Analgesic activity of Alpinia galanga extract in mice models and TNF-alpha receptor computational docking analysis on its leads with pharmacokinetics prediction

Background: Alpinia galanga is an ayurvedic herb recognized and used across many traditional medicine systems for its analgesic and anti-inflammatory activity. The present study scientifically validates the potential anti nociceptive action of ethanolic extract of Alpinia galanga by chemical, neurogenic and inflammatory nociception model in mice followed by identification of potential lead compound by computational analysis.Methods: The assessment of anti nociceptive action is evaluated by Acetic acid induced abdominal constrictions and Formalin assay on ethonolic extract of Alpinia galanga, followed by 20 compounds with known chemical structure of Alpinia galanga is subjected to computational analysis to predict possible lead compound with desirable pharmacokinetic and drug like features.Results: The percentage inhibition rate of Aspirin (100mg/kg) was 82.15% compared to Alpinia galanga (100mg/kg) 19.63%, (200mg/kg) 33.02% and (400mg/kg) 57.13% by acetic acid induced abdominal constrictions antinociceptive mice model. Alpinia galanga 400mg/kg (71.70%) had comparable percentage inhibition of nociception to standard group indomethacin (88.71%) in formalin induced nociceptive mice model. Among 20 compounds screened for pharmacokinetic and drug like features, Galanal B had the binding free energy -56.664 when compared to control compound 2AZ5-56.000.Conclusions: The Alpinia galanga extract had significant anti nociceptive activity and followed by computational analysis of 20 compounds with known chemical structure predicted Galanal B as lead compound with best insilico pharmacokinetic and drug like features

Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

Purpose Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

Purpose: Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods: Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results: The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion: We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

Chemistry of Mesoionic Sydnones as Versatile Heterocyclic Compounds

Sydnones are mesoionic heterocyclic aromatic compounds. They have been widely studied for some important biological activities like antiviral, antitumor, antimicrobial, anti-inflammatory, anticancer, analgesic, anthelmintic and antihypertensive activities. The aim of the present article is to review the available information on sydnones and the derivatives of sydnones and also a look at the future perspectives. Sydnone can be defined as a five-membered pseudo-aromatic heterocyclic molecule. Classically, 1,2,3-oxadiazole forms the main skeleton of sydnone. The molecule has delocalized balanced positive and negative charges. The five annular atoms share the positive charge and the enolate-like exocyclic oxygen atom bears the negative charge. The hydrogen atom at the position C4 was proved to have acidic and nucleophilic functionalities making the sydnone ring reactive towards electrophilic reagents. These unique chemical features enable sydnones to interact with biomolecules resulting in important therapeutic effects like anticancer, antidiabetic, antimicrobial, antioxidant and anti-inflammatory. Consequently, we aim from the current article to review the available chemical and pharmacological information on sydnone and its derivatives

Synthesis, Characterization and Anti-bacterial Activity of Schiff Bases of Sulphanilamide Derivatives

A Schiff base (azomethine) is named after its inventor, Hugo Schiff and it is a functional group that contains a carbon-nitrogen double bond with the nitrogen atom connected to an aryl or alkyl group but not hydrogen. Schiff bases have the general formula of R1R2C=NR3, where R3 is an aryl or alkyl group that makes the Schiff base a stable imine. Schiff bases can be synthesized from a reaction of an aromatic amine and a carbonyl compound by a nucleophilic addition forming a hemiaminal, followed by dehydration to generate an imine. The scheme includes Sulphanilamide react with various aromatic aldehydes to give Schiff base derivatives. (SFB-1 to SFB-5). All the synthesized compounds were purified by appropriate solvents, identified and characterized by TLC, Melting Point, IR spectroscopy. In the present study all synthesized compounds tested for anti bacterial activity and have shown significant activity when compared with standard drug Streptomycin. But as the biological and pharmacological screening conducted were preliminary

Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules

<div><p>Introduction</p><p>Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage.</p><p>Results</p><p>The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with ‘High’ reliability scoring), DEREK (accuracy = 72.73% and CCR = 71.44%) and TOPKAT (accuracy = 60.00% and CCR = 61.67%). Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%), the coverage was very low (only 10 out of 77 molecules were predicted reliably).</p><p>Conclusions</p><p>Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.</p></div

Comparative performance of our prediction workflows with VEGA v1.08.

<p>Panel A: Molecules of challenge set-1 processed by our prediction workflows (= 74) and VEGA v1.08 (= 69) used for computation; Panel B: 69 molecules of challenge set-1 processed by our prediction workflows as well as VEGA v1.08 were used for computation; Panel C: Molecules of challenge set-2 processed by our prediction workflows (= 77) and VEGA v1.08 (= 68) used for computation; Panel D: 68 molecules of challenge set-2 processed by our prediction workflows as well as VEGA v1.08 were used for computation. VEGA v1.08: orange bars; PW-1: blue bars; PW-2: green bars. CCR: Correct Classification Rate.</p

Integration of QSAR models, similarity information and sub-structure pattern into prediction workflows (PWs).

<p>Blue and red colors depict components that differ in the two Prediction Workflows, PW-1 and PW-2. Components in black and grey are those that are common in both PW-1 and PW-2. QSAR: Quantitative Structure-Activity Relationship; MLP: Multi-Layer Perceptron; SMO: Sequential Minimal Optimization; E_o: Energy-optimized dataset; RTS: Representative test set; Challenge-1: Challenge set-1; Challenge-2: Challenge set-2; m₂, m₃, m₄, s_similarity and s_substr are predictions from QSAR. models-2, 3 and 4, similarity information and sub-structure pattern, and w_m2, w_m3, w_m4, w_similarity and w_substr are their corresponding weights.</p