Opportunities For Discovery Research Articles

Microneedles for Efficient and Precise Drug Delivery in Cancer Therapy.

Cancer is the leading cause of death, acting as a global burden, severely impacting the patients' quality of life and affecting the world economy despite the expansion of cumulative advances in oncology. The current conventional therapies for cancer which involve long treatment duration and systemic exposure of drugs leads to premature degradation of drugs, a massive amount of pain, side effects, as well as the recurrence of the condition. There is also an urgent demand for personalized and precision-based medicine, especially after the recent pandemic, to avoid future delays in diagnosis or treatments for cancer patients as they are very essential in reducing the global mortality rate. Recently, microneedles which consist of a patch with tiny, micron-sized needles attached to it have been quite a sensation as an emerging technology for transdermal application to diagnose or treat various illnesses. The application of microneedles in cancer therapies is also being extensively studied as they offer a myriad of benefits, especially since microneedle patches offer a better treatment approach through self administration, painless treatment, and being an economically and environmentally friendly approach in comparison with other conventional methods. The painless gains from microneedles significantly improves the survival rate of cancer patients. The emergence of versatile and innovative transdermal drug delivery systems presents a prime breakthrough opportunity for safer and more effective therapies, which could meet the demands of cancer diagnosis and treatment through different application scenarios. This review highlights the types of microneedles, fabrication methods and materials, along with the recent advances and opportunities. In addition, this review also addresses the challenges and limitations of microneedles in cancer therapy with solutions through current studies and future works to facilitate the clinical translation of microneedles in cancer therapies.

Opportunity discovery or judgment? Value investing’s incompatibility with Austrian economics revisited

Opportunity discovery or judgment? Value investing’s incompatibility with Austrian economics revisited

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery.

Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis-Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.

Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs.

The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.

Experimental design considerations for studies of human tear proteins

PurposeHuman tears contain abundant, diverse sets of proteins that may serve as biomarkers of ocular surface health. There is a need for reproducible methods that consider multiple factors influencing the tear proteome, in addition to the variable of interest. Here we examined a workflow for proteomic analysis of tear proteins without the need to pool tear samples from multiple individuals, thus allowing for analyses based on individual factors, and increasing opportunities for protein biomarker discovery. MethodsTears were collected by Schirmer strip following topical ocular anesthetic application then individually stored at −80 °C prior to processing for proteomics. Tear proteins were extracted from Schirmer strips, digested using suspension trapping spin columns (S-Trap), and labeled with high multiplicity tandem mass tags (TMT). Peptide digests were then extensively fractionated by two-dimensional chromatography and analyzed by mass spectrometry to identify and measure changes in protein abundance in each sample. Analysis of select samples was performed to test protocols and to compare the impact of clinically relevant parameters. To facilitate comparison of separate TMT experiments, common pool samples were included in each TMT instrument run and internal reference scaling (IRS) was performed. ResultsDifferences in subsets of tear proteins were noted for: geographic site of tear collection, contact lens use, and differences in tear fluid volume among individuals. ConclusionThese findings demonstrate that proteomic analysis of human tear proteins can be performed without the need to pool samples, and that development of analytic workflows must consider factors that may affect outcomes in studies focused on diverse clinical samples.

UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

Unmanned and aerial systems as interactors among different system components for communications, have opened up great opportunities for truth data discovery in Mobile Crowd Sensing (MCS) which has not been properly solved in the literature. In this paper, an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery (UAV-ITD) scheme is proposed to obtain truth data at low-cost communications for MCS. The main innovations of the UAV-ITD scheme are as follows: (1) UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization (DMF) to discover truth data based on the trust mechanism for an Information Elicitation Without Verification (IEWV) problem in MCS. (2) This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy, which saves more communication costs than most previous data collection schemes, where they collect n or kn data samples. Finally, we conducted extensive experiments to evaluate the UAV-ITD scheme. The results show that compared with previous schemes, our scheme can reduce estimated truth error by 52.25%–96.09%, increase the accuracy of workers’ trust evaluation by 0.68–61.82 times, and save recruitment costs by 24.08%–54.15% in truth data discovery.

