SUCCESSFUL TREATMENT OF VENTILATOR ASSOCIATED PNEUMONIA CAUSED BY MULTIDRUG RESISTANT ACINETOBACTER BAUMANNII WITH A COMBINATION THERAPY OF CSE1034 AND COLISTIN: A CASE REPORT.

 Objective: One of the major causes of ventilator-associated pneumonia (VAP) in hospital settings is Acinetobacter baumannii. The propensity of acquiring antimicrobial resistance rapidly through a multiple number of mechanisms makes the selection of an appropriate empirical antimicrobial agent exceedingly challenging for this pathogen.Methods: The present case report explores the option of treating VAP infection due to carbapenem-resistant pathogens with antibiotic adjuvant entities.Results: We present a case of VAP due to carbapenem-resistant A. baumannii that was successfully treated with CSE-1034 and colistin combination therapy.Conclusion: Early recognition and appropriate antibiotic therapy are essential to eliminate poor outcomes in multidrug-resistant (MDR) bacterial infections. The present case highlights the antibiotic adjuvant entity CSE-1034†as an empiric option for the treatment of VAP due to MDR A. baumannii in intensive care unit

SEAL: Scientific Keyphrase Extraction and Classification

Automatic scientific keyphrase extraction is a challenging problem facilitating several downstream scholarly tasks like search, recommendation, and ranking. In this paper, we introduce SEAL, a scholarly tool for automatic keyphrase extraction and classification. The keyphrase extraction module comprises two-stage neural architecture composed of Bidirectional Long Short-Term Memory cells augmented with Conditional Random Fields. The classification module comprises of a Random Forest classifier. We extensively experiment to showcase the robustness of the system. We evaluate multiple state-of-the-art baselines and show a significant improvement. The current system is hosted at http://lingo.iitgn.ac.in:5000/.Comment: Accepted at JCDL 202

Odontogenic Keratocyst: An Enigma

Odontogenic Keratocyst (Primordial cyst) is known amongst clinicians for its peculiar behaviour, origin of varied nature, discussions regarding its development and a unique tendency to recur. Hence, it has been the subject of interest over the past 40 years. These cysts can occur singly or multiply, as a part of Nevoid Basal Cell Carcinoma Syndrome (NBCCS). The objective of this case report and discussion is to emphasize that proper examination, diagnosis and appropriate treatment is vital to prevent recurrence of the lesion

Role of Laser Biostimulation in Treatment of Oral Submucous Fibrosis: A Clinical Trial

AIM: To evaluate the efficacy of Low Level Laser Therapy (LLLT) in treatment of Oral Submucous Fibrosis (OSMF).MATERIAL & METHODS: 20 patients with a clinical diagnosis of OSMF were included in the study after informed consents and measurements of mouth opening (mm) and burning sensation (VAS) were made at day 0. Laser biostimulation was performed on right and left cheeks in anterior and posterior bands for 3 cycles of 10 seconds each. They were recalled for follow-up measurements and Laser biostimulation at 3rd, 7th and 15th day. The paired t-test was applied for analysing significant differences, if any, using SPSS version 21.0.RESULTS: In the follow up recordings, generally, there was an increase in mouth opening after LLLT therapy and a significant difference was seen in males(p=.04) as well as the total population(p=0.02). Burning sensation(VAS Scale), on day zero was 5.5±1.20, which was reduced to 3.4±.084 on the 15th day with a significant difference seen in the entire study population(p=0.03).CONCLUSION: Biostimulation by Laser in the treatment of OSMF is a good non-invasive, painless and quick alternative treatment modality for the management of diseases

Self-Supervised Learning of Action Affordances as Interaction Modes

When humans perform a task with an articulated object, they interact with the object only in a handful of ways, while the space of all possible interactions is nearly endless. This is because humans have prior knowledge about what interactions are likely to be successful, i.e., to open a new door we first try the handle. While learning such priors without supervision is easy for humans, it is notoriously hard for machines. In this work, we tackle unsupervised learning of priors of useful interactions with articulated objects, which we call interaction modes. In contrast to the prior art, we use no supervision or privileged information; we only assume access to the depth sensor in the simulator to learn the interaction modes. More precisely, we define a successful interaction as the one changing the visual environment substantially and learn a generative model of such interactions, that can be conditioned on the desired goal state of the object. In our experiments, we show that our model covers most of the human interaction modes, outperforms existing state-of-the-art methods for affordance learning, and can generalize to objects never seen during training. Additionally, we show promising results in the goal-conditional setup, where our model can be quickly fine-tuned to perform a given task. We show in the experiments that such affordance learning predicts interaction which covers most modes of interaction for the querying articulated object and can be fine-tuned to a goal-conditional model. For supplementary: https://actaim.github.io