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6. Analysis of Code Smells Using Machine Learning Algorithms

Author

Jain, Varun, Singh, Rohinish, Rai, Nishant, Gupta, Aakanshi, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Singh, Sanjay, editor, Ramulu, Perumalla Janaki, editor, and Gautam, Sachin Singh, editor

Published
2024
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7. Contributors

Author

Anand, Jigisha, primary, Bhattarai, Karishma, additional, Chakravarthi, G., additional, Dutta, Rajiv, additional, Joshi, Namini, additional, Kumar, Vinay, additional, Kumar, Devendra, additional, Kumar, Awanish, additional, Kumar, Sunil, additional, Lehari, Khyati, additional, Madan, Ayush, additional, Mathur, Priti, additional, Mishra, Sameer, additional, Mishra, Divisha, additional, Mishra, Vaanya, additional, Rai, Sachchidanand, additional, Rai, Nishant, additional, Ramakrishna, Kakarla, additional, Rawat, Balwant, additional, Rawat, Janhvi Mishra, additional, Sahai, Pragati, additional, Shyam, Perugu, additional, Singh, Devendra, additional, Singh, Vivek Kumar, additional, Singh, Payal, additional, Sinha, Somya, additional, Sundriyal, Manju, additional, Tandon, Chandni, additional, Tiwari, Neeraj, additional, Verma, Sunil Kumar, additional, and Vikram, Aditi, additional

Published
2024
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10. Home Action Genome: Cooperative Compositional Action Understanding

Author

Rai, Nishant, Chen, Haofeng, Ji, Jingwei, Desai, Rishi, Kozuka, Kazuki, Ishizaka, Shun, Adeli, Ehsan, and Niebles, Juan Carlos

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Existing research on action recognition treats activities as monolithic events occurring in videos. Recently, the benefits of formulating actions as a combination of atomic-actions have shown promise in improving action understanding with the emergence of datasets containing such annotations, allowing us to learn representations capturing this information. However, there remains a lack of studies that extend action composition and leverage multiple viewpoints and multiple modalities of data for representation learning. To promote research in this direction, we introduce Home Action Genome (HOMAGE): a multi-view action dataset with multiple modalities and view-points supplemented with hierarchical activity and atomic action labels together with dense scene composition labels. Leveraging rich multi-modal and multi-view settings, we propose Cooperative Compositional Action Understanding (CCAU), a cooperative learning framework for hierarchical action recognition that is aware of compositional action elements. CCAU shows consistent performance improvements across all modalities. Furthermore, we demonstrate the utility of co-learning compositions in few-shot action recognition by achieving 28.6% mAP with just a single sample., Comment: CVPR '21

Published
2021

11. Weak Multi-View Supervision for Surface Mapping Estimation

Author

Rai, Nishant, Liaudanskas, Aidas, Rao, Srinivas, Cayon, Rodrigo Ortiz, Munaro, Matteo, and Holzer, Stefan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations. We learn the underlying surface geometry of common categories, such as human faces, cars, and airplanes, given instances from those categories. While traditional approaches solve this problem using extensive supervision in the form of pixel-level annotations, we take advantage of the fact that pixel-level UV and mesh predictions can be combined with 3D reprojections to form consistency cycles. As a result of exploiting these cycles, we can establish a dense correspondence mapping between image pixels and the mesh acting as a self-supervisory signal, which in turn helps improve our overall estimates. Our approach leverages information from multiple views of the object to establish additional consistency cycles, thus improving surface mapping understanding without the need for explicit annotations. We also propose the use of deformation fields for predictions of an instance specific mesh. Given the lack of datasets providing multiple images of similar object instances from different viewpoints, we generate and release a multi-view ShapeNet Cars and Airplanes dataset created by rendering ShapeNet meshes using a 360 degree camera trajectory around the mesh. For the human faces category, we process and adapt an existing dataset to a multi-view setup. Through experimental evaluations, we show that, at test time, our method can generate accurate variations away from the mean shape, is multi-view consistent, and performs comparably to fully supervised approaches.

Published
2021

12. CoCon: Cooperative-Contrastive Learning

Author

Rai, Nishant, Adeli, Ehsan, Lee, Kuan-Hui, Gaidon, Adrien, and Niebles, Juan Carlos

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Labeling videos at scale is impractical. Consequently, self-supervised visual representation learning is key for efficient video analysis. Recent success in learning image representations suggests contrastive learning is a promising framework to tackle this challenge. However, when applied to real-world videos, contrastive learning may unknowingly lead to the separation of instances that contain semantically similar events. In our work, we introduce a cooperative variant of contrastive learning to utilize complementary information across views and address this issue. We use data-driven sampling to leverage implicit relationships between multiple input video views, whether observed (e.g. RGB) or inferred (e.g. flow, segmentation masks, poses). We are one of the firsts to explore exploiting inter-instance relationships to drive learning. We experimentally evaluate our representations on the downstream task of action recognition. Our method achieves competitive performance on standard benchmarks (UCF101, HMDB51, Kinetics400). Furthermore, qualitative experiments illustrate that our models can capture higher-order class relationships.

Published
2021

18. Non-timber Forest Products: Current Status and Development

Author

Ahamad, Faheem, Bhutiani, Rakesh, Ruhela, Mukesh, Rai, Nishant, Bezaeva, Natalia S., Series Editor, Gomes Coe, Heloisa Helena, Series Editor, Nawaz, Muhammad Farrakh, Series Editor, Bahukhandi, Kanchan Deoli, editor, Kamboj, Nitin, editor, and Kamboj, Vishal, editor

Published
2022
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23. In silico approach to identify the potential targets of Alexidine dihydro-chloride and Hexachlorophene in human fungal pathogen C. glabrata.

Author

Ansari, Ayesha, Kumar, Darshan, Rai, Nishant, and Kumar, Navin

Subjects
BINDING energy, MOLECULAR docking, DRUG repositioning, CALCIUM channels, DRUG target, ANTIFUNGAL agents
Abstract

Widespread usage of antifungals has led to the development of antifungal resistance, causing a change in the epidemiology of the responsible agents from albicans to non-Candida albicans species. Pharmaceutical repurposing is an alternate strategy that has provided a cost-effective method to address the increasing resistance to antifungal medications. The objective of this work was to examine the antifungal properties of Alexidine dihydrochloride (AXD) and Hexachlorophene (HCP) against a non-Albicans Candida model, C. glabrata. The lowest inhibitory doses of AXD and HCP against C. glabrata were determined by in vitro methods to be 0.69-1.03 µM and 14.75-19.66 µM, respectively. The minimum doses of AXD and HCP that caused fungicidal effects were defined as 1.375 µM and 61.44 µM, respectively. Three proteins involved in crucial physiological pathways, namely cell wall production (Kre1p, Kre2p, Ecm33p), membrane calcium channel (Mid1p, Ecm7p), and ergosterol biosynthesis (Erg5p), were chosen as potential targets for the medications due to their functions in survival and disease development. SWISS MODEL was used to create the 3D structures of predicted targets of C. glabrata. The quality of these structures was assessed using Ramachandran plot statistics. AXD and HCP were analyzed by docking software AutoDock Vina against these targets. The findings of computational investigations have shown that both medicines exhibit interaction affinities with all the selected protein types. The binding energy profiles of AXD and HCP showed that Mid1p had the lowest binding energies at -10.1 kcal/mol and -9.2 kcal/mol, respectively. Kre2p had binding energies of -7.9 kcal/mol and -7.1 kcal/mol, respectively. Erg5p had binding energies of -6.6 kcal/mol and -6.2 kcal/mol, respectively. Ecm7p had binding energies of -6.6 kcal/mol and -6.1 kcal/mol, respectively. Recm7p had binding energies of -4.8 kcal/mol and -7.7 kcal/mol, respectively. These results suggest that these genes are likely targets of the two drugs in C. glabrata. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Unravelling novel molecular targets of Thymol and Carvacrol in Candida glabrata.

Author

Kumar, Darshan, Ansari, Ayesha, Rai, Nishant, and Kumar, Navin

Subjects
FUNGAL proteins, MOLECULAR docking, MUTANT proteins, DRUG target, CALCIUM channels, CARVACROL, THYMOL
Abstract

The emergence of non-albicans Candida as a potential causative agent of candidiasis following Candida albicans is occurring globally. It is known that phenolic cresols, thymol, and carvacrol have anti-Candida characteristics. However, there are few papers that disclose the mechanism of action, which is characterized by a damaged cell wall and ergosterol synthesis, as well as calcium-induced death in C. albicans. This work utilized in-silico docking analysis to examine the molecular targets of thymol and carvacrol in Candida glabrata. The results were then validated in vitro by antifungal susceptibility testing and growth curve analysis. The study incorporated C. glabrata deletion mutants for the target proteins to gain understanding of the function of these proteins in the antifungal effects of the two monoterpenoids. After conducting molecular docking and in silico toxicity evaluations, thymol and carvacrol were chosen for in vitro investigations. Experimental tests conducted in a laboratory setting have shown that both phytocompounds exhibit strong binding affinity towards cell wall synthesis proteins (Kre1p, Kre2p, Ecm33p), calcium channel proteins (Mid1p, Ecm7p), and proteins involved in the ergosterol synthesis pathway (Erg5p). These proteins are likely to be the specific targets for the anti-Candida properties of the two isomeric monoterpenoids in C. glabrata. Minimum inhibitory concentration (MIC50) values for thymol and carvacrol range from 50 µg/ml to 75 µg/ml. The wild-type strains exhibit a minimum fungicidal concentration of 100 µg/ml for thymol and 125 µg/ml for carvacrol. The results underscore the significance of these proteins in the fungal reaction to thymol and carvacrol and also indicate a promising opportunity for the development of novel antifungal treatment methods utilizing these proteins. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Exploring the bioactive potential and safety profile of Caesalpinia decapetala seeds and seed oil.

