Least-squares Linear Regression Research Articles

Quantifying Health Insurance Eligibility Impact on Inter-hospital Transfers of Injured Patients: Evidence from the Affordable Care Act’s Dependent Coverage Provision

Observational studies reported that nontrauma/low-level trauma centers less frequently transferred insured injured patients to high-level centers. The dependent coverage provision of the Affordable Care Act was a natural experiment expanding insurance eligibility in 2010 by allowing young adults to remain on their parents' insurance until age 26 years old. The objective was to determine whether expanded insurance eligibility reduced interhospital transfers. This observational, retrospective cohort study of the 2007-2013 National Trauma Data Bank used a difference-in-differences design to compare the change in interhospital transfer rates before and after dependent coverage provision implementation between dependent coverage provision-eligible (aged 19-25 years) and dependent coverage provision-ineligible (aged 27-34 years) injured patients. The exposure of interest was the dependent coverage provision. The main outcome was yearly rate of interhospital transfer. Least-squares linear regression modeled interhospital transfer rates and quantified association with the age-based dependent coverage provision eligibility binary interaction term in the pre-dependent coverage provision versus post-dependent coverage provision time periods. A total of 535,036 dependent coverage provision-eligible and 431,462 dependent coverage provision-ineligible patients were identified. The median Injury Severity Score was 6.0 (interquartile range= 4.0-13.0) for both cohorts. Private insurance rates increased from 28.1% to 38.7% among the dependent coverage provision-eligible cohort and from 27.0% to 30.5% among the older cohort between the pre-dependent coverage provision to post-dependent coverage provision time period. The average yearly interhospital transfer rate was not significantly different between the cohorts in the pre-dependent coverage provision (2.3% lower among dependent coverage provision-eligible) and the post-dependent coverage provision time periods (2.9% lower among dependent coverage provision-eligible). Difference-in-differences analysis revealed nonsignificant 14.6 fewer transfers/10,000 patients (95% confidence interval= 33.7 fewer to 4.4 more transfers/10,000 patients). Policies expanding insurance eligibility do not reduce interhospital transfers in a mature trauma system.

First-principles NMR of oxide glasses boosted by machine learning.

Solid-state NMR has established itself as a cutting-edge spectroscopy for elucidating the structure of oxide glasses thanks to several decades of methodological and instrumental progress. First-principles calculations of NMR properties combined with molecular-dynamics (MD) simulations provides a powerful complementary approach for the interpretation of NMR data, although they still suffer from limitations in terms of size, time and high consumption of computational resources. We address this challenge by developing a machine-learning framework to boost predictive modelling of NMR spectra. We use kernel ridge regression techniques (least-squares support vector regression and linear ridge regression) combined with smooth overlap of atomic position (SOAP) atom-centered descriptors to efficiently predict NMR interactions: the isotropic magnetic shielding and the electric field gradient (EFG) tensor. As illustrated in this work, this approach enables the simulation of magic-angle spinning (MAS) and multiple-quantum magic-angle spinning (MQMAS) NMR spectra of very large models (more than 10 000 atoms) and an efficient averaging of NMR properties over MD trajectories of nanoseconds for incorporating finite-temperature effects, at the computational cost of classical MD simulations. We illustrate these advances for sodium silicate glasses (SiO2-Na2O). NMR parameters (isotropic chemical shift and electric field gradient) could be predicted with an accuracy of 1 to 2% in terms of the total span of the NMR parameter values. To include vibrational effects, an approach is proposed of scaling the EFG tensor in NMR simulations with a factor obtained from the time auto-correlation functions computed on MD trajectories.

Exploring the relationship between personal and cohabiting family members’ COVID-19 infection experiences and fear of COVID-19: a longitudinal study based on the Japan COVID-19 and Society Internet Survey (JACSIS)

ObjectivesThis study aims to explore the relationship between the combined experiences of COVID-19 infection in individuals and their family members and the resulting fear of COVID-19, with a focus on...

