Search

Dark-field microscopy (DFM) is a widely used imaging tool, due to its high-contrast capability in imaging label-free specimens. Traditional DFM requires optical alignment to block the oblique illumination, and the resolution is diffraction-limited to the wavelength scale. In this work, we present deep-learning assisted plasmonic dark-field microscopy (DAPD), which is a single-frame super-resolution method using plasmonic dark-field (PDF) microscopy and deep-learning assisted image reconstruction. Specifically, we fabricated a designed PDF substrate with surface plasmon polaritons (SPPs) illuminating specimens on the substrate. Dark field images formed by scattered light from the specimen are further processed by a pretrained convolutional neural network (CNN) using a simulation dataset based on the designed substrate and parameters of the detection optics. We demonstrated a resolution enhancement of 2.8 times on various label-free objects with a large potential for future improvement. We highlight our technique as a compact alternative to traditional DFM with a significantly enhanced spatial resolution.

Abstract Background The urgent need for affordable and rapid detection methodologies for foodborne pathogens, particularly Escherichia coli (E. coli), highlights the importance of developing efficient and widely accessible diagnostic systems. Dark field microscopy, although effective, requires specific isolation of the target bacteria which can be hindered by the high cost of producing specialized antibodies. Alternatively, M13 bacteriophage, which naturally targets E. coli, offers a cost-efficient option with well-established techniques for its display and modification. Nevertheless, its filamentous structure with a large length-diameter ratio contributes to nonspecific binding and low separation efficiency, posing significant challenges. Consequently, refining M13 phage methodologies and their integration with advanced microscopy techniques stands as a critical pathway to improve detection specificity and efficiency in food safety diagnostics. Methods We employed a dual-plasmid strategy to generate a truncated M13 phage (tM13). This engineered tM13 incorporates two key genetic modifications: a partial mutation at the N-terminus of pIII and biotinylation at the hydrophobic end of pVIII. These alterations enable efficient attachment of tM13 to diverse E. coli strains, facilitating rapid magnetic separation. For detection, we additionally implemented a convolutional neural network (CNN)-based algorithm for precise identification and quantification of bacterial cells using dark field microscopy. Results The results obtained from spike-in and clinical sample analyses demonstrated the accuracy, high sensitivity (with a detection limit of 10 CFU/μL), and time-saving nature (30 min) of our tM13-based immunomagnetic enrichment approach combined with AI-enabled analytics, thereby supporting its potential to facilitate the identification of diverse E. coli strains in complex samples. Conclusion The study established a rapid and accurate detection strategy for E. coli utilizing truncated M13 phages as capture probes, along with a dark field microscopy detection platform that integrates an image processing model and convolutional neural network. Graphical Abstract

Background: The urgent need for affordable and rapid detection methodologies for foodborne pathogens, particularly Escherichia coli (E. coli), highlights the importance of developing efficient and widely accessible diagnostic systems. Dark field microscopy, although effective, requires specific isolation of the target bacteria which can be hindered by the high cost of producing specialized antibodies. Alternatively, M13 bacteriophage, which naturally targets E. coli, offers a cost-efficient option with well-established techniques for its display and modification. Nevertheless, its filamentous structure with a large length-diameter ratio contributes to nonspecific binding and low separation efficiency, posing significant challenges. Consequently, refining M13 phage methodologies and their integration with advanced microscopy techniques stands as a critical pathway to improve detection specificity and efficiency in food safety diagnostics. Methods: We employed a dual-plasmid strategy to generate a truncated M13 phage (tM13). This engineered tM13 incorporates two key genetic modifications: a partial mutation at the N-terminus of pIII and biotinylation at the hydrophobic end of pVIII. These alterations enable efficient attachment of tM13 to diverse E. coli strains, facilitating rapid magnetic separation. For detection, we additionally implemented a convolutional neural network (CNN)-based algorithm for precise identification and quantification of bacterial cells using dark field microscopy. Results: The results obtained from spike-in and clinical sample analyses demonstrated the accuracy, high sensitivity (with a detection limit of 10 CFU/μL), and time-saving nature (30 min) of our tM13-based immunomagnetic enrichment approach combined with AI-enabled analytics, thereby supporting its potential to facilitate the identification of diverse E. coli strains in complex samples. Conclusion: The study established a rapid and accurate detection strategy for E. coli utilizing truncated M13 phages as capture probes, along with a dark field microscopy detection platform that integrates an image processing model and convolutional neural network. [ABSTRACT FROM AUTHOR]