Ondes Martenot: repertoire and performers

The article is devoted to the history of performance on a rare instrument of the Waves of Martenot. Its history of creation, repertoire and performance capabilities have not yet become the subject of a separate study, while at the Paris Conservatory there is still a class of Waves of Martenot, and the repertoire is periodically replenished. In connection with these facts, the coverage of the features of a musical instrument seems to be a very relevant topic. For the analysis, the main material was the works specially written for the Waves of Martenot, which can be used to determine the specifics of a particular version of the instrument that determined its technical capabilities, as well as various interviews of performers on the Waves of Martenot. The conclusion of the study is the discovery of new opportunities for enriching the timbre due to the established repertoire and the circle of performers on the instrument, as well as a special stylistic pluralism and sound imaging capabilities that determined the specifics of the instrument and at the same time its concert and cinematic fate, where it is widely used both in academic, film music, and in rock bands. The object of the study is the electromusical instrument Martino Waves, which became widespread in performing practice and composer's work of the 20th century. The subject of the research is the existing performing repertoire of Waves Martenot and performing practice. The novelty of the study lies in the fact that this is the first study in Russian about this music instrument. The applied methodology is based on a comparative analysis of various versions of the Martenot Waves instrument, the material for which are works created by composers in different years when its creator, Maurice Martenot, modified the instrument's design.

Discovery of a red backsplash galaxy candidate near M81

ABSTRACT Understanding quenching mechanisms in low-mass galaxies is essential for understanding galaxy evolution overall. In particular, isolated galaxies are important tools to help disentangle the complex internal and external processes that impact star formation. Comparisons between quenched field and satellite galaxies in the low-mass regime offer a substantial opportunity for discovery, although very few quenched galaxies with masses below $M_{\star }\, \sim \, 10^{9} {\rm M}_{\odot }$ are known outside the virial radius, Rvir, of any host halo. Importantly, simulations and observations suggest that an in-between population of backsplash galaxies also exists that may complement interpretations of environmental quenching. Backsplash galaxies – like field galaxies – reside outside the virial radius of a host halo, but their star formation can be deeply impacted by previous interactions with more massive systems. In this paper, we report the concurrent discovery of a low-mass ($M_{\star }\, \sim \, 10^{7} {\rm M}_{\odot }$) quenched galaxy approximately 1Rvir in projection from the M81 group. We use surface brightness fluctuations (SBF) to investigate the possibility that the new galaxy, dw0910+7326 (nicknamed Blobby), is a backsplash galaxy or a more distant field galaxy. The measured SBF distance of $3.21\substack{+0.15 +0.41 \\-0.15 -0.36}$ Mpc indicates that Blobby likely lies in the range 1.0 < R/Rvir < 2.7 outside the combined M81–M82 system. Given its distance and quiescence, Blobby is a good candidate for a backsplash galaxy and could provide hints about the formation and evolution of these interesting objects.

The Singapore National Precision Medicine Strategy.

Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption.

Technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis

Papers and patents can respectively present the latest progress of scientific research and technological development. Combining the two for correlation analysis has certain reference significance for technology opportunity discovery. Therefore, this paper proposes a method for technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis. Firstly, papers and patent data are collected from papers and patent databases and the LDA model is applied to extract paper and patent topics. Secondly, the indicators of topic heat and novelty are calculated to select high-value paper topics. After that, the similarity analysis between high-value paper topics and all patent topics is considered to select the patent topics with development potential. Finally, the patent texts with high relevance to these patent topics with development potential are positioned. The technical keywords in them are extracted by text mining tool and classified into innovation dimensions according to their attributes and the TEMPEST model. After that, the innovation rules in the SCAMPER model are introduced to combine with the technical keywords in each innovation dimension to identify specific technology opportunities. An empirical study on the technology of proton exchange membrane fuel cell provides sufficient evidence of the method’s ability to discover technology opportunities. This paper contributes by generating four specific technology opportunities, which can make up for the existing defects of proton exchange membrane fuel cells and provide useful guidance for companies to carry out technological innovation.

