Curve Fitting Research Articles

Efficacy of artesunate-mefloquine combination therapy on survival in Plasmodium berghei-infected mice: a time-to-event analysis

Artesunate-mefloquine combination therapy (AR-MQ) is a standard therapy for treating uncomplicated malaria by Plasmodium falciparum. Time-to-event (TTE) analysis is used to describe the occurrence and timing of events by yielding information about the risk of an event occurring during a specific period. Therefore, the aim of the present study is to evaluate the efficacy of AR-MQ combination therapy on the survival time of Plasmodium berghei-infected mice using TTE analysis. Here, TTE analysis was used to analyze P. berghei-infected mice receiving a single oral dose of 100 mg/kg artesunate and 55 mg/kg mefloquine or dose-matched artesunate monotherapy. Median survival was higher for AR-MQ than for monotherapy. A survival analysis to evaluate the influence of treatment on survival was performed using MonolixSuite™. The Weibull model best described the mortality time of the animals. Subsequent analysis identified that AR-MQ had a significant influence on population survival time (Te_pop), estimated at 13.66 days, population parameter for curve fitting (p_pop) at 4.39, and survival time under AR-MQ treatment (beta Te_AR-MQ) at 0.77 days. The probability of survival 7, 15, and 30 days after treatment with AR-MQ was 94.4%, 88.9%, and 14.9%, respectively. The experimental and modeling data both found that AR-MQ combination therapy yielded increased survival of infected animals.

The nonlinear connection between relative fat mass and non-alcoholic fatty liver disease in the Japanese population: an analysis based on data from a cross-sectional study

BackgroundRelative fat mass (RFM) is a newly developed, sex-specific anthropometric formula designed to estimate total body fat percentage. However, research investigating the correlation between RFM and the risk of non-alcoholic fatty liver disease (NAFLD) remains limited. This study evaluates the association between RFM and the risk of NAFLD within the Japanese population.MethodsThis study including 14,250 Japanese adults who underwent physical examinations at Murakami Memorial Hospital between 2004 and 2015. We employed binary logistic regression to elucidate the direct relationship between RFM levels and the incidence of NAFLD. Additionally, a generalized additive model (GAM) coupled with smooth curve fitting techniques was utilized to map the non-linear association.ResultsThe cohort had an average age of 43.53 ± 8.89 years, with a male majority of 52.00%. NAFLD was identified in 17.59% of the participants. After adjusting for confounding factors, a significant positive correlation between RFM and NAFLD risk was observed (OR: 1.15, 95%CI: 1.10–1.21, P < 0.0001 for females; OR: 1.15, 95%CI: 1.10–1.19, P < 0.0001 for males). Additionally, a non-linear relationship between RFM and the incidence of NAFLD was detected in both genders. The RFM threshold was identified as 34.95 for women and 23.40 for men. RFM was positively associated with the risk of NAFLD when RFM was below the respective threshold (OR: 1.29, 95%CI: 1.19–1.40, P < 0.0001 for females; OR: 1.23, 95%CI: 1.17–1.29, P < 0.0001 for males), whereas no significant association was found when RFM was above the threshold (OR: 1.05, 95%CI: 0.98–1.12, P = 0.1829 for females; OR: 1.01, 95%CI: 0.95–1.08, P = 0.7392 for males).ConclusionOur findings suggest a positive, nonlinear relationship between RFM and the risk of NAFLD, with a saturation effect. These results imply that maintaining RFM at a lower level may be advantageous in mitigating the risk of NAFLD.

Nonlinear relationship between cardiometabolic index and bone mineral density in U.S. adults: the mediating role of percent body fat.