Author

Ashagrie, Yenework Nigussie, Tadesse, Mesfin Getachew, Bachheti, Rakesh Kumar, Bachheti, Archana, Tyagi, Manjusha, and Rai, Nishant

Subjects
OILSEEDS, METAL analysis, PHENOLS, FLAVONOIDS, CAESALPINIA, PHYTOCHEMICALS
Abstract

The rationale for this work was to investigate the phytoconstituents of the Caesalpinia decapetala species and their possible antibacterial and antioxidant capabilities, based on their traditional use in treating conditions such as inflammation, diabetes, purgative, and bronchitis. Analysis of the C. decapetala seed identified phytoconstituents such as flavonoids, alkaloids, phenolics, glycosides, saponins, proteins, terpenoids, and triterpenoids. The quantitative analysis revealed that the phenolic content of C. decapetala seed was greater than that of the seed oil (889 ± 0.35 mg GAE/100g of dry weight). Additionally, the total flavonoid content of the seed was estimated to be higher than that of the seed oil (225.03 ± 7.13mg QE/100g of dry weight). Furthermore, the seed of C. decapetala contained 11.5813% ± 0.242 of the total alkaloid contents. The antioxidant activity of the seed oil was investigated using the 2,2-Diphenyl-1-picrylhydrazyl (DPPH), ferric reducing power, and 2,2-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS) techniques. The measured IC50 value for DPPH was 114.86 µg/mL, while for ABTS it was 138.45µg/mL. Seed oil exhibited a more pronounced zone of inhibition in its antibacterial activity against K. pneumoniae. Overall, the present investigation validates that the seed and seed oil of C. decapetala have the potential to serve as a valuable reservoir of minerals, antioxidants, and antibacterial properties. This is due to the presence of diverse phytoconstituents, including phenolic compounds, flavonoids, alkaloids, di-α-tocopherol, stigmasterol, γ-sitosterol, and lupeol, within its fatty acid composition. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Dengue Infection During Pregnancy and Adverse Birth Outcomes: A Systematic Review and Meta‐Analysis.

Author

Shabil, Muhammed, Khatib, Mahalaqua Nazli, Zahiruddin, Quazi Syed, Rekha, M. M., Kaur, Mandeep, Rani, Bindu, Koul, Apurva, Ravi Kumar, M., Chauhan, Ashish Singh, Rai, Nishant, Sah, Sanjit, Lakhanpal, Sorabh, and Bushi, Ganesh

Abstract

Dengue is a rapidly spreading mosquito‐borne viral disease, posing significant public health challenges in tropical and subtropical regions. This systematic review and meta‐analysis aimed to evaluate the relationship between maternal dengue virus infection and adverse birth outcomes. A literature search was conducted in PubMed, Embase, and web of science databases until April 2024. Observational studies examining the association between laboratory‐confirmed maternal dengue infection and adverse birth outcomes such as preterm birth, low birth weight (LBW), small for gestational age (SGA), stillbirth, and postpartum haemorrhage were included. Data were extracted, and risk of bias was assessed using the Newcastle‐Ottawa Scale. Random‐effects meta‐analysis models were used to pool data in R software (V 4.3). Twenty studies met the inclusion criteria. The pooled prevalence of preterm birth among dengue‐affected pregnancies was 18.3% (95% CI: 12.6%–25.8%), with an OR of 1.21 (95% CI: 0.78–1.89). For LBW, the pooled prevalence was 17.1% (95% CI: 10.4%–26.6%), with an OR of 1.00 (95% CI: 0.69–1.41). SGA had a pooled prevalence of 11.2% (95% CI: 2.7%–36.9%) and an OR of 0.93 (95% CI: 0.41–2.14). The prevalence of stillbirth was 3.3% (95% CI: 1.6%–6.8%), with significant associations found in some studies (RR: 2.67; 95% CI: 1.09–6.57). Postpartum haemorrhage had an OR of 1.97 (95% CI: 0.53–2.69). While maternal dengue infection was associated with a higher prevalence of preterm birth and LBW, the associations were not statistically significant. Significant associations were observed for stillbirth in specific studies. Further research with standardized methodologies is needed to clarify these relationships and identify potential mechanisms. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Spectrum of COVID-19 induced liver injury: A review report

Author

Singh, Lokjan, primary, Kumar, Anil, additional, Rai, Maya, additional, Basnet, Bibek, additional, Rai, Nishant, additional, Khanal, Pukar, additional, Lai, Kok-Song, additional, Cheng, Wan-Hee, additional, Asaad, Ahmed Morad, additional, and Ansari, Shamshul, additional

Published
2024
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30. AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos

Author

Kar, Amlan, Rai, Nishant, Sikka, Karan, and Sharma, Gaurav

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a novel method for temporally pooling frames in a video for the task of human action recognition. The method is motivated by the observation that there are only a small number of frames which, together, contain sufficient information to discriminate an action class present in a video, from the rest. The proposed method learns to pool such discriminative and informative frames, while discarding a majority of the non-informative frames in a single temporal scan of the video. Our algorithm does so by continuously predicting the discriminative importance of each video frame and subsequently pooling them in a deep learning framework. We show the effectiveness of our proposed pooling method on standard benchmarks where it consistently improves on baseline pooling methods, with both RGB and optical flow based Convolutional networks. Further, in combination with complementary video representations, we show results that are competitive with respect to the state-of-the-art results on two challenging and publicly available benchmark datasets., Comment: CVPR 2017 Camera Ready Version

Published
2016

31. Contributors

Author

Aggarwal, Manoj, primary, Akingbasote, James, additional, Anadón, Arturo, additional, Anger, Gregory J., additional, Archibong, Anthony E., additional, Ares, Irma, additional, Aschner, Michael, additional, Avila, Daiana S., additional, Bailey, Denise C., additional, Barlow, Norman J., additional, Beedanagari, Sudheer, additional, Beltyukov, P.P., additional, Bischoff, Karyn, additional, Boeck, Carina Rodrigues, additional, Bracken, William M., additional, Brehm, Emily, additional, Breyer, Richard M., additional, Bright-Ponte, Susan, additional, Brockett, Mirjana Milosevic, additional, Campagnolo, Luisa, additional, Carney, Edward W., additional, Charrette, Andrew, additional, Chebekoue, Sandrine Fleur, additional, Chiang, Catheryne, additional, Chou, Wei-Chun, additional, Cleal, Jane K., additional, Cole, Toby B., additional, Cook, Daniel, additional, Coppock, Robert W., additional, Costa, Lucio G., additional, Curras-Collazo, Margarita, additional, Dai, Wanying, additional, Dao, Khoi, additional, Da Silva, Rosane Souza, additional, Davis, T. Zane, additional, Dominguez, Francisco, additional, Doss, Robin B., additional, Dziwenka, Margitta M., additional, Enright, Brian, additional, Estevan, Carmen, additional, Evans, Timothy J., additional, Fan, Anna M., additional, Farina, Marcelo, additional, Fenton, Suzanne E., additional, Filazi, Ayhan, additional, Fitsanakis, Vanessa A., additional, FitzGerald, Rex, additional, Flaskos, John, additional, Flaws, Jodi A., additional, Flora, S.J.S., additional, Ganjam, Vekataseshu K., additional, Gardner, Dale R., additional, Garg, Ramesh C., additional, Garrick, Jacqueline M., additional, Garry, Vincent F., additional, Gelineau-van Waes, Janee, additional, Godfrey, Keith M., additional, Golubentseva, Yu V., additional, González-Martín, Roberto, additional, Green, Benedict T., additional, Guerri, Consuelo, additional, Gulati, Kavita, additional, Gupta, P.K., additional, Gupta, Ramesh C., additional, Gupta, Rekha K., additional, Guthrie, Najla, additional, Hall, Jeffery O., additional, Hargreaves, Alan J., additional, Harris, Kenneth J., additional, Hill, Bridgett N., additional, Hilmas, Corey J., additional, Hoberman, Alan M., additional, Hougaard, Karin S., additional, Ince, Sinan, additional, Iyer, Poorni R., additional, Jacobsen, Nicklas R., additional, Kalpana, Starling, additional, Kandimalla, Ramesh, additional, Kanthasamy, Arthi, additional, Kanthasamy, Anumantha, additional, Khlebnikova, N.S., additional, Klüver, Nils, additional, Kodavanti, Prasada Rao S., additional, Krishnan, Kannan, additional, Kulkarni, Shaila, additional, Kuzukiran, Ozgur, additional, Lall, Rajiv, additional, Legradi, Jessica, additional, Lewis, Rohan M., additional, Lewis, Elise M., additional, Li, Xin, additional, Lin, Pinpin, additional, Loganathan, Bommanna G., additional, Machado, Ivo F., additional, Mahadevan, Brinda, additional, Makris, Susan L., additional, Malik, Jitendra K., additional, Marreilha dos Santos, Ana Paula, additional, Marsillach, Judit, additional, Martínez-Larrañaga, María Rosa, additional, Martínez, María Aránzazu, additional, Matouskova, Klara, additional, McClellan, Roger O., additional, Milatovic, Dejan, additional, Mohammed, Ali Mustafa, additional, Montine, Thomas J., additional, Møller, Peter, additional, Multani, Pushpinder Kaur, additional, Ni, Mingwei, additional, Nikolaidis, Efstathios, additional, Novilla, Meliton N., additional, Padilla, Stephanie, additional, Palmeira, Carlos M., additional, Pamies, David, additional, Panner Selvam, Manesh Kumar, additional, Pascual, María, additional, Patwa, Jayant, additional, Phillips, Ashley, additional, Piquette-Miller, Micheline, additional, Popov, V.B., additional, Pratt, M. Margaret, additional, Protasova, G.A., additional, Rai, Nishant, additional, Ramalho-Santos, João, additional, Ramesh, Aramandla, additional, Ray, Kausik, additional, Ray, Arunabha, additional, Ren, Aiguo, additional, Renaud, Stephen J., additional, Roberts, Drucilla J., additional, Rongzhu, Lu, additional, Sachana, Magdalini, additional, Sahlman, Heidi, additional, Saini, Nitin, additional, Saini, Vandna, additional, Shabasheva, L.V., additional, Sharma, Abha, additional, Sikka, Suresh C., additional, Silva, Marilyn Helen, additional, Simsek, Ilker, additional, Singh Kalra, Rajkumar, additional, Sinha, Anita, additional, Sogorb, Miguel A., additional, Soldin, Offie P., additional, Song, Chunjuan, additional, Srivastava, Ajay, additional, Stice, Szabina A., additional, Stoker, Tammy E., additional, Stonecipher, Clinton A., additional, Szlapinski, Sandra, additional, Tanabe, Shihori, additional, Tatiparthi, Arun, additional, Teixeira da Rocha, João Batista, additional, Thokchom, Suresh Kumar, additional, Tornesi, Belen, additional, Torous, Vanda, additional, Truran, Peter, additional, Valdez, Matthew C., additional, Vandenberg, Laura N., additional, Vargesson, Neil, additional, Vilanova, Eugenio, additional, Vähäkangas, Kirsi H., additional, Vulimiri, Suryanarayana V., additional, Warner, Genoa R., additional, Welch, Kevin D., additional, Williams, Daniel C., additional, Woldemeskel, Moges, additional, Yang, Jae-Ho, additional, Yin, Zhaobao, additional, Ying, Xiaoyou, additional, Yurdakok-Dikmen, Begum, additional, Zaja-Milatovic, Snjezana, additional, and Zhou, Changqing, additional

