Traditional Parameters Research Articles

The association between hypoxic burden and the risk of cognitive impairment in patients with obstructive sleep apnea.

Obstructive sleep apnea (OSA) is associated with increasing risk of cognitive impairment, but traditional hypoxic indicators can not accurately identify cognitive impairment. This study aimed to assess a new indicator, hypoxic burden, in cognitive impairment in OSA. A total of 116 patients with OSA were enrolled in this study. Daytime sleepiness and cognition were assessed by Epworth Sleepiness Scales (ESS) and Montreal Cognitive Assessment (MoCA), respectively. All participants underwent polysomnography. Hypoxic burden was derived from polysomnography and calculated according to a specific algorithm. All the subjects were divided into two groups. 77 were OSA with mild cognitive impairment (OSA+MCI) and 39 were OSA without mild cognitive impairment (OSA-MCI). The relationship between hypoxic burden and cognitive impairment was analyzed by establishing a series of logistic regression models. Hypoxic burden was higher in OSA+MCI group compared with OSA-MCI group, while there was no significance found for the apnea-hypopnea index (AHI) between the two groups. After adjusting for various confounders, patients with OSA who had a higher total hypoxic burden and REM-hypoxic burden in the fourth quartile were found to have an increased risk of MCI compared to those in the first quartile. The adjusted ORs were 7.69 (95%CI 1.15-51.55) and 8.87 (95%CI 1.22-64.34), respectively. However, There was no significant association between the other traditional hypoxic parameters and cognitive function after adjusting for various confounders. Compared to the conventional hypoxic parameter, higher hypoxic burden is associated with cognition and may be an important indicator for assessing MCI in OSA.

Anthracycline-Induced Subclinical Right Ventricular Dysfunction in Breast Cancer Patients: A Systematic Review and Meta-Analysis.

This meta-analysis aims to evaluate the impact of anthracycline chemotherapy on subclinical right ventricular (RV) dysfunction in breast cancer patients, using traditional echocardiographic parameters and strain-based measures, such as the RV global longitudinal strain (RV GLS) and the RV free-wall longitudinal strain (RV FWLS). A systematic search was conducted according to PRISMA guidelines, including 15 studies with a total of 1148 breast cancer patients undergoing anthracycline chemotherapy. The primary outcome was the evaluation of changes in RV GLS and RV FWLS pre- and post-chemotherapy. Secondary outcomes included changes in traditional echocardiographic parameters: TAPSE, FAC, and TDI S'. Meta-analysis revealed significant declines in RV function post-chemotherapy across all parameters. RV GLS decreased from 23.99% to 20.35% (SMD: -0.259, p < 0.0001), and RV FWLS from 24.92% to 21.56% (SMD: -0.269, p < 0.0001). Traditional parameters like TAPSE, FAC, and TDI S' also showed reductions, but these were less consistent across studies. A meta-regression analysis showed no significant relationship between post-chemotherapy left ventricular ejection fraction (LVEF) and the changes in RV GLS and RV FWLS, suggesting that RV dysfunction may not be solely a consequence of LV impairment. Anthracycline chemotherapy induces subclinical RV dysfunction in breast cancer patients. RV strain analysis, especially 3D strain, shows greater sensitivity in detecting early dysfunction. However, further research is needed to clarify the clinical significance and prognostic value of these findings, as well as the role of routine RV strain analysis in guiding early interventions.

Testing relationships between multiple regional features and biogeographic processes of speciation, extinction, and dispersal.

