Characterization study of Holoptelea integrifolia tree bark fibres reinforced epoxy composites

This study characterised the composite plate fabricated by epoxy matrix reinforced with alkaline-treated Holoptelea integrifolia tree bark fibre. Tensile and flexural test results clearly show that the mechanical characteristics of pure resin improve in direct proportion to the fibre up to 40%. However, impact test results show that 30% fibre mass ratio composite showed higher mechanical properties. The H. integrifolia fibre composites (HIFC) specimens were also characterised by using Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), Energy dispersive X-ray analysis (EDAX) and thermogravimetric analysis-differential scanning calorimetry (TGA-DSC) analysis. FESEM results show that the bonding between fibre and matrix was excellent. EDAX reveals the elemental proportion of HIFC. O-H, C- H, C-O-C, moisture content and aromatic structure are evident by FTIR spectroscopy. Thermal analysis reveals that the composites degrade rapidly when exposed above 210 °C.

Advancing entity recognition in biomedicine via instruction tuning of large language models

MOTIVATION

Large Language Models (LLMs) have the potential to revolutionize the field of Natural Language Processing, excelling not only in text generation and reasoning tasks but also in their ability for zero/few-shot learning, swiftly adapting to new tasks with minimal fine-tuning. LLMs have also demonstrated great promise in biomedical and healthcare applications. However, when it comes to Named Entity Recognition (NER), particularly within the biomedical domain, LLMs fall short of the effectiveness exhibited by fine-tuned domain-specific models. One key reason is that NER is typically conceptualized as a sequence labeling task, whereas LLMs are optimized for text generation and reasoning tasks.

RESULTS

We developed an instruction-based learning paradigm that transforms biomedical NER from a sequence labeling task into a generation task. This paradigm is end-to-end and streamlines the training and evaluation process by automatically repurposing pre-existing biomedical NER datasets. We further developed BioNER-LLaMA using the proposed paradigm with LLaMA-7B as the foundational LLM. We conducted extensive testing on BioNER-LLaMA across three widely recognized biomedical NER datasets, consisting of entities related to diseases, chemicals, and genes. The results revealed that BioNER-LLaMA consistently achieved higher F1-scores ranging from 5% to 30% compared to the few-shot learning capabilities of GPT-4 on datasets with different biomedical entities. We show that a general-domain LLM can match the performance of rigorously fine-tuned PubMedBERT models and PMC-LLaMA, biomedical-specific language model. Our findings underscore the potential of our proposed paradigm in developing general-domain LLMs that can rival SOTA performances in multi-task, multi-domain scenarios in biomedical and health applications.

AVAILABILITY AND IMPLEMENTATION

Datasets and other resources are available at https://github.com/BIDS-Xu-Lab/BioNER-LLaMA.

A High-Quality Blue Whale Genome, Segmental Duplications, and Historical Demography

The blue whale, Balaenoptera musculus, is the largest animal known to have ever existed, making it an important case study in longevity and resistance to cancer. To further this and other blue whale-related research, we report a reference-quality, long-read-based genome assembly of this fascinating species. We assembled the genome from PacBio long reads and utilized Illumina/10×, optical maps, and Hi-C data for scaffolding, polishing, and manual curation. We also provided long read RNA-seq data to facilitate the annotation of the assembly by NCBI and Ensembl. Additionally, we annotated both haplotypes using TOGA and measured the genome size by flow cytometry. We then compared the blue whale genome with other cetaceans and artiodactyls, including vaquita (Phocoena sinus), the world's smallest cetacean, to investigate blue whale's unique biological traits. We found a dramatic amplification of several genes in the blue whale genome resulting from a recent burst in segmental duplications, though the possible connection between this amplification and giant body size requires further study. We also discovered sites in the insulin-like growth factor-1 gene correlated with body size in cetaceans. Finally, using our assembly to examine the heterozygosity and historical demography of Pacific and Atlantic blue whale populations, we found that the genomes of both populations are highly heterozygous and that their genetic isolation dates to the last interglacial period. Taken together, these results indicate how a high-quality, annotated blue whale genome will serve as an important resource for biology, evolution, and conservation research.

Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models

BACKGROUND

The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: (1) they identify a relationship but not the type of relationship, (2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, (3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or (4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues.

RESULTS

We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches.

CONCLUSIONS

SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach

MOTIVATION

Automated extraction of participants, intervention, comparison/control, and outcome (PICO) from the randomized controlled trial (RCT) abstracts is important for evidence synthesis. Previous studies have demonstrated the feasibility of applying natural language processing (NLP) for PICO extraction. However, the performance is not optimal due to the complexity of PICO information in RCT abstracts and the challenges involved in their annotation.

