Testing Algorithm Research Articles

Characterizing Escherichia coli carrying plasmid-mediated AmpC β-lactamases to optimize detection in a diagnostic laboratory setting.

Plasmid-mediated AmpC β-lactamases are a cause of acquired cephalosporin resistance in Gram-negative bacteria. However, consensus regarding the optimal detection method is yet to be achieved and varies depending on local epidemiology and laboratory capacity. We determined the acquired genotypic resistance mechanisms of 250 Escherichia coli isolates with a positive AmpC screen, defined as cefoxitin MIC ≥ 8 mg/L and a positive AmpC double- disc diffusion test, using in-house designed high-resolution melt PCR, detecting plasmid-acquired genes from the CIT and DHA families. A proportion of these isolates (n = 170, 68%) underwent further genotypic characterization using whole- genome sequencing (WGS). Of 250 isolates with a positive screening test, 72 (28.8%) were determined to carry an acquired AmpC gene. There was 100% concordance between PCR and WGS in the identification of acquired AmpC genes. The phenotypic criteria were then assessed to determine their utility in predicting acquired AmpC gene carriage. Criteria 1 (cefoxitin MIC > 8 mg/L plus ceftriaxone MIC > 1 mg/L and/or ceftazidime MIC > 1 mg/L) yielded a sensitivity of 93.1% and a specificity of 47.8%. Criteria 2 (cefoxitin MIC > 16 mg/L plus ceftriaxone MIC > 4 mg/L) had a sensitivity of 33.3% and a specificity of 98.9%. DHA genes, whose expression may be induced following antibiotic exposure, were present in 19% of isolates testing susceptible to ceftriaxone (MIC ≤ 1 mg/L) and were significantly more likely than CIT genes to be detected in susceptible isolates (P < 0.0001). These findings highlight the importance of using genotypic methods to detect acquired AmpC resistance in E. coli isolates that meet phenotypic screening criteria.IMPORTANCEDetection of transmissible AmpC resistance remains a challenging problem for diagnostic laboratories, especially in Escherichia coli where the expression of its intrinsic AmpC gene can result in phenotypic resistance patterns indistinguishable from plasmid-mediated resistance. In conjunction with whole- genome sequencing (WGS), we describe the development and performance of a novel melt-curve PCR to identify the two most prevalent plasmid-mediated AmpC gene families: CIT and DHA. We then describe phenotypic testing algorithms that incorporate this PCR and can differentiate these from non-acquired resistance in E. coli. It is important to distinguish these, not only to spare patients from unnecessarily being treated with infection control precautions, but also to identify plasmid-mediated genes, especially of the DHA family, that have been associated with inducible drug resistance to third- generation cephalosporins.

Genome-wide association studies of ischemic stroke based on interpretable machine learning

Despite the identification of several dozen genetic loci associated with ischemic stroke (IS), the genetic bases of this disease remain largely unexplored. In this research we present the results of genome-wide association studies (GWAS) based on classical statistical testing and machine learning algorithms (logistic regression, gradient boosting on decision trees, and tabular deep learning model TabNet). To build a consensus on the results obtained by different techniques, the Pareto-Optimal solution was proposed and applied. These methods were applied to real genotypic data of sick and healthy individuals of European ancestry obtained from the Database of Genotypes and Phenotypes (5,581 individuals, 883,749 single nucleotide polymorphisms). Finally, 131 genes were identified as candidates for association with the onset of IS. UBQLN1, TRPS1, and MUSK were previously described as associated with the course of IS in model animals. ACOT11 taking part in metabolism of fatty acids was shown for the first time to be associated with IS. The identified genes were compared with genes from the Illuminating Druggable Genome project. The product of GPR26 representing the G-coupled protein receptor can be considered as a therapeutic target for stroke prevention. The approaches presented in this research can be used to reprocess GWAS datasets from other diseases.

