Brisk clinical response to erythrocytapheresis in a G6PD‐deficient patient with rasburicase‐induced methemoglobinemia

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139947/1/jca21540_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139947/2/jca21540.pd

EVALUATION OF ANTI-DIABETIC ACTIVTY OF DIFFERENT EXTRACTS OF MYRISTICA FRAGRANS HOUTT: IN VITRO AND IN SILICO STUDIES

Objective: This study is aimed to evaluate the anti-diabetic effect of sequentially extracted (hexane, dichloromethane, ethyl acetate, and ethanol) Myristica fragrans houtt (mace) through in vitro and in silico studies.Methods: The in vitro anti-diabetic effect of the sequentially extracted plant were evaluated for its alpha-amylase inhibitory activity and the potential binding was studied by in silico studies using Schrödinger Maestro.Results: All extracts showed dose dependent alpha-amylase inhibitory effect. At concentration 500 µg/ml, all the extracts showed more than 60% inhibition of the alpha-amylase enzyme and the highest inhibition (81.30%) at 500 µg/ml was observed in DCM extract of mace. Potential compounds were identified by in silico molecular docking studies of alpha-amylase with phytocomponents from DCM extract. Among the top three compounds from virtual screening, induced fit docking studies revealed 2,5-bis(3,4-dimethoxyphenyl)-3,4-dimethyloxolane possessed better binding affinity when compared with the drug metformin.Conclusion: The obtained in vitro and in silico results suggest that all extracts of Myristica fragrans can be used successfully for the management of diabetes mellitus.Keywords: Myristica fragrans, Mace, Sequential extraction, Alpha-amylase, Molecular docking

Towards Fair Allocation in Social Commerce Platforms

Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to provide fair earning opportunities to them. The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products. Our work systematically explores various well-studied benchmarks of fairness -- including Nash social welfare, envy-freeness up to one item (EF1), and equitability up to one item (EQ1) -- from both theoretical and experimental perspectives. We find that the existential and computational guarantees of these concepts known from the unconstrained setting do not extend to our constrained model. To address this limitation, we develop a mixed-integer linear program and other scalable heuristics that provide near-optimal approximation of Nash social welfare in simulated and real social commerce datasets. Overall, our work takes the first step towards achieving provable fairness alongside reasonable revenue guarantees on social commerce platforms

Visually Similar Products Retrieval for Shopsy

Visual search is of great assistance in reseller commerce, especially for non-tech savvy users with affinity towards regional languages. It allows resellers to accurately locate the products that they seek, unlike textual search which recommends products from head brands. Product attributes available in e-commerce have a great potential for building better visual search systems as they capture fine grained relations between data points. In this work, we design a visual search system for reseller commerce using a multi-task learning approach. We also highlight and address the challenges like image compression, cropping, scribbling on the image, etc, faced in reseller commerce. Our model consists of three different tasks: attribute classification, triplet ranking and variational autoencoder (VAE). Masking technique is used for designing the attribute classification. Next, we introduce an offline triplet mining technique which utilizes information from multiple attributes to capture relative order within the data. This technique displays a better performance compared to the traditional triplet mining baseline, which uses single label/attribute information. We also compare and report incremental gain achieved by our unified multi-task model over each individual task separately. The effectiveness of our method is demonstrated using the in-house dataset of product images from the Lifestyle business-unit of Flipkart, India's largest e-commerce company. To efficiently retrieve the images in production, we use the Approximate Nearest Neighbor (ANN) index. Finally, we highlight our production environment constraints and present the design choices and experiments conducted to select a suitable ANN index.Comment: 10 pages, 5 figure

ANTI-INFLAMMATORY ACTIVITY OF SYZYGIUM AROMATICUM SILVER NANOPARTICLES: IN VITRO AND IN SILICO STUDY

  Objective: In the present study, antioxidant and anti-inflammatory activity of Syzygium aromaticum (clove) silver nanoparticles (clove AgNP's) was evaluated.Methods: Gas chromatography-mass spectrometry technique was used to identify the compounds present in the aqueous extract of clove. Fourier transform infrared (FT-IR) analysis was done to characterize the clove silver AgNP's. 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay was performed to evaluate the antioxidant property of nanoparticles (0.05 and 0.25 mg/ml) and aqueous extracts (0.05, 0.1, and 0.25 mg/ml) of clove. The anti-inflammatory property of the clove AgNP's was determined by inhibition of protein denaturation and downregulation of interleukin-1 beta. In silico molecular docking studies was performed using Schrodinger Maestro software.Results: Eugenol was found to be highest with 16.27%. The AgNP's exhibited dose-dependent antioxidant property. AgNP's scavenged 80% of radical at the concentration of 0.25 mg/ml. The scavenging activity of AgNP's markedly increased when compared to aqueous extract at the same concentration. Inhibition of protein denaturation assay also revealed AgNP's showing the highest activity (66%) when compared with drug aspirin (55%) and aqueous extract (56%). In the enzyme-linked immunosorbent assay (ELISA) method, AgNP's showed better inhibition (80%) when compared to aqueous extract (60%). Among 15 compounds, two compounds (eugenol and methyl 14-methylpentadecanoate) showed good glide energy, docking score, and hydrogen-bonded active site interactions with the protein interleukin-1 beta.Conclusion: As AgNP's were more active than the aqueous extract, it may be considered for pharmacological activity against inflammatory disorders