Prospective Subunit Nanovaccine against Mycobacterium tuberculosis Infection─Cubosome Lipid Nanocarriers of Cord Factor, Trehalose 6,6' Dimycolate.

An improved vaccine is urgently needed to replace the now more than 100-year-old Bacillus Calmette-Guérin (BCG) vaccine against tuberculosis (TB) disease, which represents a significant burden on global public health. Mycolic acid, or cord factor trehalose 6,6' dimycolate (TDM), a lipid component abundant in the cell wall of the pathogen Mycobacterium tuberculosis (MTB), has been shown to have strong immunostimulatory activity but remains underexplored due to its high toxicity and poor solubility. Herein, we employed a novel strategy to encapsulate TDM within a cubosome lipid nanocarrier as a potential subunit nanovaccine candidate against TB. This strategy not only increased the solubility and reduced the toxicity of TDM but also elicited a protective immune response to control MTB growth in macrophages. Both pre-treatment and concurrent treatment of the TDM encapsulated in lipid monoolein (MO) cubosomes (MO-TDM) (1 mol %) induced a strong proinflammatory cytokine response in MTB-infected macrophages, due to epigenetic changes at the promoters of tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) in comparison to the untreated control. Furthermore, treatment with MO-TDM (1 mol %) cubosomes significantly improved antigen processing and presentation capabilities of MTB-infected macrophages to CD4 T cells. The ability of MO-TDM (1 mol %) cubosomes to induce a robust innate and adaptive response in vitro was further supported by a mathematical modeling study predicting the vaccine efficacy in vivo. Overall, these results indicate a strong immunostimulatory effect of TDM when delivered through the lipid nanocarrier, suggesting its potential as a novel TB vaccine.

quEHRy: a question answering system to query electronic health records.

OBJECTIVE: We propose a system, quEHRy, to retrieve precise, interpretable answers to natural language questions from structured data in electronic health records (EHRs). MATERIALS AND METHODS: We develop/synthesize the main components of quEHRy: concept normalization (MetaMap), time frame classification (new), semantic parsing (existing), visualization with question understanding (new), and query module for FHIR mapping/processing (new). We evaluate quEHRy on 2 clinical question answering (QA) datasets. We evaluate each component separately as well as holistically to gain deeper insights. We also conduct a thorough error analysis for a crucial subcomponent, medical concept normalization. RESULTS: Using gold concepts, the precision of quEHRy is 98.33% and 90.91% for the 2 datasets, while the overall accuracy was 97.41% and 87.75%. Precision was 94.03% and 87.79% even after employing an automated medical concept extraction system (MetaMap). Most incorrectly predicted medical concepts were broader in nature than gold-annotated concepts (representative of the ones present in EHRs), eg, Diabetes versus Diabetes Mellitus, Non-Insulin-Dependent. DISCUSSION: The primary performance barrier to deployment of the system is due to errors in medical concept extraction (a component not studied in this article), which affects the downstream generation of correct logical structures. This indicates the need to build QA-specific clinical concept normalizers that understand EHR context to extract the "relevant" medical concepts from questions. CONCLUSION: We present an end-to-end QA system that allows information access from EHRs using natural language and returns an exact, verifiable answer. Our proposed system is high-precision and interpretable, checking off the requirements for clinical use.

Cubosome Lipid Nanocarriers As a Drug Delivery Vehicle for Intracellular Mycobacterium tuberculosis Infections.

Mycobacterium tuberculosis (MTB) causes the infectious disease tuberculosis (TB), responsible for more deaths than any other single infectious disease in history. Intracellular MTB are slow growing and difficult to target with traditional antitubercular drugs, leading to the emergence of multidrug resistance in TB infection, which is a major global public health issue. Recent advances in innovative lipid nanotechnologies for drug delivery have demonstrated promising outcomes for chronic infectious diseases but have not yet been tested as potential delivery systems for intracellular infections such as TB. The current study evaluates the potential of monoolein (MO)-based cationic cubosomes for the encapsulation and delivery of the first line antitubercular drug rifampicin (RIF) against an MTB-H37Ra in vitro culture model. In particular, we show that the use of cationic cubosomes as delivery vehicles reduced the minimum inhibitory concentration (MIC) of RIF by 2-fold against actively replicating MTB-H37Ra (compared to that of the free drug) and also shortened the lifecycle duration of axenic MTB-H37Ra from 5 to 3 days. The cubosome-mediated delivery was also found to be effective against intracellular MTB-H37Ra within THP-1 human macrophages, with a 2.8 log reduction in viability of the bacilli after 6 days incubation at the MIC. The killing time was also reduced from 8 to 6 days without distressing the host macrophages. Mechanistic studies on the uptake of RIF-loaded cationic cubosomes using total internal reflection fluorescence microscopy (TIRFM) demonstrated the capacity of these lipid particles to effectively target intracellular bacteria. Overall, these results demonstrate that cationic cubosomes are a potent delivery system for the antitubercular drug RIF for therapeutic management of TB.

