One-Pot, Four-Component Reaction for the Design, Synthesis, and SAR Studies of Novel Pyridines for Insecticidal Bioefficacy Screening against Cowpea Aphid (Aphis craccivora).

In this paper, novel pyridines 2-8 were designed and synthesized via the one-pot, four-component reaction of 2-formylphenyl 4-tolylsulfonate with malononitrile, ammonium acetate, and phenols or 2-thioxo-1,3-thiazolidin-4-one or 6-aminopyrimidine-2,4(1H,3H)-dione under microwave irradiation in an aqueous solution of water and ethanol (1:1 ratio). The structures of new pyridines 2-8 were elucidated by elemental and spectral analyses such as IR, 1H NMR, and 13CNMR. This application has many advantages, such as having easy workup, eco-friendliness, reaction time being short (6-13 min), high production (94-98%), inexpensiveness, and avoiding the use of harmful solvents. Moreover, all compounds have been investigated as insecticidal agents against cowpea aphid (Aphis craccivora) insects, and the toxicity effect was studied, followed by the structure-activity relationship. From the LC50 values, it has been found that compounds 7 and 8 were excellent and promising insecticidal agents, with LC50 values of 0.05 and 0.09 ppm against nymphs and 0.93 and 1.01 ppm against adults of cowpea aphid. Furthermore, the obtained results indicated that compounds 2-8 can be applied as insecticidal agents for the control of cowpea aphids and to protect agricultural crops from this destructive pest, which effects crop production and causes major economic damage.

Synthesis of tetrazole hybridized with thiazole, thiophene or thiadiazole derivatives, molecular modelling and antimicrobial activity.

Background: Tetrazole-based derivatives and their electronic structures have displayed interesting antimicrobial activity. Methods: The tetrazole-based hybrids linked with thiazole, thiophene and thiadiazole ring systems have been synthesized through various chemical reactions. The computational method DFT/B3LYP has been utilized to calculate their electronic properties. The antimicrobial effectiveness was investigated against representative bacterial and fungal strains. Additionally, the synthesized derivatives binding interaction was stimulated by docking program against PDB ID: 4URO as a model of the ATP binding domain of S. aureus DNA Gyrase subunit B. Results: The structures of the synthesized tetrazole-based derivatives were confirmed by IR, NMR, and Mass spectroscopic data. The DFT/B3LYP method showed that the thiadiazole derivatives 9a-c had lower ΔEH-L than the thiophenes 7a-c and thiazoles 5a-c. The hybrids 5b, 5c, and 7b exhibited proper antibacterial activity against Gram's +ve bacterial strains (S. aureus and S. pneumonia), while 9a displayed potent activity towards Gram's -ve bacterial strains (S. typhimurium and E. coli). Meanwhile, derivatives 5a-b, 7a, 7c, and 9c showed good effectiveness towards fungal strain (C. albicans). Conclusion: The study provides valuable tetrazole core-linked heterocyclic rings and opens the door to further research on their electrical characteristics and applications. Tetrazoles and thiazoles have antibacterial properties in pharmacological frameworks, making these hybrids potential lead molecules for drug development. The conclusion summarizes the data and suggests that the synthesized chemicals' interaction with a particular protein domain suggests focused biological activity.

C3-Spirooxindoles: Divergent chemical synthesis and bioactivities (2018-2023).

This scientific review documents the recent progress of C3-spirooxindoles chemistry (synthesis and reaction mechanism) and their bioactivities, focusing on the promising results as well as highlighting the biological mechanism via the reported molecular docking findings of the most bioactive derivatives. C3-Spirooxindoles are attractive bioactive agents and have been found in a variety of natural compounds, including alkaloids. They are widely investigated in the field of medicinal chemistry and play a key role in medication development, such as antivirals, anticancer agents, antimicrobials, etc. Regarding organic synthesis, several traditional and advanced strategies have been reported, particularly those that started with isatin derivatives.

Synthesis, spectrophotometric, pharmacology and theoretical investigation of a new electron transfer complex of 8-hydroxyquinoline with oxalic acid in different polar solvents.