RESULTS

We propose a two-step NLP pipeline to extract PICO elements from RCT abstracts: (i) sentence classification using a prompt-based learning model and (ii) PICO extraction using a named entity recognition (NER) model. First, the sentences in abstracts were categorized into four sections namely background, methods, results, and conclusions. Next, the NER model was applied to extract the PICO elements from the sentences within the title and methods sections that include >96% of PICO information. We evaluated our proposed NLP pipeline on three datasets, the EBM-NLPmoddataset, a randomly selected and reannotated dataset of 500 RCT abstracts from the EBM-NLP corpus, a dataset of 150 COVID-19 RCT abstracts, and a dataset of 150 Alzheimer's disease (AD) RCT abstracts. The end-to-end evaluation reveals that our proposed approach achieved an overall micro F1 score of 0.833 on the EBM-NLPmod dataset, 0.928 on the COVID-19 dataset, and 0.899 on the AD dataset when measured at the token-level and an overall micro F1 score of 0.712 on EBM-NLPmod dataset, 0.850 on the COVID-19 dataset, and 0.805 on the AD dataset when measured at the entity-level.

AVAILABILITY

Our codes and datasets are publicly available at https://github.com/BIDS-Xu-Lab/section_specific_annotation_of_PICO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

Background

The PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues.

Results

We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches.

Conclusions

SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

Fungal Planet description sheets: 1436-1477

Novel species of fungi described in this study include those from various countries as follows: Argentina, Colletotrichum araujiae on leaves, stems and fruits of Araujia hortorum. Australia, Agaricus pateritonsus on soil, Curvularia fraserae on dying leaf of Bothriochloa insculpta, Curvularia millisiae from yellowing leaf tips of Cyperus aromaticus, Marasmius brunneolorobustus on well-rotted wood, Nigrospora cooperae from necrotic leaf of Heteropogon contortus, Penicillium tealii from the body of a dead spider, Pseudocercospora robertsiorum from leaf spots of Senna tora, Talaromyces atkinsoniae from gills of Marasmius crinis-equi and Zasmidium pearceae from leaf spots of Smilaxglyciphylla. Brazil, Preussia bezerrensis from air. Chile, Paraconiothyrium kelleni from the rhizosphere of Fragaria chiloensis subsp. chiloensis f. chiloensis. Finland, Inocybe udicola on soil in mixed forest with Betula pendula, Populus tremula, Picea abies and Alnus incana. France, Myrmecridium normannianum on dead culm of unidentified Poaceae. Germany, Vexillomyces fraxinicola from symptomless stem wood of Fraxinus excelsior. India, Diaporthe limoniae on infected fruit of Limonia acidissima, Didymella naikii on leaves of Cajanus cajan, and Fulvifomes mangroviensis on basal trunk of Aegiceras corniculatum. Indonesia, Penicillium ezekielii from Zea mays kernels. Namibia, Neocamarosporium calicoremae and Neocladosporium calicoremae on stems of Calicorema capitata, and Pleiochaeta adenolobi on symptomatic leaves of Adenolobus pechuelii. Netherlands, Chalara pteridii on stems of Pteridium aquilinum, Neomackenziella juncicola (incl. Neomackenziella gen. nov.) and Sporidesmiella junci from dead culms of Juncus effusus. Pakistan, Inocybe longistipitata on soil in a Quercus forest. Poland, Phytophthora viadrina from rhizosphere soil of Quercus robur, and Septoria krystynae on leaf spots of Viscum album. Portugal (Azores), Acrogenospora stellata on dead wood or bark. South Africa, Phyllactinia greyiae on leaves of Greyia sutherlandii and Punctelia anae on bark of Vachellia karroo. Spain, Anteaglonium lusitanicum on decaying wood of Prunus lusitanica subsp. lusitanica, Hawksworthiomyces riparius from fluvial sediments, Lophiostoma carabassense endophytic in roots of Limbarda crithmoides, and Tuber mohedanoi from calcareus soils. Spain (Canary Islands), Mycena laurisilvae on stumps and woody debris. Sweden, Elaphomyces geminus from soil under Quercus robur. Thailand, Lactifluus chiangraiensis on soil under Pinus merkusii, Lactifluus nakhonphanomensis and Xerocomus sisongkhramensis on soil under Dipterocarpus trees. Ukraine, Valsonectria robiniae on dead twigs of Robinia hispida. USA, Spiralomyces americanus (incl. Spiralomyces gen. nov.) from office air. Morphological and culture characteristics are supported by DNA barcodes. Citation: Tan YP, Bishop-Hurley SL, Shivas RG, et al. 2022. Fungal Planet description sheets: 1436-1477. Persoonia 49: 261-350. https://doi.org/10.3767/persoonia.2022.49.08.

A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes

Background

The Nile rat (Avicanthis niloticus) is an important animal model because of its robust diurnal rhythm, a cone-rich retina, and a propensity to develop diet-induced diabetes without chemical or genetic modifications. A closer similarity to humans in these aspects, compared to the widely used Mus musculus and Rattus norvegicus models, holds the promise of better translation of research findings to the clinic.

