Q2 Values Research Articles

Development of an integrative empowerment model to care for patients with schizophrenia disorder.

The main factor that causes a family the inability to care for patients with schizophrenia disorder is inadequate family-centered empowerment. Nevertheless, the family-integrated empowerment model has not been developed yet. This study aims to develop a integrative empowerment model to care for patients with schizophrenia disorder. The mixed methods research design was divided into two stages. The first stage used a cross-sectional method with a questionnaire to 135 families who cared for patients with schizophrenia disorder using purposive sampling. Data analysis was obtained using Partial Least Squares (PLS). The second stage is a focused group discussion (FGD) conducted with six families, seven health workers, and six social workers, and discussions with two experts for model development. The integrative empowerment-based family empowerment model is developed from Outside-in empowerment (path coefficient = 0.309; t = 3.292) and Inside-out empowerment (path coefficient = 0.478; t = 4.850). Family factors is the most potent variable in shaping Inside-out empowerment (path coefficient = 0.217; t = 2.309). Moreover, re-meaning of caregiving is the strongest indicator that builds the Inside-out empowerment variable (t = 42.643). The value of Q2 is 0.433 indicates that this model can be generalized, since 61% of family ability to provide care for schizophrenia patients. Re-meaning of caregiving is the most potent indicator in shaping Inside-out empowerment, which is the strongest factor forming this model. Nurses assist families to be able to find activities that can generate positive meaning when caring for patients with schizophrenia disorder.

The Influence of Digital Talent and Green Innovation and Its Impact on the MSME Business Model in the Metaverse Era

Objectives: The study aims to look at MSME Business Models Entering the Metaverse Era Through Digital Transformation with an Increase of Digital Talent and Green InnovationMethodology: The type of research used is quantitative research. A study was carried out in 5 regencies/cities on Bangka Island with respondents 10 2 SMEs, dominated in the culinary sector. In this study, the author uses the analytical method used to test the PLS (Partial Least Square) variable using SmartPLS software. The path analysis model of all latent variables in Partial Least Square (PLS) consists of an outer model, convergent validity, and discriminant validity which is then carried out with a reliability test and a structural model consisting of coefficient of determination (R 2 Value), Effect Size (f 2), and Predictive Relevance (Q2 Value). Ending with a test on the hypothesis and the moderating effect.Conclusion: The results of this study indicate that hypothesis testing concluded that two research hypotheses are accepted and the other three are rejected. The accepted research hypotheses are Digital Talent effect on Digital Transformation and Digital Talent effect on MSME Business Model in the Metaverse Era. These results indicate that only digital talent currently contributes significantly to Digital Transformation and the MSME business model in the metaverse Era.

The Effect of Organizational Climate and Work Engagement on Organizational Citizenship Behavior (OCB) through Work Motivation as a Mediator Variable for Special School Teachers

This study aims to analyze the model of the influence organizational climate and work engagement on Organizational Citizenship Behavior (OCB) through work motivation as a mediator variable. This study examines the direct and indirect influence of organizational climate and work engagement on OCB, with work motivation as a mediator variable for special school teachers. The data analysis used is SEM-PLS validity and reliability of measuring instruments using reflective measurement models, confirmatory factor analysis (CFA), and second order. Hypothesis testing is done by looking at R2, Q2, and GoF values. The results showed that work motivation as a mediator variable functioned partially. The model in this study has good predictive relevance with a hypothesis test result of 0.084.

Atomic connectivity group contribution (ACGC) method for critical properties prediction

The atomic connectivity group contribution (ACGC) method is developed for predicting critical properties of organic compounds for the first time in this present work. Herein, a new group defining method, namely atomic adjacent group (AAG) method, is proposed by the relationship between core atom and its adjacent atoms. Facing the challenge of dealing with isomers in group contribution method, shape factors (SFs) are put forward to describe the effect of molecular shape on the property, and atomic connectivity factors (ACFs) are defined for describing the position of each group in a molecule. ACGC models using a general formula are established for predicting the phase transition properties of critical temperature, critical pressure, and critical volume. The R2test values are 0.9898, 0.9886, and 0.9983 for critical temperature, critical pressure, critical volume, respectively. Q2 values are 0.9826, 0.9820 and 0.9978 for the above three critical properties. Both external validation and cross validation demonstrate the good predictability and robustness of ACGC models. Compared with AAG models without SFs and ACFs, the average relative error of ACGC method decreases by 24.19–62.25 %, which demonstrates that SFs and ACFs are effective in distinguishing isomers. Hence, it is believed that ACGC method is accurate in calculating the critical properties of various organic compounds containing C, H, O, N, S, F, Cl and Br elements with C atom numbers greater than 2.

