Opportunities For Applications Research Articles

Research on clustering algorithms based on the Iris dataset

In the burgeoning field of machine learning, clustering algorithms play a quintessential role in uncovering hidden patterns and structures within data. This study commences by highlighting the critical importance of machine learning and the expansive application of clustering algorithms across various disciplines. It then provides a brief background, tracing the development history of clustering algorithms and elucidating the unique characteristics and methodologies inherent to different algorithms. Through empirical analysis conducted on the Iris dataset, this research evaluates the performance of the K-means, hierarchical clustering, and DBSCAN algorithms, leveraging experimental charts and datasets for a nuanced assessment. The comparative analysis reveals distinct advantages and disadvantages of each algorithm, facilitating a balanced discussion on their practical implications. The conclusion synthesizes these findings, offering insights into the comparative merits of the algorithms and suggesting avenues for future research. This investigation aims to deepen the comprehension of the application challenges and opportunities presented by clustering algorithms, thus offering a guiding framework for future explorations in the field.

Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics.

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study.

Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.

Opportunities and challenges for commercial encryption applications in the Internet of Vehicles

The rapid development of vehicle networking technology has brought broad opportunities for commercial encryption applications, but it is also accompanied by many challenges. As an important guarantee for the information security of the Internet of vehicles, commercial encryption applications have obtained significant development opportunities in terms of technological innovation, market demand and policy support. However, the commercial encryption applications of connected vehicles are also facing challenges such as technical difficulties, lack of standardization, and increasing security threats. This paper aims to deeply analyze the opportunities and challenges of the commercial encryption application of the Internet of vehicles, and put forward corresponding development suggestions, in order to provide reference for the security development of the Internet of vehicles industry.

Structural characterization of the polysaccharide from the black crystal region of Inonotus obliquus and its effect on AsPC-1 and SW1990 pancreatic cancer cell apoptosis

In this study, one water soluble polysaccharide (IOP1–1) with a weight average molecular weight of 6886 Da was obtained from the black crystal region of Inonotus obliquus by hot water extraction, DEAE-52 cellulose extraction and Sephadex-100 column chromatography purification. Structural analysis indicated that IOP1–1 was a glucan with a main chain composed of α-Glcp-(1 → 4)-α-Glcp-(1 → 4)-β-Glcp-(1 → 4)-β-Glcp-(1 → 4)-α-Glcp-(1 → 6)-β-Glcp-(1 → 4)-α-Glcp-(1 → 3)-β-Glcp-(1→. The CCK-8 assay results showed that IOP1–1 inhibited AsPC-1 and SW1990 pancreatic cancer cell proliferation in a concentration-dependent manner. Flow cytometric analysis revealed that IOP1–1 induced cell cycle arrest in AsPC-1 and SW1990 cells. Hoechst 33342 staining and Annexin V-FITC/PI double staining analysis showed that IOP1–1 could induce apoptosis in AsPC-1 and SW1990 cells. Furthermore, western blot analysis confirmed that IOP1–1 could induce apoptosis in AsPC-1 and SW1990 pancreatic cancer cells through three pathways: the mitochondrial pathway, the death receptor pathway, and endoplasmic reticulum stress. According to these research data, IOP1–1 may be utilized as an adjuvant treatment to anticancer medications, opening up new application prospects and opportunities.

Broadband silicon nitride integrated polarization rotators at 780 nm

Polarization management, and in particular polarization rotation, is becoming increasingly important for photonic integrated circuits (PICs). While fiber-optic networks are generally polarization insensitive, the large aspect ratio of high-index-contrast PIC waveguides leads to a large polarization-dependent response of integrated components such as waveguides, optical cavities, couplers, etc. Although foundry-processed polarization rotators operating at telecom and datacom wavelengths (C- and O-band) have been demonstrated, to date, there have been few reports of devices operating at shorter wavelengths. This work demonstrates silicon nitride (SiN) polarization rotators operating from λ=700-1000 nm (the I/Z-band) that take advantage of optical coupling between two waveguiding layers in a standard foundry process. We demonstrate a broadband white-light polarization measurement setup that enables precise characterization of the polarization-dependent transmission of photonic waveguide devices. Measurements on foundry-processed devices confirm full TE-to-TM rotation exhibiting a maximum polarization extinction ratio (PER) approaching 20 dB (limited by our measurement setup), and an exceptionally large bandwidth of up to 160 nm with an insertion loss less than 0.2 dB. Beam propagation method (3D-BPM) simulations show good agreement with experimental data and enable the device parameters to be adjusted to accommodate different operating wavelengths and geometries with no changes to the existing foundry process. This work opens up opportunities for applications in quantum information and bio-sensing where operation at λ<1000nm is needed.