A Method for Filling Missing Values in Multivariate Sequence Bidirectional Recurrent Neural Networks Based on Feature Correlations

Multivariate real-life time series data often contain missing values. These missing values often affect subsequent prediction tasks. Traditional imputation methods generally consider only some of the characteristics of multivariate time series data. This can easily lead to inaccurate filling results. In this paper, a feature correlation-based bidirectional recurrent network (BRNN-FR) is proposed to solve the problem of missing values in multivariate sequence data. First, this method involves the design of a bidirectional prediction network based on time intervals and the use of forward and reverse time series information between data points to obtain the characteristics of data changes with time to the greatest extent. Second, considering the correlation between features, a combined feature selection strategy based on the Pearson correlation coefficient and mutual information was proposed. A multiple regression model was established to predict between features. Finally, a bidirectional network ensemble filling algorithm based on the relationships between features is established to predict missing values. Comprehensive experiments on four public datasets show that the mean absolute error (MAE), root mean square error (RMSE) and maximum R2 value (R2_score) of the BRNN-FR algorithm in the direct imputation test are better than those of the other comparison methods in most cases. BRNN-FR also achieved a better area under the curve (AUC) in the indirect comparison experiment of two classifications of in-hospital death after filling the medical dataset. Using the AIR air quality dataset and the power transformer temperature dataset from the ETTH1 interpolation regression to predict the next 3hours and 6hours of average numerical results, most of the optimal regression results are obtained.

Role of admission rapid antigen testing (RATs) for COVID-19 on patients transferred from acute hospitals to a post-acute rehabilitation setting.

BackgroundRapid antigen tests (RATs) are suitable for point-of -care testing, require no laboratory time and give immediate results. However, are RATs useful for detecting asymptomatic COVID-19 infection when compared with polymerase chain reaction (PCR) testing in healthcare settings? AimThe aim of this study was to implement a reliable testing system utilising RATs to promptly detect COVID-19 infection in predominantly asymptomatic patients transferred from acute hospitals to a post-acute rehabilitation unit (PARU). MethodsRAT testing was carried out on all new admissions without a history of confirmed Covid-19 infection within three months of admission. PCR testing was carried out on all patients with a positive RAT for confirmation purposes. The cycle threshold (Ct) values of COVID-19 detected results on PCR testing were examined to determine the utility of the RATs. ResultsA total of 1,403 patients were transferred to the PARU from January to December 2023. The results of the study revealed an 85% accuracy of RATs with a 15% rate of false negative results at the time of admission. All patients that had a positive RAT at the time of admission also had a positive PCR test. ConclusionThis testing algorithm resulted in early detection and prompt isolation of positive cases reducing the likely spread of COVID-19 infection, hospital outbreaks and bed/ward closures.

Instance-optimal information-based voting

The classical Condorcet Jury Theorem considers a voting scenario in which there exists a candidate whose election would be ideal for each voter; each voter, though, has only a limited understanding of the world and is thus unable to determine exactly who this candidate is. The main question in this scenario is whether the voters, acting individually, can cast their ballots so that the unknown optimal candidate wins the election, and the welfare of the group of voters is maximized.In this setting, each candidate is represented by a known probability distribution over signals about the world that the voters can perceive, that is, over bits. One of these candidates is chosen (secretively, by an adversary) to be the ideal candidate. Afterwards, each voter samples this unknown candidate's distribution once and casts a ballot with the hope that the unknown ideal candidate wins the election.In this paper, we consider the famous Condorcet voting system, as well as some of its variants. First, we give a positive answer to an open question of Chierichetti and Kleinberg [8], and show that, with Condorcet voting, there exists a uniform voting strategy that makes the group of voters succeed with probability 1−δ provided that Θ(ϵtv−2⋅ln⁡δ−1) voters take part in the election — here, ϵtv is the minimum total variation distance between the distributions of two candidates.We also give a uniform voting strategy for the Copeland voting system (a variant of Condorcet) that makes the group succeed with probability 1−δ with Θ(ϵH−2⋅ln⁡δ−1) voters, where ϵH is the minimum Hellinger distance between the distributions. Our uniform Copeland strategy, then, is an instance-optimal hypothesis testing algorithm: constants aside, the strategy is as efficient as the optimal omniscient algorithm which determines the unknown candidate after having directly observed each of the signals perceived by the voters. Then, we “derandomize” our uniform Copeland strategy, and obtain a Condorcet strategy that achieves instance-optimality at the cost of losing uniformity; finally, we prove that this loss of uniformity is necessary: no uniform Condorcet strategy can achieve instance-optimality, in general.Thus, the right voting strategies let these classical combinatorial voting systems attain the same efficiency of centralized, optimal, hypothesis testers.

