Critical Value Research Articles

Quantitative evaluation of meniscus injury using synthetic magnetic resonance imaging.

Magnetic resonance imaging (MRI) can diagnose meniscal lesions anatomically, while quantitative MRI can reflect the changes of meniscal histology and biochemical structure. Our study aims to explore the association between the measurement values obtained from synthetic magnetic resonance imaging (SyMRI) and Stoller grades. Additionally, we aim to assess the diagnostic accuracy of SyMRI in determining the extent of meniscus injury. This potential accuracy could contribute to minimizing unnecessary invasive examinations and providing guidance for clinical treatment. Total of 60 (n=60) patients requiring knee arthroscopic surgery and 20 (n=20) healthy subjects were collected from July 2022 to November 2022. All subjects underwent conventional MRI and SyMRI. Manual measurements of the T1, T2 and proton density (PD) values were conducted for both normal menisci and the most severely affected position of injured menisci. These measurements corresponded to the Stoller grade of meniscus injuries observed in the conventional MRI. All patients and healthy subjects were divided into normal group, degeneration group and torn group according to the Stoller grade on conventional MRI. One-way analysis of variance (ANOVA) was employed to compare the T1, T2 and PD values of the meniscus among 3 groups. The accuracy of SyMRI in diagnosing meniscus injury was assessed by comparing the findings with arthroscopic observations. The diagnostic efficiency of meniscus degeneration and tear between conventional MRI and SyMRI were analyzed using McNemar test. Furthermore, a receiver operating characteristic curve (ROC curve) was constructed and the area under the curve (AUC) was utilized for evaluation. According to the measurements of SyMRI, there was no statistical difference of T1 value or PD value measured by SyMRI among the normal group, degeneration group and torn group, while the difference of T2 value was statistically significant among 3 groups (P=0.001). The arthroscopic findings showed that 11 patients were meniscal degeneration and 49 patients were meniscal tears. The arthroscopic findings were used as the gold standard, and the difference of T1 and PD values among the 3 groups was not statistically significant, while the difference of T2 values (32.81±2.51 of normal group, 44.85±3.98 of degeneration group and 54.42±3.82 of torn group) was statistically significant (P=0.001). When the threshold of T2 value was 51.67 (ms), the maximum Yoden index was 0.787 and the AUC value was 0.934. The measurement values derived from SyMRI could reflect the Stoller grade, illustrating that SyMRI has good consistency with conventional MRI. Moreover, the notable consistency observed between SyMRI and arthroscopy suggests a potential role for SyMRI in guiding clinical diagnoses.

CovXtreme : MATLAB software for non-stationary penalised piecewise constant marginal and conditional extreme value models

The covXtreme software provides functionality for estimation of marginal and conditional extreme value models, non-stationary with respect to covariates, and environmental design contours. Generalised Pareto (GP) marginal models of peaks over threshold are estimated, using a piecewise-constant representation for the variation of GP threshold and scale parameters on the (potentially multidimensional) covariate domain of interest. The conditional variation of one or more associated variates, given a large value of a single conditioning variate, is described using the conditional extremes model of Heffernan and Tawn (2004), the slope term of which is also assumed to vary in a piecewise constant manner with covariates. Optimal smoothness of marginal and conditional extreme value model parameters with respect to covariates is estimated using cross-validated roughness-penalised maximum likelihood estimation. Uncertainties in model parameter estimates due to marginal and conditional extreme value threshold choice, and sample size, are quantified using a bootstrap resampling scheme. Estimates of environmental contours using various schemes, including the direct sampling approach of Huseby et al. 2013, are calculated by simulation or numerical integration under fitted models. The software was developed in MATLAB for metocean applications, but is applicable generally to multivariate samples of peaks over threshold data. The software and case study data can be downloaded from GitHub, with an accompanying user guide.

Singular value thresholding two-stage matrix completion for drug sensitivity discovery

Incomplete data presents significant challenges in drug sensitivity analysis, especially in critical areas like oncology, where precision is paramount. Our study introduces an innovative imputation method designed specifically for low-rank matrices, addressing the crucial challenge of data completion in anticancer drug sensitivity testing. Our method unfolds in two main stages: Initially, the singular value thresholding algorithm is employed for preliminary matrix completion, establishing a solid foundation for subsequent steps. Then, the matrix rows are segmented into distinct blocks based on hierarchical clustering of correlation coefficients, applying singular value thresholding to the largest block, which has been proved to possess the largest entropy. This is followed by a refined data restoration process, where the reconstructed largest block is integrated into the initial matrix completion to achieve the final matrix completion. Compared to other methods, our approach not only improves the accuracy of data restoration but also ensures the integrity and reliability of the imputed values, establishing it as a robust tool for future drug sensitivity analysis.

Three-dimensional copula framework for early warning of agricultural droughts using meteorological drought and vegetation health conditions

ABSTRACT This study develops an early warning system for crop yield (CY) failure based on meteorological drought and vegetation health conditions. The framework combines three drought indices – the Standardized Precipitation Evapotranspiration Index (SPEI), standardized Normalized Difference Vegetation Index (stdNDVI), and standardized CY (stdCY) values – using copulas. Datasets for five major wheat-producing cities in Turkey between 2000 and 2022 are used for analysis. Results indicate that the time periods used to calculate SPEI and NDVI are critical in determining agricultural drought and CY conditions. The critical threshold values for SPEI and NDVI, with a 10% probability of causing agricultural drought, are found to be ~0.28 and ~0.42, respectively. Using a three-dimensional copula model resulted in more precise CY simulations than a two-dimensional model. The validation efforts showed that all of the observed CYs fell within the simulated range, indicating the robustness of the methodology in capturing drought impacts on CY conditions.

Hierarchical Fuzzy MCDA Multi-Risk Model for Detecting Critical Urban Areas in Climate Scenarios

One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform aimed at detecting the urban areas most at risk in the presence of heatwave and pluvial flooding scenarios. The proposed model aims to detect the urban areas most vulnerable to both the two climatic phenomena and the two types of hazards as independent events; it partitions the physical component of an urban settlement into two subsystems: buildings and open spaces, and it determines the criticality of a subzone of the urban area of study by evaluating the vulnerabilities of the two subsystems to the two phenomena. The use of a hierarchical fuzzy MCDA model facilitates the modeling of the two subsystems and the assessment of their vulnerability to the two phenomena, and it provides a computationally fast tool for detecting critical urban areas. The model was tested on a study area made up of the districts of the central-eastern area of the city of Naples (Italy); it was divided into subzones made up of individual census areas. The most critical areas are represented by the subzones with criticality values higher than a specific threshold.

Analysis of Electroosmotically Modulated Peristaltic Transport of Third Grade Fluid in a Microtube Considering Slip-Dependent Zeta Potential

Abstract The present study investigates electro-osmotically modulated peristaltic transport of third-grade fluid through a microtube taking into consideration the intricate coupling of zeta potential and hydrodynamic slippage. The analytical results encompass the mathematical expressions for dimensionless electrical potential distribution as well as series solutions for stream function and axial pressure gradient up to first order utilizing the perturbation technique for small Deborah number coupled with the Cauchy product for infinite series. Critical values and ranges of wavelength have been obtained where the axial pressure gradient vanishes. Moreover, pivotal values and ranges of wavelength have also been noted for the invariance of pressure gradient with respect to Deborah number as well as Debye–Hückel parameter. Trapping phenomenon has also been investigated by contours of streamlines wherein the zones of recirculation or trapped boluses are formed predominantly near the microtube walls. Additionally, the relative enhancement in hydrodynamic slippage amplifies the trapped bolus size, whereas a diminishing behavior on bolus size is observed by the electro-osmotic parameter.

