Interaction Effects Of Variables Research Articles

Experimental Design of the Interaction Effect of Independent Variables on Pineapple (Ananas comosus) Fruit Bromelain Activity

Background: Certain factors are known to influence the rate of enzyme activity. It is paramount to study such independent factors to obtain the ideal conditions required to maximize enzyme activity. The time-consuming traditional one-factor-at-a-time (OFAT) approach of evaluating the effect of independent variables on enzyme activity does not consider the interaction effects of the independent variables on dependent responses. Methods: The impact of four independent variables on fruit bromelain activity was evaluated using the complete factorial design of the experiment approach. Results: The highest actual fruit bromelain activity (76.83 U/ml) and predicted activity (74.81 U/ml) were recorded at an optimum pH of 7.5, temperature of 50oC, and incubation period of 10 minutes using azocasein substrate. All tested variables except temperature; the interaction between substrate type and incubation time significantly affected fruit bromelain activity (p < 0.05). Conclusion: The design model analysed the main and interaction effect of the variables on bromelain activity. Therefore, complete factorial design is a better approach than OFAT to determine the effect of independent variables on fruit bromelain activity.

Mathematical modeling for operative improvement of the decoloration of Acid Red 27 by a novel microbial consortium of Trametes versicolor and Pseudomonas putida: A multivariate sensitivity analysis

In this work, it is presented a first approach of a mathematical and kinetic analysis for improving the decoloration and further degradation process of an azo dye named acid red 27 (AR27), by means of a novel microbial consortium formed by the fungus Trametes versicolor and the bacterium Pseudomonas putida. A multivariate analysis was carried out by simulating scenarios with different operating conditions and developing a specific mathematical model based on kinetic equations describing all stages of the biological process, from microbial growth and substrate consuming to decoloration and degradation of intermediate compounds. Additionally, a sensitivity analysis was performed by using a factorial design and the Response Surface Method (RSM), for determining individual and interactive effects of variables like, initial glucose concentration, initial dye concentration and the moment in time for bacterial inoculation, on response variables assessed in terms of the minimum time for: full decoloration of AR27 (R1 = 2.375 days); maximum production of aromatic metabolites (R2 = 1.575 days); and full depletion of aromatic metabolites (R3 = 12.9 days). Using RSM the following conditions improved the biological process, being: an initial glucose concentration of 20 g l-1, an initial AR27 concentration of 0.2 g l-1 and an inoculation moment in time of P. putida at day 1. The mathematical model is a feasible tool for describing AR27 decoloration and its further degradation by the microbial consortium of T. versicolor and P. putida, this model will also work as a mathematical basis for designing novel bio-reaction systems than can operate with the same principle of the described consortium.

Understanding risk for early dislocation resulting in reoperation within 90 days of reverse total shoulder arthroplasty: extreme rare event detection through cost-sensitive machine learning.

Reliable prediction of postoperative dislocation after reverse total shoulder arthroplasty (RSA) would inform patient counseling as well as surgical and postoperative decision making. Understanding interactions between multiple risk factors is important to identify those patients most at risk of this rare but costly complication. To better understand these interactions, a game theory-based approach was undertaken to develop machine learning models capable of predicting dislocation-related 90-day readmission following RSA. A retrospective review of the Nationwide Readmissions Database was performed to identify patients who underwent RSA between 2016 and 2018 with a subsequent readmission for prosthetic dislocation. Of the 74,697 index procedures included in the data set, 740 (1%) experienced a dislocation resulting in hospital readmission within 90 days. Five machine learning algorithms were evaluated for their ability to predict dislocation leading to hospital readmission within 90 days of RSA. Shapley additive explanation (SHAP) values were calculated for the top-performing models to quantify the importance of features and understand variable interaction effects, with hierarchical clustering used to identify cohorts of patients with similar risk factor combinations. Of the 5 models evaluated, the extreme gradient boosting algorithm was the most reliable in predicting dislocation (C statistic = 0.71, F2 score = 0.07, recall = 0.84, Brier score = 0.21). SHAP value analysis revealed multifactorial explanations for dislocation risk, with presence of a preoperative humerus fracture; disposition involving discharge or transfer to a skilled nursing facility, intermediate care facility, or other nonroutine facility; and Medicaid as the expected primary payer resulting in strong, positive, and unidirectional effects on increasing dislocation risk. In contrast, factors such as comorbidity burden, index procedure complexity and duration, age, sex, and presence or absence of preoperative glenohumeral osteoarthritis displayed bidirectional influences on risk, indicating potential protective effects for these variables and opportunities for risk mitigation. Hierarchical clustering using SHAP values identified patients with similar risk factor combinations. Machine learning can reliably predict patients at risk for postoperative dislocation resulting in hospital readmission within 90 days of RSA. Although individual risk for dislocation varies significantly based on unique combinations of patient characteristics, SHAP analysis revealed a particularly at-risk cohort consisting of young, male patients with high comorbidity burdens who are indicated for RSA after a humerus fracture. These patients may require additional modifications in postoperative activity, physical therapy, and counseling on risk-reducing measures to prevent early dislocation after RSA.

Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography reimaging after core needle biopsy. Of 276 women who underwent short-term (<3 mo) serial digital mammograms followed by breast cancer surgery from Jan. to Dec. 2017, 550 breasts were included. All core needle biopsies for breast lesions were performed between serial exams. All mammography images were analyzed using a commercially available AI-based software providing an abnormality score (0-100). Demographic data for age, interval between serial exams, biopsy, and final diagnosis were compiled. Mammograms were reviewed for mammographic density and finding. Statistical analysis was performed to evaluate the distribution of variables according to biopsy and to test the interaction effects of variables with the difference in AI-based score according to biopsy. AI-based score of 550 exams (benign or normal in 263 and malignant in 287) showed significant difference between malignant and benign/normal exams (0.48 vs. 91.97 in first exam and 0.62 vs. 87.13 in second exam, P<0.0001). In comparison of serial exams, no significant difference was found in AI-based score. AI-based score difference between serial exams was significantly different according to biopsy performed or not (-0.25 vs. 0.07, P = 0.035). In linear regression analysis, there was no significant interaction effect of all clinical and mammographic characteristics with mammographic examinations performed after biopsy or not. The results from AI-based diagnostic support software for digital mammography was relatively consistent in short-term reimaging even after core needle biopsy.

Optimization of the Biodiesel Production via Transesterification Reaction of Palm Oil using Response Surface Methodology (RSM): A Review

The optimization method is vital in chemical synthesis and has been applied in many fields nowadays. Response surface methodology (RSM) is an example of an optimization method that is useful in examining the effects of multiple independent variables. RSM was applied in many studies to optimize the transesterification of biodiesel production from palm oil in the presence of a catalyst. This paper aims to provide an overview of recent catalyzed transesterification trends, as well as the benefits and drawbacks of heterogeneous, homogeneous, and enzyme catalysts in biodiesel production. RSM was used to design the process and statistically analyze the interaction effects of the independent reaction variables. The reaction variables, such as reaction time, reaction temperature, catalyst amount, and the molar ratio of the substrate, were optimized during the process. A statistical model and response surface plots were visualized graphically in the contour plots and three-dimensional figures to explain the interactive effects of variables on a response. In sum, this paper discussed the relationships between the reaction parameters and the production of biodiesel and the optimum conditions for biodiesel production using RSM.

Application of extreme gradient boosting and Shapley Additive explanations to predict temperature regimes inside forests from standard open-field meteorological data

Forest microclimate can buffer biotic responses to summer heat waves, which are expected to become more extreme under climate warming. Prediction of forest microclimate is limited because meteorological observation standards seldom include situations inside forests. We use eXtreme Gradient Boosting ‒ a Machine Learning technique ‒ to predict the microclimate of forest sites in Brandenburg, Germany, using seasonal data comprising weather features. The analysis was amended by applying a SHapley Additive explanation to show the interaction effect of variables and individualised feature attributions. We evaluate model performance in comparison to artificial neural networks, random forest, support vector machine, and multi-linear regression. After implementing a feature selection, an ensemble approach was applied to combine individual models for each forest and improve robustness over a given single prediction model. The resulting model can be applied to translate climate change scenarios into temperatures inside forests to assess temperature-related ecosystem services provided by forests.

