Identification of Constituents and Exploring the Mechanism for Toutongning Capsule in the Treatment of Migraine

Toutongning capsule (TTNC) is an effective and safe traditional Chinese medicine used in the treatment of migraine. In this present study, a multiscale strategy was used to systematically investigate the mechanism of TTNC in treating migraine, which contained UPLC-UESI-Q Exactive Focus network pharmacology and experimental verification. First, 88 compounds were identified by the UPLC-UESI-Q Exactive Focus method for TTNC. Then, the target fishing for these compounds was performed by means of an efficient drug similarity search tool. Third, a series of network pharmacology experiments were performed to predict the key compounds, targets, and pathways. They were protein-protein interaction (PPI), KEGG pathway enrichment analysis, and herbs-compounds-targets-pathways (H-C-T-P) network construction. As a result, 18 potential key compounds, 20 potential key targets, and 6 potential signaling pathways were obtained for TTNC in treatment with migraine. Finally, molecular docking and experimental were carried out to verify the key targets. In short, the results showed that TTNC is able to treat migraine through multiple components, multiple targets, and multiple pathways. This work may provide a theoretical basis for further research on the molecular mechanism of TTNC in the treatment of migraine.

Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2

The ongoing COVID-19 outbreak have posed a significant threat to public health worldwide. Recently Toll-like receptor (TLR) has been proposed to be the drug target of SARS-CoV-2 treatment, the specificity and efficacy of such treatments remain unknown. In the present study we performed the investigation of repurposed drugs via a framework comprising of Search Tool for Interacting Chemicals (STITCH), Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular docking, and virus-host-drug interactome mapping. Chloroquine (CQ) and hydroxychloroquine (HCQ) were utilized as probes to explore the interaction network that is linked to SARS-CoV-2. 47 drug targets were shown to be overlapped with SARS-CoV-2 network and were enriched in TLR signaling pathway. Molecular docking analysis and molecular dynamics simulation determined the direct binding affinity of TLR9 to CQ and HCQ. Furthermore, we established SARS-CoV-2-human-drug protein interaction map and identified the axis of TLR9-ERC1-Nsp13 and TLR9-RIPK1-Nsp12. Therefore, the elucidation of the interactions of SARS-CoV-2 with TLR9 axis will not only provide pivotal insights into SARS-CoV-2 infection and pathogenesis but also improve the treatment against COVID-19.

Analysis of International Patent Applications for Inventions Like Traditional Herbal Medicines

Numerous applications have been filed for patents based on bio-inventions in the Indian patent office. Although there is not any international patent, there is a system of international patent applications whereby the applicant may designate name of countries where they wish to file application for patents nationally. According to international patent classification, the concern class for such a patent applications is A61K36/00. More particularly, the international class (IC) A61K36/00 relates to medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof (e.g., traditional herbal medicines). International applications filings under patent cooperation treaty (PCT) for patent purposes can be accessed at the Patentscope (patent search tool of WIPO). All international patent applications for such TK-based inventions have been accessed online at Patentscope using the classification code A61K36 for this study.

Molecular classification and comparative phylogeographic study of insectivorous bat species (Pipisitrellus coromandra) from Punjab, Pakistan

Molecular based identification of bat fauna in Pakistan has been relatively less explored. The current study was therefore planned to report for the first time the molecular classification of insectivorous bats (Pipistrellus coromandra) based on mitochondrion gene (COI) from Punjab, Pakistan. Specimens were collected from five different locations followed by DNA extraction with subsequent gene amplification and sequencing. All samples in the study had shown close identity matches with species (Pipistrellus coromandra) from India and (Pipistrellus tenuis) from Vietnam with percentage identity score of 96.11 and 95.58 respectively except one sequence which only revealed 86.78% identity match on Basic Local Alignment Search Tool (BLAST) and could only be assigned to genus level Pipistrellus sp. The results indicated negligible intra-population genetic distance among collected samples whereas the comparison with species from other countries had shown high intraspecific (P. coromandra) and interspecific (P. tenuis) mean genetic distances. The current study hence successfully proved the efficiency of COI gene as a molecular marker for species identification and in analyzing the patterns of genetic variation with species from other countries.

Finding light in dark archives: using AI to connect context and content in email

Email archives are important historical resources, but access to such data poses a unique archival challenge and many born-digital collections remain dark, while questions of how they should be effectively made available remain. This paper contributes to the growing interest in preserving access to email by addressing the needs of users, in readiness for when such collections become more widely available. We argue that for the content of email to be meaningfully accessed, the context of email must form part of this access. In exploring this idea, we focus on discovery within large, multi-custodian archives of organisational email, where emails’ network features are particularly apparent. We introduce our prototype search tool, which uses AI-based methods to support user-driven exploration of email. Specifically, we integrate two distinct AI models that generate systematically different types of results, one based upon simple, phrase-matching and the other upon more complex, BERT embeddings. Together, these provide a new pathway to contextual discovery that accounts for the diversity of future archival users, their interests and level of experience.