Development of A Rapid, Low-Cost Portable Detection Assay for Enterococci in Wastewater and Environmental Waters.

Waterborne diseases are known as a leading cause of illness and death in both developing and developed countries. Several pathogens can be present in contaminated water, particularly waters containing faecal material; however, routine monitoring of all pathogens is not currently possible. Enterococcus faecalis, which is present in the microflora of human and animals has been used as a faecal indicator in water due to its abundance in surface water and soil. Accurate and fast detection methods are critical for the effective monitoring of E. faecalis in the environment. Although conventional and current molecular detection techniques provide sufficient sensitivity, specificity and throughput, their use is hampered by the long waiting period (1-6 days) to obtain results, the need for expensive laboratory equipment, skilled personnel, and cold-chain storage. Therefore, this study aimed to develop a detection system for E. faecalis that would be simple, rapid, and low-cost, using an isothermal DNA amplification assay called recombinase polymerase amplification (RPA), integrated with a lateral flow assay (LFA). The assay was found to be 100% selective for E. faecalis and capable of detecting rates as low as 2.8 × 103 cells per 100 mL from water and wastewater, and 2.8 × 104 cells per 100 mL from saline water. The assay was completed in approximately 30 min using one constant temperature (38 °C). In addition, this study demonstrated the quantitation of E. faecalis using a lateral flow strip reader for the first time, enhancing the potential use of RPA assay for the enumeration of E. faecalis in wastewater and heavily contaminated environmental waters, surface water, and wastewater. However, the sensitivity of the RPA-LFA assay for the detection of E. faecalis in tap water, saline water and in wastewater was 10-1000 times lower than that of the Enterolert-E test, depending on the water quality. Nevertheless, with further improvements, this low-cost RPA-LFA may be suitable to be used at the point-of-need (PON) if conjugated with a rapid field-deployable DNA extraction method.

Toward a Neural Semantic Parsing System for EHR Question Answering.

Clinical semantic parsing (SP) is an important step toward identifying the exact information need (as a machine-understandable logical form) from a natural language query aimed at retrieving information from electronic health records (EHRs). Current approaches to clinical SP are largely based on traditional machine learning and require hand-building a lexicon. The recent advancements in neural SP show a promise for building a robust and flexible semantic parser without much human effort. Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR question answering (QA). We found that the performance of these advanced neural models on two clinical SP datasets is promising given their ease of application and generalizability. Our error analysis surfaces the common types of errors made by these models and has the potential to inform future research into improving the performance of neural SP models for EHR QA.

Detection of a target protein (GroEl2) in Mycobacterium tuberculosis using a derivative of 1,2,4-triazolethiols.

Herein, we identified a potent lead compound RRA2, within a series of 54 derivatives of 1,2,4-triazolethiols (exhibit good potency as an anti-mycobacterial agents) against intracellular Mycobacterium tuberculosis (Mtb). Compound RRA2 showed significant mycobactericidal activity against active stage Mycobacterium bovis BCG and Mtb with minimum inhibitory concentration (MIC) values of 2.3 and 2.0 µg/mL, respectively. At MIC value, RRA2 compound yielded 0.82 log reduction of colony-forming unit (cfu) against non-replicating Mtb. Furthermore, RRA2 compound was selected for further target identification due to the presence of alkyne group, showing higher selectivity index (> 66.66 ± 0.22, in non-replicating stage). Using "click" chemistry, we synthesized the biotin linker-RRA2 conjugate, purified with HPLC method and confirmed the conjugation of biotin linker-RRA2 complex by HR-MS analysis. Furthermore, we successfully pulled down and identified a specific target protein GroEl2, from Mtb whole-cell extract. Furthermore, computational molecular modeling indicated RRA2 could interact with GroEl2, which explains the structure-activity relationship observed in this study. GroEL-2 identified a potent and specific target protein for RRA 2 compound in whole cell extract of Mtb H37Ra.

Cuboplex-Mediated Nonviral Delivery of Functional siRNA to Chinese Hamster Ovary (CHO) Cells.

