Multivariate data analysis in pharmaceutics: A tutorial review

Polysorbate stability: Effects of packaging materials, buffers, counterions, and pH

Polysorbates, widely used excipients in drug formulations, present a stability challenge due to complex degradation processes. This study investigates the hydrolysis of polysorbate (PS) under temperature stress (50 °C), focusing on the impact of primary packaging materials (glass vs. plastic vials), buffers (histidine and acetic acid), counterions (chloride vs. malate), and pH (4-7). Our findings reveal that leachables from plastic vials inhibit PS degradation in both histidine and acetic acid buffers. Kinetic parameters derived from sigmoidal fitting suggest distinct degradation mechanisms for each buffer. Furthermore, the malate counterion with histidine displays inhibitory effects on PS hydrolysis. Principal component analysis was employed to identify key factors. These results highlight the critical role of excipients and packaging in PS stability, providing valuable insights for biopharmaceutical formulation development and a deeper understanding of PS degradation complexities.

Quantification of Lanthanides on a PMMA Microfluidic Device with Three Optical Pathlengths Using PCR of UV-Visible, NIR, and Raman Spectroscopy

Microfluidic devices (MFDs) offer customizable, low-cost, and low-waste platforms for performing chemical analyses. Optical spectroscopy techniques provide nondestructive monitoring of small sample volumes within microfluidic channels. Optical spectroscopy can probe speciation, oxidation state, and concentration of analytes as well as detect counterions and provide information about matrix composition. Here, ultraviolet-visible (UV-vis) absorbance, near-infrared (NIR) absorbance, and Raman spectroscopy are utilized on a custom poly(methyl methacrylate) (PMMA) MFD for the detection of three lanthanide nitrates in solution. Absorbance spectroscopies are conducted across three pathlengths using three portions of a contiguous channel within the MFD. Univariate and chemometric multivariate modeling, specifically Beer's law regression and principal component regression (PCR), respectively, are utilized to quantify the three lanthanides and the nitrate counterion. Models are composed of spectra from one or multiple pathlengths. Models are also constructed from multiblock spectra composed of UV-vis, NIR, and Raman spectra at one or multiple pathlengths. Root-mean-square errors (RMSE), limit of detection (LOD), and residual predictive deviation (RPD) values are compared for univariate, multivariate, multi-pathlength, and multiblock models. Univariate modeling produces acceptable results for analytes with a simple signal, such as samarium cations, producing an LOD of 5.49 mM. Multivariate and multiblock models produce enhanced quantification for analytes that experience spectral overlap and interfering nonanalyte signals, such as holmium, which had an LOD reduction from 7.21 mM for the univariate model down to 3.96 mM for the multiblock model. Multi-pathlength models are developed that maintain model errors in line with single-pathlength models. Multi-pathlength models have RPDs from 9.18 to 46.4, while incorporating absorbance spectra collected at optical paths of up to 10-fold difference in length.

Associations of accelerometer-estimated free-living daily activity impact intensities with 10-year probability of osteoporotic fractures in adults

PURPOSE

Accelerometers are used to objectively measure physical activity; however, the relationship between accelerometer-based activity parameters and bone health is not well understood. This study examines the association between accelerometer-estimated daily activity impact intensities and future risk estimates of major osteoporotic fractures in a large population-based cohort.

METHODS

Participants were 3165 adults 46 years of age from the Northern Finland Birth Cohort 1966 who agreed to wear a hip-worn accelerometer during all waking hours for 14 consecutive days. Raw accelerometer data were converted to resultant acceleration. Impact magnitude peaks were extracted and divided into 32 intensity bands, and the osteogenic index (OI) was calculated to assess the osteogenic effectiveness of various activities. Additionally, the impact peaks were categorized into three separate impact intensity categories (low, medium, and high). The 10-year probabilities of hip and all major osteoporotic fractures were estimated with FRAX-tool using clinical and questionnaire data in combination with body mass index collected at the age of 46 years. The associations of daily activity impact intensities with 10-year fracture probabilities were examined using three statistical approaches: multiple linear regression, partial correlation, and partial least squares (PLS) regression.

RESULTS

On average, participants' various levels of impact were 8331 (SD = 3478) low; 2032 (1248) medium; and 1295 (1468) high impacts per day. All three statistical approaches found a significant positive association between the daily number of low-intensity impacts and 10-year probability of hip and all major osteoporotic fractures. In contrast, increased number of moderate to very high daily activity impacts was associated with a lower probability of future osteoporotic fractures. A higher OI was also associated with a lower probability of future major osteoporotic fractures.

CONCLUSION

Low-intensity impacts might not be sufficient for reducing fracture risk in middle-aged adults, while high-intensity impacts could be beneficial for preventing major osteoporotic fractures.

Data-Driven Modeling of the Spray Drying Process. Process Monitoring and Prediction of the Particle Size in Pharmaceutical Production

Spray drying is used in the pharmaceutical industry for particle engineering of amorphous solid dispersions (ASDs). The particle size of the spray-dried (SD) powders is one of their key attributes due to its impact on the downstream processes and the drug product's functional properties. Offline and inline laser diffraction methods can be used to estimate the product's particle size; however, the final release of these ASDs is based on offline instruments. This paper presents a novel data-driven modeling approach for predicting the particle size of SD products. The model-based characterization of the process and the product's particle size, as a critical quality attribute, follows the quality by design principles. The resulting model can be used for online process monitoring, reducing the risks of out-of-specifications products and supporting their real-time release. A Tucker3 model is trained to capture and factorize the deterministic variability of the process. Subsequently, a partial least-squares regression model is calibrated to model the impact that variability in the input material properties, the process parameters, and the spray nozzle have on the products' particle size. This strategy has been calibrated and validated using large scale production data for two intermediate drug products under high sparsity of particle size data. Despite the challenges, high accuracy was obtained in predicting the median particle size (dv50) for release. The 99% confidence interval results in an error of maximum 2.5 μm, which is less than 10% of the allowed range of variation.

Assessing Ethical Climate: Adaptation and Psychometric Properties in the Indonesian Context

Background

Despite the importance of understanding ethical climates in Indonesian organizations, a standardized scale for measuring this is lacking. Therefore, this study aims to adapt, validate, and ensure the consistency of the Ethical Climate Questionnaire (ECQ) within Indonesia's cultural context.

Methods

Data were collected from 565 Indonesian individuals aged 18 or older, using an online survey and convenience sampling. To ensure accurate measurements, Beaton's guidelines were followed. Reliability was assessed using Cronbach's alpha and McDonald's omega, while validity was examined through various analyses including content validity index and confirmatory factor analysis.

Results

The Indonesian version of the ECQ measures egoist, benevolent, and principled ethical climates reliably (Cronbach's alpha: egoism=0.809, principle=0.920, benevolence=0.910). Validity analyses confirm the questionnaire's validity. Demographic analysis shows age impacts the principle dimension, while organizational type affects all dimensions.

Conclusion

The Indonesian version of the ECQ demonstrates strong psychometric properties and cross-cultural adaptability, making it a valuable tool for assessing ethical climates among Indonesian individuals aged 18 or older.

