making in swarm systems, considering the effects of nonlinear dynamics and time delays. It investigated how the collective behavior of swarms, such as those engaged in foraging, can be affected by the dynamics of the individual agents and the communication delays. The findings have implications for designing robust foraging algorithms. The challenge of coordinating and synchronizing multiple robots is a significant focus in robotics research. This study examines the cooperation of two autonomous robots, termed "twin robots," as they carry a stick. Various PSO methods are assessed for this stick-carrying task, with a concise overview of PSO extensions and enhancements to pinpoint parameter usage. The research also involves using PSO variants for twin robot path planning. The performance assessment of these variants considers execution time, steps taken, turns made, path length, and path deviations. Fitness values guide each twin's movement in the algorithms. Comparisons with Artificial Bee Colony Optimization (ABCO) and Differential Evolutionary (DE) techniques reveal that PSO variants excel, particularly in terms of distance covered [ 30 ]. In a study conducted [ 31 ], the ant colony algorithm, a bionic optimization method, showed benefits like robustness, parallel computation, and ease of combination. It effectively tackles complex combinatorial optimization problems. However, the traditional algorithm's random selection strategy results in slow evolution. To address this, an enhanced ant colony algorithm is introduced. It employs the density peak clustering algorithm for site classification and integrates a local optimization strategy. Simulations across various traveling salesman problem instances demonstrate the improved algorithm's strong optimization capability. It significantly enhances solution quality and optimization speed, effectively overcoming the traditional algorithm's sluggishness and tendencies. These articles contribute to the existing body of knowledge and provide insights into utilizing bio-inspired nanorobot swarming techniques, particularly ant colony optimization, to combat E. coli infections in the urinary tract. By leveraging the principles of swarm intelligence and optimization algorithms, researchers aim to develop efficient strategies for targeted drug delivery and eradication of bacteria, ultimately advancing the field of nanorobotics in biomedicine. 3. Proposed Methodology The proposed nanorobot swarming algorithm based on ACO is a bio-inspired approach that draws inspiration from the behavior of ants to coordinate the movement and task allocation of a swarm of nanobots. Ant colony optimization is a metaheuristic algorithm that mimics the foraging behavior of ants to solve optimization problems [ 32 ]. 3.1 Biological Entity For nanobots as entities for drug delivery to treat urinary tract infections caused by E. coli , inspiration was drawn from the foraging behavior of ants in a colony, specifically the foraging behavior based on the ACO algorithm. Similar to ants in a colony, the nanobots in this scenario exhibit foraging behavior inspired by ACO. They work collectively as a swarm to optimize the drug delivery process and effectively treat UTIs. Each nanobot represents a virtual ant in the algorithm, contributing to the overall foraging strategy. The primary objective of the nanobots is to efficiently deliver drugs to the E. coli patch in the urinary tract, targeting the UTI infection. They mimic the cooperative foraging behavior of ant colonies to achieve this goal. They communicate and exchange information, similar to how ants leave pheromone trails to share information [ 33 ]. Instead of physical trails, the nanobots use wireless or other means of communication to transmit relevant data related to the drug delivery process. They probabilistically select paths based on pheromone trails or other heuristic information, adapting their movement patterns and trajectories to efficiently reach the infection sites and avoid obstacles within the urinary tract. Similar to ants intensifying pheromone trails to mark successful paths, the nanobots evaluate the effectiveness of drug delivery at different locations. If a particular area shows positive results, they reinforce the corresponding communication channels or data pathways to increase the likelihood of subsequent nanobots choosing the same routes for drug delivery. The nanobots exhibit heightened sensitivity to cues indicating infection sites, similar to how ants differentiate between food and neighboring ants. They can distinguish between healthy cells and infected cells in the urinary tract, allowing them to precisely target drug delivery to the E. coli patch while minimizing potential damage to healthy tissues. As ant colonies optimize their foraging efforts collectively, the nanobots collaborate and share information to enhance the drug delivery process. They dynamically allocate drug doses among themselves based on the severity of the infection and the requirements of different areas within the urinary tract, ensuring targeted and efficient delivery. By mimicking the foraging behavior based on ant colony optimization, the nanobots can optimize their drug delivery strategies in a manner that is robust, adaptable, and capable of efficiently treating UTIs. 3.2 Theory Employed The research is based on ACO principles, where nanobots are employed for efficient drug delivery to the affected area in the urinary tract. The nanobots initially move randomly within the urinary tract, exploring different regions. Once they encounter the affected area ( E. coli patch), they release pheromones to signal its presence, attracting more nanobots toward the infection site. This pheromone release strengthens over time, intensifying the concentration of pheromones in the affected area and guiding the collective efforts of the nanobots toward efficient drug delivery. By combining random movement and pheromone-based attraction, the research aims to concentrate the drug administration on the UTI infection, optimizing the delivery process and increasing the effectiveness of treatment. Utilizing a swarm of nanobots to treat urinary tract infections caused by E. coli can be considered a Multi-agent System (MAS). In this system, multiple interacting intelligent agents, represented by the nanobots, operate within the dynamic and narrow environment of the blood vessels. This concept draws similarities to the principles of ant colony optimization, which can be used to explain the behavior of the nanobots. In the paper context, each nanorobot can be viewed as a simplistic intelligence agent akin to an individual ant in the context of ant colony optimization. While the individual intelligence of each nanorobot may be limited, the collective behavior of the multi-agent system enables the accomplishment of complex tasks, such as targeting and treating UTIs caused by E. coli . By leveraging the principles of ant colony optimization, the nanobots can exhibit coordinated behavior to locate and target E. coli bacteria within the urinary tract. Similar to how ants communicate through pheromone trails, the nanobots can exchange information and signals to navigate the blood vessels and identify infection sites. This collective intelligence allows the swarm of nanobots to optimize their search for E. coli and deliver targeted treatment, enhancing the effectiveness of the UTI treatment process. When targeting nanobots to treat UTIs caused by E. coli , the challenge lies in the unknown positions of the E. coli bacteria within the urinary tract. The nanobots are equipped with pH sensors capable of detecting variations in pH levels within the urinary tract. E. coli infections often cause a localized decrease in pH due to the release of acidic by-products, which can be considered. This analogy allows us to draw a connection between the principles of ant colony optimization and the behavior of the nanobots in locating E. coli bacteria. Each nanorobot is equipped with sensors to detect the presence of E. coli bacteria and can determine its position within the urinary tract. The nanobots establish a search strategy based on the principles of ant colony optimization. As they move through the urinary tract, each nanorobot periodically releases a chemical substance called Nanorobot-Nanorobot Response (NNS) to communicate with other nanobots [ 34 ]. The released NNS forms a chemical gradient in the environment, which is a guiding signal for other nanobots. Similar to how ants follow pheromone trails, the nanobots sense the NNS concentration and adjust their movements to follow the chemical gradient towards regions of higher concentration. Nanobots reach regions with a higher concentration of NNS, indicating potential infection sites with a higher density of E. coli bacteria. The nanobots confirm the presence of bacteria through additional sensing and analysis. Once the nanobots have located infection sites, they can cooperate in the eradication process. Nanobots already present at the site release Drug Doses (DD) to target and eliminate the detected E. coli bacteria. The released DD contributes to localized treatment while also serving as a signal for other nanobots to reinforce the concentration of DD in the area. Through chemical communication, the nanobots exchange information about the detected infection sites and the concentration of DD. This allows them to collectively optimize their search and treatment efforts, focusing on areas with higher bacterial density. 3.3 Communication between Nanobots Nanobots for drug delivery to treat urinary tract infections based on ACO principles, information sharing between the nanobots, and targeting infected cells can be facilitated through ACO-inspired communication, pH-based detection, and pheromone release/NNS. The nanobots, acting as virtual ants in the ACO algorithm, communicate with each other through ACO-inspired mechanisms, pheromone trails/NNS. In addition to pH-based detection of infected cells, the nanobots can release pheromones when they encounter the infected area. The pheromones act as attractants for other nanobots, following the principle of ACO, where ants leave pheromone trails to guide the behavior of the colony, as depicted in Eq. 1. As the infected cells release substances or exhibit characteristics that trigger a pheromone response from the nanobots, the concentration of pheromones increases in the vicinity of the infection site, as represented in Eq. 2. This intensified pheromone trail serves as a signal to attract additional nanobots toward the infected cells. The attracted nanobots, guided by the pheromone trails left by the initial nanobots, join the collective effort to optimize drug delivery. They contribute to the information sharing process, adding their observations and measurements to the collective knowledge of the swarm. Through the combined information of pH changes, ACO-inspired communication, and the attractant effect of pheromones, the nanobots coordinate their actions to target and deliver drugs to the infected cells in the urinary tract, representing Eq. 4. This collective behavior enables them to optimize the drug delivery process by concentrating efforts on the areas with high pheromone concentrations and pH changes associated with infection. Assume that the communication strength or intensity between nanobots is represented by C, and the concentration of pheromones released by nanobots is denoted by P [ 34 ]. The communication between nanobots can be modeled as follows: C = k * P ……………………………………………………………… …………. (1) Here, k represents a proportionality constant that determines the relationship between the concentration of pheromones and the communication strength. It represents the sensitivity of nanobots to the presence of pheromones. The concentration of pheromones, P, is determined by the cumulative effect of pheromone deposition by all nanobots in the vicinity [ 35 ]. It can be calculated as the sum of the pheromone trails left by individual nanobots: P = ∑(pi) …………………………………………………………………… ……. (2) Where pi represents the pheromone deposited by the i th nanobot. By combining these equations, the communication between nanobots based on the pheromone release can be expressed as shown in Eq. 3: C = k * ∑(pi)…………………………………………………………………………. (3) This equation represents the communication strength or intensity between nanobots, which is influenced by the concentration of pheromones released by the nanobots. A higher