A comprehensive experimental study on the effects of hexagonal boron nitride particle size and loading ratio on thermal and mechanical performance in epoxy composites

Harnessing the potential of hexagonal boron nitride (h-BN) in epoxy composites for tailoring thermal conductivity is a promising avenue in materials science. However, achieving balanced enhancements in both in-plane and through-plane directions remains a challenge that requires innovative solutions. The primary objective of this research is to evaluate how thermal and mechanical characteristics of an epoxy matrix are affected by the size and amount of h-BN particles. To achieve this goal, h-BN particles with varying sizes (micro and nano) are incorporated into the epoxy matrix at different weight ratios spanning from 0.5 wt % to 20 wt % using a pre-dispersion technique. The epoxy composites reinforced with h-BN through a molding process exhibits enhanced mechanical and thermal performance in contrast to the pristine epoxy material. During the flexural test, acoustic emission data is collected to identify the initiation and progression of damage within the specimens under testing conditions. The most notable enhancement in thermal conductivity is observed when incorporating 20 wt% of micron-sized h-BN particles. This leads to a remarkable 107% increase in the in-plane direction and an impressive 112% increase in the through-plane direction. These results can be attributed to the formation of a three-dimensional thermally conductive network by the larger h-BN particles, which extends the path of phonon scattering. Furthermore, there are significant improvements in both flexural modulus and tensile modulus. Epoxy composites containing 10 wt% of micron-sized h-BN experiences an approximate 42% increase, while those with 20 wt% of the same particles displays a substantial 47% rise in these properties. This study effectively addresses the challenges associated with tailoring the thermal properties of epoxy composites, opening up new opportunities for applications in various industries, including electronics, aerospace and thermal management systems.

Diffusional Electron Transport Coupled to Thermodynamically Driven Electron Transfers in Redox-Conductive Multivariate Metal-Organic Frameworks.

The development of redox-conductive metal-organic frameworks (MOFs) and the fundamental understanding of charge propagation through these materials are central to their applications in energy storage, electronics, and catalysis. To answer some unresolved questions about diffusional electron hopping transport and redox conductivity, mixed-linker MOFs were constructed from two statistically distributed redox-active linkers, pyromellitic diimide bis-pyrazolate (PMDI) and naphthalene diimide bis-pyrazolate (NDI), and grown as crystalline thin films on conductive fluorine-doped tin oxide (FTO). Owing to the distinct redox properties of the linkers, four well-separated and reversible redox events are resolved by cyclic voltammetry, and the mixed-linker MOFs can exist in five discrete redox states. Each state is characterized by a unique spectroscopic signature, and the interconversions between the states can be followed spectroscopically under operando conditions. With the help of pulsed step-potential spectrochronoamperometry, two modes of electron propagation through the mixed-linker MOF are identified: diffusional electron hopping transport between linkers of the same type and a second channel that arises from thermodynamically driven electron transfers between linkers of different types. Corresponding to the four redox events of the mixed-linker MOFs, four distinct bell-shaped redox conductivity profiles are observed at a steady state. The magnitude of the maximum redox conductivity is evidenced to be dependent on the distance between redox hopping sites, analogous to the situation for apparent electron diffusion coefficients, Deapp, that are obtained in transient experiments. The design of mixed-linker redox-conductive MOFs and detailed studies of their charge transport properties present new opportunities for future applications of MOFs, in particular, within electrocatalysis.

Impedimetric sensor based on molecularly imprinted polythionine from deep eutectic solvent for epinephrine determination