Results

We report a 2.5 Gb, chromosome-level reference genome assembly with fully resolved parental haplotypes, generated with the Vertebrate Genomes Project (VGP). The assembly is highly contiguous, with contig N50 of 11.1 Mb, scaffold N50 of 83 Mb, and 95.2% of the sequence assigned to chromosomes. We used a novel workflow to identify 3613 segmental duplications and quantify duplicated genes. Comparative analyses revealed unique genomic features of the Nile rat, including some that affect genes associated with type 2 diabetes and metabolic dysfunctions. We discuss 14 genes that are heterozygous in the Nile rat or highly diverged from the house mouse.

Conclusions

Our findings reflect the exceptional level of genomic resolution present in this assembly, which will greatly expand the potential of the Nile rat as a model organism.

AB0227 TREATMENT SEQUENCING PATTERNS AND COMPARATIVE EFFICACY IN PATIENTS WITH RHEUMATOID ARTHRITIS FROM A REAL-WORLD SETTING

Background

The European League Against Rheumatism (EULAR)1 recently provided updated guidelines regarding the initiation and modification of disease-modifying antirheumatic drug (DMARD) therapy in patients with Rheumatoid Arthritis (RA). Therefore, real-world evidence studies are warranted to provide insights into first-line DMARD utilization and durability of response in the second-line setting.

Objectives

To analyze RA treatment patterns in real-world data and compare durability of response between second-line DMARDs + anti-TNF (TNFi) therapies vs. TNFi monotherapy.

Methods

Electronic health records (EHRs) from a large health system in the Northeast US were used to identify RA patients. Lines of therapy were defined based on confirmed prescriptions for DMARDs and TNFi therapies. Time to next treatment (TTNT) was the primary outcome to estimate durability of response. Time-to-event analyses were performed using Kaplan-Meier and log-rank test methods. In addition, a Cox Proportional-Hazards (CoxPH) model was used to evaluate covariates as independent predictors of disease progression.

Results

Our study cohort consisted of 8,040 patients who had at least one line of therapy for RA. Conventional synthetic DMARDs (csDMARDs) were the predominant first line of therapy in this dataset (71.3%), followed by TNFi alone (11.1%) or TNFi combined with csDMARD (9.1%) (Figure 1).

For patients who had csDMARD as their first line of therapy, 22.93% progressed to second line treatment. Among them 36.2% patients were TNFi with or without in combination with csDMARDs. In the second-line, TNFi + csDMARDs were associated with a longer TTNT (median time: 13.1 months vs 6.1 months, P < 0.005) compared to TNFi monotherapy. The multiple variable CoxPH model (adjusted for age, gender, and race) demonstrated that second-line TNFi + csDMARDs had a lower hazard rate when compared to TNFi monotherapy (HR = 0.74, 95% CI: 0.36 - 1.12, p < 0.005).

Conclusion

We demonstrated the first comprehensive treatment sequencing patterns in RA from a real-world setting. As a second-line therapy for patients with inadequate response to csDMARDS, the TNFi + csDMARDs combination may improve duration of response when compared to TNFi monotherapy. Results from this study will inform future sequencing strategies to improve patient outcomes.

References

[1]Smolen, Josef S., Robert B. M. Landewé, Johannes W. J. Bijlsma, Gerd R. Burmester, Maxime Dougados, Andreas Kerschbaumer, Iain B. McInnes, et al. 2020. “EULAR Recommendations for the Management of Rheumatoid Arthritis with Synthetic and Biological Disease-Modifying Antirheumatic Drugs: 2019 Update.” Annals of the Rheumatic Diseases 79 (6): 685–99.

Disclosure of Interests

Lei Ai: None declared, Mitchell Higashi: None declared, Kyeryoung Lee: None declared, Zongzhi Liu: None declared, Lan Jin: None declared, Kalpana Raja: None declared, Yun Mai: None declared, Tomi Jun: None declared, William Oh Consultant of: Janssen

Pfizer, Aviva Beckmann: None declared, Emilio Schadt: None declared, Zachary Schadt: None declared, Rick Wallsten: None declared, Ediz Calay: None declared, Andrew Kasarskis: None declared, Qi Pan: None declared, Eric Schadt Speakers bureau: Eli Lilly, Consultant of: SAB of Eli Lilly

Celgene, Xiaoyan Wang: None declared

Phenotypic and endotypic features of COPD associated with lung cancer development