#5040 UNTARGETED METABOLOMIC ANALYSIS IDENTIFIES A SPECIFIC METABOLOMIC PROFILE IN PATIENTS WITH EARLY CHRONIC KIDNEY DISEASE

Abstract Background and Aims Chronic kidney disease (CKD) has become one of the most challenging diseases of the twenty-first century and is characterized by increased mortality and morbidity. The pathogenesis of CKD is heterogeneous and the evaluation of renal function is performed with biomarkers such as serum creatinine and blood urea, which have low specificity and sensitivity. Metabolomics, one of the omics sciences, has become of interest in the nephrology field of research. Metabolic pathways can impact both glomerular and tubular structures and can offer a better understanding of the pathogenic mechanisms of CKD. Also, metabolomics provides more sensitive biomarkers for an early detection of CKD. The aim of this study was to perform metabolomic profiling of serum and urine in CKD patients by means of untargeted metabolomics in relation with the glomerular filtration rate (eGFR) and to identify potential serum and urinary biomarkers of early CKD. Method In this cross-sectional study were included 99 patients with CKD, staged by eGFR in six subgroups, according to the KDIGO Guidelines and 20 healthy control subjects. Serum and urinary metabolomic profiling were performed by using ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry. Blood and urine samples were evaluated by multivariate analysis, followed by univariate analysis. First, the PLSDA score plot and VIP score were applied. By using the cross-validation algorithm for the first 4 molecules identified, a high accuracy, high R² values, and a significant Q2 value were obtained. Therefore, the model could be considered predictive. Biomarker analysis and prediction by Random Forest analysis was performed. The specificity and sensibility of the molecules identified as potential biomarkers were evaluated by applying the Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC). Results Significant direct correlations with eGFR were demonstrated for serum levels of Oleoyl glycine (p < 0.05, Log2 = 0.773), alpha-lipoic acid (p < 0.05), Propylthiouracil (p< 0.05), and L-cysteine (p< 0.05, Log2 = 0.363). Interestingly, serum levels of 5-Hydroxyindoleacetic acid, Phenylalanine, Pyridoxamine, Cysteinylglycine, Propenoylcarnitine, and Uridine increased gradually from C group to subgroups G5. Also, 5-Hydroxyindoleacetic acid, Phenylalanine, Pyridoxamine, Cysteinylglycine, Propenoylcarnitine, Uridine, and All-trans retinoic acid levels correlated negatively with eGFR (p< 0.05). Moreover, in urine samples the majority of molecules were increased in group G4 and G5 versus G1-G3b and C groups, respectively. Urinary levels of Indoxyl sulfate, Glycylprolylarginine, and Butenoylcarnitine correlated negatively with eGFR (p < 0.05). Conclusion Amino acids, antioxidants, uremic toxins, and acylcarnitines are increased in all CKD stages. The impact of this metabolites on the glomerular and tubular structures, even in the early stages of CKD, can be explained by their dual serum and urinary variation. The study provided a particular metabolomic profile that can offer new biomarkers useful for the evaluation of early CKD and its progression, as well as potential therapeutic targets.

Spotting of Volatile Signatures through GC-MS Analysis of Bacterial and Fungal Infections in Stored Potatoes (Solanum tuberosum L.).