Mathematical characterization of dose uncertainty effects on functions summarizing findings of community noise attitudinal surveys.

Previous Monte Carlo simulations have quantified the extent to which dose (sound level) uncertainty in community noise dose-response surveys can bias the shape of inferred dose-response functions. The present work extends the prior findings to create a mathematical model of the biasing effect. The exact effect on any particular data set depends on additional attributes (situational variables) beyond dose uncertainty itself. Several variables and their interaction effects are accounted for in the model. The model produced identical results to the prior Monte Carlo simulations and thereby demonstrated the same slope reduction effect. This model was further exercised to demonstrate the nature and extent of situational variable interaction effects related to the range of doses employed and their distribution across the range. One manifestation was a false asymptotic behavior in the observed dose-response relationship. The mathematical model provides a means to not only predict dose uncertainty effects but also to serve as a foundation for correcting for such effects in regression analyses of transportation noise dose-response relationships.

A Novel Estimation Method in Generalized Single Index Models

The single index and generalized single index models have been demonstrated to be a powerful tool for studying nonlinear interaction effects of variables in the low-dimensional case. In this article, we propose a new estimation approach for generalized single index models E ( Y | θ ⊤ X ) = ψ ( g ( θ ⊤ X ) ) with ψ ( · ) known but g ( · ) unknown. Specifically, we first obtain a consistent estimator of the regression function by using a local linear smoother, and then estimate the parametric components by treating ψ ( g ̂ ( θ ⊤ X i ) ) as our continuous response. The resulting estimators of θ are asymptotically normal. The proposed procedure can substantially overcome convergence problems encountered in generalized linear models with discrete response variables when sparseness occurs and misspecification. We conduct simulation experiments to evaluate the numerical performance of the proposed methods and analyze a financial dataset from a peer-to-peer lending platform of China as an illustration.

Effect of surface treatment with laser on repair bond strength of composite resin to ceramic

Small chipping or fracture of ceramic restorations may be repaired by composite resin instead of replacing the restoration. This method is faster and cheaper compared to restoration replacement. Several strategies have been suggested to obtain a high repair shear bond strength (SBS). This study aimed to assess the efficacy of some new ceramic surface treatments (laser and universal adhesive) to enhance the repair bond strength of composite resin to ceramic compared to the conventional method. This in vitro study evaluated 80 IPS Empress Esthetic ceramic plates in eight groups (n = 10). The ceramic surface was polished with 320-grit silicon carbide paper under running water, rinsed with water spray for 10 s and dried. The samples were then divided into two subgroups for mechanical surface preparation with hydrofluoric (HF) acid and Er: YAG laser (2 W, 200 m J, 10 Hz, 10 s). Each group was divided into two subgroups for use/no use of silane. The conventional or universal adhesive was then applied on the samples in each subgroup. Composite cylinders were bonded to the ceramic surface using plastic tubes. The samples were stored in distilled water at 37°C for 24 h and subjected to an SBS test. Data were analyzed using one-way ANOVA (P < 0.05). The interaction effect of variables on SBS was significant. Maximum SBS was noted in HF acid + silane + conventional adhesive group (mean: 12.0481 MPa). Minimum SBS was noted in the laser + conventional adhesive group (mean: 2.5766 MPa). Surface treatment with HF acid yielded significantly higher SBS than laser (P < 0.001). The interaction effect of conventional/universal adhesive and use/no use of silane on SBS was statistically significant. The repair SBS was higher in groups treated with HF acid compared to laser. Ceramic surface treatment with HF plus silane plus conventional adhesive yielded a higher SBS as well as HF plus Universal adhesive. Thus, the application of silane as a separate step can be omitted in the repair of ceramic restorations with universal adhesives.

