Biological evaluation of 5-fluorouracil nanoparticles for cancer chemotherapy and its dependence on the carrier, PLGA

Nanoscaled devices have great potential for drug delivery applications due to their small size. In the present study, we report for the first time the preparation and evaluation of antitumor efficacy of 5-fluorouracil (5-FU)-entrapped poly (D, L-lactic-co-glycolic acid) (PLGA) nanoparticles with dependence on the lactide/glycolide combination of PLGA. 5-FU-loaded PLGA nanoparticles with two different monomer combinations, 50-50 and 90-10 were synthesized using a modified double emulsion method, and their biological evaluation was done in glioma (U87MG) and breast adenocarcinoma (MCF7) cell lines. 5-FU-entrapped PLGA 50-50 nanoparticles showed smaller size with a high encapsulation efficiency of 66%, which was equivalent to that of PLGA 90-10 nanoparticles. Physicochemical characterization of nanoparticles using differential scanning calorimetry and X-ray diffraction suggested the presence of 5-FU in molecular dispersion form. In vitro release studies showed the prolonged and sustained release of 5-FU from nanoparticles with both the PLGA combinations, where PLGA 50-50 nanoparticles showed faster release. Nanoparticles with PLGA 50-50 combination exhibited better cytotoxicity than free drug in a dose- and time-dependent manner against both the tumor cell lines. The enhanced efficiency of PLGA 50-50 nanoparticles to induce apoptosis was indicated by acridine orange/ethidium bromide staining. Cell cycle perturbations studied using flow cytometer showed better S-phase arrest by nanoparticles in comparison with free 5-FU. All the results indicate that PLGA 50-50 nanoparticles possess better antitumor efficacy than PLGA 90-10 nanoparticles and free 5-FU. Since, studies have shown that long-term exposure of ailing tissues to moderate drug concentrations is more favorable than regular administration of higher concentration of the drug; our results clearly indicate the potential of 5-FU-loaded PLGA nanoparticles with dependence on carrier combination as controlled release formulation to multiplex the therapeutic effect of cancer chemotherapy

Evaluation of the incidence and outcome of gestational diabetes mellitus using the current international consensus guidelines for diagnosing hyperglycaemia in pregnancy

Background: Diabetes Mellitus in pregnancy has long been recognized as a serious problem for both mother and fetus. Gestational Diabetes Mellitus (GDM) is defined as carbohydrate intolerance of variable severity with onset or first recognition during pregnancy. Even though there are many diagnostic criteria and guidelines for management of GDM, there still exists lack of consensus regarding diagnosis and management of patients with GDM. After Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, International Association of Diabetes in Pregnancy Study Group (IADPSG) has formulated a new consensus guideline for diagnosing hyperglycaemia in pregnancy which has formed the back bone for this particular study. The aim of this study was to assess the incidence of GDM using current international consensus guidelines with 75g Oral Glucose Tolerance Test (OGTT) and evaluation of maternal and fetal outcome.Methods: All antenatal patients were screened for GDM with 75g OGTT and their glycaemic control was evaluated throughout pregnancy. Either Medical Nutritional Therapy or Oral Hypoglycaemic Agents or Insulin Therapy was advised for glycaemic control. Maternal and neonatal outcomes were evaluated.Results: A total of 856 Antenatal patients were screened and 111 were diagnosed as GDM, showing an incidence of 13%. Medical Nutritional Therapy was found to be an effective method for glycaemic control in GDM.Conclusions: The incidence of GDM in the studied population was found to be 13%. Previous history of GDM was found to be the most significant high risk factor associated with GDM followed by family history of Diabetes. Medical Nutritional Therapy was found to be highly effective in the management of GDM. Only 9% of GDM patients required insulin therapy. With adequate glycaemic control, all late pregnancy complications and neonatal complications can be alleviated

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MTar: a computational microRNA target prediction architecture for human transcriptome

