Asymmetric organocatalysis of the addition of acetone to 2-nitrostyrene using N-diphenylphosphinyl-1,2-diphenylethane-1,2-diamine (PODPEN)

The highly enantioselective addition of acetone to 2-nitrostyrene, using N–diphenylphosphinyl-trans-1,2-diphenylethane-1,2-diamine (PODPEN) as catalyst, is described

An Unusual Case of Liver Abscess Caused by Trematodes

Liver abscess caused by trematodes is considered highly unusual. Here we present a case of an elderly female with no knowncomorbidities, who presented with fever and right upper quadrant pain. Upon evaluation, she was found to have features ofliver abscess on abdominal ultrasound and 640-slice computed tomography (CT) of the abdomen. Ultrasound-guided needlebiopsy of the liver showed features of trematode. Patient was treated with oral nitazoxanide. Patient’s fever and abdominalpain subsided after the treatment

Hypokalemia Secondary to Distal Renal Tubular Acidosis as a Manifestation of Primary Sjögren Syndrome

The classical symptoms of primary Sjögren syndrome such as dry eyes and mouth are not always the initial manifestations.Herein, we report the case of a middle-aged female with documented multiple hypokalemic paralytic episodes along withnonspecific symptoms. Upon evaluation, she was found to have type 1 – distal renal tubular acidosis (RTA), which is anextraglandular manifestation of Sjögren syndrome. She was administered oral alkali salts of sodium along with potassiumcitrate and oral prednisolone. Both hypokalemia and acidosis recovered on 6th week follow-up. She was advised to continuealkali supplementatio

Copper oxide nanoparticles-loaded zeolite and its characteristics and antibacterial activities

In the present work, a simple and green co-precipitation method was used to prepare copper oxide-zeolite nanocomposites (CuO-zeolite NCs). The weight ratio (1, 3, 5, 8 and 10 wt%) of CuO nanoparticles (NPs) loaded into zeolite was investigated to obtain the optimum CuO distribution for antibacterial activities. The prepared CuO-zeolite NCs were characterized by ultraviolet-visible (UV–vis) spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, powder X-ray diffraction (XRD), and energy dispersive X-ray fluorescence spectrometry (EDXRF). The transmission electron microscopy (TEM) and field emission scanning electron microscopy (FE-SEM) revealed a uniform surface morphology of the CuO-zeolite NCs. The UV–vis spectrum of NCs showed absorption peaks between 230 and 280 nm for nano-CuO in the XRD patterns, and new peaks appeared between (36.56°–38.83°) related to the CuO. At weight ratio less than 10 wt%, the CuO nanoparticles loaded to the zeolite exhibited spherical shapes with average particle diameter of 6.5 nm measured by TEM and XRD. Antibacterial activities were tested against Gram-negative and Gram-positive bacteria. The obtained results showed that, CuO-zeolite NCs with 8 wt% CuO nanoparticles had the highest antibacterial activities against Bacillus Subtilis B29 and Salmonella Choleraesuis ATCC 10708, which can be attributed to the good dispersion of CuO NPs on the zeolite surface

Amyloidosis cutis dyschromica in two female siblings: cases report

<p>Abstract</p> <p>Background</p> <p>Cutaneous amyloidosis has been classified into primary cutaneous amyloidosis (PCA, OMIM #105250), secondary cutaneous amyloidosis and systemic cutaneous amyloidosis. PCA is the deposition of amyloid in previously apparent normal skin without systemic involvement. Amyloidosis cutis dyschromica (ACD) is a rare distinct type of PCA. Here, the unique clinical and histological findings of two Chinese female siblings with ACD were described.</p> <p>Cases presentations</p> <p>Patient 1 was a 34-year-old female, presented with mildly pruritic, diffuse mottled hyperpigmentation and hypopigmentation. The lesions involved all over the body since she was 10 years old. There were a few itchy blisters appearing on her arms, lower legs and truck, especially on the sun-exposed areas in summer. Some hypopigmented macules presented with slight atrophy. Patient 2 was 39-year-old, the elder sister of patient 1. She had similar skin lesions since the same age as the former. The atrophy and blisters on the skin of the patient with amyloidosis cutis dyschromica have not been described in previous literature. Histological examinations of the skin biopsies taken from both patients revealed amyloid deposits in the whole papillary dermis. Depending on the histological assessment, the two cases were diagnosed as amyloidosis cutis dyschromica.</p> <p>Conclusion</p> <p>The two cases suggest that the atrophy and blisters may be the uncommon manifestations of amyloidosis cutis dyschromica. It alerts clinicians to consider the possibility of ACD when meeting patients with cutaneous dyschromia. Skin biopsy is essential and family consultation of genetic investigation is very important in such cases.</p