Circulating RNAs have been investigated systematically for over 20 years, both as constituents of circulating extracellular vesicles (EVs) or, more recently, non‐EV RNA carriers, such as exomeres and supermeres. The high level of variability and low reproducibility rate of EV/extracellular RNA (exRNA) results generated even on the same biofluids promoted several efforts to limit pre‐analytical variability by standardizing sample collection and sample preparation, along with instrument validation, setup and calibration. Anticoagulants (ACs) are often chosen based on the initial goal of the study and not necessarily for the later EV and/or exRNA analyses. We show the effects of blood collection on EV size, abundance, and antigenic composition, as well on the miRNAs. Our focus of this work was on the effect of ACs on the number and antigenic composition of circulating EVs and on a set of circulating miRNA species, which were shown to be relevant as disease markers in several cancers and Alzheimer's disease. Results show that while the number of plasma EVs, their relative size, and post‐fluorescence labeling profile varied with each AC, their overall antigenic composition, with few exceptions, did not change significantly. However, the number of EVs expressing platelet and platelet‐activation markers increased in serum samples. For overall miRNA expression levels, EDTA was a better AC, although this may have been associated with stimulation of cells in the blood collection tube. Citrate and serum rendered better results for a set of miRNAs that were described as circulating markers for Alzheimer's disease, colon, and papillary thyroid cancers. [ABSTRACT FROM AUTHOR]

Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <2% residual error margin. Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification. [ABSTRACT FROM AUTHOR]

Abstract Circulating RNAs have been investigated systematically for over 20 years, both as constituents of circulating extracellular vesicles (EVs) or, more recently, non‐EV RNA carriers, such as exomeres and supermeres. The high level of variability and low reproducibility rate of EV/extracellular RNA (exRNA) results generated even on the same biofluids promoted several efforts to limit pre‐analytical variability by standardizing sample collection and sample preparation, along with instrument validation, setup and calibration. Anticoagulants (ACs) are often chosen based on the initial goal of the study and not necessarily for the later EV and/or exRNA analyses. We show the effects of blood collection on EV size, abundance, and antigenic composition, as well on the miRNAs. Our focus of this work was on the effect of ACs on the number and antigenic composition of circulating EVs and on a set of circulating miRNA species, which were shown to be relevant as disease markers in several cancers and Alzheimer's disease. Results show that while the number of plasma EVs, their relative size, and post‐fluorescence labeling profile varied with each AC, their overall antigenic composition, with few exceptions, did not change significantly. However, the number of EVs expressing platelet and platelet‐activation markers increased in serum samples. For overall miRNA expression levels, EDTA was a better AC, although this may have been associated with stimulation of cells in the blood collection tube. Citrate and serum rendered better results for a set of miRNAs that were described as circulating markers for Alzheimer's disease, colon, and papillary thyroid cancers.

Herein, we report the experimental and theoretical study on scattering spectra of single-sprouted potato-shaped Au-Ag bimetallic nanoparticles. In detail, first, the sprouted potato-shaped nanoparticles are synthesized using the wet-chemistry method, and the morphology of the nanoparticles is studied using a field emission scanning electron microscope. Moreover, the scattering images and spectra of single nanoparticles of different sizes are captured using a dark field microscope. The finite element method (FEM) simulations are performed on these nanoparticles and investigated the dependence of the far-field scattering spectra and local field distribution on the morphology of the nanoparticles (i.e. size and shape of the sprouts and the nanoparticle bases or cores on which the sprouts are anchored). [ABSTRACT FROM AUTHOR]

