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  1. Article ; Online: Production of syngas by methane dry reforming over the catalyst ZNi1‐xCex: effects of catalyst calcination and reduction temperature

    Rajput, Akanksha Singh / Das, Taraknath

    Journal of Chemical Technology & Biotechnology. 2023 Mar., v. 98, no. 3 p.691-705

    2023  

    Abstract: BACKGROUND: A nickel‐based catalyst is active in the dry reforming of methane. However, the nickel‐metal particles' sintering at high reaction temperatures and the rapid catalyst deactivation due to coke deposition are still significant issues. RESULTS: ... ...

    Abstract BACKGROUND: A nickel‐based catalyst is active in the dry reforming of methane. However, the nickel‐metal particles' sintering at high reaction temperatures and the rapid catalyst deactivation due to coke deposition are still significant issues. RESULTS: A series of catalysts, Ni₁‐ₓCeₓ, was prepared by the sol–gel technique and the synthesized catalysts were used for the methane dry reforming reaction considering various parameters, such as the ceria to nickel ratio, total loading, catalyst calcination, and reduction temperature. The addition of ceria to nickel increased the CH₄ and CO₂ percent conversion. The catalyst 40Ni₀.₇₅Ce₀.₂₅/Al₂O₃ calcined at 700 °C possessed a high conversion of CO₂ and CH₄. The reduced catalyst (40Ni₀.₇₅Ce₀.₂₅/Al₂O₃) showed better catalytic activity than the calcined catalyst. However, the reduced catalyst's performance declined due to coke deposition. The calcined catalyst was more stable than the reduced catalyst. The time‐on‐stream study (up to 16 h) reflected that the percent conversion and yield dropped more sharply for the reduced catalyst (% CO₂ Conversion dropped: 100% to 94%) than for the calcined catalyst (% CO₂ conversion dropped: 94% to 93%). The accumulation of ceria and support (alumina) increased the catalyst surface area, which improved the overall activity and stability of the catalyst. Scanning Electron Microscopy (SEM) and Raman spectroscopy analyses detected the formation of Multi Walled ‐ Carbon Nanotubes (MW‐CNT) on the used catalyst. They also show the formation of a smaller diameter of MW‐CNT (60–70 nm) over the calcined catalyst than the reduced catalyst (80–139 nm). CONCLUSION: The calcined catalyst, 40Ni₀.₇₅Ce₀.₂₅/Al₂O₃–700 °C, was very active with high methane (91%) and CO₂ (94%) conversions; also, the reduced catalyst was active and possessed high methane (88%) and CO₂ (100%) conversions at low reaction temperatures. Overall, the calcined catalyst was comparatively more stable than the reduced catalyst. © 2022 Society of Chemical Industry (SCI).
    Keywords Raman spectroscopy ; aluminum oxide ; biotechnology ; carbon dioxide ; carbon nanotubes ; catalysts ; catalytic activity ; electron microscopy ; methane ; nickel ; surface area ; synthesis gas ; temperature
    Language English
    Dates of publication 2023-03
    Size p. 691-705.
    Publishing place John Wiley & Sons, Ltd.
    Document type Article ; Online
    Note JOURNAL ARTICLE
    ZDB-ID 1479465-2
    ISSN 1097-4660 ; 0268-2575
    ISSN (online) 1097-4660
    ISSN 0268-2575
    DOI 10.1002/jctb.7276
    Database NAL-Catalogue (AGRICOLA)

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  2. Article ; Online: Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing.

    Gautam, Sakshi / Thakur, Anamika / Rajput, Akanksha / Kumar, Manoj

    Viruses

    2023  Volume 16, Issue 1

    Abstract: Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a ... ...

    Abstract Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure-activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC
    MeSH term(s) Drug Repositioning ; Dengue Virus ; Reproducibility of Results ; Machine Learning ; Antiviral Agents/pharmacology
    Chemical Substances Antiviral Agents
    Language English
    Publishing date 2023-12-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2516098-9
    ISSN 1999-4915 ; 1999-4915
    ISSN (online) 1999-4915
    ISSN 1999-4915
    DOI 10.3390/v16010045
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning.