Significant changes in global maize yield sensitivity to vapor pressure deficit during 1983–2010

Vapor pressure deficit (VPD) is a critical factor in crop growth, and low yields are often associated with high VPD. The adverse effects of VPD on crop yield have been well-documented; however, whether and where yield sensitivity to VPD (SVPD) changes over time across global cropping areas remain elusive. Based on the observed maize yield and VPD at the grid scale, the SVPD calculated using least-squares linear regression for 27.5 % and 25.9 % of the global maize-growing areas, respectively, showed a significant (p < 0.05) decreasing and increasing trend from 1983 to 2010. Spatially, a statistically negative trend in SVPD was found in southeastern Brazil, the central United States, most of Europe, and northeastern China, whereas a positive trend was observed in the eastern United States and northern China. In particular, negative SVPD was alleviated in 23.2 % of global maize areas, primarily in the northeastern United States and northern China, but was aggravated in 18.4 % of maize areas in the middle latitudes of the Northern Hemisphere. In addition, the sign of SVPD was reversed in 34.4 % of the global maize areas, notably in the central United States, northern China, and southern Brazil. The results of the random forest model show that shortwave radiation was identified as the primary co-varying factor that modulated the pattern of changing SVPD in about 26 % of the global maize area, followed by maximum temperature (24.3 %) and minimum relative humidity (21.5 %). By assessing the 14 process-based crop models, we found that their ensemble mean could reproduce the annual patterns of SVPD well but failed to capture its decadal change trends.

A Long Wave-Infrared Miniatured Quantum Dot Spectrometer.

In this work, a long-wave infrared (LWIR) quantum dot (QD) spectrometer was constructed for the first time by integrating a 255-element HgSe QD filter array with an LWIR array detector. The filter array was fabricated using a combination inkjet printing strategy with eight types of T-dodecyl mercaptan-terminated HgSe QD inks. The stability and morphology of the QDs were improved by optimizing the purification methodology and ligand modification. Combined with the compressive-sensing-based least-squares linear regression (CS-LS) algorithm, the LWIR QD spectrometer achieved a spectral resolution of 5.4 cm-1 over a wide spectral range of 8 to 14 μm, enabling the detection of the chemical warfare agent simulant dimethyl methylphosphonate. This technology is expected to facilitate the development of smaller volumes and more accurate identification of various targets in the future. This paper offers an approach to fabricating low-cost LWIR spectrometers and promoting the scale-up applications of QD devices.

Mirtazapine blood levels and antidepressant response

Objective Therapeutic drug monitoring (TDM) is an important tool for treatment optimisation. Its usefulness has recently been demonstrated for some first-line antidepressants; however, few studies have been reported on the relationship between blood levels of mirtazapine and its antidepressant effects. The aim of this study was to investigate the association between blood concentration of mirtazapine and antidepressant response. Methods 59 outpatients treated with mirtazapine for depression were recruited and followed up for three months in a naturalistic setting. Hamilton Depression Rating Scale-21 (HAMD-21) was administered at baseline, month 1, and month 3 to assess antidepressant response. Mirtazapine serum concentration was measured at steady state. Linear regression analysis and nonlinear least-squares regression were used to estimate association between serum concentration of mirtazapine and antidepressant response. Results Our results showed no overall association between serum concentration of mirtazapine and symptom improvement at month 1 and month 3. A marginally significantly higher serum concentration of mirtazapine was found in responders vs non-responders at month 3. Conclusions The study suggests that serum concentration of mirtazapine is not strongly associated with the antidepressant efficacy of mirtazapine. This is probably attributed to its pharmacodynamic profile, even though higher blood levels seem to be marginally more effective.

Machine learning approaches for simultaneous spectrophotometric determination of heavy metal ions in water samples

In this study, the simultaneous determination of Co, Cd, Ni, Cu, and Pb was carried out as a color complex with 4-(2-pyridylazo) resorcinol in an aqueous solution under the assesting of machine learning. A partial least-squares multivariate linear regression and artificial neuron network for the analysis of mixtures of metals were developed. MATLAB is a powerful software machine learning program that was used to support matrix calculations and displays. The benefit of MATLAB in the construction of the machine learning model allows the development of a rapid and highly effective analysis of multiple components in the mixtures without separation and enrichment. For individual determinations, the working ranges were discovered as the important information for choosing the initial concentration of each heavy metal in a mixture, r. The results of analysis of Ni2+, Pb2+, and Cd2+ by two methods Partial Least Squares - PLS and Artificial Neural Networks - ANN are sensitive and accurate for simultaneous determination of the concentration of these ions in the synthesis mixture with a high regression coefficient of 0.993, respectively, 0.997, 0.997 for Ni2+, Pb2+ and Cd2+. As for Cu2+ and Co2+, the accuracy is higher when using the ANN method.

Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)

This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and the AGFW of each plot was measured. Sugarcane crown heights and volumes were obtained by isolating individual tree crowns using a LiDAR-derived digital surface model of the area. Multiple linear regression (MLR) and partial least-squares regression (PLSR) models were tested for the field-sampled AGFWs (dependent variable) and individual canopy heights and volumes, and spectral indices were used as independent variables or predictors. The PLSR model showed more promising results than the MLR model when predicting the AGFW over the study area. Although PLSR is well-suited to a large number of collinear predictor variables and a limited number of field samples, this study showed moderate results (R2 = 0.5). The visual appearance of the spatial distribution of the AGFW map is satisfactory. The limited no. of field samples overfitted the MLR prediction results. Overall, this research highlights the potential of integrating remote sensing technologies in the sugarcane industry, thereby improving yield estimation and effective crop management.

Association of Fructosamine Levels With Glycemic Management in Children With Type 1 Diabetes as Determined by Continuous Glucose Monitoring: Results From the CGM TIME Trial

ObjectiveOur aim in this study was to determine the correlation between serum fructosamine and average blood glucose, as measured by continuous glucose monitoring (CGM) in children with type 1 diabetes. MethodsNinety-seven blood samples were collected from 70 participants in the Timing of Initiation of continuous glucose Monitoring in Established pediatric diabetes (CGM TIME) Trial. Each eligible participant had 3 weeks of CGM data with at least 60% CGM adherence before blood collection. Ordinary least-squares linear regression incorporating restricted cubic splines was used to determine the association between fructosamine levels and mean blood glucose. ResultsAn association was found between fructosamine and mean blood glucose, with an F statistic of 9.543 (p<0.001). Data were used to create a formula and conversion chart for calculating mean blood glucose from fructosamine levels for clinical use. ConclusionsThere is a complex relationship between average blood glucose, as determined by CGM and fructosamine. Fructosamine levels may be clinically useful for assessing short-term glycemic management when CGM is not available.

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination (R2) values of 0.72 and 0.67 and root mean squared error (RMSE) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics.

Self-identified barriers to health services among migrants 50 years of age or older: population-based survey study of Russian speakers in Finland

BackgroundThe compounded effect of a migratory background and ageing increases the risk of unequal medical treatment opportunities. The aim of this article is to investigate the social determinants of barriers to health services.MethodsThe study uses population-based survey data of Russian-speaking migrants (50 + years) residing in Finland (n = 1082, 57% of men, mean age 63 years). Multiple correspondence analysis was performed as a dimension reduction procedure on six barriers to health services. Multiple ordinary least-squares linear regression was used for the predicted score of the barriers as an outcome variable.ResultsMost of the sociodemographic characteristics were not associated with barriers to health services, except gender, as women tended to face more disadvantages. Migration-related factors, such as the need for interpreters for health services and experienced discrimination, were associated with an increased likelihood of reporting barriers to health services. Using the internet as a primary source of health information was associated with more access barriers to health services.ConclusionsMigrants 50 years of age or older face multiple barriers to health services. Given that the healthcare needs increase with age, addressing this issue becomes crucial, necessitating improved access to health services for older migrants.

IDaPS — Improved data-locality aware data placement strategy based on Markov clustering to enhance MapReduce performance on Hadoop

The execution of Map-Reduce applications on the Hadoop cluster poses significant challenges due to the non-consideration of data locality, i.e., assigning tasks to compute nodes where input data sets are located. Due to such non-consideration, high data transfer overheads are caused. Further, it increases latency, which may arise if input data needs to be transferred across the network, thereby significantly increasing execution time. To address this issue, an Improved DAta Placement Strategy IDaPS based on the intra-dependency among the data is proposed. IDaPS re-organizes the default data layouts in HDFS to ensure higher degree of parallelism. The efficiency of IDaPS is demonstrated in Hadoop clusters (10 and 15 nodes) by executing Hadoop Benchmark performance tests viz. WordCount, Grep on Project-Gutenberg book dataset (50 GB) and Least Square Linear Regression (LSLR) on weather dataset (10.67 GB). The results were compared with state-of-the-art algorithms viz. Hadoop Default Data Placement (HDDP), Load-Balancer and literary work RENDA. The results demonstrate that IDaPS significantly reduces execution time by 28.2% and 38.4% in 10-node and 15-node clusters while executing WordCount, and 35% and 38.1% in 10-node and 15-node clusters for Grep. Similarly, for LSLR, it reduces execution time by 32.7%.

Is the Bland-Altman plot method useful without inferences for accuracy, precision, and agreement?

This study aims to propose a comprehensive alternative to the Bland-Altman plot method, addressing its limitations and providing a statistical framework for evaluating the equivalences of measurement techniques. This involves introducing an innovative three-step approach for assessing accuracy, precision, and agreement between techniques, which enhances objectivity in equivalence assessment. Additionally, the development of an R package that is easy to use enables researchers to efficiently analyze and interpret technique equivalences. Inferential statistics support for equivalence between measurement techniques was proposed in three nested tests. These were based on structural regressions with the goal to assess the equivalence of structural means (accuracy), the equivalence of structural variances (precision), and concordance with the structural bisector line (agreement in measurements obtained from the same subject), using analytical methods and robust approach by bootstrapping. To promote better understanding, graphical outputs following Bland and Altman's principles were also implemented. The performance of this method was shown and confronted by five data sets from previously published articles that used Bland and Altman's method. One case demonstrated strict equivalence, three cases showed partial equivalence, and one showed poor equivalence. The developed R package containing open codes and data are available for free and with installation instructions at Harvard Dataverse at https://doi.org/10.7910/DVN/AGJPZH. Although easy to communicate, the widely cited and applied Bland and Altman plot method is often misinterpreted, since it lacks suitable inferential statistical support. Common alternatives, such as Pearson's correlation or ordinal least-square linear regression, also fail to locate the weakness of each measurement technique. It may be possible to test whether two techniques have full equivalence by preserving graphical communication, in accordance with Bland and Altman's principles, but also adding robust and suitable inferential statistics. Decomposing equivalence into three features (accuracy, precision, and agreement) helps to locate the sources of the problem when fixing a new technique.

Identification of Photovoltaic Array Model Parameters by Robust Linear Regression Methods

The aim of this paper is to propose an approach for photovoltaic (PV) sources modeling based on robust least squares linear regression (LSR) parameter identification method. On the basis of experimental data of solar irradiance, cell temperature and voltage and currents at maximum power points for a given PV array, correlation functions among the considered quantities are defined. By implementing these functions in a Matlab® Simulink model, accurate I-V characteristics for the considered array are obtained, managing only the solar irradiance. The method is validated comparing the computed and the experimental maximum power points (MPPs). Its effectiveness is proven to be better with respect of parameter identification methods based on discrete approaches and standard LSR method

Mapping and Assessing Riparian Vegetation Response to Drought along the Buffalo River Catchment in the Eastern Cape Province, South Africa

The increasing drought frequency poses a significant threat to global and regional river systems and ecosystem functioning, especially in the complex topographical Buffalo River catchment area of the Eastern Cape Province, South Africa. This study explored the impact of drought on riparian vegetation dynamics using the Normalize Difference Vegetation Index (NDVI), Transformed Difference Vegetation Index (TDVI) and Modified Normalized Difference Water Index (MNDWI) from satellite-derived Landsat data from 1990 to 2020. The least-squares linear regression and Pearson’s correlation coefficient were used to evaluate the long-term drought in riparian vegetation cover and the role of precipitation and streamflow. The correlation results revealed a moderate positive correlation (r = 0.77) between precipitation and streamflow with a significant p-value of 0.04 suggesting consequences on riparian vegetation health. Concurrent with the precipitation, the vegetation trends showed that precipitation increased insignificantly with less of an influence while the reverse was the case with the streamflow in the long term. The results show that the NDVI and TDVI were significant indices for detecting water-stressed vegetation in river catchment dynamics. Much of these changes were reflected for MNDWI in dry areas with a higher accuracy (87.47%) and dense vegetation in the upper catchment areas. The standardized precipitation index (SPI) revealed the inter-annual and inter-seasonal variations in drought-stressed years between 1991–1996, 2000–2004, 2009–2010, 2015, and 2018–2019, while 2020 exhibited slight sensitivity to drought. The findings of this study underscore the need for heightened efforts on catchment-scale drought awareness for policy development, programs, and practices towards ecosystem-based adaptation.

