Cell Image Analysis Research Articles

Development of a microfluidic system with a bearing micropump for applying fluid shear force in an intervertebral disc degeneration model involving mechanical stimulation

Background: The intervertebral disc (IVD) functions as a shock absorber with viscoelastic tissues, including the gelatinous nucleus pulposus and the collagenous annulus fibrosus (AF). External mechanical loading influences IVD homeostasis but can cause damage and inflammatory reactions, contributing to disc degeneration. There is a lack of in vitro platforms for modeling IVD disease with mechanical stimulation.Methods: This study aimed to create a mechanical stimulation-based model by subjecting AF cells to shear stress using a micropump system. A micropump platform was used to measure pulsation, flow rate, and shear stress. AF cells were exposed to fluid shear stress of 0.5 and 1 dyne/cm², and morphological changes, cytotoxicity, and cell viability were analyzed through a live/dead assay. Perimeter, area, diameter, and elongation were assessed using live cell imaging and imaging analysis software.Results: The micropump platform exhibited optimal rotor characteristics for uniform pulsation and shear stress. Fluid shear stress at 0.5 dyne/cm² showed no significant difference from the control group, while 1 dyne/cm² reduced cell adhesion and survival. Comparing the 0.5 dyne/cm² shear stress group and interleukin-1β group to the control, significant decreases in perimeter, area, and diameter were observed.Conclusion: The study successfully developed a micro-peristaltic pump platform for applying fluid shear stress to cells, identifying an optimal rotor for uniform stress application. The platform effectively modeled IVD disease, revealing reduced cell adhesion and survival under specific shear stress conditions. This platform has potential promise for discovering biomarkers and exploring cellular responsiveness to external stimuli.

B-014 Performance evaluation of the automated cell image analyzer DI-60 for leukocyte differential count

Abstract Background The Sysmex DI-60 system (Sysmex, kobe, Japan) is an automated cell image analyzer that captures cell images and analyzes them. The aim of this study was to evaluate the performance of DI-60 for leukocyte differential count in comparison with manual differential count or XN-20. Methods A total of 205 samples were analyzed. The agreement between DI-60 pre-classification and post-verification by medical technicians was determined. The correlation and ability to identify clinically important abnormal cells of DI-60 post-verification were evaluated. Results The overall agreement of DI-60 pre-classification was 84.7% (Table 1). The correlation between DI-60 post-verification and manual differential counts were exellent (r2 > 0.85) for neutrophil, lymphocyte, monocyte and eosinophil, but was very low for basophil (r2 = 0.3759). The correlation between DI-60 post-verification and leukocyte differential counts by XN-20 were exellent (r2 > 0.90) for neutrophil, lymphocyte and eosinophil. However, monocyte and basophil showed a relatively low correlation of r2 = 0.6343 and r2 = 0.3118, respectively but was very low for basophil (r2 0.3759). The ability of DI-60 post-verification to identify clinically important abnormal cells including blast, promyelocyte, myelocyte, metamyelocyte and NRBC demonstrated excellent efficiency (80.8 to 96.3%) except for NRBC (51.0%). Overall sensitivity was 63.7-88.2%, and specificity was 84.3-95.5% excluding NRBC (43.7%). Conclusions DI-60 showed excellent pre-classification and generally high correlation compared to manual leukocyte differential counts. Although additional verification with PBS review by experienced medical technician may still be required, the performance DI-60 makes it an efficient screening tool in clinical laboratories.

