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We study the influence of nodal structures in two-dimensional quantum mechanical densities on wave packet entanglement. This is motivated by our recent study [Entropy, 25, 970 (2023)], which showed that the mutual information derived from the momentum-space probability density of a coupled two-particle system exhibits an unusual time dependence, which is not encountered if the position-space density is employed in the calculation. In studying a model density, here, we identify cases where the mutual information increases with the number of nodes in the wave function and approaches a finite value, whereas in this limit, the linear correlation vanishes. The results of the analytical model are then applied to interpret the correlation measures for coupled electron-nuclear dynamics, which are treated by numerically solving the time-dependent Schrödinger equation.
In DNA-encoded library synthesis, amine-substituted building blocks are prevalent. We explored isocyanide multicomponent reactions to diversify DNA-tagged amines and reported the Ugi-azide reaction with high yields and a good substrate scope. In addition, the Ugi-aza-Wittig reaction and the Ugi-4-center-3-component reaction, which used bifunctional carboxylic acids to provide lactams, were explored. Five-, six-, and seven-membered lactams were synthesized from solid support-coupled DNA-tagged amines and bifunctional building blocks, providing access to structurally diverse scaffolds.
The binding of drugs to plasma proteins is an important process in the human body and has a significant influence on pharmacokinetic parameter. Human serum albumin (HSA) has the most important function as a transporter protein. The binding of ketamine to HSA has already been described in literature, but only of the racemate. The enantiomerically pure S-ketamine is used as injection solution for induction of anesthesia and has been approved by the Food and Drug Administration for the therapy of severe depression as a nasal spray in 2019. The question arises if there is enantioselective binding to HSA. Hence, the aim of this study was to investigate whether there is enantioselective binding of S-and R-ketamine to HSA or not. Ultrafiltration (UF) followed by chiral capillary electrophoretic analysis was used to determine the extent of protein binding. Bound fraction to HSA was 71.2 % and 64.9 % for enantiomerically pure R- and S-ketamine, respectively, and 66.5 % for the racemate. Detailed binding properties were studied by Saturation Transfer Difference (STD)-, waterLOGSY- and Carr-Purcell-Meiboom-Gill (CPMG)-NMR spectroscopy. With all three methods, the aromatic ring and the N-methyl group could be identified as the structural moieties most strongly involved in binding of ketamine to HSA. pK\(_{aff}\) values determined using UF and NMR indicate that ketamine is a weak affinity ligand to HSA and no significant differences in binding behavior were found between the individual enantiomers and the racemate.
DNA alkyltransferase and alkyltransferase-like family proteins are responsible for the repair of highly mutagenic and cytotoxic O\(^6\)-alkylguanine and O\(^4\)-alkylthymine bases in DNA. Their mechanism involves binding to the damaged DNA and flipping the base out of the DNA helix into the active site pocket in the protein. Alkyltransferases then directly and irreversibly transfer the alkyl group from the base to the active site cysteine residue. In contrast, alkyltransferase-like proteins recruit nucleotide excision repair components for O\(^6\)-alkylguanine elimination. One or more of these proteins are found in all kingdoms of life, and where this has been determined, their overall DNA repair mechanism is strictly conserved between organisms. Nevertheless, between species, subtle as well as more extensive differences that affect target lesion preferences and/or introduce additional protein functions have evolved. Examining these differences and their functional consequences is intricately entwined with understanding the details of their DNA repair mechanism(s) and their biological roles. In this review, we will present and discuss various aspects of the current status of knowledge on this intriguing protein family.
Highlights
• Synthesis of a new tracer molecule.
• Robust and easy screening method for a broad range of compound activities.
• FP assay validation considering limited use of starting material, DMSO tolerance, variation in incubation time and temperature.
• Possibility of extension to HTP assay.
Abstract
The macrophage infectivity potentiator (Mip) protein belongs to the immunophilin superfamily. This class of enzymes catalyzes the interconversion between the cis and trans configuration of proline-containing peptide bonds. Mip has been shown to be important for the virulence of a wide range of pathogenic microorganisms, including the Gram-negative bacterium Burkholderia pseudomallei. Small molecules derived from the natural product rapamycin, lacking its immunosuppression-inducing moiety, inhibit Mip's peptidyl-prolyl cis-trans isomerase (PPIase) activity and lead to a reduction in pathogen load in vitro. Here, a fluorescence polarization assay (FPA) to enable the screening and effective development of BpMip inhibitors was established. A fluorescent probe was prepared, derived from previous pipecolic scaffold Mip inhibitors labeled with fluorescein. This probe showed moderate affinity for BpMip and enabled a highly robust FPA suitable for screening large compound libraries with medium- to high-throughput (Z factor ∼ 0.89) to identify potent new inhibitors. The FPA results are consistent with data from the protease-coupled PPIase assay. Analysis of the temperature dependence of the probe's binding highlighted that BpMip's ligand binding is driven by enthalpic rather than entropic effects. This has considerable consequences for the use of low-temperature kinetic assays.