Potatoes inoculated with Pectobacterium carotovorum spp., Aspergillus flavus and Aspergillus niger, along with healthy (control) samples, were stored at different storage temperatures (4 ± 1 °C, 8 ± 1 °C, 25 ± 1 °C) for three weeks. Volatile organic compounds (VOCs) were mapped using the headspace gas analysis through solid phase micro extraction-gas chromatography-mass spectroscopy every week. The VOC data were arranged into different groups and classified using principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) models. Based on a variable importance in projection (VIP) score > 2 and the heat map, prominent VOCs were identified as 1-butanol and 1-hexanol, which can act as biomarkers for Pectobacter related bacterial spoilage during storage of potatoes in different conditions. Meanwhile, hexadecanoic acid and acetic acid were signature VOCs for A. flavus, and hexadecane, undecane, tetracosane, octadecanoic acid, tridecene and undecene were associated with A. niger. The PLS-DA model performed better at classifying the VOCs of the three different species of infection and the control sample compared to PCA, with high values of R2 (96-99%) and Q2 (0.18-0.65). The model was also found to be reliable for predictability during random permutation test-based validation. This approach can be adopted for fast and accurate diagnosis of pathogenic invasion of potatoes during storage.

3D-QSAR, docking and molecular dynamics simulations of novel Pyrazolo-pyridazinone derivatives as covalent inhibitors of FGFR1: a scientific approach for possible anticancer agents

Developing highly potent covalent inhibitors of Fibroblast growth factor receptors 1 (FGFR1) has always been a challenging task. In the current study, various computational techniques, such as 3D-QSAR, covalent docking, fingerprinting analysis, MD simulation followed by MMGB/PBSA, and per-residue energy decomposition analysis were used to explore the binding mechanism of pyrazolo[3,4-d]pyridazinone derivatives to FGFR1. The high q2 and r2 values for the CoMFA and CoMSIA models, suggest that the constructed 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The structural requirements revealed by the model’s contour maps were strategically used to computationally create an in-house library of more than 100 new FGFR1 inhibitors using the R-group exploration technique implemented in the SparkTM software. The compounds from the in-house library were also mapped in the 3D-QSAR model that predicts comparable pIC50 values with the experimental values. A comparison between 3D-QSAR generated contours and molecular docking conformation of ligands was performed to reveal the fundamentals to design potent FGFR1 covalent inhibitors. The estimated binding free energies (MMGB/PBSA) for the selected compounds were in agreement with the experimental value ranking of their binding affinities towards FGFR1. Furthermore, per-residue energy decomposition analysis has identified Arg627 and Glu531 to contribute significantly in improved binding affinity of compound W16. During ADME analysis, the majority of in-house library compounds exhibited pharmacokinetic properties superior to those of experimentally produced compounds. These new compounds may help researchers better understand FGFR1 inhibition and lead to the creation of novel, potent FGFR1 inhibitors. Communicated by Ramaswamy H. Sarma

Discrimination between Korean and Chinese Kimchi using inductively coupled plasma-optical emission spectroscopy and mass spectrometry: A multivariate analysis of Kimchi

Kimchi has been designated as one of the world’s five healthiest foods, and it is a traditional Korean fermented food. The purpose of this study was to investigate whether the origin of Kimchi can be discriminated against by using inorganic elements to develop a more accurate method. The OPLS-DA showed that the R2 and Q2 values were 0.908 and 0.81, a high-quality model. We selected 24 elements (133Cs, 238U, 88Sr, K, 157Gd, Na, Mg, 139La, P, 141Pr, 72Ge, 146Nd, 147Sm, 153Eu, 55Mn, 165Ho, 163Dy, 166Er, Fe, 172Yb, 169Tm, 185Re, 175Lu, and 118Sn) with VIP 1 or higher in the OPLS-DA model. In ROC, the selected elements had an accuracy of 100%. The heatmap confirmed the contents of rare earth elements (REEs) in Korean and Chinese Kimchi, the accuracy of distinguishing classification in CDA is 100%. Such results will help to distinguish the origin of agricultural products through inorganic analysis.

1H nuclear magnetic resonance-based metabolomics study of serum and pectoralis major for different commercial chicken breeds.