Oogenesis and Spermatogenesis of the Triploid Black Tiger Shrimp, Penaeus monodon

Triploidy (3n) induction of Penaeus monodon was performed separately by Australian and Thai groups of researchers 14 years ago, incidentally at the same period. The Australian group employed the chemical induction method, while the Thai group the cold shock one, and both groups obtained 3n P. monodon in their attempts. The success has led to several studies on the physiology of the 3n P. monodon, including growth, survival and reproductive functions. Both groups reported sterility of the 3n shrimp. The Australian group reported defected oogenesis and spermatogenesis of the 3n shrimp examined at subadult stage. Since the evaluation of the reproductive functions should be performed at the adult stage of the animals, we, therefore, performed in-depth studies on oogenesis and spermatogenesis of adult 3n P. monodon induced by cold shock. The studies include gross observation of the ovary, light (LM) and transmission electron (TEM) microscopy and image analysis of sex cells. The ovary of the 3n P. monodon females could not develop to full-maturation and their gonadosomatic index (GSI) was significantly lower than that of the 2n shrimp. Oogenesis of the 3n females proceeded from oogonia to cortical rod oocytes, but with significantly lower percentage than that of the 2n shrimp. The GSI of the 3n males was significantly lower than that of the 2n shrimp. Their spermatogenesis, however, proceeded normally from spermatogonia to spermatozoa, but with low density of spermatozoa in the seminiferous tubule and vas deferens, and none in the terminal ampoule. The average nuclear area of the 3n spermatozoa was significantly lower than that of the 2n shrimp as well. Altogether, these results suggest that 3n P. monodon induced by cold shock had defect in both oogenesis and spermatogenesis, probably more in quantity than in quality. HIGHLIGHTS Oogenesis and spermatogenesis of the triploid black tiger shrimp, monodon, induced by cold shock were studied. The study found abnormalities in the spermatogenesis, but not oogenesis, of the triploid P. monodon induced by cold shock. It may be concluded that triploidy condition of P. monodon induced by cold shock affects meiotic, but not mitotic activity of the sex cells. GRAPHICAL ABSTRACT

Determination of adipogenesis stages of human umbilical cord-derived mesenchymal stem cells using three-dimensional label-free holotomography

Adipogenesis involves complex changes in gene expression, morphology, and cytoskeletal organization. However, the quantitative analysis of live cell images to identify their stages through morphological markers is limited. Distinct adipogenesis markers on human umbilical cord-derived mesenchymal stem cells (UC-MSCs) were identified through holotomography, a label-free live cell imaging technique. In the MSC-to-preadipocyte transition, the nucleus-to-cytoplasm ratio (0.080 vs. 0.052) and lipid droplet (LD) refractive index variation decreased (0.149 % vs. 0.061 %), whereas the LD number (20 vs. 65) increased. This event was also accompanied by the downregulation and upregulation of THY1 and Preadipocyte Factor-1 (PREF-1), respectively. In the preadipocyte to immature adipocyte shift, cell sphericity (0.20 vs. 0.43) and LD number (65 vs. 200) surged, large LDs (>10 μm3) appeared, and the major axis of the cell was reduced (143.7 μm vs. 83.12 μm). These findings indicate features of preadipocyte and immature adipocyte stages, alongside the downregulation of PREF-1 and upregulation of Peroxisome Proliferator-Activated Receptor gamma (PPARγ). In adipocyte maturation, along with PPARγ and Fatty Acid-Binding Protein 4 upregulation, cell compactness (0.15 vs. 0.29) and sphericity (0.43 vs. 0.59) increased, and larger LDs (>30 μm3) formed, marking immature and mature adipocyte stages. The study highlights the distinct adipogenic morphological biomarkers of adipogenesis stages in UC-MSCs, providing potential applications in biomedical and clinical settings, such as fostering innovative medical strategies for treating metabolic disease.

Artificial intelligence and machine learning applications for cultured meat.

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.

As deep learning technology has progressed, automated medical image analysis is becoming ever more crucial in clinical diagnosis. However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in blood cell detection. To address blood cell detection, we developed the TW-YOLO approach, leveraging multi-scale feature fusion techniques. Firstly, traditional CNN (Convolutional Neural Network) convolution has poor recognition capabilities for certain blood cell features, so the RFAConv (Receptive Field Attention Convolution) module was incorporated into the backbone of the model to enhance its capacity to extract geometric characteristics from blood cells. At the same time, utilizing the feature pyramid architecture of YOLO (You Only Look Once), we enhanced the fusion of features at different scales by incorporating the CBAM (Convolutional Block Attention Module) in the detection head and the EMA (Efficient Multi-Scale Attention) module in the neck, thereby improving the recognition ability of blood cells. Additionally, to meet the specific needs of blood cell detection, we designed the PGI-Ghost (Programmable Gradient Information-Ghost) strategy to finely describe the gradient flow throughout the process of extracting features, further improving the model's effectiveness. Experiments on blood cell detection datasets such as BloodCell-Detection-Dataset (BCD) reveal that TW-YOLO outperforms other models by 2%, demonstrating excellent performance in the task of blood cell detection. In addition to advancing blood cell image analysis research, this work offers strong technical support for future automated medical diagnostics.

A systematic review on deep learning based methods for cervical cell image analysis

Cervical cytology image analysis is indispensable for the detection of abnormal cervical cells. Traditionally, manual screening is time-consuming and labor-intensive. Therefore, a lot of deep learning (DL)-based automatic detection methods have been employed in this field to provide timely, accurate and objective results. In this study, we systematically review the current developments in cervical cell image analysis with DL methods. Specifically, we first present the most popular DL models that are widely applied in cervical cell analysis. Second, we describe the methodology for conducting this review. Third, we provide all publicly available datasets related to cervical cell images to the best of our knowledge. Then, we introduce relevant evaluation metrics and loss functions. Next, we summarize and assort the applications for cervical cell classification and segmentation. Afterwards, we discuss about current challenges and future research directions in this field. Finally, we draw the conclusion of this review.According to the analysis, we conclude that the studies based on DL models have maintained an increasing trend in recent years, which indicates the potential of DL in cervical cell image analysis. In cervical cell image classification, CNN is the most commonly used DL model. Among CNN models, we can find that VGGNet and ResNet are the most popular network architectures for the classification of cervical cells. Transformer is the second commonly used DL model. Moreover, Herlev and SIPaKMeD are the most popular public datasets used for cervical cell classification. In cervical cell segmentation, U-Net and FCN are the two most popular DL architectures. In addition, ISBI2014 and Herlev datasets are the most frequently used among the existing publicly available segmentation datasets. However, there are some issues in this field, such as poor cervical cell classification performance as a result of similar pathological properties between different cell categories. Therefore, it is necessary to develop more effective methods with DL models to improve these issues in the future research.

PROTAC-mediated vimentin degradation promotes terminal erythroid differentiation of pluripotent stem cells

BackgroundHuman pluripotent stem cells (hPSCs), including human embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs), can undergo erythroid differentiation, offering a potentially invaluable resource for generating large quantities of erythroid cells. However, the majority of erythrocytes derived from hPSCs fail to enucleate compared with those derived from cord blood progenitors, with an unknown molecular basis for this difference. The expression of vimentin (VIM) is retained in erythroid cells differentiated from hPSCs but is absent in mature erythrocytes. Further exploration is required to ascertain whether VIM plays a critical role in enucleation and to elucidate the underlying mechanisms.MethodsIn this study, we established a hESC line with reversible vimentin degradation (dTAG-VIM-H9) using the proteolysis-targeting chimera (PROTAC) platform. Various time-course studies, including erythropoiesis from CD34+ human umbilical cord blood and three-dimensional (3D) organoid culture from hESCs, morphological analysis, quantitative real-time PCR (qRT-PCR), western blotting, flow cytometry, karyotyping, cytospin, Benzidine-Giemsa staining, immunofluorescence assay, and high-speed cell imaging analysis, were conducted to examine and compare the characteristics of hESCs and those with vimentin degradation, as well as their differentiated erythroid cells.ResultsVimentin expression diminished during normal erythropoiesis in CD34+ cord blood cells, whereas it persisted in erythroid cells differentiated from hESC. Depletion of vimentin using the degradation tag (dTAG) system promotes erythroid enucleation in dTAG-VIM-H9 cells. Nuclear polarization of erythroblasts is elevated by elimination of vimentin.ConclusionsVIM disappear during the normal maturation of erythroid cells, whereas they are retained in erythroid cells differentiated from hPSCs. We found that retention of vimentin during erythropoiesis impairs erythroid enucleation from hPSCs. Using the PROTAC platform, we validated that vimentin degradation by dTAG accelerates the enucleation rate in dTAG-VIM-H9 cells by enhancing nuclear polarization.Graphical

Diverse calcium signaling profiles regulate migratory behavior in avascular wound healing and aberrant signal hierarchy occurs early in diabetes.