A Low-Speed Position Sensorless Scheme From Standstill for Low-Cost SRM Drives Based on Triple Current Slope Difference Threshold

In this paper, a triple current slope difference threshold based low-speed sensorless scheme for low-cost SRM drives is proposed. Firstly, slope based methods, including the current slope difference method, are proposed and the advantages in terms of anti-interference ability over traditional instantaneous value based methods have been clarified. Secondly, the traditional single/double threshold methods and corresponding outgoing/incoming phase injection types are introduced and comprehensively compared. The limitations of conventional methods while adopted in low-cost SRM drives are stated. Accordingly, to solve the problem that the current cannot be obtained during the demagnetization stage in the resistor-based current sampling scheme, the triple current slope difference threshold method is proposed, whose 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> threshold indicates the zero-current position. Thirdly, a general threshold value determination method considering the three-phase unbalanced inductances is proposed, which only requires the unsaturated inductance vs. rotor position characteristics that is very easy to be obtained. The proposed method only needs to store six threshold values in the microprocessor and needs no complex calculations and additional hardware, which can be realized in low-cost drive systems. Finally, the experiments of both the steady-state and starting transient-state under over-load conditions are implemented to verify the feasibility and accuracy of the proposed method.

M3W: Multistep Three-Way Clustering.

Three-way clustering has been an active research topic in the field of cluster analysis in recent years. Some efforts are focused on the technique due to its feasibility and rationality. We observe, however, that the existing three-way clustering algorithms struggle to obtain more information and limit the fault tolerance excessively. Moreover, although the one-step three-way allocation based on a pair of fixed, global thresholds is the most straightforward way to generate the three-way cluster representations, the clusters derived from a pair of global thresholds cannot exactly reveal the inherent clustering structure of the dataset, and the threshold values are often difficult to determine beforehand. Inspired by sequential three-way decisions, we propose an algorithm, called multistep three-way clustering (M3W), to address these issues. Specifically, we first use a progressive erosion strategy to construct a multilevel structure of data, so that lower levels (or external layers) can gather more available information from higher levels (or internal layers). Then, we further propose a multistep three-way allocation strategy, which sufficiently considers the neighborhood information of every eroded instance. We use the allocation strategy in combination with the multilevel structure to ensure that more information is gradually obtained to increase the probability of being assigned correctly, capturing adaptively the inherent clustering structure of the dataset. The proposed algorithm is compared with eight competitors using 18 benchmark datasets. Experimental results show that M3W achieves superior performance, verifying its advantages and effectiveness.

Thermodynamical analysis with extended phase transition of AdS hairy black hole in gravitational decoupling theory

This article explores various thermodynamical aspects and the extended phase space of the AdS hairy black hole within the context of gravitational decoupling. This study focused on thermodynamic properties such as geometrical mass, Hawking temperature, entropy, specific heat, Gibbs free energy (both locally and globally) and P-V criticality at the horizon radius. The thermodynamic stability of the AdS hairy black hole has been established based on its positive heat capacity. The analysis of Gibbs free energy is utilized to investigate the behavior of the first-order phase transition. Thermodynamic pressure, denoted by P, is related to the cosmological constant by the formula P=−Λ8π . In addition, a numerical investigation has been carried out to examine the equation of the state parameter and its critical values.

Flow cytometric minimal residual disease measurement accounting for cytogenetics in children with non-high-risk acute lymphoblastic leukemia treated according to the ALL-MB 2008 protocol.

