Quantitative Image Analysis Research Articles

Abstract A031: Harnessing the potential of circulating tumour cells for precision diagnostics via multiplexed imaging of the levels and subcellular localizations of over 50 cell identity, state and signaling markers per cell

Abstract Due to their longitudinal accessibility through liquid biopsy, circulating tumour cells (CTCs) hold huge potential as sources of molecular information capable of underpinning precision diagnostics in cancer. Yet key methodological limitations have thus far constrained the practical utility of CTCs as diagnostic analytes, including: a) reliance on positive CTC selection strategies that can bias against distinctive CTC subpopulations (e.g. low EpCAM, low size, low density), and; b) standard fluorescence imaging methods that capture only 4-6 molecular markers per cell. Since 3-4 markers are required to identify CTCs with even moderate confidence, such low marker plexity acutely restricts derivation of additional molecular insights about the cellular states and molecular signals that may be driving disease. Yet these insights are essential to guide selection of optimal therapies, to adapt therapeutic strategies as cycles of resistance arise, and even to stratify patients to the trials needed to expand the therapeutic arsenal. To overcome these challenges and harness the potential of CTCs for precision diagnostics, we have developed a comprehensive pipeline for CTC isolation, storage and batch processing via multiplexed immunofluorescence imaging of 50+ molecular markers per cell. These markers illuminate CTC identity, state (epithelial vs mesenchymal vs stemness), fate (proliferation, death, senescence) and signalling (spanning ∼10 alternate ‘driver’ signalling pathways), providing truly unprecedented molecular insights per CTC, across heterogeneous CTC populations, and regarding potential therapeutic targets. Downstream single-cell quantitative image analyses enable robust but adaptable computational parsing of CTC identity, removing false-positive and false-negative instances otherwise resulting from standard 4-plex CTC definitions (e.g. DAPI, CD45, EpCAM, pan-Cytokeratin) and complementing our upstream use of negative selection methods that capture the full, unbiased diversity of CTCs. The remaining 40+ markers facilitate integrated analyses of relationships between CTC state, fate and signalling pathways, capturing not only protein and phospho-protein expression levels per cell, but also variations in subcellular localisation that constitute additional layers of functional regulation not captured by most omics methods. Excitingly, when combined with machine learning, our approach can discern therapy- resistant cell subpopulations with over 98% accuracy in models of prostate cancer therapy resistance. We are now assessing CTC diversity across patients ranging from treatment naïve to advanced castrate-resistance prostate cancers. Overall, our unique high-plexity analysis now enables interrogation of detailed states, signals and dependencies in each patient CTC, providing the foundations for next-generation precision diagnostics to continuously match individual patients to optimal therapies at each stage of their disease journey. We believe this marks a significant step towards harnessing the true potential of CTCs for precision diagnostics. Citation Format: John G Lock, Tim J Mann, Ye Zheng, Tanzila Khan, Daniel Neumann, Alexander James, Therese Becker, Tara Roberts. Harnessing the potential of circulating tumour cells for precision diagnostics via multiplexed imaging of the levels and subcellular localizations of over 50 cell identity, state and signaling markers per cell [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A031.

Analyzing Multimodal Construction of China’s Image Based on the Illustrated Articles in The Economist

The shaping of national image is a two-way process, including “self-shaping” and “shaping by others”. Magazines are one of the important channels for disseminating information. Taking The Economist’s China-related reports in 2023 as a case, this article focuses on 195 reports on politics and diplomacy as well as economy, conducting quantitative and qualitative analyses of the visual images from the perspective of visual grammar, and further exploring the multimodal construction of China's image in The Economist. The research shows that the images in The Economist fully realize the representational meaning, interactive meaning and compositional meaning. In terms of the representational meaning, narrative representation is the major process. Images that reflect the reactional process, action process and symbolic process are used more frequently. In terms of the interactive meaning, “offer” images, long shot images and images with horizontal perspective appear more frequently. In addition, information value, salience, and framing are interrelated to achieve the compositional meaning. The political and diplomatic image of China constructed by The Economist is that China is a country with increasing power, which is authoritarian internally and threatening externally. In terms of economy, China is portrayed as a country with a fast growing economy which can withstand the global economic crisis, but its economic prospects are not optimistic, and the “China threat theory” still exists.