In avascular wound repair, calcium signaling events are the predominant mechanism cells use to transduce information about stressors in the environment into an effective and coordinated migratory response. Live cell imaging and computational analysis of corneal epithelial wound healing revealed that signal initiation and propagation at the wound edge are highly ordered, with groups of cells engaging in cyclical patterns of initiation and propagation. The cells in these groups exhibit a diverse range of signaling behavior, and dominant "conductor cells" drive activity in groups of lower-signaling neighbors. Ex vivo model systems reveal that conductor cells are present in wing cell layers of the corneal epithelium and that signaling propagates both within and between wing and basal layers. There are significant aberrations in conductor phenotype and interlayer propagation in type II diabetic murine models, indicating that signal hierarchy breakdown is an early indicator of disease. In vitro models reveal that signaling profile diversity and conductor cell phenotype is eliminated with P2X7 inhibition and is altered in Pannexin-1 or P2Y2 but not Connexin-43 inhibition. Conductor cells express significantly less P2X7 than their lower-signaling neighbors and exhibit significantly less migratory behavior after injury. Together, our results show that the postinjury calcium signaling cascade exhibits significantly more ordered and hierarchical behavior than previously thought, that proteins previously shown to be essential for regulating motility are also essential for determining signaling phenotype, and that loss of signal hierarchy integrity is an early indicator of disease state. NEW & NOTEWORTHY Calcium signaling in corneal epithelial cells after injury is highly ordered, with groups of cells engaged in cyclical patterns of event initiation and propagation driven by high-signaling cells. Signaling behavior is determined by P2X7, Pannexin-1, and P2Y2 and influences migratory behavior. Signal hierarchy is observed in healthy ex vivo models after injury and becomes aberrant in diabetes. This represents a paradigm shift, as signaling was thought to be random and determined by factors in the environment.

Topical and oral peroxisome proliferator-activated receptor-α agonist ameliorates diabetic corneal neuropathy

Diabetic corneal neuropathy (DCN) is a common diabetic ocular complication with limited treatment options. In this study, we investigated the effects of topical and oral fenofibrate, a peroxisome proliferator-activated receptor-α agonist, on the amelioration of DCN using diabetic mice (n = 120). Ocular surface assessments, corneal nerve and cell imaging analysis, tear proteomics and its associated biological pathways, immuno-histochemistry and western blot on PPARα expression, were studied before and 12 weeks after treatment. At 12 weeks, PPARα expression markedly restored after topical and oral fenofibrate. Topical fenofibrate significantly improved corneal nerve fibre density (CNFD) and tortuosity coefficient. Likewise, oral fenofibrate significantly improved CNFD. Both topical and oral forms significantly improved corneal sensitivity. Additionally, topical and oral fenofibrate significantly alleviated diabetic keratopathy, with fenofibrate eye drops demonstrating earlier therapeutic effects. Both topical and oral fenofibrate significantly increased corneal β-III tubulin expression. Topical fenofibrate reduced neuroinflammation by significantly increasing the levels of nerve growth factor and substance P. It also significantly increased β-III-tubulin and reduced CDC42 mRNA expression in trigeminal ganglions. Proteomic analysis showed that neurotrophin signalling and anti-inflammation reactions were significantly up-regulated after fenofibrate treatment, whether applied topically or orally. This study concluded that both topical and oral fenofibrate ameliorate DCN, while topical fenofibrate significantly reduces neuroinflammation.

Differential endothelial cell cycle status in postnatal retinal vessels revealed using a novel PIP-FUCCI reporter and zonation analysis.

