Random forest algorithm-enhanced dual-emission molecularly imprinted fluorescence sensing method for rapid detection of pretilachlor in fish and water samples

Advances on chalcogenide quantum dots-based sensors for environmental pollutants monitoring

Water contamination represents a significant ecological impact with global consequences, contributing to water scarcity worldwide. The presence of several pollutants, including heavy metals, pharmaceuticals, pesticides, and pathogens, in water resources underscores a pressing global concern, prompting the European Union (EU) to establish a Water Watch List to monitor the level of these substances. Nowadays, the standard methods used to detect and quantify these contaminants are mainly liquid or gas chromatography coupled with mass spectrometry (LC/GC-MS). While these methodologies offer precision and accuracy, they require expensive equipment and experienced technicians, and cannot be used on the field. In this context, chalcogenide quantum dots (QDs)-based sensors have emerged as promising, user-friendly, practical, and portable tools for environmental monitoring. QDs are semiconductor nanocrystals that possess excellent properties, and have demonstrated versatility across various sensor types, such as fluorescent, electrochemical, plasmonic, and colorimetric ones. This review summarizes recent advances (2019-2023) in the use of chalcogenide QDs for environmental sensing, highlighting the development of sensors capable of detect efficiently heavy metals, anions, pharmaceuticals, pesticides, endocrine disrupting compounds, organic dyes, toxic gases, nitroaromatics, and pathogens.

Molecularly imprinted polymers-isolated AuNP-enhanced CdTe QD fluorescence sensor for selective and sensitive oxytetracycline detection in real water samples

A molecularly imprinted polymers (MIPs)-isolated AuNP-enhanced fluorescence sensor, AuNP@MIPs-CdTe QDs, was developed for highly sensitive and selective detection of oxytetracycline (OTC) in aqueous medium. The developed sensor combined the advantages of strong fluorescence signal of metal-enhanced fluorescence (MEF), high selectivity of MIPs, and stability of CdTe QDs. The MIPs shell with specific recognition served as an isolation layer to adjust the distance between AuNP and CdTe QDs to optimize the MEF system. The sensor demonstrated the detection limit as low as 5.22 nM (2.40 μg/L) for a concentration range of 0.1-3.0 μM OTC and good recovery rates of 96.0-103.0% in real water samples. In addition, high specificity recognition for OTC over its analogs was achieved with an imprinting factor of 6.10. Molecular dynamics (MD) simulation was utilized to simulate the polymerization process of MIPs and revealed H-bond formation as the mainly binding sites of APTES and OTC, and finite-difference time-domain (FDTD) analysis was employed to obtain the distribution of electromagnetic field (EM) for AuNP@MIPs-CdTe QDs. The experimental results combined with theoretical analyses not only provided a novel MIP-isolated MEF sensor with excellent detection performance for OTC but also established a theoretical basis for the development of a new generation of sensors.

Portable smartphone-integrated AuAg nanoclusters electrospun membranes for multivariate fluorescent sensing of Hg2+, Cu2+ and l-histidine in water and food samples

Detection of heavy metals have been pivotal due to their non-biodegradability and food chain accumulation. Herein, a multivariate ratiometric sensor was developed by in situ integrating AuAg nanoclusters (NCs) into electrospun cellulose acetate nanofibrous membranes (AuAg-ENM) for visual detection of Hg²⁺, Cu²⁺ and consecutive sensing of l-histidine (His), which was integrated into a smartphone platform for quantitative on-site detection. AuAg-ENM achieved multivariate detection of Hg²⁺ and Cu²⁺ by fluorescence quenching, and subsequent selective recovery of the Cu²⁺-quenched fluorescence by His, which distinguished Hg²⁺ and Cu²⁺ and fulfilled determination of His simultaneously. Notably, AuAg-ENM achieved selective monitoring of Hg²⁺, Cu²⁺ and His in water, food and serum samples with high accuracy comparable to ICP and HPLC tests. A logic gate circuit was devised to further explain and promote the application of AuAg-ENM detection by smartphone App. This portable AuAg-ENM provides a promising reference for fabricating intelligent visual sensors for multiple detection.