Among the physical and chemical properties of nanomaterials, hydrophobicity is considered to play a key role in their impact on the environment. Changes in hydrophobicity resulting from abiotic and biotic processes can be used to predict the behaviours of nanoparticles (NPs) in the environment (e.g., aggregation, toxicity, and bioaccumulation). Hydrophobicity changes induced by sulfidation and natural organic matter (NOM) corona formation were evaluated by monitoring the binding rate of silver (Ag) NPs on engineered surfaces using dark-field microscopy (DFM). It was found that this DFM-based method was more capable of distinguishing the hydrophobicity of environmentally relevant AgNPs than the dye adsorption method. Under the conditions tested in this study, sulfidation and adsorption of sulfidized NOM/“lipid-free” (LF-)NOM increased the hydrophobicity of AgNPs. Both methods demonstrate the tendency of AgNPs to become more hydrophobic after sulfidation. This study shows that DFM-based methods can effectively measure the hydrophobicity of environmentally relevant NPs and have the potential to be widely used as fate predictors in the future.

Circular differential scattering (CDS) spectroscopy has been developed as a powerful method for the characterization of the optical activity of individual plasmonic nanostructures and their complexes with chiral molecules. However, standard measurement setups often result in artifacts that have long raised concerns on the interpretation of spectral data. In fact, the detection limit of CDS setups is constrained by the high level of artifacts, to ±10%. We address this issue by means of a detailed theoretical description of changes in the polarization state when circularly polarized light is reflected at a dark-field condenser. As a result, we propose a modified CDS configuration based on sequentially placing the quarter-wave plate and linear polarizer within the detection optical path, to analyze the circular polarization state of the light scattered by individual particles. Extensive analysis demonstrates a detection limit of ±1.5% for the modified configuration, which is significantly lower than that for the conventional setup. As a standard system for CDS measurements, both achiral and chiral gold nanorods (AuNRs) were characterized using both setups. With achiral AuNRs, linear dichroism (LD) artifacts in the conventional setup are found to originate from LD present in the excitation light and are only present if anisotropic excitation is produced as a result of the misalignment of the excitation light to the condenser. With chiral AuNRs, CDS spectra recorded with the conventional setup depend on the orientation of the chiral AuNRs with respect to the x -axis of the microscope and are reversed compared to those on the colloid and measured in the modified configuration. The results are in good agreement with theoretical simulations for both configurations.

Dark field scattering microscopy can create large hyperspectral data sets that contain a wealth of information on the properties and the molecular environment of noble metal nanoparticles. For a quick screening of samples of microscopic dimensions that contain many different types of plasmonic nanostructures, we propose a multivariate analysis of data sets of thousands to several hundreds of thousands of scattering spectra. By using non-negative matrix factorization for decomposing the spectra, components are identified that represent individual plasmon resonances and relative contributions of these resonances to particular microscopic focal volumes in the mapping data sets. Using data from silver and gold nanoparticles in the presence of different molecules, including gold nanoparticle-protein agglomerates or silver nanoparticles forming aggregates in the presence of acrylamide, plasmonic properties are observed that differ from those of the original nanoparticles. For the case of acrylamide, we show that the plasmon resonances of the silver nanoparticles are ideally suited to support surface enhanced Raman scattering (SERS) and the two-photon excited process of surface enhanced hyper Raman scattering (SEHRS). Both vibrational tools give complementary information on the in situ formed polyacrylamide and the molecular composition at the nanoparticle surface. [ABSTRACT FROM AUTHOR]

Lipid droplets are the major organelles for fat storage in a cell and analyzing lipid droplets in Caenorhabditis elegans (C. elegans) can shed light on obesity-related diseases in humans. In this work, we propose to use a label free scattering-based method, namely dark field microscopy, to visualize the lipid droplets with high contrast, followed by deep learning to perform automatic segmentation. Our method works through combining epi-illumination dark field microscopy, which provides high spatial resolution, with asymmetric illumination, which computationally rejects multiple scattering. Due to the raw data’s high quality, only 25 images are required to train a Convolutional Neural Network (CNN) to successfully segment lipid droplets in dense regions of the worm. The performance is validated on both healthy worms as well as those in starvation conditions, which alter the size and abundance of lipid droplets. Asymmetric illumination substantially improves CNN accuracy compared with standard dark field imaging from 70% to be 85%, respectively. Meanwhile, standard segmentation methods such as watershed and DIC object tracking (DICOT) failed to segment droplets due to the images’ complex label-free background. By successfully analyzing lipid droplets in vivo and without staining, our method liberates researchers from dependence on genetically modified strains. Further, due to the “open top” of our epi-illumination microscope, our method can be naturally integrated with microfluidic chips to perform large scale and automatic analysis.

Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries., Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples., Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K -means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes., Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K -means algorithm. The unsupervised K -means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within < 2 % residual error margin., Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to < 2 % residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification., (© 2024 The Authors.)

Dark field scattering microscopy can create large hyperspectral data sets that contain a wealth of information on the properties and the molecular environment of noble metal nanoparticles. For a quick screening of samples of microscopic dimensions that contain many different types of plasmonic nanostructures, we propose a multivariate analysis of data sets of thousands to several hundreds of thousands of scattering spectra. By using non-negative matrix factorization for decomposing the spectra, components are identified that represent individual plasmon resonances and relative contributions of these resonances to particular microscopic focal volumes in the mapping data sets. Using data from silver and gold nanoparticles in the presence of different molecules, including gold nanoparticle-protein agglomerates or silver nanoparticles forming aggregates in the presence of acrylamide, plasmonic properties are observed that differ from those of the original nanoparticles. For the case of acrylamide, we show that the plasmon resonances of the silver nanoparticles are ideally suited to support surface enhanced Raman scattering (SERS) and the two-photon excited process of surface enhanced hyper Raman scattering (SEHRS). Both vibrational tools give complementary information on the in situ formed polyacrylamide and the molecular composition at the nanoparticle surface.

Leptospirosis is a zoonotic infectious disease prevalent in the tropics caused by the pathogenic spirochaetes of the genus Leptospira. Dogs act as reservoir host for Leptospira species and can transmit the infection to humans especially dog owners. A study was conducted in Thrissur, Ernakulam and Palakkad districts of Kerala to find out the occurrence of leptospirosis among suspected and healthy dogs. The results of the study revealed that 4.82 per cent of the samples were found positive based on Dark Field Microscopy. District-wise occurrence of Leptospirosis by Microscopic Agglutination Test (MAT) was found to be 2.22 per cent, 4.61 per cent and 18.88 per cent in Ernakulam, Palakkad and Thrissur districts respectively. The common serovars identified were Australis (18.89 per cent), Grippotyphosa (17.39 per cent), Icterohaemorrhagiae (14.49 per cent) and Autumnalis (14.49 per cent). [ABSTRACT FROM AUTHOR]

The thermal conductivity of a magnetic fluid is expected to enhance by adding micron-sized particles with high thermal conductivity. The thermal conductivity of the magnetic fluid containing micron-sized graphite particles was investigated experimentally. The shapes of the graphite particles were sphere and flake-like. As the results of visualization experiments using the dark field microscope, the spherical graphite particles form chain-like clusters under applied magnetic field, while the fibrous structure is formed by the flake-like graphite particles, however, chain-like cluster formation cannot be clearly observed. The experiments using the transient hot-wire method showed that the thermal conductivity of magnetic fluid containing graphite particles is enhanced by applying magnetic field and by increasing the volume fraction of graphite particles. [ABSTRACT FROM AUTHOR]

A novel approach for ultrasensitive ochratoxin A (OTA) detection is reported based on dark field microscope-based single nanoparticle identification coupled with a statistical analysis method. OTA aptamers were firstly hybridized with a single-stranded DNA (DNA1) to form an identification probe (DNA1-Apt). The aptamers separate from DNA1 in the presence of OTA and are released from the identification probe. Then, another single-stranded DNA (DNA2) hybridizes with DNA1 and result in the aggregation of gold nanoparticles (AuNPs). Therefore, the presence of AuNP aggregates is the evidence of the presence of OTA, while AuNP aggregates can be easily identified together with the monomers under dark field microscopic inspection. On the other hand, by counting the aggregation rate (the number of AuNP aggregates versus the number of AuNP monomers) with a statistical analysis method, OTA could be quantitatively detected. The detection range for OTA was 0.1 pg/mL ~ 30 ng/mL and the limit of detection was 0.1 pg/mL. The proposed sensor has comparative detection performance to sensors utilizing a number of signal amplification procedures, with the additional advantages of simplicity and high efficiency. The sensor can also be adopted for other target detection simply by replacing the identification probes. [ABSTRACT FROM AUTHOR]

The present study was carried out in eleven animals that was diagnosed with leptospirosis. Observation of jaundice, myalgia, bloody diarrhea, haematuria and signs of renal failure formed the criteria for inclusion in the study. Clinico-pathological and ultrasonographic evaluation was carried out to study the changes in blood and internal organs. Confirmation of leptospirosis was done by dark field microscopy (DFM) and microscopic agglutination test (MAT). The findings were compared with control animals.