This study aimed to characterize the metabolic composition of four types of commercially available chicken breeds [village chicken, colored broiler (Hubbard), broiler (Cobb), and spent layers (Dekalb)] by 1H NMR coupling and discriminate them using multivariate analysis. Five chickens were collected for each chicken breed based on the marketing age from the respective commercial farms. The orthogonal partial least squares discriminant analysis (OPLS-DA) results showed an obvious separation of local village chickens from the other breeds based on the metabolites present in their serum and meat (pectoralis major). The cumulative values of Q 2, R 2 X, and R 2 Y of the OPLS-DA model for chicken serum were 0.722, 0.877, and 0.841. For the pectoralis major muscle, the cumulative values of Q 2, R 2 X, and R 2 Y of the OPLS-DA model were reported as 0.684, 0.781, and 0.786, respectively. The quality of both OPLS-DA models was accepted by the cumulative values of Q 2≥ 0.5 and R 2≥ 0.65. The 1H NMR result with multivariate analysis has successfully distinguished local village chicken from the other three commercial chicken breeds based on serum and pectoralis major muscle. Nonetheless, colored broiler (Hubbard) was not distinguished from broiler (Cobb) and spent layers (Dekalb) in serum and pectoralis major, respectively. The OPLS-DA assessment in this study identified 19 and 15 potential metabolites for discriminating different chicken breeds in serum and pectoralis major muscle, respectively. Some of the prominent metabolites identified include amino acids (betaine, glycine, glutamine, guanidoacetate, phenylalanine, and valine), nucleotides (IMP and NAD+), organic acids (lactate, malate, and succinate), peptide (anserine), and sugar alcohol (myo-inositol).

Relationship between hemoglobin and serum uric acid in adults with various glucose metabolism status

Objective: To investigate the relationship between hemoglobin and serum uric acid in adults with various glucose metabolism status. Methods: The demographic data and biochemical indicators of the adult population who had received physical examination in the Second Medical Center of the PLA General Hospital from January 2018 to December 2021 were collected. The subjects were divided into two groups according to the level of serum uric acid: the normal uric acid group and the hyperuricemia group. The relationship between hemoglobin (stratified into four levels of Q1 to Q4 by the quartile) and serum uric acid was quantified by using Pearson correlation and logistic regression analysis. The effects of age and glucose metabolism status on the relationship between hemoglobin and serum uric acid were analyzed. Results: A total of 33 183 adults were enrolled with age (50.6±10.0) years. The level of hemoglobin in the normal uric acid group (142.61±14.24) g/L was significantly lower than that in the hyperuricemia group [(151.79±11.24) g/L, P<0.001]. Univariate Pearson correlation analysis showed that hemoglobin was positively associated with serum uric acid (r=0.444, P<0.001). After adjusting for related confounding factors, multivariate logistic regression analysis showed that hemoglobin was associated with serum uric acid, and the OR values (95%CI) of hemoglobin Q2 to Q4 group were 1.29 (1.13-1.48), 1.42 (1.24-1.62) and 1.51 (1.32-1.72), respectively (Ptrend<0.001) when compared with hemoglobin Q1 group. Subgroup analysis and hierarchical interaction analysis suggested that with the increase of hemoglobin, the serum uric acid in the age<60 years subgroup, normal glucose subgroup and prediabetes subgroup increased gradually (Ptrend<0.05 and Pinteraction<0.001). Conclusion: The association between hemoglobin and serum uric acid in adults is affected by age and glucose metabolism status.

Fragment-Based Drug Design, 2D-QSAR and DFT Calculation: Scaffolds of 1, 2, 4, triazolo [1, 5-a] pyrimidin-7-amines as Potential Inhibitors of Plasmodium falciparum Dihydroorotate Dehydrogenase

Background: Plasmodium falciparum dihydroorotate dehydrogenase (PfDODH) is one of the enzymes currently explored in the treatment of malaria. Although there is currently no clinically approved drug targeting PfDODH, many of the compounds in clinical trials have [1, 2, 4,] triazolo [1, 5-a] pyrimidin- 7-amine backbone structure. Objective: This study sought to design new compounds from the fragments of known experimental inhibitors of PfDODH. Methods: Nine experimental compounds retrieved from Drug Bank online were downloaded and broken into fragments using the Schrodinger power shell; the fragments were recombined to generate new ligand structures using the BREED algorithm. The new compounds were docked with PfDODH crystal structure, after which the compounds were filtered with extensive drug-likeness and toxicity parameters. A 2D-QSAR model was built using the multiple linear regression method and externally validated. The electronic properties of the compounds were calculated using the density functional theory method. Results: Structural investigation of the six designed compounds, which had superior binding energies than the standard inhibitors, showed that five of them had [1, 2, 4,] triazolo [1, 5-a] pyrimidin-7-amine moieties and interacted with essential residues at the PfDODH binding site. In addition to their drug-like and pharmacokinetic properties, they also showed minimal toxicities. The externally validated 2D-QSAR model with R2 and Q2 values of 0.6852 and 0.6691 confirmed the inhibitory prowess of these compounds against PfDODH. The DFT calculations showed regions of the molecules prone to electrophilic and nucleophilic attacks. Conclusion: The current study thus provides insight into the development of a new set of potent PfDODH inhibitors.