concentration of pheromones indicates a stronger communication signal, attracting other nanobots to join the collective effort in drug delivery [ 36 ]. Let's introduce the pH change as ΔpH, which represents the difference in pH levels between infected and healthy regions in the urinary tract [ 37 ]. A larger pH change indicates a more significant difference between infected and healthy regions, resulting in a stronger communication signal. As mentioned earlier, the concentration of pheromones, P, is determined by the cumulative effect of pheromone deposition by all nanobots in the vicinity. Thus, the modified Eq. 4 captures the combined influence of pheromone release and pH change on the communication strength between nanobots: C = k * P * ΔpH…………………………………………………………………………. (4) By incorporating both the pheromone release and the pH change, this equation provides a more comprehensive representation of the communication strength between nanobots. It highlights how the communication signal is influenced by both the concentration of pheromones and the pH change associated with infection, allowing for more targeted and effective coordination in drug delivery for UTIs. 3.4 Random Movement of Nanobots toward the Infected Cells Assume that the random movement of nanobots toward the infected cell can be represented by a probability function, P move , which determines the likelihood of a nanobot moving toward the infected cell. The probability of movement is influenced by the information shared among the nanobots through ACO-inspired communication and the pheromone trails left by other nanobots [ 38 ]. The probability of movement can be calculated as given in Eq. 5: P move = α * P pheromone + β * P info …………………………………………………………. (5) P info represents the information shared among the nanobots. This information includes knowledge about paths, routes, or other relevant data that can be used to make decisions during the movement process. The nanobots exchange information with each other to collectively make better decisions and navigate through the environment efficiently. The nanobots share this information with one another, helping them coordinate their movement and collectively deliver drugs more effectively. P pheromone represents the pheromone trail strength or intensity at the current location of the nanobot. The concentration or strength of P pheromone at a specific location indicates the attractiveness of that location based on previous movements and interactions of nanobots. A higher concentration of pheromone suggests that other nanobots have previously visited and found that location promising. As a result, nanobots are more likely to follow the pheromone trails, leading to the emergence of collective behavior and coordination in their movements. α and β are weighting factors that determine the relative importance of the pheromone trail (P pheromone ) and the shared information (P info ) in influencing the movement of the nanobots. By adjusting the values of α and β, researchers or designers can control how much impact each factor has on the nanobots' decision-making process. This allows fine-tuning the algorithm to achieve optimal behavior based on the application's specific requirements. The pheromone trail strength, P pheromone , can be calculated as the sum of pheromone concentrations left by previous nanobots, represented as Eq. 6 [ 39 ]: P pheromone = ∑(pj) …………………………………………………………… ……. (6) Where pj represents the pheromone concentration left by the j th nanobot. The information shared among nanobots, P info , can be represented by a combination of factors such as the proximity to the infected cell, obstacles or barriers in the urinary tract, and other relevant information for drug delivery optimization. The calculation of P info depends on the specific implementation and characteristics of the nanobots and the information-sharing mechanism employed. By combining equations 5 and 6, the probability of random movement towards the infected cell can be expressed as: Pmove = α * ∑(pj) + β * Pinfo……………………………………………………. (7) This equation captures the influence of both the pheromone trail and the shared information on the random movement of nanobots toward the infected cell. The weighting factors α and β determine the relative contributions of these factors in determining the movement probability [ 27 ]. 3.5 Design of the Objective Function Using nanobots as entities for drug delivery to treat urinary tract infections based on ACO foraging behavior, the objective function can be defined in terms of optimizing the effectiveness of drug delivery to the infected area. The objective function represents the measure of success for the nanobots in performing their task of drug delivery. In this research, the objective function is closely tied to the ability of the nanobots to target and deliver drugs to the UTI infection site accurately. Maximizing drug delivery's success is the optimization problem's primary goal. An important factor in achieving this objective is the ability of the nanobots to differentiate between healthy cells and infected cells in the urinary tract. This differentiation is analogous to the nanobots' ability to differentiate between pH gradient from infected cells and healthy cells in the urinary tract. By leveraging this pH information, the nanobots can react differently to the infected cells compared to healthy cells, enabling targeted drug delivery to the UTI infection site. The objective function considers the successful detection and response to the specific pH gradient emitted by the infected cells, aiming to maximize the precision and effectiveness of drug delivery. Let’s assume that pHh represents the pH value in the vicinity of healthy cells, and the pH value in the vicinity of infected cells is represented by pH i . The calculation of the pH change gradient, indicating the pH rate of change per unit distance as illustrated in Eq. 8 [ 40 ], involves determining how the pH value alters concerning spatial displacement. The distance from a particular point to a healthy cell is denoted as rh, and the distance to an infected cell is denoted as ri. The gradient of pH change, d(pH)/dr, can be calculated using the following equation: d(pH)/dr = (pH i - pH h ) / (ri - rh) ………………………………………………………. (8) In this equation: d(pH)/dr represents the rate of change of pH with respect to the distance r (pHi − pHh) is the difference in pH values between infected cells (pHi) and healthy cells (pHh). This difference indicates the variation in pH levels between the infected and healthy regions. (ri − rh) calculates the difference in distances from the point of interest to the infected cells (ri) and healthy cells (rh). This distance difference accounts for the spatial separation between the infected and healthy regions. The equation describes how the pH value changes as a movement away from a specific point of interest in a biological system, such as a region affected by a urinary tract infection or any other disease. It considers two main factors: the difference in pH values between infected and healthy cells and the difference in distances from the point of interest to the infected and healthy cells. 3.6 Algorithm The given algorithm represents a foraging behavior inspired by ant colonies, where multiple bots or agents participate in searching for resources. The algorithm operates as follows: Initially, the algorithm takes several inputs: the list of agents (A) participating in the foraging process, the list of resource locations (R), the pheromone decay rate (δ), the pheromone strength (τ), and the deposit amount (α). The algorithm proceeds with the following steps for each bot (a) in the list of agents (A). First, it identifies the neighboring patches (P) that contain available resources or food patches. If any resource patches are available, the algorithm calculates the total pheromone level of these patches by summing up the pheromone levels of each patch in P. Then, it performs pheromone evaporation by reducing the pheromone levels of all the resource patches in P based on the decay rate δ. This mimics the natural decay of pheromones over time. Next, the algorithm creates a list of probabilities for each available resource patch in P. These probabilities are calculated by dividing the pheromone level of each patch by the total pheromone level, ensuring that patches with higher pheromone levels have higher probabilities. Based on these probabilities, a target-patch is selected probabilistically. The selection process involves choosing a patch from P with a probability based on the calculated probabilities. If a valid target-patch is selected, the pheromone level of the target-patch is increased by the deposit amount α, indicating successful discovery. The bot is then oriented towards the target-patch and moved towards it, utilizing a specific navigation mechanism relevant to the real-life scenario. This movement mechanism is not explicitly defined in the given algorithm. These steps are repeated for each bot in A, allowing them to search for resources, adjust pheromone levels, and navigate toward potential food sources. 3.7 Motion Planning Nanobots reach the target area with the assistance of pH sensors, which are applied to detect the pH gradient change in the vicinity of bacterial patches, discover the chemical marker among nanobots and check the concentration of released drug doses. Figure 3 describes the motion planning for the nanobots. In the flow diagram, the parameter β is a threshold value that represents the standard level of the drug dose. On the one hand, if the level of the drug dose is more than β, it demonstrates that the DD is enough to destroy all E. coli at the position. So, the nanobots will leave the position and move to the next local area. On the other hand, the nanobots will immediately release drug doses to eradicate cancer cells in their current position. 4. Simulation Results To demonstrate the viability of the proposed algorithm for targeted drug delivery for urinary tract infections caused by E. coli , the results of the experiment conducted is demonstrated in this section. The pH-based sensors are employed, and nearest-neighbor search communication is established between nanobots. The simulation is conducted using the NetLogo software, which provides a modeling environment for simulating multi-agent systems. NetLogo software offers a suitable platform for constructing social and natural phenomena models, making it ideal for simulating our targeted drug delivery system. The simulation involves setting several parameters to control the motion behavior of the nanobots. These parameters can be determined either deterministically or through random inputs, depending on the desired experimental setup, as shown in Fig. 4 . 