The spatial and environmental features of regions where clades are evolving are expected to impact biogeographic processes such as speciation, extinction, and dispersal. Any number of regional features (such as elevation, distance, area, etc.) may be directly or indirectly related to these processes. For example, it may be that distances or differences in elevation or both may limit dispersal rates. However, it is difficult to disentangle which features are most strongly related to rates of different processes. Here, we present an extensible Multi-feature Feature-Informed GeoSSE (MultiFIG) model that allows for the simultaneous investigation of any number of regional features. MultiFIG provides a conceptual framework for incorporating large numbers of features of different types, including categorical, quantitative, within-region, and between-region features, along with a mathematical framework for translating those features into biogeographic rates for statistical hypothesis testing. Using traditional Bayesian parameter estimation and reversible-jump Markov chain Monte Carlo, MultiFIG allows for the exploration of models with different numbers and combinations of feature-effect parameters, and generates estimates for the strengths of relationships between each regional feature and core process. We validate this model with a simulation study covering a range of scenarios with different numbers of regions, tree sizes, and feature values. We also demonstrate the application of MultiFIG with an empirical case study of the South American lizard genus Liolaemus, investigating sixteen regional features related to area, distance, and elevation. Our results show two important feature-process relationships: a negative distance/dispersal relationship, and a negative area/extinction relationship. Interestingly, although speciation rates were found to be higher in Andean versus non-Andean regions, the model did not assign significance to Andean- or elevation-related parameters. These results highlight the need to consider multiple regional features in biogeographic hypothesis testing.

Rapid detection of corn moisture content based on improved ICEEMDAN algorithm combined with TCN-BiGRU model

Rapid detection of corn moisture content(MC) during maturity is of great significance for field cultivation, mechanical harvesting, storage, and transportation management. However, cumbersome operation, time-consuming and labor-intensive operation were the bottleneck in the traditional drying process and dielectric parameter method. Thus, to overcome the above problems, a rapid detection method for corn MC based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU) model. First, based on the 405 groups of NIR spectral data of corn seeds, the crested Porcupine Optimizer (CPO) algorithm was used to optimize ICEEMDAN to reduce the noise of the original spectral data. Then the Chaotic-Cuckoo Search (CCS) algorithm was applied to extract 203 characteristic wavenumbers from the original spectrum, which were input into the constructed TCN-BiGRU network model to realize corn MC detection. Finally, the CPO-ICEEMDAN-CCS-TCN-BiGRU corn MC classification detection model was constructed. The result showed that the model accuracy was 97.54 %, which was 9.22 %, 5.58 %, 2.34 %, 4.74 %, and 5.94 % higher than those of convolutional neural networks (CNN), long short-term memory networks (LSTM), temporal convolutional network (TCN), partial least squares (PLS), and support vector machine (SVM) models, respectively. The research results can provide a reliable basis for improving corn yield, quality and economic benefits.

Myocardial deformation in children post cardiac surgery, a cross-sectional prospective study

BackgroundMyocardial deformation by speckle tracking echocardiography provides valuable information on the left ventricular function. The study aims to assess myocardial deformation in terms of left ventricular strain as an indicator of myocardial function in children after cardiac surgery at outpatient follow-up visits.MethodsThe study design was a prospective observational cross-sectional study that included pediatric patients after biventricular cardiac surgery during the postoperative follow-up visits in the outpatient department. In addition to conventional echocardiographic examination, two-dimensional speckle tracking echocardiography was done to evaluate myocardial deformation in terms of left ventricular strain. Echocardiographic measurements were done offline and were compared to published reference normal values for age. Study subjects were divided according to age at follow-up into four groups (1 month–1 year, 1–2 years, 2–5 years, and 5–11 years).ResultsOver ten months, 100 patients (64 males and 36 females) were included in the study. The median age was 30.8 months (IQR 12.8–65.3 months), the median weight was 11.7 kg (IQR 8–17 kg) and the median duration after surgery was 7.3 months (IQR 3.2–30.8 months). Longitudinal strain values were significantly (p < 0.001) lower than reference values for different age groups. Global circumferential strain showed no significant difference from the reference values. The duration after surgery had a statistically significant effect on longitudinal strain values, with improvement of the strain values with increasing intervals after surgery.ConclusionUsing myocardial deformation method to evaluate cardiac function may detect underlying cardiac function abnormalities even with normal traditional functional parameters, which could have implications for patient management and follow-up.