e13563

Background: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with multiple phenotypes and endotypes and associated with lung cancer development. In this study, we analyzed the clinical features of COPD including various phenotypic and endotypic features from the electronic health records (EHR) as the potential risk factors for lung cancer development in a large cohort of COPD patients. Methods: We identified a COPD cohort based on electronic Medical Records and Genomics (eMERGE) network COPD cohort identification algorithms with minor modifications from the Mount Sinai Data Warehouse (2000-2020) and followed the patients on the first diagnosis of lung cancer. The development of lung cancer in COPD patients was confirmed by manual chart review. We retrieved the clinical features from EHRs and conducted Kaplan Meier (KM) analysis and multivariable Cox-regression modeling for hazard ratio (HR) analysis. Results: We found that 3.8 % of COPD patients (824 out of 21,658) developed lung cancer. While COPD patients with emphysema and smoking history (former or current) showed an increased risk of lung cancer onset, patients with concurrent asthma and corticosteroid (ICS) inhalation history showed a reduced risk of lung cancer onset (adjusted HR in Table). Interestingly, COPD patients with higher eosinophil counts showed late onset of lung cancer (>300 cells/ul, cancer rate at 5y=2.4%, p=< 0.002; 150-300 cells/ul, cancer rate at 5y=2.9%, p=0.003) when compared to patients with low eosinophils count (<150 cells/ul, cancer rate at 5y=3.4%) in a KM analysis. A multivariable analysis adjusted for age, gender, race, smoking status, COPD sub-phenotypes, severe exacerbation history, and ICS inhalation history showed a significantly lower risk of lung cancer in COPD patients with higher eosinophils count (150-300 cells/ul; HR: 0.82, 95% CI: 0.69-0.97, p=0.021 and >300 cells/ul; HR:0.72, 95% CI: 0.57-0.89, p=0.003) when compared to those with low eosinophils count (<150 cells/ul). Conclusions: Our study shows that many phenotypic and endotypic features of COPD are differentially associated with lung cancer development. High eosinophil levels, ICS usage, and concurrent asthma in COPD patients may reduce the risk of lung cancer development. [Table: see text]

Water evaporation algorithm optimized cascade controller for frequency regulation of integrated microgrid

Integration of renewable energy sources into existing grid influence the stability of the power system. This article introduces the application of cascade controller in hybrid power system which enhance the frequency stability during power perturbations of the load and generation. For this study, a thermal power unit is considered with integration of a microgrid consist of regular diesel generator, renewable power generating units, energy storage and other power managing devices. Proportional-integral and proportional-integral-derivative (PI-PID) cascade controller is provided for this hybrid power system to reduce the frequency oscillations during system uncertainties. The optimal values of the PI-PID controller are achieved by using water evaporation optimization (WEO) algorithm with fast convergence rate. Investigations are carried out in different scenarios of the IM and results are compared with the PID controller to showcase the advantages of the cascade controller for frequency regulation. Simulations are carried out in MATLAB-SIMULINK ® software environment.

Biomedical Literature Mining and Its Components

The published biomedical articles are the best source of knowledge to understand the importance of biomedical entities such as disease, drugs, and their role in different patient population groups. The number of biomedical literature available and being published is increasing at an exponential rate with the use of large scale experimental techniques. Manual extraction of such information is becoming extremely difficult because of the huge number of biomedical literature available. Alternatively, text mining approaches receive much interest within biomedicine by providing automatic extraction of such information in more structured format from the unstructured biomedical text. Here, a text mining protocol to extract the patient population information, to identify the disease and drug mentions in PubMed titles and abstracts, and a simple information retrieval approach to retrieve a list of relevant documents for a user query are presented. The text mining protocol presented in this chapter is useful for retrieving information on drugs for patients with a specific disease. The protocol covers three major text mining tasks, namely, information retrieval, information extraction, and knowledge discovery.

Extracting Significant Comorbid Diseases from MeSH Index of PubMed

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level.

A Text Mining Protocol for Extracting Drug-Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease

Drug-drug interactions (DDIs) and adverse drug reactions (ADR) are experienced by many patients, especially by elderly population due to their multiple comorbidities and polypharmacy. Databases such as PubMed contain hundreds of abstracts with DDI and ADR information. PubMed is being updated every day with thousands of abstracts. Therefore, manually retrieving the data and extracting the relevant information is tedious task. Hence, automated text mining approaches are required to retrieve DDI and ADR information from PubMed. Recently we developed a hybrid approach for predicting DDI and ADR information from PubMed. There are many other existing approaches for retrieving DDI and ADR information from PubMed. However, none of the approaches are meant for retrieving DDI and ADR specific to patient population, gender, pharmacokinetics, and pharmacodynamics. Here, we present a text mining protocol which is based on our recent work for retrieving DDI and ADR information specific to patient population, gender, pharmacokinetics, and pharmacodynamics from PubMed.

A Simple Computational Approach to Identify Potential Drugs for Multiple Sclerosis and Cognitive Disorders from Expert Curated Resources

Multiple sclerosis, a disease of central nervous system leads to potential disability. In the USA, one million cases are diagnosed with multiple sclerosis in 2019. Multiple sclerosis is identified as one of the diseases causing global burden. Cognitive disorder is highly prevalent among 43-70% of multiple sclerosis patients. However, treating cognitive disorder in multiple sclerosis patients is mostly ignored and this leads to several complications. We utilized various expert curated resources to identify potential drugs for multiple sclerosis and cognitive disorder, with specific focus on identifying drugs that are capable of treating both the conditions. We used simple text mining techniques to compile two databases, disease-drug association database and gene-drug interaction database from various existing standard resources. Our study suggests four drugs, Baclofen, Levodopa, Minocycline, and Vitamin B12, for treating both multiple sclerosis and cognitive disorder. In addition, our approach suggests six drugs for multiple sclerosis and 10 drugs for cognitive disorder. We obtained pharmacologist opinion on the drugs suggested for each condition and provided literature evidence for our claim. Here, we present our computational approach as a protocol such that it can be applied to other comorbid diseases that did not gain much attention so far.