Quantitative measurement of minimal residual disease (MRD) is the "gold standard" for estimating the response to therapy in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Nevertheless, the speed of the MRD response differs for different cytogenetic subgroups. Here we present results of MRD measurement in children with BCP-ALL, in terms of genetic subgroups with relation to clinically defined risk groups. A total of 485 children with non-high-risk BCP-ALL with available cytogenetic data and MRD studied at the end-of-induction (EOI) by multicolor flow cytometry (MFC) were included. All patients were treated with standard-risk (SR) of intermediate-risk (ImR) regimens of "ALL-MB 2008" reduced-intensity protocol. Among all study group patients, 203 were found to have low-risk cytogenetics (ETV6::RUNX1 or high hyperdiploidy), while remaining 282 children were classified in intermediate cytogenetic risk group. For the patients with favorable and intermediate risk cytogenetics, the most significant thresholds for MFC-MRD values were different: 0.03% and 0.04% respectively. Nevertheless, the most meaningful thresholds were different for clinically defined SR and ImR groups. For the SR group, irrespective to presence/absence of favorable genetic lesions, MFC-MRD threshold of 0.1% was the most clinically valuable, although for ImR group the most informative thresholds were different in patients from low-(0.03%) and intermediate (0.01%) cytogenetic risk groups. Our data show that combining clinical risk factors with MFC-MRD measurement is the most useful tool for risk group stratification of children with BCP-ALL in the reduced-intensity protocols. However, this algorithm can be supplemented with cytogenetic data for part of the ImR group.

Validation of a Field-Portable, Handheld Real-Time PCR System for Detecting Pseudogymnoascus destructans, the Causative Agent of White-Nose Syndrome in Bats.

White-nose syndrome (WNS), caused by the fungus Pseudogymnoascus destructans, has decimated bat populations across North America. Despite ongoing management programs, WNS continues to expand into new populations, including in US states previously thought to be free from the pathogen and disease. This expansion highlights a growing need for surveillance tools that can be used to enhance existing monitoring programs and support the early detection of P. destructans in new areas. We evaluated the feasibility of using a handheld, field-portable, real-time (quantitative) PCR (qPCR) thermocycler known as the Biomeme two3 and the associated field-based nucleic acid extraction kit and assay reagents for the detection of P. destructans in little brown bats (Myotis lucifugus). Results from the field-based protocol using the Biomeme platform were compared with those from a commonly used laboratory-based qPCR protocol. When using dilutions of known conidia concentrations, the lowest detectable concentration with the laboratory-based approach was 108.8 conidia/mL, compared with 1,087.5 conidia/mL (10 times higher, i.e., one fewer 10× dilution) using the field-based approach. Further comparisons using field samples suggest a high level of concordance between the two protocols, with positive and negative agreements of 98.2% and 100% respectively. The cycle threshold values were marginally higher for most samples using the field-based protocol. These results are an important step in establishing and validating a rapid, field-assessable detection platform for P. destructans, which is urgently needed to improve the surveillance and monitoring capacity for WNS and support on-the-ground management and response efforts.

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.

Analytical expressions for the first passage time distribution and hit distribution in two and three dimensions

The distribution of the time elapsed before a random variable reaches a threshold value for the first time, called the first passage time (FPT) distribution, is a fundamental characteristic of stochastic processes. Here, by solving the standard macroscopic diffusion equation, we derive analytical expressions for the FPT distribution of a diffusing particle hitting a spherical object in two dimensions (2D) and three dimensions (3D) in the course of unrestricted diffusion in open space. In addition, we calculate, analytically, the angular dependence of the FPT, known as the hit distribution. The analytical results are also compared to simulations of the motions of a random walker on a discrete lattice. This topic could be of wide pedagogical interest because the FPT is important not only in physics but also in chemistry, biology, medicine, agriculture, engineering, and finance. Additionally, the central equations often appear in physics and engineering with only trivial variations, making the solution techniques widely applicable.