Both lipid metabolism and obesity are crucial factors in osteoporosis, influencing the relevance of the cardiometabolic index (CMI), a new body fat index incorporating obesity and lipid metrics. Our study aims to explore the relationship between CMI and lumbar spine bone mineral density (BMD) and the mediating role of body fat percentage. Utilizing the National Health and Nutrition Examination Survey (NHANES) data from 2011 to 2018, we conducted a cross-sectional analysis. We employed multiple linear regression models, subgroup analyses, generalized additive models (GAM), smooth curve fitting, and mediation analysis to evaluate the linear and nonlinear relationships between CMI and lumbar spine BMD. The study involved 5,124 participants with an average lumbar spine BMD of 1.03 ± 0.15g/cm2. We identified a negative correlation between CMI and lumbar spine BMD (β = -0.015; 95% CI: -0.023, -0.008). Nonlinear associations were evident, with inflection points at CMI values of 1.12 and 2.86. Subgroup analyses showed consistent negative correlations across all categories without significant differences (p for interaction > 0.05). Moreover, body fat percentage negatively correlated with BMD (β = -0.005; 95% CI: -0.006, -0.004) and mediated 9.41% of the relationship between CMI and BMD. Increased CMI levels are associated with lower lumbar spine BMD, with body fat percentage significantly mediating this relationship. This underscores the importance of managing body composition in the context of bone health, highlighting CMI's potential utility in osteoporosis risk assessment.

Association between high-density lipoprotein-related inflammation index and periodontitis: insights from NHANES 2009-2014.

Periodontitis, a persistent inflammatory condition, significantly impairs individuals' overall quality of life. Lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR), monocyte-to-high-density lipoprotein cholesterol ratio (MHR), neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), and platelet-to-high-density lipoprotein cholesterol ratio (PHR) are new convenient and economical biomarkers. However, whether the above high-density lipoprotein-related inflammatory biomarkers are associated with periodontitis has rarely been investigated. Therefore, the research endeavor focused on uncovering potential relationships. The research encompassed a diverse and extensive sample, comprising 9,470 participants, selected from the National Health and Nutrition Examination Survey spanning the years 2009 to 2014. The association between high-density lipoprotein-related inflammatory biomarkers and periodontitis was explored utilizing a multivariable logistic regression model with weighted analysis. Additionally, the study employed smoothed curve fitting to explore potential nonlinear relationships. Further stratified analyses and interaction tests were performed. This study indicated no apparent association between MHR and PHR with periodontitis, whereas LHR and NHR demonstrated a statistically significant positive relationship with the prevalence of periodontitis. In the fully adjusted model, participants belonging to the highest tertile of both LHR and NHR showed a notably higher likelihood of having periodontitis compared to those in the lowest tertile (LHR: OR = 1.22, 95% CI: 1.06, 1.39; NHR: OR = 1.27, 95% CI: 1.09, 1.49). Furthermore, smooth curve fitting was employed to investigate the potential nonlinear relationship between LHR, NHR, and periodontitis. The results indicated that there was a significant increase in the occurrence of periodontitis when Log2 (LHR) exceeded 1.01 and Log2(NHR) surpassed 2.16 (Log2(LHR): OR = 1.42; 95% CI: 1.19, 1.69; Log2(NHR): OR = 1.40; 95% CI: 1.15, 1.71). The subgroup analysis revealed that the associations between periodontitis and either LHR or NHR, separately, were more pronounced among individuals under the age of 50 and those without hypertension. This cross-sectional study revealed a positive relationship between LHR、NHR and periodontitis, particularly when these indicators exceeded their thresholds. LHR and NHR may serve as potential inflammatory markers for identifying periodontitis, thereby facilitating early warning for both patients and dentists, and enabling early intervention in the oral environment. Besides, extensive prospective cohort investigations are essential to confirm and solidify this observation.

