Major Errors Research Articles

Comparison of disk diffusion and broth microdilution methods for antimicrobial susceptibility testing of Campylobacter isolates of meat origin

The aim of this study was to compare the disk diffusion (DD) and the broth microdilution (BMD) methods in determining the antimicrobial susceptibility of 36 Campylobacter isolates of meat-origin to six antibacterial drugs (erythromycin, ciprofloxacin, tetracycline, streptomycin, gentamicin and nalidixic acid). All the available zone diameter and minimum inhibitory concentration (MIC) breakpoints of C. jejuni and C. coli as recommended by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) were utilized. In addition, the zone diameter breakpoints of Enterobacterales for nalidixic acid, gentamicin, and streptomycin, as recommended by the Clinical and Laboratory Standards Institute (CLSI), were applied. All Campylobacter isolates were categorised as susceptible to erythromycin and gentamicin by both methods indicating completely concordant classification results. The overall highest ‘Very major error’ (VME) and ‘Major error’ (ME) rates were detected for nalidixic acid (13.3%) and tetracycline (26.3%), respectively, whereas a ‘Minor error’ (mE) rate was detected only for ciprofloxacin (60.1%). However, the Cohen's kappa statistic indicated a substantial concordance between the DD and BMD classification results for tetracycline and streptomycin, and almost perfect agreement for nalidixic acid, with corresponding categorical agreement rates of over 86% and approximately up to 92%. The correlation between the complementary inhibition zones and MIC breakpoints was strong and statistically highly significant (p < 0.001) for ciprofloxacin, tetracycline, streptomycin, and nalidixic acid.

Advanced spectral reconstruction (ASR) for setup-independent universal Raman spectroscopy models

Raman spectroscopy is a versatile tool for the non-invasive determination of system or material properties. The desired information about a property or state must always be extracted from the acquired spectra. To that end, a variety of techniques ranging from data-driven machine learning methods to physics-based modeling of spectra are applied. However, most of these techniques suffer from setup dependency, i.e. even minor changes in the experimental setup can lead to major prediction errors when applying previously generated models. Consequently, laborious retraining, recalibration, or even complete rebuilding of a method are required.To weaken this constraint and therefore enable model transfer we propose a novel physics-based method for the advanced spectral reconstruction (ASR) of Raman spectra. It is established on the basic understanding and corresponding modeling of the signal formation process covering all relevant steps from excitation to detection. In contrast to common methods, error-prone preprocessing or other signal manipulation steps are omitted. Instead, experimental influences are explicitly incorporated into the reconstruction model. Hence, this model consists of two independent modules: one modeling the setup-independent fundamental Raman signal and one representing the setup influences. The setup model is determined a priori based on a straightforward setup characterization.To demonstrate the model transfer capabilities of the ASR routine we analyzed multiple liquid samples (water, toluene, and ethanol), polymers (polyethylene and polyamide 6), and a protein (lysozyme) with four different setups. For every pure substance, a parametrized fundamental Raman model could be established from high-quality reference spectra and utilized subsequently in conjunction with the respective setup influence model to reconstruct all experimentally acquired spectra very accurately. Furthermore, an ASR mixture model for the composition determination of binary ethanol/toluene mixtures was set up and calibrated. Again, this model (including the calibration factor) was transferred to evaluate spectra from the same mixtures recorded with different Raman sensors. Without any recalibration (just by implementing the respective instrument model) the evaluation yields an average root mean square error of prediction (RMSEP) of 1.19% highlighting the ability of ASR to transfer or share models for spectral quantification among sensors and/or researchers.

DNN-Based Approach to Mitigate Multipath Errors of Differential GNSS Reference Stations

One of the major error components of differential global navigation satellite systems is a multipath error in a reference station. This paper introduces a deep neural network based multipath modeling method. A signal to noise ratio, as well as satellite geometry, is used as a feature parameter to capture the variation of the multipath error caused by unavoidable changes in the vicinity of the reference station. The performance of the proposed method is demonstrated for both normal and varying multipath cases using experimental data. The remaining multipath error after mitigation is well bounded by the standardized error model.

Development of a Conceptual Model for Musical Instrument Major Secondary Students' Performance Error Management

Development of a Conceptual Model for Musical Instrument Major Secondary Students' Performance Error Management

Detection of colistin resistance via four methods in multidrug-resistant Gram-negative rods isolated from blood cultures.

