cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25429420
title: ANN multi-layer perceptron for prediction of blood–brain barrier permeable compounds for central nervous system therapeutics

description: Endothelial cells produce a semipermeable barrier known as the blood–brain barrier (BBB) to keep undesired chemicals out of the central nervous system (CNS). However, this barrier also restricts the exploration of potential new medications due to insufficient exposure. To address this challenge, machine learning (ML) algorithms can be useful to predict the BBB permeability of chemical compounds. Support vector machines, continuous neural networks, and deep learning approaches have been used to identify compounds that can penetrate the BBB. However, predicting BBB permeability based solely on chemical structure can be difficult. In the current research, we developed an ML model using a large dataset to predict BBB permeability, which could be used for early-stage drug screening of potential CNS medications. Our artificial neural network ANN algorithm exhibited an accuracy of 0.94, specificity of 0.83, sensitivity of 0.97, AUC of 0.96, and MCC of 0.83. These metrics suggest that our model has a high accuracy rate in predicting BBB permeability and therefore has the potential to advance drug discovery efforts in the CNS. This study’s outcomes demonstrate the potential for ML models to predict BBB permeability accurately, aiding in the identification of new CNS therapeutic options.
DOI: 10.6084/m9.figshare.25429420.v1
title: ANN multi-layer perceptron for prediction of blood–brain barrier permeable compounds for central nervous system therapeutics

description: Endothelial cells produce a semipermeable barrier known as the blood–brain barrier (BBB) to keep undesired chemicals out of the central nervous system (CNS). However, this barrier also restricts the exploration of potential new medications due to insufficient exposure. To address this challenge, machine learning (ML) algorithms can be useful to predict the BBB permeability of chemical compounds. Support vector machines, continuous neural networks, and deep learning approaches have been used to identify compounds that can penetrate the BBB. However, predicting BBB permeability based solely on chemical structure can be difficult. In the current research, we developed an ML model using a large dataset to predict BBB permeability, which could be used for early-stage drug screening of potential CNS medications. Our artificial neural network ANN algorithm exhibited an accuracy of 0.94, specificity of 0.83, sensitivity of 0.97, AUC of 0.96, and MCC of 0.83. These metrics suggest that our model has a high accuracy rate in predicting BBB permeability and therefore has the potential to advance drug discovery efforts in the CNS. This study’s outcomes demonstrate the potential for ML models to predict BBB permeability accurately, aiding in the identification of new CNS therapeutic options.
DOI: 10.6084/m9.figshare.25359361
title: In silico development of a novel anti-mutation, multi-epitope mRNA vaccine against MPXV variants of emerging lineage and sub-lineages by using immunoinformatics approaches

description: Over the past year, an unexpected surge in human monkeypox (hMPX) cases has been observed. This outbreak differs from previous ones, displaying distinct epidemiological characteristics and transmission patterns, believed to be influenced by a newly emerging monkeypox virus (MPXV) lineage. Notably, this emerging MPXV lineage has exhibited several non-synonymous mutations, some of which are linked to immunomodulatory activities and antigenic characteristics that aid in host detection. However, specific treatments or vaccines for human monkeypox are currently lacking. Hence, we aim to develop a multi-epitope mRNA vaccine by using immunoinformatics approaches against the MPXV, particularly its emerging variants. Six proteins (A29L, A35R, B6R, M1R, H3L, and E8L) were chosen for epitope and mutation site identification. Seventeen top-performing epitopes and eight epitopes containing mutation sites were selected and combined with adjuvants, the PADRE sequence, and linkers for vaccine development. The molecular and physical properties of the designed vaccine (WLmpx) were favorable. Immunological characteristics of WLmpx were assessed through molecular docking, molecular dynamics (MD) simulations, and immune simulations. Finally, the vaccine sequence was utilized to formulate an mRNA-based vaccine. The informatics-based predicted results indicated that the designed vaccine exhibits significant potential in eliciting high-level humoral and cellular immune responses, but further validation through <i>in vivo</i> and vitro studies is warranted.
DOI: 10.6084/m9.figshare.25359361.v1
title: In silico development of a novel anti-mutation, multi-epitope mRNA vaccine against MPXV variants of emerging lineage and sub-lineages by using immunoinformatics approaches

