Curve Fitting Technique Research Articles

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.

A model for effect of partial shading on PV panels with experimental validation

This paper aims to develop and validate an empirical model to quantify the impact of partial shading on photovoltaic (PV) panel performance. Partial shading, a significant challenge in solar power generation, can drastically reduce energy output, yet predicting its effects remains difficult using conventional models. This study introduces a methodology that models partial shading as an equivalent reduction in solar insolation across the entire panel. The model was derived from empirical data gathered under various shading scenarios, and curve-fitting techniques were employed to establish an empirical relationship between shading patterns and power loss. The proposed model was experimentally validated on a rooftop PV system in Bengaluru, India, and demonstrated precise predictions of power loss under different partial shading conditions. The framework presented can be adapted to accommodate location-specific variables, providing a versatile tool for evaluating and optimizing PV system performance in partially shaded urban environments.

Association between triglyceride glucose body mass index and urinary incontinence: a cross-sectional study from the National Health and Nutrition Examination Survey (NHANES) 2001 to 2018

BackgroundUrinary incontinence (UI) is a prevalent, health-threatening condition that causes isolation and psychological strain, leading to significant personal distress. The connection between the triglyceride glucose body mass index (TyG-BMI) and UI remains elusive. The purpose of the current research was to investigate any possible relationships between raised TyG-BMI levels and a higher likelihood of UI.MethodsFor a thorough examination, adults 20 years and older with UI were included in cross-sectional research using the data obtained from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2018. Our investigation centred on three of the significant varieties of UI: Urgent Urinary Incontinence (UUI), Mixed Urinary Incontinence (MUI), and Stress Urinary Incontinence (SUI), employing weighted multivariate logistic regression models for an in-depth evaluation. The TyG-BMI, a possible biomarker, was arranged in increasing order among participants and then assessed with a trend test (P for trend). Moreover, this investigation delved into the non-linear relationships using advanced smoothed curve fitting techniques. Meticulous subgroup analyses were executed to verify the uniformity of the UI and TyG-BMI relationship across diverse demographic groups.ResultsA thorough investigation was conducted with 18,751 subjects to analyze the prevalence and types of UI, showing that 23.59% of individuals suffered from SUI, 19.42% from UUI, and 9.32% from MUI. Considering all possible confounding variables, Multivariate logistic regression analysis showed a substantial relationship between elevated TyG-BMI values and a greater likelihood across all UI categories. Specifically, stratifying the TyG-BMI into quartiles revealed a pronounced positive correlation in the top quartile relative to the bottom, reflected in increased odds ratios for SUI, UUI, and MUI (SUI: OR = 2.36, 95% CI 2.03–2.78, P < 0.0001; UUI: OR = 1.86, 95% CI 1.65–2.09, P < 0.0001; MUI: OR = 2.07, 95% CI 1.71–2.51, P < 0.0001).ConclusionsAmong US adults, an association has been observed wherein increased TyG-BMI values correlate with a higher chance of UI. This suggests that TyG-BMI might be a helpful marker for identifying individuals at risk of UI, providing novel insights into its assessment and management.

Association between lymphocyte-to-monocyte ratio and stroke-associated pneumonia: a retrospective cohort study

Background Stroke-associated pneumonia (SAP) is a common complication of acute ischemic stroke (AIS) and is associated with increased mortality and prolonged hospital stays. The lymphocyte-to-monocyte ratio (LMR) is a novel inflammatory marker that has been shown to be associated with various diseases. However, the relationship between the LMR and SAP in patients with AIS remains unclear. Methods A retrospective cohort study was conducted on 1,063 patients with AIS admitted to our hospital within 72 hours of symptom onset. Patients were divided into two groups: the SAP group (n = 99) and the non-SAP group (n = 964). The LMR was measured within 24 hours of admission, and the primary outcome was the incidence of SAP. We used univariate and multivariate logistic regression analyses to assess the relationship between the LMR and SAP. Additionally, curve-fitting techniques and subgroup analyses were conducted. Result The incidence of SAP was 9.31%. We found that the LMR was significantly lower in the SAP group than in the non-SAP group (2.46 ± 1.44 vs. 3.86 ± 1.48, P &lt; 0.001). A nonlinear relationship was observed between the LMR and the incidence of SAP. Subgroup analysis revealed that an elevated LMR was associated with a reduced incidence of SAP in individuals with an LMR below 4. Multivariate logistic regression analysis demonstrated that LMR was an independent predictor of SAP (OR = 0.37, 95% CI [0.27–0.53]). Conclusion Our study suggests that the LMR is an independent predictor of SAP in patients with AIS, particularly when the LMR is less than 4. The LMR may serve as a promising biomarker for the early identification of patients with AIS at a high risk of SAP.

