Reducing Peptidoglycan Crosslinking by Chemical Modulator Reverts ß-lactam Resistance in Methicillin-Resistant Staphylococcus aureus.

Small molecule can be utilized to restore the effectiveness of existing major classes of antibiotics against antibiotic-resistant bacteria. In this study, it is demonstrated that celastrol, a natural compound, can modify the bacterial cell wall and subsequently render bacteria more suceptible to ß-lactam antibiotics. It is shown that celastrol leads to incomplete cell wall crosslinking by modulating levels of c-di-AMP, a secondary messenger, in methicillin-resistant Staphylococcus aureus (MRSA). This mechanism enables celastrol to act as a potentiator, effectively rendering MRSA susceptible to a range of penicillins and cephalosporins. Restoration of in vivo susceptibility of MRSA to methicillin is also demonstrated using a sepsis animal model by co-administering methicillin along with celastrol at a much lower amount than that of methicillin. The results suggest a novel approach for developing potentiators for major classes of antibiotics by exploring molecules that re-program metabolic pathways to reverse ß-lactam-resistant strains to susceptible strains.

Maximization of 3-hydroxyhexanoate fraction in poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) using lauric acid with engineered Cupriavidus necator H16.

As polyhydroxybutyrate (P(3HB)) was struggling with mechanical properties, efforts have been directed towards increasing mole fraction of 3-hydroxyhexanoate (3HHx) in P(3HB-co-3HHx) to improve the properties of polyhydroxyalkanoates (PHAs). Although genetic modification had significant results, there were several issues related to cell growth and PHA production by deletion of PHA synthetic genes. To find out easier strategy for high 3HHx mole fraction without gene deletion, Cupriavidus necator H16 containing phaC2Ra-phaACn-phaJ1Pa was examined with various oils resulting that coconut oil gave the highest 3HHx mole fraction. When fatty acid composition analysis with GC-MS was applied, coconut oil was found to have very different composition from other vegetable oil containing very high lauric acid (C12) content. To find out specific fatty acid affecting 3HHx fraction, different fatty acids from caproic acid (C6) to stearic acid (C18) was evaluated and the 3HHx mole fraction was increased to 26.5 ± 1.6 % using lauric acid. Moreover, the 3HHx mole fraction could be controlled from 9 % to 31.1 % by mixing bean oil and lauric acid with different ratios. Produced P(3HB-co-3HHx) exhibited higher molecular than P(3HB-co-3HHx) from phaB-deletion mutant. This study proposes another strategy to increase 3HHx mole fraction with easier way by modifying substrate composition without applying deletion tools.

A Methylazanediyl Bisacetamide Derivative Sensitizes Staphylococcus aureus Persisters to a Combination of Gentamicin And Daptomycin.

Infections caused by Staphylococcus aureus, notably methicillin-resistant S. aureus (MRSA), pose treatment challenges due to its ability to tolerate antibiotics and develop antibiotic resistance. The former, a mechanism independent of genetic changes, allows bacteria to withstand antibiotics by altering metabolic processes. Here, a potent methylazanediyl bisacetamide derivative, MB6, is described, which selectively targets MRSA membranes over mammalian membranes without observable resistance development. Although MB6 is effective against growing MRSA cells, its antimicrobial activity against MRSA persisters is limited. Nevertheless, MB6 significantly potentiates the bactericidal activity of gentamicin against MRSA persisters by facilitating gentamicin uptake. In addition, MB6 in combination with daptomycin exhibits enhanced anti-persister activity through mutual reinforcement of their membrane-disrupting activities. Crucially, the "triple" combination of MB6, gentamicin, and daptomycin exhibits a marked enhancement in the killing of MRSA persisters compared to individual components or any double combinations. These findings underscore the potential of MB6 to function as a potent and selective membrane-active antimicrobial adjuvant to enhance the efficacy of existing antibiotics against persister cells. The molecular mechanisms of MB6 elucidated in this study provide valuable insights for designing anti-persister adjuvants and for developing new antimicrobial combination strategies to overcome the current limitations of antibiotic treatments.