HIV and viremia prevalence in non-migrating members of migrant households in southern Uganda: A cross-sectional population-based study.

In Africa, migrants are more likely to be living with HIV and HIV viremic than non-migrants but less is known about HIV outcomes among non-migrants living in households with migrants. We compared HIV outcomes in non-migrating persons in households with and without migration. We analyzed cross-sectional data collected between August 2016-May 2018 from non-migrating participants aged 15-49 in the Rakai Community Cohort Study in Uganda. Migrant households were classified as those reporting ≥1 member moving into or out of the household since the prior survey. HIV serostatus was determined using a validated testing algorithm, and viremia defined as >1,000 copies/mL. Modified Poisson regression was used to estimate prevalence ratios (PR) between household migration and HIV outcomes. Analyses were stratified by gender, direction of migration (into/out of household), and relationship between non-migrants and migrants (e.g., spouse). There were 14,599 non-migrants (52% women) and 4,415 (30%) lived in a household with ≥1 migrant. Of these, 972 (22%) had migrant spouses, 1,102 (25%) migrant children, and 875 (20%) migrant siblings. Overall, HIV prevalence and viremia did not differ between non-migrants in households with and without migration. However, in stratified analyses, non-migrant women with migrant spouses were significantly more likely to be HIV seropositive compared to non-migrant women with non-migrant spouses (adjPR:1.44, 95%CI:1.21-1.71). Conversely, non-migrant mothers living with HIV who had migrant children were less likely to be viremic (adjPR:0.34, 95%CI:0.13-0.86). Non-migrating women with migrating spouses are more likely be living with HIV, and may benefit from additional HIV support services.

Comparison of 2 Sets of Immunoassays Used in Modified 2-Tiered Testing Algorithms for the Diagnosis of Lyme Disease.

Since 2019, modified 2-tiered testing (MTTT) algorithms have been available for the diagnosis of Lyme disease. MTTTs replaced the standard algorithms that utilized enzyme immunoassays and immunoblots with sequential enzyme immunoassays that detect different antigens. We compared the performance of serological assays from ZEUS Scientific Inc. and DiaSorin Inc. that are used for the diagnosis of Lyme disease. Serological results were compared with clinical information gathered by chart review. Percent positive agreement (PPA) and percent negative agreement (PNA) for total immunoglobulin G (IgG)/immunoglogulin M (IgM) (n = 120) were 64% (95% confidence interval 54% to 73%) and 100% (87% to 100%), respectively. PPA and PNA for IgG (n = 93) were 91% (80% to 97%) and 66% (52% to 78%), respectively. PPA and PNA for IgM (n = 93) were 75% (62% to 85%) and 95% (82% to 99%), respectively. Fewer positive total IgG/IgM results confirmed positive for either IgG or IgM for ZEUS compared to DiaSorin. Overall MTTT algorithm interpretation was concordant in 58% (55/95) of samples, and concordance improved when the results were limited to IgM in patients with symptom duration <30 days. Treatment with antibiotics was most strongly associated with IgM positivity. This analysis highlights differences in the performance characteristics between commercially available diagnostic assays for Lyme disease. Our data suggest that the DiaSorin assays would result in fewer positive total IgG/IgM tests, decreasing the required number of confirmatory IgG and IgM tests. This would potentially lead to fewer patients treated with antibiotics.