Published
2022
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33. Antibiotic pollution: The forerunner of global health catastrophe?

Author

Lahiri, Dibyajit, Nag, Moupriya, Dey, Ankita, Ray, Rina Rani, Rai, Nishant, and Chaubey, Kundan Kumar

Subjects
ANTIBIOTIC residues, WORLD health, ANTIBIOTICS, ANTI-infective agents, HORIZONTAL gene transfer, POLLUTION
Abstract

The rapid enhancement in the emergence of resistant bacteria occurring globally has endangered the efficacy of the conventional antibiotics causing threat to human at a large extent. Traces of antimicrobial drugs and their metabolites in various natural environments have resulted in enhancement of antimicrobial resistance (AMR) among the natural flora and fauna of the water resources. Contamination of the environment with antimicrobials encourages the bacteria to evolve ways to acquire competence which may result in the conversion of antibiotic susceptible strains to resistant bacteria. The spread of AMR varies from planktonic to sessile forms of the bacterial species by cross resistance or horizontal gene transfer resulting in blooming populations that would otherwise can be kept in check. This has created a tremendous threat to human health due to the enhancement in the resistance of microorganisms and other microbes to various types of antimicrobial drugs. Rampant use of antibiotics and improper detection of antibiotics from the sewages of the factories has resulted in the enhancement of concentration of antibiotics in the environment. This has resulted in the enhancement of death due to the persistence of such infections those cannot be treated with the conventional drugs causing a global health crisis. Hence, mitigation of this problem is of utmost importance to save lives of human beings and animals. This has forced to find out some alternate way on an urgent basis for risk management including adoption of stringent guidelines for surveillance and monitoring of these industries and development of policies are warranted to develop policies on checking antibiotic pollution as a "One Health Approach". [ABSTRACT FROM AUTHOR]

Published
2024
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37. List of contributors