Retraction Note: Studying efficacy of traditional software quality parameters in quantum software engineering

Retraction Note: Studying efficacy of traditional software quality parameters in quantum software engineering

Impulse oscillometry system and pulmonary function test assessment of the impact of tumor location, staging, and pathological type on lung function in primary lung cancer

PurposeTo study the effects of tumor site, stage, pathologic type and imaging findings on lung function in primary lung cancer, as well as the correlation between impulse oscillometry system (IOS) and pulmonary function test (PFT) parameters.MethodsThe impact of tumor location, staging, and pathological type on lung function were evaluated in 219 patients with primary lung cancer through IOS and PFT. Spearman correlation coefficient was used to analyze the relationship between IOS parameters and PFT parameters.ResultsThe PFT parameters in adenocarcinoma were significantly higher than those in SCLC, while the other parameters in IOS were obviously lower than those in SCLC except X5Hz (P < 0.05). The PFT parameters of FVC%, FEV1% and MVV% in SCC were evidently higher than those in SCLC, while the parameters of IOS were significantly lower than those in SCLC (P < 0.05). The PFT parameters of adenocarcinoma were higher than those of SCC (P < 0.05). In the PFT parameters of stage I patients, FEV1/FVC%, MEF50%, MMEF75/25%, and DLCO% were markedly higher than those of stage II patients, and FVC%, FEV1%, FEV1/FVC%, MEF50%, MEF25%, PEF%, MMEF75/25%, TLC%, and DLCO% were obviously higher than those of stage III and IV patients; and the MVV%, Z5Hz%, R5Hz% in IOS parameters were obviously lower than those in stage III, while Fres (1/s) and X5Hz were significantly lower than those in stage IV (P < 0.05). Compared with Phase IV, the X5Hz of stage II patients was clearly higher (P = 0.023). Besides, PFT parameters of peripheral lung cancer were obviously higher than those of central lung cancer (P < 0.05), while Z5Hz%, Fres (1/s) and R5Hz% of IOS parameters were clearly lower than those of central lung cancer (P < 0.05). Moreover, for patients without and with other pulmonary imaging manifestations, the PFT parameters of the former were significantly higher than those of the latter (P < 0.05), while only Fres (1/s) of IOS parameters was significantly lower than the latter (P < 0.05). Furthermore, there is a low to moderate correlation between IOS parameters and PFT parameters.ConclusionPatients with central SCLC and SCC and advanced lung cancer had the worst lung function. The IOS parameters show a good correlation with the traditional PFT parameters, and IOS can be used as an alternative measurement method for PFT when necessary.

Fracturability of shale based on rock brittleness and natural fracture tensile reactivation

Exploration and development practices have proved that fracturability evaluation is the primary basis for successful fracturing operations. Current studies mainly rely on using traditional physical parameters to evaluate shale fracturability. However, fracture morphology and the extension of natural fractures during fracturing are also crucial for shale fracturability evaluation. In this study, correlations between factors and rock fracture complexity and the degree of natural fracture opening were analysed using 40 sets of related experiments, and a new method is proposed for shale fracturability evaluation that considers Young's modulus, the shear expansion angle, residual strain, the approach angle and the stress variance coefficient. Finally, taking the shale gas field in the Sichuan Basin as an example, a comprehensive evaluation model of shale fracturability was established using the grey correlation method combined with core experimental data. One well was selected in order to compare and analyse the fracturability profile with the stimulated reservoir volume. The results show that peak strain has the most significant influence on shale fracture complexity, followed by the shear expansion angle and Young's modulus. The approach angle and the stress difference coefficient affect the degree of natural fracture opening at the same time. The model is accurate because the fracturability values match the study block's stimulated reservoir volume data. This new method of shale fracturability evaluation provides important technology for predicting the fracturability of shale gas reservoirs and optimizing operation schemes.

Quantification and Sensitivity Analysis of the Model Uncertainty in the Projectile-Barrel Coupled Artillery Dynamics System Based on Random Matrix Theory

Simplifications in the engineering of artillery firing dynamics modeling introduce model uncertainty, which seriously affects the model’s predictive performance. To this end, a typical variable-section hollow cylindrical cantilever beam structure in artillery firing dynamics is investigated in this paper. The statistical approximation methods quantify parameter and model uncertainty separately, and their respective confidence intervals were calculated and compared. The results show that the traditional parameter uncertainty approach overestimates the parameter uncertainty ranges, and introducing the model uncertainty approach dramatically improves the model prediction performance. Additionally, a projectile-barrel coupled artillery dynamics model considering model uncertainty is established, and its effectiveness is validated through the actual launch experiments. The sensitivity of the dynamic responses to the system matrix, namely model uncertainty, is further analyzed. This paper provides theoretical references for the study of uncertainty in artillery launch dynamics and proposes effective methods to enhance the predictability of computational models for complex structural systems.