4.1 Simulation Setup The simulations are conducted with the setting of the following parameters. The environment of the simulation is presented in Fig. 5 . The initial positions of nanobots are randomly set. The initial position of the nanobots, E. coli patch, extracellular membrane, and lumen of the blood vessel are shown respectively in the figure. Nanobot- Nanobot Response and DD are not displayed because the figure is the initial state of the simulation. The blood fluid is assumed to be steady to decrease the simulation complexity, so the flow rate is not changed over time. 4.2 Simulation Results and Analysis Firstly, a simulation was conducted on a group of nanobots to observe the difference in eradicating E. coli patches using the proposed algorithm. Figure 6 depicts the various stages of the simulation. Figures 6 (a) and (e) show the first and last stages of the simulation, respectively. In addition, Figs. 6 (b) to 6(d) depict the intermediate stages of the simulation. In Fig. 7, the horizontal axis shows the time, whose unit is seconds, and the vertical axis shows the number of live E. coli patches. Based on the observed data, the first column represents the number of nanobots used in a simulation. In contrast, the second column represents the number of E. coli patches left uncured after the simulation. Table 1 lists the results of the experimental run under a defined conditional setup on NetLogo. Table 1 Results of experimental run under defined conditional setup on NetLogo No of Nanobots E. coli Patches Left Uncured 0 50 100 32 200 16 300 10 400 13 500 3 600 3 700 3 800 2 900 1 1000 1 Based on the given data, simulations were conducted using varying numbers of nanobots to assess their effectiveness in curing E. coli patches. A closer examination of the results reveals interesting trends and dynamics in the relationship between the number of nanobots employed and the remaining E. coli patches. Upon analyzing the data, it is evident that increasing the number of nanobots generally reduces the number of E. coli patches left uncured. This trend suggests a positive correlation between the number of nanobots and the efficacy of the simulation in eliminating E. coli . As the nanobot population grows, their collective efforts become more efficient at targeting and eradicating the E. coli patches within the urinary tract. The relationship between the number of nanobots and the remaining E. coli patches is not strictly linear. It exhibits a more complex pattern. There are instances where a relatively small increase in the number of nanobots results in a significant decrease in the number of uncured patches. This implies that a modest boost in the nanobot population can substantially enhance the simulation's curative capabilities. There are also occasions where a larger increase in the number of nanobots does not yield a proportionate decrease in uncured patches. In these cases, the diminishing returns phenomenon may come into play, indicating that adding more nanobots beyond a certain threshold does not significantly contribute to further improvements in the simulation's efficacy. This could be due to factors such as limited available targets or overlapping coverage areas of the nanobots. As the number of nanobots approaches higher values, the number of uncured patches reaches a plateau or diminishes to extremely low levels. This suggests that an optimal or near-optimal number of nanobots exists beyond which additional nanobots provide marginal benefits in terms of eliminating E. coli patches. This optimal threshold can be crucial in resource allocation and decision-making when deploying nanobots for effective UTI treatment. Experimental data showed that utilizing 1000 nanobots was remarkably effective in eliminating 98% of the E. coli patch during the initial trial. To obtain a comprehensive assessment of their accuracy, 10 runs were conducted, as seen in Fig. 8 . The outcome was impressive, as 99% accuracy was achieved in successfully curing the E. coli patch. These findings demonstrate the promising potential of nanobot technology in combating E. coli infections. This article presents an algorithm that combines parallel processing with Q-learning and ACO to improve nano-medicine delivery, optimize path length, and increase accuracy. The autonomous path planning method is used for post-nanite injection in vessels, achieving environmental perception and faster navigation while reducing processing power and memory usage. The proposed adaptive agent method can achieve continuous objectives, such as recalculation of optimal paths, time efficiency in decision-making, and decreased error ratios. 4.3 Comparative Analysis Nanorobots hold transformative potential in medical practices, aided by AI's growth. Yet, precise nano-sample path planning remains challenging. Another study [ 41 ] combined parallel processing, Q-learning, and ACO for optimized nano-medicine delivery, achieving quicker navigation, reduced power usage, and continuous objective achievement. Comparing both studies, these utilize simulation-based approaches to assess nanobot efficacy in distinct medical contexts. While our study targets E. coli treatment, the other addresses tumor drug delivery. Both compare algorithms, with our study linking nanobot numbers to E. coli patch reduction and the other exploring path planning methods. Our study identifies nanobot numbers' positive correlation with efficacy, establishing optimal thresholds, while the other optimizes algorithm performance. Also, the current study underscores UTI treatment, while the other prioritizes drug delivery. These studies spotlight nanobot potential across medical challenges, showcasing consistent trends and unique insights. In comparison, our research excels in addressing UTI concerns directly, offering practical solutions while emphasizing nanobot number correlation and optimal thresholds, elevating its impact. In contrast to traditional treatment methods for urinary tract infections [ 42 ], our research findings present a promising and innovative approach with distinct advantages. Traditional methods often involve antibiotic treatments that may lead to antibiotic resistance, prolonged medication regimens, and potential side effects. In contrast, our study harnesses nanorobot technology to deliver targeted treatments with greater precision, reducing the need for extended medication consumption. We introduce a data-driven strategy that optimizes the curative process by establishing a positive correlation between nanobot numbers and treatment efficacy. This approach offers quicker and more effective results and minimizes the risk of developing antibiotic resistance. Moreover, our research emphasizes the potential for resource-efficient deployment of nanobots, aligning with patient-centric and sustainable healthcare practices. Additionally, it is noteworthy that no prior studies have explored the utilization of E. coli patches for nanobot interventions. As such, our comparison extends beyond the existing literature in the domain of tumor cell eradication, shedding new light on nanobot applications in a previously unexplored context. 5. Conclusion The present study introduces an enhanced approach utilizing the ant colony optimization algorithm to address the challenge of eliminating urinary tract infections caused by E. coli bacteria. Through the deployment of nanobots within the local field of the urinary tract, our method specifically targets and eradicates the E. coli patch. Our Improved ant colony optimization algorithm outperforms the conventional ant colony optimization algorithm, exhibiting superior performance in terms of reduced time consumption and enhanced global search capabilities. While our research has shown promising results, certain areas require further investigation and development. The introduction of a new fitness function has resulted in a slightly longer path length for each nanobot from the starting point to the target. Optimizing the fitness function is crucial to maximize the algorithm's performance. Additionally, studying the control of the nanobot population and drug release rate is essential to enhance the efficiency of the drug delivery process. During the experimental simulations, the systematic variation in the number of nanobots was employed, and their elimination probability was to ensure accuracy. Notably, with the utilization of 1000 nanobots, a 99.99% elimination rate of the E. coli patch was achieved, as shown in Fig. 7. These findings underscore the effectiveness and potential of our approach in combatting UTIs caused by E. coli bacteria. Nanorobot swarming and ant colony optimization hold great potential for revolutionizing bacterial infection treatment. By emulating ant colonies' collective intelligence, nanobots can efficiently target E. coli bacteria in the urinary tract, offering an alternative to antibiotics and reducing resistance. Minimally invasive interventions and real-time monitoring through sensor-equipped swarms enable targeted treatment and personalized medicine. These principles can extend beyond UTIs, opening new avenues for efficient treatment and management of bacterial infections while addressing antibiotic resistance. Declarations Compliance with Ethical Standards: Funding: The authors declare that they do not have any funding or grant for the manuscript. Conflict of Interest: The authors declare that they do not have any conflict of interests that influence the work reported in this paper. Data Availability : The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethical Approval : No animals were involved in this study. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. References Foxman, B. (2002). 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Sajanikar","email":"data:image/png;base64,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","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Atharva","middleName":"Y.","lastName":"Sajanikar","suffix":""},{"id":408411417,"identity":"f253a000-879c-4c2c-a47a-98557473227f","order_by":6,"name":"Yu-Chen Hu","email":"","orcid":"","institution":"Tunghai University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Chen","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-01-26 09:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5905599/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5905599/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75303451,"identity":"14aaddd8-a32b-4bb3-9d24-5a575358a164","added_by":"auto","created_at":"2025-02-03 08:02:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":333671,"visible":true,"origin":"","legend":"\u003cp\u003eSystematic diagram representing urinary tract infection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/624d4aea3ff9f1ae929e90b2.png"},{"id":75304839,"identity":"9413f620-8417-4452-b5ac-f835de1a62c7","added_by":"auto","created_at":"2025-02-03 08:10:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":211553,"visible":true,"origin":"","legend":"\u003cp\u003eNanobots for targeted drug delivery for treatment of urinary tract infection caused by \u003cem\u003eE. coli.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/fc37f8fd51dd7f59c65ee920.png"},{"id":75303437,"identity":"5f338dad-35a9-4a82-99d7-f99c197deb63","added_by":"auto","created_at":"2025-02-03 08:02:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24684,"visible":true,"origin":"","legend":"\u003cp\u003eMotion planning in nanobots through the bloodstream\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/4fd2a310e73a39c2a046a0b3.png"},{"id":75304831,"identity":"67a1ac4c-7bee-4b60-88f8-1e9dd1604f1a","added_by":"auto","created_at":"2025-02-03 08:10:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68617,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of NetLogo simulation interface\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/f61d632b89f7e9107482672d.png"},{"id":75303412,"identity":"4984f3df-aaf2-482a-ba0f-745f02f41a2c","added_by":"auto","created_at":"2025-02-03 08:02:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89385,"visible":true,"origin":"","legend":"\u003cp\u003eSystematic representation of components of NetLogo simulation under experimental setup\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/ac611d333f3b52919ade61f5.png"},{"id":75304828,"identity":"9fd597fb-7ac5-4515-91e7-2cc4f130e580","added_by":"auto","created_at":"2025-02-03 08:10:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69614,"visible":true,"origin":"","legend":"\u003cp\u003eThe various stages of the simulation. (a)being the first stage of the simulation and (e) being the last stage of the simulation (b) (c) (d) representing the intermediate stages of the simulation.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/1700d9c5f67815bab07b3363.png"},{"id":75303430,"identity":"2d29f9bc-612d-47a6-a920-bf9c2924cfb2","added_by":"auto","created_at":"2025-02-03 08:02:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":208576,"visible":true,"origin":"","legend":"\u003cp\u003eSimulation result of different populations of nanobots using ACO. In the given simulation run using (a) 1000 nanobots (b) 950 nanobots (c) 900 nanobots (d) 850 nanobots (e) 800 nanobots (f) 750 nanobots (g) 700 nanobots (h) 650 nanobots (i) 600 nanobots (j) 550 nanobots (k) 500 nanobots (l) 450 nanobots (m) 400 nanobots (n) 350 nanobots (o) 300 nanobots (p) 250 nanobots (q) 200 nanobots (r) 150 nanobots (s) 100 nanobots (t) 50 nanobots respectively.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/9648e6319f813375f8e70fb8.png"},{"id":75303422,"identity":"069f0fbe-7474-404c-bdd8-80531a558264","added_by":"auto","created_at":"2025-02-03 08:02:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":210285,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the simulation run using the most effective concentration of nanobots\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/8a7db14a2801e9c989272bd8.png"},{"id":75305104,"identity":"19203073-e010-4044-99a9-4d04833a2548","added_by":"auto","created_at":"2025-02-03 08:18:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1960188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5905599/v1/8f4f79e8-239c-4376-90c0-204fff5f2311.