Author

Ahmad, Abul H., primary, Akingbasote, James, additional, Anadón, Arturo, additional, Anand, Rashmi, additional, Anantharam, Vellareddy, additional, András, Csaba, additional, Ares, Irma, additional, Avdonin, Pavel V., additional, Ay, Muhammet, additional, Banerjee, Aryamitra, additional, Barnett, Reid E., additional, Belinskaia, Daria A., additional, Bertero, Alessia, additional, Bharti, Vijay K., additional, Bian, Wan-Ping, additional, Bischoff, Karyn L., additional, Caloni, Francesca, additional, Castela, Ana Catarina, additional, Chand, Naresh, additional, Chanderbhan, Ronald F., additional, Charli, Adhithiya, additional, Charrette, Andrew, additional, Chen, Si, additional, Chen, Tao, additional, Choudhuri, Supratim, additional, Choudhury, Soumen, additional, Coccini, Teresa, additional, Cope, Rhian B., additional, Coppock, Robert W., additional, Costa, Lucio G., additional, Damodaran, Tirupapuliyur, additional, Darvesh, Altaf S., additional, Dhruw, Leena, additional, Dormán, György, additional, Doss, Robin B., additional, DuBourdieu, Dan, additional, Dziwenka, Margitta, additional, Eades, Joshua, additional, Esch, Harald L., additional, Filazi, Ayhan, additional, Fitsanakis, Vanessa A., additional, Flachner, Beáta, additional, Fossati, Paola, additional, Gambaryan, Stepan P., additional, Garg, Ramesh C., additional, Garg, Satish K., additional, Gil, Fernando, additional, Goncharov, Nikolay V., additional, Gulati, Kavita, additional, Guo, Lei, additional, Guo, Xiaoqing, additional, Gupta, Bhanushree, additional, Gupta, Ramesh C., additional, Guthrie, Najla, additional, Gwaltney-Brant, Sharon M., additional, Hajdú, István, additional, Hartsel, Joshua A., additional, Hazzah, Trina, additional, Hernández, Antonio F., additional, Hickory, Brian, additional, Hilmas, Corey J., additional, Hoeng, Julia, additional, Jain, Amul, additional, Jenkins, Richard O., additional, Jin, Huajun, additional, Joshi, Jagdish Chandra, additional, Kalinovsky, Tatiana, additional, Kalpana, Starling, additional, Kalidindi, Sanyasi R., additional, Kanthasamy, Anumantha G., additional, Kanthasamy, Arthi, additional, Kaore, Navinchandra M., additional, Kaore, Shilpa N., additional, Kapoor, Vijay Kumar, additional, Kaur, Gurjot, additional, Kolanos, Renata, additional, Korf, Ekaterina A., additional, Krishna, Gopala, additional, Krishna, Mayur, additional, Kuča, Kamil, additional, Kudryavtsev, Igor V., additional, Kumar, Amit, additional, Kumar, Dinesh, additional, Kumar, Prafull, additional, Lall, Rajiv, additional, La Mesa, Camillo, additional, Latino, Diogo A.R.S., additional, Lehmann, Leane, additional, Li, Xilin, additional, Machado, Ivo F., additional, Makriyannis, Alexandros, additional, Malik, Jitendra K., additional, Martín-Domingo, M. Concepción, additional, Martínez, María-Aránzazu, additional, Martínez-Larrañaga, María-Rosa, additional, Mei, Nan, additional, Messinis, Dimitris E., additional, Mindukshev, Igor V., additional, Negi, Poonam, additional, Niedzwiecki, Aleksandra, additional, Ning, Baitang, additional, Novozhilov, Artem V., additional, Palmeira, Carlos Marques, additional, Papas, Andreas M., additional, Pei, De-Sheng, additional, Peitsch, Manuel C., additional, Peterson, Jeffrey D., additional, Pistollato, Francesca, additional, Pitt, Jason, additional, Pitt, Yiuka, additional, Pospisil, Pavel, additional, Poussin, Carine, additional, Pradhan, Aanchal, additional, Prakash, Atul, additional, Prasad, Sahdeo, additional, Rahal, Anu, additional, Rai, Nishant, additional, Raina, Rajinder, additional, Rath, Matthias, additional, Ray, Arunabha, additional, Rehman, Sana, additional, Richter, Gary, additional, Risuleo, Gianfranco, additional, Roberts, Ashley, additional, Rolo, Anabela Pinto, additional, Roomi, M. Waheed, additional, Sachana, Magdalini, additional, Satoh, Tetsuo, additional, Serebriakova, Maria K., additional, Sharma, Rahul, additional, Shukla, Amit, additional, Sinha, Anita, additional, Sinha, Priynak, additional, Spicer, Leon J, additional, Srivastava, Ajay, additional, Stice, Szabina A., additional, Sudnitsyna, Julia S., additional, Taku, Ila, additional, Talukder, Jamil, additional, Teodoro, João Soeiro, additional, Thokchom, Suresh Kumar, additional, Trulioff, Andrey S., additional, Ukolov, Anton I., additional, Umegaki, Keizo, additional, Verma, Pankaj, additional, Verma, Pawan K., additional, Wan, Dan, additional, Wilson-Frank, Christina, additional, Woldemeskel, Moges, additional, Wu, Qinghua, additional, Yang, Mildred S., additional, Yurdakok-Dikmen, Begum, additional, Zaja-Milatovic, Snjezana, additional, and Zoltani, Csaba K., additional

Published
2021
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41. Antimycotic activity of green tea phytocompounds against Candida glabrata

Author

Sirari, Priyanka, Anand, Jigisha, Tyagi, Manjusha, Bachheti, Rakesh Kuamar, Thapliyal, Ashish, Rai, Nishant, Sirari, Priyanka, Anand, Jigisha, Tyagi, Manjusha, Bachheti, Rakesh Kuamar, Thapliyal, Ashish, and Rai, Nishant