The broth microdilution (BMD) method recommended for the detection of colistin resistance is labor-intensive, time-consuming, and difficult to apply in routine laboratories. Thus, various methods, such as disk elution, commercial microdilution, and rapid polymyxin-NP tests have been developed for the detection of colistin resistance. In this study, a total of 102 multi-resistant Gram-negative bacteria isolated from blood cultures were evaluated by four different methods for the detection of colistin resistance, and compared with the reference method. For the detection of the compatibility of these methods with the reference method, categorical and essential agreements, very major, major, and minor error rates were determined. Colistin-tigecycline and colistin-meropenem combinations were investigated in colistin-resistant isolates. Of the isolates, 15 (15%) [K. pneumoniae (n = 12), A. baumannii (n = 2), E. coli (n = 1)] were resistant to colistin with reference BMD method. MIC50 and MIC90 values of all isolates were ≤ 0.25 μg/mL and 16 μg/mL, respectively. The categorical agreement rates were 100% for commercial microdilution, disk elution, and RPNP test. The essential agreement rates of commercial microdilution, disk elution, and broth macrodilution were 78.4%, 86.3%, and 100%, respectively. Although there were no major errors in these methods, the macrodilution (12%) and commercial microdilution (20.6%) methods showed the most minor errors. Colistin-meropenem combination showed a 100% synergistic effect, but the colistin-tigecycline combination showed an 80% synergistic effect and 20% indifference effect. Disk elution and RPNP tests are suitable for routine use because they are the most efficient, easiest, low-cost, and good performance tests in detecting colistin resistance.

Rapid pathogen identification and phenotypic antimicrobial susceptibility directly from urine specimens

Implementing effective antimicrobial therapy close to the onset of infection lowers morbidity and mortality and attenuates the spread of antimicrobial resistance. Current antimicrobial susceptibility testing (AST) methods, however, require several days to determine optimal therapies. We present technology and an automated platform that identify (ID) Urinary Tract Infection pathogens in 45 min and provide phenotypic AST results in less than 5 h from urine specimens without colony isolation. The ID and AST tests count cells fluorescently labeled with specific rRNA probes using non-magnified digital imaging. The ID test detected five pathogens at ≤ 7,000 CFU/mL and had a linear range of ~ 4 orders of magnitude. For contrived specimens, AST tests gave 93.1% categorical agreement with 1.3% Very Major Errors (VME), 0.3% Major Errors (ME), and 6.3% minor Errors (mE) compared to the broth microdilution (BMD) reference method. For clinical specimens, the ID test had 98.6% agreement and the AST test had 92.3% categorical agreement with 4.2% mE, 3.4% ME and 4.0% VME compared to BMD. Data presented demonstrates that direct-from-specimen AST tests can accurately determine antimicrobial susceptibility/resistance for each pathogen in a specimen containing two pathogens. The method is robust to urine matrix effects and off-target commensal and contaminating bacteria.

Web-based prediction of antimicrobial resistance in enterococcal clinical isolates by whole-genome sequencing.

Besides phenotypic antimicrobial susceptibility testing (AST), whole genome sequencing (WGS) is a promising alternative approach for detection of resistance phenotypes. The aim of this study was to investigate the concordance between WGS-based resistance prediction and phenotypic AST results for enterococcal clinical isolates using a user-friendly online tools and databases. A total of 172 clinical isolates (34 E. faecalis, 138 E. faecium) received at the French National Reference Center for enterococci from 2017 to 2020 were included. AST was performed by disc diffusion or MIC determination for 14 antibiotics according to CA-SFM/EUCAST guidelines. The genome of all strains was sequenced using the Illumina technology (MiSeq) with bioinformatic analysis from raw reads using online tools ResFinder 4.1 and LRE-finder 1.0. For both E. faecalis and E. faecium, performances of WGS-based genotype to predict resistant phenotypes were excellent (concordance > 90%), particularly for antibiotics commonly used for treatment of enterococcal infections such as ampicillin, gentamicin, vancomycin, teicoplanin, and linezolid. Note that 100% very major errors were found for quinupristin-dalfopristin, tigecycline, and rifampicin for which resistance mutations are not included in databases. Also, it was not possible to predict phenotype from genotype for daptomycin for the same reason. WGS combined with online tools could be easily used by non-expert clinical microbiologists as a rapid and reliable tool for prediction of phenotypic resistance to first-line antibiotics among enterococci. Nonetheless, some improvements should be made such as the implementation of resistance mutations in the database for some antibiotics.