Platelet count has a nonlinear association with 30-day in-hospital mortality in ICU end-stage kidney disease patients: a multicenter retrospective cohort study

This study addresses the relationship between platelet count and 30-day in-hospital mortality in End-Stage Kidney Disease (ESRD) patients in the intensive care unit (ICU), a topic with limited existing evidence. Utilizing data from the US eICU-CRD v2.0 database (2014–2015), a retrospective cohort study was conducted involving 3700 ICU ESRD patients. We employed binary logistic regression, smooth curve fitting, and subgroup analyses to explore the association between platelet count and 30-day in-hospital mortality. The 30-day in-hospital mortality rate was 13.27% (491/3700), with a median platelet count of 188 × 109/L. After adjusting for covariates, we observed a relationship between platelet count and 30-day in-hospital mortality (OR = 0.98, 95% CI 0.97, 0.99). Subgroup analyses supported these findings. More importantly, a nonlinear association was detected, with an inflection point at 222 × 109/L. The effect sizes (OR) on the left and right sides of the inflection point were 0.94 (0.92, 0.96) and 1.03 (1.00, 1.05), respectively. The most significant finding of this study is the revelation of a nonlinear relationship between baseline platelet count and 30-day in-hospital mortality in ICU patients with ESRD. This discovery explicitly suggests that when ESRD patients are admitted to the ICU, a platelet level closer to 222 × 10⁹/L may predict a lower 30-day in-hospital mortality risk.

Nonlinear association between atherogenic index of plasma and chronic kidney disease: a nationwide cross-sectional study

BackgroundThe interplay between metabolic disorders and chronic kidney disease (CKD) has been well-documented. However, the connection between CKD and atherogenic index of plasma (AIP) remains understudied. This research delves into the correlation between these two factors, aiming to shed new light on their potential association.MethodsThe relationship between AIP and CKD was evaluated using a weighted multivariate logistic regression model, and the curvilinear relationship between AIP and CKD was explored through smooth curve fitting. We engaged a recursive partitioning algorithm in conjunction with a two-stage linear regression model to precisely determine the inflection point. By conducting stratified analyses, the heterogeneity within subpopulations was explored.ResultsIn the regression model that accounted for all covariates, ORs (95% CI) for the association between CKD and AIP were 1.12 (0.91, 1.36), indicating no significant association between AIP and CKD. However, sensitivity analyses suggested that the relationship between them may be non-linear. Smooth curve analysis confirmed the non-linear relationship between AIP and CKD, identifying an inflection point at -0.55. Below this threshold, AIP exhibited a significant inverse correlation with CKD. Conversely, above this threshold, a pronounced positive correlation was detected. Stratified analyses elucidated that a non-linear association between AIP and CKD was observed among female participants and those aged 50 and above.ConclusionWe found a curvilinear relationship between chronic kidney disease and atherogenic index of plasma.

The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) is associated with thyroid hormones and thyroid hormone sensitivity indices: a cross-sectional study

BackgroundLipids and thyroid hormones (TH) are closely interrelated. However, previous studies have not mentioned the linkage encompassing the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) alongside TH level, as well as sensitivity indices.MethodsThis cross-sectional study leverages expansive datasets from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2012. Weighted multivariate linear regression, smoothed curve fitting and sensitivity analyses were used to investigate the associations of the NHHR with the thyroid. Subgroup analyses and interaction tests were conducted to determine the robustness of the findings across diverse segments of the population, ensuring the consistency and generalizability of the observed associations.ResultsThe NHHR was significantly positively correlated with free triiodothyronine (FT3) levels, thyroid-stimulating hormone (TSH) levels, the FT3 to FT4 ratio (FT3/FT4), and the quantile-based thyroid feedback index for FT3 (TFQIFT3) and negatively correlated with free thyroxin (FT4) levels [0.17(0.07–0.27), P = 0.001; 0.60 (0.03–1.17), P = 0.040; 0.06 (0.04–0.08), P < 0.0001; 0.23 (0.16–0.30), P < 0.0001; and -0.65 (-1.05–-0.24), P = 0.002]. Smoothed curve fitting revealed nonlinear correlations of the NHHR with thyroid function and thyroid hormone sensitivity indices. In subgroup analyses, interaction tests, and smoothed curve fitting analyses, different populations presented largely consistent statistical differences.ConclusionAmong American adults, the NHHR was significantly positively correlated with FT3 levels, TSH levels, the FT3/FT4 and the TFQIFT3. Conversely, a negative association was noted between the NHHR and FT4 levels.

