Notes

Self-catalytic procedure associated with prebiotic reactions: coming from formamide for you to pterins and guanine.
We sought to review the principles underlying the metabolic dimension of healthy and AD brains, and suggest that the integration of these concepts in the preventive, diagnostic and treatment strategies for AD is key to improving the precision of these interventions.α-Aminoamidines are promising reagents for the synthesis of a diverse family of pyrimidine ring derivatives. Here, we demonstrate the use of α-aminoamidines for the synthesis of a new series of 5,6,7,8-tetrahydroquinazolines by their reaction with bis-benzylidene cyclohexanones. The reaction occurs in mild conditions and is characterized by excellent yields. It has easy workup, as compared to the existing methods of tetrahydroquinazoline preparation. Newly synthesized derivatives of 5,6,7,8-tetrahydroquinazoline bear protecting groups at the C2-tert-butyl moiety of a quinazoline ring, which can be easily cleaved, opening up further opportunities for their functionalization. Moreover, molecular docking studies indicate that the synthesized compounds reveal high binding affinity toward some essential enzymes of Mycobacterial tuberculosis, such as dihydrofolate reductase (DHFR), pantothenate kinase (MtPanK), and FAD-containing oxidoreductase DprE1 (MtDprE1), so that they may be promising candidates for the molecular design and the development of new antitubercular agents against multidrug-resistant strains of the Tubercle bacillus. Finally, the high inhibition activity of the synthesized compounds was also predicted against β-glucosidase, suggesting a novel tetrahydroquinazoline scaffold for the treatment of diabetes.Telomeres are crucial structures that preserve genome stability. Their progressive erosion over numerous DNA duplications determines the senescence of cells and organisms. As telomere length homeostasis is critical for cancer development, nowadays, telomere maintenance mechanisms are established targets in cancer treatment. Besides telomere elongation, telomere dysfunction impinges on intracellular signaling pathways, in particular DNA damage signaling and repair, affecting cancer cell survival and proliferation. This review summarizes and discusses recent findings in anticancer drug development targeting different "telosome" components.Despite numerous studies conducted over the past decade, the exact role of the cannabinoid system in cancer development remains unclear. Though research has focused on two cannabinoid receptors (CB1, CB2) activated by most cannabinoids, CB2 holds greater attention due to its expression in cells of the immune system. In particular, cytokine-induced killer cells (CIKs), which are pivotal cytotoxic immunological effector cells, express a high-level of CB2 receptors. Herein, we sought to investigate whether inducing CIK cells with cannabidiol can enhance their cytotoxicity and if there are any possible counter effects in its downstream cascade of phosphorylated p38 and CREB using a pancreatic ductal adenocarcinoma cell line (PANC-1). Our results showed that IL-2 modulates primarily the expression of the CB2 receptor on CIK cells used during ex vivo CIK expansion. The autophagosomal-associated scaffold protein p62 was found to co-localize with CB2 receptors in CIK cells and the PANC-1 cell line. CIK cells showed a low level of intracellular phospho-p38 and, when stimulated with cannabidiol (CBD), a donor specific variability in phospho-CREB. CBD significantly decreases the viability of PANC-1 cells presumably by increasing the cytotoxicity of CIK cells. Taken together, in our preclinical in vitro study, we propose that a low effective dose of CBD is sufficient to stimulate the cytotoxic function of CIK without exerting any associated mediator. Thus, the combinatorial approach of non-psychoactive CBD and CIK cells appears to be safe and can be considered for a clinical perspective in pancreatic cancer.The infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be the cause of a fatal disease known as coronavirus disease 2019 (COVID-19) affecting the lungs and other organs. Particular attention has been given to the effects of the infection on the brain due to recurring neurological symptoms associated with COVID-19, such as ischemic or hemorrhagic stroke, encephalitis and myelitis, which are far more severe in the elderly compared to younger patients. The specific vulnerability of the aged brain could derive from the impaired immune defenses, from any of the altered homeostatic mechanisms that contribute to the aging phenotype, and from particular changes in the aged brain involving neurons and glia. While neuronal modifications could contribute indirectly to the damage induced by SARS-CoV-2, glia alterations could play a more direct role, as they are involved in the immune response to viral infections. In aged patients, changes regarding glia include the accumulation of dystrophic forms, reduction of waste removal, activation of microglia and astrocytes, and immunosenescence. It is plausible to hypothesize that SARS-CoV-2 infection in the elderly may determine severe brain damage because of the frail phenotype concerning glial cells.Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI's deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet.The prevalence of liver cancer is constantly rising, with increasing incidence and mortality in Europe and the USA in recent decades. Among the different subtypes of liver cancers, hepatocellular carcinoma (HCC) is the most commonly diagnosed liver cancer. Besides advances in diagnosis and promising results of pre-clinical studies, HCC remains a highly lethal disease. In many cases, HCC is an effect of chronic liver inflammation, which leads to the formation of a complex tumor microenvironment (TME) composed of immune and stromal cells. 2-APV in vitro The TME of HCC patients is a challenge for therapies, as it is involved in metastasis and the development of resistance. However, given that the TME is an intricate system of immune and stromal cells interacting with cancer cells, new immune-based therapies are being developed to target the TME of HCC. Therefore, understanding the complexity of the TME in HCC will provide new possibilities to design novel and more effective immunotherapeutics and combinatorial therapies to overcome resistance to treatment. In this review, we describe the role of inflammation during the development and progression of HCC by focusing on TME. We also describe the most recent therapeutic advances for HCC and possible combinatorial treatment options.Lignocellulosic biomass is renewable and one of the most abundant sources for the production of high-value chemicals, materials, and fuels. It is of immense importance to develop new efficient technologies for the industrial production of chemicals by utilizing renewable resources. Lignocellulosic biomass can potentially replace fossil-based chemistries. The production of fuel and chemicals from lignin powered by renewable electricity under ambient temperatures and pressures enables a more sustainable way to obtain high-value chemicals. More specifically, in a sustainable biorefinery, it is essential to valorize lignin to enhance biomass transformation technology and increase the overall economy of the process. Strategies regarding electrocatalytic approaches as a way to valorize or depolymerize lignin have attracted significant interest from growing scientific communities over the recent decades. This review presents a comprehensive overview of the electrocatalytic methods for depolymerization of lignocellulosic biomass with an emphasis on untargeted depolymerization as well as the selective and targeted mild synthesis of high-value chemicals. Electrocatalytic cleavage of model compounds and further electrochemical upgrading of bio-oils are discussed. Finally, some insights into current challenges and limitations associated with this approach are also summarized.Cancer represents a large group of diseases accounting for nearly 10 million deaths each year. Various treatment strategies, including surgical resection combined with chemotherapy, radiotherapy, and immunotherapy, have been applied for cancer treatment. However, the outcomes remain largely unsatisfying. Melatonin, as an endogenous hormone, is associated with the circadian rhythm moderation. Many physiological functions of melatonin besides sleep-wake cycle control have been identified, such as antioxidant, immunomodulation, and anti-inflammation. In recent years, an increasing number of studies have described the anticancer effects of melatonin. This has drawn our attention to the potential usage of melatonin for cancer treatment in the clinical setting, although huge obstacles still exist before its wide clinical administration is accepted. The exact mechanisms behind its anticancer effects remain unclear, and the specific characters impede its in vivo investigation. In this review, we will summarize the latest advances in melatonin studies, including its chemical properties, the possible mechanisms for its anticancer effects, and the ongoing clinical trials. Importantly, challenges for the clinical application of melatonin will be discussed, accompanied with our perspectives on its future development. Finally, obstacles and perspectives of using melatonin for cancer treatment will be proposed. The present article will provide a comprehensive foundation for applying melatonin as a preventive and therapeutic agent for cancer treatment.Methylmercury (MeHg) is one of the most dangerous toxic pollutants spread throughout the earth. Chronic MeHg intoxication by contaminated food ingestion is the most common threat to human health, including impairment to the developing fetus. The present study aims at investigating the effects of maternal exposure to MeHg during gestation and lactation on the spinal cord of offspring. Pregnant rats received oral doses of MeHg (40 μg/kg/day) over a period of 42 days (21 gestation and 21 lactation). Control animals received the vehicle only. Total mercury concentration was measured in blood samples from offspring collected at the 41st postnatal day. Counting of motor neurons and immunoreactivity for myelin basic protein (MBP) were assessed in the spinal cords in both control and MeHg-intoxicated animals. Our results showed that MeHg promoted an increase in blood Hg levels. In addition, it caused a reduction in the number of spinal cord motor neurons as well as decreased MBP immunoreactivity in the cervical, thoracic and lumbar segments.
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