Cell cycle regulation is critical to blood vessel formation and function, but how the endothelial cell cycle integrates with vascular regulation is not well-understood, and available dynamic cell cycle reporters do not precisely distinguish all cell cycle stage transitions in vivo. Here we characterized a recently developed improved cell cycle reporter (PIP-FUCCI) that precisely delineates S phase and the S/G2 transition. Live image analysis of primary endothelial cells revealed predicted temporal changes and well-defined stage transitions. A new inducible mouse cell cycle reporter allele was selectively expressed in postnatal retinal endothelial cells upon Cre-mediated activation and predicted endothelial cell cycle status. We developed a semi-automated zonation program to define endothelial cell cycle status in spatially defined and developmentally distinct retinal areas and found predicted cell cycle stage differences in arteries, veins, and remodeled and angiogenic capillaries. Surprisingly, the predicted dearth of S-phase proliferative tip cells relative to stalk cells at the vascular front was accompanied by an unexpected enrichment for endothelial tip and stalk cells in G2, suggesting G2 stalling as a contribution to tip-cell arrest and dynamics at the front. Thus, this improved reporter precisely defines endothelial cell cycle status in vivo and reveals novel G2 regulation that may contribute to unique aspects of blood vessel network expansion.

A generative benchmark for evaluating the performance of fluorescent cell image segmentation

Fluorescent cell imaging technology is fundamental in life science research, offering a rich source of image data crucial for understanding cell spatial positioning, differentiation, and decision-making mechanisms. As the volume of this data expands, precise image analysis becomes increasingly critical. Cell segmentation, a key analysis step, significantly influences quantitative analysis outcomes. However, selecting the most effective segmentation method is challenging, hindered by existing evaluation methods' inaccuracies, lack of graded evaluation, and narrow assessment scope. Addressing this, we developed a novel framework with two modules: StyleGAN2-based contour generation and Pix2PixHD-based image rendering, producing diverse, graded-density cell images. Using this dataset, we evaluated three leading cell segmentation methods: DeepCell, CellProfiler, and CellPose. Our comprehensive comparison revealed CellProfiler's superior accuracy in segmenting cytoplasm and nuclei. Our framework diversifies cell image data generation and systematically addresses evaluation challenges in cell segmentation technologies, establishing a solid foundation for advancing research and applications in cell image analysis.

Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification.

Tradescantia plant is a complex system that is sensible to environmental factors such as water supply, pH, temperature, light, radiation, impurities, and nutrient availability. It can be used as a biomonitor for environmental changes; however, the bioassays are time-consuming and have a strong human interference factor that might change the result depending on who is performing the analysis. We have developed computer vision models to study color variations from Tradescantia clone 4430 plant stamen hair cells, which can be stressed due to air pollution and soil contamination. The study introduces a novel dataset, Trad-204, comprising single-cell images from Tradescantia clone 4430, captured during the Tradescantia stamen-hair mutation bioassay (Trad-SHM). The dataset contain images from two experiments, one focusing on air pollution by particulate matter and another based on soil contaminated by diesel oil. Both experiments were carried out in Curitiba, Brazil, between 2020 and 2023. The images represent single cells with different shapes, sizes, and colors, reflecting the plant's responses to environmental stressors. An automatic classification task was developed to distinguishing between blue and pink cells, and the study explores both a baseline model and three artificial neural network (ANN) architectures, namely, TinyVGG, VGG-16, and ResNet34. Tradescantia revealed sensibility to both air particulate matter concentration and diesel oil in soil. The results indicate that Residual Network architecture outperforms the other models in terms of accuracy on both training and testing sets. The dataset and findings contribute to the understanding of plant cell responses to environmental stress and provide valuable resources for further research in automated image analysis of plant cells. Discussion highlights the impact of turgor pressure on cell shape and the potential implications for plant physiology. The comparison between ANN architectures aligns with previous research, emphasizing the superior performance of ResNet models in image classification tasks. Artificial intelligence identification of pink cells improves the counting accuracy, thus avoiding human errors due to different color perceptions, fatigue, or inattention, in addition to facilitating and speeding up the analysis process. Overall, the study offers insights into plant cell dynamics and provides a foundation for future investigations like cells morphology change. This research corroborates that biomonitoring should be considered as an important tool for political actions, being a relevant issue in risk assessment and the development of new public policies relating to the environment.