Lipid nanoparticles of internal cubic symmetry, termed cuboplexes, are potential nonviral delivery vehicles for gene therapy due to their "topologically active" nature, which may enhance endosomal escape and improve delivery outcomes. In this study, we have used cationic cuboplexes, based on monoolein (MO) doped with a cationic lipid, for the encapsulation and delivery of antisense green fluorescent protein (GFP)-small interfering RNA (siRNA) into Chinese Hamster Ovary (CHO)-GFP cells. Agarose gel electrophoresis has confirmed the successful encapsulation of siRNA within cationic cubosomes, while synchrotron small-angle X-ray scattering (SAXS) demonstrated that the underlying cubic nanostructure of the particles was retained following encapsulation. The cationic cubosomes were shown to be reasonably nontoxic against the CHO-GFP cell line. Fluorescence-activated cell sorting (FACS) provided evidence of the successful transfection to CHO-GFP cells. Knockdown efficiency was strongly linked to the type of cationic lipid used, although all cubosomes had essentially the same internal nanostructure. The gene knockdown efficiency for some cationic cubosomes was shown to be higher than lipofectamine, which is a commercially available liposome-based formulation, while the controlled release of the siRNA from the cubosomes over a 72 h period was observed using confocal microscopy. This combination exemplifies the potential of cationic cuboplexes as a novel, nonviral, controlled-release delivery vector for siRNA.

An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature.

The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, leading to both corpora for COVID-19 literature and search engines to query such data. While most search engine research is performed in academia with rigorous evaluation, major commercial companies dominate the web search market. Thus, it is expected that commercial pandemic-specific search engines will gain much higher traction than academic alternatives, leading to questions about the empirical performance of these tools. This paper seeks to empirically evaluate two commercial search engines for COVID-19 (Google and Amazon) in comparison with academic prototypes evaluated in the TREC-COVID task. We performed several steps to reduce bias in the manual judgments to ensure a fair comparison of all systems. We find the commercial search engines sizably underperformed those evaluated under TREC-COVID. This has implications for trust in popular health search engines and developing biomedical search engines for future health crises.

Self-assembled nanostructures of phosphomolybdate, nucleobase and metal ions synthesis and their in vitro cytotoxicity studies on cancer cell lines.

The ability of the multidentate nucleobases, adenine and thymine, to coordinate polyoxometalate and metal ions leading to the formation of self-assembled nanostructures and their strong cytotoxicity toward cancer cell lines have been demonstrated. A unique synthetic approach is developed to make a series of functional nanoscale hybrid materials consisting of nucleobases (adenine and thymine) and phosphomolybdic acid (PMA) through solid state chemical reaction and self-assembly process. Adenine was protonated through its ring nitrogen, while the ketone group in thymine was protonated during the addition of PMA to these nucleobases. The self-assembled nanostructures formed as a result of the electrostatic interaction between the protonated nucleobases and polyanionic PMA. To promote the base pairing between the nucleobases, chloroaurate ions and silver ions were added to each PMA/adenine and PMA/thymine nanostructures. The complexation between the nucleobases and the added metal ions was found to drive the formation of subsequent self-assembled nanostructures. All the materials were screened for their anticancer activity against breast (MDAMB-231) and prostate (PC-3) cancer cells, and non-cancerous keratinocyte cells HaCaT. PMA/adenine/[AuCl4]- and PMA/thymine/Ag+ nanostructures were found to have strong anti-cancer activity, while PMA/adenine/Ag+, PMA/thymine/[AuCl4]-, and PMA/pdenine, PMA/thymine nanostructures did not exhibit such activity. The unique redox properties of these materials and the self-assembly of the PMA and metal ions were the major factors responsible for the cytotoxicity. This unique approach of making functional nanomaterials incorporate the nucleobase, PMA and metal ions using solid state self-assembly and their anti-cancer applications are considered to be an effective approach for the development of inorganic nucleoside analogue bio-pharmaceutical agents.

Developing a Search Engine for Precision Medicine.

Precision medicine focuses on developing new treatments based on an individual's genetic, environmental, and lifestyle profile. While this data-driven approach has led to significant advances, retrieving information specific to a patient's condition has proved challenging for oncologists due to the large volume of data. In this paper, we propose the PRecIsion Medicine Robust Oncology Search Engine (PRIMROSE) for cancer patients that retrieves scientific articles and clinical trials based on a patient's condition, genetic profile, age, and gender. Our search engine utilizes Elasticsearch indexes for information storage and retrieval, and we developed a knowledge graph for query expansion in order to improve recall. Additionally, we experimented with machine learning and learning-to-rank components to the search engine and compared the results of the two approaches. Finally, we developed a front-facing ReactJS website and a REST API for connecting with our search engine. The development of this front-facing website allows for easy access to our system by healthcare providers.