A Text Mining Protocol for Predicting Drug-Drug Interaction and Adverse Drug Reactions from PubMed Articles

Drug-drug interactions (DDIs) and adverse drug reactions (ADRs) occur during the pharmacotherapy of multiple comorbidities and in susceptible individuals. DDIs and ADRs limit the therapeutic outcomes in pharmacotherapy. DDIs and ADRs have significant impact on patients' life and health care cost. Hence, knowledge of DDI and ADRs is required for providing better clinical outcomes to patients. Various approaches are developed by the scientific community to document and report the occurrences of DDIs and ADRs through scientific publications. Due to the enormously increasing number of publications and the requirement of updated information on DDIs and ADRs, manual retrieval of data is time consuming and laborious. Various automated techniques are developed to get information on DDIs and ADRs. One such technique is text mining of DDIs and ADRs from published biomedical literature in PubMed. Here, we present a recently developed text mining protocol for predicting DDIs and ADRs from PubMed abstracts.

GC-MS Analysis, Molecular Docking and Pharmacokinetic Properties of Phytocompounds from Solanum torvum Unripe Fruits and Its Effect on Breast Cancer Target Protein

This study was designed to identify phytocompounds from the aqueous extract of Solanum torvum unripe fruits using GC–MS analysis against breast cancer. For this, the identified phytocompounds were subjected to perform molecular docking studies to find the effects on breast cancer target protein. Pharmacokinetic properties were also tested for the identified phytocompounds to evaluate the ADMET properties. Molecular docking studies were done using docking software PyRx, and pharmacokinetic properties of phytocompounds were evaluated using SwissADME. From the results, ten best compounds were identified from GC–MS analysis against breast cancer target protein. Of which, three compounds showed very good binding affinity with breast cancer target protein. They are ergost-25-ene-3,6-dione,5,12-dihydroxy-,(5.alpha.,12.beta.) (− 7.3 kcal/mol), aspidospermidin-17-ol,1-acetyl-16-methoxy (− 6.7 kcal/mol) and 2-(3,4-dichlorophenyl)-4-[[2-[1-methyl-2-pyrrolidinyl]ethyl amino]-6-[trichloromethyl]-s-triazine (− 6.7 kcal/mol). Further, docking study was performed for the synthetic drug doxorubicin to compare the efficiency of phytocompounds. The binding affinity of ergost-25-ene-3,6-dione,5,12-dihydroxy-,(5.alpha.,12.beta.) is higher than the synthetic drug doxorubicin (− 7.2 kcal/mol), and the binding affinity of other compounds is also very near to the drug. Hence, the present study concludes that the phytocompounds from the aqueous extract of Solanum torvum unripe fruits have the potential ability to treat breast cancer.

Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources

OBJECTIVE

Drug-drug interactions (DDIs) can result in adverse and potentially life-threatening health consequences; however, it is challenging to predict potential DDIs in advance. We introduce a new computational approach to comprehensively assess the drug pairs which may be involved in specific DDI types by combining information from large-scale gene expression (984 transcriptomic datasets), molecular structure (2159 drugs), and medical claims (150 million patients).

MATERIALS AND METHODS

Features were integrated using ensemble machine learning techniques, and we evaluated the DDIs predicted with a large hospital-based medical records dataset. Our pipeline integrates information from >30 different resources, including >10 000 drugs and >1.7 million drug-gene pairs. We applied our technique to predict interactions between 37 611 drug pairs used to treat psoriasis and its comorbidities.

RESULTS

Our approach achieves >0.9 area under the receiver operator curve (AUROC) for differentiating 11 861 known DDIs from 25 750 non-DDI drug pairs. Significantly, we demonstrate that the novel DDIs we predict can be confirmed through independent data sources and supported using clinical medical records.

CONCLUSIONS

By applying machine learning and taking advantage of molecular, genomic, and health record data, we are able to accurately predict potential new DDIs that can have an impact on public health.

892 Emergency General Surgical Practice at a District General Hospital during COVID-19 Pandemic: Implementing Royal College of Surgeons Guidance

Introduction

COVID-19 pandemic had a significant impact on surgical practice across NHS. RCS released guidance on altering surgical practise during the pandemic to deliver safe surgical care in March, 2020. We present an audit conducted at a DGH comparing practice of emergency general surgery (EGS) with RCS guidance at the peak of COVID-19 pandemic.

Method

Consecutive patients undergoing EGS from 1^st April to 15^th May,2020. Data of demographics, ASA grade, comorbidities, type of surgery, hospital stay, informed COVID-19 pneumonia consent, complications and 30-day mortality were collected. Pre- and post-operative COVID-19 status was determined.

Results

Forty-four (n = 44) patients, mean age 47.5 and IQR (26-69). Male (55.8%) and females (44.2%). Preoperative COVID19 status was confirmed in around 79.1% patients. All (100%) patients who underwent CT imaging preoperatively had CT chest performed. Informed consent for COVID19 pneumonia was taken in 4.7% patients. 30-day mortality risk was 7% and complications risk was 4.7%. RR of 30-day mortality in preoperative COVID19 status positive patients was RR = 0.92 (CI 0.85-1.01) and for complications was RR = 0.95 (CI 0.88-1.02).

Conclusions

RCS guidance on managing and altering practice in EGS during COVID-19 pandemic is reliable, implementable, and measurable in a DGH setting. Simple improvements in consent process can achieve full compliance with RCS guidelines.

Molecular epidemiology of HBV among HIV infected individuals in Chennai, south India

Hepatitis B is a major co-infection among people with HIV (PWHIV) worldwide. There is a paucity of data on HBV genetic diversity in India, which would be useful for targeted preventive and management interventions. To characterize the distribution of HBV genotypes and sub-genotypes, samples of 180 HIV-HBV co-infected individuals from a study previously conducted to estimate the prevalence of HBV co-infection were analyzed. Nested PCR using type-specific primers was used to identify the various HBV genotypes. Partial HBV S sequences were generated for a subset of samples using Sanger sequencing. Mutation analysis was done using the online HBVseq program. PCR based genotyping documented D (69.4 %) and A (5.6 %) to be the major genotypes in the study population. Infection with multiple genotypes was observed in 25 % co-infected individuals. D2, D5, A2, and A1 were the sub-genotypes detected. Mutations 184K and 173L were identified. HBV genotypes/ sub-genotypes play a pivotal role in the clinical outcome of chronic hepatitis B (CHB). Therefore, monitoring of CHB cases is needed to track disease progression, including early detection of hepatocellular carcinoma.

Caenorhabditis elegans as a possible model to screen anti-Alzheimer's therapeutics

Alzheimer's disease (AD) is regarded as one of the significant health burdens, as the prevalence is raising worldwide and gradually reaching to epidemic proportions. Consequently, a number of scientific investigations have been initiated to derive therapeutics to combat AD with a concurrent advancement in pharmacological methods and experimental models. Whilst, the available experimental pharmacological approaches both in vivo and in vitro led to the development of AD therapeutics, the precise manner by which experimental models mimic either one or more biomarkers of human pathology of AD is gaining scientific attentions. Caenorhabditis elegans (C. elegans) has been regarded as an emerging model for various reasons, including its high similarities with the biomarkers of human AD. Our review supports the versatile nature of C. elegans and collates that it is a well-suited model to elucidate various molecular mechanisms by which AD therapeutics elicit their pharmacological effects. It is apparent that C. elegans is capable of establishing the pathological processes that links the endoplasmic reticulum and mitochondria dysfunctions in AD, exploring novel molecular cascades of AD pathogenesis and underpinning causal and consequential changes in the associated proteins and genes. In summary, C. elegans is a unique and feasible model for the screening of anti-Alzheimer's therapeutics and has the potential for further scientific exploration.

Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC

We recently conducted a large association analysis to compare the genetic profiles between patients with psoriatic arthritis (PsA) and cutaneous-only psoriasis (PsC). Despite including over 7,000 genotyped patients, only the MHC achieved genome-wide significance. In this study, we hypothesized that appropriate epigenomic elements (H3K27ac marks for active enhancers) can guide us to reveal valuable information about the loci with suggestive evidence of association. Our aim is to investigate these loci and explore how they may lead to the development of PsA. We evaluated this potential by investigating the genes connected with these loci from the perspective of pharmacogenomics and gene expression. We illustrated that markers with suggestive evidence of association outside the MHC region are enriched in H3K27ac marks for osteoblast and chondrogenic differentiated cells; using pharmacogenomics resources, we showed that genes near these markers are targeted by existing drugs used to treat psoriatic arthritis. Significantly, six of the ten suggestive significant loci overlapping the regulatory elements encompass genes differentially expressed (FDR < 5%) in differentiated osteoblasts, including genes participating in the Wnt signaling such as RUNX1, FUT8, and CTNNAL1. Our approach shows that epigenomic information can be used as cost-effective approach to enhance the inferences for GWAS results, especially in situations when few genome-wide significant loci are available. Our results also point the way to more directed investigations comparing the genetics of PsA and PsC.

Hypersensitive IFN Responses in Lupus Keratinocytes Reveal Key Mechanistic Determinants in Cutaneous Lupus