IoT based weather monitoring system for temperature, humidity, rainfall and seismic detection in real time

This paper presents the design and implementation of an IoT-based weather monitoring system enhanced with seismic detection capabilities, employing Node MCU microcontroller and the Blynk mobile application platform. The system incorporates various sensors including a rain sensor, Light light-dependent resistor (LDR), a DHT11 sensor for temperature and humidity measurement, and an accelerometer (MPU6050) for earthquake detection. The hardware components are interconnected with the Node MCU microcontroller, facilitating real-time data acquisition from the sensors. Programming of the Node MCU is conducted using the Arduino IDE, enabling data transmission to the Blynk app via Wi-Fi connectivity. The Blynk app serves as the user interface, allowing seamless visualization and monitoring of weather parameters and seismic activity on smartphones. Key functionalities of the system include periodic data collection from sensors, transmission of sensor data to the Blynk server, and updating of graphical representations in the Blynk app interface. The accelerometer sensor is utilized to detect changes in acceleration, indicative of seismic disturbances. Threshold values are defined to trigger alerts within the Blynk app in the event of potential seismic activity. The implementation undergoes rigorous testing to ensure accuracy, reliability, and responsiveness under diverse environmental conditions. Considerations such as network stability, power efficiency, and data security are addressed to enhance system performance and longevity. The system demonstrates promising capabilities for real-time weather monitoring and seismic event detection, offering valuable insights for disaster preparedness and environmental analysis. IoT-based weather monitoring system integrated with seismic detection provides an effective solution for comprehensive environmental monitoring, leveraging the power of IoT technology and mobile applications for enhanced data accessibility and analysis. Further advancements and optimizations can be explored to extend the functionality and scalability of the system for broader applications in environmental science and disaster management.

Performance Evaluation of Collision Avoidance for Multi-node LoRa Networks based on TDMA and CSMA Algorithm

LoRa can be used as the communication technology for the intelligent monitoring system. However, LoRa is usually used for outdoor communication. The usage of LoRa as indoor communication has many challenges. One of the challenges is that collision happens when using standard LoRa devices with only one channel. The algorithms based on TDMA (Time-division Multiple Access) and CSMA (Carrier-sense Multiple Access) protocols can be used to address this challenge. These two algorithms can be modified by applying the device that is the center of the network (gateway) as a central control and the data transmitter (sensor node) as a passive device. The test was conducted on the Intelligent Laboratory Monitoring System to evaluate this design on a multi-node LoRa network. RSSI testing proves that the distance and building interference affect the signal strength or RSSI of sensor nodes, so the average RSSI value is -73.75 with an RSSI threshold of value -106. The gateway successfully collected each sensor node data with an average success of about 64.953%. The experiment results show the success rate of the CSMA-based algorithm is 10% versus 100% in TDMA-based algorithm; the delay is 4125 ms for CSMA-based and 428.3 ms for TDMA-based. This result means that the CSMA-based algorithm is more complex, takes more time to process the data than the TDMA-based algorithm, has a low success rate, and is more prone to collisions.

Is the organic carbon-to-clay ratio a reliable indicator of soil health?

Climate action plans under the Paris Climate Agreement and other national commitments aimed at improving soil-based ecosystem services require the operational monitoring of soil carbon (C). The European Union is aiming to enhance soil health, and as part of the proposed Soil Monitoring Law, the European Commission recommends the monitoring of the soil C loss indicator among other soil health indicators. In this study, we evaluate the feasibility of the proposed soil C loss indicator by assessing its performance using the EU-wide 2009 LUCAS soil survey data. The proposed indicator is the soil organic carbon (SOC) to clay ratio, with a threshold value of 1:13. The results are also compared with the C stock changes reported by countries to the climate convention (UNFCCC). Our results reveal that the variation in SOC and clay content at European scale exceeds that of the data used to develop the proposed indicator. We also found that the variation in the SOC content was influenced not only by clay content but also by climate and land-use reflecting C input levels. Therefore, the defined threshold is inadequate for detecting degraded soils if the SOC and clay content are beyond the conditions used to establish the criteria. Furthermore, major discrepancies were observed between the soil carbon stock changes reported by the national greenhouse gas (GHG) inventories and the proportions of degraded soils identified by using the soil C loss indicator. We conclude that employing a single indicator such as SOC:Clay ratio with one threshold value for all soils across various land covers, management practices, and climatic conditions, as defined by the European Commission for the Soil Monitoring Law, is inappropriate for monitoring soil C loss.

