Identification, analysis and prediction of valid and false information related to vaccines from Romanian tweets.

Introduction: The online misinformation might undermine the vaccination efforts. Therefore, given the fact that no study specifically analyzed online vaccine related content written in Romanian, the main objective of the study was to detect and evaluate tweets related to vaccines and written in Romanian language. Methods: 1,400 Romanian vaccine related tweets were manually classified in true, neutral and fake information and analyzed based on wordcloud representations, a correlation analysis between the three classes and specific tweet characteristics and the validation of several predictive machine learning algorithms. Results and discussion: The tweets annotated as misinformation showed specific word patterns and were liked and reshared more often as compared to the true and neutral ones. The validation of the machine learning algorithms yielded enhanced results in terms of Area Under the Receiver Operating Characteristic Curve Score (0.744-0.843) when evaluating the Support Vector Classifier. The predictive model estimates in a well calibrated manner the probability that a specific Twitter post is true, neutral or fake. The current study offers important insights regarding vaccine related online content written in an Eastern European language. Future studies must aim at building an online platform for rapid identification of vaccine misinformation and raising awareness for the general population.

Cetirizine and Levetiracetam as Inhibitors of Monoacylglycerol Lipase: Investigating Their Repurposing Potential as Novel Osteoarthritic Pain Therapies.

Osteoarthritis is characterized by progressive articular cartilage degradation, subchondral bone changes, and synovial inflammation, and affects various joints, causing pain and disability. Current osteoarthritis therapies, primarily focused on pain management, face limitations due to limited effectiveness and high risks of adverse effects. Safer and more effective treatments are urgently needed. Considering that the endocannabinoid 2-arachidonoyl glycerol is involved in pain processing, increasing its concentration through monoacylglycerol lipase (MAGL) inhibition reduces pain in various animal models. Furthermore, drug repurposing approaches leverage established drug safety profiles, presenting a cost-effective route to accelerate clinical application. To this end, cetirizine and levetiracetam were examined for their MAGL inhibitory effects. In vitro studies revealed that cetirizine and levetiracetam inhibited MAGL with IC50 values of 9.3931 µM and 3.0095 µM, respectively. In vivo experiments demonstrated that cetirizine, and to a lesser extent levetiracetam, reduced mechanical and thermal nociception in complete Freund adjuvant (CFA)-induced osteoarthritis in rats. Cetirizine exhibited a notable anti-inflammatory effect, reducing CFA-induced inflammation, as well as the inflammatory infiltrate and granuloma formation in the affected paw. These findings suggest that cetirizine may serve as a promising starting point for the development of novel compounds for osteoarthritis treatment, addressing both pain and inflammation.

The Influence of Sildenafil-Metformin Combination on Hyperalgesia and Biochemical Markers in Diabetic Neuropathy in Mice.

Background and objectives: Worldwide, approximately 500 million people suffer from diabetes and at least 50% of these people develop neuropathy. Currently, therapeutic strategies for reducing diabetic neuropathy (DN)-associated pain are limited and have several side effects. The purpose of the study was to evaluate the antihyperalgesic action of different sildenafil (phosphodiesterase-5 inhibitor) and metformin (antihyperglycemic agent) combinations in alloxan-induced DN. Methods: The study included 100 diabetic mice and 20 non-diabetic mice that were subjected to hot and cold stimulus tests. Furthermore, we determined the influence of this combination on TNF-α, IL-6 and nitrites levels in brain and liver tissues. Results: In both the hot-plate and tail withdrawal test, all sildenafil-metformin combinations administered in our study showed a significant increase in pain reaction latencies when compared to the diabetic control group. Furthermore, all combinations decreased blood glucose levels due to the hypoglycemic effect of metformin. Additionally, changes in nitrite levels and pro-inflammatory cytokines (TNF-α and IL-6) were observed after 14 days of treatment with different sildenafil-metformin combinations. Conclusions: The combination of these two substances increased the pain reaction latency of diabetic animals in a dose-dependent manner. Moreover, all sildenafil-metformin combinations significantly reduced the concentration of nitrites in the brain and liver, which are final products formed under the action of iNOS.

Natural Active Ingredients and TRPV1 Modulation: Focus on Key Chemical Moieties Involved in Ligand-Target Interaction.

