Formulation of a sedative film coated tablet from extracts of Melissa Officinalis and Valeriana officinalis

Anxiety is feeling of danger in a person who is threatening with something. Nowadays, anxiety is increased in different populations and, thus it is became an important problem in many countries. Melissa officinalis (Melissa) and Valeriana officinalis (Valerian) are two medicinal plants. These two plants are traditionally used as sedative herbal drugs and new studies on their pharmacological activity present the same results. Aerial parts of M.officinalis and rhizomes of V.officinalis were extracted with ethanol: water (70:30). The hydroalcoholic extracts were concentrated in vacuum evaporator and then added to an absorbent matrix. Amount of the dried used extracts per one tablet is 180 mg and 60 mg of Melissa and Valerian respectively. After granulation process, other ingredients were added to wet granules and tablets were prepared using a tabletting machine. Tablets tested physically and some modifications were done on the formulation. At last tablets were coated with a film coat by spraying method. As a criterion of active constituents transmission, the amount of trans-caryophillene was determined by GC-MS method. Results of this study indicated that after modification of formulation, tablets with desirable characteristics for film coating had been produced. Physical examinations indicated that the tablets have proper physical characteristics. GC-MS analysis also showed that in spite of long processes of extraction, granulation, and coating, active ingredients transmission was about 38% from plant to film coated tablets. Keywords: Sedative tablet, Melissa officinalis, Valeriana officinali

Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records

Temporal phenotyping enables clinicians to better understand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories. These models will enable clinicians to identify early warning signs of progression in specific sub-types and therefore to make informed decisions tailored to individual patients. In this paper, we explore two approaches to building temporal phenotypes based on the topology of data: topological data analysis and pseudo time-series. Using type 2 diabetes data, we show that the topological data analysis approach is able to identify disease trajectories and that pseudo time-series can infer a state space model characterized by transitions between hidden states that represent distinct temporal phenotypes. Both approaches highlight lipid profiles as key factors in distinguishing the phenotypes.Medical Research Council; Engineering and Physical Sciences Research Council; National Institute for Health Researc