The diagnosis of Lyme disease is becoming more common in Turkey. Nonetheless, some physicians are not aware of the diagnostic principles that should be followed when faced with a suspected patient and could use tests that are not recommended, such as darkfield microscopy. Dark field microscopy is a diagnostic technique to visualize the spirochetes that cause Lyme disease; however, it is not recommended for the diagnosis of Lyme disease. One of the main limitations of dark field microscopy is its low sensitivity. Another limitation is its high false-positivity rate, as other microorganisms and cellular debris can be mistaken for spirochetes, leading to a misdiagnosis thatmay result in unnecessary treatment. Therefore, this study aimed to review the literature on the role of dark field microscopy as a diagnostic method for Lyme disease and inform physicians about recommended approaches in line with the recommendations of national or international guidelines. An electronic search of Pubmed, Scopus, and Web of Science was performed using the following medical subject headings (MeSH) search terms: Lyme borreliosis, Lyme disease, Borrelia burgdorferi , diagnosis, and microscopy. With this narrative review, we aimed to inform physicians better and improve patient care for patients with suspected Lyme disease., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Infectious Diseases and Clinical Microbiology.)

Circulating tumor cells (CTCs) are nowadays one of the most promising tumor biomarkers. It is well correlated with overall survival and progression-free survival in breast cancer, as well as in many other types of human cancer. In addition, enumeration and analysis of CTCs could be important for monitoring the response to different therapeutic agents, thus guiding the treatment of cancer patients and offering the promise of a more personalized approach. In this article, we present a new method that could be used for the automatic detection of CTC in blood, based on the microscopic appearance of unstained cells. The proposed method is based on the evaluation of image characteristics and boosting techniques. A dataset of 263 dark field microscopy images was constructed and used for our tests, containing blood spiked with three different types of tumor cells. An overall sensitivity of 92.87% and a specificity of 99.98% were obtained for the detection of CTC, performances which proved to be comparable to those obtained by human experts. [ABSTRACT FROM AUTHOR]

Abstract We revealed the magnetoelectric activity effect of vertical Bloch lines of ferrite garnets film in external electric AC field. Effect visually manifested as a broadening of the regions of their localizations. These perturbations are revealed without additional impact of the magnetic field. Magneto-optical measurements and method of optical dark field microscopy with the direct visual observations were used for the investigations. Highlights • We revealed the magnetoelectric activity effect of vertical Bloch lines of ferrite garnets film in external electric field. • Effect visually manifested as a broadening of the regions of their localizations. • These perturbations are revealed without additional impact of the magnetic field. [ABSTRACT FROM AUTHOR]

Owing to their unique physical and chemical properties like stability, non-toxic, biocompatibility and feasible to modification with various biomolecules, gold nanoparticle has become a versatile nanomaterial in the field of therapeutic, diagnostic and analytical studies. Various surface plasmon resonance based pathogen detection systems, relying on change in colour, have been proposed. However, all the approaches developed so far were designed for the detection of a single pathogen. In the present study, we have designed a new colorimetric approach based on distant-dependent properties of gold nanoparticle for the detection of multiple targets. A modified multiplex asymmetric PCR in which a universal primer amplifies the multiple targets with the same efficiency was performed. The Limit of detection (LOD) of the designed visual assay is 10 pg of Brucella and Leptospira target DNA and 100 pg of Bovine herpes virus-1 (BoHV-1) target DNA. LOD of 0.5 pg, 0.7 pg and 3.8 pg for Brucella , Leptospira and BoHV-1 respectively was obtained spectrophotometrically. A study on dark field microscopy as a qualitative supporting detection system has also been presented in this study. The designed assay has advantages over earlier reports in terms of multiple organisms detection, specificity and sensitivity of the test. [ABSTRACT FROM AUTHOR]

