Microporous carbon fibers prepared from cellulose as efficient sorbents for removal of chlorinated phenols

Research on Chemical Intermediates - Cellulose-based microporous carbon fibers (CFs) were evaluated for the adsorption of 2-Chlorophenol (CP), 2,4-Dichlorophenol (DCP), and 2,4,6-Trichlorophenol...     

Structure and stereodynamics of N,N-bis(silyloxy)enamines

The structure and stereodynamics of N,N-bis(silyloxy)enamines (1), a new class of enamines with extraordinary reactivity, have been simulated by the DFT PBE/TZP method. The computed pattern of dynamic behavior and structural peculiarities of 1 was shown to reflect adequately the results of the studies by a series of physical methods including X-ray analysis and dynamic NMR and UV spectroscopies, which provided evidence of a rather low barrier for rotation around the C,N single bond, a negligible contribution of the n-pi-conjugation, a high barrier of inversion, and high pyramidality of the nitrogen atom.     

A new depsidone derivative from the leaves of Garcinia polyantha

One new depsidone, polyanthadepsidone A (1), together with four known compounds were isolated from the dichloromethane extract of the leaves of Garcinia polyantha. The structures of all compounds were determined by comprehensive analyses of their 1D and 2D NMR and EI mass spectral data. All the isolates exhibited suppressive effect on phagocytosis response upon activation with serum opsonised zymosan in the IC₅₀ range of 4.5–23.80 μM, tested in vitro for oxidative burst studies of whole blood.     

Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis

Artificial Intelligence (AI) is critical for data-driven decision making to increase resource utilization, operational performance, and service quality in various industry domains, particularly in healthcare. Using AI in healthcare operations can significantly improve treatment outcomes and enhance patient satisfaction while reducing costs. In this paper, we propose a multi-stage framework to build an AI-based decision support tool that can predict the 5-year survivability of lung cancer patients. We evaluate the proposed framework using the Surveillance, Epidemiology, and End Results dataset pertaining to the 1973–2015 period obtained from the National Institutes of Health. The first stage entails data preprocessing and target creation. The second stage applies six AI algorithms with feature selection through Particle Swarm Optimization and hyperparameter tuning with Cross-Validation. These Algorithms include Logistic Regression, Decision Trees, Random Forests (RF), Adaptive Boosting (AdaBoost), Artificial Neural Network, and Naïve Bayes. The results show that RF and AdaBoost models yield an AUC rate of 0.94 and outperform the other models. Stage 3 utilizes permutation importance to interpret the RF and AdaBoost models and applies Tree-based Augmented Naïve Bayes to gain insights regarding the interrelations among important features. The results of Stage 3 delineate that the number of lymph nodes containing metastases), the number of tumors that patients have had in their lifetime, the patient’s age, and the microscopic composition of cells rank among the topmost important features and can significantly impact patient survivability. We think this study has significant practical implications in helping physicians predict prognosis and develop treatment plans for lung cancer patients.

     

TARTROL-derived chiral phosphine–phosphite ligands and their performance in enantioselective Cu-catalyzed 1,4-addition reactions

By using (R,R,R,R)-2,3-dimethoxy-2,3-dimethyl-1,4-dioxane-5,6-bis-diphenylmethanol (TARTROL) as a chiral building block, a set of six modular phosphine–phosphite ligands (with a 1,2-phenylene backbone) were synthesized and evaluated in the Cu-catalyzed asymmetric 1,4-addition of Grignard reagents to cyclohexenone. Ligands with bulky substituents at the ortho- and para-positions to the chiral phosphite moiety were found to be the most selective affording the 1,4-addition products with enantioselectivities of up to 84% ee.     

Unified presentation of p-adic L-functions associated with unification of the special numbers

By using partial differential equations (PDEs) of the generating functions for the unification of the Bernoulli, Euler and Genocchi polynomials and numbers, we derive many new identities and recurrence relations for these polynomials and numbers. In [33], Srivastava et al. defined a unified presentation of certain meromorphic functions related to the families of the partial zeta type functions. By using these functions, we construct p-adic functions which are related to the partial zeta type functions. By applying these p-adic function, we construct unified presentation of p-adic L-functions. These functions give us generalization of the Kubota–Leopoldt p-adic L-functions, which are related to the Bernoulli numbers and the other p-adic L-functions, which are related to the Euler numbers and polynomials. We also give some remarks and comments on these functions.     

Concise preparation of azenes by oxidation of aromatic amines with molecular oxygen in subcritical water

Abstract  

Reaction of organic substrates with molecular oxygen, the most abundant and accessible oxidant, has always been an attractive method for preparation of target molecules. In terms of green chemistry, non-metal-catalyzed oxidation of organic substrates is very attractive. This paper describes a general procedure for synthesis of azenes by oxidation of primary aromatic amines with molecular oxygen (³O₂) in subcritical water. The reactions afforded the corresponding azenes in moderate to good yield.     

Evaluation and discrimination of simvastatin-induced structural alterations in proteins of different rat tissues by FTIR spectroscopy and neural network analysis

Statins are commonly used to control hypercholesterolemia and to prevent cardiovascular diseases. Among the statins, Simvastatin is one of the most frequently prescribed statins because of its efficacy in reducing LDL lipoprotein cholesterol levels, its tolerability, and its reduction of cardiovascular risk and mortality. Conflicting results have been reported with regard to benefits (pleiotropic effects) as well as risks (adverse effects) of simvastatin on different soft and hard tissues. In the current study, Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was used to obtain detailed information about protein conformational changes due to simvastatin therapy of soft tissues namely liver, testis, sciatic nerve and hard tissues such as femur and tibia. Protein secondary structural changes were predicted by intensity calculations from second derivative spectra and neural network (NN) analysis, using the amide I band (1700-1600 cm(-1)) of FTIR spectra. Moreover, based on protein secondary structural differences, hierarchical cluster analysis was carried out in the 1700-1600 cm(-1) region. The results of our study in liver, testis and sciatic nerve tissues revealed that simvastatin treatment significantly decreased alpha helix structure and beta sheet structure at 1638 cm(-1), while increased the anti-parallel and aggregated beta sheet and random coil structures implying a simvastatin-induced protein denaturation in treated groups. Different to soft tissues, the results of hard tissue studies on femur and tibia bones revealed increased alpha helix structure and decreased anti-parallel beta sheet, aggregated beta sheet and random coil structures implying more strengthened bone tissues in simvastatin-treated groups. Finally, the simvastatin-treated and control groups for all soft and bone tissues were successfully differentiated using cluster analysis. According to the heterogeneity values in the cluster analysis of these tissues, the sciatic nerve tissue was found to be the most affected tissue from simvastatin treatment among the studied soft tissues. In addition, the high heterogeneity value implied high secondary structural difference between control and simvastatin-treated groups in tibia bone tissues. These findings reveal that FTIR spectroscopy with bioinformatic analyses such as neural network and hierarchical clustering, allowed us to determine the simvastatin-induced protein conformational changes as adverse and pleiotropic effects of the drug on different soft and hard tissues.