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Chandrasekaran, R.
- Psychiatric Morbidity Pattern - A Retrospective Study in a General Hospital
Abstract Views :154 |
Authors
Source
NIMHANS Journal, Vol 2, No 2 (1984), Pagination: 149-152Abstract
822 cases who attended the psychiatric out-patient department of a general hospital were retrospectively analysed. In the neurotic group, cases of depressive neurosis were found to be higher in incidence. Cases of endogenous depressions were found to be higher in the psychotic group. More number of cases had a positive family history of alcohol dependence. Many cases sought the help of general practitioners before attending the psychiatric clinic. An attempt has been made to compare the findings of the present study with the findings of some relevant studies.Keywords
Psychiatric Morbidity, Alcohol Dependence, Depressive Neurosis
- Receptor Interactions of Transpeptidase Involved in Peptidoglycan Biosynthesis
Abstract Views :434 |
PDF Views:0
Authors
Affiliations
1 Department of Bioinformatics, Marudu pandiyar College, Thanjavur
2 Department of Botany and Microbiology, A.V.V.M Sri Pushpam College, Thanjavur.
1 Department of Bioinformatics, Marudu pandiyar College, Thanjavur
2 Department of Botany and Microbiology, A.V.V.M Sri Pushpam College, Thanjavur.
Source
Journal of Computational Intelligence in Bioinformatics, Vol 6, No 1 (2013), Pagination: 59-67Abstract
Analyse the various docking modes of peptidoglycan formation and inhibition is by selecting the test sets from PDB. These test sets were docked by Hex and the resulting docking complex submitted to Spdbv. Distance of the residues in three different states viz. interface area, contact surface area and near native area. The results revealed that ARG 642 (1.33 Å) is found to be the best fit for peptidoglycan formation, GLN 422 (1.45 Å) and TYR 423 (2.15Å) are found to be the best fit for peptidoglycan inhibition using penicillin G and penicillin V respectively. Based on our study it is concluded that penicillin G has the best interacting activity as compared to penicillin V.Keywords
Docking, Peptidoglycan, Penicillin, InteractionsReferences
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- Palma, P.N., Krippahl, L., Wampler, J.E., and Moura, J.J.G. (2000) BiGGER: A new (soft) docking algorithm for predicting Protein interactions. Proteins: Structure, Function and Genetics,Vol.39,pp:372-384.
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- Talaro, K.P. (2005) Foundations in Microbiology. McGraw Hill, New York. [16] Taufer, M., Crowley, M., Price, D., Chien, A.A., and Brooks, C.L (2004) Study of a Highly Accurate and Fast Protein-Ligand Docking Algorithm Based on Molecular Dynamics. Third IEEE International Workshop on High Performance Computational Biology (HICOMB 2004),pp:1627-1641.
- Wenfen, H., (2005) Rigid Body Protein Docking by Fast Fourier Transform. Honor Year Project Report, Department of Computer Science, National University of Singapore, Vol.1.
- Observations on the Vegetation of Parali Forest, Nilgiri-Tamil Nadu
Abstract Views :197 |
PDF Views:131
Authors
Affiliations
1 Botanical Survey of India, Coimbatore, IN
1 Botanical Survey of India, Coimbatore, IN
Source
Nelumbo - The Bulletin of the Botanical Survey of India, Vol 23, No 3-4 (1981), Pagination: 146-148Abstract
No Abstract.Full Text
- Notes on Some Rare and Interesting Plants from Nilgiris, South India
Abstract Views :173 |
PDF Views:107
Authors
Affiliations
1 Botanical Survey of India, Jodhpur, IN
2 Botanical Survey of India, Coimbatore, IN
1 Botanical Survey of India, Jodhpur, IN
2 Botanical Survey of India, Coimbatore, IN
Source
Nelumbo - The Bulletin of the Botanical Survey of India, Vol 26, No 3-4 (1984), Pagination: 211-214Abstract
No Abstract.Full Text
- IoT Based Ketoacidosis Detection
Abstract Views :549 |
PDF Views:0
Authors
Affiliations
1 Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, IN
1 Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, IN
Source
Indian Journal of Public Health Research & Development, Vol 10, No 12 (2019), Pagination: 369-373Abstract
Ketoacidosis also known as DKA (diabetic ketoacidosis) is a serious condition which occurs in patients who suffers from diabetes. It affects people above 25 years of age. It occurs due to insufficiency of insulin. Detection of ketone is done by the nitroprusside-based urinary dipstick ketone test and plasmlserum ketone analyses. Non-invasive detection of ketoacidosis is done by breath analyzer. In this paper we are using breath acetone as a biomarker for ketoacidosis. The resultant ketoacidosis values are transmitted through ESP8266. The ESP8266 sends the sensorvalues to think speak (private cloud). The new channel is created in Think Speak private clouding and Channel API keys are generated to read and write the sensor data from ESP8266. Through this simple IoT device the physician is always connected to the patient and can be able to monitor thet ketoacidosis condition of the subjectKeywords
Ketoacidosis, Think Speak, IoT, API Keys.- Smart Aid for the Blind
Abstract Views :472 |
PDF Views:0
Authors
Affiliations
1 Department of Biomedical Engineering, Vels Institute of Science Technology and Advanced Studies, Chennai, IN
1 Department of Biomedical Engineering, Vels Institute of Science Technology and Advanced Studies, Chennai, IN