Expert Rules Research Articles

Are You with Us or Against Us?

Abstract When the COVID-19 pandemic hit, two contrasting images quickly became representative of the crisis. On the one hand, there were heroic doctors working day and night with the novel virus, risking their lives and making sacrifices to save others. On the other, there were ‘anti-maskers’ and ‘anti-vaxxers’: people doubting if the virus is real, questioning the effectiveness of protective measures, suspicious that the crisis is nothing more than an elaborate plot, a scam aimed to redesign their world and to destroy the values they hold dear. Reflecting on research conducted in Ireland with people separated by the conspiratorial divide, this paper examines some methodological and analytical challenges of doing simultaneous research with opposing stakeholders. Analysing my own entanglements in the conflicts over vaccines and conspiracy theories in this paper I argue that the pandemic was not just a battle to secure the acceptability of specific medical technology (the COVID-19 vaccine) but was also about safeguarding respectability of science and maintaining the rule of experts. It was about preventing ontological turn, the end of the era of reason, a dawn of modernity.

A proposal of a mixed diagnostic system based on decision trees and probabilistic experts rules

Decision trees and rule-based expert systems (RBES) are standard diagnostic tools. We propose a mixed technique that starts with a probabilistic decision tree where information is obtained from a real world data base. The decision tree is automatically translated into a set of probabilistic rules. Meanwhile a panel of experts proposes their own set of probabilistic rules, according with their experience on the subject. Both sets of rules are combined, generating a mixed RBES with probabilistic rules. The expected probabilities of the rules translating the knowledge in the decision tree are discretized by considering a mapping from intervals of expected probabilities into a set of five values. This way, knowledge coming from real data is completed with the experience of the panel of experts in order to provide a more accurate prediction of suffering from type 2 diabetes mellitus (T2DM) before seven and a half years in the future.The proposed technique is illustrated with a real case using a diabetes diagnosis probabilistic decision tree built using 1350 out of 1800 real cases and the rules provided by a panel of experts in diabetes. The final result takes into account both the probabilities of the rules and the number of times that each possible consequent is reached, giving a probabilistic result among seven possibilities.For modeling the decision tree, 75% of the individuals in the database (randomly selected) have been used and the rest (25%) have been used to test the results. The results of the Mixed RBES have been compared with the results of the Tree RBES (the RBES built using only the rules from the decision tree) and the results of the Experts’ RBES (the RBES built using only the rules from the panel of experts). The accuracy of the predictions of the Mixed RBES is much better.

Epistocracy vs constitutional democracy: A Hayekian response to Jason Brannan

Jason Brennan, who proposes assessing democratic decisions based on non-procedural expert knowledge from a pure utilitarian standpoint, holds a prominent position among libertarian critiques of democracy. Brennan contends that epistocratic regimes can outperform democracies since democracies perform badly due to the phenomena of rational ignorance and deliberative democratic methods cannot solve this problem. Brennan, who compares epistocratic institutions to constitutional institutions, wants to tame democracies using negative externality arguments. In this study, we demonstrate that constitutional democracies cannot be assessed by the Brennanian metrics and that epistocracy will erode the libertarian political successes of constitutional democracy. Two important arguments back up this conclusion. First, transforming libertarian ideals beyond constitutional rules into the standard for daily politics allows experts tremendous discretion. Even with good intentions, the unchecked discretion of experts would most likely undermine the general, abstract, and egalitarian rules required by a complex society. Second, taking the concept of rational choice out of its original context will make the distinction between constitutional and unconstitutional governments unclear. Therefore, the libertarian ideal of the limited government established by the separation of powers and the procedures of checks and balances would lose its significance, giving place to the unchecked discretion of expert rule.

Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model

A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role in the blockchain ecosystem. While it also holds a large number of digital assets, the frequent occurrence of smart contract vulnerabilities have caused huge economic losses and destroyed the blockchain-based credit system. Currently, the security and reliability of smart contracts have become a new focus of research, and there are a number of smart contract vulnerability detection methods, such as traditional detection tools based on static or dynamic analysis. However, most of them often rely on expert rules, and therefore have poor scalability and high false negative and false positive rates. Recent deep learning methods alleviate this issue, but without considering the semantic information and context of source code. To this end, we propose a hybrid attention mechanism (HAM) model to detect security vulnerabilities in smart contracts. We extract code fragments from the source code, which focus on key points of vulnerability. We conduct extensive experiments on two public smart contract datasets (a total of 24,957 contracts). Empirical results show remarkable accuracy improvement over the state-of-the art methods on five kinds of vulnerabilities, where the detection accuracy could achieve 93.36%, 80.85%, 82.56%, 85.62%, and 82.19% for reentrancy, arithmetic vulnerability, unchecked return value, timestamp dependency, and tx.origin, respectively.

СИСТЕМА АВТОМАТИЧЕСКОЙ РАЗМЕТКИ НЕСТРУКТУРИРОВАННЫХ ПРОТОКОЛОВ РЕНТГЕНОЛОГИЧЕСКИХ ИССЛЕДОВАНИЙ ГРУДНОЙ КЛЕТКИ С ИСПОЛЬЗОВАНИЕМ МЕТОДОВ СЕМАНТИЧЕСКОГО АНАЛИЗА

Currently, a unified structured standard for describing radiological chest examination does not exist. The complexity of developing such text report templates lies in the diversity of instrumental methods, variety of diagnostic objectives and specific work characteristics of individual medical organizations. Development of tools for marking the unstructured radiological chest examination protocols makes it possible to improve the system of electronic document management in healthcare due to automation of data formalization processes as well as develop data sets for machine learning. The purpose of this study is to develop a system for automated marking of text reports of the unstructured radiological chest examination protocols using heuristic approach and machine learning algorithms. Material and methods. The study used patient data on radiological chest examinations of medical organizations connected to the Unified Radiological Information Service of the Unified Medical Information and Analysis System of inpatient and outpatient medical organizations of Moscow and the Moscow region. Semantic analysis methods, expert rules and machine learning algorithms were used for processing the unstructured text reports. Results. The study has identified language patterns associated with important pathological conditions and “norm” class as well as developed regular expressions for these classes. A dictionary of radiological concepts and abbreviations (397 items) was compiled, followed by the development of an algorithm for correcting grammar mistakes in the protocols. In collaboration with the expert group, the rules of multilabel classification of the radiological examination protocols were created and their efficiency was tested. When solving the multilabel classification problem using only the expert rules, the percentage of exact matches equaled to 84%. Inasmuch as classifiers for conditions such as “infiltration/consolidation” and “blackout focus” were not effective, we have adjusted the models of machine learning. Conclusion. The best classification results were demonstrated by the recurrent neural network with the long-short term memory architecture ensuring sensitivity of 89% and 99% for “infiltration/consolidation” and “blackout focus” classes, respectively. This made it possible to statistically significantly (p=0.039) increase the total percentage of the exact matches up to 87%.

Closed-Loop Dynamic Blending Optimization Based on Variational Bayesian and its Application in Industry

There exist uncertainties in raw material components and distribution parameters in blending process. Considering the dynamic uncertainty of distribution parameters and the dynamic change of raw material inventory, a general closed-loop dynamic blending optimization (CDBO) is proposed. The proposed method consists of both data-driven system and model-based system. In the data-driven system, the feedback optimization problem is reconstructed into a linear regression problem, and the variational Bayesian is proposed to obtain the mean and variance of the distribution. Then, according to the incoming raw materials, the mean value of each distribution parameter is adjusted by expert rules. Last, in the model-based system, blending ratio is obtained by chance-constrained programming. To verify the effectiveness of CDBO, a detailed derivation process of variable Bayesian, expert rule and chance-constrained programming are established for zinc smelting process. Numerical studies and an industrial application for zinc smelting process are presented to demonstrate the advantages of the proposed method. Compared with the manual blending, the volatility index of chemical tests data is greatly reduced and the compliance rates of zinc and lead is increased by 6.7% and 3.3%, respectively.