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revolutionizing UTI Treatment: Harnessing Ant Colony Optimization for Effective E. coli Combat in the Urinary Tract","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrinary tract infections are a common health issue, with over 6\u0026nbsp;million outpatient visits annually [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Elderly individuals, both men and women, are particularly susceptible to UTIs due to increased urinary tract abnormalities and catheter use. Most simple UTIs are attributed to uropathogenic strains of \u003cem\u003eE. coli\u003c/em\u003e, accounting for up to 90% of cases [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. These uropathogenic \u003cem\u003eE. coli\u003c/em\u003e strains possess specific serotypes and adhesive organelles known as fimbriae, such as P fimbriae, which facilitate attachment to uroepithelial cells. Recent research has shown the involvement of type 1 fimbriae in UTIs, promoting bacterial persistence and enhancing the host\u0026apos;s inflammatory response. Other factors, such as aerobactin-mediated iron uptake and hemolysin, may contribute to bacterial growth and host-cell injury. These virulence factors are often found together on \u0026quot;pathogenicity islands,\u0026quot; large segments of DNA acquired through horizontal gene transfer [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eBehavioral elements, such as sexual behavior and urination habits, can influence urethral colonization by uropathogens. Certain menstrual cycle stages and hormonal variations may also influence UTI susceptibility. An association between recurrent UTIs and specific Lewis\u0026rsquo;s blood-group phenotypes is observed. Clinical manifestations of UTIs include dysuria, increased frequency of urination, cloudy urine, and sometimes blood in the urine. Acute pyelonephritis, a more severe form of UTI, may present with high fever and costovertebral pain [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Elderly patients may exhibit atypical symptoms such as gastrointestinal discomfort and absent fever. Quantitative bacterial counts are used to determine significant bacteriuria, but the clinical relevance of the traditional threshold of 10\u003csup\u003e5\u003c/sup\u003e colony-forming units/mL has been reassessed. Low-count bacteriuria (10\u003csup\u003e2\u003c/sup\u003e to 10\u003csup\u003e4\u003c/sup\u003e colony-forming units/mL) may still indicate infection if the patient is symptomatic and pyuria is present [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. depicts the urinary tract infection process.\u003c/p\u003e\n\u003cp\u003eUrinary tract infections present significant challenges in diagnosis and treatment due to their high prevalence and impact on patient well-being and healthcare costs. The rise of antibiotic-resistant bacteria, including \u003cem\u003eE. coli\u003c/em\u003e strains with multidrug resistance, further complicates treatment and necessitates targeted interventions. Recurrent infections and limited drug delivery to the urinary tract pose additional hurdles in managing UTIs. The presence of biofilms and individual variations in immune response, urinary tract anatomy, and hormonal changes contribute to the complexity of UTI treatment [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Overcoming these challenges requires tailored interventions that address antibiotic resistance, enhance drug delivery, and target bacterial reservoirs. Innovative approaches, such as nanobots or bio-inspired algorithms, offer promising solutions to improve treatment outcomes by combating antibiotic resistance, enhancing drug delivery to the urinary tract, and effectively targeting bacterial reservoirs, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eNanobots are nanoscale devices capable of sensing, actuating, and performing various tasks. They possess promising applications in healthcare, encompassing cancer therapy, surgical procedures, precision medicine, diabetes management, dental care, blood analysis, and drug administration [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nanobots are capable of precise drug targeting and delivery, early cancer diagnosis, tissue engineering, gene delivery systems, minimally invasive surgery, and imaging. There are challenges to overcome, such as high costs, complexity, interface difficulties, and the unpredictable behavior of nanobots in the bloodstream. Other challenges include developing feedback sensors for autonomous control and designing nanobots at the molecular scale [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nanobots must be small enough to avoid tissue damage but large enough to handle signals and be effective in their applications. The manufacturing of nanobots faces obstacles because of the absence of efficient methodologies in nanotechnology. Controlling the behavior of nanobots and coordinating large numbers of them (swarms) are also challenges. The obstacles related to sensing, navigation, power, communication, locomotion, and manipulation must be tackled [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eEstablishing valid connections between biological and non-biological components is crucial for developing nanobots. These can be powered through passive or active driving modes. The passive drive is used for body entry and control, while the active drive employs molecular motors, electric nanomotors, pumps, or membranes to power the nanorobot [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Energy supply is a critical factor for nanobots, and both internal (microchemical cells, fuel cells, ATP motors) and external (energy conversion) sources are explored. Implementing motor proteins for autonomous propulsion has been challenging due to their instability in different environments. Nanobots must overcome limitations in energy storage and often rely on harnessing energy from the external environment. Fuel-based and fuel-free propulsion mechanisms are used, and factors like fabrication, scale-up, and cost are important for commercial availability. Various nanobots, such as nanoscale trains, actuators using DNA components, spinning motors, and biomimetic engines, have been developed for different applications in biomedicine [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Microrobots have been proposed for minimally invasive procedures, targeted drug delivery, and cell manipulation in fluidic or soft environments, often drawing inspiration from natural microscale locomotion methods. Four different concepts for propulsion are as follows [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e(a) \u003cstrong\u003eChemical Propulsion\u003c/strong\u003e: Chemical propulsion uses chemical reactions to generate thrust and propel nanobots. This can be achieved by incorporating microchemical or fuel cells onboard the nanorobot, which converts chemical energy into mechanical motion. The reaction products are ejected to create a propulsive force, enabling the nanorobot to move.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e(b) \u003cstrong\u003eMagnetic Propulsion\u003c/strong\u003e: Magnetic propulsion relies on the interaction between magnetic fields and materials integrated into the nanorobot. By manipulating external magnetic fields, the nanorobot can be propelled and controlled. This approach offers precise control over the motion of nanobots and allows for navigation in complex environments.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e(c) \u003cstrong\u003eAcoustic Propulsion\u003c/strong\u003e: Acoustic propulsion exploits the generation of acoustic waves to propel nanobots. Using sound waves or ultrasonic vibrations, the nanobots can experience acoustic radiation forces that propel them through fluids or surfaces. Acoustic propulsion has the advantage of being non-invasive and compatible with biological environments.\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e(d) \u003cstrong\u003eBiological Propulsion\u003c/strong\u003e: Biological propulsion takes inspiration from natural microorganisms and biological systems to achieve nanorobot locomotion. This approach integrates biological components, such as living microorganisms or biomolecular motors, and incorporates artificial elements to construct biohybrid or organism-derived nanobots. The natural propulsion mechanisms of microorganisms, such as flagella-driven movement, can be harnessed for controlled propulsion and navigation.\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eRecent research focuses on bioinspired nanobots composed of biological elements like DNA, carbon nanotubes, and proteins [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. These bio nanobots utilize biomolecular motors to transform chemical energy into kinetic energy, offering advantages such as biocompatibility, self-replication, and reliability. Connecting organic and inorganic components remains a challenge in their fabrication. Another approach involves combining natural microbes with synthetic elements to generate biohybrid motors that can be propelled and guided to targeted areas [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. The use of rotating flagella in these systems aids locomotion and sensing of temperature and concentration changes. Biohybrid nanobots have shown promise in efficient therapeutic delivery with precise control and navigation. Overcoming challenges in maintaining viability and stable physiological parameters while minimizing complexity is crucial in developing these biohybrid machines. Different designs, such as helical propellers, flexible filaments, flexible magnetic composites, and catalytic nanobots, have been explored for specific requirements [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe rest of the paper is structured as follows. Section 2 contains the related work, reviews existing literature on nanorobot swarming and UTI treatments, and identifies research gaps. Section 3 provides the methodology, and Section \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the simulation results and presents the setup and analysis of experiments. Section \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e contains the conclusion, summarises the findings, and suggests prospects for the research. Section 6 discusses the prospects of the research.\u003c/p\u003e"},{"header":"2. Related Work","content":"\u003cp\u003eThe exploration of bio-inspired nanorobot swarming has shown great potential in tackling various challenges, and one promising application is combating \u003cem\u003eE. coli\u003c/em\u003e in urinary tract infections. Extensive research has been conducted in this field to unveil the capabilities of different algorithms in targeted drug delivery. A study by Tabrizi \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] highlighted the potential of nanobots and artificial intelligence in revolutionizing modern medical approaches. It introduced an algorithm combining Q-learning and ACO for parallel processing of path planning, aiming to improve nanomedicine delivery and optimize path length. The algorithm enabled autonomous path planning for post-nanite injection in vessels, resulting in quicker and more accurate navigation. The experiments demonstrated the method's ability to adapt to sudden changes in patient tumor location, improve decision-making efficiency, and reduce error ratios during operations.\u003c/p\u003e \u003cp\u003eIn 2014 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], researchers demonstrated the motion of a swarm of nanorobots in a human bloodstream environment, using red and white cells as obstacles. The problem involved predicting obstacle movement and studying the fluid flow of blood. Modified obstacle avoidance and improved PSO algorithms were combined to achieve collision avoidance and communication. A simulation of the performance and behavior of nanobots was conducted, demonstrating the effectiveness of the proposed algorithms in swarm nanorobot navigation systems. The use of communication techniques between nanobots and their movements in a human blood-streaming environment for target area tracking and arrival was studied in 2014 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The nanobots were programmed to detect and avoid blood cells as obstacles using a modified PSO algorithm. The research analyses the performance and behavior of nanobots, demonstrating the significant impact of combining communication and obstacle avoidance algorithms on their movement.