Pathogens that play a role in the development and progression of periodontitis have gained significant attention due to their implications in the onset of various systemic diseases. Periodontitis is characterized as an inflammatory disease of the gingival tissue that is mainly caused by bacterial pathogens. Among them, Porphyromonas gingivalis, Treponema denticola, Fusobacterium nucleatum, Aggregatibacter actinomycetemcomitans, and Tannerella forsythia are regarded as the main periodontal pathogens. These pathogens elicit the release of cytokines, which in combination with their virulence factors induce chronic systemic inflammation and subsequently impact neural function while also altering the permeability of the blood–brain barrier. The primary objective of this review is to summarize the existing information regarding periodontal pathogens, their virulence factors, and their potential association with neuroinflammation and neurodegenerative diseases. We systematically reviewed longitudinal studies that investigated the association between periodontal disease and the onset of neurodegenerative disorders. Out of the 24 studies examined, 20 showed some degree of positive correlation between periodontal disease and neurodegenerative disorders, with studies focusing on cognitive function demonstrating the most robust effects. Therefore, periodontal pathogens might represent an exciting new approach to develop novel preventive treatments for neurodegenerative diseases.

Molecularly imprinted polymers (MIPs) are promising sorbents for selectively capturing pharmaceutically active compounds (PhACs), but design remains slow because candidate screening is largely experimental or based on computationally expensive methods. We present MIP–PhAC, an open, curated resource of polymer–pharmaceutical interaction energies generated from molecular dynamics (MD) followed by MM/PBSA analysis, with a small DFT subset for cross-method comparison. This resource is comprised of two complementary datasets: MIP–PhAC-Calibrated, a benchmark set with manually verified pH-7 microstates that reports both monomeric (pre-polymerized) and polymeric (short-chain) MD/MMPBSA energies and includes a DFT subset; and MIP–PhAC-Screen, a broader, high-throughput collection produced under a uniform automated workflow (including automated protonation) for rapid within-polymer ranking and machine learning development. For each MIP—PhAC pair we provide ΔG* components (electrostatics, van der Waals, polar and non-polar solvation; −TΔS omitted), summary statistics from post-convergence frames, simulation inputs, and chemical metadata. To our knowledge, MIP–PhAC is the largest open, curated dataset of polymer–pharmaceutical interaction energies to date. It enables benchmarking of end-point methods, reproducible protocol evaluation, data-driven ranking of polymer–pharmaceutical combinations, and training/validation of machine learning (ML) models for MIP design on modest compute budgets.

Pharmaceutical compounds are small, invisible, and biologically powerful molecules that, due to widespread production and consumption, have become part of the environment, causing long-term adverse effects on biota even at low doses. Advances in sensitive and reliable analytical methods have made their detection possible in complex environmental matrices such as wastewater. Given the large number of synthesized pharmaceuticals with various therapeutic purposes, the occurrence of a synergistic effect is to be expected, interfering with their analysis. Therefore, the challenging analysis is often improved through the application of different sample preparation techniques. This paper includes the development of an SPE-HPLC-DAD method for the determination of eleven pharmaceuticals from water samples. To achieve better recoveries for the specified pharmaceutical (sulfamethoxazole) and possibly other components of the mixture, a sulfamethoxazole-imprinted polymer (MIP-SMETOX) was prepared and used in combination with a commercial sorbent (Oasis HLB) for MIP-SPE-HPLC-DAD. After optimization of the extraction conditions, both methods were validated. The LOD was 0.1 to 0.5 µg/L for SPE-HPLC-DAD and 0.1 to 0.25 µg/L for MIP-SPE-HPLC-DAD, depending on the pharmaceuticals. The matrix effect is different (77–196%) for both methods. A decrease in the signal for sulfamethoxazole (77%) was observed with SPE-HPLC-DAD, while MIP-SMETOX as a sorbent is not suitable for procaine (196%), and this is also the highest matrix effect. To extend the data obtained, additional in silico methods were used to gain deeper insights into the nature and strength of the binding interactions. Both methods (with and without MIP) confirmed their purpose by determining various validation performance features, and the final goal of the developed methods was tested using complex wastewater. The MIP-SMETOX produced justified its production, as the MIP-SPE-HPLC-DAD method is generally slightly better than the method using only a commercial sorbent.