Core Genome Multilocus Sequence Typing and Antibiotic Susceptibility Prediction from Whole-Genome Sequence Data of Multidrug-Resistant Pseudomonas aeruginosa Isolates.

Over the past decade, whole-genome sequencing (WGS) has overtaken traditional bacterial typing methods for studies of genetic relatedness. Further, WGS data generated during epidemiologic studies can be used in other clinically relevant bioinformatic applications, such as antibiotic resistance prediction. Using commercially available software tools, the relatedness of 38 clinical isolates of multidrug-resistant Pseudomonas aeruginosa was defined by two core genome multilocus sequence typing (cgMLST) methods, and the WGS data of each isolate was analyzed to predict antibiotic susceptibility to nine antibacterial agents. The WGS typing and resistance prediction data were compared with pulsed-field gel electrophoresis (PFGE) and phenotypic antibiotic susceptibility results, respectively. Simpson's Diversity Index and adjusted Wallace pairwise assessments of the three typing methods showed nearly identical discriminatory power. Antibiotic resistance prediction using a trained analytical pipeline examined 342 bacterial-drug combinations with an overall categorical agreement of 92.4% and very major, major, and minor error rates of 3.6, 4.1, and 4.1%, respectively. IMPORTANCE Multidrug-resistant Pseudomonas aeruginosa isolates are a serious public health concern due to their resistance to nearly all or all of the available antibiotics, including carbapenems. Utilizing molecular approaches in conjunction with antibiotic susceptibility prediction software warrants investigation for use in the clinical laboratory workflow. These molecular tools coupled with antibiotic resistance prediction tools offer the opportunity to overcome the extended turnaround time and technical challenges of phenotypic susceptibility testing.

Microsporia in Young Children: Clinical Cases

Background. Microsporia is a zooanthroponotic mycosis of skin and hair caused by fungi genus Microsporum. Microsporia is most common among children, including infants. The microsporia incidence has slow steady increase over recent years.Clinical cases description. Follow-up results of two children of different ages (younger/older than 1 year) with microsporia are presented. Successful management approach is shown. Major limitations of drug therapy in infants as well as common therapeutic errors were analyzed.Conclusion. Diagnosis and management of microsporia in infants and young children is a challenging task. Major errors in microsporia management in patients of these age groups are associated with peculiarities of clinical picture as well as limited variations of drugs that are appropriate for the requirements on efficacy and safety of therapy among young children.

Twenty Significant Problems in AI Research, with Potential Solutions via the SP Theory of Intelligence and Its Realisation in the SP Computer Model

This paper highlights 20 significant problems in AI research, with potential solutions via the SP Theory of Intelligence (SPTI) and its realisation in the SP Computer Model. With other evidence referenced in the paper, this is strong evidence in support of the SPTI as a promising foundation for the development of human-level broad AI, aka artificial general intelligence. The 20 problems include: the tendency of deep neural networks to make major errors in recognition; the need for a coherent account of generalisation, over- and under-generalisation, and minimising the corrupting effect of ‘dirty data’; how to achieve one-trial learning; how to achieve transfer learning; the need for transparency in the representation and processing of knowledge; and how to eliminate the problem of catastrophic forgetting. In addition to its promise as a foundation for the development of AGI, the SPTI has potential as a foundation for the study of human learning, perception, and cognition. And it has potential as a foundation for mathematics, logic, and computing.

Penerapan Metode WASPAS Dalam Pemilihan Perguruan Tinggi Bagi Siswa Sekolah

There are so many high school students who do not understand their own criteria because they are confused about choosing the best college. So that most students choose colleges according to the interests of their friends, without finding out which colleges match their criteria. Not even a few of these students follow the directives of their parents. As a result of this, it causes major errors, regrets studying at these institutions and some even move and some even stop. Though each major is tailored to the wishes of students in the future. So that these problems can be solved, then a decision support system is made which will make it easier for students who want to continue to a bachelor's degree or diploma. There are many methods used in decision support systems, one of which is used in this study is the WASPAS method. The way WASPAS works is easy to reach, resulting in a sequence of universities and majors according to their respective criteria, starting from the highest to the lowest. After being applied with the WASPAS method, the final result is the best alternative in alternative A2 with a result value of 14,297.

Genomic analysis of Fosfomycin resistance in multi-drug resistant uropathogens and comparison of in-vitro susceptibility methods uropathogens.