\u003c/p\u003e \u003cp\u003eA research study by Bahri \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] introduced a bio-inspired molecular communication algorithm that employs ant colony optimization to aid target nodes in discovering the most efficient route to a source node. The algorithm used molecules emitted and diffused by the source, similar to ant colony pheromones. Performance measures included arrival time and number of contacts, with time step size being dominant in molecular communication. Ahmed and his co-researchers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] presented an innovative technique for swarm nanobots to navigate within the human environment, considering obstacles like blood elements, flow, and physical properties. The combined MPSO-MOA algorithm, based on swarm intelligence and obstacle avoidance, efficiently routed nanobots past blood elements while navigating within the human body. The simulation results demonstrate the effectiveness of this approach in navigating nanobots safely. Nanobots are designed to treat various ailments by circulating in the vascular system and destroying cancer cells. They require a swarm intelligence system for efficient coordination, collaboration, adaptability, and biocompatibility.\u003c/p\u003e \u003cp\u003eThe research work by Sharma and Srivastava [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] addressed existing limitations and proposed a new algorithm based on task allocation and quorum sensing. The study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] showed the simulated behavior of cell rovers, bio nanobots injected into astronauts, using a swarm of kilobots. The simulation captured searching, movement, and protective behavior but failed to simulate body repair. The paper explored themes from particle swarm optimization, ant colony optimization, and the wolfpack algorithm. The algorithm could be used as an optimization problem, allowing for the development of assembler nanobots capable of actively repairing damaged Mars suits.\u003c/p\u003e \u003cp\u003eCanonical particle swarm optimization was chosen to control early-stage nanobots' locomotion in a simulation system designed by Kaewkamnerdpong and his colleagues [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The study demonstrated nanobots acting as artificial platelets for wound healing, potentially treating platelet diseases. The standard particle swarm optimization algorithm effectively guided early-stage nanobots with fundamental attributes in Newtonian and non-Newtonian flow simulations. Ezzat \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] designed nanorobots that were being used in cancer treatment by injecting them into the body, searching for cancer cells, and releasing drugs to destroy infected cells while leaving uninfected ones untreated. This approach aimed at overcoming traditional methods like chemotherapy and radiotherapy. This paper compared TLBO and Directed PSO (DPSO) algorithms for the same problem. Cancer is a deadly disease, with current treatments causing side effects on healthy tissues. A study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] showed that DPSO enables efficient delivery of nanorobots to cancer areas without harming healthy tissues in 2014.\u003c/p\u003e \u003cp\u003eThe rise of drug-resistant pathogenic microorganisms is a pressing global public health concern. Antimicrobial resistance is causing existing agents to lose effectiveness over time. Ozaydin \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] demonstrated that Micro-/nanobots offer promising treatment due to their intrinsic antimicrobial properties, oxidative stress induction, and autonomous delivery of antibiotics. Micro- and nanoscale robots are rapidly emerging in robotics research, converting energy sources into movement and force. A thorough review unveiling Advances in design, fabrication, and operation that have enhanced their power, function, and versatility was shown by Li \u003cem\u003eet al.\u003c/em\u003e in 2017 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These untethered machines have immense potential in biomedical applications, including directed drug delivery, precision surgery, medical diagnosis, and detoxification. Collaboration between robotics, medical, and nanotechnology experts is crucial for their success. DNA nanotechnology has enabled the development of nanoscale robotics that are capable of executing sophisticated tasks in a programmed and automatic manner. Designer DNA nanobots offer chemistry, biology, and medicine research potential.\u003c/p\u003e \u003cp\u003eThis review [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] summarized the latest progress in designer DNA nanorobotics, focusing on engineering principles, functional modules, sensing tasks, sensing-and-actuation, and continuous actuation. Challenges and opportunities in developing functional DNA nanorobotics for biomedical applications are discussed. The study recently carried out in 2022 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] assessed AI and metaheuristic algorithms for designing nano vector drug delivery systems. Adaptive Neuro-Fuzzy Inference System (ANFIS) outperformed Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) in modeling nano vector data. In contrast, cuckoo search, symbiotic organism search, and firefly algorithm outperformed Genetic Algorithm (GA). The results indicate that AI and metaheuristic algorithms are not the most efficient for modeling and optimizing the physicochemical properties of nanoparticles. Implementing these algorithms and sample size calculations could yield better predictions for optimizing NV properties.\u003c/p\u003e \u003cp\u003eIn a research study [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], a comparison of the Jaya and DPSO algorithms for delivering nanorobots for cancer treatment was demonstrated. The proposed Directed Jaya (DJaya) algorithm combines these algorithms, resulting in higher efficiency. Experiments show DJaya starts delivering nanorobots earlier than DPSO, completes drug doses earlier, and groups them. The strategy also saves 40% of nanorobots, efficiently destroying cancer cells. Zhao et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] presented a nanorobot control algorithm for targeted drug delivery in local blood vessels. It uses acoustic signals and blood fluid velocity to coordinate nanobots' movements. The algorithm increases the nanorobot population and reduces the cost of reaching target sites, making it a better strategy for targeting tumor tissue in blood vessels.\u003c/p\u003e \u003cp\u003eIn another seminal paper by Dorigo \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] introduced the ant colony optimization algorithm, which simulates the foraging behavior of ants. It describes how individual ants deposit and follow pheromone trails to collectively find the shortest path between a nest and a food source. The paper lays the foundation for subsequent research on ACO. The particle swarm optimization (PSO) algorithm, inspired by the social behavior of bird flocking and fish schooling, was demonstrated in another research conducted [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. While not specifically focused on foraging, PSO demonstrates the effectiveness of swarm intelligence for optimization problems. PSO's collective behavior and exploration-exploitation balance have implications for foraging scenarios. Early research by Gazi and Passino [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] focused on the stability analysis of collective decision-making in swarm systems, considering the effects of nonlinear dynamics and time delays. It investigated how the collective behavior of swarms, such as those engaged in foraging, can be affected by the dynamics of the individual agents and the communication delays. The findings have implications for designing robust foraging algorithms.\u003c/p\u003e \u003cp\u003eThe challenge of coordinating and synchronizing multiple robots is a significant focus in robotics research. This study examines the cooperation of two autonomous robots, termed \"twin robots,\" as they carry a stick. Various PSO methods are assessed for this stick-carrying task, with a concise overview of PSO extensions and enhancements to pinpoint parameter usage. The research also involves using PSO variants for twin robot path planning. The performance assessment of these variants considers execution time, steps taken, turns made, path length, and path deviations. Fitness values guide each twin's movement in the algorithms. Comparisons with Artificial Bee Colony Optimization (ABCO) and Differential Evolutionary (DE) techniques reveal that PSO variants excel, particularly in terms of distance covered [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a study conducted [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], the ant colony algorithm, a bionic optimization method, showed benefits like robustness, parallel computation, and ease of combination. It effectively tackles complex combinatorial optimization problems. However, the traditional algorithm's random selection strategy results in slow evolution. To address this, an enhanced ant colony algorithm is introduced. It employs the density peak clustering algorithm for site classification and integrates a local optimization strategy. Simulations across various traveling salesman problem instances demonstrate the improved algorithm's strong optimization capability. It significantly enhances solution quality and optimization speed, effectively overcoming the traditional algorithm's sluggishness and tendencies.\u003c/p\u003e \u003cp\u003eThese articles contribute to the existing body of knowledge and provide insights into utilizing bio-inspired nanorobot swarming techniques, particularly ant colony optimization, to combat \u003cem\u003eE. coli\u003c/em\u003e infections in the urinary tract. By leveraging the principles of swarm intelligence and optimization algorithms, researchers aim to develop efficient strategies for targeted drug delivery and eradication of bacteria, ultimately advancing the field of nanorobotics in biomedicine.\u003c/p\u003e"},{"header":"3. Proposed Methodology","content":"\u003cp\u003eThe proposed nanorobot swarming algorithm based on ACO is a bio-inspired approach that draws inspiration from the behavior of ants to coordinate the movement and task allocation of a swarm of nanobots. Ant colony optimization is a metaheuristic algorithm that mimics the foraging behavior of ants to solve optimization problems [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Biological Entity\u003c/h2\u003e\n \u003cp\u003eFor nanobots as entities for drug delivery to treat urinary tract infections caused by \u003cem\u003eE. coli\u003c/em\u003e, inspiration was drawn from the foraging behavior of ants in a colony, specifically the foraging behavior based on the ACO algorithm. Similar to ants in a colony, the nanobots in this scenario exhibit foraging behavior inspired by ACO. They work collectively as a swarm to optimize the drug delivery process and effectively treat UTIs. Each nanobot represents a virtual ant in the algorithm, contributing to the overall foraging strategy.