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in important cell functions, there is a growing interest in identifying the relationship between miRNAs and their target mRNAs. So, there is now a imperative need to develop a computational method by which we can identify the target mRNAs of existing miRNAs. Here, we proposed an efficient machine learning model to unravel the relationship between miRNAs and their target mRNAs.</p> <p>Results</p> <p>We present a novel computational architecture MTar for miRNA target prediction which reports 94.5% sensitivity and 90.5% specificity. We identified 16 positional, thermodynamic and structural parameters from the wet lab proven miRNA:mRNA pairs and MTar makes use of these parameters for miRNA target identification. It incorporates an Artificial Neural Network (ANN) verifier which is trained by wet lab proven microRNA targets. A number of hitherto unknown targets of many miRNA families were located using MTar. The method identifies all three potential miRNA targets (5' seed-only, 5' dominant, and 3' canonical) whereas the existing solutions focus on 5' complementarities alone.</p> <p>Conclusion</p> <p>MTar, an ANN based architecture for identifying functional regulatory miRNA-mRNA interaction using predicted miRNA targets. The area of target prediction has received a new momentum with the function of a thermodynamic model incorporating target accessibility. This model incorporates sixteen structural, thermodynamic and positional features of residues in miRNA: mRNA pairs were employed to select target candidates. So our novel machine learning architecture, MTar is found to be more comprehensive than the existing methods in predicting miRNA targets, especially human transcritome.</p

SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper, we propose SPINN, a distributed inference system that employs synergistic device-cloud computation together with a progressive inference method to deliver fast and robust CNN inference across diverse settings. The proposed system introduces a novel scheduler that co-optimises the early-exit policy and the CNN splitting at run time, in order to adapt to dynamic conditions and meet user-defined service-level requirements. Quantitative evaluation illustrates that SPINN outperforms its state-of-the-art collaborative inference counterparts by up to 2x in achieved throughput under varying network conditions, reduces the server cost by up to 6.8x and improves accuracy by 20.7% under latency constraints, while providing robust operation under uncertain connectivity conditions and significant energy savings compared to cloud-centric execution.Comment: Accepted at the 26th Annual International Conference on Mobile Computing and Networking (MobiCom), 202

XVI Agricultural Science Congress 2023: Transformation of Agri-Food Systems for Achieving Sustainable Development Goals

The XVI Agricultural Science Congress being jointly organized by the National Academy of Agricultural Sciences (NAAS) and the Indian Council of Agricultural Research (ICAR) during 10-13 October 2023, at hotel Le Meridien, Kochi, is a mega event echoing the theme “Transformation of Agri-Food Systems for achieving Sustainable Development Goals”. ICAR-Central Marine Fisheries Research Institute takes great pride in hosting the XVI ASC, which will be the perfect point of convergence of academicians, researchers, students, farmers, fishers, traders, entrepreneurs, and other stakeholders involved in agri-production systems that ensure food and nutritional security for a burgeoning population. With impeding challenges like growing urbanization, increasing unemployment, growing population, increasing food demands, degradation of natural resources through human interference, climate change impacts and natural calamities, the challenges ahead for India to achieve the Sustainable Development Goals (SDGs) set out by the United Nations are many. The XVI ASC will provide an interface for dissemination of useful information across all sectors of stakeholders invested in developing India’s agri-food systems, not only to meet the SDGs, but also to ensure a stable structure on par with agri-food systems around the world. It is an honour to present this Book of Abstracts which is a compilation of a total of 668 abstracts that convey the results of R&D programs being done in India. The abstracts have been categorized under 10 major Themes – 1. Ensuring Food & Nutritional Security: Production, Consumption and Value addition; 2. Climate Action for Sustainable Agri-Food Systems; 3. Frontier Science and emerging Genetic Technologies: Genome, Breeding, Gene Editing; 4. Livestock-based Transformation of Food Systems; 5. Horticulture-based Transformation of Food Systems; 6. Aquaculture & Fisheries-based Transformation of Food Systems; 7. Nature-based Solutions for Sustainable AgriFood Systems; 8. Next Generation Technologies: Digital Agriculture, Precision Farming and AI-based Systems; 9. Policies and Institutions for Transforming Agri-Food Systems; 10. International Partnership for Research, Education and Development. This Book of Abstracts sets the stage for the mega event itself, which will see a flow of knowledge emanating from a zeal to transform and push India’s Agri-Food Systems to perform par excellence and achieve not only the SDGs of the UN but also to rise as a world leader in the sector. I thank and congratulate all the participants who have submitted abstracts for this mega event, and I also applaud the team that has strived hard to publish this Book of Abstracts ahead of the event. I wish all the delegates and participants a very vibrant and memorable time at the XVI ASC