A novel strategy for the quality control of carbonized Typhae pollen using colorimeter, liquid chromatography-mass spectrometry, and efficacy evaluation coupled with multivariate statistical analysis

In this study, a novel quality control strategy was proposed, aiming to establish a multivariate specification for the processing step by exploring the correlation between colors, chemical components, and hemostatic effects of the carbonized Typhae pollen (CTP) using multivariate statistical analysis. The CTP samples were stir-fried at different durations. Afterward, the colorimeter and LC-MS techniques were applied to characterize the CTP samples, followed by the determination of bleeding time and clotting time using mice to evaluate their hemostatic effect. Then, principal component analysis, hierarchical cluster analysis, and multi-block partial least squares were used for data analysis on colors, chemical components, and their correlation with the hemostatic effect. Consequently, 13 critical quality attributes (CQAs) of CTP were identified via multivariate statistical analysis-L*, a*, b*, 3,4-dihydroxybenzoic acid, 4-hydroxybenzoic acid, 3-hydroxybenzoic acid, quercetin-3-O-glucoside, azelaic acid, kaempferol-3-O-glucoside, quercetin, naringenin, kaempferol, and isorhamnetin. The multivariate specification method involving the 13 CQAs was developed and visualized in the latent variable space of the partial least squares model, indicating that the proposed method was successfully applied to assess the quality of CTP and the degree of carbonization. Most importantly, this study offers a novel insight into the control of processing for carbonized Chinese herbal medicines.

Implications of confounding from unmodeled interactions between explanatory variables when using latent variable regression models to make inferences

With linear dependency between the explanatory variables, partial least squares (PLS) regression is commonly used for regression analysis. If the response variable correlates to a high degree with the explanatory variables, a model with excellent predictive ability can usually be obtained. Ranking of variable importance is commonly used to interpret the model and sometimes this interpretation guides further experimentation. For instance, when analyzing natural product extracts for bioactivity, an underlying assumption is that the highest ranked compounds represent the best candidates for isolation and further testing. A problem with this approach is that in most cases the number of compounds is larger than the number of samples (and usually much larger) and that the concentrations of the compounds correlate. Furthermore, compounds may interact as synergists or as antagonists. If the modelling process does not account for this possibility, the interpretation can be thoroughly wrong since unmodelled variables that strongly influence the response will give rise to confounding of a first order PLS model and send the experimenter on a wrong track. We show the consequences of this by a practical example from natural product research. Furthermore, we show that by including the possibility of interactions between explanatory variables, visualization using a selectivity ratio plot may provide model interpretation that can be used to make inferences.

Electronic waste analysis using laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF): Critical evaluation of data fusion for the determination of Al, Cu and Fe

BACKGROUND

Electronic waste (e-waste) proliferation and its implications underscore the imperative for advanced analytical methods to mitigate its environmental impact. It is estimated that e-waste production stands at a staggering 20-50 million tons yearly, of which merely 20-25% undergo formal recycling. The e-waste samples evaluated contain computers, laptops, smartphones, and tablets.

RESULTS

Forty-one samples were processed, involving the disassembly and separation of components. Subsequently, two analytical techniques, laser-induced breakdown spectroscopy (LIBS) and energy dispersive X-ray fluorescence (ED-XRF), were applied to quantify aluminum (Al), copper (Cu), and iron (Fe) in the e-waste samples. The samples were then analyzed after acid mineralization with 50% v v-1 aqua regia in a digester block and finally by ICP OES. A solid residue composed of Si and Ti was observed after the digestion of the samples. Multivariate calibration strategies such as partial least-squares regression (PLS), principal component regression (PCR), maximum likelihood principal component regression (MLPCR), and error covariance penalized regression (ECPR) were used for calibration. Finally, the figures of merit were calculated to verify the most suitable models. The results revealed robust models with notable sensitivity, varying from 8.98 to 35.04 Signal (a.u.)(% w w-1) -1, low Limits of Detection (LoD) within the range of 0.001-0.2 % w w-1, and remarkable relative errors ranging from 2% to 33%, particularly for Cu and Fe.

SIGNIFICANCE

Notably, the models for Al faced inherent challenges, thus highlighting the complexities associated with its quantification in e-waste samples. In conclusion, this research represents an important step toward a more sustainable and efficient future for electronic waste recycling, signifying its relevance to global environmental welfare and resource conservation.

Current Applications of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in Pharmaceutical Analysis: Review

The present review encompasses various applications of multivariate curve resolution- alternating least squares (MCR-ALS) as a promising data handling, which is issued by analytical techniques in pharmaceutics. It involves different sections starting from a concise theory of MCR-ALS and four detailed applications in drugs analysis. Dissolution, stability, polymorphism, and quantification are the main four detailed applications. The data generated by analytical techniques associated with MCR-ALS deals accurately with different challenges compared to other chemometric tools. For each reviewed purpose, it was explained how MCR-ALS was applied and detailed information was given. Different approaches were introduced to overcome challenges that limit the use of MCR-ALS efficiently in pharmaceutical mixture were also discussed.

Mitigating the impact of gelatin capsule variability on detection of substandard and falsified pharmaceuticals with near-IR spectroscopy

Portable NIR spectrometers are effective in detecting authentic pharmaceutical products in intact capsule formulations, which can be used to screen for substandard or falsified versions of those authentic products. However, the chemometric models are trained on libraries of authentic products, and are generally unreliable for detection of quality problems in products from outside their training set, even for products that are nominally the same active pharmaceutical ingredient and same dosage as products in the training set. As part of our research directed at developing better non-brand-specific strategies for pharmaceutical screening, we investigated the impact of capsule composition on NIR modeling. We found that capsule features like gelatin type, color, or thickness, give rise to a similar amount of variance in the NIR spectra as the type of API stored within the capsules. Our results highlight the efficacy of orthogonal projection to latent structures in mitigating the impacts of different types of capsules on the accuracy of NIR chemometric models for classification and regression analysis of lab-made samples. The models showed good performance for classification of field-collected doxycycline capsules as good or bad quality when an NIR-based % w/w metric was used, identifying five samples that were adulterated with talc. However, the % w/w was systematically underestimated, so when evaluating the capsules based on their absolute API content according to the monograph standard, the classification accuracy decreased from 100% to 70%. The underestimation was attributed to an unforeseen variability in the quantities and types of excipients present in the capsules.

Multivariate pattern analysis: a method and software to reveal, quantify, and visualize predictive association patterns in multicollinear data

BACKGROUND

Strongly multicollinear covariates, such as those typically represented in metabolomics applications, represent a challenge for multivariate regression analysis. These challenges are commonly circumvented by reducing the number of covariates to a subset of linearly independent variables, but this strategy may lead to loss of resolution and thus produce models with poorer interpretative potential. The aim of this work was to implement and illustrate a method, multivariate pattern analysis (MVPA), which can handle multivariate covariates without compromising resolution or model quality.