Energy Consumption Prediction of Injection Molding Process Based on Rolling Learning Informer Model.

Accurate energy consumption prediction in the injection molding process is crucial for optimizing energy efficiency in polymer processing. Traditional parameter optimization methods face challenges in achieving optimal energy prediction due to complex energy transmission. In this study, a data-driven approach based on the Rolling Learning Informer model is proposed to enhance the accuracy and adaptability of energy consumption forecasting. The Informer model addresses the limitations of long-sequence prediction with sparse attention mechanisms, self-attention distillation, and generative decoder techniques. Rolling learning prediction is incorporated to enable continuous updating of the model to reflect new data trends. Experimental results demonstrate that the RL-Informer model achieves a normalized root mean square error of 0.1301, a root mean square error of 0.0758, a mean absolute error of 0.0562, and a coefficient of determination of 0.9831 in energy consumption forecasting, outperforming other counterpart models like Gated Recurrent Unit, Temporal Convolutional Networks, Long Short-Term Memory, and two variants of the pure Informer models without Rolling Learning. It is of great potential for practical engineering applications.

The Effect of Channel Surface Roughness on Two–Phase Flow Patterns: A Review

This review article highlights the critical impact of surface roughness in modifying the structure of two-phase flow within mini- and microchannels, particularly in processes such as boiling and condensation. Channel surface roughness enhances flow resistance, affects the distribution of vapor bubbles, and enhances heat transfer by providing additional nucleation sites. Several experiments have shown that while increased surface roughness enhances the efficiency of heat transfer, increased flow resistance may hurt system performance. This is so because too high a surface roughness negatively impacts flow resistance, a factor of importance in the optimization for a balance between heat transfer and flow resistance, especially in high-performance compact heat exchangers. Furthermore, the review identifies that higher-degree measurement and characterization techniques of the surface roughness are increasingly required, as traditional 2D parameters may not fully represent the actual physics of complex surface interactions in two-phase flow systems. Consequently, the article calls for further research that can examine the exact relationship between roughness, flow structure, and thermal performance with the aim of improving design strategies for future heat exchanger technologies.

Are Oxidative Stress Biomarkers Reliable Part of Multimarker Panel in Female Patients with Type 2 Diabetes Mellitus?

Background: Oxidative stress and inflammation are the key features of metabolic diseases, including type 2 diabetes mellitus (T2D). However, studies that explored redox homeostasis parameters in relation to T2D show discrepant results. Accordingly, we aimed to examine the potential reliability of oxidative stress biomarkers [i.e., determined by malondialdehyde (MDA), advanced oxidation protein products (AOPP) and catalase (CAT)] in addition to traditional cardiometabolic parameters in relation to T2D in female cohort. Methods: A total of 214 women (of them 40.6% T2D) were consecutively recruited in the study. Principal component analysis with varimax rotation was performed to determine the adequate number of factors consisting of anthropometric, traditional cardiometabolic and redox status markers. Results: MDA and AOPP concentrations were lower, but CAT activity was higher in T2D group as compared with controls (P < 0.001, P = 0.002, P < 0.001). Traditional markers related factor (i.e., with positive loading of waist circumference, triglycerides, uric acid, high sensitivity C-reactive protein and negative loadings of high-density lipoprotein cholesterol) was found to be independently related with T2D in multivariate binary regression analysis, whereas oxidative stress related factor (i.e., with positive loading of MDA and AOPP) lost its independent prediction after adjustment for confounding factors (i.e., age, menopausal status, antihypertensive, and hypolipemic therapies). Increased Traditional markers related factor was associated with more than three times higher probability for T2D onset (OR = 3.319, p < 0.001). Conclusion: Oxidative stress biomarkers, i.e., MDA, AOPP, and CAT are not superior over traditional cardiometabolic markers in relation to T2D in female population. Future studies with both gender included are needed to confirm such results.