Nonlinear association between remnant cholesterol and reversion from impaired fasting glucose to normoglycemia: a multicenter cohort study

BackgroundRemnant cholesterol (RC), a potent atherogenic lipid, has been shown to be strongly correlated with insulin resistance and the pathogenesis of diabetes mellitus. However, the relationship between RC and normoglycemia reversal in individuals with impaired fasting glucose (IFG) is crucial and remains unclear. This investigation, which aimed to clarify this association, is important for understanding and potentially improving the management of diabetes.MethodThis study, which included 15,019 IFG participants from 11 Chinese cities between 2010 and 2016, was conducted with a rigorous research process. Cox regression analysis revealed intriguing findings regarding the relationship between RC and normoglycemia reversal in individuals with IFG. Potential nonlinear associations were further explored via smooth curve-fitting techniques and 4-knot restricted cubic spline functions, ensuring a comprehensive analysis. To examine the validity of the results, an array of subgroup and sensitivity analyses were conducted, further bolstering the robustness of the findings.ResultsBy the end of the 2.89-year median follow-up period, 6,483 of the 15,019 IFG participants (43.17%) had reverted to normoglycemia. The findings, which reveal that increased RC levels are inversely associated with the likelihood of normoglycemia reversal, are novel and significant. According to the fully adjusted Cox proportional hazards model analysis, an increase of one standard deviation in RC was associated with a 20% decrease in the likelihood of normoglycemia reversal among IFG participants (HR: 0.80, 95% CI: 0.77–0.82). A nonlinear association between RC and normoglycemia reversal was observed, with an inflection point at 41.37 mg/dL. This suggests that the growth rate of the likelihood of reversion decreased and stabilized after the inflection point was reached. Moreover, significant interactions were observed between the age groups, providing a more nuanced understanding of this complex relationship.ConclusionAmong Chinese adults with IFG, RC exhibited a negative nonlinear relationship with the probability of normoglycemia reversal. When RC levels reached or exceeded 41.38 mg/dL, the probability of achieving normoglycemia progressively diminished and subsequently stabilized. Maintaining RC levels below 41.38 mg/dL can significantly improve the probability of normoglycemia reversal among individuals with IFG, especially those aged 60 years or older.

Associations between life's essential 8 and femoral neck bone mineral density among adults: A national population-based study.

Osteoporosis represents a significant public health issue, impacting both health outcomes and economic costs. This research investigates how cardiovascular health, as indicated by the LE8 score, correlates with bone mineral density (BMD). Data from the National Health and Nutrition Examination Survey (NHANES) spanning 2011 to 2018 were analyzed in this cross-sectional analysis, including 9018 subjects following the exclusion of individuals lacking BMD or LE8 data. The LE8 score, comprising factors such as diet, physical activity, smoking status, sleep quality, body mass index, lipid profiles, blood glucose, and blood pressure, was used to evaluate cardiovascular health. BMD was determined through dual-energy X-ray absorptiometry (DXA). Relationships between the LE8 scores and BMD at the femoral neck were assessed using linear regression and smooth curve fitting techniques. Enhanced LE8 scores were linked to improved BMD at the femoral neck. Notably, a 10-point increment in the LE8 score was associated with a rise in BMD by 0.04 g/cm² [β = 0.04, 95% CI: 0.03-0.05]. The data indicate a strong positive association between cardiovascular health, as measured by LE8, and BMD. These results support the development of holistic health strategies that promote cardiovascular health to potentially improve bone density.