Urinary tract infection is one of the most common bacterial infections causing high morbidity and mortality. The alarming rise of multidrug-resistant uropathogens worldwide forced the clinician to rethink the old drugs like Fosfomycin for its therapeutic management. Our objective was to compare agar dilution, disc diffusion and E-test method for antimicrobial susceptibility testing of Fosfomycin against different drug-resistant uropathogens. Consecutive 181 uropathogens were tested for Fosfomycin susceptibility using agar dilution, disc diffusion and E-test. Results were interpreted using Clinical and Laboratory Standards Institute (CLSI) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints. Whole genome sequencing analysis was done on the 4 XDR/PDR Fosfomycin resistant Klebsiella pneumoniae isolates. Escherichia coli was found as the most common (62.4%) uropathogen followed by Klebsiella pneumoniae (21%). Considering agar dilution as the gold standard, 6.1% of isolates were resistant to Fosfomycin. Following CLSI breakpoints, the susceptibility of Escherichia coli, Klebsiella pneumoniae, other Enterobacterales and Pseudomonas aeruginosa were 92.9%, 92.1%, 100%, 100%; whereas using EUCAST breakpoints the susceptibility rates were 85.7%, 86.9%, 92.9%, and 100%, respectively. The essential agreement, categorical agreement, major error, and very major error for E-test/disc diffusion for all the organisms were 91.2%/Not Applicable, 95%/93.9%, 1.8%/4.7%, 9.1%/9.1%, respectively. Whole-genome sequencing showed mutation UhpT gene as well as the presence of plasmid-mediated fosA5 or fosA6 genes conferring Fosfomycin resistance. This result supports very low resistance of Enterobacterales against Fosfomycin; hence should be considered a valuable option to treat multidrug-resistant uropathogens. Disc diffusion was observed to be a convenient method for Fosfomycin susceptibility testing compared to agar dilution.

A Multi-Layer Auto-Segmentation Quality Assurance and Correction Pipeline for MR-Guided Adaptive Radiotherapy

<h3>Purpose/Objective(s)</h3> Fast and accurate auto-segmentation on daily image is essential for MR-guided adaptive radiotherapy (MRgART). However, the state-of-the-art deep learning auto-segmentation (DLAS) still has limited success particularly for complex structures such as those in the abdomen. Inaccurate contours from DLAS need to be inspected and edited and this manual process can be labor intensive, time consuming and subject to human errors. This work aims to develop a multi-layer pipeline that includes an automatic contour quality assurance (ACQA) test, an automatic contour refinement (ACR) process, and an interactive semi-automatic segmentation (ISS) tool to quickly identify and correct for inaccurate auto-segmented contours, thus replacing the manual process in MRgART. <h3>Materials/Methods</h3> The proposed pipeline includes the ACQA, ACR and ISS modules that are developed to detect and correct for inaccurate auto-segmented contours in the following steps: (1) identifying contours/image slices of acceptable or with minor or major errors from the input auto-segmented contours using ACQA, (2) correcting the identified inaccurate contours/slices using ACR, (3) repeating steps (1) and (2) on the contours obtained from the previous ACR until the contours/slices become acceptable or the performance of ACR becomes saturated, and (4) correcting the un-correctable contours from ACR using ISS tool. The steps 1-3 are designed to be fully automated. Details on ACQA, ACR and ISS are being investigated in separate studies. Briefly, the ACQA module uses either machine learning or DL classification algorithms based on organ and image properties to validate contour quality. The ACR module utilizes multiple correction methods, including an active contour model (ACM), U-Net and Dense UNet DL models to sequentially refine the inaccurate contours with minor and/or major errors. The ISS tool uses the accurate contours on an adjacent slice as prior knowledge informant to guide a 2D UNet DL model to predict contours on a subsequent slice. We have demonstrated the performance of the multi-layer pipeline on complex abdominal organs on various MRIs. <h3>Results</h3> The ACQA models achieved AUC/accuracy of 0.95/0.89 for pancreas and 0.92/0.88 for stomach contours. The ACM model improved 54%/49% of bowels/pancreas contours from major into minor errors and the UNet/Dense UNet ACR model improved 43%/33% of the minor bowel/stomach contours to be acceptable. The ISS tool achieved DSC of 0.92, 0.91, 0.91, 0.87, and 0.87 for colon, small bowel, stomach, pancreas, and duodenum. The expected execution time of the pipeline on an auto-segmented MRI was within 5 minutes. <h3>Conclusion</h3> This proposed multi-layer pipeline can complement deep learning auto-segmentation and can improve the segmentation efficiency and accuracy. As part of our on-going efforts, the pipeline is being implemented as a post DLAS process, substantially speeding up daily recontouring in MRgART.