A Smart Contract Vulnerability Detection Mechanism Based on Deep Learning and Expert Rules

A Smart Contract Vulnerability Detection Mechanism Based on Deep Learning and Expert Rules

Toward a Superintelligent Action Recommender for Network Operation Centers Using Reinforcement Learning

Today’s Network Operation Centres (NOC) consist of teams of network professionals responsible for monitoring and taking actions for their network’s health. Most of these NOC actions are relatively complex and executed manually; only the simplest tasks can be automated with rules-based software. But today’s networks are getting larger and more complex. Therefore, deciding what action to take in the face of non-trivial problems has essentially become an art that depends on collective human intelligence of NOC technicians, specialized support teams organized by technology domains, and vendors’ technical support. This model is getting increasingly expensive and inefficient, and the automation of all or at least some NOC tasks is now considered a desirable step towards autonomous and self-healing networks. In this article, we investigate whether an autonomous NOC can achieve superintelligence; i.e., it can recommend or take actions that lead to better results than those achieved by the collective human intelligence, in this case rules designed by human experts. Our investigation is inspired by the superintelligence achieved in computer games recently. Specifically, we build an Action Recommendation Engine based on Reinforcement Learning, a type of Artificial Intelligence method, and we train it with expert rules and lets it explore actions by itself. We then show that it can learn new and more efficient strategies that outperform expert rules designed by humans. This can be used in face of network problems to either quickly recommend actions to NOC technicians or autonomously take actions for fast recovery.

AMRexpert – online platform for interpretation, verification and validation of antimicrobial susceptibility testing

Objective. To review the key principles and functionality of AMRexpert online platform. Materials and Methods. The information part of the platform is comprised of rules based on the EUCAST recommendations and various standards for interpreting the results of antimicrobial susceptibility testing (EUCAST, CLSI versions 2020-2022). The technical part of the platform was developed using C# programming language, Angular and Bootstrap frameworks. AST results of Serratia marcescens, Pseudomonas aeruginosa, Staphylococcus saprophyticus, Enterococcus faecium specific isolates were analyzed for practical testing of the platform using EUCAST v.12.0, 2022 interpretation criteria. Results. The developed platform for the evaluation of microbiological reports includes a wide list of expert rules, various standards for the interpretation of the AST results. Consistent data input, the ability to switch forms between several microorganisms, and the presentation of evaluation results in the form of blocks allows all necessary information to be structured. Practical use of the platform is available for various infectious pathogens. Fast and efficient interaction between users is provided by different options for sharing and saving the results. Conclusions. The web-based application evaluates microbiological reports in a comprehensive approach, with the ability to apply the results later to prescribe antimicrobial therapy. The platform for the interpretation, verification and validation of the AST results – AMRexpert can be accessed at https://amrexpert.ru.

Application of a fuzzy controller in adaptive e-learning content used to evaluate student activity

This article presents an e-learning system that supports the adaptation of course content to the student's way of learning. The adaptability of an e-learning course is a challenging task that requires appropriate tools and a well-thought-out architecture. The solution presented in this article is based on the concept of information imprecision and fuzzy control. The proposed fuzzy controller, based on an expert rule base calculates the student's activity in the course in order to determine the questions in tests at the end of successive parts of the course. The solution proposed was implemented in an e-learning Applications of Fuzzy Sets course, which was part of an Artificial Intelligence class. The proposed system successfully adapts the course content to the student's learning style. The article highlights the potential of fuzzy controllers in adaptive e-learning systems. The use of fuzzy controllers makes it possible to take into account the imprecision of information, enabling better handling of uncertainty and variability in the student's learning style.