Deep eutectic solvents have obtained growing interest in analytical chemistry due to opportunities related the green chemistry paradigm, i.e., substitution of organic solvents, application of nontoxic ingredients and minimal quantities of wastes formed in their synthesis and usage. In this work, the advantages of deep eutectic solvents were shown in the development of electrochemical sensor for epinephrine determination. Polythionine molecularly imprinted with epinephrine (PTNMIP) was electrodeposited from natural deep eutectic solvent (NADES) consisted of citric acid, glucose, and water in 1:1:6 molar ratio and tested with standard solutions of epinephrine, pharmaceutical preparations, and artificial samples of biological liquids. Only one drop (100 µL) of the monomer dissolved in NADES was applied for sensor preparation on the platform of screen-printed carbon electrode. Electropolymerization was performed by multiple scanning of the potential. The formation of molecular imprints was confirmed by scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). After the template removal, the charge transfer resistance of the surface layer depended on the epinephrine concentration in the range from 100 nM to 316 µM (limit of detection 50 nM). Selectivity of the signal was confirmed against serotonin and norepinephrine commonly present in biological liquids. Sensor was successfully tested for the determination of epinephrine in spiked solutions mimicking influence of the plasma electrolytes, serum proteins, and in real saliva samples. The recoveries calculated for 10 μM epinephrine ranged from 98.3 to 102.6%. Insignificant influence of the additives and stabilizers present in medications was eliminated by sample dilution. Impedimetric sensor developed showed good opportunities for further application in clinical diagnostics, pharmacokinetics studies and medication manufacture control.

Can 2D Semiconductors Be Game-Changers for Nanoelectronics and Photonics?

2D semiconductors have interesting physical and chemical attributes that have led them to become one of the most intensely investigated semiconductor families in recent history. They may play a crucial role in the next technological revolution in electronics as well as optoelectronics or photonics. In this Perspective, we explore the fundamental principles and significant advancements in electronic and photonic devices comprising 2D semiconductors. We focus on strategies aimed at enhancing the performance of conventional devices and exploiting important properties of 2D semiconductors that allow fundamentally interesting device functionalities for future applications. Approaches for the realization of emerging logic transistors and memory devices as well as photovoltaics, photodetectors, electro-optical modulators, and nonlinear optics based on 2D semiconductors are discussed. We also provide a forward-looking perspective on critical remaining challenges and opportunities for basic science and technology level applications of 2D semiconductors.

COMPUTER VISION IN SMART HOMES: ENHANCING LIVING WITH AI

This research focuses on the use of computer vision technology in smart homes. It starts by outlining the present developing state of intelligent homes and explaining the idea. After that, a thorough review of the literature is done in order to examine the current difficulties and roadblocks related to using computer vision in smart home settings. The paper then suggests a number of development and implementation strategies for computer vision-based intelligent home systems. Comprehensive research methods are covered, including data gathering, preprocessing approaches, training models, and system evaluation strategies.The study ends with a review of its main conclusions and an explanation of possible directions for future research and opportunities for application in the field of computer vision inside smart home ecosystems. Keywords: Computer vision, Smart homes, Data collection, Model training, Application opportunities.

Exploration of novel DPP-IV inhibitory peptides from discarded eggshell membrane: An integrated in silico and in vitro study

Dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides exhibit great potential to alleviate type II diabetes. This study aimed to prepare, identify and explore the molecular mechanism of DPP-IV inhibitory peptides from eggshell membrane (ESM), an abundant byproduct of the food industry, based on combining in silico and in vitro assessments. In silico evaluation of ten major ESM proteins revealed the great potential of ESM as a novel source of DPP-IV inhibitors. Moreover, the optimal combined-enzymatic strategy (alkaline protease + papain) for the efficient production of DPP-IV inhibitory peptides from ESM was designed by simulated proteolysis. Corresponding with in silico predictions, in vitro assessments confirmed that sodium sulfite assisted the screened dual enzymes led to better production of DPP-IV inhibitory peptides from ESM, with preferable total nitrogen recovery (86.61% ± 4.83%), DPP-IV inhibitory activity (64.61% ± 2.75%) and total antioxidant activity (215.27 ± 1.75 μg/mL). Due to the correlation between diabetes and oxidative stress, the antioxidant and DPP-IV inhibitory activities of the hydrolysate facilitate its amelioration of diabetes. Three novel DPP-IV inhibitory peptides with high abundance in ESM hydrolysates (YPEPPPQ, HDGADVS and VTDGQPH) were identified by LC-MS/MS, in silico characterization and molecular docking. These peptides were synthesized and confirmed to possess DPP-IV inhibitory activity, potentially relating to their stable binding with key residues within the S1, S2 pockets and catalytic triad of DPP-IV via hydrogen bonding and hydrophobic interaction. Overall, this research suggests an effective and time-saving strategy to investigate novel DPP-IV inhibitory peptides from ESM, as well as opens new opportunities for ESM application in antidiabetic foods.

