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Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images
Prabhakar Ramachandran,Ben Perrett,Orrie Dancewicz,Venkatakrishnan Seshadri,Catherine Jones,Akash Mehta,Matthew Foote 대한방사선종양학회 2021 Radiation Oncology Journal Vol.39 No.2
Purpose: The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). Materials and Methods: A retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm. Results: The mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. Conclusion: The artifacts from the CBCT’s limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution.
Prabhakar, M. Manoj,Vasudevan, K.,Karthikeyan, S.,Baskaran, N.,Silvan, S.,Manoharan, S. Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.10
The present study was designed to explore the anti-cell proliferative efficacy of ferulic acid by analysing the expression pattern of cell proliferative markers, proliferating cellular nuclear antigen (PCNA) and cyclin D1, in the buccal mucosa of golden Syrian hamsters treated with 7,12-dimethylbenz(a)anthracene (DMBA). Oral squamous cell carcinomas developed in the buccal pouch of hamsters using topical application of 0.5% DMBA three times a week for 14 weeks. Immunohistochemical (PCNA) and RT-PCR (Cyclin D1) analysis revealed over expression of PCNA and cyclin D1 in the buccal mucosa of hamsters treated with DMBA alone (tumor bearing hamsters). Oral administration of ferulic acid at a dose of 40 mg/kg bw to hamsters treated with DMBA not only completely prevented the tumor formation but also down regulated the expression of PCNA and cyclin D1. The results of the present study thus suggests that ferulic acid might have inhibited tumor formation in the buccal mucosa of hamsters treated with DMBA through its anti-cell proliferative potential as evidenced by decreased expression of PCNA and cyclin D1.
Prabhakar, Arun K.,Potroz, Michael G.,Tan, Ee-Lin,Jung, Haram,Park, Jae Hyeon,Cho, Nam-Joon American Chemical Society 2018 ACS APPLIED MATERIALS & INTERFACES Vol.10 No.34
<P>Pine pollen offers an all-natural multicavity structure with dual hollow air sacs, providing ample cargo capacity available for compound loading. However, the pollen exhibits reduced permeability because of the presence of a thin natural water-proofing layer of lipidic compounds. Herein, we explore the potential for compound loading within pine pollen and the potential for developing all-natural formulations for targeted delivery to the intestinal tract. Removal of the surface-adhered lipidic compounds is shown to improve surface wetting, expose nanochannel structures in the outer pollen shell and enhance water uptake throughout the whole pollen structure. Optimization of loading parameters enabled effective compound loading within the outer pollen shell sexine structure, with bovine serum albumin (BSA) serving as a representative protein. All-natural oral delivery formulations for targeted intestinal delivery are developed based on tableting of BSA-loaded defatted pine pollen, with the incorporation of xanthan gum as a natural binder, or ionotropically cross-linked sodium alginate as an enteric coating. Looking forward, the large cargo capacity, ease of compound loading, competitive cost, abundant availability, and extensive historical usage as food and medicine make pine pollen an attractive microencapsulant for a wide range of potential applications.</P> [FIG OMISSION]</BR>
Prabhakar, M. N.,Sudhakara, P.,Subha, M. C. S.,Rao, K. Chowdoji,Song, Jung Il Taylor Francis 2015 Polymer-Plastics Technology and Materials Vol.54 No.16
<P>Nanocomposite hydrogels were prepared through a blend solution of poly(lactic acid) and poly(N-isopropylacrylamide)-co-acrylamide via free radical polymerization. Plant extractions were used for the synthesis of Ag nanoparticles to study the antibacterial activity of the hydrogels. Similarly, 5-Fluorouracil drug was loaded through both in situ and ex situ methods to study thecontrolled release profiles. The nanocomposite hydrogels were characterized by ultraviolet-visible spectroscopy, Fourier transform infrared spectroscopy, Thermo gravimetric analysis - Differential scanning calorimetry (TGA-DSC), X-ray diffractometer, scanning electron microscopy, and transmission electron microscope. The dissolution and the agar diffusion test were performed to evaluate the drug release and antibacterial activity, respectively. The results suggested that the fabricated nanocomposite hydrogels can be used as a promising candidate for dual functions in biomedical applications.</P>
Gas sensing properties of single crystalline ZnO nanowires grown by thermal evaporation technique
Prabhakar Rai,리쥔찬,Rafiq Ahmad,한윤봉,이인환,유연태 한국물리학회 2013 Current Applied Physics Vol.13 No.8
The ZnO NWs were applied as effective material for the fabrication of ethanol (C2H5OH) and carbon monoxide (CO) gas sensor. The ZnO NWs were grown by thermal evaporation techniques on non-catalytic Si (100) substrates. The average width and length of ZnO NWswas 60 nmand 20 mm, respectively and they were single crystalline in nature. The maximumresponsewas 51.64 at 300 ℃ for 1000 ppm of CO gas, while 104.23 at 400 ℃ for 250 ppm of ethanol gas. The response of ZnO NWswas very high for ethanol compared to the CO, whereas the recovery time for ethanol was very poor compare to CO gas. The response of ZnO NWs was about 25 times higher for ethanol compare to CO, at 400 ℃ for 100 ppm of each gas. The high response for ethanol is related to electron donating effect of ethanol (10e-) which was higher than the CO gas (2e-). The high response of ZnO NWs was attributed to large contacting surface area for electrons,oxygen, target gas molecule, and abundant channels for gas diffusion.
Electroencephalography-based imagined speech recognition using deep long short-term memory network
Prabhakar Agarwal,Sandeep Kumar 한국전자통신연구원 2022 ETRI Journal Vol.44 No.4
This article proposes a subject-independent application of brain–computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.
Implicit vs. Explicit Solvent Models for Calculating X-ray Solution Scattering Curves
Prabhakar Ganesan,김종구,이재혁,김정호,이효철 대한화학회 2015 Bulletin of the Korean Chemical Society Vol.36 No.3
Theoretical calculation of X-ray solution scattering curves of proteins in the solution phase is strongly influenced by solvent contributions in the form of solvent-excluded volume and hydration layer that are generally represented either implicitly or explicitly. To investigate the effect of the implicit and explicit solvent models on the calculated scattering curves, we developed a new program, X-ray Solution Scattering (XSoS) based on implicit (XSoS-implicit) and explicit (XSoS-explicit) solvent models. Both XSoS-implicit and XSoS-explicit can calculate X-ray solution scattering curves with high accuracy. Overall, the implicit solvent model has practical advantages over the explicit solvent model for the analysis of experimental X-ray solution scattering data.