Robust continuous-variable quantum key distribution in the finite-size regime

Quantum key distribution (QKD) has been proven to be theoretically unconditionally secure. However, any theoretical security proof relies on certain assumptions. In QKD, the assumption in the theoretical proof is that the security of the protocol is considered under the asymptotic case where Alice and Bob exchange an infinite number of signals. In the continuous-variable QKD (CV-QKD), the finite-size effect imposes higher requirements on block size and excess noise control. However, the local local oscillator (LLO) CV-QKD system cannot be considered time-invariant under long blocks, especially in cases of environmental disturbances. Thus, we propose an LLO CV-QKD scheme with time-variant parameter estimation and compensation. We first establish an LLO CV-QKD theoretical model under the temporal modes of continuous-mode states. Then, a robust method is used to compensate for arbitrary frequency shift and arbitrary phase drift in CV-QKD systems with longer blocks, which cannot be achieved under traditional time-invariant parameter estimation. Besides, the digital signal processing method predicated on high-speed reference pilots can achieve a time complexity of O(1). In the experiment, the frequency shift is up to 89.05 MHz/s and phase drift is up to 3.036 Mrad/s using a piezoelectric transducer (PZT) to simulate the turbulences in the practical channel. With a signal-to-interference ratio (SIR) of −51.67 dB, we achieve a secret key rate (SKR) of 0.29 Mbits/s with an attenuation of 16 dB or a standard fiber of 80 km. This work paves the way for future long-distance field-test experiments in the finite-size regime.

Developing Predictive Models for Financial Stability: Integrating Behavioral Analytics into Credit Risk Management

In this article, its author takes an attempt at investigating the use of behavioral analytics to complement credit risk management in the world of finance. Current mainstream credit risk models very much depend on conventional methods that incorporate historical financial information like credit scores and income while ignoring client behavioral contemporaneous patterns essential for modeling their risk levels. By this, the transactional patterns; payment behaviour and digital activities included besides the traditional financial parameters allows the financial institution to make superior models that gives better results on credit worthiness and risk identification earlier. It is crucial for both better risk assessment and for embracing more inclusion within lending networks and financial creativity. Analyzing the informational component of behavioral data as well as its applicability in credit risk modeling and considering the difficulties of implementing it in practice, the article provides an outline of the approaches to telemetry integration.

Echocardiographic left and right ventricular strain reference values and their associated characteristics in a healthy adult population from the ELSA-Brasil Study

Abstract Background/Introduction Impaired left ventricular (LV) systolic function is a well-known predictor of cardiovascular morbidity and mortality. Global longitudinal strain (GLS) analysis has been shown a robust measure to detect early systolic dysfunction and a prognostic parameter beyond traditional echocardiographic systolic parameters. Standard reference values for echocardiography measures have been established by echocardiography societies. However, these data may not accurately represent diverse world populations. Purpose We aimed to describe, from a Brazilian multiethnic population, age and sex-specific reference values for left (LV) and right ventricles (RV) GLS in a sample free of cardiovascular disease and risk factors, and to describe the impact of these risk factors according to GLS distribution. Methods ELSA-Brasil is a prospective cohort to investigate cardiovascular disease and diabetes including 15,105 participants aged 35 to 74 years. A predefined representative randomly selected sample with echocardiographic exam was included. We categorized the sample in tertiles according to severity of LV and RV systolic function measured by GLS, and applied trend tests across ordered groups to illustrate the association between GLS and demographic characteristics and echocardiographic measures of cardiac structure and function. Results In the study population (n=527), the mean age was 50.2 years and 59% were women. The overall mean value of LVGLS was -19.0 ± 2.4%, while the mean value of RVFWLS was -28.35 ± 3.06%. Women had higher absolute values of LV and RV GLS compared to men. Conclusion Our study provided the reference ranges for LV and RV GLS in a large middle-aged healthy Brazilian sample, with sex-differences reflected on these values.Central FigureTable 1:Tertiles of LVGLS and RVFWLS

