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Spectrum of fungal keratitis: clinicopathologic study of 44 cases

International Journal of Ophthalmology(English edition) 2014年第1期7卷 114-117页

作者： rajpal singh punia Reetu Kundu Jagdish Chander Sudesh Kumar Arya Uma Handa Harsh Mohan Department of Pathology Government Medical College and Hospital Department of Microbiology Government Medical College and Hospital Department of Ophthalmology Government Medical College and Hospital

AIM: To determine the causative agents of fungal keratitis and study the predisposing factors over a period of ten years in a single tertiary care hospital. ·METHODS: A retrospective analysis of fungal corneal ulcers was done from 2003-2012. Patients’ clinical data were noted from the file records. Correlation of histopathological diagnosis was done with the report on fungal culture. · RESULTS: Mycotic keratitis was established in 44 cases by a positive fungal culture. Direct microscopic examination of potassium hydroxide(KOH) mounts revealed fungal elements in 39 cases while 40 cases showed fungus on Gram stained smears. Males(54.55%) were more commonly affected than the females(45.45%). The age ranged from 18 to 82 years. Most common age group to be involved was 41-60 years. Predisposing risk factors were seen in 34(77.27%) cases. Most common findings on clinical examination were anterior chamber reaction and conjunctival injection seen in all the cases. Other common findings were stromal infiltration and hypopyon seen in 20(45.45%) and 18(40.91%) cases respectively. On histopathological examination the fungus was typed,as aspergillus in 34 cases while no definite typing was possible in 10 cases. The predominant isolate was aspergillus flavus(59.09%) followed by fusarium(15.91%). Mixed fungal and bacterial infection was seen in 3(6.82%) cases. ·CONCLUSION: Although culture is the gold standard for definitive diagnosis of fungal keratitis,direct microscopic examination of corneal scrapings or histomorphological evaluation of biopsies allow a rapidpreliminary diagnosis. Early administration of antifungal treatment helps in preventing dreadful complications.

关键词： mycotic keratitis fungal culture infection cornea

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Random Amplification Polymorphic DNA and Agro-Morphological Traits-Based Fingerprinting for Detection of Genetic Divergence in Indian Black Rice

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Rice science 2024年第4期31卷 366-370,I0004-I0014页

作者： Manisha SHARMA Dhananjay RATURI Vijay Rani rajpal Manju CHAUDHARY Soom Nath RAINA Apekshita singh Amity Institute of Biotechnology Amity University Uttar PradeshSector-125UP 201313India Department of Botany Hansraj CollegeUniversity of DelhiDelhi 110007India

In this study, 32 black rice genotypes spanning diverse geographies in India were evaluated at both phenotypic and DNA sequence levels to gain insights into their genetic makeup. Significant variations were observed f... 详细信息

关键词： Indian Morphological Traits

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Adaptive Neuro-Fuzzy Inference System for Prediction of Effective Thermal Conductivity of Polymer-Matrix Composites

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Modeling and Numerical Simulation of Material Science 2012年第3期2卷 43-50页

作者： rajpal singh Bhoopal Ramvir singh Pradeep Kumar Sharma Department of Physics Thermal Physics Laboratory University of Rajasthan Jaipur India

In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained with data collected from various sources of literature. ETC is predicted using ANFIS with volume fraction and thermal conductivities of fillers and matrixes as input parameters, respectively. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.

关键词： Neuro-fuzzy Inference System Effective Thermal Conductivity Polymer Composites Volume Fraction Fuzzy Inference Systems

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