Quantifying the recovery process of skeletal muscle on hematoxylin and eosin stained images via learning from label proportion

Visual observing muscle tissue regeneration is used to measure experimental effect size in biological research to discover the mechanism of muscle strength decline due to illness or aging. Quantitative computer imaging analysis for support evaluating the recovery phase has not been established because of the localized nature of recovery and the difficulty in selecting image features for cells in regeneration. We constructed MyoRegenTrack for segmenting cells and classifying their regeneration phase in hematoxylin–eosin (HE) stained images. A straightforward approach to classification is supervised learning. However, obtaining detailed annotations for each fiber in a whole slide image is impractical in terms of cost and accuracy. Thus, we propose to learn individual recovery phase classification utilizing the proportions of cell class depending on the days after muscle injection to induce regeneration. We extract implicit multidimensional features from the HE-stained tissue images and train a classifier using weakly supervised learning, guided by their class proportion for elapsed time on recovery. We confirmed the effectiveness of MyoRegenTrack by comparing its results with expert annotations. A comparative study of the recovery relation between two different muscle injections shows that the analysis result using MyoRegenTrack is consistent with findings from previous studies.

Improving Image Quality and Visualization of Hepatocellular Carcinoma in Arterial Phase Imaging Using Contrast Enhancement-Boost Technique.

This study aimed to evaluate the image quality and visualization of hepatocellular carcinoma (HCC) on arterial phase computed tomography (CT) using the contrast enhancement (CE)-boost technique. This retrospective study included 527 consecutive patients who underwent dynamic liver CT between June 2021 and February 2022. Quantitative and qualitative image analyses were performed on 486 patients after excluding 41 patients. HCC conspicuity was evaluated in 40 of the 486 patients with at least one HCC in the liver. Iodinated images obtained by subtracting nonenhanced images from arterial phase images were combined to generate CE-boost images. For quantitative image analysis, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for the liver, pancreas, muscles, and aorta. For qualitative analysis, the overall image quality and noise were graded using a 3-point scale. Artifact, sharpness, and HCC lesion conspicuity were assessed using a 5-point scale. The paired-sample t test was used to compare quantitative measures, whereas the Wilcoxon signed-rank test was used to compare qualitative measures. The mean SNR and CNR of the aorta, liver, pancreas, and muscle were significantly higher, and the image noise was significantly lower in the CE-boost images than in the conventional images (P < 0.001). The mean CNR of HCC was also significantly higher in the CE-boost images than in the conventional images (P < 0.001). In the qualitative analysis, CE-boost images showed higher scores for HCC lesion conspicuity than conventional images (P < 0.001). The overall image quality and visibility of HCC were improved using the CE-boost technique.

Qualitative and quantitative analysis of 18F-GP1 positron emission tomography in thrombotic cardiovascular disease.

18F-GP1 is a novel highly specific radiotracer that binds to activated platelets and thrombus. We aimed to establish the observer repeatability of coronary, carotid and cerebral 18F-GP1 uptake in patients presenting with acute myocardial infarction or ischaemic stroke. Forty-three patients presenting with acute myocardial infarction or ischaemic stroke underwent hybrid positron emission tomography (PET) and computed tomography (CT) angiography. Qualitative and quantitative assessment of 18F-GP1 uptake was performed on coronary arteries, carotid arteries and brain parenchyma. Qualitative uptake of 18F-GP1 had excellent intraobserver and interobserver agreement, with complete agreement for the presence or absence of visual 18F-GP1 uptake. For quantitative analysis, there were excellent intraclass correlation coefficients for intraobserver repeatability for coronary artery, carotid artery and brain parenchymal SUVmax and TBRmax measurements (all ≥ 0.92). Coronary artery and brain parenchymal analyses showed the strongest agreement in SUVmax values with mean biases of - 0.04 (limits of agreement - 0.21 to 0.20) and 0.02 (limits of agreement - 0.29 to 0.32) respectively. There was good interclass correlation coefficients for interobserver repeatability for coronary artery, carotid artery and brain parenchymal SUVmax and TBRmax measurements (all ≥ 0.89). The strongest interobserver agreement was seen with brain parenchymal SUVmax (mean SUVmax 1.95 ± 0.94) and TBRmax (mean TBRmax 9.55 ± 6.56) with mean biases of - 0.05 (limits of agreement - 0.37 to 0.27) and 0.04 (limits of agreement - 0.59 to 0.52) respectively. Visual qualitative and quantitative 18F-GP1 PET-CT image analyses provide robust and repeatable measurements of activated platelets and thrombi within the coronary arteries, carotid arteries and brain parenchyma.

Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence.

With the rising incidence of pulmonary nodules (PNs), lung adenocarcinoma in situ (AIS) is a critical early stage of lung cancer, necessitating accurate diagnosis for early intervention. This study applies artificial intelligence (AI) for quantitative imaging analysis to differentiate AIS from atypical adenomatous hyperplasia (AAH) and minimally invasive adenocarcinoma (MIA), aiming to enhance clinical diagnosis and prevent misdiagnosis. The study analyzed 1215 PNs with confirmed AAH, AIS, and MIA from six centers using the Shukun AI diagnostic module. Parameters evaluated included demographic data and various CT imaging metrics to identify indicators for clinical application, focusing on the mean CT value's predictive value. Significant differences were found in several parameters between AAH and AIS, with nodule mass showing the highest predictive value. When comparing AIS to MIA, total nodule volume was the best predictor, followed by the maximum CT value. The mean CT value has limited discriminative power for AIS diagnosis. Instead, the maximum CT value and maximum 3D diameter are recommended for clinical differentiation. Nodule mass and volume of solid components are strong indicators for differentiating AIS from AAH and MIA, respectively.

Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations

In situ TEM is by far the most commonly used microscopy method for imaging dislocations, i.e., line-like defects in crystalline materials. However, quantitative image analysis so far was not possible, implying that also statistical analyses were strongly limited. In this work, we created a deep learning-based digital twin of an in situ TEM straining experiment, additionally allowing to perform matching simulations. As application we extract spatio-temporal information of moving dislocations from experiments carried out on a Cantor high entropy alloy and investigate the universality class of plastic strain avalanches. We can directly observe “stick–slip motion” of single dislocations and compute the corresponding avalanche statistics. The distributions turn out to be scale-free, and the exponent of the power law distribution exhibits independence on the driving stress. The introduced methodology is entirely generic and has the potential to turn meso-scale TEM microscopy into a truly quantitative and reproducible approach.

Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification

Abstract Background Analyzing cine cardiac magnetic resonance (CMR) images is a task that demands substantial experience and time, and it is subject to both inter- and intra-observer variability. Existing automated tools in the clinics for image analysis often necessitate substantial editing to enable accurate quantification. Objective To tackle this issue, in this study, we developed and validated a fully automated artificial intelligence (AI)-based system for the analysis of cine CMR images, aiming to address these challenges. Method In this study, 1,038 patients with CMR images were enrolled. Out of these, 350 CMR images, along with corresponding segmentations for the left ventricle myocardium (LVM) and the left- and right ventricles (LV and RV, respectively), sourced from open access data from 5 centers and databases (with different cardiac diseases), were utilized for model development. The remaining images from two local registries were used to evaluate the model. A 2D U-Net network with deep supervision, enhanced with various image augmentation techniques, was developed for automatic segmentation. The developed model was subsequently utilized to segment the test dataset of 4D (3D+time) short-axis cine CMR images. End-diastolic volumes (EDV) and end-systolic volumes (ESV) were automatically detected from these segmentations and evaluated against the ground truth calculations performed by two experienced physicians using commercially validated software. Results In the test set, Dice scores of 0.84, 0.90, and 0.93 were achieved for the RV, LV, and LVM, respectively. The mean percentage error for EDV was 0.6% for LV and -6.32% for RV. Regarding ESV, the mean percentage error was 2.95% for LV and -6.32% for RV. A -6.95% error was achieved for LV mass calculation. The stroke volumes for LV and RV were calculated with a mean percent error of -2.52% and -8.43%, respectively. We achieved a mean percent error of -3.22% for LV and -1.64% for RV ejection fraction. Conclusion The fully automated AI-based quantitative analysis of cine cardiac MR images developed in this study demonstrated high agreement with the ground truth across large datasets, including those from external centers. This method can significantly reduce the time required for image analysis and enhance the accuracy and consistency of cardiac quantitative assessments in clinical settings.