Optimized bp Neural Network Based on Improved Dung Beetle Optimization Algorithm to Predict High-Performance Concrete Compressive Strength

In modern architecture, the structural safety of buildings largely depends on the compressive strength of high-performance concrete (HPC), which is determined by the complex nonlinear relationships between its components. In order to more accurately forecast HPC’s compressive strength, this paper proposes a prediction model based on an improved dung beetle optimization algorithm (OTDBO)-optimized backpropagation neural network (BPNN). Extreme Gradient Boosting (XGBoost) is employed to determine the inputs for the BPNN, enhancing the computational efficiency under high-dimensional data feature conditions. To address the issues of local optima entrapment and slow convergence in the dung beetle optimization algorithm (DBO), four improvements were made to enhance its performance. In the initial population generation stage, the optimal Latin hypercube method was used to increase the population diversity. In the rolling stage, the osprey optimization algorithm’s global exploration strategy was introduced to improve the global search capability. The variable spiral search strategy was employed in the reproduction stage, and an adaptive t-distribution perturbation strategy was combined in the foraging stage to enhance the algorithm’s adaptability and search efficiency. The improved dung beetle optimization algorithm (OTDBO) outperformed other algorithms in performance tests on the CEC2017 benchmark functions. In terms of predicting the compressive strength of HPC, the XG-OTDBO-BP model developed in this study outperformed models optimized by other algorithms in terms of fitting outcomes and prediction accuracy. These findings support the XG-OTDBO-BP model’s superiority in the compressive strength of HPC prediction.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Efficient algorithm for thermal nondestructive testing and evaluation by considering the heteroscedastic nature of noise sources in infrared thermography

Abstract Thermal Imaging is a promising Non Destructive Testing &amp; Evaluation (NDT &amp; E) approach to monitor the health&amp;#xD;of composite materials. Among various post processing approaches adopted in thermal imaging for NDT &amp; E, statistical&amp;#xD;analysis schemes gained importance due to their reliability and data reduction capabilities. This paper provides an insight&amp;#xD;to a factor analysis-based statistical approach to detect the hidden defects in the Glass Fiber Reinforced Polymer (GFRP)&amp;#xD;sample. The proposed approach models the observed data covariance into combination of temporal signal covariance&amp;#xD;and noise covariance matrices. The modeling of the diagonal covariance matrix (with different elements) is motivated by&amp;#xD;the presence of heterogeneity in the experimental data obtained from GFRP sample. This novel method is based on the&amp;#xD;coordinate descent technique, which estimates the covariance matrix of the noise variances iteratively by minimizing the&amp;#xD;negative log likelihood function. The obtained results from the chosen GFRP samples compared with the widely used&amp;#xD;statistical Principal Component Thermography (PCT) technique illustrate the improved performance in terms of defect&amp;#xD;detection with the proposed technique.&amp;#xD;

Sputum and tongue swab molecular testing for the in-home diagnosis of tuberculosis in unselected household contacts: a cost and cost-effectiveness analysis.

Delayed and missed diagnosis are a persistent barrier to tuberculosis control, partly driven by limitations associated with sputum collection and an unmet need for decentralized testing. Household contact investigation with point-of-care testing of non-invasive specimens like tongue swabs are hitherto undescribed and may be a cost-effective solution to enable community-based active case finding. In-home, molecular point-of-care testing was conducted using sputum and tongue specimens collected from all household contacts of confirmed tuberculosis cases. A health economic assessment was executed to estimate and compare the cost and cost-effectiveness of different in-home, point-of-care testing strategies. Incremental cost effectiveness ratios of strategies utilizing different combination testing algorithms using sputum and/or tongue swab specimens were compared. The total implementation cost of delivering the standard of care for a 2-year period was $84 962. Strategies integrating in-home point-of-care testing ranged between $87 844 - $93 969. The cost-per-test for in-home, POC testing of sputum was the highest at $20·08 per test. Two strategies, Point-of-Care Sputum Testing and Point-of-Care Combined Sputum and Individual Tongue Swab Testing were the most cost-effective with ICERs of $543·74 and $547·29 respectively, both below a $2,760 willingness-to-pay threshold. An in-home, point-of-care molecular testing strategy utilizing combination testing of tongue swabs and sputum specimens would incur an additional 10.6% program cost, compared to SOC, over a 2-year period. The increased sample yield from tongue swabs combined with immediate result notification following, in-home POC testing would increase the number of new TB cases detected and linked to care by more than 800%. Evidence before this study: We searched PubMed for original research published between January 1, 1950 and June 30, 2024 that evaluated the cost-effectiveness of in-home POC molecular testing, as part of HCI strategies for tuberculosis. PubMed search terms used included ["household contact investigation" OR "household contact tracing"] AND "tuberculosis" AND "cost-effectiveness". The search revealed 8 studies, of which one was removed as HCIs were leveraged for the provision of short course preventative therapy and not tuberculosis testing. None of the studies were conducted in South Africa. All seven remaining studies relied on a hub-and-spoke model of sputum collection and transportation with sputum tested at a centralized laboratory facility. Although active case finding strategies like HCIs are endorsed by the WHO to improve early case detection and treatment initiation, limited research has been done to assess its cost-effectiveness in low- and middle-income countries.Added value of this study: To our knowledge, this is the first example of in-home molecular point-of-care (POC) testing as part of HCI. The use of primary data to estimate and compare the incremental cost effectiveness of different combination, in-home testing strategies utilizing alternative sample types equips policy makers with a selection of strategy options to choose from. The tradeoff between sample types with high collection yield and those with increased accuracy becomes evident in the economic analysis, highlighting the need to consider both yield and accuracy in effective clinical decision making and use-case development. The success of in-home, POC tongue swab testing of all contacts, irrespective of symptom presentation shows great promise for universal testing programs.Implications of all available evidence: Results from our economic modeling provide evidence in support for the integration of in-home, POC tuberculosis (TB) testing during HCI. The use of less invasive tongue swab samples to increase sample yield in the absence of sputum expectoration highlights the value of combination testing strategies. Immediate result notification resulting from rapid, in-home POC testing shows great promise for increasing early case detection and improving treatment uptake. In-home, POC testing strategies, when incorporated into HCI could curb ongoing community transmission and reduce the overall burden of TB. Considerations for adopting novel POC testing strategies in future active case finding programs like HCI should strongly be considered. We evaluated the cost-effectiveness of in-home, point-of-care TB testing of household contacts. The findings indicate that combined testing strategies using tongue swab and sputum specimens could significantly increase TB case detection, with modest additional program costs.

An enhanced slime mould algorithm with triple strategy for engineering design optimization

Abstract This paper introduces an enhanced slime mould algorithm (EESMA) designed to address critical challenges in engineering design optimization. The EESMA integrates three novel strategies: the Laplace logistic sine map technique, the adaptive t-distribution elite mutation mechanism, and the ranking-based dynamic learning strategy. These enhancements collectively improve the algorithm’s search efficiency, mitigate convergence to local optima, and bolster robustness in complex optimization tasks. The proposed EESMA demonstrates significant advantages over many conventional optimization algorithms and performs on par with, or even surpasses, several advanced algorithms in benchmark tests. Its superior performance is validated through extensive evaluations on diverse test sets, including IEEE CEC2014, IEEE CEC2020, and IEEE CEC2022, and its successful application in six distinct engineering problems. Notably, EESMA excels in solving economic load dispatch problems, highlighting its capability to tackle challenging optimization scenarios. The results affirm that EESMA is a competitive and effective tool for addressing complex optimization issues, showcasing its potential for widespread application in engineering and beyond.

Diagnosis and management of patients with chronic coronary syndromes

Chronic coronary syndromes (CCS) are associated with reduced quality of life, an increased risk of cardiovascular events and mortality, and substantial healthcare costs. Diagnostic testing algorithms and treatment strategies for this condition have evolved significantly. Importantly, the results of several randomised controlled trials assessing invasive versus medical management of patients with CCS have recently been reported. In this review, we examine the latest evidence on the diagnosis and treatment of patients with CCS, with a particular focus on the use of invasive strategies in these individuals.

Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios

Before being deployed on roads, Autonomous Vehicles (AVs) must undergo comprehensive testing. Safety-critical situations, however, are infrequent in usual driving conditions, so simulated scenarios are used to create them. A test scenario comprises static and dynamic features related to the AV and the test environment; the representation of these features is complex and makes testing a heavy process. A test scenario is effective if it identifies incorrect behaviors of the AV. In this article, we present a technique for identifying key features of test scenarios associated with their effectiveness using Instance Space Analysis (ISA). ISA generates a ( \(2D\) ) representation of test scenarios and their features. This visualization helps to identify combinations of features that make a test scenario effective. We present a graphical representation of each feature that helps identify how well each testing technique explores the search space. While identifying key features is a primary goal, this study specifically seeks to determine the critical features that differentiate the performance of algorithms. Finally, we present metrics to assess the robustness of testing algorithms and the scenarios generated. Collecting essential features in combination with their values associated with effectiveness can be used for selection and prioritization of effective test cases.

A Review Paper on Memory Fault Models and its Algorithms

The significance of testing semiconductor memories has grown significantly in the semiconductor industry due to the increased density of modern memory chips. This paper aims to investigate and analyze different types of functional faults present in today's memory technology. These faults include stuck-at faults, transition faults, coupling faults, address decoder faults, and neighborhood pattern-sensitive faults. The paper also delves into the techniques utilized to identify and detect these faults. In particular, the focus is placed on the importance of zero-one, checkerboard, and March pattern tests, which are widely employed to assess functional memory defects at different levels, such as the chip level, array level, and board level. Furthermore, the study provides an in-depth exploration of various test algorithms and thoroughly examines their fault coverage capabilities. Overall, this review paper provides valuable insights into the challenges posed by the dense nature of modern memory chips and offers a comprehensive analysis of functional faults in memory technology. By emphasizing the importance of testing and presenting a detailed exploration of fault detection methods and test algorithms, this study contributes to the advancement of reliable and high-performance memory devices in the electronic industry suggesting that MARCH algorithms outperform others when considering factors like fault coverage, power efficiency, area optimization, and time complexity parameters, making them the preferable choice for reliable and high-performance memory devices in the electronic industry.

Transformer object tracking algorithm based on spatiotemporal template focal attention

Existing visual object tracking methods only use the target area of the first frame as a template, which makes it easy to fail in fast-changing and complex backgrounds. To solve this problem, this paper proposes a Transformer-based object tracking algorithm that focuses on the target focus information in the template and dynamically updates the template features. In order to reduce the interference of background information on attention, this algorithm uses a sparse Transformer module to achieve the interaction of feature information; a template focus attention module is also proposed to focus on the dynamic template features and the initial template features to retain the highly reliable feature information in the initial template. Experimental results show that the success rate and accuracy of this algorithm in the 0TB100 benchmark test are and respectively 70.9%, 91.6%and the success rate and accuracy are improved by 4.11%and respectively compared with the similar template update algorithm STARK 3.27%. This algorithm effectively addresses the limitations of existing visual object tracking methods and improves the accuracy and robustness of tracking.

Implementation of an algorithm for testing, diagnosis, and antibiotic stewardship of asymptomatic bacteriuria in pregnancy

BackgroundAsymptomatic bacteriuria (ASB) affects 2-15% of pregnant women, with 20-40% developing symptoms later. Symptomatic urinary tract infections (UTIs) are more common in pregnancy, with a prevalence of 33%, posing risks like preterm delivery, low birth weight, and maternal pyelonephritis. The gold standard for UTI detection is a urine culture, but point-of-care urinalysis dipsticks are frequently performed as screens during regular obstetric visits. Leukocyte esterase has been used to justify treatment in the asymptomatic, even with low sensitivity and specificity. Confirmatory tests are crucial to avoid false positives and ensure optimal outcomes. Current guidelines for urinalysis dipstick interpretation and the decision to treat ASB in pregnancy are limited. It remains unclear if an evidence-based algorithm can improve test utilization, diagnosis, and treatment decisions for ASB in pregnancy. ObjectivesThe primary objective of our study is to develop, implement, and evaluate an evidence-based algorithm to guide urinalysis interpretation, culturing, diagnosis, and antibiotic stewardship of asymptomatic bacteriuria in pregnant patients during routine obstetric visits. Study DesignThe project involves both retrospective and quasi-experimental prospective chart reviews of pregnant patients aged 18 and older, beyond 20 weeks gestation, from routine obstetric visits with urinalysis dipstick tests. A doctorate in clinical laboratory sciences student developed an educational algorithm to guide urinalysis dipstick interpretation, culturing necessity, and treatment decisions based on evidence-based practice. Our study considered patient records from February 1 – 28, 2022 as retrospective (pre-algorithm implementation) data and January 24 - February 22, 2023, as prospective (post-algorithm implementation) data. Data collected from the electronic medical record included de-identified patient information, urinalysis results, culture dates and outcomes, antibiotic prescriptions, UTI or ASB diagnoses, provider details, adverse pregnancy outcomes, and demographics. Data analysis using SPSS version 29 involved chi-square tests, likelihood ratios, and effect size calculations, with P-values <0.05 considered statistically significant. ResultsIn our study, we examined a total of 1,176 patient records. Pre-implementation data included 440 records, with 224 abnormal and 216 normal urinalyses. Post-implementation data encompassed 736 records, of which 255 were abnormal and 481 were normal. The patient demographics predominantly featured White (87%), with a median maternal age of 27 years and a gestational age of 32 weeks. Our pre-implementation analyses revealed significant associations between algorithm deviations with both culture utilization (P <.001) and antibiotic stewardship (P <.001). However, no significant associations were observed between algorithm deviations and adverse patient outcomes. Culture underutilization decreased significantly from 43.0% (189/440) pre-implementation to 29.5% (217/736) post-implementation (P < .001). The overall reduction in ASB prevalence from 16.3% (8/49) to 6.7% (10/67) suggests a decrease of nearly 60%. Additionally, antibiotic overprescription decreased significantly from 1.6% (4/258) pre-implementation to 0.8% (4/522) post-implementation (P = .003), with a reduction from 7.1% (3/42) to 2.4% (1/41) among abnormal urinalyses. ConclusionOur findings show a strong alignment between the use of the algorithm and subsequent clinical decisions, underscoring its potential to enhance patient care and management in obstetric settings. Notably, adherence to the algorithm was higher among providers displaying prudent antibiotic use.

The effects of HIV self-testing on HIV incidence and awareness of status among men who have sex with men in the United States: Insights from a novel compartmental model

BackgroundThe OraQuick In-Home HIV self-test represents a fast, inexpensive, and convenient method for users to assess their HIV status. If integrated thoughtfully into existing testing practices, accompanied by efficient pathways to formal diagnosis, self-testing could enhance both HIV awareness and reduce HIV incidence. However, currently available self-tests are less sensitive, particularly for recent infection, when compared to gold-standard laboratory tests. It is important to understand the impact if some portion of standard testing is replaced by self-tests. We used a compartmental model to evaluate the effects of self-testing in diverse scenarios among gay, bisexual and other men who have sex with men (MSM) in the United States for the period 2020–2030, and to understand which scenarios maximize the advantages of self-testing. MethodsWe introduced a novel 4-compartment model for HIV self-testing. We employed the model under different screening rates, self-test proportions, and delays to diagnosis for those identified through self-tests to determine the potential effects of self-testing on HIV incidence and awareness of status when applied to the US MSM population. We studied scenarios in which self-tests supplement laboratory-based tests, with no replacement, and scenarios in which some replacement occurs. We also examined how future improvements in self-test sensitivity may affect our results. ResultsWhen HIV self-tests are supplemental rather than substitutes for laboratory-based testing, self-testing can decrease HIV incidence among MSM in the US by up to 10 % and increase awareness of status among MSM from 85 % to 91 % over a 10-year period, provided linkage to care and formal diagnosis occur promptly following a positive self-test (90 days or less). As self-tests replace a higher percentage laboratory-based testing algorithms, increases in overall testing rates were necessary to ensure reductions in HIV incidence. However, such needed increases were relatively small (under 10 % for prompt engagement in care and moderate levels of replacement). Improvements in self-test sensitivity and/or decreases in the detection period may further reduce any necessary increases in overall testing by up to 40 %. ConclusionsIf properly utilized, self-testing can provide significant long-term reductions to HIV incidence and improve awareness of HIV status. Ensuring that self-testing increases overall testing and that formal diagnosis and engagement in care occur promptly following a positive self-test are necessary to maximize the benefits of self-testing. Future improvements in self-test sensitivity and reductions in the detection period would further reduce HIV incidence and the potential risks associated with replacing laboratory tests with self-tests.