description: Over the past year, an unexpected surge in human monkeypox (hMPX) cases has been observed. This outbreak differs from previous ones, displaying distinct epidemiological characteristics and transmission patterns, believed to be influenced by a newly emerging monkeypox virus (MPXV) lineage. Notably, this emerging MPXV lineage has exhibited several non-synonymous mutations, some of which are linked to immunomodulatory activities and antigenic characteristics that aid in host detection. However, specific treatments or vaccines for human monkeypox are currently lacking. Hence, we aim to develop a multi-epitope mRNA vaccine by using immunoinformatics approaches against the MPXV, particularly its emerging variants. Six proteins (A29L, A35R, B6R, M1R, H3L, and E8L) were chosen for epitope and mutation site identification. Seventeen top-performing epitopes and eight epitopes containing mutation sites were selected and combined with adjuvants, the PADRE sequence, and linkers for vaccine development. The molecular and physical properties of the designed vaccine (WLmpx) were favorable. Immunological characteristics of WLmpx were assessed through molecular docking, molecular dynamics (MD) simulations, and immune simulations. Finally, the vaccine sequence was utilized to formulate an mRNA-based vaccine. The informatics-based predicted results indicated that the designed vaccine exhibits significant potential in eliciting high-level humoral and cellular immune responses, but further validation through <i>in vivo</i> and vitro studies is warranted.
DOI: 10.6084/m9.figshare.25265827
title: Virtual screening and site-directed mutagenesis-derived aptamers for precise <i>Salmonella typhimurium</i> prediction: emphasizing OmpD targeting and G-quadruplex stability

description: The efficient detection of the foodborne pathogen <i>Salmonella typhimurium</i> has historically been hampered by the constraints of traditional methods, characterized by protracted culture periods and intricate DNA extraction processes for PCR. To address this, our research innovatively focuses on the crucial and relatively uncharted virulence factor, the Outer Membrane Protein D (OmpD) in <i>Salmonella typhimurium</i>. By harmoniously integrating the power of virtual screening and site-directed mutagenesis, we unveiled aptamers exhibiting marked specificity for OmpD. Among these, aptamer 7ZQS stands out with its heightened binding affinity. Capitalizing on this foundation, we further engineered a repertoire of mutant aptamers, wherein APT6 distinguished itself, reflecting unmatched stability and specificity. Our rigorous validation, underpinned by cutting-edge bioinformatics tools, amplifies the prowess of APT6 in discerning and binding OmpD across an array of <i>Salmonella typhimurium</i> strains. This study illuminates a transformative approach to the prompt and accurate detection of <i>Salmonella typhimurium</i>, potentially redefining boundaries in applied analytical chemistry and bolstering diagnostic precision across diverse research and clinical domains.
DOI: 10.6084/m9.figshare.25265827.v1
title: Virtual screening and site-directed mutagenesis-derived aptamers for precise <i>Salmonella typhimurium</i> prediction: emphasizing OmpD targeting and G-quadruplex stability

description: The efficient detection of the foodborne pathogen <i>Salmonella typhimurium</i> has historically been hampered by the constraints of traditional methods, characterized by protracted culture periods and intricate DNA extraction processes for PCR. To address this, our research innovatively focuses on the crucial and relatively uncharted virulence factor, the Outer Membrane Protein D (OmpD) in <i>Salmonella typhimurium</i>. By harmoniously integrating the power of virtual screening and site-directed mutagenesis, we unveiled aptamers exhibiting marked specificity for OmpD. Among these, aptamer 7ZQS stands out with its heightened binding affinity. Capitalizing on this foundation, we further engineered a repertoire of mutant aptamers, wherein APT6 distinguished itself, reflecting unmatched stability and specificity. Our rigorous validation, underpinned by cutting-edge bioinformatics tools, amplifies the prowess of APT6 in discerning and binding OmpD across an array of <i>Salmonella typhimurium</i> strains. This study illuminates a transformative approach to the prompt and accurate detection of <i>Salmonella typhimurium</i>, potentially redefining boundaries in applied analytical chemistry and bolstering diagnostic precision across diverse research and clinical domains.
DOI: 10.6084/m9.figshare.25375397
title: A network pharmacological approach for the identification of potential therapeutic targets of <i>Brahmi Nei</i> – a complex traditional Siddha formulation