The effects of processing parameters on structure of mesoporous γ-alumina granules synthesized by sol-gel/oil-drop method: Modelling via response surface methodology

Sol-gel/oil-drop granulation technique was used to prepare mesoporous ?-Al2O3 granules of 1.5-2mm with high specific surface area and pore volume of 295m2/g and 0.47 cm3/g, respectively. The structure of granules was studied using powder XRD, DTA, TEM and N2 adsorption/desorption techniques. A response surface methodology (RSM) holding CCD approach was used for modelling the influence of process variables on the granule pore structure, having a quadratic function relating the independent variables (calcination temperature, acid amount and ammonia concentration) to the responses (specific surface area, mean pore diameter and pore volume). The interaction effect of variables on each response was studied and all were in acceptable accordance with experimental values. Accordingly, the granules synthesized using 20ml of nitric acid, aged in a 9.25wt.% ammonia solution and calcined at 450?C showed the best properties. The predicted values for the specific surface area and pore volume were 315.64m2/g, 7 nm and 0.53 cm3/g, respectively, which were compatible with experimental values.

Spexin Friend or Foe? Novel Role of Spexin During Thermogenesis of White Adipose Tissue

Spexin (SPX) is a novel adipokine playing an emerging role in metabolic diseases due to its involvement in carbohydrate homeostasis, weight loss, appetite control, gastrointestinal movement, among others. Moreover, plasma levels are reduced in obese and type II diabetic patients. In vitro, SPX favors lipolysis in adipocytes and hepatocytes and inhibits white adipogenesis. Therefore, the aim of this study was to evaluate the role of SPX in white adipose tissue (AT) thermogenesis. C57BL/6J male mice were treated or not with SPX for ten days (ip. 29 µg/kg/day; CTR and SPX). At day 3 mice were randomly divided: a group was kept at room temperature (RT) and the other at 4°C to stimulate thermogenesis (CTR-C and SPX-C). Caloric intake and body weight was daily recorded. At the end of the protocol plasma, Brown AT (BAT), abdominal AT (Epidydimal, EAT) and subcutaneous AT (Inguinal, IAT) depots were collected for several measurements. We found that caloric intake was increased when animals were exposed to cold (P<0.001). Body weight change revealed a differential effect of SPX depending on temperature (interaction SPX x Cold, P<0.05): SPX animals weighted less than CTR at RT, but upon cold stimulation there was no difference. No changes were observed for plasma glucose levels, however plasma triglycerides (Tg) levels decreased after cold exposure regardless SPX treatment (Cold P<0.01). Liver Tg content showed a SPX x Cold interaction effect (P<0.0001), where, upon cold stimulation, CTR-C animals increased their levels, but on the contrary SPX-C mice decreased it. EAT, IAT and BAT relative mass showed an interaction effect of variables (SPX x Cold P<0.05). When compared upon cold, SPX-C mice had less AT mass compared to CTR-C mice. IAT and EAT mRNA expression of UCP1 and Cox8b showed SPX x Cold interaction (P<0.05), with a tendency of reduction or no difference in SPX at RT, but with a significant decrease in SPX-C compared to CTR-C mice upon cold exposure. PGC1a expression was increased in EAT from cold exposed-mice and in IAT only in CTR-C mice. UCP1 protein levels showed different results depending on the AT depot. For IAT SPX x Cold interaction (P<0.05) was observed, where SPX inhibited UCP1 stimulation only upon cold exposure. On the contrary, for EAT UCP1 levels decreased in SPX-treated mice, regardless cold exposure (SPX P<0.05). In conclusion, SPX treatment in vivo reduced the thermogenic process in subcutaneous and abdominal AT, being more evident upon cold stimulation. PICT2017-2038, PICT2017-2314.

The biosorption of Alizarin Red S by Spirulina platensis; process modelling, optimisation, kinetic and isotherm studies

ABSTRACT Spirulina platensis is abundant biomass of cyanobacteria (blue-green algae) which is applied in the current study as a biosorbent. A statistical Box–Behnken approach was adopted to remove the toxic dye of alizarin red S (ARS) from synthetic solutions. A polynomial equation was developed to predict the dye removal efficiency and elucidate the interaction effects of variables. The maximum removal efficiency of ARS (69.1%) was obtained at pH 6.5, mixing time of 42.5 min, dye concentration of 100 mg. L−1 and S. platensis dose of 1.5 g. L−1. The findings indicated that the biosorption rate of ARS had a direct relationship with the ARS concentration, pH, reaction time, and S. platensis dose. The quadratic model suggested that the ARS concentration is the major variable in ARS removal. The isotherms and kinetics data could be suitably reflected by the Langmuir model and the pseudo-first-order equation. The maximum adsorption capacity of S. cerevisiae was obtained to be 17.15 mg. g−1 based on the Langmuir model. The results showed that the removal rates of ARS from the first cycle up to the fourth cycle were varied from 55% to 20%, respectively. In conclusion, S. platensis shows suitable adsorptive characteristics and hence could be suggested as a viable option for future studies.