Automatic Contour Refinement of Inaccurate Auto-Segmentation Using an Active Contour Model for MR-Guided Adaptive Radiotherapy

<h3>Purpose/Objective(s)</h3> One of the bottleneck problems of MR-guided adaptive radiotherapy (MRgART) is the impractically long time required to segment the patient's anatomy of the day. Deep learning auto-segmentation (DLAS) has a limited success for complex structures such as those in abdomen. We have previously proposed an active contour model (ACM) to automatically refine the inaccurate contours from DLAS. This study aims to improve the performance of the ACM method by further optimizing the method and testing it using large and diversified abdominal MRI datasets. <h3>Materials/Methods</h3> An improved ACM method that does not require any manual parameter adjustment was implemented. The ACM utilizes the probability maps generated from DLAS models to establish 2D parameter maps and to initialize contour evolution. The performance of the ACM method was tested on the inaccurate DLAS contours of abdominal organs from 91 MRI sets (76 sets of T2-HASTE sequence from a 3T MR-simulator and 15 sets of BTFE sequence from a 1.5T MR-Linac). Bowels were divided into multiple subregions with each including a bowel loop. Levels of contour inaccuracies were divided into two groups: (1) major error group with Dice similarity coefficient (DSC) <0.5 or mean distance to agreement (MDA) >8 mm, and (2) minor error group with remaining contours of 0.5≤ DSC<0.8 or 3 mm<MDA≤8 mm. The performance of the ACM was evaluated based on various metrics, e.g., DSC, MDA, and the added path length (APL, a metric that is correlated with contour editing time). Manually delineated contours were used as ground truth. <h3>Results</h3> As shown in the table below, the average DSC, MDA and APL values for all the contours were improved after the ACM correction. By applying this ACM, approximately half of the contours in major error group were improved to minor error group, and 12-40% of the contours became practically acceptable per TG-132 recommendation (DSC>0.8 and MDA<3 mm). The largest improvement was seen in the major error group of pancreases, where APL reduced by one third, indicating substantial reductions in the subsequent contour editing effort. The execution time of this method to correct one contour was around 1 second. <h3>Conclusion</h3> The ACM method was successfully implemented to automictically and quickly correct for inaccurate contours from MRI-based DL auto-segmentation for complex anatomy, e.g., bowels and pancreas, in abdomen. The ACM method may be used to reduce the manual contour editing workload and accelerate the segmentation process for MRgART.

Modified extended Kalman filtering algorithm for precise voltage and state-of-charge estimations of rechargeable batteries

With the popularization of electric vehicles (EVs), the voltage and state-of-charge (SOC) estimations of rechargeable batteries are of great significance. The SOC parameter has been used as an indicator for delivering the electrical energy of rechargeable lithium-ion batteries (LIBs), while the voltage has been a critical parameter needed to monitor to prevent the cause of battery damage especially during the charging and discharging process. Thus, the research focus is to estimate the SOC and voltage accurately using algorithms. With the capability of avoiding major estimation errors, conventional extended Kalman Filtering (EKF) has been employed to estimate the optimal value of SOC using the parameters obtained by indirect measurements, such as voltage and current. However, the algorithm suffers from limited precision in SOC and voltage estimations, and there is still no in-depth investigation of the error reduction in voltage prediction. Although the SOC accuracy can be improved by a joint algorithm such as double Kalman filtering, optimization of EKF itself is still needed due to the superposition of nonlinear errors. This study reveals that the conventional EKF algorithm induces estimation errors especially when the current abruptly changes. In this study, the research on the modified extended Kalman filtering (MEKF) algorithm was conducted for estimating the voltage and SOC of the LIBs with great improvement in estimation accuracy. The YUASA LEV50 cell was subjected to a standard discharging rate of 0.2C at 298 K to acquire offline parameters, followed with the newly proposed dynamic estimation mathematical battery model (DBOFT) for the optimization of estimations. This is the first time to propose a method of combining gain matrix and noise to reduce the error of voltage estimation at the current turning points, which greatly improves the accuracy of voltage estimation. Specifically, the MEKF algorithm is capable of adjusting the parameters in real-time and reducing the SOC estimation error. The SOC error estimated by the MEKF algorithm was reduced to 0.0052 % under the current rate of 0.2C. Finally, the maximum estimated voltage error was reduced to 0.972 % while the SOC estimation error was reduced to 0.01016 % under the same working current. The precise voltage and SOC estimations obtained by the MEKF with higher accuracy than traditional methods were validated under experimental verification, which is crucial for the life extension of LIBs and the safety of the battery management system (BMS).