Willingness to pay for internet speed and quality

This paper adds to a growing literature on the willingness to pay (WTP) for internet services. We surveyed 5,200 respondents across four demographically diverse United States (US) states, and developed a curve-fitting WTP estimation technique that builds on existing conjoint analysis models. We find that users are willing to pay an extra $1.13 per month for a 1 megabits per second (Mbps) faster internet and $45.52 per month for better connection quality. We document a strong non-linearity: a given speed increment generates the most value for users with the slowest internet but confers negligible value on users with the fastest internet. Specifically, improving speed from 1 Mbps to 25 Mbps creates 2.32 times more value than improving from 25 Mbps to 100 Mbps. A similar non-linearity arises in terms of internet quality. We report large WTP discrepancies by income levels and employment status. The results corroborate that internet subsidies are most impactful if they first assist users with the poorest internet access. In this sense, subsidizing the right population could be as important as – if not more important than – raising funds to subsidize internet access.

A compartmental modal for type2 diabetes on the effect of plasma glucose -insulin regulatory system by Jacobes model

A compartmental model for diabetes is developed. The model describes the dynamics of the spread of Type 2 diabetes. A theoretical analysis in the non-adherence to drugs is investigated. A system of differential equations is analyzed by stability analysis; the non-trivial critical point obtained is locally asymptotically firm under the given conditions. In consideration of Mathematical model for glucose tolerance test (GTT) is considered, actual glucose data values are fixed using MATLAB least squares curve fitting technique. Two methods are used to numerically work out the distributions of steady states of diabetic sub-populations. The Gauss-Seidel method is more accurate than the Jacobi method. The result show that more than 50% of clinical diagnosis attempt needs to be applied to have more diagnosed population than undiagnosed. The GTT model shows that if severe diet and medication is followed diabetes can be controlled.

Association of blood urea nitrogen to creatinine ratio with incident type 2 diabetes mellitus: A retrospective cohort study in the Chinese population.

Renal dysfunction can lead to insulin resistance and increase the incidence of type 2 diabetes mellitus (T2DM). The blood urea nitrogen to creatinine ratio (NCR) is a frequently used indicator to assess renal dysfunction and differentiate between prerenal and intrinsic renal injury. However, the association between NCR and T2DM in the Chinese population remains unclear. Hence, this study aimed to investigate the association between NCR and the incidence of T2DM in the Chinese population. The relationship between NCR and T2DM was examined using the Cox proportional hazards model and curve fitting techniques. In addition, a comprehensive set of sensitivity and subgroup analyses were performed. All results were presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Between 2010 and 2016, 189,416 Chinese people were recruited from the Rich Healthcare Group for this retrospective cohort study. Of the participants, 3755 (19.8%) were diagnosed with T2DM during the follow-up period. After full adjustment, the Cox proportional hazards model revealed a positive connection between NCR and the incidence of T2DM (HR = 1.03, 95% CI: 1.02-1.04, P < .001). Compared with individuals with lower NCR Q1 (≤13.536), the multivariate HR for NCR and T2DM in Q2 (13.536-16.256), Q3 (16.256-19.638), Q4 (>19.638) were 1.08 (0.98-1.19), 1.16 (1.05-1.28), 1.39 (1.26-1.53). The higher NCR groups (≥20) had a higher ratio of T2DM (HR = 1.28, 95% CI: 1.18-1.38, P < .001) than the lowest NCR group (<20). These findings were validated using sensitivity and subgroup analyses. In conclusion, this study found a positive and independent association between NCR and the incidence of T2DM after adjusting for confounding variables.

The Culprit of Bias for Paleointensity Estimation in the Shaw‐Type Protocol and an Innovative Calculation Method

AbstractPaleointensity records are vital for understanding the Earth's evolution, but obtaining accurate paleointensity is a challenging task. The Shaw‐type method, a widely‐used paleointensity protocol, produces biased results occasionally despite strict selection criteria. By examining the relationships between paleointensities and rock magnetic parameters from a pseudo‐Tsunakawa‐Shaw experiment, we ascertain that changes in the ratio of thermal to anhysteretic remanent magnetization (R) are proportional to bias in paleointensity, especially prominent in samples with hundred‐nanometer‐scale magnetic grains, and thus proved to be the culprit for the bias. Furthermore, we develop a method exploiting the Linear regression of R with Diverse cut‐off coercivity intervals for estimating Shaw‐type paleointensities (LoRD‐Shaw). Combined with a curve fit technique for samples with “folding" phenomenon, the LoRD‐Shaw method yields high‐accuracy results in all tested samples, demonstrating its efficiency in mitigating paleointensity bias from thermal alteration. The new method will enhance acquisition of high‐precision paleointensities for constraining the geodynamo evolution.