    Rajput, Akanksha / Kumar, Manoj

    Molecular diversity

    2021  Volume 26, Issue 3, Page(s) 1635–1644

    Abstract: Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are ... ...

    Abstract Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC
    MeSH term(s) Antiviral Agents/pharmacology ; Antiviral Agents/therapeutic use ; Ebolavirus ; Hemorrhagic Fever, Ebola/drug therapy ; Humans ; Machine Learning ; Quantitative Structure-Activity Relationship ; Support Vector Machine
    Chemical Substances Antiviral Agents
    Language English
    Publishing date 2021-08-06
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1376507-3
    ISSN 1573-501X ; 1381-1991
    ISSN (online) 1573-501X
    ISSN 1381-1991
    DOI 10.1007/s11030-021-10291-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Anti-Biofilm: Machine Learning Assisted Prediction of IC

    Rajput, Akanksha / Bhamare, Kailash T / Thakur, Anamika / Kumar, Manoj

    Journal of molecular biology

    2023  Volume 435, Issue 14, Page(s) 168115

    Abstract: Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of ... ...

    Abstract Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed 'anti-Biofilm', a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC
    MeSH term(s) Humans ; Anti-Bacterial Agents/pharmacology ; Anti-Bacterial Agents/chemistry ; Biofilms/drug effects ; Drug Repositioning ; Drug Resistance, Bacterial ; Machine Learning ; Microbial Sensitivity Tests ; Inhibitory Concentration 50
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2023-04-20
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80229-3
    ISSN 1089-8638 ; 0022-2836
    ISSN (online) 1089-8638
    ISSN 0022-2836
    DOI 10.1016/j.jmb.2023.168115
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Anti-Biofilm: Machine Learning Assisted Prediction of IC50 Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing

    Rajput, Akanksha / Bhamare, Kailash T. / Thakur, Anamika / Kumar, Manoj

    Journal of Molecular Biology. 2023 July, v. 435, no. 14 p.168115-

    2023  

    Abstract: Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of ... ...

    Abstract Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed ‘anti-Biofilm’, a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC₅₀) values extracted from aBiofilm resource. Out of the five MLTs, the Support Vector Machine performed best with Pearson’s correlation coefficient of 0.75 on the training/testing data set. The robustness of the developed model was further checked using an independent validation dataset. While analyzing the chemical diversity of the anti-biofilm compounds, we observed that they occupy diverse chemical spaces with parent molecules like furanone, urea, phenolic acids, quinolines, and many more. Use of diverse chemicals as input further signifies the robustness of our predictive models. The three best-performing machine learning models were implemented as a user-friendly ‘anti-Biofilm’ web server (https://bioinfo.imtech.res.in/manojk/antibiofilm/) with different other modules which make ‘anti-Biofilm’ a comprehensive platform. Therefore, we hope that our initiative will be helpful for the scientific community engaged in identifying effective anti-biofilm agents to target the problem of antimicrobial resistance.
    Keywords Internet ; antibiotic resistance ; biofilm ; data collection ; drugs ; humans ; immune system ; inhibitory concentration 50 ; models ; molecular biology ; prediction ; quinolines ; support vector machines ; urea
    Language English
    Dates of publication 2023-07
    Publishing place Elsevier Ltd
    Document type Article ; Online
    Note Pre-press version
    ZDB-ID 80229-3
    ISSN 1089-8638 ; 0022-2836
    ISSN (online) 1089-8638
    ISSN 0022-2836
    DOI 10.1016/j.jmb.2023.168115
    Database NAL-Catalogue (AGRICOLA)

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  6. Article ; Online: Biofilm-

    Rajput, Akanksha / Bhamare, Kailash T / Thakur, Anamika / Kumar, Manoj

    Molecules (Basel, Switzerland)

    2022  Volume 27, Issue 15

    Abstract: Antibiotic drug resistance has emerged as a major public health threat globally. One of the leading causes of drug resistance is the colonization of microorganisms in biofilm mode. Hence, there is an urgent need to design novel and highly effective ... ...