Morphological, phytochemical and molecular characterization of five common Jatropha species in the Niger Delta Region of Nigeria

The economic and medicinal important genus Jatropha contains many distinctly different species. To elucidate the genetic relationship of five common occurring Jatropha species namely J. multifida, J. podagrica, J.tanjorernsis, J. curcas, and J. gossypifolia, thirty-nine morphological, six phytochemical features and one arbitrary marker was used to screen and explore their similarity. Morphological data was obtained from the measurement of vegetative and reproductive parts while the presence of five phytochemicals was determined using differentphytochemical tests. The DNA of all five Jatropha species were amplified and sequenced using Ribolose 1, 5- biphosphate carboxylase molecular marker. The DNA sequences were then aligned using the Basic Local Alignment Search Tool for nucleotide 2.8.0 version of the National Center for Biotechnology Information database and phylogenetic trees were constructed using Paleontological Statistical software and Molecular Evolutionary Genetics Analysis version 7.0.26 software. From the results of the classical and phylogenetic cluster analysis, the five Jatropha species was separated into two major clusters. The highly distinctive J. gossypifolia was the only species that clustered separately from the other Jatropha species. Although, J. tanjorensis has been reported to be a hybrid from J. curcas and J. gossypifolia, the species did not segregate and cluster with these species, but segregated with J. multifida, and J.podagrica, indicating that this species is more closely related to J. multifida, and J. podagrica than J. curcas and J.gossypifolia. The result therefore provide information that would be useful in the plant improvement programs for the genus Jatropha.

Association of Hidradenitis Suppurativa (HS) with Autoimmune Disease and Autoantibodies

Objective There is thought to be an association between Hidradenitis Suppurativa (HS) and autoimmune diseases. This retrospective longitudinal cohort study looked to identify whether certain autoimmune diseases or autoantibody specificities are more closely associated with HS than others and, whether such associations are related to severity of HS. Methods Patients were identified using the SlicerDicer search tool in Epic from January 1, 2010 through August 15, 2020. Search criteria included HS diagnosis by ICD-10 code and at least one visit in dermatology. Charts were reviewed to determine HS disease severity, treatment modalities, presence of autoimmune disease, and autoantibody positivity. Results 627 patients were identified. Most patients were females (75.3%) and had obese BMIs (71.1%), but there were no significant demographic differences between HS patients with and without autoimmune diseases. 101 (16.1%) patients in the total cohort had at least one autoimmune disease, most commonly, thyroid disease, lupus, psoriasis, and inflammatory bowel disease (IBD). 212 patients were also tested for the presence of autoantibodies. The most common positive autoantibody, found in 54 patients (28.4%), was antinuclear antibody (ANA). 54 patients with more severe HS disease manifestations required biologic medications to treat their HS. Neither HS severity nor biologic treatment was associated with presence of autoimmune disease or positive autoantibodies. Conclusion In a large cohort of patients with HS followed longitudinally, autoimmune disorders (especially lupus, psoriasis and IBD) and presence of autoantibodies were more commonly observed than expected in the normal population.

PITA, a pre-bilaterian p75NTR, is the evolutionary ancestor of TNF receptors

Tumor necrosis factor receptors (TNFRs) regulate a diverse array of biological functions, including adaptive immunity, neurodevelopment, and many others. Although TNFRs are expressed in all metazoan phyla, a coherent model of the molecular origins of mammalian TNFRs—and how they relate to TNFRs in other phyla—has remained elusive. To address this, we executed a large-scale, systematic Basic Local Alignment Search Tool (BLAST)-based approach to trace the evolutionary ancestry of all 29 human TNFRs. We discovered that all human TNFRs are descendants of a single pre-bilaterian TNFR with strong sequence similarity to the p75 neurotrophin receptor (p75NTR), which we designate as PITA for ‘ p75NTR is the TNFR Ancestor’ . A distinct subset of human TNFRs—including EDAR, XEDAR and TROY—share a unique history as descendants of EDAR-XEDAR-TROY (EXT), which diverged from PITA in a bilaterian ancestor. Most PITA descendants possess a death domain (DD) within their intracellular domain (ICD) but EXTs do not. PITA descendants are expressed in all bilaterian phyla and Cnidaria, but not in non-planulozoan ParaHoxozoa, suggesting that PITA originated in an ancestral planulozoan. Drosophila melanogaster TNFRs (Wengen (Wgn) and Grindelwald (Grnd)) were identified as divergent PITA descendants, providing the first evolutionary link between this model TNFR system and the mammalian TNFR superfamily. This study reveals PITA as the ancestor to human and Drosophila TNFR systems and describes an evolutionary model that will facilitate deciphering TNF-TNFR functions in health and disease.

Daisychain: Search and Interactive Visualisation of Homologs in Genome Assemblies

Daisychain is an interactive graph visualisation and search tool for custom-built gene homology databases. The main goal of Daisychain is to allow researchers working with specific genes to identify homologs in other annotation releases. The gene-centric representation includes local gene neighborhood to distinguish orthologs and paralogs by local synteny. The software supports genome sequences in FASTA format and GFF3 formatted annotation files, and the process of building the homology database requires a minimum amount of user interaction. Daisychain includes an integrated web viewer that can be used for both data analysis and data publishing. The web interface extends KnetMaps.js and is based on JavaScript.