RESULTS

MVPA has been implemented in an open-source R package of the same name, mvpa. To facilitate the usage and interpretation of complex association patterns, mvpa has also been integrated into an R shiny app, mvpaShiny, which can be accessed on www.mvpashiny.org . MVPA utilizes a general projection algorithm that embraces a diversity of possible models. The method handles multicollinear and even linear dependent covariates. MVPA separates the variance in the data into orthogonal parts within the frame of a single joint model: one part describing the relations between covariates, outcome, and explanatory variables and another part describing the "net" predictive association pattern between outcome and explanatory variables. These patterns are visualized and interpreted in variance plots and plots for pattern analysis and ranking according to variable importance. Adjustment for a linear dependent covariate is performed in three steps. First, partial least squares regression with repeated Monte Carlo resampling is used to determine the number of predictive PLS components for a model relating the covariate to the outcome. Second, postprocessing of this PLS model by target projection provided a single component expressing the predictive association pattern between the outcome and the covariate. Third, the outcome and the explanatory variables were adjusted for the covariate by using the target score in the projection algorithm to obtain "net" data. We illustrate the main features of MVPA by investigating the partial mediation of a linearly dependent metabolomics descriptor on the association pattern between a measure of insulin resistance and lifestyle-related factors.

CONCLUSIONS

Our method and implementation in R extend the range of possible analyses and visualizations that can be performed for complex multivariate data structures. The R packages are available on github.com/liningtonlab/mvpa and github.com/liningtonlab/mvpaShiny.

Quantification of ciprofloxacin in pharmaceutical products from various brands using FT-NIR: A comparative investigation of PLS and MCR-ALS

This study aims to quantify ciprofloxacin in commercial tablets with varying excipient compositions using Fourier Transform Near-Infrared Spectroscopy (FT-NIR) and chemometric models: Partial Least Squares (PLS) and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). Matrix variation, arising from differences in excipient compositions among the tablets, can impact quantification accuracy. We discuss this phenomenon, emphasizing potential issues introduced by varying certain excipients and its importance in reliable ciprofloxacin quantification. We evaluated the performance of PLS and MCR-ALS models independently on two sets of tablets, each containing the same drug substance but different excipients. The statistical results revealed promising results with PLS prediction error of 0.38% w/w of the first set and 0.47% w/w of the second set, while MCR-ALS achieved prediction errors of 0.67% w/w of the first set and 1.76% w/w of the second set. To address the challenge of matrix variation, we developed single models for PLS and MCR-ALS using a dataset combining both first and second sets. The PLS single model demonstrated a prediction error of 4.3% w/w and a relative error of 6.41% w/w, while the MCR-ALS single model showed a prediction error of 1.88% w/w and a relative error of 1.29% w/w. We then assessed the performance of the single PLS and MCR-ALS models developed based on the combination of the first and the second set in quantifying ciprofloxacin in various commercial tablet brands containing new excipients. The PLS model achieved a prediction error ranging between 6.2% w/w and 8.39% w/w, with relative errors varied between 8.53% w/w and 12.82% w/w. On the other hand, the MCR-ALS model had a prediction error between 1.11% w/w and 2.66% w/w, and the relative errors ranging from 0.8% to 1.74% w/w.

Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability

Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the model's performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications.

Association between biomarkers of redox status and cytokines with different patterns of habitual physical activity in eutrophic and overweight/obese preschoolers: multivariate analysis of a cross-sectional study

BACKGROUND

Although it is well known that obesity is frequently associated with reduced levels of habitual physical activity (HPA), which contributes to determining severe oxidative stress and inflammatory state, this association is however unknown in preschoolers so far. This study aimed to investigate the association between biomarkers of redox status and cytokines with different patterns of HPA according to the adiposity of preschoolers.

METHODS

A cross-sectional study was conducted in 50 preschoolers (25 overweight/obese, OW/OB and 25 eutrophic, EU), matched for age, sex, economic level, and maternal education. Total antioxidant capacity (TAC), superoxide dismutase (SOD) and catalase (CAT) activities, substances reactive to thiobarbituric acid (TBARS), soluble tumor necrosis factor receptors (sTNFRs), and leptin levels were evaluated. HPA levels were evaluated by accelerometry (ActiGraph GT9X accelerometer). Correlation, multiple linear regression, and partial least squares regression analysis were used to determine the association between redox status biomarkers and cytokines with different patterns of HPA (HPA level, bouts of moderate to vigorous physical activity [MVPA], and multivariate pattern of HPA) in EU and OW/OB preschoolers.

RESULTS

OW/OB preschoolers had lower CAT activity, higher levels of TAC, TBARS, and cytokines, and similar levels of HPA to EU preschoolers. In EU preschoolers, SOD activity exhibited a stronger negative association with moderate intensity ranges of HPA (R2 = 0.18), and negative correlation with sTNFRs (r = -0.40 to -0.46). TBARS had a stronger positive association with ranges of light intensity in the multivariate pattern of HPA (R2 = 0.10). In OW/OB preschoolers, the HPA multivariate associative pattern was predominantly from vigorous intensity ranges. Thus, SOD activity had a positive association with the multivariate pattern of HPA (R2 = 0.38) and MVPA bouts (β [95% CI] = 0.457 [0.0026. 0.0576]). TAC had a negative association with the multivariate pattern of HPA (R2 = 0.38) and MVPA bouts (β [95% CI] = -0.718 [-0.0025. -0.0003]). Additionally, leptin levels were lower in OW/OB preschoolers engaged in vigorous physical activity (VPA) (8000-9999 counts/min) for longer periods of time.

CONCLUSION

The results of this study indicate that OW/OB preschoolers have higher levels of oxidative stress biomarkers and pro-inflammatory cytokines compared to EU preschoolers. Moreover, VPA may exert antioxidative and anti-inflammatory effects in OW/OB preschoolers.

Soft sensor based on Raman spectroscopy for the in-line monitoring of metabolites and polymer quality in the biomanufacturing of polyhydroxyalkanoates

Polyhydroxyalkanoates (PHA) are among the most promising bio-based alternatives to conventional petroleum-based plastics. These biodegradable polyesters can in fact be produced by fermentation from bacteria like Cupriavidus necator, thus reducing the environmental footprint of the manufacturing process. However, ensuring consistent product quality attributes is a major challenge of biomanufacturing. To address this issue, the implementation of real-time monitoring tools is essential to increase process understanding, enable a prompt response to possible process deviations and realize on-line process optimization. In this work, a soft sensor based on in situ Raman spectroscopy was developed and applied to the in-line monitoring of PHA biomanufacturing. This strategy allows the collection of quantitative information directly from the culture broth, without the need for sampling, and at high frequency. In fact, through an optimized multivariate data analysis pipeline, this soft sensor allows monitoring cell dry weight, as well as carbon and nitrogen source concentrations with root mean squared errors (RMSE) equal to 3.71, 7 and 0.03 g/L, respectively. In addition, this tool allows the in-line monitoring of intracellular PHA accumulation, with an RMSE of 14 gPHA/gCells. For the first time, also the number and weight average molecular weights of the polymer produced could be monitored, with RMSE of 8.7E4 and 11.6E4 g/mol, respectively. Overall, this work demonstrates the potential of Raman spectroscopy in the in-line monitoring of biotechnology processes, leading to the simultaneous measurement of several process variables in real time without the need of sampling and labor-intensive sample preparations.

Identifying possible biomarkers of lower urinary tract symptoms using metabolomics and partial least square regression

INTRODUCTION

The objective of this study was to explore potential novel biomarkers for moderate to severe lower urinary tract symptoms (LUTS) using a metabolomics-based approach, and statistical methods with significant different features than previous reported.

MATERIALS AND METHODS

The patients and the controls were selected to participate in the study according to inclusion/exclusion criteria (n = 82). We recorded the following variables: International prostatic symptom score (IPSS), prostate volume, comorbidities, PSA, height, weight, triglycerides, glycemia, HDL cholesterol, and blood pressure. The study of 41 plasma metabolites was done using the nuclear magnetic resonance spectroscopy technique. First, the correlations between the metabolites and the IPSS were done using Pearson. Second, significant biomarkers of LUTS from metabolites were further analysed using a multiple linear regression model. Finally, we validated the findings using partial least square regression (PLS).

RESULTS

Small to moderate correlations were found between IPSS and methionine (-0.301), threonine (-0.320), lactic acid (0.294), pyruvic acid (0.207) and 2-aminobutyric-acid (0.229). The multiple linear regression model revealed that only threonine (p = 0.022) was significantly associated with IPSS, whereas methionine (p = 0.103), lactic acid (p = 0.093), pyruvic acid (p = 0.847) and 2-aminobutyric-acid (p = 0.244) lost their significance. However, all metabolites lost their significance in the PLS model.

CONCLUSION

When using the robust PLS-regression method, none of the metabolites in our analysis had a significant association with lower urinary tract symptoms. This highlights the importance of using appropriate statistical methods when exploring new biomarkers in urology.

Using diffusion of innovation framework with attitudinal factor to predict the future of mobility in the Indian market

Rising temperatures across the world is posing a serious challenge to the longevity of earth's natural cycle. Use of private and conventional cars is growing year by year as standard of living continues to increase and affordability remaining no more of an issue for a significant proportion of the population. However, there has been a growing voice from segments of the society for a behavioural shift towards more sustainable modes of transport like usage of electric vehicles (EVs), mobility sharing and rental services and public transportation powered by electricity. One of the biggest importers of foreign crude oil, India, is reeling under immense pressure to reduce its carbon dioxide emissions and thereby contributing to environment protection. In India, the statistics or stock of EVs continues to remain poor when compared to its counterparts like China. Hence, this study is conducted to predict the influence of factors on purchase intention of consumers with respect to EVs in the Indian context. A questionnaire was prepared and circulated in New Delhi region to gather the responses people hold about this new technology based on the diffusion of innovation framework. The data of 225 respondents was analysed with the help of SPSS and AMOS software. Structural equation modelling technique was applied to test the hypotheses of this research. The results indicate that attitude has the most influential impact on purchase intention followed by related advantages but consumers also believe EVs to be complex in terms of their usability, limited availability and the absence of required public charging infrastructure. The mediating role of attitude between determinants of diffusion and purchase intention was also established except in the case of complexity. This study is contemporary and adds to the limited literature available on EV adoption in the Indian market by integrating attitudinal factor within the DOI framework. It also provides suitable insights to further the development of EVs in the domestic and global market.

Multivariate physical activity association patterns for fundamental motor skills and physical fitness in preschool children aged 3-5 years

Physical activity (PA) is important for children's development of fundamental motor skills (FMS) and physical fitness (FIT) but evidence regarding which intensities are associated with these outcomes in early childhood is limited. The aim of this study was to determine the cross-sectional multivariate PA intensity signatures associated with FMS and FIT in children aged 3-5 years. We used a sample of 952 Norwegian preschoolers (4.3 years, 51% boys) who provided data on PA (ActiGraph GT3X+), at least one FMS (locomotor, object control and/or balance skills) or FIT (speed agility, standing long jump, and/or handgrip strength) outcome, body mass index, and socioeconomic status in 2019-2020. We created 17 PA intensity variables (0-99 to ≥15000 counts per minute) from the vertical axis and used multivariate pattern analysis for analyses. The PA intensity spectrum (including sedentary time) was significantly associated with all outcomes. Associations for PA intensities were positive (negative for sedentary time), strongest for moderate and vigorous intensities, and were significant across sex and age groups. Our findings show that the PA intensity spectrum is associated with FMS and FIT in young children and that promotion of PA, in particular moderate- and vigorous-intensity activity, from an early age benefits children's physical development.

Quality by Design - Spray drying of Ciprofloxacin-Quercetin Fixed-Dose Combination Intended for Inhalation

Spray drying is a well-suited technique for producing fixed-dose drug combinations. There has been a growing interest in utilizing spray drying to engineer carrier-free inhalable drug particles. The aim of this study was to understand and optimise the spray drying process of a ciprofloxacin-quercetin fixed dose combination intended for pulmonary administration. A 2^4-1 fractional factorial design and multivariate data analysis was used to identify important process parameters and investigate correlations with particle characteristics. The independent variables were solute concentration along with the processing parameters: solution flow rate, atomizing air flow rate and inlet temperature. The dependent variables included particle size distribution, yield and residual moisture content (RMC). Correlations between dependent and independent variables were further investigated via principal component analysis. Overall, solution flow rate, atomizing air flow rate and inlet temperature were found to affect the particle size D_(v,50) and D_(v,90) while the solute concentration and the atomizing air flow rate mainly affected the span. The inlet temperature was the most important parameter affecting the RMC and the yield. The formulation with optimized independent variables had a D_(v,50) and span values of 2.42 µm and 1.81 with excellent process yield > 70% and low RMC i.e. 3.4%. The optimized formulation was further investigated for its in vitro aerosolization performance using next generation impactor (NGI); it exhibited high emitted dose (ED > 80%) and fine particle fractions (FPF > 70%) for both drugs.

Kvalheim O, Cech NB. Interaction Metabolomics to Discover Synergists in Natural Product Mixtures

Mass spectrometry metabolomics has become increasingly popular as an integral aspect of studies to identify active compounds from natural product mixtures. Classical metabolomics data analysis approaches do not consider the possibility that interactions (such as synergy) could occur between mixture components. With this study, we developed "interaction metabolomics" to overcome this limitation. The innovation of interaction metabolomics is the inclusion of compound interaction terms (CITs), which are calculated as the product of the intensities of each pair of features (detected ions) in the data matrix. Herein, we tested the utility of interaction metabolomics by spiking known concentrations of an antimicrobial compound (berberine) and a synergist (piperine) into a set of inactive matrices. We measured the antimicrobial activity for each of the resulting mixtures against Staphylococcus aureus and analyzed the mixtures with liquid chromatography coupled to high-resolution mass spectrometry. When the data set was processed without CITs (classical metabolomics), statistical analysis yielded a pattern of false positives. However, interaction metabolomics correctly identified berberine and piperine as the compounds responsible for the synergistic activity. To further validate the interaction metabolomics approach, we prepared mixtures from extracts of goldenseal (Hydrastis canadensis) and habañero pepper (Capsicum chinense) and correctly correlated synergistic activity of these mixtures to the combined action of berberine and several capsaicinoids. Our results demonstrate the utility of a conceptually new approach for identifying synergists in mixtures that may be useful for applications in natural products research and other research areas that require comprehensive mixture analysis.