Novel antiproliferative inhibitors from salicylamide derivatives with dipeptide moieties using 3D-QSAR, molecular docking, molecular dynamic simulation and ADMET studies

Currently, most cancers cannot be cured permanently. Therefore, the search of new anticancer drugs is an urgent unmet need. In order to solve this problem, we can use computational chemistry as it represents a sensitive step in the procedure of rational drugs development and design. For this study, we performed several in silico approaches such as: 3D QSAR study, molecular docking and molecular dynamics simulation for a database forming of 28 salicylamide compounds with dipeptide moieties that are inhibitors of antiproliferative activity on CEM cancer cell lines. The established CoMSIA/SH model showed a high reliability reflected by an important value of the coefficient of determination (R2 = 0.969), and a reasonable value of the cross-validation coefficient Q² = 0.637, the validity of this model is ensured by the high value of the prediction coefficient for the test set with R2pred = 0.913. The treatment of the different extracted results provides important information about the structures favored to improve the inhibitory activity. These results have guided to propose new antiproliferative inhibitors with high pGI50 values. Subsequently, these new compounds were analyzed using a molecular docking for determine the essential ligand-receptor interactions, then we performed a molecular dynamics simulation that showed the stability of the proposed new compounds, since no significant fluctuation is detected in these validity parameters, finally, to test the possibility of these new inhibitors to become drug candidates we used the ADMET properties.

Evaluating the indotecan-neutropenia relationship in patients with solid tumors by population pharmacokinetic modeling and sigmoidal Emax regressions.

This study aimed at characterizing indotecan population pharmacokinetics and explore the indotecan-neutropenia relationship in patients with solid tumors. Population pharmacokinetics were assessed using nonlinear mixed-effects modeling of concentration data from two first-in-human phase 1 trials evaluating different dosing schedules of indotecan. Covariates were assessed in a stepwise manner. Final model qualification included bootstrap simulation, visual and quantitative predictive checks, and goodness-of-fit. A sigmoidal Emax model was developed to describe the relationship between average concentration and maximum percent neutrophil reduction. Simulations at fixed doses were conducted to determine the mean predicted decrease in neutrophil count for each schedule. 518 concentrations from 41 patients supported a three-compartment pharmacokinetic model. Body weight and body surface area accounted for inter-individual variability of central/peripheral distribution volume and intercompartmental clearance, respectively. Estimated typical population values were CL 2.75 L/h, Q3 46.0 L/h, and V3 37.9 L. The estimated value of Q2 for a typical patient (BSA = 1.96 m2) was 17.3 L/h, while V1 and V2 for a typical patient (WT = 80kg) was 33.9 L and 132 L. The final sigmoidal Emax model estimated that half-maximal ANC reduction occurs at an average concentration of 1416µg/L and 1041µg/L for the daily and weekly regimens, respectively. Simulations of the weekly regimen demonstrated lower percent reduction in ANC compared to the daily regimen at equivalent cumulative fixed doses. The final PK model adequately describes indotecan population pharmacokinetics. Fixed dosing may be justified based on covariate analysis and the weekly dosing regimen may have a reduced neutropenic effect.