The association between adult asthma in the United States and dietary total energy intake: a retrospective cross-sectional analysis from NHANES

BackgroundEpidemiological research links asthma progression to dietary nonallergic factors, particularly high-calorie intake. However, evidence supporting the relationship with total dietary calorie consumption remains scarce.ObjectiveThis study aimed to explore the potential correlation between asthma occurrence and total dietary energy intake.MethodsA retrospective cross-sectional study of 21,354 US adults collected comprehensive participant data, including demographics, blood parameters, fatty acids, zinc, fiber intake, and asthma outcomes. Statistical analyses included interaction effects analysis, smooth curve fitting, and logistic regression.ResultsOf 21,354 participants, 14.77% self-reported asthma diagnosis. After adjusting for confounders, odds ratios (OR) for asthma decreased with higher energy intake: Q2 (OR = 0.77, 95% CI: 0.69–0.86, p < .001), Q3 (OR = 0.66, 95% CI: 0.59–0.75, p < .001), and Q4 (OR = 0.61, 95% CI: 0.53–0.69, p < .001) compared to Q1 (< 17.73 kcal/kg/day). A non-linear (L-shaped) association between energy intake and asthma was observed (p < .001), with a critical threshold around 24 kcal/kg/day, supported by subgroup and sensitivity analyses.ConclusionThis study reveals an L-shaped trend between total energy intake and asthma in US adults, with a significant threshold at approximately 24 kcal/kg/day.

Fungibility, accessibility and clinical utility of remote electronic fetal monitoring in improving maternal emotional status compared with traditional method: A multicenter prospective cohort analysis.

Supported by remote signal processing techniques and wireless communication technology, remote electronic fetal monitoring (REFM) has emerged as a promising alternative to traditional electronic fetal monitoring (TEFM) in clinical practice. The aim of this study was to evaluate the comparability, accessibility, and clinical utility of REFM in contrast to TEFM. This was a multicenter prospective cohort study. A cohort of 2900 pregnant women were enrolled from three medical centers between June 1, 2021 and June 31, 2022. Among them, 800 utilized REFM, with 760 of them completing the self-rating anxiety scale (SAS) and self-rating depression scale (SDS) assessments using the devices for 1 month. The control group comprised 2100 pregnant women who did not use REFM. Additionally, 80 pregnant women concurrently employed both REFM and TEFM, and their respective curve coincidence rates were determined through curve fitting. Primary outcomes encompassed pregnancy outcomes in both groups, average curve coincidence rates between REFM and TEFM, as well as SDS and SAS scores. Among the 760 pregnant women who completed SAS and SDS assessments, their average SAS scores before and after 1 month of REFM usage were 43.09 ± 8.04 and 41.58 ± 6.59, respectively. Concurrently, the average SDS scores before and after 1 month of REFM usage were 45.45 ± 9.60 and 44.80 ± 9.17, respectively. A statistically significant decrease was observed in SAS scores (P = 0.005), whereas no significant difference was noted in SDS scores (P = 0.340). Furthermore, a statistically significant difference in the rate of adverse pregnancy outcomes (neonatal asphyxia) emerged between the two groups, those who employed REFM and those who did not (P = 0.021). In the subset of 80 pregnant women employing both REFM and TEFM, all 80 results showed precise congruence between the two methods. The average coincidence rate was determined to be 79.45% ± 12.64%. REFM contributes to improved pregnancy outcomes and exhibits a high level of concordance with TEFM, thereby accurately reflecting the quality of fetal heart monitoring. Additionally, REFM effectively mitigates pregnant women's anxiety. Thus, REFM demonstrates comparability, accessibility, and clinical utility.