Abstract

One of the medically important opportunistic fungal pathogen for humans is Candida glabrata that causes various types of candidiasis. Its environmental adaptations and antimicrobial resistance is now a great concern for public health. In the present study, the green tea phytocompounds; EGCg, Chlorogenic acid, Coumaroyl quinic acid and Rutin trihydrate along with a known antimycotic Fluconazole were studied for their antimycotic activity against Candida glabrata. The MIC90 for C. glabrata was observed at 125µg/ml for EGC g, 250 µg/mlf or Chlorogenic acid, 500µg/ml for Coumaroyl quinic acid and Rutin trihydrate while 12.5µg/ml for Fluconazole in macro dilution assay while the MFC values were 1000 µg/ml for EGC g, 500 µg/ml for Chlorogenic acid, Coumaroyl quinic acid, Rutin trihydrate and 50 µg/ml for Fluconazole. In microdilution assay, the MIC90 for C. glabrata was observed 125µg/ml for EGC g and chlorogenic acid, 500µg/ml for Coumaroyl quinic acid, Rutin trihydrate and 12.5µg/ml for Fluconazole while the MFC values were 31.25 µg/ml for Fluconazole, 250 µg/ml for chlorogenic acid and 500 µg/ml for EGC g, Coumaroyl quinic acid and Rutin trihydrate. EGCg and Chlorogenic acid was found to be more effective against C. glabrata and therefore these two were used for synergistic study along with Fluconazole. The viability of HeLa cells (in per cent) was observed ≥100% green tea phyto compounds. The viability of treated cells (in per cent) with a combination of Green tea, phytocompounds and fluconazole was observed between ≥98± 0.79 to ≥ 98± 0.87. Green tea phytocompounds mainly EGC g and chlorogenic acid can be used as synergistic molecules having antimycotic activity against C. glabrata.

Published
2023

45. Traditional Uses, Active Ingredients, and Biological Activities of Paris polyphylla Smith: A Comprehensive Review of an Important Himalayan Medicinal Plant

Author

Rawat, Janhvi Mishra, Pandey, Shweta, Rawat, Balwant, Rai, Nishant, Preeti, P., Thakur, Ajay, Butola, Jitendra Singh, and Bachheti, Rakesh Kumar

Abstract

Paris polyphylla Smith (family: Melanthiaceae), a high-value medicinal herb endemic to the Himalayan region, has drawn much attention recently due to its immense use in the traditional healthcare system since ancient times. In the present review, an extensive database on P. polyphylla was systematically searched from databases such as Medline/PubMed, Scopus, the Web of Science, and the online service E-library.ru and SCImago (https://www.scimagojr.com/). Information on species, ecology, distribution, trade, ethnopharmacology, pharmacology, biotechnology, and molecular biology was gathered from 1979 to 2023 using 116 research publications. Major steroidal saponins such as Paris saponin I, V, VI, VII, and H have been found substantially effective in anticancer activity, abnormal uterine bleeding, dysfunctional uterine bleeding, and menorrhagia. Traditional breeding and propagation techniques cannot keep up with the world’s growing demand for herbal drugs. Therefore, it is critically necessary to take conservation measures and develop novel techniques for growing and cultivating this economically significant and highly valuable therapeutic herb. The advanced biotechnological approaches like micropropagation and genetic analysis introduced long back are either rare or lacking in the case of P. polyphylla. It contains a wealth of information that will serve as a baseline data source for various stakeholders, researchers working on various research aspects, and policymakers to define appropriate utilization and conservation plans for a high-value commercial medicinal plant called P. polyphylla. The review provides an updated overview and critical assessment of secondary data regarding the past and recent applications and interventions of P. polyphylla.

Published
2023
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49. List of Contributors