Anxious depression represents a subtype of major depressive disorder and is associated with increased suicidality, severity, chronicity and lower treatment response. Only a few studies have investigated the differences between anxious depressed (aMDD) and non-anxious depressed (naMDD) patients regarding treatment dosage, serum-concentration and drug-specific treatment response. In our naturalistic and prospective study, we investigated whether the effectiveness of therapy including antidepressants (SSRI, SNRI, NaSSA, tricyclics and combinations) in aMDD patients differs significantly from that in naMDD patients. In a sample of 346 patients, we calculated the anxiety somatization factor (ASF) and defined treatment response as a reduction (≥50%) in the Hamilton Depression Rating Scale (HDRS)-21 score after 7 weeks of pharmacological treatment. We did not observe an association between therapy response and the baseline ASF-scores, or differences in therapy outcomes between aMDD and naMDD patients. However, non-responders had higher ASF-scores, and at week 7 aMDD patients displayed a worse therapy outcome than naMDD patients. In subgroup analyses for different antidepressant drugs, venlafaxine-treated aMDD patients showed a significantly worse outcome at week 7. Future prospective, randomized-controlled studies should address the question of a worse therapy outcome in aMDD patients for different psychopharmaceuticals individually.
Dimethyl fumarate attenuates lymphocyte infiltration and reduces infarct size in experimental stroke
(2023)
Ischemic stroke is associated with exacerbated tissue damage caused by the activation of immune cells and the initiation of other inflammatory processes. Dimethyl fumarate (DMF) is known to modulate the immune response, activate antioxidative pathways, and improve the blood–brain barrier (BBB) after stroke. However, the specific impact of DMF on immune cells after cerebral ischemia remains unclear. In our study, male mice underwent transient middle cerebral artery occlusion (tMCAO) for 30 min and received oral DMF (15 mg/kg) or a vehicle immediately after tMCAO, followed by twice-daily administrations for 7 days. Infarct volume was assessed on T2-weighted magnetic resonance images on days 1 and 7 after tMCAO. Brain-infiltrating immune cells (lymphocytes, monocytes) and microglia were quantified using fluorescence-activated cell sorting. DMF treatment significantly reduced infarct volumes and brain edema. On day 1 after tMCAO, DMF-treated mice showed reduced lymphocyte infiltration compared to controls, which was not observed on day 7. Monocyte and microglial cell counts did not differ between groups on either day. In the acute phase of stroke, DMF administration attenuated lymphocyte infiltration, probably due to its stabilizing effect on the BBB. This highlights the potential of DMF as a therapeutic candidate for mitigating immune cell-driven damage in stroke.
RBM20 mutations account for 3 % of genetic cardiomypathies and manifest with high penetrance and arrhythmogenic effects. Numerous mutations in the conserved RS domain have been described as causing dilated cardiomyopathy (DCM), whereas a particular mutation (p.R634L) drives development of a different cardiac phenotype: left-ventricular non-compaction cardiomyopathy. We generated a mutation-induced pluripotent stem cell (iPSC) line in which the RBM20-LVNC mutation p.R634L was introduced into a DCM patient line with rescued RBM20-p.R634W mutation. These DCM-634L-iPSC can be differentiated into functional cardiomyocytes to test whether this RBM20 mutation induces development of the LVNC phenotype within the genetic context of a DCM patient.
During the COVID-19 pandemic, social distancing restricted psycho-oncological care. Therefore, this secondary analysis examines the changes in anxiety, fear of progression, fatigue, and depression in cancer patients after a video-based eHealth intervention. We used a prospective observational design with 155 cancer patients with mixed tumor entities. Data were assessed before and after the intervention and at a three-month follow-up using self-reported questionnaires (GAD-7, FOP-Q-SF, PHQ-8, and EORTC QLQ-FA12). The eight videos included psychoeducation, Acceptance and Commitment Therapy elements, and yoga and qigong exercises. The results showed that three months after finishing the video-based intervention, participants showed significantly reduced fear of progression (d = −0.23), depression (d = −0.27), and fatigue (d = −0.24) compared to the baseline. However, there was no change in anxiety (d = −0.09). Findings indicated marginal improvements in mental distress when using video-based intervention for cancer patients for up to three months, but long-term effectiveness must be confirmed using a controlled design.
During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags #MentalHealth and #Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags #Depression and especially #MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with #MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81% for #MentalHealth and 79% for #Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.