Abstract PO1-26-04: Patient-derived organotypic tissue cultures for organoid isolation and probing unique metabolic features of individual breast cancer patients

Abstract Patient-derived organotypic tissue cultures (PD-OTC) are unique models for probing how individual patient’s tumor microenvironment (TME) influences cancer development and treatment responses. They retain the patient tumor architecture and TME, that are difficult to recapitulate in other models, while affording flexible treatment options and comparison of cancer (CA) versus matched non-cancer (NC) tissue responses to treatments. We have developed a culturing method for establishing PD-OTC of breast cancer patients that exhibit CA tissue growth and extensive metabolic reprogramming between CA versus matched NC tissues while enabling patient-derived organoids (PDO) and fibroblasts to be isolated. Pairs of CA and NC tissues freshly resected from six patients bearing early-stage estrogen receptor/progesterone receptor positive ductal carcinoma were sliced at 750 µm, embedded in Matrigel, and cultured in a 6-well plate with a Biopore membrane insert and custom medium at 37°C/5% CO2 with gentle rocking (16-40 mg wet weight; n=3). Excess tissue slices were cryopreserved and kept at -196 °C. Culture media were sampled periodically for metabolite analysis by NMR. Microscopic examination revealed significant tissue, organoid-like, and fibroblast outgrowth after 1-1.5 month of culturing. Two to three days prior to the harvest of two patients’ (CZ16 and 17) OTC, the culture medium was replaced with [U-2H]-glucose + [U-13C,15N]-glutamine-containing medium to allow the two stable isotope tracers to be metabolized, followed by Stable Isotope-Resolved Metabolomic (SIRM) analysis by NMR and ion chromatography coupled with ultra high-resolution Fourier transform mass spectrometry (IC-UHRFTMS). Two separate pieces of the OTC were cut and each was assayed for glucose uptake and mitochondrial membrane potential by live fluorescence spectroscopy via a portable custom-built fluorescence microscope, and by histochemical analysis. Histological analysis showed that both CA and NC OTC of all patients maintained their structural integrity. SIRM analysis of two patients’ OTC revealed active catabolic and anabolic metabolism including glucose uptake/glycolysis, the Krebs cycle/mitochondrial membrane potential, the pentose phosphate pathway (PPP), gluconeogenesis, glycogen synthesis, and purine/pyrimidine/sugar nucleotides synthesis. The CA OTC of both patients showed enhanced glucose uptake, glycolysis, mitochondrial membrane potential/Krebs cycle, PPP, glycogen synthesis, and purine nucleotide synthesis, when compared with the matched NC OTC. Treatment-naïve CA OTC of CZ16 showed much more significant metabolic reprogramming (NAD+ metabolism, in particular) than CA OTC of CZ17 who has undergone neoadjuvant treatment, chemotherapy, and hormone therapy prior to surgery. Distinct metabolic features of breast CA tissues can be exploited as therapeutic targets. Organoids and fibroblasts were isolated from CA and/or NC OTC cultures and their characterization is in progress. When revived, cryopreserved OTC exhibited active metabolism and tissue/organoid/fibroblast outgrowth similarly to fresh OTC. In conclusion, our culturing method enables PD-OTC models to be established efficiently for early-stage breast cancer patients for downstream functional studies including live cell imaging and SIRM analysis. Organoids and fibroblasts can also be derived from < 20 mg of tissues for further studies. Future direction includes therapeutic studies on these patient-derived models to better understand and predict individual patient’s responses to therapy. Citation Format: Teresa Fan, Carlos Goncalves, Jing Yan, Jahid Islam, Penghui Lin, Mohamed Kaddah, Richard Higashi, Andrew Lane, Caigang Zhu. Patient-derived organotypic tissue cultures for organoid isolation and probing unique metabolic features of individual breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-26-04.