Iron Bearing Minerals Flotation from Silica Sand Using Hydroxyl Surfactants

A technological clayey sandstone sample from Wadi Qena locality, Eastern Desert of Egypt, was directed to processing. Fine kaolin and clean silica sand were produced after intensive attrition scrubbing of the sample. To increase the quality of the produced silica, it was subjected to reverse anionic flotation to minimize its iron content. In this respect, conventional flotation tests using three anionic oxyhydryl surfactants namely: sodium dodecyl benzene sulphonate, sodium naphtha sulphonate, and sodium dodecyl sulphate, were tried. Results showed a privilege action for sodium dodecyl sulphate to remove most of the iron oxide content of the sample. A statistical Box-Behnken design was constructed to optimize the process efficiency. It was shown that from a flotation feed contained 360 ppm Fe2O3 and 1190 ppm Al2O3, sand concentrate contained 29 ppm Fe2O3 and 564 ppm Al2O3, was produced. The optimum flotation conditions were 3.86 kg/t sodium dodecyl sulphate dose, 3.22 flotation pulp pH, 1226 rpm impeller speed, and 22.24 L/min airflow rate. Variables interaction effects results showed that flotation separation efficiency was significantly influenced by air flow rate and cell impeller speed of the process.

Application of Cooperative Game Theory Principles to Interpret Machine Learning Models of Nonhome Discharge Following Spine Surgery.

Retrospective analysis of prospectively acquired data. The aim of this study was to identify interaction effects that modulate nonhome discharge (NHD) risk by applying coalitional game theory principles to interpret machine learning models and understand variable interaction effects underlying NHD risk. NHD may predispose patients to adverse outcomes during their care. Previous studies identified potential factors implicated in NHD; however, it is unclear how interaction effects between these factors contribute to overall NHD risk. Of the 11,150 reviewed cases involving procedures for degenerative spine conditions, 1764 cases (15.8%) involved NHD. Gradient boosting classifiers were used to construct predictive models for NHD for each patient. Shapley values, which assign a unique distribution of the total NHD risk to each model variable using an optimal cost-sharing rule, quantified feature importance and examined interaction effects between variables. Models constructed from features identified by Shapley values were highly predictive of patient-level NHD risk (mean C-statistic = 0.91). Supervised clustering identified distinct patient subgroups with variable NHD risk and their shared characteristics. Focused interaction analysis of surgical invasiveness, age, and comorbidity burden suggested age as a worse risk factor than comorbidity burden due to stronger positive interaction effects. Additionally, negative interaction effects were found between age and low blood loss, indicating that intraoperative hemostasis may be critical for reducing NHD risk in the elderly. This strategy provides novel insights into feature interactions that contribute to NHD risk after spine surgery. Patients with positively interacting risk factors may require special attention during their hospitalization to control NHD risk.Level of Evidence: 3.

WITHDRAWN: Optimization and modeling of drilling variables on AMCs using Taguchi technique and regression analysis

This research work explores the optimum variables for the drilling process of AA7075 matrix composite filled with 10 wt% ZrO 2 particles was synthesized via stir casting route. A Taguchi approach was used to determine the optimum variables such as rotational speed (500, 750, 1000 and 1250 rpm), feed rate (0.06, 0.08, 0.10 and 0.12 mm/rev) and depth of cut (1.0, 1.5, 2.0 and 2.5 mm) on the surface roughness (SR) of the composite. The experiments were performed on a CNC machining centre using HSS tool. The signal-to-noise (S/N) ratio and analysis of variance (ANOVA) was applied to find out the optimum parameters and also compute the notable effects of factors on the SR. The result reveals that, the rotational speed of 1250 rpm; feed rate of 0.12 mm/rev and depth of cut of 1.5 mm were achieved the low SR. The ANOVA results understood that the feed rate was the most impactful factor on SR subsequently by rotational speed with a contribution of 60.37% and 32.66% respectively. The interaction effects of variables on SR were also reported. At last, regression equation was formulated to predict the SR and it was compared with experimental SR.