Direct MALDI-TOF MS and Antimicrobial Susceptibility Testing of Positive Blood Cultures Using the FASTTM System and FAST-PBC Prep Cartridges-Performance Evaluation in a Clinical Microbiology Laboratory Serving High-Risk Patients.

Bloodstream infections are a leading cause of morbidity and mortality. The rapid diagnostic testing of positive blood cultures (PBCs) shortens times to effective therapy and the de-escalation of broad-spectrum empiric therapy. This is the first study examining the Qvella FASTTM System for the rapid (~20 min) purification of microorganisms directly from PBCs using BacT/Alert® FA/FAN bottles in the bioMérieux Virtuo instrument. We compared the performance of the FASTTM System Liquid ColonyTM (LC), for immediate downstream ID and phenotypic AST, to standard workflow involving colonies obtained by overnight subculture. The LC yielded a concordant species ID by VITEK MS in 121/138 (87.7%) samples, identifying 32 different Gram-positive and Gram-negative species with 3/123 (2.6%) discordances. Compared to standard workflow, direct AST of the LC using VITEK® 2 yielded 98.4% categorical agreement and 98.0% essential agreement. Very major error, major error, and minor error rates were 1.0%, 0.0%, and 1.8%, respectively, for Gram-negative organisms; and 1.9%, 0.2%, and 1.2%, respectively, for Gram-positive organisms. The median times from positive blood culture flag to results by FASTTM System for ID and AST were 7.8 h and 15.7 h, respectively, versus 22.4 h and 36.6 h for standard workflow, respectively. In conclusion, the FASTTM System provides reliable results for direct ID and AST from PBCs with significantly decreased turnaround times.

Multicentre Evaluation of the EUCAST Rapid Antimicrobial Susceptibility Testing (RAST) Extending Analysis to 16-20 Hours Reading Time.

The aim of the study was to evaluate the EUCAST RAST method by extending analysis to 16–20 h reading time and performance with new β-lactam/β-lactamase inhibitor combinations. A total of 676 positive blood cultures (BCs) were enrolled. Results at 4 h, 6 h, 8 h and 16–20 h were interpreted according to bacterial species using EUCAST RAST breakpoints (version 5.1). For species for which no breakpoints were available, tentative breakpoints were used. Categorical agreement with the Microscan microdilution system was analysed. Among the 676 BCs enrolled, 641 were monomicrobial and were included in the analysis. Categorical agreement ranged from 98.9% at 4 h to 99.4% at 16–20 h. The rates of very major errors were 3.3%, 3.7% and 3.4% at 4 h, 6 h and 8 h, respectively, and decreased to 1% at 16–20 h (p < 0.001). The number of major errors was low for each reading time (0.2% and 0.4% at 4 h and 6 h, respectively, and 0.3% at both 8 h and 16–20 h). The proportions of results in the area of technical uncertainty were 9.9%, 5.9%, 5% and 5.2% for readings at 4 h, 6 h, 8 h and 16–20 h, respectively. Tentative breakpoints proposed for Enterobacterales other than E.coli/K.pneumoniae and coagulase-negative staphylococci showed overall performances comparable to those observed for E. coli/K. pneumoniae and S. aureus. In conclusion, EUCAST RAST has been shown to be reliable to determine microbial susceptibility to main antimicrobials, including ceftazidime/avibactam and ceftolozane/tazobactam. A poorer performance was observed for certain species/antimicrobial agent combinations. The better performance observed at 16–20 h compared to the early readings may confer to the method greater potential for antimicrobial de-escalation interventions.

Evaluation of the Sensititre YeastOne and Etest in Comparison with CLSI M38-A2 for Antifungal Susceptibility Testing of Three Azoles, Amphotericin B, Caspofungin, and Anidulafungin, against Aspergillusfumigatus and Other Species, Using New Clinical Breakpoints and Epidemiological Cutoff Values.