Plant secondary metabolites as an alternative to combat antimicrobial resistance: An overview of medicinal plants of Pir Panjal Himalayas

Antibiotics have made formidable benefactions to human civilization and found extensive usage in infectious disease treatment. However, indiscriminate and overuse of antibiotics have evolved resistant bacterial strains, which is a cause of concern to healthcare set-ups all around the world. This situation has necessitated the need of introducing new antimicrobials or combination therapies. The slow development of novel synthetic antibiotics has diverted the research towards naturally occurring antimicrobial molecules. Plant secondary metabolites, being diverse and distributed across different habitats, provide fair chances for the exploration and discovery of novel antimicrobials, mainly due to the huge reservoir of phytochemicals produced like alkaloids, flavonoids, tannins and phenolic compounds. The North-Western Himalaya fosters great diversity of medicinal plants, which present a lucrative bioresource available to be explored for the development of novel antimicrobials or potentiating molecules in combination therapies. This review presents an overview of plant-based antimicrobials reported in recent years, along with their targets and mode of action and provides an appraisal of medicinal plant resource of Pir Panjal Himalayan region, along with their reported antimicrobial phytochemicals, which provides excellent opportunity for further exploration and discovery of novel antimicrobials.

Molecular-Engineered Nanoparticle’s Intensive Use in Nanotechnology Challenges like Nanotoxicity and Breakthrough Opportunities

This critical review focuses on Engineered nanoparticles (ENPs) types, properties, toxicities, applications, and future scopes. ENPs are specially designed chemical substances or materials with a particle size between 1 and 100 nm in at least one dimension, while the term Engineered nanomaterials (ENMs) is used collectively for ENPs and nanostructured materials. They can be classified into different classes based on their properties, shapes, or sizes. The different groups include fullerenes, metal NPs, ceramic NPs, and polymeric NPs. NPs possess unique physical and chemical properties due to their high surface area and nanoscale size. NPs like carbon nanotubes (CNTs), quantum dots (QDs), fullerenes, nanocomposites, metal-oxide nanoparticles (TiO2, ZnO, and iron oxides), and fibrous nanomaterials (NMs). Exposure to such ENP increases as nanotechnology advances, though it is an emerging field with a wide range of applications as an interdisciplinary, integrated science with huge potential. Aims to present a comprehensive review of nanotoxicity to human health and impact on the environment, future aspects breakthrough opportunities. Nanowaste as a new and challenging research topic is also highlighted. NPs’ intensive use, persistence, and retention in the environment need to evaluate nanogenotoxicity to humans and other organisms. ENMs used in therapeutic targeted drug delivery, their toxicokinetic, surface chemistry, mechanism of cellular interactions, effect on chromosomes, and DNA started being studied by researchers, however, not fully understood. Nano genotoxicity assessment should be done thoroughly following regulatory guidelines in order to safeguard humans from its harmful effects like cancer and reduce fertility. ENM retention time in the environment should be traced keeping in mind Eco nanotoxicity, bioaccumulation, and biomagnification in aquatic and forest ecosystems. The unique properties of NPs can be utilized fully by knowing all useful characteristics for breakthrough inventions in health care, cancer treatment, anti-aging (longevity), aeronautics, illuminating diodes, nanofertilizers and Artificial Intelligence (AI)-powered robotics for noble innovations for human use. Finally, the challenges of nanowaste management and gaps in breakthrough opportunities are highlighted to reflect the need for more serious action and advancement in nanoscience with novel inventions.

ISH-PL-1: GENETIC MECHANISMS OF BLOOD PRESSURE REGULATION AND HUMAN HYPERTENSION – WHAT HAVE WE LEARNT FROM KIDNEY MULTI-OMICS?

Genome-wide association studies (GWAS) have identified > 1000 genetic variants associated with blood pressure (BP) and hypertension. However, the identity of genes mediating these associations and the downstream molecular mechanisms remain largely unknown. Large panels of human kidney tissue characterised at multiple molecular levels together with the newest statistical advances have accelerated translation of GWAS signals into specific kidney mRNA isoforms, genes and pathways causally associated with BP. The lecture will illustrate how the human kidney transcriptomics, epigenomics and proteomics have enhanced our understanding of BP regulation and how they are paving the way to discovery of new diagnostic and therapeutic opportunities for hypertensive patients.