Simulating the methodological bias in the ATLS classification of hypovolemic shock: a critical reappraisal of the base deficit renaissance

BackgroundThe Advanced Trauma Life Support classification (ATLS) of hypovolemic shock is a widely used teaching and treatment reference in emergency medicine, but oversimplifies clinical reality. A decade ago, a landmark study compared vital parameters to base deficit (BD) in trauma patients. The investigators concluded that BD had higher accuracy to detect the need for early blood product administration. BD was subsequently introduced in the ATLS shock classification and has since been widely accepted as a laboratory standard for hypovolemia. The aim of this study is to investigate whether a methodological bias may have inadvertently contributed to the study’s results and interpretation.MethodsIn the current study, we replicate the original study by simulating a cohort of trauma patients with randomly generated data and applying the same methodological strategies. First, a predefined correlation between all predictor variables (vital parameters and BD) and outcome variable (transfusion) was set at 0.55. Then, in accordance with the methods of the original study we created a composite of ATLS parameters (highest class amongst heart rate, systolic blood pressure, and Glasgow Coma Scale) and compared it with BD for resulting transfusion quantity. Given the preset correlations between predictors and outcome, no predictor should exhibit a stronger association unless influenced by methodological bias.ResultsApplying the original imbalanced grouping and composite allocation strategies caused a systematic overestimation of shock class for traditional ATLS parameters, favoring the association between BD and transfusion. This effect persisted when the correlation between BD and transfusion was set substantially worse (rho = 0.3) than the correlation between ATLS parameters and transfusion (rho = 0.8).ConclusionsIn this fully reproducible simulation, we confirm the inadvertent presence of methodological bias. It is physiologically reasonable to include a metabolic parameter to classify hypovolemic shock, but more evidence is needed to support widespread and preferred use of BD.

Echocardiographic parameters for weaning from Extracorporeal Membrane Oxygenation - the role of longitudinal function and cardiac time intervals.

Limited data exist on echocardiographic predictors of weaning from veno-arterial extracorporeal membrane oxygenation (V-A ECMO). We aimed to test the performance of different echocardiographic indices to predict weaning from V-A ECMO. and free survival after weaning. Observational study including patients with cardiogenic shock submitted to V-AECMO. Echocardiography was performed after V-AECMO placement and daily during the weaning trial to assess cardiac recovery. Echocardiography data after V-A ECMO implantation and during the last weaning-trial before V-A ECMO removal were analyzed. Besides traditional parameters, total isovolumic time (t-IVT, a left ventricular performance index) and mitral annular plane systolic excursion (MAPSE) were also tested.76 patients were included. A greater ventricular velocity-time integral (LVOT VTI) at baseline was associated with a 5-fold increase in weaning success (p < 0.001) as MAPSE lateral >6.15 mm (p 0.001) did. TAPSE and S' at tricuspid annulus showed an analogous association. During the weaning trial t-IVT, LVEF, MAPSE, LVOT VTI and TAPSE all improved significantly (p <0.001 for all). At regression analysis t-IVT <14.4second/minute (<0.001), LVOT VTI >12.3cm (p <0.001), MAPSE >8.9mm (p < 0.001), TAPSE> 16mm (<0.001) and E/e' <15.5 (p 0.001) were associated with weaning success and free survival after weaning. LVEF did not predict the weaning success and survival at any time-point (p 0.230). Longitudinal function, t-IVT and native ejection, measured with LVOT VTI, are reliable parameters to predict weaning success in V-A ECMO whereas the LVEF, although dynamically changing during weaning trial, it is not.

Elevated 1-Hour Post Load Glucose as a Predictor for Telomere Attrition: a study based on a Chinese Community population.