description: <i>Brahmi Nei</i> (BN), a traditional Indian polyherbal formulation has been described in classical texts for the treatment of anxiety and depression, as well as to fortify the immune system. The individual herbs of BN have been used for treatment of wide range of disorders including cognition, inflammation, skin ailments and cancer etc., This diverse basket of therapeutic activity suggests that BN may possess therapeutic benefits to other disorders. So, the present study aims to identify the potential therapeutic targets of BN using a network pharmacological approach to comprehend the multi target action of its multiple phytoconstituents. We have employed Randić Index for the first time to calculate the contribution score of module segregated targets towards diseases. Our results suggests that BN targets could also be effective in other diseases such as lysosomal storage disorders, respiratory disorders etc., apart from neurological disorders. The key targets with highest topological measures of Targets-(Pathway)-Targets network were identified as potential therapeutic targets of BN. And the top hit target PTGS2, a gene encoding for cyclooxygenase-2 was further evaluated using molecular docking, molecular dynamic simulation and <i>in vitro</i> studies. Our findings open up new therapeutic facets for BN that can be explored systematically in future.
DOI: 10.6084/m9.figshare.25375397.v1
title: A network pharmacological approach for the identification of potential therapeutic targets of <i>Brahmi Nei</i> – a complex traditional Siddha formulation

description: <i>Brahmi Nei</i> (BN), a traditional Indian polyherbal formulation has been described in classical texts for the treatment of anxiety and depression, as well as to fortify the immune system. The individual herbs of BN have been used for treatment of wide range of disorders including cognition, inflammation, skin ailments and cancer etc., This diverse basket of therapeutic activity suggests that BN may possess therapeutic benefits to other disorders. So, the present study aims to identify the potential therapeutic targets of BN using a network pharmacological approach to comprehend the multi target action of its multiple phytoconstituents. We have employed Randić Index for the first time to calculate the contribution score of module segregated targets towards diseases. Our results suggests that BN targets could also be effective in other diseases such as lysosomal storage disorders, respiratory disorders etc., apart from neurological disorders. The key targets with highest topological measures of Targets-(Pathway)-Targets network were identified as potential therapeutic targets of BN. And the top hit target PTGS2, a gene encoding for cyclooxygenase-2 was further evaluated using molecular docking, molecular dynamic simulation and <i>in vitro</i> studies. Our findings open up new therapeutic facets for BN that can be explored systematically in future.
DOI: 10.6084/m9.figshare.28006470
title: Green synthesis of ZnO nanoparticles: <i>in-silico</i> and <i>in-vitro</i> assessment of anticancer and antibacterial activity and biomolecule (DNA) binding analysis

description: The zinc oxide nanoparticles (ZnO NPs) were synthesized by a green chemistry approach utilizing <i>Stachys schtschegleevii</i> Sosn.) <i>S. schtchegleevii</i> (extract, aims to innovate by employing environmentally friendly techniques. The production of ZnO NPs was confirmed by FT-IR, zeta potential, TEM, SEM-EDX, DLS and ultraviolet-visible techniques. The antibacterial potency of the ZnO NPs was evaluated toward pathogenic strains of <i>E. coli</i> and <i>S. aureus.</i> The antibacterial efficacy of this NPs against the selected bacteria followed the sequence: <i>S. aureus</i> &gt; <i>E. coli.</i> The results of the MTT assay indicate that ZnO NPs have significant anticancer potential against the MCF-7 cell line. Furthermore, the synthesized ZnO NPs demonstrate a stronger inhibitory effect compared to the extract on the cancer cell line. To find out the potential of ZnO NPs as therapeutics, interaction process with calf-thymus DNA (ct-DNA) was performed by using absorption, and fluorescence studies. The evaluation of the fluorescence spectra and UV–visible absorption showed a satisfactory association of the ZnO NPs with ct-DNA. Besides, to clarify the binding interactions of ZnO NPs with the enzymes and ct-DNA, molecular docking simulation was performed. The molecular docking’ results show a well compromise with our experimental results. In total, the present investigation employs ZnO NPs synthesized through green methods as an efficient strategy for the purpose of the potentiation of anticancer and antibacterial activities in a biocompatible manner. Environmentally green protocol was employed to biosynthesis of ZnO NPs from <i>Stachys schtschegleevii</i> extract.The MTT assay demonstrated cytotoxicity of green synthesized ZnO NPs against MCF-7 cancer cell line.The green synthesized ZnO NPs demonstrated remarkable antibacterial effects against <i>S. aureus</i> and <i>E. coli</i>.Molecular docking studies were performed to explore the binding interactions of ZnO NPs with the enzymes and ct-DNA.ZnO NPs bind to ct-DNA through an unusual binding mode (partial intercalation). Environmentally green protocol was employed to biosynthesis of ZnO NPs from <i>Stachys schtschegleevii</i> extract. The MTT assay demonstrated cytotoxicity of green synthesized ZnO NPs against MCF-7 cancer cell line. The green synthesized ZnO NPs demonstrated remarkable antibacterial effects against <i>S. aureus</i> and <i>E. coli</i>. Molecular docking studies were performed to explore the binding interactions of ZnO NPs with the enzymes and ct-DNA. ZnO NPs bind to ct-DNA through an unusual binding mode (partial intercalation).
DOI: 10.6084/m9.figshare.28006470.v1
title: Green synthesis of ZnO nanoparticles: <i>in-silico</i> and <i>in-vitro</i> assessment of anticancer and antibacterial activity and biomolecule (DNA) binding analysis