Aspergillosis is an invasive fungal disease associated with high mortality. Antifungal susceptibility testing (AFST) is receiving increasing consideration for managing patients, as well as for surveilling emerging drug resistance, despite having time-consuming and technically complex reference methodologies. The Sensititre YeastOne (SYO) and Etest methods are widely utilized for yeasts but have not been extensively evaluated for Aspergillus isolates. We obtained Posaconazole (POS), Voriconazole (VCZ), Itraconazole (ITC), Amphotericin B (AMB), Caspofungin (CAS), and Anidulafungin (AND) minimum inhibitory concentrations (MICs) for both the Etest (n = 330) and SYO (n = 339) methods for 106 sequenced clinical strains. For 84 A. fumigatus, we analyzed the performance of both commercial methods in comparison with the CLSI-AFST, using available cutoff values. An excellent correlation could be demonstrated for Etest-AMB and Etest-VCZ (p < 0.01). SYO-MICs of AMB, VCZ, and POS resulted in excellent essential agreement (>93%), and >80% for AMB, VCZ, and ITC Etest-MICs. High categoric agreement was found for AMB, ITC, and CAS Etest-MICs (>85%) and AMB SYO-MICs (>90%). The considerable number of major/very major errors found using Etest and SYO, possibly related to the proposed cutoffs and associated with the less time-consuming processes, support the need for the improvement of commercial methods for Aspergillus strains.

Incorrectly corrected? QT interval analysis in rats and mice.

QT interval, a surrogate measure for ventricular action potential duration (APD) in the surface ECG, is widely used to identify cardiac abnormalities and drug safety. In humans, cardiac APD and QT interval are prominently affected by heart rate (HR), leading to widely accepted formulas to correct the QT interval for HR changes (QT corrected - QTc). While QTc is widely used in the clinic, the proper way to correct the QT interval in small mammals such as rats and mice is not clear. Over the years, empiric correction formulas were developed for rats and mice, which are widely used in the literature. Recent experimental findings obtained from pharmacological and direct pacing experiments in unanesthetized rodents show that the rate-adaptation properties are markedly different from those in humans and the use of existing QTc formulae can lead to major errors in data interpretation. In the present review, these experimental findings are summarized and discussed.

Analysis of In Vitro Activity of Cefiderocol Against Carbapenem-Resistant Gram-Negative Bacilli by Broth Microdilution and Disk Diffusion Method: A Single-Center Study in Odisha, India

IntroductionCefiderocol (CFDC), a novel semi-synthetic siderophore cephalosporin has been developed to combat the menace of infections caused by carbapenem-resistant Gram-negative bacilli (CR-GNB) including Carbapenem-resistant Enterobacterales (CRE) and Carbapenem-resistant Nonfermenting Gram-negative bacilli (CR-NFGNB).MethodsWe determined the in vitro activity of CFDC against a contemporary collection of 503 CR-GNB isolates by the reference broth microdilution method (BMD) using Iron depleted cation adjusted Mueller-Hinton broth (ID-CAMHB). Performance of CFDC disk diffusion (DD) was evaluated against the reference BMD, as an alternative convenient testing method. Molecular characterization of carbapenemase in CR-GNB was performed by PCR targeting blaNDM-1, blaOXA-48like alleles, blaKPC, blaIMP, and, blaVIM. Minimum inhibitory concentration (MIC) distribution of CFDC in CR-GNB harbouring different carbapenemase enzymes was also analyzed.ResultsIn our study, 81.7% (411/503) of CR-GNB isolates [81.3%, (278/342) CRE and 82.6% (133/161) CR-NFGNB] were susceptible to CFDC (p>0.05). Categorical agreement (CA) of DD ranged from 79.8% to 87.5%, Minor error (mE) ranged from 0 to 14%, Major error (ME) ranged from 0 to 3.5%, and Very Major error (VME) ranged from 0 to 12.5% with variations by species tested. Overall CFDC MIC50 and MIC90 values of CR-GNB isolates without any carbapenemase genes were higher as compared to those with the presence of carbapenemase genes (4 µg/mL and 128 µg/mL versus 2 µg/mL and 16 µg/mL respectively).DiscussionCFDC is not yet available for clinical use in India. Hence, multicentric studies are the need of the hour in India for standardization of CFDC susceptibility using disks and CAMHB from different manufacturers as well as understanding mechanisms of high MIC values.