Paraphrasing to improve the performance of Electronic Health Records Question Answering.

This paper describes a paraphrasing approach to improve the performance of question answering (QA) for electronic health records (EHRs). QA systems for structured EHR data usually rely on semantic parsing, which aims to generate machine-understandable logical forms from free-text questions. Training semantic parsers requires large datasets of question-logical form (QL) pairs, which are labor-intensive to create. Considering the scarcity of large QL datasets in the clinical domain, we propose a framework for expanding an existing dataset using paraphrasing. We experiment with different heuristics for multiple sample sizes and iterations to assess the effect of adding paraphrasing to the task of semantic parsing. We found that adding paraphrases to an existing dataset based on TERTHRESHOLD scores results in an improved performance in the majority (74%) of the experimental runs. Hence, the proposed paraphrasing-based framework has the potential to improve the performance of QA systems using a limited set of existing QL annotations.

Deep learning in clinical natural language processing: a methodical review.

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a "long tail" of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. DISCUSSION: Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). CONCLUSION: Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field.

Patient Cohort Retrieval using Transformer Language Models.

We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task ofCR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a given query. Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks. In this paper, we propose a framework for retrieving patient cohorts using neural language models without the need of explicit feature engineering and domain expertise. We find that a majority of our models outperform the BM25 baseline method on various evaluation metrics.

Size-Dependent Encapsulation and Release of dsDNA from Cationic Lyotropic Liquid Crystalline Cubic Phases.

The potential of gene therapy has not yet been realized, largely due to difficulties in the targeted delivery of DNA to tissues and cells. Lipid-based nanovectors are of potential use in gene therapy due to their ability to enhance fusion with cellular membranes and transport the large polyanionic DNA molecules into the cytoplasm. While the research to date has mainly focused on liposome-based vectors, recently, nonlamellar phases with more complex internal architectures based on hexagonal or cubic symmetry have received increasing research attention due to their fusogenic properties, which may promote uptake of the DNA into the cell. Herein, we have carried out a fundamental physicochemical study to systematically analyze the encapsulation and release of nonfunctional double-stranded (ds) DNA fragments within monoolein (MO)-based cationic lipid phases of cubic symmetry (cationic cubic phases) and their dispersed submicron particles (cationic cubosomes). MO-based cationic cubic phases, both as the bulk phase and cubosomes, were formulated using six different cationic lipids, and their nanostructure was characterized in a high-throughput manner by synchrotron small-angle X-ray scattering (SAXS). dsDNA encapsulation was confirmed using agarose gel electrophoresis, and the effect on the internal nanostructure, size, and morphology of the cubosomes was investigated using synchrotron SAXS, dynamic light scattering, and cryo-transmission electron microscopy. Synchrotron radiation circular dichroism confirmed that the structure of the dsDNA fragments was unaffected by encapsulation within the cationic cubosome. The use of commercially available dsDNA ladders consisting of a controlled mixture of dsDNA fragments allowed us to determine release rates as a function of fragment size in a reasonably high throughput manner. An improved understanding of the loading capacity and release profile of nonfunctional biomolecules in cationic cubosomes will assist in the design of novel lipid nanovectors for gene delivery.

Viability determination of Ascaris ova in raw wastewater: a comparative evaluation of culture-based, BacLight Live/Dead staining and PMA-qPCR methods.

Accurate evaluation of viable Ascaris ova in wastewater is the key to mitigating Ascaris reinfections in endemic regions. In this study, the viability of Ascaris ova in raw wastewater was determined using three different detection methods: culture-based, BacLight Live/Dead staining and propidium monoazide-quantitative polymerase chain reaction (PMA-qPCR). Furthermore, comparative assessment of viability utilising the aforementioned detection methods was performed using seeded experiments in wastewater. The percentage of viability was: culture-based (82%), BacLight Live/Dead staining (87%) and PMA-qPCR (85%) respectively. Despite the fact that no statistical difference was shown in the viability determination among the three methods, PMA-qPCR-based viability determination would be preferable over the other two methods for evaluating potential public health risks with A. suum ova due to its accuracy, being least subjective and its rapid reaction time.

Detection of helminth ova genera using in-situ biosynthesis of gold nanoparticles.

