ChemTextMiner: An open source tool kit for mining medical literature abstracts

Text mining involves recognizing patterns from a wealth of information hidden latent in unstructured text and deducing explicit relationships among data entities by using data mining tools. Text mining of Biomedical literature is essential for building biological network connecting genes, proteins, drugs, therapeutic categories, side effects etc. related to diseases of interest. We present an approach for textmining biomedical literature mostly in terms of not so obvious hidden relationships and build biological network applied for the textmining of important human diseases like MTB, Malaria, Alzheimer and Diabetes. The methods, tools and data used for building biological networks using a distributed computing environment previously used for ChemXtreme[1] and ChemStar[2] applications are also described

Context specific text mining for annotating protein interactions with experimental evidence

Indiana University-Purdue University Indianapolis (IUPUI)Proteins are the building blocks in a biological system. They interact with other proteins to make unique biological phenomenon. Protein-protein interactions play a valuable role in understanding the molecular mechanisms occurring in any biological system. Protein interaction databases are a rich source on protein interaction related information. They gather large amounts of information from published literature to enrich their data. Expert curators put in most of these efforts manually. The amount of accessible and publicly available literature is growing very rapidly. Manual annotation is a time consuming process. And with the rate at which available information is growing, it cannot be dealt with only manual curation. There need to be tools to process this huge amounts of data to bring out valuable gist than can help curators proceed faster. In case of extracting protein-protein interaction evidences from literature, just a mere mention of a certain protein by look-up approaches cannot help validate the interaction. Supporting protein interaction information with experimental evidence can help this cause. In this study, we are applying machine learning based classification techniques to classify and given protein interaction related document into an interaction detection method. We use biological attributes and experimental factors, different combination of which define any particular interaction detection method. Then using predicted detection methods, proteins identified using named entity recognition techniques and decomposing the parts-of-speech composition we search for sentences with experimental evidence for a protein-protein interaction. We report an accuracy of 75.1% with a F-score of 47.6% on a dataset containing 2035 training documents and 300 test documents

MONOCROTOPHOS INDUCED BEHAVIORAL STRESS, BIOCHEMICAL AND HISTOLOGICAL ALTERATIONS IN LAMELLIDENS MARGINALIS (LAMARCK)

The present study aimed to investigate the possible effects of acute exposure of monocrotophos on behavioural response, inhibition of protein and glycogen activity, and histopathological changes in mantle and muscle tissues of freshwater bivalve, Lamellidens marginalis. Animals were exposed to sub lethal LC5 concentration (47.45 ppm) of monocrotophos. The glycogen and protein content decreased significantly (p<0.05) in mantle (28.27%, 35.96%) gill, (16.59%, 53.05%) foot (28.05%, 71.41%) and adductor muscle (27.37%, 64.21%) respectively after 96 hours exposure to monocrotophos. Decrease in glycogen content shows greater utilization of glycogen for metabolic purposes and to combat with monocrotophos stress. Fragmentation of muscle fibre and hypertrophy in mucous cells of mantle was observed after acute exposure to monocrotophos. Protein and glycogen content recovered significantly (p<0.05) in mantle and muscle after 14 days, while all the tissues recovered significantly (p<0.05) after 28 days. These results revealed that there was a significant recovery in biochemical parameters in bivalve after a recovery period of 28 days

Biometric Personal Identification based on Iris Patterns

This paper discusses an analysis of human iris patterns for recognition of biometric system which consists of a segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region is then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. To encode the unique pattern of the iris into a bit-wise biometric template, 1D Log-Gabor filter is used.Finally to match two iris templates hamming distance is used as matching metric. The system performance is analyzed on 312 iris images taken from standard CASIA Iris Interval database version 4. To establish the verification accuracy of iris representation and matching approach, each iris image in the database is matched with all the other iris images in the database and genuine and imposter distribution is found .The performance of the system is implemented by evaluating the Decidability Index (DI), False match rate (FMR), False Non-match rate (FNMR), Genuine Accept Rate (GAR) and Equal error rate (EER)

Hypophosphatemic Rickets/ Osteomalacia: A Case Report and Review of Literature

Hypophosphatemic rickets/ osteomalacia comprises of a group of disorders of bone mineralization caused due to defect in renal handling of phosphorus. The group includes X linked hypophosphatemic rickets, autosomal dominant hypophosphatemic rickets and tumor induced osteomalacia. Here, we report the case of a young male who presented with mechanical low backache, muscular pains and proximal muscle weakness resulting in severe debility. He was diagnosed to have hypophosphatemic osteomalacia on the basis of hypophosphatemia, hyperphosphaturia, normal 25 hydroxy- and 1, 25 dihydroxy- vitamin D, normal intact PTH and raised serum FGF23 levels. Despite extensive search, no tumor was localized. He showed marked improvement with oral phosphate and calcitriol replacement and is under follow up

The Effect of Modifications of Activated Carbon Materials on the Capacitive Performance: Surface, Microstructure, and Wettability

none7siopenKouao Dujearic-Stephane; Meenal Gupta; Ashwani Kumar; Vijay Sharma; Soumya Pandit; Patrizia Bocchetta; Yogesh KumarDujearic-Stephane, Kouao; Gupta, Meenal; Kumar, Ashwani; Sharma, Vijay; Pandit, Soumya; Bocchetta, Patrizia; Kumar, Yoges

Evanescent wave sensor for potassium ion detection with special reference to agricultural application

We are introducing 4 & PRIME;-aminodibenzo-18-crown-6 ether (A2BC) modification of gold nanoparticles coated optical fiber as a new sensor for evanescent wave trapping on the polymer optical fiber to detect low-level potassium ions. We characterized these gold nanoparticles by X-ray Diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), Nanoparticle tracking analysis (NTA), Field Emission scanning electron microscopes (FE-SEM), and UV-Visible spectroscopy. In the present study, we modified the gold nanoparticles with A2BC for selective sensing of potassium (K+) ions. The interaction between A2BC and K+ ions leads to the temporary formation of a sandwich structure as crown ethers form steady complexes with metal ions. This sandwich structure leads to potassium detection. In our implementation, related operational parameters such as cladding length, roughness, and concentration of A2BC and gold nanoparticles, were optimized to achieve a detection threshold of 1 ppm. Additionally, we optimized the optical fiber sensor to increase its detection sensitivity from the μV range to the mV range. The sensor demonstrates a fast response time (10 s) and high sensitivity, selectivity, and stability, which cause a wide linear range (1-100 ppm) and a low limit of detection (LOD = 0.14 ppm). Lastly, we tested the sensor for a soil-sensing application.Acknowledgments. The authors would like to thank Rajiv Gandhi Science and Technology Commission, Mumbai. Maharashtra for providing financial assistance and Director, The Institute of Science, Dr. Homi Bhabha State University, Mumbai, for providing laboratory access for carrying out experiments. The authors would also like to thank DST for providing instruments to the Institute of Science under their FIST scheme. The authors would like to thank the researchers supporting project number (RSP2023R370), King Saud University, Riyadh, Saudi Arabia, for financial support

Speaking the Same Language: Leveraging LLMs in Standardizing Clinical Data for AI

The implementation of Artificial Intelligence (AI) in the healthcare industry has garnered considerable attention, attributable to its prospective enhancement of clinical outcomes, expansion of access to superior healthcare, cost reduction, and elevation of patient satisfaction. Nevertheless, the primary hurdle that persists is related to the quality of accessible multi-modal healthcare data in conjunction with the evolution of AI methodologies. This study delves into the adoption of large language models to address specific challenges, specifically, the standardization of healthcare data. We advocate the use of these models to identify and map clinical data schemas to established data standard attributes, such as the Fast Healthcare Interoperability Resources. Our results illustrate that employing large language models significantly diminishes the necessity for manual data curation and elevates the efficacy of the data standardization process. Consequently, the proposed methodology has the propensity to expedite the integration of AI in healthcare, ameliorate the quality of patient care, whilst minimizing the time and financial resources necessary for the preparation of data for AI.11 pages, 2 figures, 4 table

Decision Support System For Geriatric Care

poster abstractGeriatrics is a branch in medicine that focuses on the healthcare of the elderly. We propose to build a decision support system for the elderly care based on a knowledgebase system that incorporates best practices that are reported in the literature. A Bayesian network model is then used for decision support for the geriatric care tool that we develop