Modulation of in Vitro SARS-CoV-2 Infection by Stephania tetrandra and Its Alkaloid Constituents

Botanical natural products have been widely consumed for their purported usefulness against COVID-19. Here, six botanical species from multiple sources and 173 isolated natural product compounds were screened for blockade of wild-type (WT) SARS-CoV-2 infection in human 293T epithelial cells overexpressing ACE-2 and TMPRSS2 protease (293TAT). Antiviral activity was demonstrated by an extract from Stephania tetrandra. Extract fractionation, liquid chromatography-mass spectrometry (LC-MS), antiviral assays, and computational analyses revealed that the alkaloid fraction and purified alkaloids tetrandrine, fangchinoline, and cepharanthine inhibited WT SARS-CoV-2 infection. The alkaloids and alkaloid fraction also inhibited the delta variant of concern but not WT SARS-CoV-2 in VeroAT cells. Membrane permeability assays demonstrate that the alkaloids are biologically available, although fangchinoline showed lower permeability than tetrandrine. At high concentrations, the extract, alkaloid fractions, and pure alkaloids induced phospholipidosis in 293TAT cells and less so in VeroAT cells. Gene expression profiling during virus infection suggested that alkaloid fraction and tetrandrine displayed similar effects on cellular gene expression and pathways, while fangchinoline showed distinct effects on cells. Our study demonstrates a multifaceted approach to systematically investigate the diverse activities conferred by complex botanical mixtures, their cell-context specificity, and their pleiotropic effects on biological systems.

Associations between educators' and children's physical activity and sedentary time in Norwegian preschools: A cross-sectional study

Preschool educators may be important role models influencing children's physical activity (PA) behaviours. The main aim of this paper was to examine the relationship between educators' and children's PA and sedentary time (SED) by including 1230 children (mean age 4.8 yrs, 48% girls) and 422 educators (mean age 42.4 yrs, 90% women) from 68 preschools in Western Norway. PA and SED were measured over 10 preschool days using hip worn ActiGraph GT3×+ accelerometers. Associations between child and educator PA and SED during preschool hours, determined by multivariate pattern analyses, provided explained variances (R²) = 2.8-5.2%. Levels of educator moderate PA were positively related to child PA (all intensities) and educator vigorous PA were positively related to child vigorous PA, while educator light PA was negatively associated with PA and positively associated with child SED. Educator SED were positively associated with child vigorous PA and negatively associated to child SED. Association patterns were similar for boys and girls, while educator moderate and vigorous PA were more strongly related to the younger vs. the older children's PA. The positive relationships between educators' moderate and vigorous PA and children's PA found herein suggest educators' PA behaviours should be addressed in future interventions.

The influence of reliability and variability of objectively measured physical activity on associations with lower body muscle strength in young children

It is not known how extended or multiple monitoring periods affect associations between accelerometer-measured physical activity and outcomes. The aim of this study was to examine how accelerometer monitoring length influenced cross-sectional and prospective associations for physical activity with lower body muscle strength in young children. 176 Norwegian 2-6-year-old children had 3 valid 14-day periods of accelerometer monitoring (ActiGraph GT3×+) between September 2015 and May 2016 (baseline) as well as baseline and 4-year follow-up measurements of standing long jump. We analysed physical activity using a descriptor with 4 intensities using 6 different monitoring lengths both within and across monitoring periods (1 day, 3 days, 1 week, 2 weeks, 3 weeks, 6 weeks) and determined associations with lower body muscle strength using multivariate pattern analysis. We found that the strength of cross-sectional associations with lower body muscle strength improved for longer monitoring periods (explained variances = 7.7%, 9.8%, 11.8%, 15.8%, 18.4% and 22.9% for 1 day, 3 days, 1 week, 2 weeks, 3 weeks and 6 weeks of measurement). Longitudinal associations were weaker and less clear. Our findings suggest that multiple extended physical activity monitoring periods improve the validity of the study findings regarding associations between physical activity and relevant outcomes.

Configurational analysis of conditions influencing customers' channel switching intention in omnichannel retailing: a fuzzy-set analysis

This research examined the interplay among personal factors, namely channel lock-in, cross-channel synergy, attribute-based decision making (ADM); environmental factors, namely others' past switching behaviour (OPB), pressure to switch from others (PSO); and behavioural factors, namely perceived self-efficacy and perception on facilitating conditions as antecedents to customers' channel switching intention in an omnichannel context. Drawing on the complexity theory and set theory, we applied configurational analysis using the fuzzy-set Qualitative Comparative Analysis. The result of the analysis indicated two (2) sufficient configurations that led to an intention to switch channels. Both configurations contained ADM, OPB, and PSO conditions that highlight the importance of personal factors and environmental factors needed for the presence of an intention to switch channels. However, no sufficient configurations were obtained that indicate an absence of intention to switch channels. This study challenges theoretical underpinnings by demonstrating that omnichannel channel-switching behaviours can be explained from a configurational perspective. The configurations produced by this study can serve as a basis for researchers who plan to conduct asymmetric modelling of customers' channel-switching behaviour in an omnichannel context. Finally, this paper suggests omnichannel retail strategies and management as informed by these configurations.

How is the problematic smartphone use affected by social support? A research model supported by the mediation of Ikigai

Social support and ikigai (making life worthwhile) have positive effects on problematic smartphone use and are seen to be related to each other. However, what mediates these relationships has not been adequately analyzed. This study aims to examine how the effect between social support and problematic smartphone use occurs by suggesting the mediator role of ikigai. The study was designed as quantitative and cross-sectional, and 1189 university participants aged 18 and over were reached online. The multidimensional scale of perceived social support, ikigai-9 scale, smartphone application-based addiction scale and sociodemographic information form were used as data collection tools in the study. The data obtained were carried out using SPSS 24 and Amos 25 statistical programs. Established hypotheses were tested with correlation, multiple regression and mediation analyzes. The results confirmed that social support was positively associated with ikigai, and ikigai was negatively associated with problematic smartphone use. In addition, interaction analyzes found that ikigai had a mediating effect. These findings show the importance of implementing specific applications based on the meaning and purpose of life (ikigai), especially for vulnerable groups, to minimize the problems that may arise with the excessive use of smartphones.

Variable importance for projection (VIP) scores for analyzing the contribution of risk factors in severe adverse events to Xiyanping injection

BACKGROUND

Age and herb-drug combination are risk factors for the severity of Xiyanping injection (XYP) associated adverse events (AEs).

OBJECTIVE

To analyze risk factors contributing to the severity of XYP's AEs using a variable importance for projection (VIP) method.

METHODS

AEs related to the use of XYP were extracted from the China National Adverse Drug Reaction Monitoring Information System (2004-2017) and classified as general or severe. Data were analyzed with respect to age and 12 herb-drug combinations, including ribavirin (RB), ceftriaxone, penicillin sodium, ambroxol hydrochloride (AH), clindamycin, cefoxitin sodium, azithromycin (AZM), ceftazidime, amoxicillin sodium/potassium clavulanate, levofloxacin hydrochloride, sodium cefazolin pentahydrate, and acyclovir according to VIP scores and correlation coefficient (Coeff).

