Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Thermal-tagging photoacoustic remote sensing flowmetry.

Ultrasound coupling is one of the critical challenges for traditional photoacoustic (or optoacoustic) microscopy (PAM) techniques transferred to the clinical examination of chronic wounds and open tissues. A promising alternative potential solution for breaking the limitation of ultrasound coupling in PAM is photoacoustic remote sensing (PARS), which implements all-optical non-interferometric photoacoustic measurements. Functional imaging of PARS microscopy was demonstrated from the aspects of histopathology and oxygen metabolism, while its performance in hemodynamic quantification remains unexplored. In this Letter, we present an all-optical thermal-tagging flowmetry approach for PARS microscopy and demonstrate it with comprehensive mathematical modeling and ex vivo and in vivo experimental validations. Experimental results demonstrated that the detectable range of the blood flow rate was from 0 to 12 mm/s with a high accuracy (measurement error:±1.2%) at 10-kHz laser pulse repetition rate. The proposed all-optical thermal-tagging flowmetry offers an effective alternative approach for PARS microscopy realizing non-contact dye-free hemodynamic imaging.

Bichromatic tetraphasic full-field optical coherence microscopy.

Significance: Full-field optical coherence microscopy (FF-OCM) is a prevalent technique for backscattering and phase imaging with epi-detection. Traditional methods have two limitations: suboptimal utilization of functional information about the sample and complicated optical design with several moving parts for phase contrast. Aim: We report an OCM setup capable of generating dynamic intensity, phase, and pseudo-spectroscopic contrast with single-shot full-field video-rate imaging called bichromatic tetraphasic (BiTe) full-field OCM with no moving parts. Approach: BiTe OCM resourcefully uses the phase-shifting properties of anti-reflection (AR) coatings outside the rated bandwidths to create four unique phase shifts, which are detected with two emission filters for spectroscopic contrast. Results: BiTe OCM overcomes the disadvantages of previous FF-OCM setup techniques by capturing both the intensity and phase profiles without any artifacts or speckle noise for imaging scattering samples in three-dimensional (3D). BiTe OCM also utilizes the raw data effectively to generate three complementary contrasts: intensity, phase, and color. We demonstrate BiTe OCM to observe cellular dynamics, image live, and moving micro-animals in 3D, capture the spectroscopic hemodynamics of scattering tissues along with dynamic intensity and phase profiles, and image the microstructure of fall foliage with two different colors. Conclusions: BiTe OCM can maximize the information efficiency of FF-OCM while maintaining overall simplicity in design for quantitative, dynamic, and spectroscopic characterization of biological samples.

Multi-scale fractal Fourier Ptychographic microscopy to assess the dose-dependent impact of copper pollution on living diatoms.

Accumulation of bioavailable heavy metals in aquatic environment poses a serious threat to marine communities and human health due to possible trophic transfers through the food chain of toxic, non-degradable, exogenous pollutants. Copper (Cu) is one of the most spread heavy metals in water, and can severely affect primary producers at high doses. Here we show a novel imaging test to assay the dose-dependent effects of Cu on live microalgae identifying stress conditions when they are still capable of sustaining a positive growth. The method relies on Fourier Ptychographic Microscopy (FPM), capable to image large field of view in label-free phase-contrast mode attaining submicron lateral resolution. We uniquely combine FPM with a new multi-scale analysis method based on fractal geometry. The system is able to provide ensemble measurements of thousands of diatoms in the liquid sample simultaneously, while ensuring at same time single-cell imaging and analysis for each diatom. Through new image descriptors, we demonstrate that fractal analysis is suitable for handling the complexity and informative power of such multiscale FPM modality. We successfully tested this new approach by measuring how different concentrations of Cu impact on Skeletonema pseudocostatum diatom populations isolated from the Sarno River mouth.

Molecular tools are crucial for malaria elimination.

The eradication of Plasmodium parasites, responsible for malaria, is a daunting global public health task. It requires a comprehensive approach that addresses symptomatic, asymptomatic, and submicroscopic cases. Overcoming this challenge relies on harnessing the power of molecular diagnostic tools, as traditional methods like microscopy and rapid diagnostic tests fall short in detecting low parasitaemia, contributing to the persistence of malaria transmission. By precisely identifying patients of all types and effectively characterizing malaria parasites, molecular tools may emerge as indispensable allies in the pursuit of malaria elimination. Furthermore, molecular tools can also provide valuable insights into parasite diversity, drug resistance patterns, and transmission dynamics, aiding in the implementation of targeted interventions and surveillance strategies. In this review, we explore the significance of molecular tools in the pursuit of malaria elimination, shedding light on their key contributions and potential impact on public health.

Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting.

BACKGROUND: Infections caused by soil-transmitted helminths (STHs) are the most prevalent neglected tropical diseases and result in a major disease burden in low- and middle-income countries, especially in school-aged children. Improved diagnostic methods, especially for light intensity infections, are needed for efficient, control and elimination of STHs as a public health problem, as well as STH management. Image-based artificial intelligence (AI) has shown promise for STH detection in digitized stool samples. However, the diagnostic accuracy of AI-based analysis of entire microscope slides, so called whole-slide images (WSI), has previously not been evaluated on a sample-level in primary healthcare settings in STH endemic countries. METHODOLOGY/PRINCIPAL FINDINGS: Stool samples (n = 1,335) were collected during 2020 from children attending primary schools in Kwale County, Kenya, prepared according to the Kato-Katz method at a local primary healthcare laboratory and digitized with a portable whole-slide microscopy scanner and uploaded via mobile networks to a cloud environment. The digital samples of adequate quality (n = 1,180) were split into a training (n = 388) and test set (n = 792) and a deep-learning system (DLS) developed for detection of STHs. The DLS findings were compared with expert manual microscopy and additional visual assessment of the digital samples in slides with discordant results between the methods. Manual microscopy detected 15 (1.9%) Ascaris lumbricoides, 172 (21.7%) Tricuris trichiura and 140 (17.7%) hookworm (Ancylostoma duodenale or Necator americanus) infections in the test set. Importantly, more than 90% of all STH positive cases represented light intensity infections. With manual microscopy as the reference standard, the sensitivity of the DLS as the index test for detection of A. lumbricoides, T. trichiura and hookworm was 80%, 92% and 76%, respectively. The corresponding specificity was 98%, 90% and 95%. Notably, in 79 samples (10%) classified as negative by manual microscopy for a specific species, STH eggs were detected by the DLS and confirmed correct by visual inspection of the digital samples. CONCLUSIONS/SIGNIFICANCE: Analysis of digitally scanned stool samples with the DLS provided high diagnostic accuracy for detection of STHs. Importantly, a substantial number of light intensity infections were missed by manual microscopy but detected by the DLS. Thus, analysis of WSIs with image-based AI may provide a future tool for improved detection of STHs in a primary healthcare setting, which in turn could facilitate monitoring and evaluation of control programs.

Functional Plasmonic Microscope: Characterizing the Metabolic Activity of Single Cells via Sub-nm Membrane Fluctuations.

Metabolic abnormalities are at the center of many diseases, and the capability to film and quantify the metabolic activities of a single cell is important for understanding the heterogeneities in these abnormalities. In this paper, a functional plasmonic microscope (FPM) is used to image and measure metabolic activities without fluorescent labels at a single-cell level. The FPM can accurately image and quantify the subnanometer membrane fluctuations with a spatial resolution of 0.5 µm in real time. These active cell membrane fluctuations are caused by metabolic activities across the cell membrane. A three-dimensional (3D) morphology of the bottom cell membrane was imaged and reconstructed with FPM to illustrate the capability of the microscope for cell membrane characterization. Then, the subnanometer cell membrane fluctuations of single cells were imaged and quantified with the FPM using HeLa cells. Cell metabolic heterogeneity is analyzed based on membrane fluctuations of each individual cell that is exposed to similar environmental conditions. In addition, we demonstrated that the FPM could be used to evaluate the therapeutic responses of metabolic inhibitors (glycolysis pathway inhibitor STF 31) on a single-cell level. The result showed that the metabolic activities significantly decrease over time, but the nature of this response varies, depicting cell heterogeneity. A low-concentration dose showed a reduced fluctuation frequency with consistent fluctuation amplitudes, while the high-concentration dose showcased a decreasing trend in both cases. These results have demonstrated the capabilities of the functional plasmonic microscope to measure and quantify metabolic activities for drug discovery.

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology.

Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

Dynamic Mode Decomposition of Multiphoton and Stimulated Emission Depletion Microscopy Data for Analysis of Fluorescent Probes in Cellular Membranes.