\u003c/p\u003e\n \u003cp\u003eThe primary objective of the nanobots is to efficiently deliver drugs to the E. coli patch in the urinary tract, targeting the UTI infection. They mimic the cooperative foraging behavior of ant colonies to achieve this goal. They communicate and exchange information, similar to how ants leave pheromone trails to share information [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Instead of physical trails, the nanobots use wireless or other means of communication to transmit relevant data related to the drug delivery process. They probabilistically select paths based on pheromone trails or other heuristic information, adapting their movement patterns and trajectories to efficiently reach the infection sites and avoid obstacles within the urinary tract. Similar to ants intensifying pheromone trails to mark successful paths, the nanobots evaluate the effectiveness of drug delivery at different locations. If a particular area shows positive results, they reinforce the corresponding communication channels or data pathways to increase the likelihood of subsequent nanobots choosing the same routes for drug delivery. The nanobots exhibit heightened sensitivity to cues indicating infection sites, similar to how ants differentiate between food and neighboring ants. They can distinguish between healthy cells and infected cells in the urinary tract, allowing them to precisely target drug delivery to the \u003cem\u003eE. coli\u003c/em\u003e patch while minimizing potential damage to healthy tissues. As ant colonies optimize their foraging efforts collectively, the nanobots collaborate and share information to enhance the drug delivery process. They dynamically allocate drug doses among themselves based on the severity of the infection and the requirements of different areas within the urinary tract, ensuring targeted and efficient delivery. By mimicking the foraging behavior based on ant colony optimization, the nanobots can optimize their drug delivery strategies in a manner that is robust, adaptable, and capable of efficiently treating UTIs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Theory Employed\u003c/h2\u003e\n \u003cp\u003eThe research is based on ACO principles, where nanobots are employed for efficient drug delivery to the affected area in the urinary tract. The nanobots initially move randomly within the urinary tract, exploring different regions. Once they encounter the affected area (\u003cem\u003eE. coli\u003c/em\u003e patch), they release pheromones to signal its presence, attracting more nanobots toward the infection site. This pheromone release strengthens over time, intensifying the concentration of pheromones in the affected area and guiding the collective efforts of the nanobots toward efficient drug delivery. By combining random movement and pheromone-based attraction, the research aims to concentrate the drug administration on the UTI infection, optimizing the delivery process and increasing the effectiveness of treatment.\u003c/p\u003e\n \u003cp\u003eUtilizing a swarm of nanobots to treat urinary tract infections caused by \u003cem\u003eE. coli\u003c/em\u003e can be considered a Multi-agent System (MAS). In this system, multiple interacting intelligent agents, represented by the nanobots, operate within the dynamic and narrow environment of the blood vessels. This concept draws similarities to the principles of ant colony optimization, which can be used to explain the behavior of the nanobots. In the paper context, each nanorobot can be viewed as a simplistic intelligence agent akin to an individual ant in the context of ant colony optimization. While the individual intelligence of each nanorobot may be limited, the collective behavior of the multi-agent system enables the accomplishment of complex tasks, such as targeting and treating UTIs caused by \u003cem\u003eE. coli\u003c/em\u003e. By leveraging the principles of ant colony optimization, the nanobots can exhibit coordinated behavior to locate and target \u003cem\u003eE. coli\u003c/em\u003e bacteria within the urinary tract. Similar to how ants communicate through pheromone trails, the nanobots can exchange information and signals to navigate the blood vessels and identify infection sites. This collective intelligence allows the swarm of nanobots to optimize their search for \u003cem\u003eE. coli\u003c/em\u003e and deliver targeted treatment, enhancing the effectiveness of the UTI treatment process.\u003c/p\u003e\n \u003cp\u003eWhen targeting nanobots to treat UTIs caused by \u003cem\u003eE. coli\u003c/em\u003e, the challenge lies in the unknown positions of the \u003cem\u003eE. coli\u003c/em\u003e bacteria within the urinary tract. The nanobots are equipped with pH sensors capable of detecting variations in pH levels within the urinary tract. \u003cem\u003eE. coli\u003c/em\u003e infections often cause a localized decrease in pH due to the release of acidic by-products, which can be considered. This analogy allows us to draw a connection between the principles of ant colony optimization and the behavior of the nanobots in locating \u003cem\u003eE. coli\u003c/em\u003e bacteria.\u003c/p\u003e\n \u003cp\u003eEach nanorobot is equipped with sensors to detect the presence of \u003cem\u003eE. coli\u003c/em\u003e bacteria and can determine its position within the urinary tract. The nanobots establish a search strategy based on the principles of ant colony optimization. As they move through the urinary tract, each nanorobot periodically releases a chemical substance called Nanorobot-Nanorobot Response (NNS) to communicate with other nanobots [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. The released NNS forms a chemical gradient in the environment, which is a guiding signal for other nanobots. Similar to how ants follow pheromone trails, the nanobots sense the NNS concentration and adjust their movements to follow the chemical gradient towards regions of higher concentration. Nanobots reach regions with a higher concentration of NNS, indicating potential infection sites with a higher density of \u003cem\u003eE. coli\u003c/em\u003e bacteria. The nanobots confirm the presence of bacteria through additional sensing and analysis. Once the nanobots have located infection sites, they can cooperate in the eradication process. Nanobots already present at the site release Drug Doses (DD) to target and eliminate the detected \u003cem\u003eE. coli\u003c/em\u003e bacteria. The released DD contributes to localized treatment while also serving as a signal for other nanobots to reinforce the concentration of DD in the area. Through chemical communication, the nanobots exchange information about the detected infection sites and the concentration of DD. This allows them to collectively optimize their search and treatment efforts, focusing on areas with higher bacterial density.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Communication between Nanobots\u003c/h2\u003e\n \u003cp\u003eNanobots for drug delivery to treat urinary tract infections based on ACO principles, information sharing between the nanobots, and targeting infected cells can be facilitated through ACO-inspired communication, pH-based detection, and pheromone release/NNS. The nanobots, acting as virtual ants in the ACO algorithm, communicate with each other through ACO-inspired mechanisms, pheromone trails/NNS. In addition to pH-based detection of infected cells, the nanobots can release pheromones when they encounter the infected area. The pheromones act as attractants for other nanobots, following the principle of ACO, where ants leave pheromone trails to guide the behavior of the colony, as depicted in Eq.\u0026nbsp;1. As the infected cells release substances or exhibit characteristics that trigger a pheromone response from the nanobots, the concentration of pheromones increases in the vicinity of the infection site, as represented in Eq.\u0026nbsp;2. This intensified pheromone trail serves as a signal to attract additional nanobots toward the infected cells. The attracted nanobots, guided by the pheromone trails left by the initial nanobots, join the collective effort to optimize drug delivery. They contribute to the information sharing process, adding their observations and measurements to the collective knowledge of the swarm. Through the combined information of pH changes, ACO-inspired communication, and the attractant effect of pheromones, the nanobots coordinate their actions to target and deliver drugs to the infected cells in the urinary tract, representing Eq.\u0026nbsp;4. This collective behavior enables them to optimize the drug delivery process by concentrating efforts on the areas with high pheromone concentrations and pH changes associated with infection.\u003c/p\u003e\n \u003cp\u003eAssume that the communication strength or intensity between nanobots is represented by C, and the concentration of pheromones released by nanobots is denoted by P [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. The communication between nanobots can be modeled as follows:\u003c/p\u003e\n \u003cp\u003eC\u0026thinsp;=\u0026thinsp;k * P \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (1)\u003c/p\u003e\n \u003cp\u003eHere, k represents a proportionality constant that determines the relationship between the concentration of pheromones and the communication strength. It represents the sensitivity of nanobots to the presence of pheromones.\u003c/p\u003e\n \u003cp\u003eThe concentration of pheromones, P, is determined by the cumulative effect of pheromone deposition by all nanobots in the vicinity [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. It can be calculated as the sum of the pheromone trails left by individual nanobots:\u003c/p\u003e\n \u003cp\u003eP = \u0026sum;(pi) \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; \u0026hellip;\u0026hellip;. (2)\u003c/p\u003e\n \u003cp\u003eWhere pi represents the pheromone deposited by the i\u003csup\u003eth\u003c/sup\u003e nanobot.\u003c/p\u003e\n \u003cp\u003eBy combining these equations, the communication between nanobots based on the pheromone release can be expressed as shown in Eq.\u0026nbsp;3:\u003c/p\u003e\n \u003cp\u003eC\u0026thinsp;=\u0026thinsp;k * \u0026sum;(pi)\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (3)\u003c/p\u003e\n \u003cp\u003eThis equation represents the communication strength or intensity between nanobots, which is influenced by the concentration of pheromones released by the nanobots. A higher concentration of pheromones indicates a stronger communication signal, attracting other nanobots to join the collective effort in drug delivery [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eLet\u0026apos;s introduce the pH change as \u0026Delta;pH, which represents the difference in pH levels between infected and healthy regions in the urinary tract [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. A larger pH change indicates a more significant difference between infected and healthy regions, resulting in a stronger communication signal.\u003c/p\u003e\n \u003cp\u003eAs mentioned earlier, the concentration of pheromones, P, is determined by the cumulative effect of pheromone deposition by all nanobots in the vicinity. Thus, the modified Eq.\u0026nbsp;4 captures the combined influence of pheromone release and pH change on the communication strength between nanobots:\u003c/p\u003e\n \u003cp\u003eC\u0026thinsp;=\u0026thinsp;k * P * \u0026Delta;pH\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (4)\u003c/p\u003e\n \u003cp\u003eBy incorporating both the pheromone release and the pH change, this equation provides a more comprehensive representation of the communication strength between nanobots. It highlights how the communication signal is influenced by both the concentration of pheromones and the pH change associated with infection, allowing for more targeted and effective coordination in drug delivery for UTIs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Random Movement of Nanobots toward the Infected Cells\u003c/h2\u003e\n \u003cp\u003eAssume that the random movement of nanobots toward the infected cell can be represented by a probability function, P\u003csub\u003emove\u003c/sub\u003e, which determines the likelihood of a nanobot moving toward the infected cell. The probability of movement is influenced by the information shared among the nanobots through ACO-inspired communication and the pheromone trails left by other nanobots [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe probability of movement can be calculated as given in Eq.\u0026nbsp;5:\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003emove\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026alpha; * P\u003csub\u003epheromone\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u0026beta; * P\u003csub\u003einfo\u003c/sub\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (5)\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003einfo\u003c/sub\u003e represents the information shared among the nanobots. This information includes knowledge about paths, routes, or other relevant data that can be used to make decisions during the movement process. The nanobots exchange information with each other to collectively make better decisions and navigate through the environment efficiently. The nanobots share this information with one another, helping them coordinate their movement and collectively deliver drugs more effectively.