In this study, a presumptive colorimetric method was used to detect and differentiate the ova of two major soil transmitted helminths in wastewater, Ascaris and Trichuris. Gold nanoparticles were synthesised following the reduction of tetrachloroauric acid by the surface moiety of Ascaris suum, resulting in a colour change. In contrast there was no colour change with Trichuris suis indicating the absence of gold nanoparticle synthesis. Analysis of the ova using scanning electron microscopy (SEM) revealed that the synthesis of nanoparticles on the surface of ova was confirmed as gold nanoparticles (91 w/w %) by energy dispersive X-ray analysis (EDX). This study indicated that the surface moieties of helminth ova could be a potential target for ova detection and differentiation using the biosynthesis of nanoparticles by colorimetry methods. Three advantages: •Simple colorimetry based method requiring no sophisticated devices.•No trained personnel required.•Cost-effective and can be a potential candidate for biosensors.

Novel biocompatible poly(acrylamide)-grafted-dextran hydrogels: Synthesis, characterization and biomedical applications.

An electro-responsive PAAm-g-Dxt copolymer was synthesized and characterized by 1HNMR & FTIR spectroscopy, neutralization equivalent, elemental and thermogravimetric analysis to ascertain the grafting reaction. Further, we developed an electro-responsive transdermal drug delivery system (ETDS) utilizing PAAm-g-Dxt copolymer for rivastigmine tartarate delivery through skin. The ETDS were developed using drug-loaded PAAm-g-Dxt hydrogel as the reservoir, and cross-linked dextran-poly(vinyl alcohol) blend films as rate controlling membranes (RCM). In the absence of electrical stimuli, a small amount of drug was permeated from the ETDS, while in the presence of electrical stimuli, the drug permeability was increased. On application of electric stimulus, the flux was increased by 1.6 fold; drug permeability was enhanced when the strength of applied electric current was raised to 8 mA from 2 mA. The drug permeability characteristics studied under "on-off" stimuli suggested that there was faster drug permeation when electrical stimuli was 'on' and it decreased when electrical stimuli was 'off.' The histopathology study confirmed the altered skin structural integrity after application of electrical stimuli. Hence, the PAAm-g-Dxt based ETDS are useful for transdermal drug delivery triggered by an electric stimulus to deliver on-demand release of drug into systemic circulation.

Evaluation of plasmid DNA stability against ultrasonic shear stress and its in vitro delivery efficiency using ionic liquid [Bmim][PF6].

The hydrophobic ionic liquid (IL) 1-butyl-3-methylimidazolium hexafluorophosphate [Bmim][PF6] forms nanostructures with negatively charged plasmid DNA through electrostatic interactions. The formation of plasmid DNA/IL nanostructures was confirmed by measuring the zeta potential of plasmid DNA as well as plasmid DNA/IL nanostructures. The zeta potential of the nanostructures was positive, although plasmid DNA is negatively charged. The positive zeta potential is due to the complexation between plasmid DNA and positively charged ionic liquid [Bmim][PF6]. The ability of ionic liquid [Bmim][PF6] to protect plasmid DNA against ultrasonic shear stress was also investigated using an agarose gel electrophoretic assay and showed that ionic liquid stabilizes plasmid DNA against ultrasonication. The plasmid DNA and plasmid DNA/IL nanostructures were subjected to ultrasonic shear stress for different time periods and the biological functionality of pristine plasmid DNA (i.e., expression of the eGFP gene) as well as the self-assembled nanostructures was investigated in vitro using three different cell lines, COS7, HEK293 and HeLa. Ionic liquid [Bmim][PF6] protected the plasmid DNA against ultrasonic shear stress and also enhanced gene transfection efficiency in vitro. Furthermore, the cytotoxicity of ionic liquid [Bmim][PF6] was assayed in vitro using all three cell lines and the toxicity was very low. Therefore, the ionic liquid [Bmim][PF6] stabilizes plasmid DNA against ultrasonic shear stress and also enhances its in vitro delivery efficiency.

Using FHIR to Construct a Corpus of Clinical Questions Annotated with Logical Forms and Answers.

This paper describes a novel technique for annotating logical forms and answers for clinical questions by utilizing Fast Healthcare Interoperability Resources (FHIR). Such annotations are widely used in building the semantic parsing models (which aim at understanding the precise meaning of natural language questions by converting them to machine-understandable logical forms). These systems focus on reducing the time it takes for a user to get to information present in electronic health records (EHRs). Directly annotating questions with logical forms is a challenging task and involves a time-consuming step of concept normalization annotation. We aim to automate this step using the normalized codes present in a FHIR resource. Using the proposed approach, two annotators curated an annotated dataset of 1000 questions in less than 1 week. To assess the quality of these annotations, we trained a semantic parsing model which achieved an accuracy of 94.2% on this corpus.