Comparison of MnO2 modified and unmodified magnetic Fe3O4 nanoparticle adsorbents and their potential to remove iron and manganese from aqueous media

In this work, several magnetic nanoparticles (Fe3O4) with silica (SiO2) and Mg–Fe–CO32− layered double hydroxide (LDH) as the shell was prepared then modified with manganese dioxide (MnO2) to the removal of iron (Fe2+) and manganese (Mn2+) ions and compared with each other. Results of adsorption experiments showed that magnetic nanoparticles modified by MnO2 are very effective in the adsorption of Fe2+ and Mn2+ from aqueous solutions. The textural properties of the adsorbents were done by FT-IR, TGA, TEM, XRD, FESEM, BET, EDX, and VSM. The central composite design (CCD) defined under response surface methodology (RSM) was employed to optimize and interaction effects of variables on removal efficiency of Fe2+ and Mn2+ with Fe3O4@LDH@MnO2. The optimum adsorption conditions were determined at the pH of 6.0, dose of 0.01 g, contact time 30 min, initial Fe2+concentration of 100 mg/L and initial Mn2+ concentration of 80 mg/L. The maximum adsorption capacity of Fe2+ and Mn2+ at optimum conditions were found to be 238.09 and 81.30 mg/g respectively. The experimental equilibrium data were fitted better by using the Langmuir model than the Freundlich or the Temkin model, suggesting that the adsorption feature be monolayer. The pseudo-first order, pseudo-second order, Elovich, intra-particle diffusion, and particle diffusion kinetic models were used to describe the obtained data. The pseudo-second-order model was found to describe adequately the experimental kinetic data. Thermodynamic parameters studies indicated that heavy metals adsorption onto Fe3O4@LDH@MnO2 is a spontaneous and endothermic. Regeneration process was performed with NaCl and proven it was a suitable desorbing reagent in the discharge of Fe2+ and Mn2+. The reusability of prepared magnetic Fe3O4 nano particle was investigated up to six cycles. The adsorption method was utilized for the adsorptive removal of Fe2+ and Mn2+ in two water samples.

Aluminum ASA 6061 Anodizing Process by Chromic Acid Using Box–Wilson Central Composite Design: Optimization and Corrosion Tendency

The behavior of anodic porous alumina film formed on an aluminum surface by anodizing in chromic acid solutions was optimized using Box–Wilson experimental design. The film thickness was correlated as a function of temperature, acid concentration, applied voltage, and time. A mathematical model was suggested and optimized. Results of coating thickness indicate that both temperature and time play an important role in the anodizing process. The interaction effect of variables on the film thickness is less pronounced compared with the effect of the main variables. Optimum film thickness obtained during the anodizing process was 10.43 µm at the optimum value of temperature (45 °C) and time (68 min). The polarization measurement of anodized aluminum ASA 6061 in chromic acid shows a high corrosion resistance in the presence of coating. The results confirmed by using surface morphology investigations.

Microbiological Status as a Factor of Airway Complications After Lung Transplantation

BackgroundLung transplantation (LTx) is the only treatment for patients with end-stage lung disease. This procedure is associated with a risk of complications related to airway stenosis, which can be treated by means of bronchoscopic interventions (BI). Microbiological colonization may have an impact on airway complications. The aim of the study was to investigate the effect of presence of microbiological pathogens in graft among lung recipients and frequency of BI, considered as the indicator of severe complications. Materials and MethodsThe study design was single-center retrospective cohort research; cases of 116 patients with complete microbiological data who underwent LTx from April 2013 to June 2019 were reviewed (70.3% of transplanted patients). All statistical analyses were performed with SPSS version 25.0 and R 3.5.3. For analyses involving the number of bronchoscopy interventions, univariate and multivariate Poisson regression were used. Interaction effect of variables in multivariate Poisson regression was assessed with partial response plot. ResultsThe mean number of pathogens colonizing each patient was approximately 4.66 (range, 0 to 19) with Candida albicans (n = 42, 36.2%), Aspergillus spp. (n = 33, 28.4%), Pseudomonas aeruginosa (n = 32, 27.59%), and methicillin-sensitive Staphylococcus aureus (MSSA) (n = 29, 25%) being the most prominent. Microbiological agents causing the greatest increase in the risk of intervention are as follows: Proteus mirabilis by 3.84 times, Aspergillus spp. by 3.53 times, and Stenotrophomonas maltophilia by 3.09 times. Burkholderia multivorans, Enterococcus spp., and Klebsiella spp. do not have a statistically significant impact on the number of BI. ConclusionsSome pathogens increase the frequency of complications, which are associated with deterioration of the general condition. Therefore, patients should be monitored for the presence of pathogens in the airways.