Summary: We use near‐infrared dark‐field optical microscopy to probe isothermal time variation of the volume fraction of naturally‐occurring, subsurface microstructures in PG 64‐22 asphalt binders at temperature T = 30 ∘ C, following a rapid heating (cooling) increment | Δ T | = 20 ∘ C from initial temperature T 0 = 10 ∘ C ( 50 ∘ C ). We compare these microstructure variations with isothermal time variations of the magnitude | G 30 ∗ ( t ) | of the bulk complex shear modulus measured for identical sample conditions with a Dynamic Shear Rheometer. The main findings are: (1) Microstructure volume fraction (inferred from intensity I ( t ) of near‐infrared optical scatter) and | G ∗ ( t ) | both continue to change appreciably long after measurable changes of binder temperature cease. Moreover, delayed time variations in I ( t ) and | G ∗ ( t ) | (2) correlate closely with each other; (3) evolve on three distinct time scales – several minutes, ∼1 h, >1 day; (4) depend on binder aging; (5) are more pronounced after a cooling step ( Δ T = − 20 ∘ C) than after a heating step ( Δ T = + 20 ∘ C); and (6) account for hysteresis in I ( t ) and | G ∗ ( t ) | curves observed during heating–cooling cycles. [ABSTRACT FROM AUTHOR]

This paper reported a new method to observe the catalytic progress of the natural horseradish peroxidase (HRP) in-situ on single gold nanoparticles (GNPs) by the combination of dark field imaging and plasmonic resonance scattering spectra. The produced single HRP-GNP exhibited localized catalytic property toward H 2 O 2 -Diaminobenzidine (DAB), which could be used to detect the concentration of H 2 O 2 in micro/nanospace. The linear range for H 2 O 2 sensing was from 0.01 μM to 5 μM with a detection limit of 10 nM. The new design strategy could be applied for a broader bioanalysis situation by substituting the HRP with other specified biocatalyst. [ABSTRACT FROM AUTHOR]

Enumeration techniques were compared for quantification of the South African isolate of Cryptophlebia leucotreta granulovirus (CrleGV-SA), used as a biopesticide to control false codling moth ( Thaumatotibia leucotreta ), an insect pest of various fruits and nuts, including citrus. The routine enumeration method for CrleGV-SA virus particles in experimentation and production of CrleGV-SA biopesticides is dark field microscopy. This method was compared with spectrophotometry, scanning electron microscopy (SEM) and real time quantitative polymerase chain reaction (qPCR). The purpose was to develop an accurate and reliable routine enumeration method for CrleGV-SA occlusion bodies (OBs) and to validate the use of dark field microscopy. Purified and semi-purified CrleGV-SA viral stocks were used. Spectrophotometry was not a suitable or accurate enumeration method. Dark field microscopy and SEM were accurate and statistically comparable (p = 0.064), validating the use of dark field microscopy as an enumeration method for granulovirus (GV). However, SEM has superior resolution and the advantage of easily distinguishing virus particles from debris in semi-purified viral stock preparations. A quantitative PCR technique has been developed based on use of specific oligonucleotide primers for the granulin gene. This has the advantage of not being affected by contamination with non-biological debris or biological material, which impact on the other methods. [ABSTRACT FROM AUTHOR]

A highly sensitive homogeneous method for DNA detection has been developed. The system relies on two kinds of gold nanorod (AuNR) probes with complementary DNA sequences to the target DNA. In the presence of the target DNA, two kinds of AuNR probes are assembling into dimers or small aggregates. The target-induced AuNR aggregate has higher scattering intensity than that of a single AuNR because of the plasmonic coupling effect. Dark field microscopy was utilized to image the single particle and measure its scattering intensity. We wrote our own Matlab code and used it to extract the scattering signal of all particles. Difference in distribution of scattering intensity between the single AuNR and its aggregate provides a quantitative basis for the detection of target DNA. A linear dynamic range spanning from 0.1 pM to 1 nM and a detection limit of ~ 30 fM were achieved for the detection of DNA in serum sample. [ABSTRACT FROM AUTHOR]