An efficient Fusion-Purification Network for Cervical pap-smear image classification

Background and Objectives:In cervical cell diagnostics, autonomous screening technology constitutes the foundation of automated diagnostic systems. Currently, numerous deep learning-based classification techniques have been successfully implemented in the analysis of cervical cell images, yielding favorable outcomes. Nevertheless, efficient discrimination of cervical cells continues to be challenging due to large intra-class and small inter-class variations. The key to dealing with this problem is to capture localized informative differences from cervical cell images and to represent discriminative features efficiently. Existing methods neglect the importance of global morphological information, resulting in inadequate feature representation capability. Methods:To address this limitation, we propose a novel cervical cell classification model that focuses on purified fusion information. Specifically, we first integrate the detailed texture information and morphological structure features, named cervical pathology information fusion. Second, in order to enhance the discrimination of cervical cell features and address the data redundancy and bias inherent after fusion, we design a cervical purification bottleneck module. This model strikes a balance between leveraging purified features and facilitating high-efficiency discrimination. Furthermore, we intend to unveil a more intricate cervical cell dataset: Cervical Cytopathology Image Dataset (CCID). Results:Extensive experiments on two real-world datasets show that our proposed model outperforms state-of-the-art cervical cell classification models. Conclusions:The results show that our method can well help pathologists to accurately evaluate cervical smears.

Remote Disease Diagnosis through IoMT-Enhanced Blood Cell Classification with Deep Learning

Background For the purpose of diagnosing diseases and developing treatment plans, blood cell pictures must be accurately classified. This procedure can be greatly enhanced by automated systems that make use of deep learning and the Internet of Medical Things (IoMT). Objective In order to improve illness detection and increase healthcare accessibility, this work suggests an IoMT-based system for remote blood cell picture transmission and classification utilizing deep learning algorithms. Methods High-resolution pictures of blood cells are captured by an IoMT tiny camera and wirelessly sent to a cloud-based infrastructure. The blood cells are divided into groups according to a, deeplearning classification algorithm, including neutrophils, lymphocytes, monocytes, and eosinophils. Results The IoMT-enabled system excels in transmitting and analyzing blood cell images, achieving precise classification. Utilizing deep learning models with multi-scale feature extraction and attention mechanisms, the system demonstrates robust performance. Numerical results showcase a high accuracy of approximately 97.21%, along with noteworthy precision, recall, and F1 scores for individual blood cell classes. Eosinophil, Lymphocyte, Monocyte, and Neutrophil classes exhibit strong performance metrics, emphasizing the system's effectiveness in accurate blood cell classification. Conclusion By combining IoMT and deep learning with blood cell image analysis, diagnostic accessibility and efficiency are improved. The suggested approach has the potential to completely transform healthcare by facilitating prompt interventions, individualized treatment regimens, and better patient outcomes. It is essential to continuously enhance and validate the system in order to maximize its efficacy and dependability in a variety of healthcare settings.

Live Cell Monitoring of Separase Activity, a Key Enzymatic Reaction for Chromosome Segregation, with Chimeric FRET-Based Molecular Sensor upon Cell Cycle Progression.

Separase is a key cysteine protease in the separation of sister chromatids through the digestion of the cohesin ring that inhibits chromosome segregation as a trigger of the metaphase-anaphase transition in eukaryotes. Its activity is highly regulated by binding with securin and cyclinB-CDK1 complex. These bindings prevent the proteolytic activity of separase until the onset of anaphase. Chromosome missegregation and aneuploidy are frequently observed in malignancies. However, there are some difficulties in biochemical examinations due to the instability of separase in vitro and the fact that few spatiotemporal resolution approaches exist for monitoring live separase activity throughout mitotic processes. Here, we have developed FRET-based molecular sensors, including GFP variants, with separase-cleavable sequences as donors and covalently attached fluorescent dyes as acceptor molecules. These are applicable to conventional live cell imaging and flow cytometric analysis because of efficient live cell uptake. We investigated the performance of equivalent molecular sensors, either localized or not localized inside the nucleus under cell cycle control, using flow cytometry. Synchronized cell cycle progression rendered significant separase activity detections in both molecular sensors. We obtained consistent outcomes with localized molecular sensor introduction and cell cycle control by fluorescent microscopic observations. We thus established live cell separase activity monitoring systems that can be used specifically or statistically, which could lead to the elucidation of separase properties in detail.