Стратегическая диагностика отраслевого развития России

The issues of ensuring the long-term socio-economic development of the state and its subjects are of a strategic character and hold one of the key places in the system of public administration in Russia. One of the prospective directions is industrial development, with the right methodological approach, economic sectors and industry can act as long-term drivers of the development of the economy and society. It is indicated that for this purpose it is advisable to use the methodology of industrial strategizing, which is one of the directions of the general theory of strategy and the strategizing methodology of the national scientific school, the founder of which is Foreign Member of the Russian Academy of Sciences, Dr. Sc. (Econ.), professor Vladimir L. Kvint. In accordance with this, in order to initiate the process of industrial strategizing, the author conducts an in-depth study of the strategic object, which consists in conducting strategic diagnostics, namely, assessing the impact of industries on the long-term socioeconomic development of Russia, studying the character and initial trajectory of industrial development. It is noted that the purpose of strategizing the industrial development of Russia should be the formation of a strong industrial core through the discovery and implementation of such strategic opportunities that effectively use the existing competitive advantages, as well as guarantee the generation of multiplicative effects that ensure the socio-economic development of the country and its subjects.

Beyond nature's base pairs: machine learning-enabled design of DNA-stabilized silver nanoclusters.

Sequence-encoded biomolecules such as DNA and peptides are powerful programmable building blocks for nanomaterials. This paradigm is enabled by decades of prior research into how nucleic acid and amino acid sequences dictate biomolecular interactions. The properties of biomolecular materials can be significantly expanded with non-natural interactions, including metal ion coordination of nucleic acids and amino acids. However, these approaches present design challenges because it is often not well-understood how biomolecular sequence dictates such non-natural interactions. This Feature Article presents a case study in overcoming challenges in biomolecular materials with emerging approaches in data mining and machine learning for chemical design. We review progress in this area for a specific class of DNA-templated metal nanomaterials with complex sequence-to-property relationships: DNA-stabilized silver nanoclusters (AgN-DNAs) with bright, sequence-tuned fluorescence colors and promise for biophotonics applications. A brief overview of machine learning concepts is presented, and high-throughput experimental synthesis and characterization of AgN-DNAs are discussed. Then, recent progress in machine learning-guided design of DNA sequences that select for specific AgN-DNA fluorescence properties is reviewed. We conclude with emerging opportunities in machine learning-guided design and discovery of AgN-DNAs and other sequence-encoded biomolecular nanomaterials.

Identification and characterisation of anti-IL-13 inhibitory single domain antibodies provides new insights into receptor selectivity and attractive opportunities for drug discovery.

Interleukin-13 (IL-13) is a cytokine involved in T-cell immune responses and is a well validated therapeutic target for the treatment of asthma, along with other allergic and inflammatory diseases. IL-13 signals through a ternary signalling complex formed with the receptors IL-13Rα1 and IL-4Rα. This complex is assembled by IL-13 initially binding IL-13Rα1, followed by association of the binary IL-13:IL-13Rα1 complex with IL-4Rα. The receptors are shared with IL-4, but IL-4 initially binds IL-4Rα. Here we report the identification and characterisation of a diverse panel of single-domain antibodies (VHHs) that bind to IL-13 (KD 40 nM-5.5 μM) and inhibit downstream IL-13 signalling (IC50 0.2-53.8 μM). NMR mapping showed that the VHHs recognise a number of epitopes on IL-13, including previously unknown allosteric sites. Further NMR investigation of VHH204 bound to IL-13 revealed a novel allosteric mechanism of inhibition, with the antibody stabilising IL-13 in a conformation incompatible with receptor binding. This also led to the identification of a conformational equilibrium for free IL-13, providing insights into differing receptor signalling complex assembly seen for IL-13 compared to IL-4, with formation of the IL-13:IL-13Rα1 complex required to stabilise IL-13 in a conformation with high affinity for IL-4Rα. These findings highlight new opportunities for therapeutic targeting of IL-13 and we report a successful 19F fragment screen of the IL-13:VHH204 complex, including binding sites identified for several hits. To our knowledge, these 19F containing fragments represent the first small-molecules shown to bind to IL-13 and could provide starting points for a small-molecule drug discovery programme.