Author

Aggarwal, Manoj, primary, Agrawal, Shruti, additional, Anadón, Arturo, additional, Anger, Gregory J., additional, Archibong, Anthony E., additional, Ares, Irma, additional, Aschner, Michael, additional, Avila, Daiana S., additional, Bailey, Denise C., additional, Baker, Nancy C., additional, Barlow, Norman J., additional, Beedanagari, Sudheer, additional, Bischoff, Karyn, additional, Bracken, William M., additional, Brehm, Emily, additional, Breyer, Richard M., additional, Bright-Ponte, Susan, additional, Campagnolo, Luisa, additional, Carney, Edward W., additional, Castellano, Victor, additional, Chebekoue, Sandrine Fleur, additional, Chiang, Catheryne, additional, Chou, Wei-Chun, additional, Choudhuri, Supratim, additional, Cleal, Jane K., additional, Coppock, Robert W., additional, Costa, Lucio G., additional, Curras-Collazo, Margarita, additional, da Rocha, João Batista Teixeira, additional, Da Silva, Rosane S., additional, Davis, T. Zane, additional, Doss, Robin B., additional, Dziwenka, Margitta M., additional, Enright, Brian, additional, Eriksson, Per, additional, Estevan, Carmen, additional, Evans, Timothy J., additional, Fadeel, Bengt, additional, Fan, Anna M., additional, Faqi, Ali S., additional, Farina, Marcelo, additional, Fenton, Suzanne E., additional, Feuston, Maureen H., additional, Filazi, Ayhan, additional, Fitsanakis, Vanessa A., additional, Flaskos, John, additional, Flaws, Jodi A., additional, Flora, Swaran J.S., additional, Ganjam, Vekataseshu K., additional, Gardner, Dale R., additional, Gardner, Nicole M., additional, Garg, Ramesh C., additional, Garry, Vincent F., additional, Gelineau-van Waes, Janee, additional, Godfrey, Keith M., additional, Guerri, Consuelo, additional, Guizzetti, Marina, additional, Gulati, Kavita, additional, Gulumian, Mary, additional, Gupta, Pawan K., additional, Gupta, Ramesh C., additional, Gupta, Rekha K., additional, Gwaltney-Brant, Sharon M., additional, Hall, Jeffrey O., additional, Hansen, Deborah K., additional, Hargreaves, Alan J., additional, Harris, Kenneth J., additional, Hoberman, Alan M., additional, Hougaard, Karin S., additional, Ince, Sinan, additional, Inselman, Amy L., additional, Iyer, Poorni, additional, Jacobsen, Nicklas R., additional, Kagan, Valerian E., additional, Kalpana, Starling, additional, Kaneko, Kotaro, additional, Kanthasamy, Anumantha, additional, Kanthasamy, Arthi, additional, Karttunen, Vesa, additional, Kim, Hyung Sik, additional, Knudsen, Thomas B., additional, Kodavanti, Prasada Rao S., additional, Krishnan, Kannan, additional, Kulkarni, Shaila, additional, Lall, Rajiv, additional, Lasher, Michelle A., additional, Laudert, Scott B., additional, Lee, Byung-Mu, additional, Legradi, Jessica, additional, Leung, Maxwell C.K., additional, Lewis, Elise M., additional, Lewis, Rohan M., additional, Li, Xin, additional, Lin, Pinpin, additional, Loganathan, Bommanna G., additional, Lyche, Jan Ludvig, additional, MacPhail, Robert C., additional, Mahadevan, Brinda, additional, Malik, Jitendra K., additional, Marreilha dos Santos, Ana Paula, additional, Martínez, Maria Aranzazu, additional, Martínez-Larrañaga, Maria Rosa, additional, McClary, David, additional, Menezes, Fabiano P., additional, Meyer, Jerrold S., additional, Milatovic, Dejan, additional, Miller Mukherjee, Ida R., additional, Mohammed, Ali Mustafa, additional, Møller, Peter, additional, Montine, Thomas J., additional, Ni, Mingwei, additional, Nikolaidis, Efstathios, additional, Novilla, Meliton N., additional, Padilla, Stephanie, additional, Palmeira, Carlos M., additional, Pamies, David, additional, Panter, Kip E., additional, Pascual, María, additional, Pellacani, Claudia, additional, Piper, Brian J., additional, Piquette-Miller, Micheline, additional, Popov, Vadim B., additional, Pratt, M. Margaret, additional, Protasova, Galina A., additional, Rai, Nishant, additional, Ramalho-Santos, João, additional, Ramesh, Aramandla, additional, Ray, Arunabha, additional, Ray, Kausik, additional, Ren, Aiguo, additional, Renaud, Stephen J., additional, Riley, Ronald T., additional, Roberts, Drucilla J., additional, Rongzhu, Lu, additional, Sachana, Magdalini, additional, Savolainen, Kai M., additional, Shabasheva, Lilia V., additional, Shiverick, Kathleen T., additional, Sikka, Suresh C., additional, Sogorb, Miguel A., additional, Soldin, Offie P., additional, Song, Chunjuan, additional, Srivastava, Ajay, additional, Stice, Szabina A., additional, Stoker, Tammy E., additional, Toor, Jaideep S., additional, Tornesi, Belen, additional, Truran, Peter, additional, Vähäkangas, Kirsi, additional, Valdez, Joseph, additional, Vargesson, Neil, additional, Viberg, Henrik, additional, Vilanova, Eugenio, additional, Voss, Kenneth A., additional, Vulimiri, Suryanarayana V., additional, Welch, Kevin D., additional, Williams, Daniel C., additional, Woldemeskel, Moges, additional, Yang, Jae-Ho, additional, Yin, Zhaobao, additional, Ying, Xiaoyou, additional, Yurdakok-Dikmen, Begum, additional, Zaja-Milatovic, Snjezana, additional, and Zhou, Changqing, additional

Published
2017
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