Hyperspectral (VNIR-SWIR) analysis of roll front uranium host rocks and industrial minerals from Karnes and Live Oak Counties, Texas Coastal Plain

VNIR-SWIR (400–2500 nm) reflectance measurements were made on the surfaces of various cores, cuttings and sample splits of sedimentary rocks from the Tertiary Jackson Group, and Catahoula, Oakville and Goliad Formations. These rocks vary in composition and texture from mudstone and claystone to sandstone and are known host rocks for roll front uranium occurrences in Karnes and Live Oak Counties, Texas. Spectral reflectance profiles, 569 in total, were reduced to 125 representative spectral signatures, which were analyzed using the U.S. Geological Survey's (USGS) Material Identification and Characterization Algorithm (MICA). MICA uses an automated continuum-removal procedure together with a least-squares linear regression to determine the fit of observed sample spectral absorption features to those of reference mineral standards in a spectral library. The reference minerals include various clay, mica, carbonate, ferric and ferrous iron minerals and their mixtures. In addition, absorption feature band-depth analysis was done to identify rock surfaces exhibiting absorption features related to uranium and zeolite minerals, which were not included in the command files used to execute MICA.Rocks from each of the four geologic units produced broadly similar spectral signatures as a result of comparable mineral compositions, but there were some notable differences. For example, Ca- and Na-montmorillonite was matched most frequently to the spectral absorption features in 2-μm (∼2000–2500 nm) wavelengths, while goethite occurred often at 1-μm (∼400–1000 nm) wavelengths. The latter is related to limonitic iron-staining in and around oxidized zones of the uranium roll front as described in previous papers. Rocks of the Jackson Group differed from those of the Catahoula, Oakville and Goliad units in that the former exhibited spectral features we interpret as being due to the presence of lignite-bearing mudstone layers. Goliad rocks exhibit spectral features related to dolomite, gypsum, anhydrite, and an unidentified green clay mineral that is possibly glauconite. Jackson Group rocks also exhibit weak but well-resolved absorption features at 964 and 1157 nm related to either or both zeolite minerals clinoptilolite and heulandite. These zeolite minerals and a few spectra exhibiting hydrous silica absorption features are indicative of alteration of volcanic glass in tuffaceous mudstone and claystone layers. A few sample spectra exhibited strong absorption features at around 1135 nm related to the uranium mineral coffinite. Both the 1135 nm coffinite and 1157 nm zeolite absorption features overlap somewhat, potentially making them difficult to distinguish without additional hyperspectral field, laboratory or remote sensing data.The results of this study were compared to mixtures of minerals described for ore, gangue and alteration minerals in deposit models for sandstone-hosted uranium, sedimentary bentonite and sedimentary zeolite. Use of these spectra can help facilitate mapping of both waste materials from the legacy mining of the above commodities, as well as future exploration and resource assessment activities.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods.

Harnessing dual-energy CT for glycogen quantification: a phantom analysis.

Non-invasive glycogen quantification in vivo could provide crucial information on biological processes for glycogen storage disorder. Using dual-energy computed tomography (DECT), this study aimed to assess the viability of quantifying glycogen content in vitro. A fast kilovolt-peak switching DECT was used to scan a phantom containing 33 cylinders with different proportions of glycogen and iodine mixture at varying doses. The virtual glycogen concentration (VGC) was then measured using material composition images. Additionally, the correlations between VGC and nominal glycogen concentration (NGC) were evaluated using least-square linear regression, then the calibration curve was constructed. Quantitative estimation was performed by calculating the linearity, conversion factor (inverse of curve slope), stability, sensitivity (limit of detection/limit of quantification), repeatability (inter-class correlation coefficient), and variability (coefficient of variation). In all conditions, excellent linear relationship between VGC and NGC were observed (P<0.001, coefficient of determination: 0.989-0.997; residual root-mean-square error of glycogen: 1.862-3.267 mg/mL). The estimated conversion factor from VGC to NGC was 3.068-3.222. In addition, no significant differences in curve slope were observed among different dose levels and iodine densities. The limit of detection and limit of quantification had respective ranges of 6.421-15.315 and 10.95-16.46 mg/mL. The data demonstrated excellent scan-repeat scan agreement (inter-class correlation coefficient, 0.977-0.991) and small variation (coefficient of variation, 0.1-0.2%). The pilot phantom analysis demonstrated the feasibility and efficacy of detecting and quantifying glycogen using DECT and provided good quantitative performance with significant stability and reproducibility/variability. Thus, in the future, DECT could be used as a convenient method for glycogen quantification to provide more reliable information for clinical decision-making.