Macrophages in necrotic liver lesion repair: opportunities for therapeutical applications.

Healthy livers contain 80% of body resident macrophages known as Kupffer cells. In diseased livers, the number of Kupffer cells usually drops but is compensated by infiltration of monocyte-derived macrophages, some of which can differentiate into Kupffer-like cells. Early studies suggest that Kupffer cells play important roles in both promoting liver injury and liver regeneration. Yet, the distinction between the functionalities of resident and infiltrating macrophages is not always made. By using more specific macrophage markers and targeted cell depletion and single-cell RNA sequencing, recent studies revealed several subsets of monocyte-derived macrophages that play important functions in inducing liver damage and inflammation as well as in liver repair and regeneration. In this review, we discuss the different roles that hepatic macrophages play in promoting necrotic liver lesion resolution and dead cell clearance, as well as the targeting of these cells as potential tools for the development of novel therapies for acute liver failure and acute-on-chronic liver failure.

A survey of medical schoolaspirant perceptions of an unexpected lottery-facilitated admissions adaptation.

Due to the COVID-19 pandemic, the Undergraduate Medical Doctor (MD) Programme at McMaster University (Hamilton, Canada) was unable to run in-person medical school interviews in March 2020, prompting an alternate solution that maximised admission opportunities for Indigenous applicants, prioritised admission for those rated most highly in the interview determination process, and allocated subsequent offers via lottery. A short survey was administered to applicants who had been offered an admissions interview and were subsequently impacted by the admissions adaptations. The survey elicited perceptions of the adaptation through Likert scale ratings and free-text responses. Survey data were analysed via a sequential (quantitative to qualitative) mixed-methods design. 196 of 552 potential participants completed the survey. Across quantitative and qualitative analyses, respondents reported that the adaptation had a negative impact on their professional development and personal life. Ratings of negative perception were greater for those who did not receive an offer than for those who accepted or declined an offer. Free text responses emphasised considerable criticism for the lottery portion of the adaptation and displeasure that efforts made in constructing applications were less relevant than anticipated. The negative responses to this unexpected change highlight the profound upstream impact admission policies have on the preapplication behaviours of aspiring medical students. The outcomes support a refined understanding of the value candidates place on the interview in appraising their own suitability for a career as a physician.

CANDIL: A federated data fabric for network analytics

The availability of data sources during the Big Data era provides the opportunity for new analytical applications in the networking domain, which are envisioned as one of the main enablers of the future autonomous networks. But the proliferation of heterogeneous data sources has resulted into a sea of data silos, in which finding data, understanding data, and dealing with the complexities of each data source becomes a challenge. Aiming to tackle the connection of data silos, the data fabric has appeared as a new paradigm that provides a uniform access to all the data, abstracting consumers from the underlying complexities of the data sources. In this regard, the knowledge graph has raised as a promising solution that can integrate data from heterogeneous silos based on common concepts captured in ontologies. Building upon knowledge graph standards, this paper introduces CANDIL, a federated data fabric to support the integration of data from distributed systems, mainly focused on networking domain aspects. CANDIL defines an ontology that captures network topology and interface concepts, along with a reference architecture to ingest and integrate data in a federated knowledge graph that spans across the Edge-Cloud continuum. The proposal is validated with a prototype implementation and two example use cases of network analytics.

Research progress and applications of epigenetic biomarkers in cancer.

Epigenetic changes are heritable changes in gene expression without changes in the nucleotide sequence of genes. Epigenetic changes play an important role in the development of cancer and in the process of malignancy metastasis. Previous studies have shown that abnormal epigenetic changes can be used as biomarkers for disease status and disease prediction. The reversibility and controllability of epigenetic modification changes also provide new strategies for early disease prevention and treatment. In addition, corresponding drug development has also reached the clinical stage. In this paper, we will discuss the recent progress and application status of tumor epigenetic biomarkers from three perspectives: DNA methylation, non-coding RNA, and histone modification, in order to provide new opportunities for additional tumor research and applications.