Development of fluorescence-linked immunosorbent assay for rapid detection of Staphylococcus aureus.

Staphylococcus aureus is a major pathogen that causes infections and life-threatening diseases. Although antibiotics, such as methicillin, have been used, methicillin-resistant S. aureus (MRSA) causes high morbidity and mortality rates, and conventional detection methods are difficult to be used because of time-consuming process. To control the spread of S. aureus, a development of a rapid and simple detection method is required. In this study, we generated a fluorescent anti-S. aureus antibody, and established a novel fluorescence-linked immunosorbent assay (FLISA)-based S. aureus detection method. The method showed high sensitivity and low limit of detection toward MRSA detection. The assay time for FLISA was 5 h, which was faster than that of conventional enzyme-linked immunosorbent assay (ELISA) or rapid ELISA. Moreover, the FLISA-based detection method was applied to diagnose clinically isolated MRSA samples that required only 5.3 h of preincubation. The FLISA method developed in this study can be widely applied as a useful tool for convenient S. aureus detection. KEY POINTS: • A fluorescence-linked immunosorbent assay-based S. aureus detection method • Simultaneous quantification of a maximum of 96 samples within 5 h • Application of the novel system to diagnosis clinical isolates.

Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data.

Since the WHO's 2021 aging redefinition emphasizes "healthy aging" by focusing on the elderly's ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008-2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population.

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.

Simultaneous Screening of 322 Residual Pesticides in Fruits and Vegetables Using GC-MS/MS and Deterministic Health Risk Assessments.

The development of efficient methods for evaluating pesticide residues is essential in order to ensure the safety and quality of agricultural products since the Republic of Korea implemented the Positive List System (PLS). The objective of this research was to establish a method for the simultaneous analysis of 322 pesticide residues in fruits and vegetables (such as coffee, potato, corn, and chili pepper), using the quick, easy, cheap, effective, rugged, and safe (QuEChERS) approach in combination with gas chromatography-tandem mass spectrometry (GC-MS/MS). This study introduces a robust, high-throughput GC-MS/MS method for screening the target pesticide residues in agricultural products, achieving the PLS criterion of 0.01 mg/kg LOQ. Despite some compounds not aligning with the CODEX recovery guideline, sufficient reproducibility was confirmed, attesting to the method's applicability in qualitative analyses. A health risk assessment conducted using estimated daily intake/acceptable daily intake ratios indicated low risks associated with product consumption (<0.035391%), thereby confirming their safety. This efficient method holds significant implications for the safe distribution of agricultural products, including during import inspections.

Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy.

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.

A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network.

Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.

Sarcopenia Prediction for Elderly People Using Machine Learning: A Case Study on Physical Activity.

Sarcopenia is a well-known age-related disease that can lead to musculoskeletal disorders and chronic metabolic syndromes, such as sarcopenic obesity. Numerous studies have researched the relationship between sarcopenia and various risk factors, leading to the development of predictive models based on these factors. In this study, we explored the impact of physical activity (PA) in daily life and obesity on sarcopenia prediction. PA is easier to measure using personal devices, such as smartphones and watches, or lifelogs, than using other factors that require medical equipment and examination. To demonstrate the feasibility of sarcopenia prediction using PA, we trained various machine learning models, including gradient boosting machine (GBM), xgboost (XGB), lightgbm (LGB), catboost (CAT), logistic regression, support vector classifier, k-nearest neighbors, random forest (RF), multi-layer perceptron, and deep neural network (DNN), using data samples from the Korea National Health and Nutrition Examination Survey. Among the models, the DNN achieved the most precise accuracy on average, 81%, with PA features across all data combinations, and the accuracy increased up to 90% with the addition of obesity information, such as total fat mass and fat percentage. Considering the difficulty of measuring the obesity feature, when adding waist circumference to the PA features, the DNN recorded the highest accuracy of 84%. This model accuracy could be improved by using separate training sets according to gender. As a result of measurement with various metrics for accurate evaluation of models, GBM, XGB, LGB, CAT, RF, and DNN demonstrated significant predictive performance using only PA features including waist circumference, with AUC values at least around 0.85 and often approaching or exceeding 0.9. We also found the key features for a highly performing model such as the quantified PA value and metabolic equivalent score in addition to a simple obesity measure such as body mass index (BMI) and waist circumference using SHAP analysis.

Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks.

Blockchain has introduced a new era for online payment services and its economy with tamper-proof cryptocurrencies. However, blockchain, which is based on global peer-to-peer networks, has its limitations due to payment delays from global consensus and transaction costs for maintenance. Thus, payment channel networks (PCN) have been proposed as one of the most promising off-chain solutions, allowing users to pay directly through payment channels (PC), with minimal blockchain involvement. However, payment delays and cost problems still exist, especially given the large size of the PCN. This study proposes a multiparty payment channel (MPC) that enables multiple users to join the same PC and exchange payment transactions, compared to the legacy PC. To avoid a consensus procedure among users in the PC, we introduce sequential and parallel updates for the PC status. Since increasing the MPC size limits the advantages in terms of the delay and cost, we propose a distributed coalition formation algorithm to form the MPC group, in which each user has the choice to join or leave the group. Simulations show that the proposed algorithm establishes MPCs successfully, considering the trade-off between the payoff gain and the MPC delay cost.

Design and Experiment of Satellite-Terrestrial Integrated Gateway with Dynamic Traffic Steering Capabilities for Maritime Communication.

This study presents the architectural design and implementation of a multi-RAT gateway (MRGW) supporting dual satellite and terrestrial connectivity that enables moving maritime vessels, such as autonomous surface ships, to be connected to multiple radio access networks in the maritime communication environment. We developed an MRGW combining LTE and very-small-aperture terminal (VSAT) access networks to realize access traffic steering, switching, and splitting functionalities between them. In addition, we developed communication interfaces between the MRGW and end-devices connecting to their corresponding radio access networks, as well as between the MRGW and the digital bridge system of an autonomous surface ship, enabling the MRGW to collect wireless channel information from each RAT end-device and provide the collected data to the digital bridge system to determine the optimal navigation route for the autonomous surface ship. Experiments on the MRGW with LTE and VSAT end-devices are conducted at sea near Ulsan city and the Kumsan satellite service center in Korea. Through validation experiments on a real maritime communication testbed, we demonstrate the feasibility of future maritime communication technologies capable of providing the minimum performance necessary for autonomous surface ships or digitized aids to navigation (A to N) systems.

Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms.

The health and productivity of animals, as well as farmers' financial well-being, can be significantly impacted by cattle illnesses. Accurate and timely diagnosis is therefore essential for effective disease management and control. In this study, we consider the development of models and algorithms for diagnosing diseases in cattle based on Sugeno's fuzzy inference. To achieve this goal, an analytical review of mathematical methods for diagnosing animal diseases and soft computing methods for solving classification problems was performed. Based on the clinical signs of diseases, an algorithm was proposed to build a knowledge base to diagnose diseases in cattle. This algorithm serves to increase the reliability of informative features. Based on the proposed algorithm, a program for diagnosing diseases in cattle was developed. Afterward, a computational experiment was performed. The results of the computational experiment are additional tools for decision-making on the diagnosis of a disease in cattle. Using the developed program, a Sugeno fuzzy logic model was built for diagnosing diseases in cattle. The analysis of the adequacy of the results obtained from the Sugeno fuzzy logic model was performed. The processes of solving several existing (model) classification and evaluation problems and comparing the results with several existing algorithms are considered. The results obtained enable it to be possible to promptly diagnose and perform certain therapeutic measures as well as reduce the time of data analysis and increase the efficiency of diagnosing cattle. The scientific novelty of this study is the creation of an algorithm for building a knowledge base and improving the algorithm for constructing the Sugeno fuzzy logic model for diagnosing diseases in cattle. The findings of this study can be widely used in veterinary medicine in solving the problems of diagnosing diseases in cattle and substantiating decision-making in intelligent systems.