An analysis of the membrane organization and intracellular trafficking of lipids often relies on multiphoton (MP) and super-resolution microscopy of fluorescent lipid probes. A disadvantage of particularly intrinsically fluorescent lipid probes, such as the cholesterol and ergosterol analogue, dehydroergosterol (DHE), is their low MP absorption cross-section, resulting in a low signal-to-noise ratio (SNR) in live-cell imaging. Stimulated emission depletion (STED) microscopy of membrane probes like Nile Red enables one to resolve membrane features beyond the diffraction limit but exposes the sample to a lot of excitation light and suffers from a low SNR and photobleaching. Here, dynamic mode decomposition (DMD) and its variant, higher-order DMD (HoDMD), are applied to efficiently reconstruct and denoise the MP and STED microscopy data of lipid probes, allowing for an improved visualization of the membranes in cells. HoDMD also allows us to decompose and reconstruct two-photon polarimetry images of TopFluor-cholesterol in model and cellular membranes. Finally, DMD is shown to not only reconstruct and denoise 3D-STED image stacks of Nile Red-labeled cells but also to predict unseen image frames, thereby allowing for interpolation images along the optical axis. This important feature of DMD can be used to reduce the number of image acquisitions, thereby minimizing the light exposure of biological samples without compromising image quality. Thus, DMD as a computational tool enables gentler live-cell imaging of fluorescent probes in cellular membranes by MP and STED microscopy.

The InSciEdRS View: A User-Friendly and Accessible Microscope with Easy-to-Follow Companion Curricula.

Microscopes are essential for research and education in science. Unlike computers and online learning tools, however, microscopes are not currently a fixed element in K-12 classrooms, due to steep cost, needless complexity, and often requiring a prohibitive level of staff training to effectively deploy. In a collaboration with Area 10 Labs, Integrated Science Education Outreach (InSciEd Out) developed a state-of-the-art alternative microscope, the InSciEdRS View, to reduce the financial barrier, prohibitive per-student cost, unnecessary complexity, and extensive staff training. Utilizing a 1080p camera and a lunchbox-style case, this Wi-Fi- and USB-connectable microscope comes with all necessary components for visualization of microscopic specimens (10 × -50 × magnification). While built to handle the rigors of classroom use, its imaging capability and battery-operation can make it flexible for a laboratory or fieldwork as well. We further highlight here K-12 curricula that we have developed using larval zebrafish to enable teachers, science outreach leaders, and parents to support active hands-on science observations. The InSciEdRS View microscope and the InSciEd Out curricula are readily scalable, translatable, and accessible for traditional and neurodiverse students and integrating these in various settings can be an efficient way to achieve better outcomes in science education.

Label-free Brillouin endo-microscopy for the quantitative 3D imaging of sub-micrometre biology.

This report presents an optical fibre-based endo-microscopic imaging tool that simultaneously measures the topographic profile and 3D viscoelastic properties of biological specimens through the phenomenon of time-resolved Brillouin scattering. This uses the intrinsic viscoelasticity of the specimen as a contrast mechanism without fluorescent tags or photoacoustic contrast mechanisms. We demonstrate 2 µm lateral resolution and 320 nm axial resolution for the 3D imaging of biological cells and Caenorhabditis elegans larvae. This has enabled the first ever 3D stiffness imaging and characterisation of the C. elegans larva cuticle in-situ. A label-free, subcellular resolution, and endoscopic compatible technique that reveals structural biologically-relevant material properties of tissue could pave the way toward in-vivo elasticity-based diagnostics down to the single cell level.

Clinical Characteristics and Pathogen Spectrum of Male Genital Fungal Infections in Nanchang Area, South China.

The cutaneous fungal infections in male genitalia are relatively rare, and often present with various atypical clinical symptoms. It was mainly reported in a small number of case reports, while data with large number of patients were rarely reported. In this study, we reported 79 male patients with cutaneous fungal infections on scrotum or penis. The fungal infections were confirmed by microscopic examination directly and fungus culture. Clinical characteristics and predisposing factors were also collected. Of these 79 patients, 72 has lesions on scrotum, 5 on penis and 2 on both scrotum and penis. Trichophyton (T.) rubrum is the most common pathogen, found in 50 (67.6%) patients, which presented diverse clinical manifestation such as majorly erythematous, dry diffused scaly lesions without a clear border, slightly powdery and scutular scalings. Candida (C.) albicans is the secondly common pathogen, found in 21 (28.4%) patients, which also presented diverse lesions such as erythematous with dry whitish scaly lesions and erythematous erosion. The predisposing factors mainly included concomitant fungal infections on sites other than genitalia, especially inguinal region (tinea cruris), application of corticosteroid and high moisture. In conclusion, cutaneous fungal infections in male genitalia could be caused by different fungi, showed atypical or mild clinical appearances in most cases and might be a fungus reservoir, emphasizing the necessity to timely perform the fungi examinations and corresponding therapy.

Light-Sheet Imaging to Reveal Cardiac Structure in Rodent Hearts.