Diseases such as cancer, neurological pathologies and chronic pain represent currently unmet needs. The existing pharmacotherapeutic options available for treating these conditions are limited by lack of efficiency and/or side effects. Transient receptor potential vanilloid 1 ion channel emerged as an attractive therapeutic target for developing new analgesic, anti-cancer and antiepileptic agents. Furthermore, various natural ingredients were shown to have affinity for this receptor. The aim of this narrative review was to summarize the diverse natural scaffolds of TRPV1 modulators based on their agonistic/antagonistic properties and to analyze the structure-activity relationships between the ligands and molecular targets based on the results of the existing molecular docking, mutagenesis and in vitro studies. We present here an exhaustive collection of TRPV1 modulators grouped by relevant chemical features: vanilloids, guaiacols, phenols, alkylbenzenes, monoterpenes, sesquiterpenoids, alkaloids, etc. The information herein is useful for understanding the key structural elements mediating the interaction with TRPV1 and how their structural variation impacts the interaction between the ligand and receptor. We hope this data will contribute to the design of novel effective and safe TRPV1 modulators, to help overcome the lack of effective therapeutic agents against pathologies with high morbidity and mortality.

In Silico Drug Repurposing Framework Predicts Repaglinide, Agomelatine and Protokylol as TRPV1 Modulators with Analgesic Activity.

Pain is one of the most common symptoms experienced by patients. The use of current analgesics is limited by low efficacy and important side effects. Transient receptor potential vanilloid-1 (TRPV1) is a non-selective cation channel, activated by capsaicin, heat, low pH or pro-inflammatory agents. Since TRPV1 is a potential target for the development of novel analgesics due to its distribution and function, we aimed to develop an in silico drug repositioning framework to predict potential TRPV1 ligands among approved drugs as candidates for treating various types of pain. Structures of known TRPV1 agonists and antagonists were retrieved from ChEMBL databases and three datasets were established: agonists, antagonists and inactive molecules (pIC50 or pEC50 < 5 M). Structures of candidates for repurposing were retrieved from the DrugBank database. The curated active/inactive datasets were used to build and validate ligand-based predictive models using Bemis−Murcko structural scaffolds, plain ring systems, flexophore similarities and molecular descriptors. Further, molecular docking studies were performed on both active and inactive conformations of the TRPV1 channel to predict the binding affinities of repurposing candidates. Variables obtained from calculated scaffold-based activity scores, molecular descriptors criteria and molecular docking were used to build a multi-class neural network as an integrated machine learning algorithm to predict TRPV1 antagonists and agonists. The proposed predictive model had a higher accuracy for classifying TRPV1 agonists than antagonists, the ROC AUC values being 0.980 for predicting agonists, 0.972 for antagonists and 0.952 for inactive molecules. After screening the approved drugs with the validated algorithm, repaglinide (antidiabetic) and agomelatine (antidepressant) emerged as potential TRPV1 antagonists, and protokylol (bronchodilator) as an agonist. Further studies are required to confirm the predicted activity on TRPV1 and to assess the candidates' efficacy in alleviating pain.

Effects of Venlafaxine, Risperidone and Febuxostat on Cuprizone-Induced Demyelination, Behavioral Deficits and Oxidative Stress.

Multiple sclerosis (MS) is a demyelinating, autoimmune disease that affects a large number of young adults. Novel therapies for MS are needed considering the efficiency and safety limitations of current treatments. In our study, we investigated the effects of venlafaxine (antidepressant, serotonin-norepinephrine reuptake inhibitor), risperidone (atypical antipsychotic) and febuxostat (gout medication, xanthine oxidase inhibitor) in the cuprizone mouse model of acute demyelination, hypothesizing an antagonistic effect on TRPA1 calcium channels. Cuprizone and drugs were administered to C57BL6/J mice for five weeks and locomotor activity, motor performance and cold sensitivity were assessed. Mice brains were harvested for histological staining and assessment of oxidative stress markers. Febuxostat and metabolites of venlafaxine (desvenlafaxine) and risperidone (paliperidone) were tested for TRPA1 antagonistic activity. Following treatment, venlafaxine and risperidone significantly improved motor performance and sensitivity to a cold stimulus. All administered drugs ameliorated the cuprizone-induced deficit of superoxide dismutase activity. Desvenlafaxine and paliperidone showed no activity on TRPA1, while febuxostat exhibited agonistic activity at high concentrations. Our findings indicated that all three drugs offered some protection against the effects of cuprizone-induced demyelination. The agonistic activity of febuxostat can be of potential use for discovering novel TRPA1 ligands.