RESULTS

A total of 21,714 AEs (general 20,660; severe 1054) related to XYP combinations were included. Using XYP alone tended to produce general AEs (All VIP = 3.124; 1.329; 1.857; 2.169; 2.400, Coeff < 0). For all set, 0-6 years old patients tend to have general AEs (VIP = 2.425, Coeff < 0), while those > 41 years old patients tend to have severe AEs (VIP = 1.180; 2.323, Coeff > 0). For 0-40 years old patients, XYP-RB combination had a greater impact on the severity of AEs (VIP = 1.158; 1.360; 1.147, Coeff > 0). For 7-17 years old patients, XYP-AZM combination tended to produce general AEs (VIP = 1.502, Coeff < 0). In individuals > 65 years old, XYP-AH combination tended to result in severe AEs (VIP = 1.232, Coeff > 0).

CONCLUSIONS

VIP method was expected to effectively analyze risk factors in affecting the severity of AEs and control AEs more effectively. Age is the key factor contributing to the severity of AEs, and there are different influence directions. It is recommended that clinicians pay closer attention to the metabolic characteristics of different age groups. It is safe to use XYP alone and strictly implementing standardized operations such as medication interval and flushing will avoid undesired AEs.

Factors influencing women's entrepreneurial success: A multi-analytical approach

Women entrepreneurs are significant contributors to the economic development of any country and their role becomes more vital in improving the economic condition of developing countries. This highlights the important role of women-owned small and medium enterprises (SMEs) and their entrepreneurial success. Therefore, the current study extends the entrepreneurship literature by examining the effects of factors like personality traits (PT), motivation and commitment (MC), availability of financial resources (AFR), and government support (GS) on entrepreneurial success (ES) of women-owned SMEs. Using a purposive sampling technique data from 255 women-owned SMEs were collected. A multi-analytical approach was employed to analyze the data. The Structural equation modeling (SEM) results indicated that PT, MC, AFR, and GS have a direct effect of ES whereas MC also mediated the link between PT and ES, and the results reveal that in presence of MC the effects of PT on ES become more significant. SEM results revealed that PT and AFR are the most important factors related to entrepreneurial success. On the contrary, ANN analysis revealed that "motivation and commitment" is the most influencing factor. These findings can guide business practitioners and policymakers in the envisioned strategy formulation to encourage women entrepreneurs who can contribute to their country's sustainable economic growth.

Machine Learning-Enabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data

An increasingly large dataset of pharmaceutics disciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al.. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robust data on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset ( http://www.models.life.ku.dk/Tablets ) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. HIGHLIGHTS: • We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies • A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets • Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance • The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation • Model extension from lab-scale to pilot-scale successfully demonstrated.

Prospective Association Patterns for the Physical Activity Intensity Spectrum With Body Mass Index and Lower Body Muscle Strength in Norwegian Children Aged 3-9 Years

BACKGROUND

Limited evidence exists regarding prospective associations for physical activity (PA) and sedentary time (SED) with body mass index (BMI) and muscle strength in young children. We aimed to determine prospective associations for PA and SED with change in BMI and standing long jump over 2 and 4 years in children aged 3-5 years at baseline.

METHODS

A sample of 262 Norwegian children (50% girls) was followed from 2015 to 2017 and/or 2019. PA and SED (hip-worn ActiGraph GT3X+) were measured at baseline and BMI and standing long jump at baseline and at follow-ups. Multivariate pattern analysis was used to determine prospective associations between the triaxial PA intensity spectrum (0-99 to ≥15,000 counts per minute) and the change in outcomes.

RESULTS

We found significant prospective associations between the PA intensity spectrum and standing long jump at 2- (explained variance = 5.8%-7.7%) and 4-year (explained variance = 4.8%-5.6%) follow-ups. Associations were negative for SED and positive for all PA intensities. We found no associations between PA/SED and BMI.

CONCLUSIONS

Our findings suggest that PA and SED can predict future lower body muscle strength but not BMI in early childhood.

ex vivo-in vivo comparison of drug penetration analysis by confocal Raman microspectroscopy and tape stripping

When it comes to skin penetration analysis of a topically applied formulation, the number of suitable methods is limited, and they often lack in spatial resolution. In vivo studies are pivotal, especially in the approval of a new product, but high costs and ethical difficulties are limiting factors. For that reason, good ex vivo models for testing skin penetration are crucial. In this study, caffeine was used as a hydrophilic model drug, applied as a 2% (w/w) hydrogel, to compare different techniques for skin penetration analysis. Confocal Raman microspectroscopy (CRM) and tape stripping with subsequent HPLC analysis were used to quantify caffeine. Experiments were performed ex vivo and in vivo. Furthermore, the effect of 5% (w/w) 1,2-pentanediol on caffeine skin penetration was tested, to compare those methods regarding their effectiveness in detecting differences between both formulations.

Cross-cultural factors influencing the adoption of virtual reality for practical learning

Education is one area that was significantly affected by the COVID-19 pandemic with much of the education being transferred online. Many subjects that require hands-on experimental experience suffer when taught online. Education is also one area that many believe can benefit from the advances in virtual reality (VR) technology, particularly for remote, online learning. Furthermore, because the technology shows overall good results with hands-on experiential learning education, one possible way to overcome online education barriers is with the use of VR applications. Given that VR has yet to make significant inroads in education, it is essential to understand what factors will influence this technology's adoption and acceptance. In this work, we explore factors influencing the adoption of VR for hands-on practical learning around the world based on the Unified Theory of Acceptance and Use of Technology and three additional constructs. We also performed a cross-cultural analysis to examine the model fit for developed and developing countries and regions. Moreover, through open-ended questions, we gauge the overall feeling people in these countries have regarding VR for practical learning and how it compares with regular online learning.

Incentivizing Relationship Investment for Project Performance Improvement

Collaboration between developers and contractors is pivotal to the efficient completion of construction projects. Relational contracting practice has been used to promote working partnerships. Construction incentivization can be a valuable contractual tool to build relationships. This study proposes that incentivization can serve as an instrumental means to forge interorganizational relationships and corresponding project performance improvements. With data collected from experienced construction professionals, the hypotheses are empirically supported by structural equation modeling. It is therefore concluded that in addition to its conventional uses, incentivization can be innovatively designed to develop and manage interorganizational relationships and engender project performance improvement.

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.

The multivariate physical activity signatures associated with body mass index and waist-to-height ratio in 3-5-year-old Norwegian children

The evidence regarding associations between intensity-specific physical activity and adiposity in young children is inconclusive. The aim of this study was to determine the multivariate physical activity intensity signatures associated with body mass index and waist-to-height ratio in children aged 3-5 years. A sample of 1003 Norwegian preschool children (mean age 4.3 years, 51 % boys) from the Active Learning Norwegian Preschool(er)s study provided data on physical activity (ActiGraph GT3X+), body mass index, and waist-to-height ratio during 2019-2020. Multivariate pattern analysis was used to determine associations between the triaxial intensity spectrum (0-99 to ≥15,000 counts per minute) and the outcomes. We found significant associations for physical activity with body mass index and waist-to-height ratio (explained variances = 10.8 and 11.5 %, respectively). For the vertical axis, associations were negative for time spent sedentary (0-99 counts per minute) and positive for time spent in lower intensities (100-2999 counts per minute) for both outcomes, whereas associations for vigorous intensities (≥4000 counts per minute) differed for body mass index (no associations) and waist-to-height ratio (negative associations). Association patterns for body mass index and waist-to-height ratio were rather similar, but the stronger associations with vigorous physical activity intensities for waist-to-height ratio suggest that waist-to-height ratio might better capture adiposity resulting from inactivity than body mass index.

Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning

Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.

Performance Evaluation of Focal Plane Array (FPA)-FTIR and Synchrotron Radiation (SR)-FTIR Microspectroscopy to Classify Rice Components

The development of biochemical analysis techniques to study heterogeneous biological samples is increasing. These techniques include synchrotron radiation Fourier transform infrared (SR-FTIR) microspectroscopy. This method has been applied to analyze biological tissue with multivariate statistical analysis to classify the components revealed by the spectral data. This study aims to compare the efficiencies of SR-FTIR microspectroscopy and focal plane array (FPA)-FTIR microspectroscopy when classifying rice tissue components. Spectral data were acquired for mapping the same sample areas from both techniques. Principal component analysis and cluster imaging were used to investigate the biochemical variations of the tissue types. The classification was based on the functional groups of pectin, protein, and polysaccharide. Four layers from SR-FTIR microspectroscopy including pericarp, aleurone layer, sub-aleurone layer, and endosperm were classified using cluster imaging, while FPA-FTIR microspectroscopy could classify only three layers of pericarp, aleurone layer, and endosperm. Moreover, SR-FTIR microspectroscopy increased the image contrast of the biochemical distribution in rice tissue more efficiently than FPA-FTIR microspectroscopy. We have demonstrated the capability of the high-resolution synchrotron technique and its ability to clarify small structures in rice tissue. The use of this technique might increase in future studies of tissue characterization.

An approach to assess and adjust for the influence of multicollinear covariates on metabolomics association patterns-applied to a study of the associations between a comprehensive lipoprotein profile and the homeostatic model assessment of insulin resistance

INTRODUCTION

Comprehensive lipoprotein profiling using proton nuclear magnetic resonance (NMR) spectroscopy of serum represents an alternative to the homeostatic model assessment of insulin resistance (HOMA-IR). Both adiposity and physical (in)activity associate to insulin resistance, but quantification of the influence of these two lifestyle related factors on the association pattern of HOMA-IR to lipoproteins suffers from lack of appropriate methods to handle multicollinear covariates.

OBJECTIVES

We aimed at (i) developing an approach for assessment and adjustment of the influence of multicollinear and even linear dependent covariates on regression models, and (ii) to use this approach to examine the influence of adiposity and physical activity on the association pattern between HOMA-IR and the lipoprotein profile.

METHODS

For 841 children, lipoprotein profiles were obtained from serum proton NMR and physical activity (PA) intensity profiles from accelerometry. Adiposity was measured as body mass index, the ratio of waist circumference to height, and skinfold thickness. Target projections were used to assess and isolate the influence of adiposity and PA on the association pattern of HOMA-IR to the lipoproteins.

RESULTS

Adiposity explained just over 50% of the association pattern of HOMA-IR to the lipoproteins with strongest influence on high-density lipoprotein features. The influence of PA was mainly attributed to a strong inverse association between adiposity and moderate and high-intensity physical activity.

CONCLUSION

The presented covariate projection approach to obtain net association patterns, made it possible to quantify and interpret the influence of adiposity and physical (in)activity on the association pattern of HOMA-IR to the lipoprotein features.

Development of Inline Near-Infrared Spectroscopy Method for Real-Time Monitoring of Blend Uniformity of Direct Compression and Granulation-Based Products at Commercial Scales

Blending is a critical intermediate unit operation for all solid oral formulations. For blend uniformity testing, API content in the blend must be quantified precisely. A detailed study was conducted to demonstrate the suitability of inline NIR (near-infrared) spectroscopy for blend uniformity testing of two solid oral formulations: existing direct compression (DC) product with a multistep blending process and granulation-based product with API granules. Both qualitative and quantitative methods were developed at a laboratory scale using statistical moving block standard deviation (MBSD) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. The qualitative MBSD method demonstrated that there was no need for multiple steps for the existing DC product. Hence, a simplified single-step process was developed for blending. Quantitative PLS models for blending processes of both the products were developed, validated, and successfully implemented at a commercial scale for the real-time release of blends. Results obtained from the validated model were in good agreement with the current method of sampling and chromatography.

Nasopharyngeal Bacterial Microbiota Composition and SARS-CoV-2 IgG Antibody Maintenance in Asymptomatic/Paucisymptomatic Subjects

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the coronavirus disease 2019 (COVID-19), ranging from asymptomatic conditions to severe/fatal lung injury and multi-organ failure. Growing evidence shows that the nasopharyngeal microbiota composition may predict the severity of respiratory infections and may play a role in the protection from viral entry and the regulation of the immune response to the infection. In the present study, we have characterized the nasopharyngeal bacterial microbiota (BNM) composition and have performed factor analysis in a group of 54 asymptomatic/paucisymptomatic subjects who tested positive for nasopharyngeal swab SARS-CoV-2 RNA and/or showed anti-RBD-IgG positive serology at the enrolment. We investigated whether BNM was associated with SARS-CoV-2 RNA positivity and serum anti-RBD-IgG antibody development/maintenance 20–28 weeks after the enrolment. Shannon’s entropy α-diversity index [odds ratio (OR) = 5.75, p = 0.0107] and the BNM Factor1 (OR = 2.64, p = 0.0370) were positively associated with serum anti-RBD-IgG antibody maintenance. The present results suggest that BNM composition may influence the immunological memory against SARS-CoV-2 infections. To the best of our knowledge, this is the first study investigating the link between BNM and specific IgG antibody maintenance. Further studies are needed to unveil the mechanisms through which the BNM influences the adaptive immune response against viral infections.

Accelerometer epoch length influence associations for physical activity intensities with body mass index and locomotor skills in young children

The epoch length is decisive for the capture of physical activity intensities from accelerometry and possibly for associations between physical activity intensities and outcomes in children. The aim of the present study was to determine the multivariate physical activity intensity signatures related to body mass index and locomotor skills in preschool children using different epoch lengths. A sample of 1054 Norwegian children (mean age 4.8 years, 52% boys) from the Sogn og Fjordane Preschool Physical Activity Study provided data on physical activity (ActiGraph GT3X+), body mass index and locomotor skills in 2015-2016. Multivariate pattern analysis was used to determine associations between the physical activity intensity spectrum (0-99 to ≥15000 counts per minute) and the outcomes using files aggregated using 8 different epoch lengths from 1 to 60 seconds. We observed that associations across the intensity spectrum, from sedentary time to vigorous intensities, differed for data derived using different epoch lengths. For both outcomes, associations for moderate intensities became stronger and vigorous intensities became weaker for longer as compared to shorter epoch lengths. Aggregation of accelerometer data using different epoch lengths influences the capture of physical activity intensities and associations between physical activity intensities and related outcomes.

Effect of functional excipients on the dissolution and membrane permeation of furosemide formulated into multiple-unit pellet system (MUPS) tablets

The effect of functional excipients (i.e. chitosan, sodium lauryl sulphate, NaHCO₃, and CaCO₃) formulated in multiple-unit pellet system (MUPS) tablets has been investigated on the dissolution and permeability of furosemide, a BCS class IV compound. Spherical beads were produced and compressed into MUPS tablets. MUPS tablet formulations were evaluated for hardness, disintegration, mass variation, friability, and dissolution (pH 1.2, pH 4.6, and pH 7.4). Ex vivo permeability studies were conducted across excised pig tissues (pyloric antrum and duodenal region) on selected experimental MUPS tablet formulations. Histological analysis was conducted on the tissues after exposure to selected experimental MUPS tablet formulations. Dissolution results in the 0.1 M HCl (pH 1.2) showed the highest effect of the excipients on furosemide release. Dissolution parameters showed increased dissolution of furosemide for the MUPS tablet formulations containing functional excipients: a 4.5-10-fold increase in the AUC values, the %_max showed a 60-70% increase and up to a 19-fold increase in DRi was seen. Permeability results revealed a 2.5-fold higher cumulative percentage transport for selected formulations. The results proved that functional excipients incorporated into beads, compressed into MUPS tablet formulations increased furosemide release as well as permeation across excised intestinal tissues.

How Corporate Social Responsibility Boosts Corporate Financial and Non-financial Performance: The Moderating Role of Ethical Leadership

Corporate social responsibility has always been considered an important topic, and many studies discuss the association between corporate social responsibility (CSR) and corporate performance, but the results are still inconclusive. This study is to examine the impact of CSR on corporate performance (financial and non-financial) with the moderating impact of ethical leadership. Data is gathered from 222 companies in Morocco using a simple random sampling technique. Moreover, for measuring customer satisfaction and corporate image in the kinds of customers targeted by the CSR activities of the firms, we collected data from customers and got 209 responses. For analyzing the results of this study, structural equation modeling has been used, while for moderation, the hierarchical regression technique has been adopted. Findings revealed a significant positive association found between CSR and corporate finance as well as non-financial performance (corporate image and customer satisfaction). Ethical leadership helps in increasing the financial and non-financial performance of an organization. The findings further revealed that ethical leadership moderates the relationship between CSR and firm financial and non-financial (corporate image and customer satisfaction) performance. This study will assist management in realizing the importance and implementation of CSR practices in organizations, especially in the Moroccan context.

The effect of enabling organizational control on employees’ affective commitment: evidence from French firms

Purpose

This paper aims to find out the impact of enabling organizational control on employee affective organizational commitment. Moreover, based on self-determination theory, this paper also explains the process through which this relationship works.

Design/methodology/approach

This paper has adopted an explanatory study using a cross-sectional design. Data was collected from the alumni of a business school in France using a survey strategy. Structural equation modeling has been used to validate the measure and to test the hypotheses.

Findings

The results of this study reveal that there is a positive relationship between enabling organizational controls and employee affective organizational commitment. Moreover, this study also shows that this relationship is mediated by the satisfaction of the need for autonomy.

Practical implications

This study serves as a guide for the management to achieve organizational goals as well as employees’ organizational commitment.

Originality/value

This study enriches the literature in the field of organizational theory by showing the positive effect of enabling organizational control on employees’ affective commitment.

Improvements on multiple direct compaction properties of three powders prepared from Puerariae Lobatae Radix using surface and texture modification: comparison of microcrystalline cellulose and two nano-silicas

It has been reported that hydrophilic nano-silica (N) markedly improved direct compaction (DC) properties of Zingiberis Rhizoma alcoholic extract. This study aims to examine the broader scope and generality of the previous work by investigating (i) three powders, i.e., the directly pulverized product, ethanol extract, and water extract prepared from the same medicinal herb-Puerariae Lobatae Radix (named DP, EE, and WE) and (ii) the effects on their DC properties of co-processing with N, hydrophobic nano-silica (BN), or microcrystalline cellulose (C). Unexpectedly, C provided the best improvement on tabletability for WE, while N for both DP and EE. More importantly, only N could move all parent powders to a regime suitable for DC, and BN rather than C enabled parent WE to be directly compressed. Typically, 6/9 N-modified powders simultaneously met the requirements of DC on bulk density, flowability, and tablet tensile strength (σ_t). Principal component analysis indicated that DC properties were mainly governed by flowability and texture properties. The partial least-squares regression model revealed that flowability, texture parameters, and deformation behavior of powders were dominating factors impacting tablet σ_t and solid fraction. Overall, the findings are promising for the manufacture of high drug loading tablets of herbs by DC.

Spectral homogeneity of human platelets investigated by SERS

This paper describes a detailed study of the spectral homogeneity of human platelets using Surface-enhanced Raman spectroscopy (SERS). We used a combined approach based on multivariate methods as principal component analysis and pair correlation algorithms to investigate platelets spectral properties. The correlation coefficients for each sample have been calculated, and the average coefficient of determination has been estimated. The high degree of spectral homogeneity inside one probe and between them has been revealed. The prospects of obtained results usage for pathologies based on platelet conformations during cardiovascular diseases have been demonstrated.

The Multivariate Physical Activity Signatures Associated With Self-Regulation, Executive Function, and Early Academic Learning in 3-5-Year-Old Children

The evidence regarding associations between intensity-specific physical activity and cognitive and learning outcomes in preschoolers is inconsistent and limited by low sample sizes and analytical approaches that cannot handle the multicollinearity among multiple physical activity intensity variables. We aimed to determine the multivariate physical activity intensity signatures associated with self-regulation, executive function, and early academic learning in preschool children aged 3–5 years. A 711 Norwegian preschool children (mean age 4.6 years, 52% boys) provided valid data on physical activity (ActiGraph GT3X+), self-regulation, executive function, and early academic learning during 2019–2020. Multivariate pattern analysis was used to determine associations between uniaxial and triaxial intensity spectra (time spent in intensities from 0–99 to ≥15,000 counts per minute) and the outcomes in the total sample and in subgroups split by sex and age (median split). Uniaxial data led to the highest explained variances (R²) and were reported as the primary findings. We found significant association patterns between physical activity and numeracy (R² = 4.28%) and inhibition (R² = 1.48%) in the total sample. The associations with numeracy were negative for time spent sedentary (0–99 counts per minute) and positive for time spent in moderate to vigorous intensities (≥ 1,000 counts per minute). The associations with inhibition were positive for time spent sedentary (0–99 counts per minute) and in vigorous intensities (≥ 8,500 counts per minute) and negative for time spent in low to moderate intensities (100–3,499 counts per minute). Associations with numeracy were stronger in boys (R² = 5.58%) and older children (R² = 7.27%), and associations with inhibition were stronger in girls (R² = 3.12%) and older children (R² = 3.33%). In conclusion, we found weak associations with numeracy and inhibition across the physical activity intensity spectrum in preschool children.