Light-sheet microscopy (LSM) plays a pivotal role in comprehending the intricate three-dimensional (3D) structure of the heart, providing crucial insights into fundamental cardiac physiology and pathologic responses. We hereby delve into the development and implementation of the LSM technique to elucidate the micro-architecture of the heart in mouse models. The methodology integrates a customized LSM system with tissue clearing techniques, mitigating light scattering within cardiac tissues for volumetric imaging. The combination of conventional LSM with image stitching and multiview deconvolution approaches allows for the capture of the entire heart. To address the inherent trade-off between axial resolution and field of view (FOV), we further introduce an axially swept light-sheet microscopy (ASLM) method to minimize out-of-focus light and uniformly illuminate the heart across the propagation direction. In the meanwhile, tissue clearing methods such as iDISCO enhance light penetration, facilitating the visualization of deep structures and ensuring a comprehensive examination of the myocardium throughout the entire heart. The combination of the proposed LSM and tissue clearing methods presents a promising platform for researchers in resolving cardiac structures in rodent hearts, holding great potential for the understanding of cardiac morphogenesis and remodeling.

Two-Photon Microscopy for the Study of Tendons.

Two-photon microscopy has emerged as a potent tool for evaluating deep tissue cells and characterizing the alignment of the extracellular matrix (ECM) in various biological systems. This technique relies on nonlinear light-matter interactions to detect two distinct signals: the second harmonic generated (SHG) diffusion signal, which facilitates the visualization of collagen fibers and their orientation, and the near-infrared excitation signal for imaging ultraviolet excited autofluorescence. SHG imaging proves especially effective in visualizing collagen fibers due to the non-centrosymmetric crystalline structure of fibrillar collagen I. Given that tendons are matrix-rich tissues with a limited number of cells, their high collagen content makes them ideal candidates for analysis using two-photon microscopy. Consequently, two-photon microscopy offers a valuable means to analyze and characterize collagen abnormalities in tendons. Its application extends to studying tendon development, injuries, healing, and aging, enabling the comprehensive characterization of tendon cells and their interactions with the ECM under various conditions using two-photon microscopy tools. This protocol outlines the use of two-photon microscopy in tendon biology and presents an adapted methodology to achieve effective imaging and characterization of tendon cells during development and after injury. The method allows the utilization of thin microscopic sections to create a comprehensive image of the ECM within tendons and the cells that interact with this matrix. Most notably, the article showcases a technique to generate 3D images using two-photon microscopy in animal models.

Tools and methods for high-throughput single-cell imaging with the mother machine.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Microscopy detection and molecular characterisation of Giardia duodenalis infection in outpatients seeking medical care in Egypt.

Introduction: Giardiosis remains one of the most prevalent enteric parasitic infections globally. Earlier molecular-based studies conducted in Egypt have primarily focused on paediatric clinical populations and most were based on single genotyping markers. As a result, there is limited information on the frequency and genetic diversity of G. duodenalis infections in individuals of all age groups. Methods: Individual stool samples (n = 460) from outpatients seeking medical care were collected during January-December 2021 in Kafr El-Sheikh governorate, northern Egypt. Initial screening for the presence of G. duodenalis was conducted by coprological examination. Microscopy-positive samples were further confirmed by real-time PCR. A multilocus sequence typing approach targeted amplification of the glutamate dehydrogenase (gdh), beta-giardin (bg), and triose phosphate isomerase (tpi) genes was used for genotyping purposes. A standardised epidemiological questionnaire was used to gather basic sociodemographic and clinical features of the recruited patients. Results: Giardia duodenalis cysts were observed in 5.4% (25/460, 95% CI: 3.6-7.9) of the stool samples examined by conventional microscopy. The infection was more frequent in children under the age of 10 years and in individuals presenting with diarrhoea but without reaching statistical significance. Stool samples collected during the winter period were more likely to harbour G. duodenalis. All 25 microscopy-positive samples were confirmed by real-time PCR, but genotyping data was only available for 56.0% (14/25) of the isolates. Sequence analyses revealed the presence of assemblages A (78.6%, 11/14) and B (21.4%, 3/14). All assemblage A isolates were identified as sub-assemblage AII, whereas the three assemblage B sequences belonged to the sub-assemblage BIII. Patients with giardiosis presenting with diarrhoea were more frequently infected by the assemblage A of the parasite. Conclusion: This is one of the largest epidemiological studies evaluating G. duodenalis infection in individuals of all age groups in Egypt. Our molecular data suggest that G. duodenalis infections in the surveyed population are primarily of anthropic origin. However, because assemblages A and B are zoonotic, some of the infections identified can have an animal origin. Additional investigations targeting animal (domestic and free-living) and environmental (water) samples are warranted to better understand the epidemiology of giardiosis in Egypt.

[Preliminary Study on the Identification of Aerobic Vaginitis by Artificial Intelligence Analysis System]. / 人工智能分析系统对需氧菌性阴道炎的判读方法初探.

Objective: To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV). Methods: In this study, the vaginal secretion samples of 3769 patients receiving treatment at the Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University between January 2020 and December 2021 were selected. Using the results of manual microscopy as the control, we developed the linear kernel SVM algorithm, an artificial intelligence (AI) automated analysis software, with Python Scikit-learn script. The AI automated analysis software could identify leucocytes with toxic appearance and parabasal epitheliocytes (PBC). The bacterial grading parameters were reset using standard strains of lactobacillus and AV common isolates. The receiver operating characteristic (ROC) curve analysis was used to determine the cut-off value of AV evaluation results for different scoring items were obtained by using the results of manual microscopy as the control. Then, the parameters of automatic AV identification were determined and the automatic AV analysis scoring method was initially established. Results: A total of 3769 vaginal secretion samples were collected. The AI automated analysis system incorporated five parameters and each parameter incorporated three severity scoring levels. We selected 1.5 µm as the cut-off value for the diameter between Lactobacillus and common AV bacterial isolates. The automated identification parameter of Lactobacillus was the ratio of bacteria ≥1.5 µm to those <1.5 µm. The cut-off scores were 2.5 and 0.5, In the parameter of white blood cells (WBC), the cut-off value of the absolute number of WBC was 103 µL－1 and the cut-off value of WBC-to-epithelial cell ratio was 10. The automated identification parameter of toxic WBC was the ratio of toxic WBC toWBC and the cut-off values were 1% and 15%. The parameter of background flora was bacteria<1.5 µm and the cut-off values were 5×103 µL－1 and 3×104 µL－1. The parameter of the parabasal epitheliocytes was the ratio of PBC to epithelial cells and the cut-off values were 1% and 10%. The agreement rate between the results of automated microscopy and those of manual microscopy was 92.5%. Out of 200 samples, automated microscopy and manual microscopy produced consistent scores for 185 samples, while the results for 15 samples were inconsistent. Conclusion: We developed an AI recognition software for AV and established an automated vaginal secretion microscopy scoring system for AV. There was good overall concordance between automated microscopy and manual microscopy. The AI identification software for AV can complete clinical lab examination with rather high objectivity, sensitivity, and efficiency, markedly reducing the workload of manual microscopy.

Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks.

Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantification. However, this process's time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fluorescent images, presents challenges. In response, machine learning algorithms have been integrated into microscopy, automating tasks and constructing predictive models from vast datasets. These models adeptly learn representations for object detection, image segmentation, and target classification. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classification-something hindered when fluorescent markers are used. The aim is to introduce a model proficient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantification of cell numbers. Employing a CNN machine learning algorithm, a classification model predicting cellular lineage achieved a remarkable accuracy of 93%, with ROC curve results nearing 1.0, showcasing robust performance. However, some lineages, namely SH-SY5Y (78%), HUH7_mayv (85%), and A549 (88%), exhibited slightly lower accuracies. These outcomes not only underscore the model's quality but also emphasize CNNs' potential in addressing the inherent complexities of microscopic images.

Label-Free Techniques for Probing Biomolecular Condensates.

Biomolecular condensates play important roles in a wide array of fundamental biological processes, such as cellular compartmentalization, cellular regulation, and other biochemical reactions. Since their discovery and first observations, an extensive and expansive library of tools has been developed to investigate various aspects and properties, encompassing structural and compositional information, material properties, and their evolution throughout the life cycle from formation to eventual dissolution. This Review presents an overview of the expanded set of tools and methods that researchers use to probe the properties of biomolecular condensates across diverse scales of length, concentration, stiffness, and time. In particular, we review recent years' exciting development of label-free techniques and methodologies. We broadly organize the set of tools into 3 categories: (1) imaging-based techniques, such as transmitted-light microscopy (TLM) and Brillouin microscopy (BM), (2) force spectroscopy techniques, such as atomic force microscopy (AFM) and the optical tweezer (OT), and (3) microfluidic platforms and emerging technologies. We point out the tools' key opportunities, challenges, and future perspectives and analyze their correlative potential as well as compatibility with other techniques. Additionally, we review emerging techniques, namely, differential dynamic microscopy (DDM) and interferometric scattering microscopy (iSCAT), that have huge potential for future applications in studying biomolecular condensates. Finally, we highlight how some of these techniques can be translated for diagnostics and therapy purposes. We hope this Review serves as a useful guide for new researchers in this field and aids in advancing the development of new biophysical tools to study biomolecular condensates.