News Classification Using Machine Learning

There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement

Pattern and practice of self medication in students of health professional university in North India

Background: To determine the prevalence, pattern and practice of self-medication in health professional students of a University in North India.Methods: This cross sectional study was conducted over a period of 6 months among the students of University. Data was collected through anonymous, self-administered, structured and validated 15 questions based questionnaire. Data was analysed by using descriptive statistical analysis.Results: Out of the 1538 students enrolled, 1350 (88.6%) students of University practiced self medication in the past 6 months. Prevalence of self medication varied among students of different courses (MBBS, BDS, Nursing, Physiotherapy, Pharmacy and paramedical) and different professional years of same course. Self medication practices were comparable between MBBS and BDS students with no significant difference (p=0.293). Fever and headache were the most frequently reported illness for which self medication was taken. Analgesics and antipyretics were the frequently self medicated drugs. Most common reason for seeking self medication was minor illness (41%). Among all the students 6% of them complained of adverse effects with the use of self medication. About 85% students of University reported as treated with self medication.Conclusions: Practice of self-medication is common among the health professional students. In this situation it is important to create awareness and educate the students about pros and cons of self medication through educational programs.

Rerouting trafficking circuits through posttranslational SNARE modifications

Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) are membrane-associated trafficking proteins that confer identity to lipid membranes and facilitate membrane fusion. These functions are achieved through the complexing of Q-SNAREs with a specific cognate target R-SNARE, leading to the fusion of their associated membranes. These SNARE complexes then dissociate so that the Q-SNAREs and R-SNAREs can repeat this cycle. Whilst the basic function of SNAREs has been long appreciated, it is becoming increasingly clear that the cell can control the localisation and function of SNARE proteins through posttranslational modifications (PTMs), such as phosphorylation and ubiquitylation. Whilst numerous proteomic methods have shown that SNARE proteins are subject to these modifications, little is known about how these modifications regulate SNARE function. However, it is clear that these PTMs provide cells with an incredible functional plasticity; SNARE PTMs enable cells to respond to an ever-changing extracellular environment through the rerouting of membrane traffic. In this Review, we summarise key findings regarding SNARE regulation by PTMs and discuss how these modifications reprogramme membrane trafficking pathways.</p

Massive Liver Abscess Caused by Multidrug Resistant Citrobacter freundii

Pyogenic liver abscess is a disease entity that may follow infections of the biliary tract, blood stream and intra abdominal conditions like appendicitis, diverticulitis etc. Co- morbid conditions like diabetes mellitus, renal disease, malignancy, immune suppression etc. may contribute to the development of an abscess and affect the overall outcome of liver abscess as well. Escherichia coli and Klebsiellapneumoniae have been reported to be the most common isolations from these cases. Citrobacter infections usually occur in patients with some underlying co- morbidities and mostly in hospitalized patients. Recently, an increased emergence of multi- drug resistant strains is presenting a challenge to clinicians and microbiologists.Here, we report a case of massive liver abscess in an immunocompetent patient caused due to Citrobacterfreundii resistant to gentamicin, ciprofloxacin, cotrimoxazole, amikacin, ceftriaxone and piperacillin-tazobactam. It showed in vitro sensitivity to meropenem and aztreonam only

Make-A-Story: Visual Memory Conditioned Consistent Story Generation

There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches rely on conditional sentences that contain unambiguous descriptions of scenes and main actors in them. Therefore employing such models for more complex task of story visualization, where naturally references and co-references exist, and one requires to reason about when to maintain consistency of actors and backgrounds across frames/scenes, and when not to, based on story progression, remains a challenge. In this work, we address the aforementioned challenges and propose a novel autoregressive diffusion-based framework with a visual memory module that implicitly captures the actor and background context across the generated frames. Sentence-conditioned soft attention over the memories enables effective reference resolution and learns to maintain scene and actor consistency when needed. To validate the effectiveness of our approach, we extend the MUGEN dataset and introduce additional characters, backgrounds and referencing in multi-sentence storylines. Our experiments for story generation on the MUGEN and the FlintstonesSV dataset show that our method not only outperforms prior state-of-the-art in generating frames with high visual quality, which are consistent with the story, but also models appropriate correspondences between the characters and the background.Comment: 10 page

Frequency of Peripheral CD8+ T Cells Expressing Chemo-Attractant Receptors CCR1, 4 and 5 Increases in NPC Patients with EBV Clearance upon Radiotherapy

Radiotherapy (RT) is the standard-of-care for Epstein–Barr virus (EBV)-associated nasopharyngeal carcinoma (NPC), where the post-RT clearance of plasma EBV DNA is prognostic. Currently, it is not known whether the post-RT clearance of plasma EBV DNA is related to the presence of circulating T-cell subsets. Blood samples from NPC patients were used to assess the frequency of T-cell subsets relating to differentiation, co-signaling and chemotaxis. Patients with undetectable versus detectable plasma EBV DNA levels post-RT were categorized as clearers vs. non-clearers. Clearers had a lower frequency of PD1+CD8+ T cells as well as CXCR3+CD8+ T cells during RT compared to non-clearers. Clearers exclusively showed a temporal increase in chemo-attractant receptors CCR1, 4 and/or 5, expressing CD8+ T cells upon RT. The increase in CCR-expressing CD8+ T cells was accompanied by a drop in naïve CD8+ T cells and an increase in OX40+CD8+ T cells. Upon stratifying these patients based on clinical outcome, the dynamics of CCR-expressing CD8+ T cells were in concordance with the non-recurrence of NPC. In a second cohort, non-recurrence associated with higher quantities of circulating CCL14 and CCL15. Collectively, our findings relate plasma EBV DNA clearance post-RT to T-cell chemotaxis, which requires validation in larger cohorts.</p

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer

Aim: The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Methods: Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). Results: In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Conclusions: Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken