Machine learning model for clinical named entity recognition

To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER). It is found from the literature that several researchers have extensively used machine learning models for clinical NER.The most fundamental tasks among the medical data mining tasks are medical named entity recognition and normalization. Medical named entity recognition is different from general NER in various ways. Huge number of alternate spellings and synonyms create explosion of word vocabulary sizes. This reduces the medicine dictionary efficiency. Entities often consist of long sequences of tokens, making harder to detect boundaries exactly. The notes written by clinicians written notes are less structured and are in minimal grammatical form with cryptic short hand. Because of this, it poses challenges in named entity recognition. Generally, NER systems are either rule based or pattern based. The rules and patterns are not generalizable because of the diverse writing style of clinicians. The systems that use machine learning based approach to resolve these issues focus on choosing effective features for classifier building. In this work, machine learning based approach has been used to extract the clinical data in a required manne

Enhancement of DNVME device driver

The device driver is the interface between hardware and software applications. It includes all the functionality for handling the devices connected to it. The drivers are device-specific. The storage devices like SSD and HDD use dNVMe driver for handling them. This driver can be enhanced for supporting various features. The enhancement helps in development of storage devices. The main areas for modification includes enhancing IOCTL calls, allowing register level changes and allowance for negative testing. These features will enrich the storage devices for all the qualifications

Context Free Grammar (CFG) Analysis for simple Kannada sentences

When Computational Linguistic is concerns Kannada is lagging far behind compared to Telugu and Tamil. Writing the grammar production for any south Indian language is bit difficult. Because the languages are highly inflected with three gender forms and two number forms. This paper is an effort to write Context Free Grammar for simple Kannada sentences. Kannada Language being one of the major Dravidian languages of India and it has 27th place in most spoken language in the world. But still it does not yet have computerized grammar checking methods for a given Kannada sentence. Thus, this paper highlights the process of generating context free grammar for simple Kannada sentences

ALGORITHMS FOR CONSTRUCTING EDGE MAGIC TOTAL LABELING OF COMPLETE BIPARTITE GRAPHS

The study of graph labeling has focused on finding classes of graphs which admits a particular type of labeling. In this paper we consider a particular class of graphs which demonstrates Edge Magic Total Labeling. The class we considered here is a complete bipartite graph Km,n. There are various graph labeling techniques that generalize the idea of a magic square has been proposed earlier. The definition of a magic labeling on a graph with v vertices and e edges is a one to one map taking the vertices and edges onto the integers 1,2,3,………, v+e with the property that the sum of the label on an edge and the labels of its endpoints is constant independent of the choice of edge. We use m x n matrix to construct edge magic total labeling of Km,n

Thioridazine: a potential adjuvant in pharmacotherapy of drug resistant tuberculosis Ki

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. Despite advances in control strategies, inadequate treatment and failure to comply with drug regimens have resulted in TB to emerge as one of the most common and deadly infectious diseases worldwide. The emergence of drug-resistant TBhas evolved as a formidable obstacle for comprehensive TB control. Drug-resistant TB can be classified as multi-drug-resistant TB, extensively drug-resistant TB and totally drug resistant TB (TDR-TB). There is a paucity in the development of new drugs against drug-resistant mycobacteria. The focus has shifted to the exploration of anti-mycobacterial properties of drugs approved for other indications. Thioridazine, a drug approved for use in schizophrenia is one such potential agent, which has shown anti-mycobacterial activity. There is evidence of anti-mycobacterial action of Thioridazine in in-vitro and mouse models. There is a compelling need for new anti-mycobacterial drugs that are more effective and have less toxicity. Further clinical trials are advocated favoring the use of thioridazine as an adjuvant in the treatment of TB, especially TDR-TB

Experimental Investigations for Evaluation of Mechanical Properties of Aluminum Matrix Composites Reinforced with Copper Particles

In this study, aluminum alloy Al 6061-copper particulate metal matrix composites were prepared with three different volume fractions of reinforcement 75 μm (1%, 2% and 3%) using stir casting route. The particles distribution, mechanical and physical properties are observed using SEM and XRD. Analysis is discussed on microstructure study and hardness; compression and density are explained in sight of mechanical and physical properties. Finally, it was observed from the results that the hardness, density and compression strength were increased by increasing in wt% of reinforcement

Disruption of FDPS/Rac1 Axis Radiosensitizes Pancreatic Ductal Adenocarcinoma by Attenuating DNA Damage Response and Immunosuppressive Signalling

BACKGROUND: Radiation therapy (RT) has a suboptimal effect in patients with pancreatic ductal adenocarcinoma (PDAC) due to intrinsic and acquired radioresistance (RR). Comprehensive bioinformatics and microarray analysis revealed that cholesterol biosynthesis (CBS) is involved in the RR of PDAC. We now tested the inhibition of the CBS pathway enzyme, farnesyl diphosphate synthase (FDPS), by zoledronic acid (Zol) to enhance radiation and activate immune cells. METHODS: We investigated the role of FDPS in PDAC RR using the following methods: in vitro cell-based assay, immunohistochemistry, immunofluorescence, immunoblot, cell-based cholesterol assay, RNA sequencing, tumouroids (KPC-murine and PDAC patient-derived), orthotopic models, and PDAC patient\u27s clinical study. FINDINGS: FDPS overexpression in PDAC tissues and cells (P \u3c 0.01 and P \u3c 0.05) is associated with poor RT response and survival (P = 0.024). CRISPR/Cas9 and pharmacological inhibition (Zol) of FDPS in human and mouse syngeneic PDAC cells in conjunction with RT conferred higher PDAC radiosensitivity in vitro (P \u3c 0.05, P \u3c 0.01, and P \u3c 0.001) and in vivo (P \u3c 0.05). Interestingly, murine (P = 0.01) and human (P = 0.0159) tumouroids treated with Zol+RT showed a significant growth reduction. Mechanistically, RNA-Seq analysis of the PDAC xenografts and patients-PBMCs revealed that Zol exerts radiosensitization by affecting Rac1 and Rho prenylation, thereby modulating DNA damage and radiation response signalling along with improved systemic immune cells activation. An ongoing phase I/II trial (NCT03073785) showed improved failure-free survival (FFS), enhanced immune cell activation, and decreased microenvironment-related genes upon Zol+RT treatment. INTERPRETATION: Our findings suggest that FDPS is a novel radiosensitization target for PDAC therapy. This study also provides a rationale to utilize Zol as a potential radiosensitizer and as an immunomodulator in PDAC and other cancers. FUNDING: National Institutes of Health (P50, P01, and R01)