Machine-learning-based predictions of imprinting quality using ensemble and non-linear regression algorithms

The molecularly imprinted polymers are artificial polymers that, during the synthesis, create specific sites for a definite purpose. These polymers due to their characteristics such as stability, easy of synthesis, reproducibility, reusability, high accuracy, and selectivity have many applications. However, the variety of the functional monomers, templates, solvents, and synthesis conditions like pH, temperature, the rate of stirring, and time, limit the selectivity of imprinting. The Practical optimization of the synthetic conditions has many drawbacks, including chemical compound usage, equipment requirements, and time costs. The use of machine learning (ML) for the prediction of the imprinting factor (IF), which indicates the quality of imprinting is a very interesting idea to overcome these problems. The ML has many advantages, for example a lack of human error, high accuracy, high repeatability, and prediction of a large amount of data in the minimum time. In this research, ML was used to predict the IF using non-linear regression algorithms, including classification and regression tree, support vector regression, and k-nearest neighbors, and ensemble algorithms, like gradient boosting (GB), random forest, and extra trees. The data sets were obtained practically in the laboratory, and inputs, included pH, the type of the template, the type of the monomer, solvent, the distribution coefficient of the MIP (K_MIP), and the distribution coefficient of the non-imprinted polymer (K_NIP). The mutual information feature selection method was used to select the important features affecting the IF. The results showed that the GB algorithm had the best performance in predicting the IF, and using this algorithm, the maximum R² value (R² = 0.871), and the minimum mean absolute error (MAE = - 0.982), and mean square error were obtained (MSE = - 2.303).

Rapid quantification of 2,4-dichlorophenol in river water samples using molecularly imprinted polymers coupled to ambient plasma mass spectrometry

Rapid quantification of environmental pollutants is important for water quality control and environmental monitoring. In this work, we report the development of molecularly imprinted polymers (MIPs) obtained from poly(methyl vinyl ether-alt-maleic acid) polymer. The synthesized materials were used for selective preconcentration of 2,4-dichlorophenol, a priority pollutant which creates a threat to public health. The structure of poly(methyl vinyl ether-alt-maleic acid) was functionalized with 4-aminomethylpyridine (4-AMP) to incorporate pyridine groups presumably responsible for increased affinity towards 2,4-dichlorophenol. The synthesis was performed with different degree (10%, 20% and 30%) of 4-AMP functionalization to investigate the influence of pyridine group content on the final MIPs properties. The molecular imprinting process was conducted by amidation of polymers' anhydride groups with diethylenetriamine. Moreover, the experimental data indicated that maximum adsorption capacity was observed for the highest 4-AMP functionalization degree. Similarly, MIPs with the highest 4-AMP content proved to possess the highest selectivity towards the analyte. Finally, the functionalized MIPs were used to quantify 2,4-dichlorophenol by their direct introduction into a specially designed ambient mass spectrometry setup. The detection limits were improved significantly over the ones measured for pure analyte solution. The proposed analytical technique was used to quantify 2,4-dichlorophenol in river water and wastewater samples. Good recovery results were obtained, which proves that the method can be used for analysis of complex real-life samples.

Predictive Models for Knee Pain in Middle-Aged and Elderly Individuals Based on Machine Learning Methods

Aim

This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals.

Methods

A total of 5386 individuals above 45 years old were obtained from the National Health and Nutrition Examination Survey. Participants were randomly divided into a training set and a test set at a 7 : 3 ratio. The training set was used to create a prediction model, whereas the test set was used to validate the proposed model. We constructed multiple predictive models based on three machine learning methods: logistic regression, random forest, and Extreme Gradient Boosting. The model performance was evaluated by areas under the receiver (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. Additionally, we created a simplified nomogram based on logistic regression for better clinical application.

Results

About 31.4% (1690) individuals were with self-reported knee pain. The logistic regression showed that female gender (odds ratio [OR] = 1.28), pain elsewhere (OR = 4.64), and body mass index (OR = 1.05) were significantly associated with increased risk of knee pain. In the test set, the logistic regression (AUC = 0.71) showed similar but slightly higher accuracy than the random forest (AUC = 0.70), while the performance of the Extreme Gradient Boosting model was less reliable (AUC = 0.59). Based on mean decrease accuracy, the most important first five predictions were pain elsewhere, waist circumference, body mass index, age, and gender. Additionally, the most important first five predictions with the highest mean decrease Gini index were pain elsewhere, body mass index, waist circumference, triglycerides, and age. The nomogram model showed good discrimination ability with an AUC of 0.75 (0.73-0.77), a sensitivity of 0.72, specificity of 0.71, a positive predictive value of 0.45, and a negative predictive value of 0.88.

Conclusion

This study proposed a convenient nomogram tool to evaluate the risk of knee pain for the middle-aged and elderly US population in primary care. All the input variables can be easily obtained in a clinical setting, and no additional radiologic assessments were required.