Systemic lupus erythematosus (SLE) is a complex autoimmune disease in which 70% of patients experience disfiguring skin inflammation (grouped under the rubric of cutaneous lupus erythematosus [CLE]). There are limited treatment options for SLE and no Food and Drug Administration-approved therapies for CLE. Studies have revealed that IFNs are important mediators for SLE and CLE, but the mechanisms by which IFNs lead to disease are still poorly understood. We aimed to investigate how IFN responses in SLE keratinocytes contribute to development of CLE. A cohort of 72 RNA sequencing samples from 14 individuals (seven SLE and seven healthy controls) were analyzed to study the transcriptomic effects of type I and type II IFNs on SLE versus control keratinocytes. In-depth analysis of the IFN responses was conducted. Bioinformatics and functional assays were conducted to provide implications for the change of IFN response. A significant hypersensitive response to IFNs was identified in lupus keratinocytes, including genes (IFIH1, STAT1, and IRF7) encompassed in SLE susceptibility loci. Binding sites for the transcription factor PITX1 were enriched in genes that exhibit IFN-sensitive responses. PITX1 expression was increased in CLE lesions based on immunohistochemistry, and by using small interfering RNA knockdown, we illustrated that PITX1 was required for upregulation of IFN-regulated genes in vitro. SLE patients exhibit increased IFN signatures in their skin secondary to increased production and a robust, skewed IFN response that is regulated by PITX1. Targeting these exaggerated pathways may prove to be beneficial to prevent and treat hyperinflammatory responses in SLE skin.

Intestinal lymphangiectasia: a rare cause of intussusception in an adolescent

Intussusception in adolescents is usually idiopathic in nature. A 17-year-old woman with diffuse large B cell lymphoma presented with signs of intestinal obstruction after initiation of induction chemotherapy. On evaluation, the patient was diagnosed to have ileoileal intussusception with intestinal lymphangiectasia as the lead point. Intestinal lymphangiectasia as a rare cause for intussusception and its relationship with lymphoma is discussed in this case report.

Atopic Dermatitis Is an IL-13-Dominant Disease with Greater Molecular Heterogeneity Compared to Psoriasis

Atopic dermatitis (AD) affects up to 20% of children and adults worldwide. To gain a deeper understanding of the pathophysiology of AD, we conducted a large-scale transcriptomic study of AD with deeply sequenced RNA-sequencing samples using long (126-bp) paired-end reads. In addition to the comparisons against previous transcriptomic studies, we conducted in-depth analysis to obtain a high-resolution view of the global architecture of the AD transcriptome and contrasted it with that of psoriasis from the same cohort. By using 147 RNA samples in total, we found striking correlation between dysregulated genes in lesional psoriasis and lesional AD skin with 81% of AD dysregulated genes being shared with psoriasis. However, we described disease-specific molecular and cellular features, with AD skin showing dominance of IL-13 pathways, but with near undetectable IL-4 expression. We also demonstrated greater disease heterogeneity and larger proportion of dysregulated long noncoding RNAs in AD, and illustrated the translational impact, including skin-type classification and drug-target prediction. This study is by far the largest study comparing the AD and psoriasis transcriptomes using RNA sequencing and demonstrating the shared inflammatory components, as well as specific discordant cytokine signatures of these two skin diseases.

Molecular dynamics simulation analysis of conessine against multi drug resistant Serratia marcescens

Ornithine decarboxylase (ODC) is an immediate precursor of polyamine biosynthesis in Serratia marcescens and a potential target for inhibition of its growth. We predicted the 3D structural conformation of ODC enzyme and validated it using MDS in our previous study. In this current study, the potential inhibitors of ODC were obtained by virtual screening of potential inhibitors from ZINC database and studied in depth for their different binding pose. Among the ten virtually screened inhibitors, Conessine exhibited the best binding with ODC and its inhibition property was studied further by MDS studies. The natural compound conessine is isolated from plant Holarrhena antidysenterica and it is studied against ODC of Serratia marcenses for its inhibitory potentials. This revealed unforeseen twisted position in root mean square fluctuation (RMSF) and ODC modelled conformation that influenced ligand binding. Both predicted model and ligand bound model were compared and found to be stable with Root Mean Square Deviation (RMSD) of approximately 7 nm and 0.25 nm to that of crystallographic structure over simulation time of 55 ns and 70 ns respectively. This work paves the way for future development of new drugs against nosocomial diseases caused by Serratia marcescens.

Medicinal Plant Using Ground State Stabilization of Natural Antioxidant Curcumin by Keto-Enol Tautomerisation

Curcumin is a medicinal agent that exhibits anti-cancer properties and bioactive pigment in Turmeric has a huge therapeutic value. It has a keto-enol moiety that gives rise to many of its chemical properties. A recent study has shown that keto-enol tautomerisation at this moiety is implicated the effect of curcumin. The tautomerisation of curcumin in methanol, acetone and acetonitrile are used in nuclear magnetic resonance (1H, 13C) spectroscopy. It was characterized using UV, IR and Raman spectral values. The molecular electrostatic potential surface of the Curcumin has been visualized in electropositive potential in the region of the CH3+ group and most electronegative potential in the two oxygen atom has very strong binding group. In the following, the modality of structural and thermo dynamical parameters, electrophilicity (ω), chemical potential (μ), chemical hardness (η) and electronic charge transfer confirms the local reactivity. The rate constant of tautomerisation of curcumin shows strong temperature dependence. Molecular electrostatic potential and Temperature dependence of various thermodynamic properties like [Formula: see text] is increase with increase in temperature for monomer and dimer of various electrical fields.