New model for evaluating the impact response of steel fiber reinforced concrete subjected to the repeated drop-weight

The investigation of available experimental results obtained in the non-instrumented drop weight test recommended by American Concrete Institute on the steel-fiber-reinforced concrete reveals that the data distribution does not match a normal distribution. For this reason, logarithmic scales of the first-crack (number of blows to cause the first visible crack) and failure strengths (number of blows to spread the cracks sufficiently) are adopted to increase the fitness of the normal curve to the frequency histogram. The authors propose a novel model to predict the failure strength and evaluate the steel fiber effect on the post-cracking behavior of steel-fiber-reinforced concrete. The new model improves the prediction of the experimental results obtained in literature review for the various ranges of volume fraction of steel fiber analyzed. The proposed model is compared with other models using statistical parameters and the comparison shows a significant improvement considering different failure states. To verify the performance of the proposed model, in addition to results from another research, an experimental impact test was conducted. Besides, a reliable regression model is developed to validate experimental results. The findings underscore the potential of the proposed model to significantly enhance predictive accuracy, thereby proposing valuable insights for optimizing steel-fiber-reinforced concrete.

Green Synthesis of Boric Acid Modified Bismuth Based Non-Toxic Perovskite Quantum Dots for Highly Sensitive Detection of Oxytetracycline.

In recent years, perovskite quantum dots (PQDs) have successfully attracted widespread attention due to their excellent optical properties. However, the instability and toxicity problems of perovskite quantum dots are the main obstacles limiting their applications. In this work, bismuth-based perovskite quantum dots were synthesized by a ligand-assisted reprecipitation method, based on which a novel boric acid-functionalized bismuth-based non-toxic perovskite quantum dots fluorescent sensor (Cs3Bi2Br9-APBA) that can be stabilized in the ethanol phase was prepared by a boron affinity technique. Based on the covalent binding interaction of Cs3Bi2Br9-APBA with oxytetracycline (OTC), a highly selective and sensitive method for the detection of OTC was developed, which effectively solved the problems of poor stability and toxicity in the application of perovskite quantum dots. Under the optimal conditions, the fluorescence intensity of the synthesized Cs3Bi2Br9-APBA was linear with the concentration range of 0.1 ∼ 18 µM OTC, and the detection limit could reach 0.0802 µM. The fluorescence detection mechanism was explored and analyzed by spectral overlap analysis, suppression efficiency study of observed and corrected fluorescence, and fluorescence lifetime decay curve fitting, the mechanism of OTC detection by Cs3Bi2Br9-APBA was identified as the inner filter effect (IFE). In addition, the sensor successfully realized the quantitative detection of trace OTC in the environment, and our study provides a new idea for the preparation of green perovskite materials with high stability and selectivity.

Molecular mechanism of immunotoxicity: Binding interaction between perfluorinated compounds and human immunoglobulin G

Perfluorinated compounds (PFCs) can induce immunotoxicity effect via binding with proteins. Immunoglobulin G (IgG) is a common four chain monomer protein in serum, and plays an important role in long-term body fluid immunity. Whether PFCs can bind with IgG and further induce immunotoxicity is not clear. Herein, fluorescence quenching assay was used to verify the PFCs-IgG binding interactions. The occurrence of fluorescence quenching phenomenon suggested that PFCs could bind to IgG. Linear fitting curves demonstrated that the binding constants (KA) for perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) were 2.51 × 106 L/mol and 1.58 × 105 L/mol, respectively. UV–vis spectral analysis results showed that the PFCs-IgG interactions mainly proceeded via the intercalation binding mode. Fourier transform infrared spectroscopy results revealed that PFCs preferentially bound to the C=O/N-H of IgG structure. Circular dichroism results revealed that PFCs-IgG binding induced the decrease of α-helix. Moreover, hydrogen bonds and van der Waals force dominated PFCs-IgG binding interactions. This binding process was a stable process, and its stability depended on the number of hydrogen bonds formation. This study reveals the mechanism of interaction between PFCs and IgG at the molecular level, providing a theoretical basis for the immunotoxic mechanism of PFCs.