Tumor-associated antigen-specific cell imaging based on upconversion luminescence and nucleic acid rolling circle amplification.

Tumor-associated antigen (TAA)-based diagnosis has gained prominence for early tumor screening, treatment monitoring, prognostic assessment, and minimal residual disease detection. However, limitations such as low sensitivity and difficulty in extracting non-specific binding membrane proteins still exist in traditional detection methods. Upconversion luminescence (UCL) exhibits unique physical and chemical properties under wavelength near-infrared light excitation. Rolling circle amplification (RCA) is an efficient DNA amplification technique with amplification factorsas high as 105. Therefore, the above two excellent techniques can be employed for highly accurate imaging analysis of tumor cells. Herein, we developed a novel nanoplatform for TAA-specific cell imaging based on UCL and RCA technology. An aptamer-primer complex selectively binds to Mucin 1 (MUC1), one of TAA on cell surface, to trigger RCA reaction, generating a large number of repetitive sequences. These sequences provide lots of binding sites for complementary signal probes, producing UCL from lanthanide-doped upconversion nanoparticles (UCNPs) after releasing quencher group. The experimental results demonstrate the specific attachment of upconversion nanomaterials to cancer cells which express a high level of MUC1, indicating the potential of UCNPs and RCA in tumor imaging.

Design, synthesis, and applications of nucleic acid-specific benzoxazole-N,N-dialkylphenylamines derivatives for nucleolus imaging in the cells

Considered the brain of the nucleus, alterations in the structure and function of the nucleolus are linked to numerous cellular functions and, consequently, contribute to several diseases. The identification of nucleolar morphology and activity via novel biomarkers presents new avenues for the development of therapeutic approaches for a variety of human diseases, including cancer, neurodegeneration, and aging. Therefore, specific detection of the nucleolus with fluorescence probes is of critical importance for clinical applications. In the present study, a series of benzoxazole-N,N-dialkylphenylamines derivative compounds were designed and synthesized based on the benzothiazole-based fluorescence probe Thioflavin T (ThT). Among the compounds, BX-3 and BX-16, which carry electron-withdrawing substituents in the benzoxazole ring, were observed to have higher fluorescence emission at wavelengths of 470 and 465 nM, respectively. The general morphology and divisions of the cells were examined under inverted and light microscopes, respectively, and the fluorescence potentials of selected compounds were determined using immunofluorescence microscopy. The fluorescence intensity of molecules and 3D interactive surface plot images of cells were analyzed using ImageJ software. Cell imaging analyses showed that BX-6 and BX-13, like ThT, specifically stain the cell nucleolus. Moreover, molecular docking studies showed that the compounds could identify the RNA-rich nucleolus by binding with high affinity to the guanine region in the RNA structure. The results suggest that the compounds may be an initial route in developing specific biosensor compounds for nucleolus imaging.

Exploring fluorescent and colorimetric probes for analyte detection: Utilizing it in live cell imaging and real-time sample analysis

Fluorescent and colorimetric probes are gaining interest in analyte detection, imaging, and real-time applications. However, it faces several challenges in achieving high sensitivity and selectivity, maintaining a strong signal-to-noise ratio, detecting analytes at extremely low concentrations, ensuring stability under different conditions and addressing issues related to cost, accessibility and instrumentation. Additionally, ensuring biological compatibility and enabling real-time monitoring are also challenges that need to overcome. We have investigated the sensing application of probe molecules towards various analytes like metal ions, anions, biomolecules, reactive oxygen species (ROS), reactive sulphur species (RSS), toxic agents and nanoparticles performed by our group together with critically comparing with the existing reports. This featured article provides the impact of fluorescent and colorimetric probes in advancing analytical techniques, imaging and real-time applications that transcend traditional boundaries.