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.

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.

Short-term data suggests cognitive benefits in the elderly with single-implant overdentures.

This study was an extension of a randomized crossover clinical trial approved by the institutional ethics committee (approval number: D2014-148) and adhered to the CONSORT guidelines. The original study juxtaposed patient contentment with single-implant overdentures (1-IODs) against conventional complete dentures (CCDs), with patient satisfaction being the primary focus. In this follow-up study, the cognitive function of edentulous patients receiving 1-IODs was assessed, specifically monitoring for the emergence of mild cognitive impairment (MCI) throughout a three-year period. Patient outcomes were systematically recorded at predetermined intervals: initially, two months post-1-IOD placement, after one year (with groups alternated between denture types at eight-month marks), then after two and three years. A prosthodontist with a decade of expertise performed all denture-related procedures. This follow-up emphasized the cognitive outcomes using the Montreal Cognitive Assessment (MoCA-J), considering it alongside previously documented results on masticatory function, bone resorption, survival rates, and patient-reported outcomes. Between 2015 and 2016, a follow-up study enrolled edentulous patients over 50 years of age who were proficient in Japanese, had sufficient mandibular bone for implants, and were free of systemic health issues and habits that could impact oral health. The participants were randomly divided into two groups after receiving a central mandibular implant. Group 1 initially used 1-IODs, and Group 2 used unloaded CCDs. After two months and subsequent periods, they swapped denture types. Eventually, all patients chose 1-IODs for continued use. Implant success was monitored over three years. The design featured block randomization and accounted for a sample size of 22, determined to be sufficient for evaluating the primary outcome of patient satisfaction. All patients underwent careful allocation and received customized dental interventions, with detailed radiographic planning and surgical precision guiding the implantation process. Multivariable linear mixed models were used to assess within-group changes in both overall and specific cognitive function scores across five timepoints. Age, assessment interval, and upper jaw denture status were incorporated as consistent variables, while individual participants were considered variable elements in the analysis. SPSS software version 22.0 was utilized to conduct the statistical tests, and a p value threshold of 0.05 was predetermined to establish statistical significance. Twenty-two patients with edentulous mandibles received 1-IODs. Memory and executive functions saw significant score increases at multiple timepoints over the three-year period, with statistical significance. Though one participant dropped out and another passed away, and two did not complete the 3-year follow-up, the remaining 18 participants provided comprehensive data. Age and type of maxillary denture were significant factors, influencing MoCA-J scores with older participants and those with fixed dentures showing lower scores in certain domains. Overall, the findings illustrated the positive correlation between 1-IODs and cognitive function in older adults. Older adults with no natural teeth left in their mandible showed improved cognitive function after one and three years of using 1-IODs, as reflected by their total and specific cognitive domain scores. The study suggests that such implant therapy may offer protective benefits against cognitive decline, demonstrating clinical relevance for patient care, regardless of the maxillary arch (antagonist) condition.

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.

Sound radiation characteristics of a beam under supersonic airflow and non-uniform temperature field

This paper investigates vibro-acoustic behaviour of an isotropic beam subjected to in-plane varying temperature field and supersonic flow. For a given elevated temperature and non-uniform temperature (NUFT) field, the aerodynamic pressure (ADP) is varied in fraction of its critical value, and the consequent effect on sound radiation characteristics is investigated. Shear and normal deformable beam theory (SNDBT) based kinematics is used, and equations of motion are obtained using Hamilton's principle. Following that, the Ritz method is used to solve the equations. To start with, aeroelastic flutter and vibro-acoustic response characteristics (as a function of critical aerodynamic pressure (λcr)) are analyzed without considering any thermal stress effect. Following that, for a given temperature field, the critical buckling temperature (ΔTcr) of the beam is obtained. Then, the impact of thermal stress caused by the NUFT field on the flutter and sound radiation characteristics is studied. The study observed that the combined effects of the NUFT field and variable ADP significantly impacted the beam's flutter and vibro-acoustic responses.

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.

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.

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.

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.

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.