    Abstract Antibiotic drug resistance has emerged as a major public health threat globally. One of the leading causes of drug resistance is the colonization of microorganisms in biofilm mode. Hence, there is an urgent need to design novel and highly effective biofilm inhibitors that can work either synergistically with antibiotics or individually. Therefore, we have developed a recursive regression-based platform "Biofilm-
    MeSH term(s) Anti-Bacterial Agents/pharmacology ; Biofilms ; Drug Resistance, Microbial ; Quantitative Structure-Activity Relationship ; Support Vector Machine
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2022-07-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 1413402-0
    ISSN 1420-3049 ; 1431-5165 ; 1420-3049
    ISSN (online) 1420-3049
    ISSN 1431-5165 ; 1420-3049
    DOI 10.3390/molecules27154861
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches.

    Kamboj, Sakshi / Rajput, Akanksha / Rastogi, Amber / Thakur, Anamika / Kumar, Manoj

    Computational and structural biotechnology journal

    2022  Volume 20, Page(s) 3422–3438

    Abstract: Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5-10% cases. Therefore, it is crucial to develop new antivirals ... ...

    Abstract Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5-10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the "
    Language English
    Publishing date 2022-06-30
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2022.06.060
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Impact of processing parameters on the quality attributes of spray-dried powders: a review

    George, Sony / Thomas, Anish / Kumar, Malladi V. Pavan / Kamdod, Abdul Samad / Rajput, Akanksha / T, Jayasree Joshi / Abdullah, S.

    Eur Food Res Technol. 2023 Feb., v. 249, no. 2 p.241-257

    2023  

    Abstract: The powdered form of various food products, including dairy-based and sugar-rich, is popular in the market due to their ease of processing and instant application. Spray drying is one of the most popular processing techniques to make food powders. The ... ...

    Abstract The powdered form of various food products, including dairy-based and sugar-rich, is popular in the market due to their ease of processing and instant application. Spray drying is one of the most popular processing techniques to make food powders. The spray-drying process parameters, especially the drying temperatures and feed rate, have a significant influence on the quality attributes (such as color, moisture content, drying yield, wettability, water activity, proximate composition, and nutrient contents) of different food products. Recent studies revealed that at very high inlet/outlet air temperatures, the properties of the powdered products are adversely affected. Furthermore, sticking is a major problem during the spray-drying process. Keeping all these issues, this review focuses on providing insights on the effects of spray-drying process conditions and optimization of these conditions to overcome the problems associated with the process and ultimately obtain the best quality product with longer storage life.
    Keywords air ; color ; markets ; proximate composition ; shelf life ; spray drying ; water activity ; water content ; wettability
    Language English
    Dates of publication 2023-02
    Size p. 241-257.
    Publishing place Springer Berlin Heidelberg
    Document type Article ; Online
    Note Review
    ZDB-ID 1359456-4
    ISSN 1431-4630 ; 1438-2377
    ISSN 1431-4630 ; 1438-2377
    DOI 10.1007/s00217-022-04170-0
    Database NAL-Catalogue (AGRICOLA)

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  9. Article: Anti-flavi: A Web Platform to Predict Inhibitors of

    Rajput, Akanksha / Kumar, Manoj

    Frontiers in microbiology

    2018  Volume 9, Page(s) 3121

    Abstract: ... ...

    Abstract Flaviviruses
    Language English
    Publishing date 2018-12-18
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2018.03121
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches

    Kamboj, Sakshi / Rajput, Akanksha / Rastogi, Amber / Thakur, Anamika / Kumar, Manoj

    Computational and Structural Biotechnology Journal. 2022, v. 20

    2022  

    Abstract: Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5–10% cases. Therefore, it is crucial to develop new antivirals ... ...

    Abstract Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5–10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the “Anti-HCV” platform using machine learning and quantitative structure–activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC₅₀/EC₅₀) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson’s correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The “Anti-HCV” predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.
    Keywords Hepatitis C virus ; antiviral agents ; bioactive properties ; biotechnology ; data collection ; databases ; drug development ; hepatoma ; neural networks ; quantitative structure-activity relationships ; support vector machines ; viral hepatitis
    Language English
    Size p. 3422-3438.
    Publishing place Elsevier B.V.
    Document type Article
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2022.06.060
    Database NAL-Catalogue (AGRICOLA)

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