description: The zinc oxide nanoparticles (ZnO NPs) were synthesized by a green chemistry approach utilizing <i>Stachys schtschegleevii</i> Sosn.) <i>S. schtchegleevii</i> (extract, aims to innovate by employing environmentally friendly techniques. The production of ZnO NPs was confirmed by FT-IR, zeta potential, TEM, SEM-EDX, DLS and ultraviolet-visible techniques. The antibacterial potency of the ZnO NPs was evaluated toward pathogenic strains of <i>E. coli</i> and <i>S. aureus.</i> The antibacterial efficacy of this NPs against the selected bacteria followed the sequence: <i>S. aureus</i> &gt; <i>E. coli.</i> The results of the MTT assay indicate that ZnO NPs have significant anticancer potential against the MCF-7 cell line. Furthermore, the synthesized ZnO NPs demonstrate a stronger inhibitory effect compared to the extract on the cancer cell line. To find out the potential of ZnO NPs as therapeutics, interaction process with calf-thymus DNA (ct-DNA) was performed by using absorption, and fluorescence studies. The evaluation of the fluorescence spectra and UV–visible absorption showed a satisfactory association of the ZnO NPs with ct-DNA. Besides, to clarify the binding interactions of ZnO NPs with the enzymes and ct-DNA, molecular docking simulation was performed. The molecular docking’ results show a well compromise with our experimental results. In total, the present investigation employs ZnO NPs synthesized through green methods as an efficient strategy for the purpose of the potentiation of anticancer and antibacterial activities in a biocompatible manner. Environmentally green protocol was employed to biosynthesis of ZnO NPs from <i>Stachys schtschegleevii</i> extract.The MTT assay demonstrated cytotoxicity of green synthesized ZnO NPs against MCF-7 cancer cell line.The green synthesized ZnO NPs demonstrated remarkable antibacterial effects against <i>S. aureus</i> and <i>E. coli</i>.Molecular docking studies were performed to explore the binding interactions of ZnO NPs with the enzymes and ct-DNA.ZnO NPs bind to ct-DNA through an unusual binding mode (partial intercalation). Environmentally green protocol was employed to biosynthesis of ZnO NPs from <i>Stachys schtschegleevii</i> extract. The MTT assay demonstrated cytotoxicity of green synthesized ZnO NPs against MCF-7 cancer cell line. The green synthesized ZnO NPs demonstrated remarkable antibacterial effects against <i>S. aureus</i> and <i>E. coli</i>. Molecular docking studies were performed to explore the binding interactions of ZnO NPs with the enzymes and ct-DNA. ZnO NPs bind to ct-DNA through an unusual binding mode (partial intercalation).
DOI: 10.6084/m9.figshare.30634473
title: A combination of conserved and stage-specific lncRNA biomarkers to detect lung adenocarcinoma progression

