Etemadi regression in chemometrics: Reliability-based procedures for modeling and forecasting

The creation of predictive models with a high degree of generalizability in chemical analysis and process optimization is of paramount importance. Nonetheless, formulating a prediction model based on collected data from chemical measurements that maximize quantitative generalizability remains a challenging task for chemometrics experts. To tackle this challenge, a range of forecasting models with varying characteristics, structures, and capabilities has been developed, utilizing either accuracy-based or reliability-based modeling strategies. While the majority of models follow the accuracy-based approach, a recently proposed reliability-based approach, known as the Etemadi approach, has shown impressive performance across various scientific fields. The Etemadi models were constructed through a reliability-based parameter estimation process in such a manner that maximizes the models' reliability. However, the foundation of modeling procedures for chemometrics purposes is built upon the assumption that high generalizability in inaccessible/test data is achieved through the accuracy-based training procedure in which errors in available historical/training data are minimized. After conducting a thorough review of the current literature, we have found that none of the forecasting models for chemometrics purposes incorporate reliability into their modeling procedures. Given the dynamic and highly sensitive nature of chemistry experiments and processes, implementing a reliable model that controls performance criteria variation is a promising strategy for achieving stable and robust forecasts. To address this research gap, this paper introduces several key innovations, which can be highlighted as follows: (1) Proposing a general design structure based on a new optimal reliability-based parameter estimation process. (2) Introducing a novel risk-based modeling strategy that minimizes the performance variation of models implemented under different conditions in chemical laboratory experiments, to generate a more generalizable model for diverse applications in chemometrics. (3) Specifying the degree of influence that each reliability and accuracy factor has in enhancing the generalizability and uncertainty modeling of chemometric models. Empirical evidence confirms the effectiveness and superior performance of reliability-based models compared to accuracy-based models in 78.95% of the cases across various fields, including Pharmacology, Biochemistry, Agrochemical, Geochemical, Biological, Pollutants, Physicochemical Properties, and Gases Experiment. Furthermore, the study's findings demonstrate that the reliability-based modeling approach outperforms the accuracy-based strategy in terms of MAE, MSE, ARV, and RMSE by an average of 4.697%, 5.646%, 5.646%, and 4.342%, respectively. It is also statistically proven that reliability has a more significant impact on improving the generalizability of chemometric models than accuracy. This emphasizes the importance of including reliability as a crucial factor in chemometrics modeling, a consideration that has been overlooked in traditional modeling processes. Consequently, reliability-based modeling approaches can be regarded as a viable alternative to conventional accuracy-based modeling methods for chemical modeling purposes.

A novel reliability-based regression model for medical modeling and forecasting

BACKGROUND AND AIMS

In recent decades, modeling and forecasting have played a significant role in the diagnosis and treatment of different diseases. Various forecasting models have been developed to improve data-based decision-making processes in medical systems. Although these models differ in many aspects, they all originate from the assumption that more generalizable results are achieved by more accurate models. This means that accuracy is considered as the only prominent feature to evaluate the generalizability of forecasting models. On the other side, due to the changeable medical situations and even changeable models' results, making stable and reliable performance is necessary to adopt appropriate medical decisions. Hence, reliability and stability of models' performance is another effective factor on the model's generalizability that should be taken into consideration in developing medical forecasting models.

METHODS

In this paper, a new reliability-based forecasting approach is developed to address this gap and achieve more consistent performance in making medical predictions. The proposed approach is implemented on the classic regression model which is a common accuracy-based statistical method in medical fields. To evaluate the effectiveness of the proposed model, it has been performed by using two medical benchmark datasets from UCI and obtained results are compared with the classic regression model.

RESULTS

Empirical results show that the proposed model has outperformed the classic regression model in terms of error criteria such as MSE and MAE. So, the presented model can be utilized as an appropriate alternative for the traditional regression model in making effective medical decisions.

CONCLUSIONS

Based on the obtained results, the proposed model can be an appropriate alternative for traditional multiple linear regression for modeling in real-world applications, especially when more generalization and/or more reliability is needed.