A New Shear-Stress-Based Point-of-Care Technology for Evaluation of the Hemostatic Pattern in Whole Blood

The currently available point-of-care hemostasis tests are burdened by criticisms concerning the use of different activators and inhibitors and the lack of dynamic flow. These operating conditions may constitute an impediment to the determination of the patient’s hemostatic condition. Hence, the diffusion of these tests in clinical practice is still limited to specific scenarios. In this work, we present a new method for analyzing the patient’s global hemostasis based on the visualization of the main components of the coagulation process and its computerized quantitative image analysis. The automated “Smart Clot” point-of-care system presents a micro-fluidic chamber in which whole blood flows, without the addition of any activator or inhibitor. In this micro-channel, platelet adhesion, activation and aggregation to the type I collagen-coated surface take place (primary hemostasis), leading to the production of endogenous thrombin on the surface of platelet aggregates and the consequent fibrin mesh and thrombus formation (secondary hemostasis). These observations are verified by inhibiting primary hemostasis with the antiplatelet drugs Indomethacin (−70% on platelet aggregation, −60% on fibrin(ogen) formation) and Tirofiban (complete inhibition of platelet aggregation and fibrin(ogen) formation) and secondary hemostasis with the antithrombin drugs Heparin (−70% on platelet aggregation, −80% on fibrin(ogen) formation) and Lepirudin (−80% on platelet aggregation, −90% on fibrin(ogen) formation). Smart Clot, through a single test, provides quantitative results concerning platelet aggregation and fibrin formation and is suitable for undergoing comparative studies with other coagulation point-of-care devices.

Remote Sensing Image Segmentation Based on Probabilistic Fuzzy Local Information Clustering

Abstract. In this paper, a remote sensing image segmentation based on probabilistic fuzzy local information clustering algorithm is proposed. First, assuming that the spectral measure within the same ground object follows the same probability distribution. The dissimilarity between pixel and object is modeled by the negative logarithm of the Gaussian probability density function. It can improve the noise sensitive problem caused by the Euclidean distance which can only describe the data with isotropic distribution. Then, in order to consider the effect of local spatial constraint, on the one hand, the probability measure is used to modify the local fuzzy factor to establish the dissimilarity measure with spatial constraints. On the other hand, the hidden Markov random field is used to model the prior probability model of pixel membership. Next, the entropy regularization term of the objective function is built by combining the Kullback-Leibler(KL) maximum entropy model to further improve the robustness and noise resistance. The qualitative and quantitative analysis of simulated image and different types of real remote sensing images show that the proposed algorithm can effectively overcome the above problems and further improve the accuracy of image segmentation to over 95%.

A Novel Lipid Nanoparticle NBF-006 Encapsulating Glutathione S-Transferase P siRNA for the Treatment of KRAS-driven Non-small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancers, and KRAS mutations occur in 25-30% of NSCLC. Our approach to developing a therapeutic with the potential to target KRAS mutant NSCLC was to identify a new target involved in modulating signaling proteins in the RAS pathway. Glutathione S-Transferase P (GSTP) known as a Phase II detoxification enzyme has more recently been identified as a modulator of MAP kinase-related cell-signaling pathways. Therefore, developing a GSTP siRNA may be an effective therapeutic approach to treat KRAS mutant NSCLC. The lead drug product candidate (NBF-006) is a proprietary siRNA-based lipid nanoparticle (LNP) comprising GSTP siRNA (NDT-05-1040). Here, studies using a panel of KRAS mutant NSCLC cell lines demonstrated that NDT-05-1040 is a very potent and selective GSTP siRNA inhibitor. Our Western blot analysis showed that NDT-05-1040 effectively decreased the phosphorylation of MAPK and PI3K pathway components while upregulating apoptotic signaling cascade. Our in vivo studies revealed statistically significant higher distribution of NBF-006 to the lungs and tumor as compared to liver. In the subcutaneous and orthotopic tumor models, NBF-006 led to a statistically significant and dose dependent anti-tumor growth inhibition. Further, quantitative image analysis of PCNA and PARP staining showed that NBF-006 decreased proliferation and induced apoptosis, respectively, in tumors. Additionally, in a surgically implanted orthotopic lung tumor model, the survival rate of the NBF-006 treatment group was significantly prolonged (P <0.005) as compared to the vehicle control group. Together, these preclinical studies supported advancement of NBF-006 into clinical studies.