Improved quantitative parameter estimation for prostate T2 relaxometry using convolutional neural networks.

Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T2 in the prostate. Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.

Sex differences in the association between sleep duration and muscle quality index in adults: A cross-sectional study from NHANES 2011-2014.

The aim of this study was to investigate the association between sleep duration and muscle quality index (MQI) in middle-aged and older age groups, as limited evidence exists on this topic. In order to assess the relationship between sleep duration and MQI, a cross-sectional study was undertaken, utilizing data from the National Health and Nutrition Examination Survey (NHANES) acquired during the period from 2011 to 2014. The study comprised a total of 4598 participants aged 20 years and above. To examine the association between sleep duration and MQI, sophisticated weighted multivariate linear regression models were employed. Additionally, smooth curve fitting techniques were applied to examine the possibility of any non-linear relationship between the two variables. The average age of the adults who were enrolled in the study was 38.48±11.69 years, and 46.75% of them were female. The results of the multivariable linear regression models showed that sleep duration had a positive correlation with MQI. However, when subgroup analysis was conducted, it was found that this positive correlation only existed among women (β = 0.09, 95% CI: 0.014 to 0.167). To further confirm the differences between sexes in the relationship between sleep duration and MQI, a weighted generalized additive model (GAM) was used. This research study provides evidence that there is a positive correlation between the duration of sleep and MQI specifically in females, while no such association was observed in males. These findings shed light on the existence of gender disparities in the connection between sleep duration and MQI.

Artificial High- and Low-density Materials in Bone Mineral Densitometry Using Dual-energy X-ray Absorptiometry: A GATE Monte Carlo Simulation of “Black-hole” Artifact

Objective: The objective of the study was to evaluate the effect of artificial high- and low-density materials on Bone mineral density (BMD)scans in dual-energy X-ray absorptiometry (DXA) method and emergence of black-hole artifact through GATE Monte Carlo simulation. Materials and Methods: GATE Monte Carlo code was utilized to simulate the artifact encountered in clinical scans acquired by HOLOGIC® bone densitometer. Two simplified phantoms were designed. The first one was a rectangular box with six smaller cubes inside and the second one was a body torso. Materials of cubes were spine bone, polymethyl methacrylate (PMMA), barium sulfate suspension in water, stainless steel, titanium alloy, and gold. The torso phantom contained objects of 5 vertebrae, bowel and 3 small spherical objects near the surface of the torso as piercing objects on the abdominal wall, each overlying the vertebrae. Using 100 and 140 kVp, spectral X-rays were generated to simulate DXA. For both phantoms, two simulations were carried out. The pair of projections acquired for each phantom were then subtracted and analyzed by curve fitting techniques. Results: Except for spine bone, in which radio-opacity decreases with increasing spectral X-ray energy (from 100 to 140 kVp), other squares exhibit little changes over different energies. PMMA shows consistently very low radio-opacity. Four other materials (barium sulfate in water, stainless steel alloy, titanium alloy, and gold), all attenuate the X-ray photons substantially. Except for spine bone, other materials are barely noticeable in pairwise subtracted images. In torso phantom, piercing objects are visualized as “holes” in vertebrae. Conclusion: Both artificial high- and low-density materials, compared to bone, are eliminated during the subtraction of dual-energy X-ray profiles and therefore, can create black-hole artifact.

Experimental Study on the Dowel-Bearing Strength of Bambusa blumeana Bamboo Used for Sustainable Housing Construction

This study addresses the critical issue of dowel-bearing strength in Bambusa blumeana, a key sustainable construction material crucial for climate change mitigation. Given the lack of bamboo connection standards, this research focuses on determining the dowel-bearing strength of Bambusa blumeana, emphasizing factors such as dowel diameter, node placements, and the physical properties of bamboo. A predictive equation is derived, enhancing the practicality of bamboo in structural design. The results underscore a notable correlation between dowel diameter and characteristic strength, with implications for engineering practices. Node placements significantly affect dowel-bearing capacity, while bamboo’s physical attributes, including thickness, culm diameter, and moisture content, exhibit modest correlations with strength. The derived equation aims to assist in structural design, mitigating splitting and bearing failures in bamboo structures. This research establishes a foundation for optimizing the use of Bambusa blumeana in sustainable construction, advancing the understanding of its dowel-bearing strength for improved sustainability and resilience in the construction industry. Future research suggestions include exploring bamboo–mortar composites, additional node placements, and employing more comprehensive empirical equations and curve-fitting techniques. The study advocates for further investigations with more diverse and larger bamboo samples to bolster robustness. Additionally, delving into bamboo ductility may offer valuable insights.