Isotopic spike 190Os as internal standard and empirical coefficient LA-ICP-MS combined with Sb fire assay for the determination of ultra-trace platinum group elements in geochemical samples

In this work, a novel method of antimony fire assay (Sb-FA) enrichment combined with laser ablation ICP-MS (LA-ICP-MS) for the determination of ultra-trace platinum group elements (PGEs) in geological samples was established. The purification and recycling technology of ultra-clean and high-purity fire assay collector Sb2O3 was proposed, in addition, high-purity quartz crucible was developed to replace the usual clay crucible, then the blank values of PGEs were as low as 0.0007–0.0028 ng g−1 (for 20 g sample). 190Os isotopic diluent was used as internal standard (IS) and quantitatively added into the fire assay ingredients, and fully mixed and balanced with the PGEs in the real samples by means of high temperature melting, cupellation and horizontal rotation of crucible and dish. Both 190Os and PGEs in the real sample were pre-concentrated in microgram level Sb granules (100 mg) through Sb-remaining cupellation. After grinding and polishing, 195Pt, 105Pd, 101Ru, 103Rh, 193Ir, total 189Os and 190Os enriched in Sb slices were determined by LA-ICP-MS, 190Os in the internal standard was calculated by isotope dilution equations. The Certified Reference Materials (CRMs) for PGEs were treated by the same procedure to obtain completely matrix matched Sb slices to solve the problem of no internationally recognized uniform PGEs standard materials for LA-ICP-MS determination. Due to the similar distribution trends of different PGEs in Sb slices by LA-ICP-MS imaging, then matrix-matched internal standard calibration strategy was used to reduce the element fractionation effect and improve the determination precision and accuracy of LA-ICP-MS. The laser frequency, energy density, denudation diameter and dwell times were optimized. Under the optimal conditions, empirical coefficient method was used to fit the standard curve and excellent curve fitting of PGEs were obtained with the correlation coefficient between 0.9990 and 0.9999. The method detection limits (LODs) for PGEs ranged from 0.00042 to 0.010 ng g−1. The established method was successfully applied to analyze real geochemical samples and various matrix Certified Reference Materials (CRMs) domestic and international, the determined values were in good agreement with the results of Sb-FA ICP-MS and the certified values.

Impact of Data Corruption and Operating Temperature on Performance of Model-Based SoC Estimation

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.

Millimeter accuracy depth estimation in concrete using smartphone-equipped handheld GPR and hyperbolic curve fitting with antenna distance consideration

In the management and maintenance of structural facilities, knowing the cover thickness of rebar and the depth of buried objects is of paramount importance. Currently, Smartphone-Equipped Handheld Ground Penetrating Radars (GPR) are primarily used for detecting rebar, yet the Electromagnetic Induction (EMI) method remains the sole available technique for determining the cover thickness of rebar. This necessitates the use of two separate devices to understand the subsurface condition of rebar. In this study, we introduce a new algorithm that utilizes Hyperbolic Curve Fitting to determine depth and relative permittivity from reflection images derived from a single GPR measurement, using metal depth estimation as an exemplar. Our contributions include the automatic detection of the hyperbola's centerline before curve fitting, separation of the hyperbola into left and right for enhanced fitting accuracy by removing points near the vertex, and incorporating a rigorous theoretical model that took into account the distance between the transmitting and receiving antennas, known as the inter-antenna distance. This algorithm had enabled us to meet the performance requirements for the EMI method as stipulated by the Japanese Society for Non-Destructive Inspection (JSNDI). Notably, our method retained its accuracy even at depths exceeding the 100 mm limitation of the EMI method.

The relationship between remnant cholesterol and the risk of testosterone deficiency in US adults: a cross-sectional study based on the NHANES database.