\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003epheromone\u003c/sub\u003e represents the pheromone trail strength or intensity at the current location of the nanobot. The concentration or strength of P\u003csub\u003epheromone\u003c/sub\u003e at a specific location indicates the attractiveness of that location based on previous movements and interactions of nanobots. A higher concentration of pheromone suggests that other nanobots have previously visited and found that location promising. As a result, nanobots are more likely to follow the pheromone trails, leading to the emergence of collective behavior and coordination in their movements.\u003c/p\u003e\n \u003cp\u003e\u0026alpha; and \u0026beta; are weighting factors that determine the relative importance of the pheromone trail (P\u003csub\u003epheromone\u003c/sub\u003e) and the shared information (P\u003csub\u003einfo\u003c/sub\u003e) in influencing the movement of the nanobots. By adjusting the values of \u0026alpha; and \u0026beta;, researchers or designers can control how much impact each factor has on the nanobots\u0026apos; decision-making process. This allows fine-tuning the algorithm to achieve optimal behavior based on the application\u0026apos;s specific requirements.\u003c/p\u003e\n \u003cp\u003eThe pheromone trail strength, P\u003csub\u003epheromone\u003c/sub\u003e, can be calculated as the sum of pheromone concentrations left by previous nanobots, represented as Eq.\u0026nbsp;6 [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]:\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003epheromone\u003c/sub\u003e = \u0026sum;(pj) \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; \u0026hellip;\u0026hellip;. (6)\u003c/p\u003e\n \u003cp\u003eWhere pj represents the pheromone concentration left by the j\u003csup\u003eth\u003c/sup\u003e nanobot.\u003c/p\u003e\n \u003cp\u003eThe information shared among nanobots, P\u003csub\u003einfo\u003c/sub\u003e, can be represented by a combination of factors such as the proximity to the infected cell, obstacles or barriers in the urinary tract, and other relevant information for drug delivery optimization. The calculation of P\u003csub\u003einfo\u003c/sub\u003e depends on the specific implementation and characteristics of the nanobots and the information-sharing mechanism employed.\u003c/p\u003e\n \u003cp\u003eBy combining equations 5 and 6, the probability of random movement towards the infected cell can be expressed as:\u003c/p\u003e\n \u003cp\u003ePmove\u0026thinsp;=\u0026thinsp;\u0026alpha; * \u0026sum;(pj) + \u0026beta; * Pinfo\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (7)\u003c/p\u003e\n \u003cp\u003eThis equation captures the influence of both the pheromone trail and the shared information on the random movement of nanobots toward the infected cell. The weighting factors \u0026alpha; and \u0026beta; determine the relative contributions of these factors in determining the movement probability [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Design of the Objective Function\u003c/h2\u003e\n \u003cp\u003eUsing nanobots as entities for drug delivery to treat urinary tract infections based on ACO foraging behavior, the objective function can be defined in terms of optimizing the effectiveness of drug delivery to the infected area. The objective function represents the measure of success for the nanobots in performing their task of drug delivery. In this research, the objective function is closely tied to the ability of the nanobots to target and deliver drugs to the UTI infection site accurately. Maximizing drug delivery\u0026apos;s success is the optimization problem\u0026apos;s primary goal. An important factor in achieving this objective is the ability of the nanobots to differentiate between healthy cells and infected cells in the urinary tract. This differentiation is analogous to the nanobots\u0026apos; ability to differentiate between pH gradient from infected cells and healthy cells in the urinary tract. By leveraging this pH information, the nanobots can react differently to the infected cells compared to healthy cells, enabling targeted drug delivery to the UTI infection site. The objective function considers the successful detection and response to the specific pH gradient emitted by the infected cells, aiming to maximize the precision and effectiveness of drug delivery. Let\u0026rsquo;s assume that pHh represents the pH value in the vicinity of healthy cells, and the pH value in the vicinity of infected cells is represented by pH\u003csub\u003ei\u003c/sub\u003e. The calculation of the pH change gradient, indicating the pH rate of change per unit distance as illustrated in Eq.\u0026nbsp;8 [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], involves determining how the pH value alters concerning spatial displacement.\u003c/p\u003e\n \u003cp\u003eThe distance from a particular point to a healthy cell is denoted as rh, and the distance to an infected cell is denoted as ri. The gradient of pH change, d(pH)/dr, can be calculated using the following equation:\u003c/p\u003e\n \u003cp\u003ed(pH)/dr = (pH\u003csub\u003ei\u003c/sub\u003e - pH\u003csub\u003eh\u003c/sub\u003e) / (ri - rh) \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (8)\u003c/p\u003e\n \u003cp\u003eIn this equation:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003ed(pH)/dr represents the rate of change of pH with respect to the distance r\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e(pHi\u0026thinsp;\u0026minus;\u0026thinsp;pHh) is the difference in pH values between infected cells (pHi) and healthy cells (pHh). This difference indicates the variation in pH levels between the infected and healthy regions.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e(ri\u0026thinsp;\u0026minus;\u0026thinsp;rh) calculates the difference in distances from the point of interest to the infected cells (ri) and healthy cells (rh). This distance difference accounts for the spatial separation between the infected and healthy regions.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe equation describes how the pH value changes as a movement away from a specific point of interest in a biological system, such as a region affected by a urinary tract infection or any other disease. It considers two main factors: the difference in pH values between infected and healthy cells and the difference in distances from the point of interest to the infected and healthy cells.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Algorithm\u003c/h2\u003e\n \u003cp\u003eThe given algorithm represents a foraging behavior inspired by ant colonies, where multiple bots or agents participate in searching for resources. The algorithm operates as follows: Initially, the algorithm takes several inputs: the list of agents (A) participating in the foraging process, the list of resource locations (R), the pheromone decay rate (\u0026delta;), the pheromone strength (\u0026tau;), and the deposit amount (\u0026alpha;). The algorithm proceeds with the following steps for each bot (a) in the list of agents (A). First, it identifies the neighboring patches (P) that contain available resources or food patches. If any resource patches are available, the algorithm calculates the total pheromone level of these patches by summing up the pheromone levels of each patch in P. Then, it performs pheromone evaporation by reducing the pheromone levels of all the resource patches in P based on the decay rate \u0026delta;. This mimics the natural decay of pheromones over time. Next, the algorithm creates a list of probabilities for each available resource patch in P. These probabilities are calculated by dividing the pheromone level of each patch by the total pheromone level, ensuring that patches with higher pheromone levels have higher probabilities. Based on these probabilities, a target-patch is selected probabilistically. The selection process involves choosing a patch from P with a probability based on the calculated probabilities. If a valid target-patch is selected, the pheromone level of the target-patch is increased by the deposit amount \u0026alpha;, indicating successful discovery. The bot is then oriented towards the target-patch and moved towards it, utilizing a specific navigation mechanism relevant to the real-life scenario. This movement mechanism is not explicitly defined in the given algorithm. These steps are repeated for each bot in A, allowing them to search for resources, adjust pheromone levels, and navigate toward potential food sources.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Motion Planning\u003c/h2\u003e\n \u003cp\u003eNanobots reach the target area with the assistance of pH sensors, which are applied to detect the pH gradient change in the vicinity of bacterial patches, discover the chemical marker among nanobots and check the concentration of released drug doses. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e describes the motion planning for the nanobots.\u003c/p\u003e\n \u003cp\u003eIn the flow diagram, the parameter \u0026beta; is a threshold value that represents the standard level of the drug dose. On the one hand, if the level of the drug dose is more than \u0026beta;, it demonstrates that the DD is enough to destroy all \u003cem\u003eE. coli\u003c/em\u003e at the position. So, the nanobots will leave the position and move to the next local area. On the other hand, the nanobots will immediately release drug doses to eradicate cancer cells in their current position.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Simulation Results","content":"\u003cp\u003eTo demonstrate the viability of the proposed algorithm for targeted drug delivery for urinary tract infections caused by \u003cem\u003eE. coli\u003c/em\u003e, the results of the experiment conducted is demonstrated in this section. The pH-based sensors are employed, and nearest-neighbor search communication is established between nanobots. The simulation is conducted using the NetLogo software, which provides a modeling environment for simulating multi-agent systems. NetLogo software offers a suitable platform for constructing social and natural phenomena models, making it ideal for simulating our targeted drug delivery system. The simulation involves setting several parameters to control the motion behavior of the nanobots. These parameters can be determined either deterministically or through random inputs, depending on the desired experimental setup, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Simulation Setup\u003c/h2\u003e\n \u003cp\u003eThe simulations are conducted with the setting of the following parameters. The environment of the simulation is presented in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The initial positions of nanobots are randomly set. The initial position of the nanobots, \u003cem\u003eE. coli\u003c/em\u003e patch, extracellular membrane, and lumen of the blood vessel are shown respectively in the figure. Nanobot- Nanobot Response and DD are not displayed because the figure is the initial state of the simulation. The blood fluid is assumed to be steady to decrease the simulation complexity, so the flow rate is not changed over time.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Simulation Results and Analysis\u003c/h2\u003e\n \u003cp\u003eFirstly, a simulation was conducted on a group of nanobots to observe the difference in eradicating \u003cem\u003eE. coli\u003c/em\u003e patches using the proposed algorithm. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the various stages of the simulation. Figures \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e(a) and (e) show the first and last stages of the simulation, respectively. In addition, Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e(b) to 6(d) depict the intermediate stages of the simulation. In Fig. 7, the horizontal axis shows the time, whose unit is seconds, and the vertical axis shows the number of live \u003cem\u003eE. coli\u003c/em\u003e patches.\u003c/p\u003e\n \u003cp\u003eBased on the observed data, the first column represents the number of nanobots used in a simulation. In contrast, the second column represents the number of \u003cem\u003eE. coli\u003c/em\u003e patches left uncured after the simulation. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e lists the results of the experimental run under a defined conditional setup on NetLogo.