1-hour post-load glucose (1h-PG) detects dysglycemia-related disorders more effectively than traditional glycemic parameters. Hyperglycemia accelerates aging, whether 1h-PG outperforms in predicting aging remains unclear. To Compare the effectiveness of 1h-PG with other glycemic parameters in identifying and predicting telomere attrition. We conducted a cross-sectional and longitudinal study based on a Chinese community cohort. Multivariate linear regression and logistic regression were used to analyze the associations between glycemic parameters and telomere length. The area under the receiver operating characteristic (AUROC) curve were used to compare the differentiating and predictive ability. Populations were regrouped by glucose tolerance status and 1h-PG to compare telomere length. Analyses were separately conducted in non-diabetic and diabetic populations. The cross-sectional study included 715 participants. Only 1h-PG was significantly negatively associated with RTL in both non-diabetic (β = -0.106, 95%CI -0.068 to -0.007, P = 0.017) (odds ratio [OR] = 1.151, 95% CI 1.069 to 1.239, P = 0.005) and diabetic (β = -0.222, 95%CI -0.032 to -0.007, P = 0.002) (OR = 1.144, 95% CI 1.041 to 1.258, P = 0.035) populations. The longitudinal study recruited 437 populations and 112 remained in 7-years follow-up. 1h-PG was associated with telomere shortening in the non-diabetic group (β = -0.314, 95%CI -0.276 to -0.032, P = 0.016) (OR = 2.659, 95% CI 1.158 to 6.274, P = 0.021). AUROC analysis showed that 1h-PG outperformed other glycemic parameters in identifying and predicting telomere attrition. Reclassification revealed that normal glucose tolerance and prediabetic individuals with elevated 1h-PG had telomere lengths comparable to prediabetic and diabetic populations, respectively. 1h-PG outperforms other glycemic parameters in predicting telomere attrition and can be a valuable marker for early aging detection.

Contribution of body adiposity index and conicity index in prediction of metabolic syndrome risk and components

Background and aimsBody adiposity index (BAI) and conicity index have been known as useful measures in predicting cardio-metabolic diseases. This study aimed to evaluate the predictive potential of BAI and conicity index for the risk of metabolic syndrome (MetS) in comparison with body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR). MethodsIn this cross-sectional study, 174 adults (87 with MetS and 87 healthy individuals) were recruited from a medical weight loss center. Anthropometric parameters, systolic and diastolic blood pressures (SDP and DBP), lipid profile, and fasting blood sugar (FBS) were measured. ResultsAll anthropometric parameters were significantly higher in subjects with MetS than in healthy subjects. Both in MetS and healthy subjects, females had significantly higher BAI and BMI than males. In the fully adjusted model, the odds of MetS increased for each unit increase in BAI by 27 % (p = 0.001), in BMI by 33 % (p = 0.001), in WC by 13 % (p < 0.001), and in HC by 9 % (p = 0.005). ROC curve analysis showed that all the anthropometric parameters displayed clinical importance in predicting MetS, but WHR had the largest area under the curve (AUC) in total, male, and female patients. In participants with MetS, the conicity index was negatively correlated with FBS; BAI was positively associated with HDL level. ConclusionAll studied anthropometric parameters had acceptable accuracy for predicting MetS. Traditional parameters, particularly the WHR, exhibited a higher predictive power concerning MetS. The results underscore the reliability of conventional anthropometric measures in clinical and epidemiological settings.

Multi-parameter optimization of polarization gradient cooling for 87Rb atoms based on reinforcement learning.

Polarization gradient cooling (PGC) plays an important role in many cold atom applications including the formation of Bose-Einstein condensates (BECs) and cooling of single atoms. Traditional parameter optimization of PGC usually relies on subjective expertise, faces challenges in fine manipulation, and exhibits low optimization efficiency. Here, we propose a segmented control method that differs from the traditional PGC process by expanding the experiment parameters from 3 to 30. Subsequently, the conventional timing optimization problem is reformulated as a Markov decision process (MDP), and the experiment parameters are optimized using a reinforcement learning model. With proper settings of hyperparameters, the learning process exhibits good convergence and powerful parameter exploration capabilities. Finally, we capture ∼4.3 × 108 cold atoms, with a phase space density of ∼7.1 × 10-4 at a temperature of ∼3.7 µK in ∼18.8 min. Our work paves the way for the intelligent preparation of degenerate quantum gas.