A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda.

Indian people are at high risk for type 2 diabetes (T2DM) even at younger ages and lower body weights. Already 74 million people in India have the disease, and the proportion of those with T2DM is increasing across all strata of society. Unique aspects, related to lower insulin secretion or function, and higher hepatic fat deposition, accompanied by the rise in overweight (related to lifestyle changes) may all be responsible for this unrelenting epidemic of T2DM. Yet, research to understand the causes, pathophysiology, phenotypes, prevention, treatment, and healthcare delivery of T2DM in India seriously lags behind. There are major opportunities for scientific discovery and technological innovation, which if tapped can generate solutions for T2DM relevant to the country's context and make leading contributions to global science. We analyze the situation of T2DM in India, and present a four-pillar (etiology, precision medicine, implementation research, and health policy) strategic research framework to tackle the challenge. We offer key research questions for each pillar, and identify infrastructure needs. India offers a fertile environment for shifting the paradigm from imprecise late-stage diabetes treatment toward early-stage precision prevention and care. Investing in and leveraging academic and technological infrastructures, across the disciplines of science, engineering, and medicine, can accelerate progress toward a diabetes-free nation.

Network Proximity-based computational pipeline identifies drug candidates for different pathological stages of Alzheimer's disease

Despite the massive investment in Alzheimer’s disease (AD), there are still no disease-modifying treatments (DMTs) for AD. One major reason is attributed to the limitation of clinical "one‐size‐fits‐all” approach, since the same AD treatment solely based on clinical diagnosis was unlikely to achieve good clinical efficacy. In recent years, computational approaches based on multiomics data have provided an unprecedented opportunity for drug discovery since they can substantially lower the costs and boost the efficiency. In this study, we intended to identify potential drug candidates for different pathological stages of AD by computationally repurposing Food and Drug Administration (FDA) approved drugs. First, we assembled gene expression data from three different AD pathological stages, which include mild cognitive impairment (MCI) and early and late stages of AD (EAD, LAD). We next quantified the network distances between drug target networks and AD modules by utilizing a network proximity approach, and identified 193 candidates that possessed significant associations with AD. After searching for previous literature evidence, 63 out of 193 (32.6%) predicted drugs were demonstrated to exert therapeutic effects on AD. We further explored the novel mechanism of action (MOA) for these drug candidates by determining the specific brain cells they might function on based on AD patient single cell transcriptomic data. Additionally, we selected several promising candidates that could cross the blood brain barrier together with confirmed neuroprotective effects, and subsequently determined the antioxidative activity of these compounds. Experimental results showed that azathioprine decreased the reactive oxygen species (ROS) and malondialdehyde (MDA) levels and improved the superoxide dismutase (SOD) activity in APP-SH-SY5Y cells. Finally, we deciphered the potential MOA of azathioprine against AD via network analysis and validated several apoptosis-related proteins (Caspase 3, Cleaved Caspase 3, Bax, Bcl2) through western blotting. In summary, this study presented an effective computational strategy utilizing omics data for AD drug repurposing, which provides a new perspective for drug discovery and development.

Recent advances in the molecular design and applications of proteolysis targeting chimera-based multi-specific antiviral modality

Viral infections represent a major threat to human health and the global economy; however, most of the currently available antiviral drugs are not fully effective in restricting viral replication and selecting for drug-resistant variants. Targeted protein degradation technologies are promising strategies to avoid or delay the emergence of drug resistance. Among the protein degradation-based multi-specific approaches, proteolysis targeting chimera (PROTAC) is the main strategy applied in the antiviral field. In this review we will introduce the elements and mechanisms of action used by PROTAC technology, as well as the advantages of PROTACs over available antiviral drugs. We also summarize the latest progress in the application of PROTACs in antiviral research, discuss existing challenges and look into future opportunities for antiviral drug discovery.

Community Detection Algorithms in Healthcare Applications: A Systematic Review

Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.