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of experimental run under defined conditional setup on NetLogo\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo of Nanobots\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e Patches Left Uncured\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the given data, simulations were conducted using varying numbers of nanobots to assess their effectiveness in curing \u003cem\u003eE. coli\u003c/em\u003e patches. A closer examination of the results reveals interesting trends and dynamics in the relationship between the number of nanobots employed and the remaining \u003cem\u003eE. coli\u003c/em\u003e patches. Upon analyzing the data, it is evident that increasing the number of nanobots generally reduces the number of \u003cem\u003eE. coli\u003c/em\u003e patches left uncured. This trend suggests a positive correlation between the number of nanobots and the efficacy of the simulation in eliminating \u003cem\u003eE. coli\u003c/em\u003e. As the nanobot population grows, their collective efforts become more efficient at targeting and eradicating the \u003cem\u003eE. coli\u003c/em\u003e patches within the urinary tract. The relationship between the number of nanobots and the remaining \u003cem\u003eE. coli\u003c/em\u003e patches is not strictly linear. It exhibits a more complex pattern. There are instances where a relatively small increase in the number of nanobots results in a significant decrease in the number of uncured patches. This implies that a modest boost in the nanobot population can substantially enhance the simulation\u0026apos;s curative capabilities.\u003c/p\u003e\n \u003cp\u003eThere are also occasions where a larger increase in the number of nanobots does not yield a proportionate decrease in uncured patches. In these cases, the diminishing returns phenomenon may come into play, indicating that adding more nanobots beyond a certain threshold does not significantly contribute to further improvements in the simulation\u0026apos;s efficacy. This could be due to factors such as limited available targets or overlapping coverage areas of the nanobots. As the number of nanobots approaches higher values, the number of uncured patches reaches a plateau or diminishes to extremely low levels. This suggests that an optimal or near-optimal number of nanobots exists beyond which additional nanobots provide marginal benefits in terms of eliminating \u003cem\u003eE. coli\u003c/em\u003e patches. This optimal threshold can be crucial in resource allocation and decision-making when deploying nanobots for effective UTI treatment.\u003c/p\u003e\n \u003cp\u003eExperimental data showed that utilizing 1000 nanobots was remarkably effective in eliminating 98% of the \u003cem\u003eE. coli\u003c/em\u003e patch during the initial trial. To obtain a comprehensive assessment of their accuracy, 10 runs were conducted, as seen in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The outcome was impressive, as 99% accuracy was achieved in successfully curing the \u003cem\u003eE. coli\u003c/em\u003e patch. These findings demonstrate the promising potential of nanobot technology in combating \u003cem\u003eE. coli\u003c/em\u003e infections.\u003c/p\u003e\n \u003cp\u003eThis article presents an algorithm that combines parallel processing with Q-learning and ACO to improve nano-medicine delivery, optimize path length, and increase accuracy. The autonomous path planning method is used for post-nanite injection in vessels, achieving environmental perception and faster navigation while reducing processing power and memory usage. The proposed adaptive agent method can achieve continuous objectives, such as recalculation of optimal paths, time efficiency in decision-making, and decreased error ratios.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Comparative Analysis\u003c/h2\u003e\n \u003cp\u003eNanorobots hold transformative potential in medical practices, aided by AI\u0026apos;s growth. Yet, precise nano-sample path planning remains challenging. Another study [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] combined parallel processing, Q-learning, and ACO for optimized nano-medicine delivery, achieving quicker navigation, reduced power usage, and continuous objective achievement. Comparing both studies, these utilize simulation-based approaches to assess nanobot efficacy in distinct medical contexts. While our study targets \u003cem\u003eE. coli\u003c/em\u003e treatment, the other addresses tumor drug delivery. Both compare algorithms, with our study linking nanobot numbers to \u003cem\u003eE. coli\u003c/em\u003e patch reduction and the other exploring path planning methods. Our study identifies nanobot numbers\u0026apos; positive correlation with efficacy, establishing optimal thresholds, while the other optimizes algorithm performance. Also, the current study underscores UTI treatment, while the other prioritizes drug delivery. These studies spotlight nanobot potential across medical challenges, showcasing consistent trends and unique insights. In comparison, our research excels in addressing UTI concerns directly, offering practical solutions while emphasizing nanobot number correlation and optimal thresholds, elevating its impact.\u003c/p\u003e\n \u003cp\u003eIn contrast to traditional treatment methods for urinary tract infections [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e], our research findings present a promising and innovative approach with distinct advantages. Traditional methods often involve antibiotic treatments that may lead to antibiotic resistance, prolonged medication regimens, and potential side effects. In contrast, our study harnesses nanorobot technology to deliver targeted treatments with greater precision, reducing the need for extended medication consumption. We introduce a data-driven strategy that optimizes the curative process by establishing a positive correlation between nanobot numbers and treatment efficacy. This approach offers quicker and more effective results and minimizes the risk of developing antibiotic resistance. Moreover, our research emphasizes the potential for resource-efficient deployment of nanobots, aligning with patient-centric and sustainable healthcare practices.\u003c/p\u003e\n \u003cp\u003eAdditionally, it is noteworthy that no prior studies have explored the utilization of \u003cem\u003eE. coli\u003c/em\u003e patches for nanobot interventions. As such, our comparison extends beyond the existing literature in the domain of tumor cell eradication, shedding new light on nanobot applications in a previously unexplored context.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study introduces an enhanced approach utilizing the ant colony optimization algorithm to address the challenge of eliminating urinary tract infections caused by \u003cem\u003eE. coli\u003c/em\u003e bacteria. Through the deployment of nanobots within the local field of the urinary tract, our method specifically targets and eradicates the \u003cem\u003eE. coli\u003c/em\u003e patch. Our Improved ant colony optimization algorithm outperforms the conventional ant colony optimization algorithm, exhibiting superior performance in terms of reduced time consumption and enhanced global search capabilities. While our research has shown promising results, certain areas require further investigation and development. The introduction of a new fitness function has resulted in a slightly longer path length for each nanobot from the starting point to the target.\u003c/p\u003e \u003cp\u003eOptimizing the fitness function is crucial to maximize the algorithm's performance. Additionally, studying the control of the nanobot population and drug release rate is essential to enhance the efficiency of the drug delivery process. During the experimental simulations, the systematic variation in the number of nanobots was employed, and their elimination probability was to ensure accuracy. Notably, with the utilization of 1000 nanobots, a 99.99% elimination rate of the \u003cem\u003eE. coli\u003c/em\u003e patch was achieved, as shown in Fig.\u0026nbsp;7. These findings underscore the effectiveness and potential of our approach in combatting UTIs caused by \u003cem\u003eE. coli\u003c/em\u003e bacteria. Nanorobot swarming and ant colony optimization hold great potential for revolutionizing bacterial infection treatment. By emulating ant colonies' collective intelligence, nanobots can efficiently target \u003cem\u003eE. coli\u003c/em\u003e bacteria in the urinary tract, offering an alternative to antibiotics and reducing resistance. Minimally invasive interventions and real-time monitoring through sensor-equipped swarms enable targeted treatment and personalized medicine. These principles can extend beyond UTIs, opening new avenues for efficient treatment and management of bacterial infections while addressing antibiotic resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declare that they do not have any funding or grant for the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare that they do not have any conflict of interests that influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e: No animals were involved in this study. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFoxman, B. (2002). Epidemiology of urinary tract infections: incidence, morbidity, and economic costs. The American Journal of Medicine, 113(1), 5-13.\u003c/li\u003e\n \u003cli\u003eRusso, T. A., \u0026amp; Johnson, J. R. (2003). Medical and economic impact of extraintestinal infections due to Escherichia coli: focus on an increasingly important endemic problem. Microbes and Infection, 5(5), 449-456.\u003c/li\u003e\n \u003cli\u003eMesserer, M., Fischer, W., \u0026amp; Schubert, S. (2017). Investigation of horizontal gene transfer of pathogenicity islands in Escherichia coli using next-generation sequencing. PloS One, 12(7), e0179880.\u003c/li\u003e\n \u003cli\u003eKim, Y., Wie, S. H., Chang, U. I., Kim, J., Ki, M., Cho, Y. K., ... \u0026amp; Pai, H. (2014). 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Nature Reviews Microbiology, 15(11), 689-696.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"nanorobot, swarm algorithm, ant colony optimization, E. coli, urinary tract infections.","lastPublishedDoi":"10.21203/rs.3.rs-5905599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5905599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research presents a bio-inspired nanorobot swarming algorithm based on Ant Colony Optimization (ACO) for combating \u003cem\u003eEscherichia coli \u003c/em\u003e(\u003cem\u003eE. coli\u003c/em\u003e) in Urinary Tract Infections (UTIs). UTIs caused by \u003cem\u003eE. coli\u003c/em\u003e are a major global health concern. An approach that mimics the collective behavior of ant colonies in foraging tasks, utilizing ACO principles to guide the navigation and targeting of nanobots within the urinary tract, was proposed. Using NetLogo, an agent-based modeling platform, simulation and evaluation of the nanorobot swarm's performance was carried out. The swarm consists of autonomous agents that mimic ant behavior, with \u003cem\u003eE. coli\u003c/em\u003e bacteria as the target. The efficacy of the ACO-based algorithm in eradicating E. coli in UTIs was demonstrated through extensive experimentation. Experimental results show that the algorithm successfully guides nanobots to locate and neutralize \u003cem\u003eE. coli\u003c/em\u003e bacteria, reducing infection levels. The investigation also investigated the impact of parameters like pheromone evaporation rates and agent communication strategies on swarm performance. This research contributes to nanorobot technology advancement and highlights the potential of bio-inspired algorithms for medical challenges. The proposed ACO-based algorithm offers a targeted and efficient approach to combat \u003cem\u003eE. coli\u003c/em\u003e infections in UTIs. Insights gained from this study can guide further research and development of nanorobot-based therapies for urinary tract infections and other infectious diseases.\u003c/p\u003e","manuscriptTitle":"Revolutionizing UTI Treatment: Harnessing Ant Colony Optimization for Effective E. coli Combat in the Urinary Tract","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 08:02:40","doi":"10.21203/rs.3.rs-5905599/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1a547ac-3af5-4583-bfdb-89174ad0e12f","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T08:02:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-03 08:02:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5905599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5905599","identity":"rs-5905599","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}