Experimental investigation of retrofitting seismically damaged reinforced-concrete frames using steel jacketing

Retrofitting seismically damaged structures provides significant economic and social benefits. To investigate the rationality of using steel jacketing to retrofit seismically damaged reinforced concrete (RC) structures, we initially tested a half-scale single-span two-story frame structure under pseudo-static loads to achieve a severe damage state. Subsequently, the structure was retrofitted using steel jacketing and tested under another round of pseudo-static loads. The seismic performances of the original and retrofitted structures were compared. The test results indicated that both the original and retrofitted specimens sequentially developed plastic hinges at the beam ends and the bottom of the columns, indicating a generally similar damage evolution mode. Comparing to the original structure, the retrofitted specimen exhibited an increase of 33% and 78.5% in initial stiffness and strength, respectively. Although the ductility ratio of the retrofitted specimen was reduced by about 14%, the ultimate drift ratios of both specimens exceeded the threshold value (1/50), indicating an adequate deformation capacity. The retrofitted specimen exhibited a certain degree of pinching behavior on the hysteretic curve because of the presence of extensive inclined shear cracks at the mid span of the beams and the progressive failure of the anchoring of the strengthening steel angle at the beam ends and column base. Consequently, the energy-dissipation coefficients of the retrofitted specimen were lower than those of the original specimen. Based on the test, the finite element analysis modeling method for RC frame structures retrofitted using steel jacketing was also studied. By comparing the numerical simulation analysis results with the experimental ones, the reliability of the proposed numerical simulation method was verified. This study can serve as a foundation for the practical application of steel jacketing to retrofit seismically damaged RC structures.

Triglyceride-glucose index in early pregnancy predicts the risk of gestational diabetes: a prospective cohort study

ObjectiveThis study aimed to investigate the association between the triglyceride-glucose (TyG) index in early pregnancy and the development of gestational diabetes mellitus (GDM) in the second trimester. The primary objectives were to evaluate the predictive potential of the TyG index for GDM, determine the optimal threshold value of the TyG index for GDM assessment, and compare the predictive performance of the TyG index alone versus its combination with maternal age and pre-pregnancy body mass index on GDM. Moreover, the study explored the association between the TyG index in early pregnancy and the risk of other pregnancy-related complications (PRCs), such as placental abruption and gestational hypertension.Patients and methodsThis prospective cohort study recruited 1,624 pregnant women who underwent early pregnancy antenatal counseling and comprehensive assessments with continuous monitoring until delivery. To calculate the TyG index, health indicators, including maternal triglycerides and fasting plasma glucose, were measured in early pregnancy (< 14 weeks of gestation). The predictive power of the TyG index for evaluating GDM in Chinese pregnant women was determined using multifactorial logistic regression to derive the odds ratios and 95% confidence interval (CI). Subgroup analyses were conducted, and the efficacy of the TyG index in predicting PRCs was assessed via receiver operating characteristic (ROC) curve analysis and restricted cubic spline, with the optimal cutoff value calculated.ResultsLogistic regression analyses revealed a 2.10-fold increase in the GDM risk for every 1-unit increase in the TyG index, after adjusting for covariates. The highest GDM risk was observed in the group with the highest TyG index compared with the lowest quintile group (odds ratios: 3.25; 95% CI: 2.23–4.75). Subgroup analyses indicated that exceeding the recommended range of gestational weight gain and an increased GDM risk were significantly associated (P = 0.001). Regarding predictive performance, the TyG index exhibited the highest area under the curve (AUC) value in the ROC curve for GDM (AUC: 0.641, 95% CI: 0.61–0.671). The optimal cutoff value was 8.890, with both sensitivity and specificity of 0.617.The combination of the TyG index, maternal age, and pre-pregnancy body mass index proved to be a superior predictor of GDM than the TyG index alone (AUC: 0.672 vs. 0.641, P < 0.01). After adjusting for multiple factors, the analyses indicated that the TyG index was associated with an increased risk of gestational hypertension. However, no significant association was noted between the TyG index and the risk of preeclampsia, placental abruption, intrauterine distress, or premature rupture of membranes.ConclusionThe TyG index can effectively identify the occurrence of GDM in the second trimester, aligning with previous research. Incorporating the TyG index into routine clinical assessments of maternal health holds significant practical implications. Early identification of high-risk groups enables healthcare providers to implement timely interventions, such as increased monitoring frequency for high-risk pregnant women and personalized nutritional counseling and health education. These measures can help prevent or alleviate potential maternal and infant complications, thereby enhancing the overall health outcomes for both mothers and babies.