Bio-Directed Synthesis of Calcium Oxide (CaO) Nanoparticles Extracted from Limestone Using Honey

Calcium oxide (CaO) nanoparticles have been synthesized by two step thermal decomposition method under ambient temperature. Structural analysis was carried out by powder X-ray diffraction method and the crystallite size of CaO nanoparticles was calculated using Scherrer formula. Fourier transform infrared spectroscopy (FTIR) analysis has been carried to identify the functional groups present in the synthesized specimen. Optical absorption studies reveal very low absorption in the entire visible region. The surface analysis of the synthesized particles was analysed using scanning electron microscope (SEM).

Mining protein phosphorylation information from biomedical literature using NLP parsing and Support Vector Machines

BACKGROUND

Extraction of protein phosphorylation information from biomedical literature has gained much attention because of the importance in numerous biological processes.

OBJECTIVE

In this study, we propose a text mining methodology which consists of two phases, NLP parsing and SVM classification to extract phosphorylation information from literature.

METHODS

First, using NLP parsing we divide the data into three base-forms depending on the biomedical entities related to phosphorylation and further classify into ten sub-forms based on their distribution with phosphorylation keyword. Next, we extract the phosphorylation entity singles/pairs/triplets and apply SVM to classify the extracted singles/pairs/triplets using a set of features applicable to each sub-form.

RESULTS

The performance of our methodology was evaluated on three corpora namely PLC, iProLink and hPP corpus. We obtained promising results of >85% F-score on ten sub-forms of training datasets on cross validation test. Our system achieved overall F-score of 93.0% on iProLink and 96.3% on hPP corpus test datasets. Furthermore, our proposed system achieved best performance on cross corpus evaluation and outperformed the existing system with recall of 90.1%.

CONCLUSIONS

The performance analysis of our unique system on three corpora reveals that it extracts protein phosphorylation information efficiently in both non-organism specific general datasets such as PLC and iProLink, and human specific dataset such as hPP corpus.

Ataxia in a Young Female

Neurofibromatosis type 2 (NF2) is a genetically inherited disorder characterized by the presence of multiple central nervous system tumours, most pathognomonic being bilateral vestibular schwannomas with or without peripheral manifestations in the form of cataract or cutaneous neurofibromas. NF2 is an uncommon disorder compared to NF1. We describe a classical case of neurofibromatosis type 2 with florid clinical manifestations and characteristic neuroimaging features. We also briefly describe the literature pertaining to this rare disorder. The case also emphasizes the fact that NF2 should be considered in the list of differentials for ataxia especially when it is associated with sensory neural hearing loss.

Oral neoplasms in pickhandle barracuda Sphyraena jello from India

We report the spontaneous occurrence of oral neoplasms in pickhandle barracuda Sphyraena jello Cuvier, 1829 from Parangipettai, on the southeast coast of India. A total of 11736 fish were examined, of which 43 were affected with oral tumours, with an overall prevalence of 0.37%. Gross and clinical symptoms included reddish to grayish-white distended tumourous growths on the gingiva, intra-oral bones and tongue. The tumours exhibited delayed eruption and intra- or extra-oral swelling, varied in consistency from extremely firm to fleshy and released mucinous material. The majority of tumours consisted of numerous clumped toothlets, but some included hardened tissues. Local area invasion/transmission was observed in most cases; however, necropsy examination revealed no gross evidence of distant metastasis into visceral organs. Radiographic examination of compound odontomas revealed distinct unilocular radio-opaque mini-toothlets surrounded by defined radiolucency, whereas complex odontomas exhibited unilocular and indistinct radio-opaque masses within a much more extensive radiolucent zone. Histopathologically, the intra-oral tumour lesions were characterized by numerous imperfect (germ) toothlets consisting of a disorganized combination of dental tissues: pulp tissues with manifested and predominantly mixed hard dental tissues of immature dentine and enamel, numerous small to large and round to polyhedral ossicles embedded in hypocellular fibrous stromal tissues and sparsely spaced bland spindloid cells with cleft-like spaces of loose mucoid stroma. Histochemically, the neoplastic lesions stained positive for periodic acid-Schiff and Masson's trichrome. Based on the clinical and histological findings, the tumours were diagnosed as compound odontomas, complex odontomas, odontogenic myxomas, lingual myxomas and psammomatoid ossifying fibromas.

Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases

Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is necessary to prevent ADR, the rapid pace of drug discovery makes it challenging to maintain a strong insight into DDIs. In this study, we present a novel literature-mining framework for enhancing the predictions of DDIs and ADR types by integrating drug-gene interactions (DGIs). The ADR types were adapted from a DDI corpus, including i) adverse effect; ii) effect at molecular level; iii) effect related to pharmacokinetics; and iv) DDIs without known ADRs. By using random forest classifier our approach achieves an F-score of 0.87 across the ADRs classification using only the DDI features. We then enhanced the performance of the classifier by including DGIs (F-score = 0.90), and applied the classification model trained with the DDI corpus to identify the drugs that might interact with the drugs for cutaneous diseases. We successfully predict previously known ADRs for drugs prescribed to cutaneous diseases, and are also able to identify promising new ADRs.