Supervised learning to covering cost risk through post-construction evaluation of transportation projects by project delivery methods

PurposeThe main objectives of this study are to (1) develop and test a cost contingency learning model that can generalize initially estimated contingency amounts by analyzing back the multiple project changes experienced and (2) uncover the hidden link of the learning networks using a curve-fitting technique for the post-construction evaluation of cost contingency amounts to cover cost risk for future projects.Design/methodology/approachBased on a total of 1,434 datapoints collected from DBB and DB transportation projects, a post-construction cost contingency learning model was developed using feedforward neural networks (FNNs). The developed model generalizes cost contingencies under two different project delivery methods (i.e. DBB and DB). The learning outputs of generalized contingency amounts were curve-fitted with the post-construction schedule and cost information, specifically aiming at uncovering the hidden link of the FNNs. Two different bridge projects completed under DBB and DB were employed as illustrative examples to demonstrate how the proposed modeling framework could be implemented.FindingsWith zero or negative values of change growth experienced, it was concluded that cost contingencies were overallocated at the contract stage. On the other hand, with positive values of change growth experienced, it was evaluated that set cost contingencies were insufficient from the post-construction standpoint. Taken together, this study proposed a tangible post-construction evaluation technique that can produce not only the plausible ranges of cost contingencies but also the exact amounts of contingency under DBB and DB contracts.Originality/valueAs the first of its kind, the proposed modeling framework provides agency engineers and decision-makers with tangible assessments of cost contingency coupled with experienced risks at the post-construction stage. Use of the proposed model will help them evaluate the allocation of appropriate contingency amounts. If an agency allocates a cost contingency benchmarked from similar projects on aspects of the base estimate and experienced risks, a set contingency can be defended more reliably. The main findings of this study contribute to post-construction cost contingency verification, enabling agency engineers and decision-makers to systematically evaluate set cost contingencies during the post-construction assessment stage and achieving further any enhanced level of confidence for future cost contingency plans.

The smart nanocarrier containing zein/starch co-biopolymers enhanced by graphitic carbon nitride; exploring opportunities in brain cancer treatment

In this investigation, we present an innovative pH-responsive nanocomposite designed to address challenges associated with using 5-Fluorouracil (5-FU) in cancer therapy. The nanocomposite containing zein (Z), starch (S), and graphitic carbon nitride (g-C3N4) macromolecules is synthesized by a water-in-oil-in-water (W/O/W) double emulsion technique, serving as a carrier for 5-FU. The S/Z hydrogel matrix's entrapment and loading efficiency are greatly improved by adding g-C3N4 nanosheets, reaching noteworthy values of 45.25 % and 86.5 %, respectively, for drug loading efficiency and entrapment efficiency. Characterization through FTIR and XRD validates the successful loading of 5-FU, elucidating the chemical bonding within the nanocomposite and crystalline characteristics. Structural analysis using FESEM, along with DLS and zeta potential measurements, reveals an average nanocomposite size of 193.48 nm, indicating a controlled structure, and a zeta potential of −42.32 mV, signifying a negatively charged surface. Studies on the in vitro release of drugs reveal that 5-FU is delivered more effectively and sustainably in acidic environments than in physiological circumstances. This highlights the fact that the created nanocarrier is pH-sensitive. Modeling release kinetics involves finding the right mathematical conditions representing underlying physicochemical processes. Employing curve-fitting techniques, predominant release mechanisms are identified, and optimal-fitting kinetic models are determined. The Baker kinetic model performed best at pH 7.4, indicating that the leading cause of the drug release was polymer swelling. In contrast, the Higuchi model was most accurate for drug release at pH 5.4, illuminating the diffusion and dissolution mechanisms involved in diffusion. To be more precise, the mechanism of release at pH 7.4 and 5.4 was anomalous transport (dissolution-controlled), according to the Korsmeyer-Peppas mathematical model. The pH-dependent swelling and degradation behavior of S/Z/g-C3N4@5-FU nanocomposite showed higher swelling and faster degradation in acidic environments compared to neutral conditions. Crucially, outcomes from the MTT test affirm the significant cytotoxicity of the 5-FU-loaded nanocomposite against U-87 MG brain cancer cells, while simultaneously indicating non-toxicity towards L929 fibroblast cells. These cumulative findings underscore the potential of the engineered S/Z/g-C3N4@5-FU as a productive and targeted therapeutic approach for cancer cells.

