Investigation of nano lipid vesicles of methotrexate for anti-rheumatoid activity

Prabhakara Prabhu1, Rakshith Shetty1, Marina Koland1, K Vijayanarayana3, KK Vijayalakshmi2, M Harish Nairy1, GS Nisha11Department of Pharmaceutics, Nitte University, NGSM Institute of Pharmaceutical Sciences, Paneer, Deralakatte, Mangalore, Karnataka, India; 2Department of Applied Zoology, Mangalore University, Konaje, Mangalore, Karnataka, India; 3Department of Pharmacy Practice, Manipal University, Manipal College of Pharmaceutical Sciences, Manipal, Karnataka, IndiaBackground: The purpose of this study was to formulate and evaluate nano lipid vesicles of methotrexate (MTX) for its anti-rheumatoid activity.Methods: In this study the principle of both active as well as passive targeting using MTX-loaded stealth liposomes as per the magic gun approach was followed. Stealth liposomes of MTX were prepared by thin-film hydration method using a PEGylated phospholipid-like DSPE-MPEG 2000. Similarly, conventional liposomes were prepared using phospholipids like DPPC and DSPC. Conventional liposomes were coated with a hydrophilic biocompatible polymer like chitosan. They were investigated for their physical properties and in vitro release profile. Further, in vivo screening of the formulations for their anti-rheumatoid efficacy was carried out in rats. Rheumatoid arthritis was induced in male Wistar-Lewis rats using complete Freund’s adjuvant (1 mg/mL Mycobacterium tuberculosis, heat killed in mineral oil).Results: It was found that chitosan coating of the conventional liposomes increased the physical stability of the liposomal suspension as well as its entrapment efficiency. The size of the unsonicated lipid vesicles was found to be in the range of 8–10 µm, and the sonicated lipid vesicles in the range of 210–260 nm, with good polydispersity index. Further, chitosan-coated conventional liposomes and the PEGylated liposomes released the drug for a prolonged period of time, compared to the uncoated conventional liposomes. It was found that there was a significant reduction in edema volume in the rat group administered with the test stealth liposomal formulations and chitosan-coated conventional liposomes (PEGylated and chitosan-coated conventional) compared to that of the control and standard (administered with free MTX) group of rats. PEGylated liposomes showed almost equal efficacy as that of the chitosan-coated conventional liposomes.Conclusion: Lipid nano vesicles of MTX can be administered by intravenous route, whereby the drug selectively reaches the target site with reduced toxicity to other organs.Keywords: methotrexate, stealth liposomes, conventional liposomes, chitosan coating, targeted delivery, anti-rheumatoid efficac

An Appraisal of Sensitivity and Resistance Pattern of Organisms Isolated from Hospital Acquired Pneumonia Patients

ABSTRACT Hospital acquired pneumonia (HAP) is an infection of lung parenchyma develops at least after 48 hours of hospitalization for another illness or procedure, also known as nosocomial pneumonia. Organisms causing HAP were found in 221 (43.7%) out of 505 patients. The most common isolates found were Klebisiella pneumonia (30.9%) Acinetobacter species (29.4%), Pseudomonas aeruginosa (16.7%), Escherichia coli (9.1%) and methicillin resistant Staphylococcus aureus (MRSA; 3.7%, 73%). Sensitivity pattern of gram negative organisms such as Klebisiella pneumonia, Acinetobacter species and Escherichia coli was comparatively identical showing high sensitivity towards colistin and tigecycline where as Pseudomonas aeruginosa was highly sensitive to only colistin. Among Ggram positive organisms MRSA was highly sensitive to linezolid, tetracycline and cotrimoxazole, methicillin sensitive Staphylococcus aureus to (MSSA) cotrimoxazole (80%) and tetracycline (71.4%) and Streptococcus pneumonia to chloramphenicol, penicillin, piperacillin, cefoperazone-sulbactam and colistin. Sensitivity pattern of Gram negative organisms was different from that of Gram positive organisms causing high mortality rates. Among Gram positive organisms MRSA caused mortality as twice as that of MSSA. Multidrug resistant strains were resulted by inappropriate and inadequate antibiotic therapy, causing high rate of morbidity mortality and adding cost to therapy which is the major concern in recent times

Artificial intelligence in differentiating tropical infections: A step ahead.

Background and objectiveDifferentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections.MethodologyA cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection.ResultsA total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55-60%, whereas a binary classification machine learning algorithms showed an average of 79-84% for one vs other and 69-88% for one vs one disease category.ConclusionThis is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient care