UNASSIGNED: 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.
UNASSIGNED: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

Gold nanoparticles (AuNPs) have been widely applied to molecular sensors due to their optical properties. We previously reported a molecular detection by observing the scattered light of AuNPs at a single nanoparticle level using dark field microscopy (DFM). Recently, a molecular detection method using digital immunoassay has been reported, taking advantage of the characteristics of DFM. However, the digital immunoassays reported so far have been performed by a conventional sandwich immunoassay, which is difficult to apply to the detection of small molecules. In this study, with the aim of small molecule detection, we developed a digital immunoassay method using an anti-immunocomplex antibody that specifically recognizes immunocomplexes of small molecules with antibodies. The number of AuNPs modified with anti-immunocomplex antibody bound to immunocomplex of estradiol and anti-estradiol antibody was counted at a single nanoparticle level using DFM. We demonstrated for the first time that estradiol molecule can be detected by digital immunoassay using DFM and an anti-immunocomplex antibody with a detection sensitivity of 1 pg/mL.

The kiwi plant is dioecious, and its sex is generally identified from flower morphology at blossoming, which takes several years. It is quite necessary but challenging to on-spot identify the plant sex in juvenile stage. Here the target DNA was obtained by screening the Friendly boy (FrBy) gene which is sex-related for different kiwi plant species. Its complementary sequence was divided into two parts as primer DNA and further attached to different gold nanoparticles (GNPs). The connection between target DNA and primer DNA will promote the formation of plasmonic dimers. Dark field microscopy (DFM) can distinguish particles in different aggregation states. Various conditions were optimized based on the standard of increasing the proportion of dimers while reducing that of large aggregates. Furthermore, two Raman reporters (RR) are separately labeled on the nanoprobes, and the plasmonic dimers lead to a tremendous Raman enhancement of two reporters located at the dimer nanogap. Double-blind tests proved the feasibility of this method on the actual samples of kiwi plant leaves. Our SERS method is sensitive, specific, and reliable for rapid sex identification analysis at the kiwi seeding stage, with great promise for decision-making in field management.

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.

The increase in outbreaks of emerging and re-emerging bacterial infections over the last few decades calls for their rapid detection and treatment. Surface-enhanced Raman spectroscopy (SERS) is a technique that can be applied to develop fast screening systems for bacterial presence in biological samples. Optimizing the capping agents in nanoparticle synthesis is important because capping agents are responsible for controlling the morphological features and chemical properties of the nanoparticles that are essential for SERS. To the best of our knowledge, this paper is the first to study the application of gold nanoparticles capped with thioglucose and polyvinylpyrrolidone (PVP) in SERS detection of bacteria as an alternative to the citrate-capped gold nanoparticles that are often used in SERS detection of bacteria. Three different species of bacteria were used in this study: Cutibacterium acnes, Escherichia coli and Staphylococcus aureus (methicillin-sensitive and methicillin-resistant). This study demonstrates that the thioglucose, citrate both show good contribution in bacterial species identification and the thioglucose shows the best among the three capping agents in two types of S. aureus identification. Moreover, although PVP showed high Raman peaks in the SERS spectrum for each type of bacteria, it showed least contribution in identifying species and strains due to its low efficacy in producing responses from different nucleic acid components in the bacteria cells.

Herein, a facile and sensitive dual mode sensing strategy for amantadine (AMD) level evaluation by coupling the plasmonic Au nanorod (NR) and supramolecular SH-cyclodextrin (CD) through strong Au-S bond is proposed. Methylene blue (MB) molecules can be inserted into the cavity of CD molecules through the hydrophobic interaction, which would cause the plasmon resonance energy transfer (PRET) process as well as electrochemical signal response due to the spectrum overlap between Au NR and MB molecules and the electrochemical conversion activity of MB molecules. Subsequently, AMD would induce the replacement of MB molecules because of the stronger interaction with CD, resulting the recovery of scattering intensity of Au NR and decrease of the electrooxidation current of MB. On one hand, the increase of Au NR scattering intensity is linearly proportional to AMD with the concentration from 0.4 to 3.0 μM, and reaches a limit of detection (LOD) of 0.28 μM. On the other hand, electrochemical measurement method enlarged the detection range of AMD. The variation of electrochemical oxidation peak current of MB is linearly proportional to the logarithm value of AMD concentration from 2.5 to 375.0 μM, with LOD of 1.9 μM. Subsequently, the proposed dual mode response sensing strategy was successfully employed for the detection of AMD in human serum samples with great selectivity and sensitivity, with a recovery percentage ranged from 92.6 to 112.0%. Overall, this Au NR@SH-CD-MB nanoparticle based single particle plasmonic and electrochemical dual mode sensing method provides great potential in the field of clinical drug detection or metabolic process investigation in the future.

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. Graphical abstract The schematic of the AuNP aggregation for OTA detection. The OTA aptamers were competitively banded by OTA and induced form AuNP aggregation after adding DNA2 and AuNPs2. Subsequently, AuNPs were detected under dark field microscope and statistical analysis.

T4 polynucleotide kinase (T4 PNK) plays an essential role in DNA phosphorylation during the DNA repair process. Therefore, the sensitive, selective and convenient detection of T4 PNK activity is of great significance. In this work, we proposed a sensitive non-amplification strategy for the sensing of T4 PNK activity via dark field microscope (DFM) based on magnetic bead (MB)-gold nanoparticle (AuNP) hybrids probe, MB-dsDNA-AuNP (MDA). In the presence of T4 PNK, the 5\'-OH termini of DNA are phosphorylated and cleaved into oligonucleotides by lambda exonuclease (λexo), resulting in the destruction of the MDA probe and the separation of AuNP from the MB. Through automatic counting of AuNPs from DFM images, T4 PNK activity can be quantitatively measured. This strategy revealed a limit of detection (LOD) as low as 0.0058 U/mL and exhibited a dynamic range from 0.01 to 1 U/mL. The strategy presents an excellent ability to discriminate T4 PNK from the other proteins and enzymes. Notably, this strategy was applied to screen the T4 PNK inhibitors and test the activity in cell lysates, showing great potential for the discovery of new anticancer drugs and other related research field.

Development of sensitive methods for the determination of E. coli bacteria contamination in water distribution systems is of paramount importance to ensure the microbial safety of drinking water. This work presents a new sensing platform enabling the fast detection of bacteria in field samples by using specific antibodies as the biorecognition element and dark field microscopy as the detection technique. The development of the sensing platform was performed using non-pathogenic bacteria, with the E. coli DH5α strain as the target, and Bacillus sp. 9727 as the negative control. The identification of the captured bacteria was made by analyzing the dark field microscopy images and screening the detected objects by using object circularity and size parameters. Specificity tests revealed the low unspecific attachment of either E. coli over human serum albumin antibodies (negative control for antibody specificity) and of Bacillus sp. over E. coli antibodies. The system performance was tested using field samples, collected from a wastewater treatment plant, and compared with two quantification techniques (i.e., Colilert-18 test and quantitative polymerase chain reaction (qPCR)). The results showed comparable quantification capability. Nevertheless, the present method has the advantage of being faster, is easily adaptable to in-field analysis, and can potentially be extended to the detection of other bacterial strains.