Solving the capacitor placement problem in radial distribution networks

The optimal capacitor allocation problem is suggested in this article. This study purposes to maximize the voltage stability, minimize the power losses, and consolidate the annual net savings. Calculating the optimal locations and sizes of fixed and switched capacitors is done in two steps. The first step is using the fuzzy expert rules in calculating the most candidate buses for capacitor allocation. While the second step is using a nondominated sorting genetic algorithm II (NSGA-II) in determining the list of Pareto optimal solutions and then applying a fuzzy decision maker to pick the most compromise. To emphasize the effectiveness of the proposed method, radial distribution systems are proposed; IEEE 33-bus system and the actual Portuguese IEEE 94-bus system. To demonstrate the strength and applicability of this method, a multiobjective water cycle algorithm (MOWCA), multiobjective grey wolf optimizer (MOGWO), and other optimizers used in the published papers are used. From simulation and analysis, the proposed NSGA-II outperforms other optimizers considered for comparison in achieving the maximum percentages of minimization in a real power loss of 32.369% and 31.1011% for the 33-bus system and 25.6296% and 25.3027% for the 94-bus system, the maximum percentages of minimization in a reactive power loss of 31.7916% and 30.4948% for 33-bus system and 25.9457% and 25.8001% for the 94-bus system, the maximum annual net savings of 23,612 $ and 23,131 $ for the 33-bus system for fixed and switched capacitors, respectively, and boosting the total voltage stability, which show its superior ability to give high-grade solutions.

291: DEVELOPING DELPHI EXPERT CONSENSUS RULES FOR A DIGITAL TWIN MODEL OF THE NEURO CRITICAL CARE UNIT

Introduction: In the past 20 years, the concept of the digital twin has been utilized in other fields to track the operations of wind turbines, monitor the status of spacecraft, and even create a model of the Earth for climate research While artificial intelligence holds much promise for the neurocritical care unit, these models should be clearly based on an understanding of underlying physiological variables rather than be opaque “black box” models. Our group has previously created such a digital twin model to predict acute response to treatment of sepsis. This project expands on our previous work by developing a similar model for the critical care of acute ischemic stroke. Methods: A panel of 18 experts in the field of critical care reviewed the clinical practice and pathophysiological statements related to neurology critical care. The core group of investigators developed statements based on a Directed Acyclic Graph (DAG) describing pathophysiology surrounding acute neurological issues encountered in the practice of Neurocritical Care (NCC). A modified Delphi method (3 rounds) was used to gauge agreement on 20 statements (120 sub statements) using a 7-point Likert scale. Agreement was defined a-priori by >80% selection of a 6 (“agree”) or 7 (“strongly agree”). Results: Consensus was achieved on 58 (48.3%) expert statements after completion of 2 rounds. After completion of 3rd round, the level of consensus increased to 93 (77.5%) expert statements. Some statements garnered 100% consensus in the first round of DELPHI such as “Reperfusion of ischemic stroke can lead to improvement of stroke.” Other statements required revision to capture the nuances of clinical practice before gaining consensus. For example, when the severity of infection was incorporated into the statement “Infection can lead to low blood pressure.” consensus increased from 56% to 100%. Conclusions: Our study demonstrated the feasibility of application of the DELPHI process to generate consensus among experts for use in development of a “digital twin” artificial intelligence model for use in the Neuro ICU. Compared to other models which rely on “black-box” associative artificial intelligence, our proposed causal AI model is based on a solid foundation of expert rules and casual mechanisms.