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling

IntroductionThis study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation.MethodA pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC–MS).ResultsThe best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC–MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure.ConclusionsThe results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

Structural Insights on Hyp-Gly-Containing Peptides as Antiplatelet Compounds through Topomer CoMFA and CoMSIA Analysis

Increasing evidence has shown collagen hydrolysate involves a variety of bioactivities. In our previous study, multiple antiplatelet peptides containing Hyp/Pro-Gly were identified in collagen hydrolysates from Salmo salar and silver carp skin and exhibited anti-thrombosis activity without bleeding risks in vivo. However, the relationship between structure and activity remains unknown. We performed 3D-QSAR studies on 23 Hyp/Pro-Gly-containing peptides in which 13 peptides were reported before. CoMFA, Topomer CoMFA and CoMSIA analyses were used to generate the QSAR models. Topomer CoMFA analysis showed a q2 value of 0.710, an r2 value of 0.826, an r2pred value of 0.930, and the results showed that Hyp instead of Pro was more important for improving the antiplatelet activity. CoMSIA analysis showed a q2 value of 0.461, an r2 value of 0.999, and an r2pred value of 0.999. Compared with the electrostatic field and hydrogen bond donor field, the steric field, hydrophobic field and hydrogen bond receptor field have great influence on the activity of antiplatelet peptides. The predicted peptide EOGE exhibited antiplatelet activity induced by ADP, and inhibited thrombus formation (300 μmol/kg bw) without bleeding risks. Combined results of these studies indicate that OG-containing peptides had a potential to be developed into an effective specific medical food in the prevention of thrombotic diseases.

What drives citizens to get the COVID-19 vaccine? The integration of protection motivation theory and theory of planned behavior

PurposeIn the wake of COVID-19 pandemic, public health is associated with the acceptance of vaccination unless it reaches herd immunity level, which is 67%. Nevertheless, citizens have shown hesitancy toward acceptance of COVID-19 vaccination globally. Therefore, this study aims to investigate factors which influence citizen’s intention to get COVID-19 vaccination with two well-known theories, namely, protection motivation theory and theory of planned behavior. Aside of direct relationship, the moderating effect of perceived awareness is also tested between attitude and citizens’ intention to get COVID-19 vaccine.Design/methodology/approachThe research framework is statistically tested with data set of 215 responses. Data were collected from Pakistani citizens residing in Islamabad, Lahore, Narowal and Burban. For data collection, convenience sampling approach is used. The positivism research paradigm is adopted to design the research framework. Data were estimated with structural equation modeling approach.FindingsResults of the statistical analysis unveiled that collectively perceived severity, vulnerability, subjective norm and perceived behavioral control have explained substantial variance R2 52.9% in measuring citizens’ attitude toward acceptance of COVID-19 vaccination. Likewise, attitude and awareness have explained substantial variance R2 54.6% in measuring citizens’ intention to accept COVID-19 vaccine. Moreover, results have shown substantial Stone–Geisser Q2 value to predict citizens’ attitude and intention to accept COVID-19 vaccine.Practical implicationsIn term of theoretical contributions, this study has combined two well-known theories, namely, protection motivation theory and theory of planned behavior, to investigate citizens’ attitude and intention to get COVID-19 vaccine. Therefore, practically this study directs that government officials and health-care practitioners should create awareness among citizens about pandemic by highlighting severity and vulnerability of the COVID-19 virus toward health which, in turn, motivate citizens to get COVID-19 vaccine on time.Social implicationsThe ongoing health crisis could be reduced if citizens achieve herd immunity against COVID-19 virus, resulting in long-term welfare of the society. This research has significant social impact on citizen’s lives, as it revealed that citizens could be motivated to get COVID-19 vaccination if they get adequate awareness about pandemic, enhance behavioral control and understand severity and vulnerability of the COVID-19 virus.Originality/valueThe current research is valuable, as it unveils factors which encourage citizens to get COVID-19 vaccine. In terms of originality, to the best of the author’s knowledge, this study is the first that integrates protection motivation theory and theory of planned behavior toward citizens’ intention to get COVID-19 vaccine during pandemic.

Integration of fingerprint-based similarity searching and kernel-based partial least squares analysis to predict inhibitory activity against CSK, HER2, JAK1, JAK2, and JAK3.