The synergistic effect of turmeric aqueous extract and chitosan against multidrug-resistant bacteria

We aimed to investigate the antibacterial and antibiofilm effects of turmeric and chitosan against the planktonic and biofilm forms of multidrug-resistant (MDR) bacteria. A group of MDR bacteria, including clinical isolates of methicillin-resistant Staphylococcus aureus, carbapenem-resistant Pseudomonas, carbapenem-resistant Enterobacteriaceae and AmpC-producing Enterobacteriaceae, were collected by phenotypic and genotypic assays. The broth microdilution method was used to investigate the MIC of turmeric aqueous extract and chitosan. To investigate the synergistic effect of the combination of these natural compounds, we used the checkerboard assay. According to the results of this study, turmeric and chitosan showed inhibitory effects on MDR bacteria, especially on the planktonic form of methicillin-resistant S. aureus as a Gram-positive compared to tested Gram-negative bacteria (carbapenem-resistant Pseudomonas, carbapenem-resistant Enterobacteriaceae and AmpC-producing Enterobacteriaceae). The antibiofilm effect of turmeric and chitosan was found more often in carbapenem-resistant Pseudomonas isolates. There was no significant difference between the tested Gram-negative bacteria because most of the tested strains were inhibited in 512 and 1024 μg/mL concentrations of chitosan and turmeric aqueous extract. In this study, turmeric aqueous extract and chitosan exhibited significant antibacterial and antibiofilm properties. However, the effect of these compounds should be investigated using in vivo models for use in pharmaceutical and disinfectant formulations.

Toxoplasmosis as a complication of transfusion in hemodialysis patients

BACKGROUND

Toxoplasma Gondi is an obligate intracellular protozoan parasite that is one of the most important protozoa of blood and tissue. The medical importance of this parasite is considered from two aspects of congenital defects and opportunities among those with congenital immune deficiency. Depending on the mode of transmission through blood and the risk of infection to Toxoplasma Gondi in hemodialysis patients, this serological study was conducted on Iranian population.

MATERIALS AND METHODS

This case-control study was conducted on 37 patients that underwent regular hemodialysis that 21 were male and 16 were female, and the mean age of them was 17.52±4.10 years (rages 13-22 years). Thirty-seven healthy individuals were chosen as control group. All samples were tested by using of ELISA kits with two methods of IgG-ELISA and IgM-ELISA. Finally obtained data was analysis by SPSS software.

RESULTS

The results of this study revealed high prevalence of toxoplasmosis among hemodialysis patients. Other findings indicate that 21 out of 37 patients in the case group were positive for anti-Toxoplasma Gondi IgG in case group while in control group just 11 individuals were positive that was a statistically significant difference(p <0.05 ).

CONCLUSION

because of the high prevalence of toxoplasmosis among hemodialysis patients, identification of these patients for prevention of transfusion complications is important.

Assessment of tooth preparation designs for posterior resin-bonded porcelain restorations

Tooth preparation designs advocated for posterior resin-bonded porcelain restorations were evaluated for clinical application. This study examined the design of tooth preparations from stone dies for individual posterior resin-bonded porcelain onlays and complete porcelain crowns placed in a specialist practice, and compared features that provided retentive and porcelain fracture-resistance with those in the dental literature. Stone dies of tooth preparations for 57 onlay and 46 crown preparations were provided. Measurements were recorded for the tooth preparations with the aid of interocclusal silicone impressions. Proximal boxes, grooves and occlusal steps resulting from removal of amalgam restorations were common. These retentive features were more evident with onlays, and appeared larger on the distal than the mesial surfaces. Internal tooth preparation tapers or convergence angles were frequently greater than 20 degrees, leading to relatively-wide occlusal isthmus widths, especially for onlays. There were wide variations in occlusal surface reductions for the central fossae and supporting cusps. Tapers for tooth preparations were frequently more extensive, and occlusal surface reductions were often less than recommended. The implications of these differences for long-term clinical success should be resolved.