Neural Models for Personalized Recommendation Systems with External Information

Personalized recommendation systems use the data generated by user-item interactions (for example, in the form of ratings) to predict different users interests in available items and recommend a set of items or products to the users. The sparsity of data, cold start, and scalability are some of the important challenges faced by the developers of recommendation systems. These problems are alleviated by using external information, which can be in the form of a social network or a heterogeneous information network, or cross-domain knowledge. This thesis develops novel neural network models for designing personalized recommendation systems using the available external information. The first part of the thesis studies the top-N item recommendation setting where the external information is available in the form of a social network or heterogeneous information network. Unlike a simple recommendation setting, capturing complex relationships amongst entities (users, items, and connected objects) becomes essential when a social and heterogeneous information network is available. In a social network, all socially connected users do not have equal influence on each other. Further, estimating the quantum of influence among entities in a user-item interaction network is important when only implicit ratings are available. We address these challenges by proposing a novel neural network model, SoRecGAT, which employs a multi-head and multi-layer graph attention mechanism. The attention mechanism helps the model learn the influence of entities on each other more accurately. Further, we exploit heterogeneous information networks (HIN) to gather multiple views for the items. A novel neural network model -- GAMMA (Graph and Multi-view Memory Attention mechanism) is proposed to extract relevant information from HINs. The proposed model is an end-to-end model which eliminates the need for learning a similarity matrix offline using some manually selected meta-paths before optimizing the desired objective function. In the second part of the thesis, we focus on top-N bundle recommendation and list continuation setting. Bundle recommendation is the task of recommending a group of products instead of individual products to users. We study two interesting challenges -- (1) how to personalize and recommend existing bundles to users and (2) how to generate personalized novel bundles targeting specific users. We propose GRAM-SMOT -- a graph attention-based framework that considers higher-order relationships among the users, items, and bundles and the relative influence of items present in the bundles. For efficiently learning the embeddings of the entities, we define a loss function based on the metric-learning approach. A strategy that leverages submodular optimization ideas is used to generate novel bundles. We also study the problem of top-N personalized list continuation where the task is to curate the next items to user-generated lists (ordered sequence of items) in a personalized way by using the sequential information of the items in the list. The main challenge in this task is understanding the ternary relationships among the users, items, and lists. We propose HyperTeNet -- a self-attention hypergraph and Transformer-based neural network architecture for the personalized list continuation task. Here, graph convolutions are used to learn the multi-hop relationship among entities of the same type. A self-attention-based hypergraph neural network is proposed to learn the ternary relationships among the interacting entities via hyperlink prediction in a 3-uniform hypergraph. Further, the entity embeddings are shared with a Transformer-based architecture and are learned through an alternating optimization procedure. The final part of the thesis focuses on the personalized rating prediction setting where external information is available in the form of cross-domain knowledge. We propose an end-to-end neural network model, NeuCDCF, that provides a way to alleviate data sparsity problems by exploiting the information from related domains. NeuCDCF is based on a wide and deep framework and learns the representations jointly using matrix factorization and deep neural networks. We study the challenges involved in handling diversity between domains and learning complex non-linear relationships among entities within and across domains. We conduct extensive experiments in each of these settings using several real-world datasets and demonstrate the efficacy of the proposed models