Preparation, characterization, and investigation of a novel organic charge transfer (CT) complex were carried out, with a focus on exploring its antibacterial and antifungal characteristics. Theoretical analysis backs up the experimental findings. CT complex formed was synthesized between 8-hydroxyquinoline (8HQ) and oxalic acid (OA) at RT (room temperature). Different analyses were used to describe the CT complex, including 1H-NMR, FTIR, TGA/DTA, and UV-vis spectra (in different solvents). These indicate that the CT interaction is linked to proton transfer from OA to 8HQ and the subsequent development of 'N+__H…O-" type bonding. On the basis of wave number, the CT complex and reactants are distinguished in FTIR spectra. By using Thermo gravimetric Analysis/Differential Thermal Analysis (TGA/DTA) tests, the thermal stability of complicated and thorough corrosion was examined. Through UV-visible spectroscopy, physical characteristics like ECT (interaction energy), RN (resonance energy), ID (ionization potential), f (oscillator strength) and ΔG (free energy) were calculated. The εCT (molar extinction coefficient), the KCT (formation constant), and additional physical properties of this complex were calculated by the Benesi-Hildebrand equation in order to determine its 1:1 stoichiometry. The biological properties are also supported by theoretical study. The protein, Human Serum Albumin (HSA), is observed to bind with CT complex, as shown by molecular docking and the observed binding energy value is -167.04 kcal/mol. Molecular dynamics (MD) simulation 100 ns run was used to refine docking results and binding free energy was calculated using MM-PBSA. This study introduces a novel CT complex, offering fresh perspectives on molecular interactions.Communicated by Ramaswamy H. Sarma.

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records.

BACKGROUND: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records data for identifying such patients can ultimately help provide better health outcomes. OBJECTIVE: Our study investigated the performance of predictive models to forecast HbA1c elevation levels by employing several machine learning models. We also examined the use of patient electronic health record longitudinal data in the performance of the predictive models. Explainable methods were employed to interpret the decisions made by the black box models. METHODS: This study employed multiple logistic regression, random forest, support vector machine, and logistic regression models, as well as a deep learning model (multilayer perceptron) to classify patients with normal (<5.7%) and elevated (≥5.7%) levels of HbA1c. We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large data set from Saudi Arabia with 18,844 unique patient records. RESULTS: The machine learning models achieved promising results for predicting current HbA1c elevation risk. When coupled with longitudinal data, the machine learning models outperformed the multiple logistic regression model used in the comparative study. The multilayer perceptron model achieved an accuracy of 83.22% for the area under receiver operating characteristic curve when used with historical data. All models showed a close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data. CONCLUSIONS: This study shows that machine learning models can provide promising results for the task of predicting current HbA1c levels (≥5.7% or less). Using patients' longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies.

Molecular characterization of carbapenem-resistant Enterobacterales in thirteen tertiary care hospitals in Saudi Arabia.

BACKGROUND: Carbapenems are the antibiotics of last-resort for the treatment of bacterial infections caused by multidrug-resistant organisms. The emergence of resistance is a critical and worrisome problem for clinicians and patients. Carbapenem-resistant Enterobacterales (CRE) are spreading globally, are associated with an increased frequency of reported outbreaks in many regions, and are becoming endemic in many others. OBJECTIVES: Determine the molecular epidemiology of CRE isolates from various regions of Saudi Arabia to identify the genes encoding resistance and their clones for a better understanding of the epidemio-logical origin and national spread. DESIGN: Multicenter, cross-sectional, laboratory-based study. SETTING: Samples were collected from 13 Ministry of Health tertiary-care hospitals from five different regions of Saudi Arabia. METHODS: Isolates were tested using the GeneXpert molecular platform to classify CRE. MAIN OUTCOME MEASURES: Prevalence of various types of CRE in Saudi Arabia. SAMPLE SIZE: 519 carbapenem-resistant isolates. RESULT: Of 519 isolates, 440 (84.7%) were positive for CRE, with Klebsiella pneumoniae (410/456, 90%) being the most commonly isolated pathogen. The distribution of the CRE-positive K pneumoniae resistance genes was as follows: OXA-48 (n=292, 71.2%), NDM-1 (n=85, 20.7%), and NDM+OXA-48 (n=33, 8%). The highest percentage of a single blaOXA-48 gene was detected in the central and eastern regions (77%), while the blaNDM-gene was the predominant type in the northern region (27%). The southern regions showed the lowest percentages for harboring both blaOXA-48 and blaNDM genes (4%), while the western region isolates showed the highest percentage of harboring both genes (14%). CONCLUSION: The results illustrate the importance of molecular characterization of CRE isolates for patient care and infection prevention and control. Larger multicenter studies are needed to critically evaluate the risk factors and trends over time to understand the dynamics of spread and effective methods of control. LIMITATIONS: Lack of phenotypic susceptibility and clinical data. CONFLICT OF INTEREST: None.

Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study.

BACKGROUND: Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. OBJECTIVE: The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. METHODS: We present a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. RESULTS: The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression-based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29% . CONCLUSIONS: The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs.

A review of influenza detection and prediction through social networking sites.

Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.