The Techno-politics of Rental Housing Financialization: Real Estate Service Companies and Technocratic Expertise in Australia’s Build to Rent Market

This article argues private expertise is a driving force behind the global expansion of rental housing financialization and, particularly, the making of build to rent (BTR) assets and markets. It develops this argument by investigating Australia’s underexamined BTR market and global real estate service companies (RESCs) as ubiquitous yet unscrutinized intermediaries in this new financialization frontier. Its analysis heeds calls to attend to assetization, as the process of turning things into assets, deploying science and technology studies-inspired marketization approaches, which understand markets as sociotechnical assemblages, and their prior integration with critical political economy of financialization. This approach is enhanced by engaging with the techno-politics of market-making scholarship, which sensitizes assetization approaches to the politics of expertise. This conceptual move respecifies market devices (i.e., material and discursive assemblages of market making) as knowledge contingent (i.e., that require and assert expert knowledge) and provides conceptual terrain to explore the rule of private experts in assetization. Analysis of interviews, media, and industry reporting reveals how RESCs’ epistemic, discursive, and technical efforts format the emergent market, making BTR assets thinkable, visible, calculable, and transactable. This article repositions rental housing financialization as a techno-political project led in nontrivial ways by private experts who act as financializing champions and as intermediaries connecting global finance and local sites, through advisory, valuation, brokerage, and lobbying. This contributes to understandings of the expanding global geographies of rental housing financialization and project ecologies behind urban production. Underscoring the power of private expertise to reconfigure housing markets recasts concerns surrounding market reliance as urban housing crisis salves.

EFNC-Exp: An evolving fuzzy neural classifier integrating expert rules and uncertainty

Data stream classification processes with neuro-fuzzy approaches may involve situations where uncertainties arise, which may directly interfere with the quality of the results of the evolving models. Another factor that can help improve the performance of neuro-fuzzy evolving models is using a priori knowledge about a topic and incorporating it into the model's training procedure. The definition of fuzzy rules with a high degree of representativeness for certain classes can help models increase the significance of the representation of these labels and thus boost their predictive performance for these classes. This article proposes the integration of uncertainty in experts' feedback on the class labels and the integration of expert rules into the classifier architecture and the evolving, adaptive learning engine. This uncertainty integration occurs by combining it in defining neurons' weights in the first layer of the model and incorporating these weight values in the Gaussian neurons in the model's first layer; furthermore, uncertainty is integrated into an incremental feature weighting concept (inducing a weighted version of it) for the curse of dimensionality reduction. The proof of the new concepts will be carried out through tests on binary pattern classification problems in real-world data streams and a comparison between our approach and several related state-of-the-art works in evolving (neuro-) fuzzy modeling. The results obtained by the model showed that, by explicitly respecting the uncertainty of the class labels in the process of updating the evolving neuro-fuzzy classifier, the accuracy trend lines showed a robust behavior as the degree of distortion existing in the class labels of the samples due to uncertainty could be partially compensated.

ADT: AI-Driven network Telemetry processing on routers

Network monitoring is a pivotal part of network management and operations. It is responsible for monitoring the behavior of the network to assure its functionality within expectation and to guarantee a smooth-running environment for enabling of various services. Therefore, operators are interested in gaining a comprehensive assessment of their network elements and tracking operational changes to facilitate timely correction of any deviation. Commonly, this assessment is achieved by performing regular manual checks of different operational counters and defining expert rules from known root causes. The common approach requires the maintenance of a regularly updated set of rules and only goes as far as the operator’s pre-gained knowledge of the system. With the growing complexity of the networks as well as the availability of more data, a more efficient monitoring approach is necessary to address the emerging network monitoring requirements.In this paper, a novel unsupervised approach is proposed that is capable of exploring a broader set of counters (not limited to the handpicked Key Performance Indicators (KPIs)). The goal is to leverage the dependencies between the counters in order to discover complex state changes that might have otherwise slipped the operator’s view. This paper proposes ADT, an AI-driven telemetry processing solution that facilitates monitoring of a larger set of counters. The Detector block of ADT is known as DESTIN, a multivariate unsupervised change detection for high dimensional time-series data of originally low effective dimension, which provides near real-time state assessment of network devices. The efficiency of the proposed approach is demonstrated and compared with well-known methodologies on an experimental test-bed. The method’s performance is also explored extensively considering different criteria such as traffic type, device and the type of events to identify its potentials and limitations. The datasets used for the evaluation are made publicly available.

Implementation and Assessment of a SmartZone Alert to Notify Clinicians of Critical Hyperbilirubinemia in Preterm Infants Less Than 35 Weeks Gestation

Abstract Background Neonatal jaundice, also known as hyperbilirubinemia in term and preterm infants, is treated with phototherapy when bilirubin results exceed gestational age- and age-specific medical decision levels (MDL) to prevent kernicterus and bilirubin-induced neurological damage. During phototherapy, unconjugated bilirubin is converted to water-soluble isomers that are excreted in the urine. Presently, the electronic medical record (EMR) at our hospital cannot use gestational age to stratify reference ranges thereby the associated flags and alerts would not be triggered, leading to delays in reviewing bilirubin results and placing phototherapy orders. The aim of this project is to replace the current manual assessment process for phototherapy with a newly designed alert to notify clinicians of elevated bilirubin results for preterm infants (<35 weeks gestation) and to minimize prolonged delays in placing phototherapy orders. We hypothesized that the SmartZone alert will encourage consistent and timely consideration of phototherapy. Method A SmartZone alert, built with Cerner command language and Discern Expert rules, evaluates total or neonatal bilirubin results for patients in the Newborn and Infant Critical Care Unit (NICCU), calculates the patient’s age using the recorded gestational age and birth time, and utilizes the following rules: 1) bilirubin result must exceed the pre-defined gestational age-specific MDLs, and 2) calculated gestational age is <35 weeks. Additionally, the SmartZone alert will not be triggered if there is an active phototherapy order. Once triggered, the alert will be visible for four hours to all caregivers who view the patient’s EMR. We compared the duration of time between verified bilirubin results and phototherapy orders before and after the implementation of the SmartZone alert. Result The SmartZone alert was implemented on 01/11/2022. Between 1/1/2021 - 1/11/2022, 95 preterm infants in the NICCU had neonatal and total bilirubin results, in which 19 met the criteria for the alert to be triggered, and 17 had phototherapy orders. Further analysis revealed that 12 orders were placed <24 hours, four orders were placed 24 - 72 hours, and one order was placed >72 hours after bilirubin results were verified. Between 1/12/2022 – 2/13/2022, 13 newly admitted preterm infants in the NICCU had neonatal and total bilirubin results, in which one triggered the alert, and phototherapy was ordered within 15 hours. Conclusion A SmartZone alert was implemented to encourage consistent and timely consideration of phototherapy for preterm infants <35 weeks with critical hyperbilirubinemia. The alert is functioning as intended. This alert will be continuously monitored to further optimize its functionality and utilization.

Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction.

Finding the optimal balance between end-user's comfort, lifestyle preferences and the cost of the heating, ventilation and air conditioning (HVAC) system, which requires intelligent decision making and control. This paper proposes a heating control method for HVAC based on dynamic programming. The method first selects the most suitable modeling approach for the controlled building among three machine learning modeling techniques by means of statistical performance metrics, after which the control of the HVAC system is described as a constrained optimization problem, and the action of the controller is given by solving the optimization problem through dynamic programming. In this paper, the variable 'thermal energy storage in building' is introduced to solve the problem that dynamic programming is difficult to obtain the historical state of the building due to the requirement of no aftereffect, while the room temperature and the remaining start hours of the Primary Air Unit are selected to describe the system state through theoretical analysis and trial and error. The results of the TRNSYS/Python co-simulation show that the proposed method can maintain better indoor thermal environment with less energy consumption compared to carefully reviewed expert rules. Compared with expert rule set 'baseline-20 °C', which keeps the room temperature at the minimum comfort level, the proposed control algorithm can save energy and reduce emissions by 35.1% with acceptable comfort violation.