description: Lung adenocarcinoma is highly heterogeneous at the molecular level between different stages; therefore, understanding molecular mechanisms contributing to such heterogeneity is needed. In addition, multiple stages of progression are critical factors for lung adenocarcinoma treatment. However, previous studies showed that cancer progression is associated with altered lncRNA expression, highlighting the tissue-specific and developmental stage-specific nature of lncRNAs in various diseases. Therefore, a study using an integrated network approach to explore the role of lncRNA in carcinogenesis was done using expression profiles revealing stage-specific and conserved lncRNA biomarkers in lung adenocarcinoma. We constructed ceRNA networks for each stage of lung adenocarcinoma and analysed them using network topology, differential co-expression network, protein–protein interaction network, functional enrichment, survival analysis, genomic analysis and deep learning to identify potential lncRNA biomarkers. The co-expression networks of healthy and three successive stages of lung adenocarcinoma have shown different network properties. One conserved and four stage-specific lncRNAs are identified as genome regulatory biomarkers. These lncRNAs can successfully identify lung adenocarcinoma and different stages of progression using deep learning. In addition, we identified five mRNAs, four miRNAs and twelve novel carcinogenic interactions associated with the progression of lung adenocarcinoma. These lncRNA biomarkers will provide a novel perspective into the underlying mechanism of adenocarcinoma progression and may be further helpful in early diagnosis, treatment and prognosis of this deadly disease.
DOI: 10.6084/m9.figshare.30634473.v1
title: A combination of conserved and stage-specific lncRNA biomarkers to detect lung adenocarcinoma progression

description: Lung adenocarcinoma is highly heterogeneous at the molecular level between different stages; therefore, understanding molecular mechanisms contributing to such heterogeneity is needed. In addition, multiple stages of progression are critical factors for lung adenocarcinoma treatment. However, previous studies showed that cancer progression is associated with altered lncRNA expression, highlighting the tissue-specific and developmental stage-specific nature of lncRNAs in various diseases. Therefore, a study using an integrated network approach to explore the role of lncRNA in carcinogenesis was done using expression profiles revealing stage-specific and conserved lncRNA biomarkers in lung adenocarcinoma. We constructed ceRNA networks for each stage of lung adenocarcinoma and analysed them using network topology, differential co-expression network, protein–protein interaction network, functional enrichment, survival analysis, genomic analysis and deep learning to identify potential lncRNA biomarkers. The co-expression networks of healthy and three successive stages of lung adenocarcinoma have shown different network properties. One conserved and four stage-specific lncRNAs are identified as genome regulatory biomarkers. These lncRNAs can successfully identify lung adenocarcinoma and different stages of progression using deep learning. In addition, we identified five mRNAs, four miRNAs and twelve novel carcinogenic interactions associated with the progression of lung adenocarcinoma. These lncRNA biomarkers will provide a novel perspective into the underlying mechanism of adenocarcinoma progression and may be further helpful in early diagnosis, treatment and prognosis of this deadly disease.
DOI: 10.5281/zenodo.17627898
title: Human Detection Dataset
DOI: 10.5281/zenodo.17627899
title: Human Detection Dataset
DOI: 10.34740/kaggle/dsv/13762338
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.ezhbe9
title: Occurrence Download

description: A dataset containing 560 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stegocintractia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 560 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038598-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30634448
title: Arbuscular mycorrhizal tree dominance affect leaf nutrient status and resorption efficiency in subtropical forest with different tree diversity

description: In March 2021, we established a randomized block experiment, combining diversity level with AM tree dominance. We selected four AM tree species (Cunninghamia lanceolate (Lamb.) Hook., Schima superba Gardner & Champ., Mytilaria laosensis Lec., Liquidambar formosana Hance) and four EM tree species (Pinus massoniana Lamb., Alnus trabeculosa Hand.-Mazz., Castanopsis carlesii (Hemsl.) Hayata., Castanopsis fissa (Champ. ex Benth.) Rehder & E. H. Wilson in Sarg.) with wide distribution and economic values. Two-year-old tree seedlings were planted either in monoculture or in mixtures, at a density of 256 individuals per plot (plot size is 12 m × 12 m, tree spacing is 0.80 m). We established three diversity levels (one, two, or four species per plot) with arbuscular mycorrhizal (AM) tree dominance of 0% and 100% at 1 tree species level, 0%, 50% and 100% at 2 tree species level, and 0%, 25%, 50%, 75% and 100% at 4 tree species level. Overall, the experiment included 28 treatments, roughly distributed in four blocks, resulting in 112 plots.