Associations of life's essential 8 with MAFLD and liver fibrosis among US adults: a nationwide cross-section study.

Life's essential' 8 (LE8) is a newly updated cardiovascular health (CVH) metrics from the American Heart Association, with close relevance to metabolism. Our objective is to explore the association between LE8 scores and incidence of metabolic dysfunction-associated fatty liver disease (MAFLD) and advanced liver fibrosis in American adults. This population-based cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2018, encompassing adults aged 20 years or older. Validated non-invasive scoring systems were employed to define liver steatosis and advanced liver fibrosis. Multivariable logistic regression and smooth curve fitting techniques were applied to evaluate the associations. All analyses were adjusted for the survey' complex design parameters and accounted for sample weights. A total of 11,820 participants were included. A higher LE8 score was found to be inversely associated with the incidence of MAFLD and advanced liver fibrosis, with odds ratios (OR) of 0.64 (95% CI: 0.57-0.71) for MAFLD and 0.75 (95% CI: 0.61-0.92) for advanced liver fibrosis per 1 standard deviation (SD) increase in LE8 score. Similar patterns were found in the relationship between health behaviors/factors score and incidence of MAFLD and advanced liver fibrosis. In subgroup analyses, the interaction test showed that age, education level, marital status, CVD, hypertension and diabetes had a significant impact on the association between LE8 score and MAFLD (all P for interaction < 0.05). Among male, elderly, wealthy, other race, CVD, diabetes and depression participants, the correlation between LE8 score and advanced liver fibrosis was not statistically significant (P > 0.05). Younger participants exhibited a more pronounced negative association between the CVH metric and both MAFLD and advanced life fibrosis. LE8 and its subscales score were inversely associated with the presence of MAFLD and advanced liver fibrosis in non-linear patterns. Optimal LE8 score may significantly reduce the risk of liver steatosis and fibrosis.

Exploring the link: Systemic immune-inflammation index as a marker in endometriosis—Insights from the NHANES 2001–2006 cross-sectional study

Objective The systemic immuno-inflammatory index (SII), a novel immune marker of inflammation, has not been previously associated with endometriosis. The objective of this research is to explore the link between SII and the occurrence of endometriosis. Methods Utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2001 to 2006, we screened and extracted relevant information from the population. Participants missing data on either SII or endometriosis were excluded. We divided the remaining cohort into quartiles based on SII levels: Q1 (SII &lt; 249, n = 848), Q2 (249 ≤ SII &lt; 604.55, n = 847), Q3 (604.55 ≤ SII &lt; 825.35, n = 847), and Q4 (SII ≥ 852.35, n = 848). Multiple linear regression and smooth curve fitting techniques, were to evaluate the non-linear association between SII and endometriosis. Results The study included 3,390 adults aged 20 to 55. Multiple linear regression analysis revealed a significant positive correlation between SII and endometriosis [3.14, 95% CI (2.22, 4.45), P &lt; 0.01]. This correlation was consistent across subgroups defined by marital status, poverty income ratio, BMI, alcohol consumption, and age at first menstrual period. However, the relationship between SII and endometriosis was significantly modified by age, education, and history of pregnancy in the stratified analyses. The curve fitting indicated an S-shaped curve, with an inflection point at SII = 1105.76. Conclusion The SII may serve as a predictive marker for endometriosis risk among women in the United States, offering a potentially simple and cost-effective approach. However, given the cross-sectional design of this investigation, further validation in prospective studies is necessary.