Fingerprint-based similarity searching is an important strategy for virtual screening in drug discovery. In the present study, we carried out a systematic virtual screening study, followed by the establishment of kernel-based partial least square (KPLS) analysis prediction models for five tyrosine kinase drug targets, C-terminal SRC kinase (CSK), human epidermal growth factor 2 (HER2), and Janus kinases 1, 2, and 3 (JAK1, JAK2, and JAK3), using a dataset of 3688 compounds. These kinases are important drug discovery targets, particularly as HER2 has been validated for the treatment of metastatic breast cancer, JAK inhibitors have been validated for the clinical management of arthritis and autoimmune diseases, and CSK has been found to play an important role in bone remodeling in arthritis. We conducted similarity screenings with the most active molecule for each target in the dataset as a query using eight (8) types of two-dimensional (2D) molecular fingerprints, comprising seven Hashed fingerprints, Linear, Dendritic, Radial, Pairwise, Triplet, Torsion, and MOLSPRINT2D, and one Structural keys fingerprint, MACCS. The top ranked 1% of compounds from each target's similarity screening results was used to set up kernel-based partial least square (KPLS) prediction models, with q2 values up to 0.8. The best KPLS model for each target was selected based on its predictive ability and boot strapping results and used for prediction. This integrated study approach combining similarity screening with KPLS analysis has a high potential to enhance the accuracy and efficiency of virtual screening and thus improve the drug discovery process.

Evaluation of a Coating Process for SiO&lt;sub&gt;2&lt;/sub&gt;/TiO&lt;sub&gt;2&lt;/sub&gt; Composite Particles by Machine Learning Techniques

In this study, in order to optimize a fabrication process for SiO2/TiO2 composite particles and control their coating ratio (CTi), regression models for the coating process were constructed using various machine learning techniques. The composite particles with a core (SiO2)/shell (TiO2) structure were synthesized by mechanical stress under various fabrication conditions with respect to the supply volume of raw materials (V), addition ratio of TiO2 (rTi), operation time (t), rotor rotation speed (S), and temperature (T). Regression models were constructed by the least squares method (LSM), principal component regression (PCR), support vector regression (SVR), and the deep neural network (DNN) method. The accuracy of the constructed regression models was evaluated using the determination coefficients (R2) and the predictive performance was evaluated by comparing the prediction coefficients (Q2). From the perspective of the R2 and Q2 values, the DNN regression model was found to be the most suitable model for the present coating process. Moreover, the effects of the fabrication parameters on CTi were analyzed using the constructed DNN model. The results suggested that the t value was the dominant factor determining CTi of the composite particles, with the plot of CTi versus t displaying a clear maximum.

UPLC-Q-TOF/MS-based metabolomic analysis reveals the effects of asomate on the citrus fruit

The regulation of the sugar-acid ratio is of great significance to the improvement of citrus fruit quality. The citric acid level in fruit is influenced by many factors. Among them, cultivar selection and production practices are the most important strategies under the grower's control. In recent years, an arsenic-containing preparation called “Tianmisu”, with the main ingredient of asomate, has occasionally been reported to be used in citrus cultivation to improve the sweetness of fruits. In order to reveal the effects of the pesticide on citrus fruits, ‘Harumi’ tangor was treated with “Tianmisu”, and the impact of this pesticide on fruit quality and metabolites was investigated through UPLC-Q-TOF/MS-based metabolomic analysis. Compared with the control, the concentration of titratable acidity, in particular citric acid, in the pulp of ‘Harumi’ tangor treated with the pesticide, was significantly reduced by 60.5%. The differences in metabolites between the pesticide-treated samples and the control were illustrated by Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). The PLS-DA analysis demonstrated a clear discrimination, with R2Y and Q2 values of 0.982 and 0.933 in the positive mode and 0.984 and 0.900 in the negative mode, respectively. A total of 155 compounds were identified, and 63 characteristic components were screened out from the pesticide-treated samples compared to the control. Aside from the upregulation observed for a few metabolites, the majority of the compounds, including citric acid and various lipids, were down-regulated in the treated citrus fruits compared to the control. This study can serve as a basis for understanding the regulatory mechanism of organic acids in citrus and will be helpful in developing different strategies to improve citrus quality.

Quantitative structure–activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R2, Q2, and IIC for the validation set of splits 1 to 3 are in the range of 0.7180–0.7755, 0.6891–0.7561, and 0.4431–0.8611 respectively. The numerical result of {CR}_{p}^{2} > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC50 are recognized and used for the mechanistic interpretation of structural attributes.