Testosterone deficiency (TD) is an urgent health issue that requires attention, associated with various adverse health outcomes including cardiovascular diseases (CVD) and metabolic syndrome. Remnant cholesterol (RC) has emerged as a potential biomarker for cardiovascular risk, but its relationship with testosterone levels and TD has not been thoroughly investigated. This study aims to explore the association between RC and TD in adult American males using data from the National Health and Nutrition Examination Survey (NHANES). This cross-sectional study utilized data from three NHANES cycles (2011-2016), including 2,848 adult male participants. RC was calculated as total cholesterol minus high-density lipoprotein cholesterol (HDL) and low-density lipoprotein cholesterol (LDL). TD was defined as total testosterone levels below 300 ng/dL. Multivariable linear and logistic regression analyses, as well as smooth curve fitting and generalized additive models, were performed to assess the associations between RC and total testosterone levels and TD, adjusting for potential confounders. Subgroup analyses were conducted based on age, BMI, smoking status, diabetes, hypertension, CVD, and chronic kidney disease (CKD). Higher RC levels were significantly associated with lower total testosterone levels (β = -53.87, 95% CI: -77.69 to -30.06, p<0.001) and an increased risk of TD (OR = 1.85, 95% CI: 1.29 to 2.66, p=0.002) in fully adjusted models. When RC was analyzed as quartiles, participants in the highest quartile (Q4) had significantly lower total testosterone levels (β = -62.19, 95% CI: -93.62 to -30.76, p<0.001) and higher odds of TD (OR = 2.15, 95% CI: 1.21 to 3.84, p=0.01) compared to those in the lowest quartile (Q1). Subgroup analyses revealed consistent associations across different age groups, particularly strong in participants over 60 years, and in never smokers. The associations remained significant in both hypertensive and non-hypertensive groups, as well as in those with and without CKD. No significant interactions were found across subgroups. This study demonstrates a significant inverse association between RC levels and total testosterone levels, along with a positive association with the risk of TD. These findings suggest that RC could serve as a valuable biomarker for early identification of individuals at risk for TD. Future longitudinal studies are needed to confirm these findings and explore the underlying mechanisms.

Methodology study for determining the half-value layer using the Monte Carlo code PHITS

For most purposes, the quality of a beam can be specified in terms of the half-value layer (HVL), which is the thickness of material required to reduce the air kerma to half, at one meter from the source, from a unidirectional beam of infinitesimal width. Just like many other radiological quantities of interest, the HVL can be estimated via simulations employing the Monte Carlo Method (MCM). Among the variety of MCM based codes available, the Particles and Heavy Ions Transport Code System (PHITS) was the one adopted in this study. Primarily, simulations were conducted to determine the X-ray spectra resulting from the interaction of electron beams with different energies with a tungsten anode. Each of these spectra was then defined as a probability distribution for the photon source energies in a second round of simulations, where it was possible to estimate the X-ray beam qualities as specified by the reference literature. At this stage, it was possible to estimate the mean energy of the beam and the conversion coefficient for air kerma per unit fluence for each of the qualities. The results obtained in this phase were used to define the sources for other groups of simulations, where the air kerma was quantified for each source configuration, for different thicknesses of an aluminum metallic attenuator, which was also done with spectra for the same X-ray beam qualities obtained from a reference catalog. Curve fitting on the scatter of kerma points as a function of attenuation allowed the estimation of the HVL. The results obtained are consistent with the values described in the literature and demonstrate the efficiency of the developed methodology, as well as the use of the code in the evaluated energy range for X-rays.

Oxygen-Plasma-Treated Al/TaOX/Al Resistive Memory for Enhanced Synaptic Characteristics.