Plate shape recognition based on Gaussian function and particle swarm optimization for roller quenching process

Plate shape recognition in the roller quenching process provides effective feedback signals for plate shape control. Its accuracy directly affects the control effect. Most studies have obtained the high recognition accuracy of the plate shape defects by neural networks or parameter identification methods, but cannot provide a sufficiently detailed feedback signal with controllable accuracy for plate shape control, limiting the further improvement of the control effect. For these problems, a plate shape recognition method for roller quenching process is proposed. Firstly, the roller quenching process and the representation of the plate shape are introduced, and the structure of the plate shape recognition method is designed. Secondly, the Gaussian function is used to describe the plate shape defects, and the physical significance of each parameter in the Gaussian function is analyzed. Based on the Gaussian function, a plate shape fitting method with iterative structure and controllable fitting accuracy is designed to extract the detailed information about each defect. Thirdly, according to the actual production experience, the expert rules are designed for plate shape classification and to evaluate the quality. Finally, the method is verified by industrial production data. The results verify the correctness and effectiveness of our method, indicating that the method can provide important guidance for the improvement of plate shape quality and lays an important foundation for plate shape control.

Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space.

Asthma is among the most common occupational diseases with considerable public health and economic costs. Chemicals that induce hypersensitivity in the airways can cause respiratory distress and comorbidities with respiratory infections such as COVID. Robust predictive models for this end point are still elusive due to the lack of an experimental benchmark and the over-reliance of existing in silico tools on structural alerts and structural (vs chemical) similarities. The Computer-Aided Discovery and REdesign (CADRE) platform is a proven strategy for providing robust computational predictions for hazard end points using a tiered hybrid system of expert rules, molecular simulations, and quantum mechanics calculations. The recently developed CADRE model for respiratory sensitization is based on a highly curated data set of structurally diverse chemicals with high-fidelity biological data. The model evaluates absorption kinetics in lung mucosa using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines subsequent reactivity with cell proteins via quantum-mechanics calculations using a multi-tiered regression. The model affords an accuracy above 0.90, with a series of external validations based on literature data in the range of 0.88-0.95. The model is applicable to all low-molecular-weight organics and can inform not only chemical substitution but also chemical redesign to advance development of safer alternatives.

Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Human Blood in the Northern Netherlands between 2013 and 2019.

Background: For years, coagulase-negative staphylococci (CoNS) were not considered a cause of bloodstream infections (BSIs) and were often regarded as contamination. However, the association of CoNS with nosocomial infections is increasingly recognized. The identification of more than 40 different CoNS species has been driven by the introduction of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Yet, treatment guidelines consider CoNS as a whole group, despite increasing antibiotic resistance (ABR) in CoNS. This retrospective study provides an in-depth data analysis of CoNS isolates found in human blood culture isolates between 2013 and 2019 in the entire region of the Northern Netherlands. Methods: In total, 10,796 patients were included that were hospitalized in one of the 15 hospitals in the region, leading to 14,992 CoNS isolates for (ABR) data analysis. CoNS accounted for 27.6% of all available 71,632 blood culture isolates. EUCAST Expert rules were applied to correct for errors in antibiotic test results. Results: A total of 27 different CoNS species were found. Major differences were observed in occurrence and ABR profiles. The top five species covered 97.1% of all included isolates: S. epidermidis, S. hominis, S. capitis, S. haemolyticus, and S. warneri. Regarding ABR, methicillin resistance was most frequently detected in S. haemolyticus (72%), S. cohnii (65%), and S. epidermidis (62%). S. epidermidis and S. haemolyticus showed 50–80% resistance to teicoplanin and macrolides while resistance to these agents remained lower than 10% in most other CoNS species. Conclusion: These differences are often neglected in national guideline development, prompting a focus on ‘ABR-safe’ agents such as glycopeptides. In conclusion, this multi-year, full-region approach to extensively assess the trends in both the occurrence and phenotypic resistance of CoNS species could be used for evaluating treatment policies and understanding more about these important but still too often neglected pathogens.