The prenyl fragment is the quintessential constituent of terpenoid natural products, a diverse family which contains numerous members with diverse biological properties. In contrast, fluorinated and multifluorinated arenes make up an important class of anthropogenic molecules which are highly relevant to material, agricultural, and pharmaceutical industries. While allylation chemistry is well developed, effective prenylation strategies hav e been less forthcoming. Herein, we describe the photocatalytic defluoroprenylation, a powerful method that provides access to “hybrid molecules” that possess both the functionality of a prenyl group and fluorinated arenes. This approach involves direct prenyl group transfer under very mild conditions, displays excellent functional group tolerance, and relatively short reaction times (<4 h), which is the fastest photocatalytic C–F functionalization developed to date. Additionally, the strategy can be extended to include allyl and geranyl (10 carbon fragment) transfers. Another prominent finding is a reagent dependent switch in regioselectivity of the major product from para to ortho C–F functionalization.

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug‐drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non‐linear datasets, as well as big data of increasing depth and complexity. Various machine learning‐based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand‐based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

An innovative protocol to the synthesis of this material emerged on exploring the potential of the various form of N-aminophthalimides on its reaction with a number of aromatic aldehydes. New series of biologically active substituted Schiff bases with general formula, R1N=CHR2 where R1 = 3-nitro-N-aminophthalimide, 3-bromo-N-aminophthalimide, 4-nitro-N-aminophthalimide, 4-bromo-N-aminophthalimide, R2 =2, 6-dichlorobenzaldehyde, o-anisaldehyde and o-vanillin were synthesized by the reaction of substituted N-aminophthalimides and substituted aldehydes in ethanol. Moreover N-aminophthalimides (50-70% yield) were synthesized by reactions of corresponding phthalimides and hydrazine hydrate. Such compounds were characterized by different physico-chemical techniques like, melting point, elemental analysis, multinuclear NMR (1H, 13C).The synthesized compounds were screened for antibacterial and antifungal activities. The explorations of the biological properties of the compounds are mentioned in this paper.

Portable Automated Rapid Testing (PART) is an iPad application (https://braingamecenter.ucr.edu/games/p-a-r-t/) capable of measuring various psychoacoustic thresholds (speech on speech masking, temporal, spectral, and spectrotemporal modulation detection, temporal gap detection, frequency modulation tasks, and pure tone detection in noise) in an automated and rapid format using commercially available headphones. The app has been validated previously in native speakers of English and an adapted version of the app has been validated in young normal hearing listeners from Mexico. Here, we present psychoacoustic threshold data for a large cohort of younger college going listeners from India whose native language is not English but are proficient in English. The thresholds obtained from this group will be compared against previously published thresholds based on native speakers of English. It is hypothesized that there will not be any significant differences in thresholds between listeners from India and the thresholds published in the literature. These results will give us the required evidence to start using this freely available app to measure reliable thresholds of various central auditory processing measures in non-native speakers of English who are proficient in the language as well.

Squamous odontogenic tumor is a rare epithelial odontogenic tumor. Only a few cases have been reported in the literature, out of which central cases occurring within the jawbones are more common, and very rarely peripheral cases have been reported. Here, we report a rare case of a peripheral squamous odontogenic tumor occurring in the left lower gingivobuccal sulcus region in a 63-year-old female patient and presented as a firm and fibrotic swelling clinically. Radiographic examination revealed no evidence of a central lesion in the bone. Excisional biopsy of the lesion revealed the characteristic histopathological picture of peripheral squamous odontogenic tumor (PSOT). The healing was found to be satisfactory upon periodic evaluation of the patient done to study the healing and for any recurrence of the lesion.