6:2 Chlorinated Polyfluoroalkyl Ether Sulfonates Exert Stronger Thyroid Homeostasis Disruptive Effects in Newborns than Perfluorooctanesulfonate: Evidence Based on Bayesian Benchmark Dose Values from a Population Study.

Growing toxicologic evidence suggests that emerging perfluoroalkyl substances (PFASs), like chlorinated polyfluoroalkyl ether sulfonate (Cl-PFESA), may be as toxic or more toxic than perfluorooctanesulfonate (PFOS) and perfluorooctanoic acid (PFOA). However, further investigations are needed in terms of the human health risk assessment. This study examined the effects of emerging and legacy PFAS exposure on newborn thyroid homeostasis and compared the thyroid disruption caused by 6:2 Cl-PFESA and PFOS using a benchmark dose approach. The health effects of mixture and individual exposure were estimated using the partial least-squares (PLS) model and linear regression, respectively. A Bayesian benchmark dose (BMD) analysis determined the BMD value for adverse effect comparison between 6:2 Cl-PFESA and PFOS. The median (interquartile range) concentrations of 6:2 Cl-PFESA (0.573 [0.351-0.872] ng/mL), PFOS (0.674 [0.462-1.007] ng/mL), and PFOA (1.457 [1.034, 2.405] ng/mL) were found to be similar. The PLS model ranked the PFAS variables' importance in projection (VIP) scores as follows: 6:2 Cl-PFESA > PFOS > PFOA. Linear regression showed that 6:2 Cl-PFESA had a positive association with free triiodothyronine (FT3, P = 0.006) and triiodothyronine (T3, P = 0.014), while PFOS had a marginally significant positive association with FT3 alone (P = 0.042). The BMD analysis indicated that the estimated BMD10 for 6:2 Cl-PFESA (1.01 ng/mL) was lower than that for PFOS (1.66 ng/mL) in relation to a 10% increase in FT3. These findings suggest that 6:2 Cl-PFESA, an alternative to PFOS, has a more pronounced impact on newborns' thyroid homeostasis compared to PFOS and other legacy PFASs.

Preclinical pharmacokinetic exploration of a novel osteoporotic quinazolinone-benzopyran-indole hybrid (S019-0385) using LC-MS/MS

1. The current investigation was to develop and validate the LC-MS/MS method in order to analyse the various pharmacokinetic parameters of S019-0385. A sensitive, selective, and robust LC-MS/MS approach was established and validated for measuring S019-0385 in female mice plasma and tissue, using optimal multiple reaction monitoring (MRM) transition m/z 488.25/329.12 on positive mode. On a Waters Symmetry Shield C18 column, the analyte was separated using acetonitrile and deionised water with formic acid within 6 min at 0.7 mL/min. Linearity (R2 ≥ 0.99) was observed across 0.195–100 ng/mL concentration range using linear least-squares regression. 2. Blood-to-plasma ratio and plasma protein drug binding (%) in mice and human was assessed and found to be less than 1 and >83%, respectively. Absolute bioavailability (%F) of S019-0385 in female Swiss mice was exhibited to be 6.90%. Percent dose excreted S019-0385 in unchanged form through urine and faecal was found to be less than 2% and 0.5%, respectively. 3. Following oral administration at 5 mg/kg, the concentration of S019-0385 in tissue distribution was found to be in the order of C small intestine > C bone > C lung > C spleen > C kidney > C liver > C heart > C brain.