EFFECT OF GREEN LEAFY VEGETABLES (SPINACH AND SORREL) ON HAEMATOPOIESIS IN MALE WISTAR RATS

Spinach (Spinacia oleracea L.) is an edible flowering plant in the Amaranthaceae family and Sorrel (Hibiscus sabdariffa Linn.) is a shrub belonging to the Malvaceae family. It is thought of native to Asia (India to Malaysia) or Tropical Africa. The present study was aimed at investigating the effect of Green leafy vegetables (Spinach and Sorrel) on Hematopoiesis in male Wistar rats to know the effect on hematological parameters and oxidative stress in male rats. The study reveal that Spinach and sorrel leaves increased the RBC count, WBC count Hb%, and MCHC when compared control. The sorrel leaves exhibited prominent effect when compared to spinach leaves. However didnot exhibit prominent effect on platelet count. In addition, both the plant extracts showed significant effect on in decreased MDA levels when compared to control

Biphasic gastroretentive drug delivery system of acyclovir: formulation and in vitro evaluation

A biphasic gastroretentive drug delivery system of acyclovir consisted of loading dose tablet and floating multiple matrix tablets was prepared by direct compression process. The delivery system was designed by hydroxy propyl methyl cellulose as retardant polymer with an effervescent component to get the desired buoyant and sustained release characteristics. All formulations compile within the limits. The FTIR studies did not show any evidence of an interaction between acyclovir and polymers. Dissolution studies revealed biphasic drug release pattern, with loading dose released within 30 min and floating multiple matrix tablets provided zero order sustained release profile for 12 h. It is concluded that floating multiple matrix tablets designed were particularly suitable as gastro retentive drug delivery system with anomalous non-fickian diffusion mechanism. The stability studies showed no significant change in dissolution profiles (f2 value > 50).Colegio de Farmacéuticos de la Provincia de Buenos Aire

An Automated Pipeline for Character and Relationship Extraction from Readers' Literary Book Reviews on Goodreads.com

Reader reviews of literary fiction on social media, especially those in persistent, dedicated forums, create and are in turn driven by underlying narrative frameworks. In their comments about a novel, readers generally include only a subset of characters and their relationships, thus offering a limited perspective on that work. Yet in aggregate, these reviews capture an underlying narrative framework comprised of different actants (people, places, things), their roles, and interactions that we label the "consensus narrative framework". We represent this framework in the form of an actant-relationship story graph. Extracting this graph is a challenging computational problem, which we pose as a latent graphical model estimation problem. Posts and reviews are viewed as samples of sub graphs/networks of the hidden narrative framework. Inspired by the qualitative narrative theory of Greimas, we formulate a graphical generative Machine Learning (ML) model where nodes represent actants, and multi-edges and self-loops among nodes capture context-specific relationships. We develop a pipeline of interlocking automated methods to extract key actants and their relationships, and apply it to thousands of reviews and comments posted on Goodreads.com. We manually derive the ground truth narrative framework from SparkNotes, and then use word embedding tools to compare relationships in ground truth networks with our extracted networks. We find that our automated methodology generates highly accurate consensus narrative frameworks: for our four target novels, with approximately 2900 reviews per novel, we report average coverage/recall of important relationships of > 80% and an average edge detection rate of >89\%. These extracted narrative frameworks can generate insight into how people (or classes of people) read and how they recount what they have read to others

Decoding pituitary tumors: a systematic analysis of diagnostic methods and treatment modalities

Pituitary tumors are growths that form in the gland these tumors are rare representing 10-15% of all brain tumors. They can disrupt the production of hormones, in the body leading to symptoms related to hormone imbalance. This review offers an overview of the methods used for diagnosing and treating tumors. It is worth noting that relying solely on references may restrict the scope and depth of discussions about tumors in this paper. Suggestions for research include exploring diagnostic tools like molecular imaging and liquid biopsy to enhance early detection and accurate assessment of these tumors. Additionally, more research is required to understand the long-term effects and quality of life outcomes for patients undergoing treatment options for tumors. In conclusion, significant progress has been made in diagnosing and treating tumors over time. Advances in imaging technologies such as diffusion-weighted imaging and magnetic resonance spectroscopy have enhanced precision and treatment strategies for these tumors. The discussion also covers the roles of surgery, radiation therapy and medical interventions, in managing tumor growth and hormonal imbalances further advancements, in research and innovation are crucial, for enhancing our knowledge and treatment of tumors ultimately bringing outcomes for both patients and healthcare professionals.

Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching

Videos can be a powerful medium for acquiring soft skills, where learning requires contextualisation in personal experience and ability to see different perspectives. However, to learn effectively while watching videos, students need to actively engage with video content. We implemented interactive notetaking during video watching in an active video watching system (AVW) as a means to encourage engagement. This paper proposes a systematic approach to utilise learning analytics for the introduction of adaptive intervention - a choice architecture for personalised nudges in the AVW to extend learning. A user study was conducted and used as an illustration. By characterising clusters derived from user profiles, we identify different styles of engagement, such as parochial learning, habitual video watching, and self-regulated learning (which is the target ideal behaviour). To find opportunities for interventions, interaction traces in the AVW were used to identify video intervals with high user interest and relevant behaviour patterns that indicate when nudges may be triggered. A prediction model was developed to identify comments that are likely to have high social value, and can be used as examples in nudges. A framework for interactive personalised nudges was then conceptualised for the case study

Formulation and pharmacodynamic evaluation of meloxicam liquisolid compacts

The purpose of this study was to improve the meloxicam dissolution rate through its formulation into liquisolid compacts and then to evaluate the in vitro and in vivo performance of the prepared liquisolid compacts. Dissolution efficiency, mean dissolution time and relative dissolution rate of liquisolid compacts were calculated and compared to marketed formulation. The degree of interaction between the ME and excipients was studied by differential scanning calorimetry and X-ray diffraction were used and results revealed that, there was a loss of meloxicam crystallanity upon liquisolid formulation and almost molecularly dispersed state, which contributed to the enhanced drug dissolution properties. The optimized liquisolid compact showed higher dissolution rates and dissolution efficiency compared to commercial product. The analgesic and anti inflammatory response of optimized liquisolid compact in Swiss albino mice and Wistar rats was found to be superior compared to the marketed formulation.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Silica-Supported Oligomeric Benzyl Phosphate (Si-OBP) and Triazole Phosphate (Si-OTP) Alkylating Reagents

The syntheses of silica-supported oligomeric benzyl phosphates (Si-OBPn) and triazole phosphates (Si-OTPn) using ring-opening metathesis polymerization (ROMP) for use as efficient alkylating reagents is reported. Ease of synthesis and grafting onto the surface of norbornenyl-tagged (Nb-tagged) silica particles has been demonstrated for benzyl phosphate and triazole phosphate monomers. It is shown that these silica polymer hybrid reagents, Si-OBPn and Si-OTPn, can be used to carry out alkylation reactions with an array of different nucleophiles to afford the corresponding benzylated and (triazolyl)methylated products in good yield and high purity