Emphasizing the need for preconceptional, prenatal genetic counseling and comprehensive genetic testing in consanguinity: challenges and experience.

Preconception and prenatal genetic counseling is a well-established means of risk assessment in many parts of the world, and in recent years, an emerging concept in India. Likelihood of an offspring having autosomal recessive disorder increases based on the degree of consanguinity. Hence, genetic testing of the couple for the identification of carrier status for disease-causing variants is crucial. The purpose of this study is to understand the frequency of genetic abnormalities in consanguineous marriages by using a comprehensive genetic testing algorithm where in karyotyping, FISH, exome sequencing and microarray are used sequentially to determine the genetic etiology based on the clinical presentation and to evaluate the need and benefits of preconceptional and prenatal genetic counseling. This retrospective study includes 66 couples having consanguinity referred for genetic counseling and testing. Of the 66 couples, 58 underwent comprehensive genetic testing which included Karyotyping, Fluorescence in Situ Hybridization (FISH), Microarray and Exome sequencing based on their clinical presentation. The analyses revealed a genetic abnormality in approximately 31% and chromosomal polymorphic variations & variants of uncertain significance in 17% of the couples. Counseling in these couples helped in identifying the carrier status and enabled them to take an informed decision in subsequent pregnancies. These findings reiterate the acute need for preconception and prenatal genetic counseling services in India.

A-126 The Role of Lysozyme and Other Inflammatory Markers in Understanding GI Microbiome Changes

Abstract Background The role of GI microbiome in gut health, intestinal permeability, and the development of inflammation has been well-established however, few clinical studies assess microbiome (i.e. PCR) and biochemical markers from the same stool collection. Doctor’s Data Inc. (DDI) has been performing a GI microbiome test in conjunction with other stool biochemical markers for the past 3 years, with over 7000 patient samples analyzed. The aim of this study was to analyze GA-map® Probe results for possible correlations with elevated values for fecal calprotectin, lactoferrin, and lysozyme. Methods Microbiome characterization The microbiome analysis test used in this study identifies 48 targeted analytes across six phyla using the GA-map® Dysbiosis test (Genetic Analysis AS, Oslo). The test algorithm has been trained and validated using 48 bacterial markers signal intensity obtained from 668 adults, including healthy subjects, patients with IBS and IBD. GA-map® Dysbiosis Analyzer software calculates the degree of dysbiosis (DI) of a given sample, assigning a DI ranging from 1 to 5. DI greater than 2 indicates that the microbiota is deviant from a healthy reference population (“dysbiotic”), while a score of 2 or lower is reported as “normobiotic”. Biochemical marker characterization Each stool sample was analyzed for a suite of biochemical markers - in particular calprotectin, lactoferrin and lysozyme - with the concentration determined using commercially available ELISA kits (ALPCO, Techlab). Results Using clinical cut-offs of 100.0 ug/g (calprotectin) and 7.3 ug/g (lactoferrin) the patient data was sorted into cohorts indicating disease state of “Normal”, “Intermediate” and “IBD Likely”, and further differentiated using the lysozyme result. The biochemical marker results were correlated against DI score for each stool sample (Figure 1). Conclusions DI score increases as biochemical inflammatory markers increase, with lysozyme as the key differentiating factor between patient cohorts.