In this study, we investigate the impact of O2 plasma treatment on the performance of Al/TaOX/Al-based resistive random-access memory (RRAM) devices, focusing on applications in neuromorphic systems. Comparative analysis using scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the differences in chemical composition between O2-plasma-treated and untreated RRAM cells. Direct-current measurements showed that O2-plasma-treated RRAM cells exhibited significant improvements over untreated RRAM cells, including higher on/off ratios, improved uniformity and distribution, longer retention times, and enhanced durability. The conduction mechanism is investigated by current-voltage (I-V) curve fitting. In addition, paired-pulse facilitation (PPF) is observed using partial short-term memory. Furthermore, 3- and 4-bit weight tuning with auto-pulse-tuning algorithms was achieved to improve the controllability of the synapse weight for the neuromorphic system, maintaining retention times exceeding 103 s in the multiple states. Neuromorphic simulation with an MNIST dataset is conducted to evaluate the synaptic device.

Research on Rock Energy Constitutive Model Based on Functional Principle

The essence of rock fracture can be broadly categorized into four processes: energy input, energy accumulation, energy dissipation, and energy release. From the perspective of energy consumption, the failure of rock materials must be accompanied by energy dissipation. Dissipated energy serves as the internal driving force behind rock damage and progressive failure. Given that the process of rock loading and deformation involves energy accumulation and dissipation, the rock constitutive model theory is expanded by incorporating energy principles. By introducing the dynamic energy correction coefficient, according to the law of the conservation of energy, the total energy exerted by external loads on rocks is equal to the energy dissipated through the dynamic energy inside the rocks. A new type of energy constitutive model is established through the functional principle and momentum principle. To validate the model’s accuracy, a triaxial compression test was conducted on sandstone to examine the stress–strain behavior of the rock during the failure process. A sensitivity analysis of the parameters introduced into the model was conducted by comparing the model results, which helped to clarify the innate laws of significance of these parameters. The results indicated that the energy model more accurately captures the non-linear mechanical behavior of sandstone under high-stress loading conditions. The model curve fits the test data to a high degree. The fitting curve was basically consistent with the changing trend of the test curve, and the correlation coefficients were all above 0.90. Compared with other models, the model based on the energy principle not only accurately reflects the rock’s stress–strain curve, but also reflects the energy change law of rock. This has reference value for the safety analysis of rock mass engineering under loading conditions and aids in the development of anchoring and support schemes. The research results can fill in the blanks that exist in the energy method in terms of rock deformation and failure and provide a theoretical basis for deep rock engineering. Moreover, this research can further improve and extend the rock mechanics research system based on energy.

Nonlinear relationship between triglyceride-glucose index and the risk of prediabetes and diabetes: a secondary retrospective cohort study.

The triglyceride-glucose (TyG) index, recognized for its cost-efficiency and simplicity, serves as an accessible indicator of insulin resistance. Yet, its correlation with the risk of prediabetes and diabetes (Pre-DM/DM) in the Chinese demographic remains uncertain. Consequently, our study explored the association between the TyG index and the development of Pre-DM/DM within the Chinese population. The retrospective cohort study was carried out utilizing data from a health screening initiative. The study included 179541 adults over 20 who underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. The correlation between the TyG index and Pre-DM/DM risk was investigated using Cox regression analysis. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore their non-linear connection. The mean age of study participants was 41.18 ± 12.20 years old, and 95255 (53.05%) were male. During a median follow-up of 3.01 years, 21281 (11.85%) participants were diagnosed with Pre-DM/DM. After adjusting the potential confounding factors, the results showed that the TyG index was positively correlated with incident Pre-DM/DM (HR: 1.67, 95%CI: 1.62-1.71, P< 0.001). Additionally, a non-linear association was observed between the TyG index and the onset of Pre-DM/DM, with an inflection point identified at 8.73. Hazard ratios (HR) to the left and right of this inflection point were 1.95 (95%CI: 1.86-2.04) and 1.34 (95%CI: 1.27-1.42), respectively. Furthermore, sensitivity analyses confirmed the stability of these findings. The TyG index exhibited a non-linear positive relationship with the risk of Pre-DM/DM. These findings imply that maintaining the TyG index at a lower, specified threshold may be beneficial in mitigating the onset of Pre-DM/DM.