Derivation and validation of a risk-stratification model for patients with probable or proven COVID-19 in EDs: the revised HOME-CoV score

BackgroundThe HOME-CoV (Hospitalisation or Outpatient ManagEment of patients with SARS-CoV-2 infection) score is a validated list of uniquely clinical criteria indicating which patients with probable or proven COVID-19 can be...

Interassay Variability and Clinical Implications of Five Different Prostate-specific Antigen Assays

Background and objectiveProstate-specific antigen (PSA) remains a critical marker for prostate cancer (PCa) detection and monitoring. Recognising historical variability in PSA assays and the evolution of assay technology and calibration, this study aims to reassess interassay variability using the latest generation of five assays in a contemporary cohort of men undergoing prostate biopsy. MethodsFive different commercially available PSA assays were tested in a blood sample of 76 men before undergoing a prostate biopsy. Total PSA (tPSA) and free-to-total PSA ratio (%fPSA) were compared across assays, using Roche (Basel, Switzerland) as the benchmark, and correlated with biopsy outcome to analyse the impact on PCa diagnosis. The statistical analysis included Passing-Bablok regression and Bland-Altman plots, with a p value threshold of <0.05 for significance. Key findings and limitationsAmong the 76 men, 28 (36.8%) were diagnosed with significant PCa (defined as International Society of Urological Pathology grade ≥2). A high correlation was observed between tPSA and %fPSA values among the different PSA assays tested (r2 ≥ 0.9). The Passing-Bablok analysis showed that tPSA results varied substantially among the assays, with slopes ranging between 0.78 and 1.04. Compared with the tPSA of Roche, tPSA values were on average 20.7% lower by Beckman (Oststeinbeck, Germany), 15.2% lower by Abbott (Chicago, IL, USA), 6.1% lower by Diasorin (Saluggia, Italy), and 9.6% higher by Brahms (Hennigsdorf, Germany; p < 0.001 for all). The %fPSA values by Abbott and Brahms were higher at 15.7% and 10.6%, respectively (p < 0.001), while the Beckman and Diasorin values had minimal differences of -0.3% and 2.3%, respectively (p > 0.05). The variability across assays would have resulted in discrepancies in both the sensitivity and the specificity for tPSA and %fPSA by at least 14%, depending on the cut-offs applied. Conclusions and clinical implicationsDespite the use of the latest PSA assays, relevant variability of tPSA and %fPSA results can be observed among different assays. There is an urgent need for standardised calibration methods and greater awareness among practitioners concerning interassay variability. Clinicians should acknowledge that clinically relevant thresholds may depend on the specific PSA assay and that ideally the same assay is applied over time for better clinical decision-making. Patient summaryProstate-specific antigen (PSA) is a critical marker for prostate cancer (PCa) detection and monitoring. However, significant variations were observed in the results of the latest PSA assays. Thus, standardised calibration methods and greater awareness among practitioners concerning interassay variability are needed.