Synthetic orbit uncertainty generation through regression analysis of historical Conjunction Data Messages

In the last decades, the Earth-orbiting population of both active and non-active objects has grown significantly, leading to a substantial increase in number of possible in-orbit collisions. It is therefore crucial to monitor the orbit of space resident objects to assess in advance the threat of risky conjunctions. Within this framework, the 18th Space Defense Squadron (SDS) is consistently updating the orbit of thousands of tracked objects by processing observations of the U.S. Space Surveillance Network (SSN). The determined orbital data is continuously maintained in the Special Perturbation (SP) catalogue and used by the 19th SDS to issue close approach warnings to satellite operators around the globe in the form of Conjunction Data Messages (CDM). The Flight Dynamics (FD) group of the German Space Operation Centre (GSOC) receives on regular basis a subset of the SP catalogue data along with CDMs associated to the fleet of its controlled satellites. The SP ephemerides are in fact provided without any covariance information preventing any computation of the Probability of Collision (Pc). In GSOC FD we are implementing a service to link a series of synthetic orbital error covariance matrices to a given SP ephemeris by statistically analyzing historical CDMs of past events. More than 30 GB of past conjunction data are processed to extract state vector, covariance matrix and object size parameter of already encountered secondary objects. The orbital errors of these last are subsequently categorized and divided into orbital classes to decouple the high correlation the covariance has with respect to solar flux, object dimension, altitude of perigee, eccentricity and orbit inclination. The classification aims at collecting similar CDMs regarding the aforementioned dependencies, and approximates the predicted 1-sigma position errors in the orbital frame by optimal curve-fitting techniques. By evaluation of the curve fitting coefficients of a requested orbit class a covariance matrix can be generated for any prediction time in upcoming CDM refinements and other analyses. The work discusses the limiting cases of the classification approach, bringing possible solutions to the scenario of empty classes. An in-depth characterization of the parameters that affect the orbital errors is in fact performed to individualize the neighboring class that provides the closest and most meaningful covariance timeline. Successively, the effect of using synthetic covariance in a conjunction risk assessment is also explored, adapting the problem on real operations. Lastly, the entire data processing pipeline and how the described service fits into the GSOC Flight Dynamics System (FDS) framework is described.

The different association between fat mass distribution and intake of three major nutrients in pre- and postmenopausal women.

Obesity, characterized by excessive body fat accumulation, is associated with various chronic health conditions. Body fat plays a crucial role in health outcomes, and nutrient intake is a contributing factor. Menopause further influences body fat, but the precise relationships between nutrients and fat mass distribution in pre- and post-menopausal women are unclear. Data from 4751 adult women aged ≥18 years old (3855 pre-menopausal, 896 post-menopausal) with completed information were obtained from the National Health and Examination Survey (NHANES) from 2011 to 2018. Multivariate linear regression models were used to examine the associations between protein, carbohydrate, fat intake and total percent fat (TPF), android percent fat (APF), gynoid percent fat (GPF), android to gynoid ratio (A/G), subcutaneous adipose tissue mass (SAT), visceral adipose tissue mass (VAT). Subgroup analyses, stratified by menopausal status, were also conducted. Additionally, we employed smoothing curve fitting techniques to investigate potential non-linear relationships between fat mass distribution and nutrient intake. Compared with pre-menopausal women, post-menopausal women had higher body fat, BMI, and metabolic indicators but lower nutrient intake (All p<0.05). In the overall analysis, we found significant correlations between nutrient intake and fat mass. Specifically, protein intake was negatively correlated with TPF (β = -0.017, 95% CI: -0.030, -0.005), APF (β = -0.028, 95% CI: -0.044, -0.012), GPF (β = -0.019, 95% CI: -0.030, -0.008), while fat intake showed positive correlations with these measures (SAT: β = 2.769, 95% CI: 0.860, 4.678). Carbohydrate intake exhibited mixed associations. Notably, body fat mass-nutrient intake correlations differed by menopausal status. Generally speaking, protein intake showed negative correlations with body fat distribution in pre-menopausal women but positive correlations in post-menopausal women. Carbohydrate intake revealed significant negative associations with abdominal and visceral fat in post-menopausal women, while fat intake was consistently positive across all fat distribution indices, especially impacting visceral fat in post-menopausal women. Dietary intake plays a crucial role in body fat distribution, with menopausal status significantly influencing the impact of nutrients on specific fat distribution metrics. The study emphasizes the need for dietary guidelines to consider the nutritional needs and health challenges unique to women at different life stages, particularly concerning menopausal status, to effectively manage obesity.