Formation of Highly Tunable Periodic Plasmonic Structures on Gold Films Using Direct Laser Writing

AbstractDirect laser writing method is a promising technique for the large‐scale and cost‐effective fabrication of periodic nanostructure arrays exciting hybrid lattice plasmons. This type of electromagnetic mode manifests a narrow and deep resonance peak with a high dispersion whose precise controllability is crucial for practical applications in photonic devices. Here, the formation of differently shaped gold nanostructures using the direct laser writing method on Au layers of different thicknesses is presented. The resonance peak is demonstrated to be highly dependent on the shape of the structures in the array, thus its position in the spectra, as well as the quality, can be additionally modulated by changing the morphology. The shape of the structure and the resonance itself pertain not only on the laser pulse energy but also on the grating period. This overlapping effect occurring at distances smaller than the diameter of the focused laser beam is studied in detail. By taking advantage of the highly controllable plasmonic resonance, the fabricated gratings open up new opportunities for applications in sensing.

AI-based Environmental Sustainability: Transforming Conservation Efforts

The use of artificial intelligence (AI) technologies has the potential to enhance sustainability efforts in the face growing environmental issues, especially in the areas of resource management and climate change mitigation. This study focuses at how artificial intelligence (AI) and environmental sustainability interact, examining the various ways AI might help solve urgent environmental issues. AI provides innovative methods to maximize utilization of resources, predict environmental trends, and promote accurate decision-making processes through the use of advanced computational algorithms and data analytics. This study analyses multiple applications of AI in environmental sustainability, from pollution prevention and natural resource conservation to climate modelling and renewable energy optimization. In addition, an evaluation is done about moral implications and possible socio-economic effects of AI-driven environmental solutions. Using an in-depth review of the majority of current research by applying case studies, this research identifies significant challenges and opportunities for more research and application while highlighting the revolutionary potential of AI in achieving environmental sustainability goals. Ultimately, the paper encourages ethical application of artificial intelligence (AI) as important part of larger strategies for mitigating the effects of climate change and developing a future that is environmentally conscious.

INFORMATION AND COMMUNICATION TECHNOLOGIES IN MODERN EDUCATION IN THE PROCESS OF FORMING LEADERSHIP COMPETENCE OF STUDENTS IN PPOFESSIONAL HIGHER EDUCATION INSTITUTIONS

The article considers the peculiarities of application and potential opportunities of information and communication technologies in the process of formation of leadership competence of students of professional higher education institutions at the present stage. It is indicated that in modern conditions information and communication technologies are an integral part of the education system in Ukraine. It is proved that they play a significant role in the formation of leadership competence of students, as they contribute to the disclosure and development of their creative and leadership potential, develop critical thinking, activate cognitive processes, form skills of independent activity, self-presentation, etc. In addition, it is found that the use of information and communication technologies in the educational process contributes to the development of social, communication and organisational skills, selfpresentation skills, etc. It has been found that information and communication technologies also contribute to the development of the personal potential of students, developing their confidence, responsibility, self-discipline, readiness for continuous self-development and self-improvement, perspective vision, willingness to take risks and make responsible decisions. It is noted that information and communication technologies play a significant role in the educational work with students of professional higher education institutions, as they make it possible to optimise the educational process and actively involve young people in it. It is noted that the effectiveness of the use of information and communication technologies for the formation of leadership competence in institutions of professional higher education largely depends on a clear understanding of the place they should occupy in the educational process. It is concluded that the use of information and communication technologies in the training and education of students creates all the prerequisites for the active formation of leadership competence, development of leadership abilities and qualities, and expansion of leadership potential.

Dual Functional Microcapsule based on Monodisperse Short PEG Amphiphile for Drug Encapsulation and Protein Affinity Controlled Release.

A short monodisperse poly(ethylene glycol) (PEG) and a neutral organic rotamer conjugate TEG-BTA-2 amphiphile was designed for the construction of a stimuli-responsive switchable self-assembled structure for drug encapsulation by noncovalent interaction and targeted controlled delivery. A short PEG, tetraethylene glycol (TEG) was covalently attached with a neutral organic rotamer benzothiazole dye (BTA-2) affording the neutral TEG-BTA-2 (<500 D). The TEG-BTA-2 is self-assembled into a microsphere in an aqueous medium, but remarkably undergoes morphology change switching to a rice-like microcapsule for curcumin encapsulation. Curcumin-loaded microcapsules were stable in an aqueous solution, however, were noticed disintegrating upon the addition of BSA protein. This is possibly due to an interaction with BSA protein leading to a protein affinity-controlled curcumin release in a neutral PBS buffer. Moreover, cell internalization of the neutral amphiphile TEG-BTA-2 into A549 cells was observed by fluorescence microscopy, providing an opportunity for application as a molecular vehicle for targeted drug delivery and monitoring.