Study of SarA by DNA Affinity Capture Assay (DACA) Employing Three Promoters of Key Virulence and Resistance Genes in Methicillin-Resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA), one of the most well-known human pathogens, houses many virulence factors and regulatory proteins that confer resistance to diverse antibiotics. Although they have been investigated intensively, the correlations among virulence factors, regulatory proteins and antibiotic resistance are still elusive. We aimed to identify the most significant global MRSA regulator by concurrently analyzing protein-binding and several promoters under same conditions and at the same time point. DNA affinity capture assay (DACA) was performed with the promoters of mecA, sarA, and sarR, all of which significantly impact survival of MRSA. Here, we show that SarA protein binds to all three promoters. Consistent with the previous reports, ΔsarA mutant exhibited weakened antibiotic resistance to oxacillin and reduced biofilm formation. Additionally, production and activity of many virulence factors such as phenol-soluble modulins (PSM), α-hemolysin, motility, staphyloxanthin, and other related proteins were decreased. Comparing the sequence of SarA with that of clinical strains of various lineages showed that all sequences were highly conserved, in contrast to that observed for AgrA, another major regulator of virulence and resistance in MRSA. We have demonstrated that SarA regulates antibiotic resistance and the expression of various virulence factors. Our results warrant that SarA could be a leading target for developing therapeutic agents against MRSA infections.

Flow cytometry-based rapid detection of Staphylococcus aureus and Pseudomonas aeruginosa using fluorescent antibodies.

Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa) are major pathogens frequently detected in food and beverage poisoning, and persistent infections. Therefore, the development of a rapid method that can detect these pathogens before serious multiplication is required. In this study, we established a flow cytometry (FCM)-based detection method that allows rapid acquisition of cell populations in fluid samples by using a fluorescent antibody against S. aureus or P. aeruginosa. Using this method, we detected these pathogens with a 103 to 105 CFU order of limit of detection value within 1 hour. The FCM-based method for the detection of S. aureus and P. aeruginosa offers the possibility of high-throughput analysis of pathogens in food, environmental, and clinical sources.

DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data.

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.

Arctic Psychrotolerant Pseudomonas sp. B14-6 Exhibits Temperature-Dependent Susceptibility to Aminoglycosides.

Bacteria can evade antibiotics by acquiring resistance genes, as well as switching to a non-growing dormant state without accompanying genetic modification. Bacteria in this quiescent state are called persisters, and this non-inheritable ability to withstand multiple antibiotics is referred to as antibiotic tolerance. Although all bacteria are considered to be able to form antibiotic-tolerant persisters, the antibiotic tolerance of extremophilic bacteria is poorly understood. Previously, we identified the psychrotolerant bacterium Pseudomonas sp. B14-6 from the glacier foreland of Midtre Lovénbreen in High Arctic Svalbard. Herein, we investigated the resistance and tolerance of Pseudomonas sp. B14-6 against aminoglycosides at various temperatures. This bacterium was resistant to streptomycin and susceptible to apramycin, gentamicin, kanamycin, and tobramycin. The two putative aminoglycoside phosphotransferase genes aph1 and aph2 were the most likely contributors to streptomycin resistance. Notably, unlike the mesophilic Pseudomonas aeruginosa PA14, this cold-adapted bacterium demonstrated reduced susceptibility to all tested aminoglycosides in a temperature-dependent manner. Pseudomonas sp. B14-6 at a lower temperature formed the persister cells that shows tolerance to the 100-fold minimum inhibitory concentration (MIC) of gentamicin, as well as the partially tolerant cells that withstand 25-fold MIC gentamicin. The temperature-dependent gentamicin tolerance appears to result from reduced metabolic activity. Lastly, the partially tolerant Pseudomonas sp. B14-6 cells could slowly proliferate under the bactericidal concentrations of aminoglycosides. Our results demonstrate that Pseudomonas sp. B14-6 has a characteristic ability to form cells with a range of tolerance, which appears to be inversely proportional to its growth rate.

Generation of a recombinant antibody for sensitive detection of Pseudomonas aeruginosa.

Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen that causes nosocomial infections and often exhibits antibiotic resistance. Therefore, the development of an accurate method for detecting P. aeruginosa is required to control P. aeruginosa-related outbreaks. In this study, we established an enzyme-linked immunosorbent assay method for the sensitive detection of three P. aeruginosa strains, UCBPP PA14, ATCC 27853, and multidrug-resistant ATCC BAA-2108. We produced a recombinant antibody (rAb) against P. aeruginosa V-antigen (PcrV), which is a needle tip protein of the type III secretion system of P. aeruginosa using mammalian cells with high yield and purity, and confirmed its P. aeruginosa binding efficiency. The rAb was paired with commercial anti-P. aeruginosa Ab for a sandwich ELISA, resulting in an antigen-concentration-dependent response with a limit of detection value of 230 CFU/mL. These results suggest that the rAb produced herein can be used for the sensitive detection of P. aeruginosa with a wide range of applications in clinical diagnosis and point-of-care testing.

Leucyl-tRNA Synthetase Inhibitor, D-Norvaline, in Combination with Oxacillin, Is Effective against Methicillin-Resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is a pathogenic bacterium that causes severe diseases in humans. For decades, MRSA has acquired substantial resistance against conventional antibiotics through regulatory adaptation, thereby posing a challenge for treating MRSA infection. One of the emerging strategies to combat MRSA is the combinatory use of antibacterial agents. Based on the dramatic change in phospholipid fatty acid (PLFA) composition of MRSA in previous results, this study investigated branched-chain amino acid derivatives (precursors of fatty acid synthesis of cell membrane) and discovered the antimicrobial potency of D-norvaline. The compound, which can act synergistically with oxacillin, is among the three leucine-tRNA synthetase inhibitors with high potency to inhibit MRSA cell growth and biofilm formation. PLFA analysis and membrane properties revealed that D-norvaline decreased the overall amount of PLFA, increasing the fluidity and decreasing the hydrophobicity of the bacterial cell membrane. Additionally, we observed genetic differences to explore the response to D-norvaline. Furthermore, deletion mutants and clinically isolated MRSA strains were treated with D-norvaline. The study revealed that D-norvaline, with low concentrations of oxacillin, was effective in killing several MRSA strains. In summary, our findings provide a new combination of aminoacyl-tRNA synthetase inhibitor D-norvaline and oxacillin, which is effective against MRSA.

Topical niclosamide (ATx201) reduces Staphylococcus aureus colonization and increases Shannon diversity of the skin microbiome in atopic dermatitis patients in a randomized, double-blind, placebo-controlled Phase 2 trial.

BACKGROUND: In patients with atopic dermatitis (AD), Staphylococcus aureus frequently colonizes lesions and is hypothesized to be linked to disease severity and progression. Treatments that reduce S. aureus colonization without significantly affecting the skin commensal microbiota are needed. METHODS AND FINDINGS: In this study, we tested ATx201 (niclosamide), a small molecule, on its efficacy to reduce S. aureus and propensity to evolve resistance in vitro. Various cutaneous formulations were then tested in a superficial skin infection model. Finally, a Phase 2 randomized, double-blind and placebo-controlled trial was performed to investigate the impact of ATx201 OINTMENT 2% on S. aureus colonization and skin microbiome composition in patients with mild-to-severe AD (EudraCT:2016-003501-33). ATx201 has a narrow minimal inhibitory concentration distribution (.125-.5 µg/ml) consistent with its mode of action - targeting the proton motive force effectively stopping cell growth. In murine models, ATx201 can effectively treat superficial skin infections of methicillin-resistant S. aureus. In a Phase 2 trial in patients with mild-to-severe AD (N = 36), twice-daily treatment with ATx201 OINTMENT 2% effectively reduces S. aureus colonization in quantitative colony forming unit (CFU) analysis (primary endpoint: 94.4% active vs. 38.9% vehicle success rate, p = .0016) and increases the Shannon diversity of the skin microbiome at day 7 significantly compared to vehicle. CONCLUSION: These results suggest that ATx201 could become a new treatment modality as a decolonizing agent.