Fixed-point algorithms for solving the critical value and upper tail quantile of Kuiper's statistics

Kuiper's statistic is a good measure for the difference of ideal distribution and empirical distribution in the goodness-of-fit test. However, it is a challenging problem to solve the critical value and upper tail quantile, or simply Kuiper pair, of Kuiper's statistics due to the difficulties of solving the nonlinear equation and reasonable approximation of infinite series. In this work, the contributions lie in three perspectives: firstly, the second order approximation for the infinite series of the cumulative distribution of the critical value is used to achieve higher precision; secondly, the principles and fixed-point algorithms for solving the Kuiper pair are presented with details; finally, finally, a mistake about the critical value cnα for (α,n)=(0.01,30) in Kuiper's distribution table has been labeled and corrected where n is the sample capacity and α is the upper tail quantile. The algorithms are verified and validated by comparing with the table provided by Kuiper. The methods and algorithms proposed are enlightening and worth of introducing to the college students, computer programmers, engineers, experimental psychologists and so on.

[Establishing and verifying the threshold value of HLA mixed antigen reagent screening test results].

Objective: To establish the threshold value of human leukocyte antigen (HLA) mixed antigen reagent screening test results, and to verify it by HLA single antigen reagent confirmation test results. Methods: The results of 2 255 serum samples tested for HLA antibodies by HLA mixed antigen reagent in the department of HLA Laboratory, the First Affiliated Hospital of Soochow University from October 2017 to December 2021 were retrospectively analyzed. Among them, 1 139 samples were also tested by single antigen HLA Class-Ⅰ reagent and 1 116 samples were also tested by single antigen HLA Class-Ⅱ reagent. Based on the same antigens coated with both reagents, the Mean Fluorescence Intensity (MFI) and Nomalized Background ratio (NBG ratio) of 12 HLA Class-Ⅰ beads and 5 HLA Class-Ⅱ beads in the HLA mixed antigen reagent and the MFI of 77 anti-HLA class-Ⅰ antibodies and 35 anti-HLA class-Ⅱ antibodies detected by HLA single antigen reagent were recorded. The MFI and NBG ratio of HLA mixed antigen reagent beads in 1 139 or 1 116 samples were segmented according to the positive rate of antibodyies detected by the single antigen reagent corresponding to the antigens coated with each HLA mixed antigen reagent bead, and the results of the HLA mixed antigen screening test were verified by the HLA single antigen reagent confirmation test. Results: The threshold values of MFI and NBG ratio of HLA mixed antigen reagent's 17 beads were established. The MFI of No. 1 to No. 17 beads of HLA mixed antigen reagent ranged from 26.86 to 21 925.58, and the NBG ratio ranged from 0 to 434.65. According to the positive detection rate of HLA single antigen reagent corresponding to the coated antigens, the MFI and NBG ratio of the beads of HLA mixed antigen reagent were divided into positive interval, suspicious positive interval, suspicious negative interval and negative interval. The positive rates of anti-HLA class-Ⅰ antibodies by HLA mixed antigen reagent and single antigen HLA Class-Ⅰ reagent were 87.5% (997/1 139) and 66.3% (755/1 139). The positive rates of anti-HLA class-Ⅱ antibodies were 63.4% (707/1 116) and 44.9% (501/1 116). In the samples with suspicious negative, suspicious positive and positive results of HLA class-Ⅰ、Ⅱ antibodies detected by HLA mixed antigen reagent, the positive detection rates of single antigen HLA Class-Ⅰ reagent were 14.9% (17/114), 41.3% (145/351) and 91.3% (590/646), respectively. The positive detection rates of single antigen HLA Class-Ⅱ reagent were 15.5% (58/375), 26.5% (81/306) and 88.8% (356/401), respectively. Conclusions: In this study, the threshold values of MFI and NBG ratio of HLA mixed antigen reagent screening test are established, and the threshold values are verified by the results of HLA single antigen reagent confirmation test. HLA mixed reagent screening test can be used for screening of HLA antibodies, and if necessary, it should be combined with HLA single antigen confirmatory test for clinical detection of HLA antibodies.