Optimization using the response surface methodology for adsorption of polychlorinated biphenyls (PCBs) from transformer oil by magnetic CMCD-Fe3O4@SiO2 nanoparticles

The main objective of this work was to synthesis a novel magnetic nanoparticles functionalized with β-cyclodextrin and use it to refine transformer oil contaminated with high concentration of PCBs. Carboxymethyl-β-cyclodextrin modified Fe3O4@SiO2 magnetic nanoparticles (CMCD-Fe3O4@SiO2) were fabricated by grafting onto the core-shell silica magnetic nanoparticles surface using carbodiimide. The successful synthesis of nanocomposites was characterized by FTIR, XRD, VSM, SEM, TEM, VSM, TGA and EDX analysis. Hereinafter, the application of response surface methodology (RSM) based on the central composite design (CCD) was employed for optimization of the interactive effects of the three main variables on the removal efficiency of PCBs. A quadratic model was applied as a final model to measure the adequacy and the significance of the individual and interactive effects of variables via analysis of variance. The design matrix under the optimum conditions exhibited that the maximum PCBs removal of 82% was attained to be at adsorbent dosage, contact time and temperature of 10 mg/mL, 30 min, and 50 °C, respectively. The obtained results revealed that the adsorbent dosage is known as the key operating parameter (P < 0.0001) affecting PCB removal efficiency. Thermodynamic parameters (ΔH0 > 0, ΔG0 > 0, ΔS0 > 0) were confirmed that the adsorption process was nonspontaneous and endothermic and assess physical phenomena occurring on the surface of adsorbent. The experimental results were proposed that the as-synthesized β-cyclodextrin -modified magnetic nanoparticles could be an ideal candidate for purification of PCB compounds from contaminated transformer oil owing to its simple process and rapid separation and good reusability after five times.

Statistically Designed, Targeted Profile UPLC Method Development for Assay and Purity of Haloperidol in Haloperidol Drug Substance and Haloperidol 1 mg Tablets

In view of the US pharmacopeia (USP)-proposed general chapter $$\left\langle {1220} \right\rangle$$ “The Analytical Procedure Lifecycle” and ICH-proposed ICHQ14 guideline “Analytical Procedure Development”, application of quality by design approach during analytical method development gains a significant importance. Methods developed by QbD approach will be more robust and thereby decrease efforts for method performance verification and post approval changes. Additionally, these methods will be more QC friendly as out of trend or out of specification results due to incompetent methods will be reduced and it will support the approach of life cycle management for analytical procedures. The present study deals with the development of an analytical targeted profile (ATP)-based, rapid, specific, precise, and mass-compatible RP-UPLC method with proven design space (DS) for the quantitative determination of Haloperidol and its degradation products in Haloperidol drug substance and Haloperidol 1 mg tablets. Using 5 different column chemistries, 4 different pH ranges, oven temperatures, and flow rates as variables experiments were carried out and statistically evaluated to finalize the chromatographic conditions. The significance and interaction effects of variables on the critical attributes (tailing factor and resolution) were evaluated through statistical analysis like analysis of variance (ANOVA). DOE study was also applied for the sample preparation to prove robustness of the sample preparation procedure. Forced degradation study was carried to demonstrate the stability-indicating nature of the method. Validation for active and available impurities was performed as per ICH and ANVISA guidelines and the method was found to be linear, accurate, specific, precise, and robust. Specificity of the method was confirmed orthogonally using QDa (mass detector) along with PDA detector. The method can be employed for release and stability testing of Haloperidol drug substance as well as Haloperidol 1 mg tablets.