Mechanical Microvibration Device Enhancing Immunohistochemistry Efficiency.

Immunohistochemistry (IHC) is a widely used technique in diagnostic pathology and biomedical research, but there is still a need to shorten the operation process and reduce the cost of antibodies. This study aims to assess a novel IHC technique that incorporates mechanical microvibration (MMV) to expedite the process, reduce antibody consumption, and enhance staining quality. MMV was generated using coin vibration motors attached to glass slides mounted with consecutive tissue sections. Multiple antibodies targeting various antigens were used to stain cancerous and normal tissues, with and without microvibration. Various parameters were tested, including incubation durations, temperatures, and antibody dilutions. The novel method showed the potential to achieve comparable or superior outcomes in significantly less time, utilizing over 10 times less antibody than controls. MMV improved specific staining quality, yielding stronger, and better-defined positive reactions. This was validated through a multicenter double-blind assessment and quantitative image analysis. The possible mechanisms were also investigated. MMV shortens immunohistochemical staining duration, reduces antibody usage, and enhances staining specificity, likely by accelerating antibody movement and diffusion. These improvements translate to time and cost savings, offering clinical and financial value for diagnostic pathology and biomedical research.

Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load.

The aim of this study was to assess the effectiveness of a deep learning-based image contrast-boosting algorithm by enhancing the image quality of low-dose computed tomography pulmonary angiography at reduced iodine load. This study included 179 patients who underwent low-dose computed tomography pulmonary angiography with a reduced iodine load using 64 mL of a 1:1 mixture of contrast medium from January 1 to June 30, 2023. For single-energy computed tomography, the noise index was set at 15.4 to maintain a CTDIvol of <2 mGy at 80 kVp, and for dual-energy computed tomography, fast kV-switching between 80 and 140 kVp was employed with a fixed tube current of 145 mA. Images were reconstructed by 50% adaptive statistical iterative reconstruction (AR50) and a commercially available deep learning image reconstruction (TrueFidelity) package at a high strength level (TFH). In addition, AR50 images were further processed using a deep learning-based contrast-boosting algorithm (AR50-CB). Quantitative and qualitative image qualities and numbers of involved vessels with thrombus at each pulmonary artery level were compared in the 3 image types using the Friedman test and Wilcoxon signed rank test. Five hundred thirty-seven reconstructed image datasets of 179 patients were analyzed. Quantitative image analysis showed AR50-CB (30.8 ± 10.0 and 28.1 ± 9.6, respectively) had significantly higher signal-to-noise ratio and contrast-to-noise ratio values than AR50 (20.2 ± 6.2 and 17.8 ± 6.2, respectively) (P < 0.001) or TFH (28.3 ± 8.3 and 24.9 ± 8.1, respectively) (P < 0.001). Qualitative image analysis showed that contrast enhancement and noise scores of AR50-CB were significantly greater than those of AR50 (P < 0.001) and that AR50-CB enhancement scores were significantly higher than TFH enhancement scores (P < 0.001). The number of subsegmental pulmonary arteries affected by thrombus detected was significantly greater for AR50-CB (30 for AR50, 30 for TFH, and 55 for AR50-CB, P < 0.001). The use of a deep learning-based contrast-boosting algorithm improved image quality in terms of signal-to-noise ratio and contrast-to-noise ratio values and the detection of thrombi in subsegmental pulmonary arteries.

Biologic Correlates of Radiologic Features of Systemic Sclerosis Interstitial Lung Disease

Background and objectivesThe extent and pattern of radiological features (e.g., fibrosis, ground glass) can influence treatment approaches for systemic sclerosis-related interstitial lung disease (SSc-ILD). However, the pathobiology underlying these radiologic features is poorly understood and warrants further investigation.MethodsSixty-eight proteins were measured in bronchoalveolar lavage (BAL) fluid from 103 SSc-ILD participants in Scleroderma Lung Study I. Quantitative image analysis calculated the extent of fibrosis (QLF) and ground glass opacity (QGG) from concurrent high-resolution computerized tomography (HRCT) scans. The relationship between BAL proteins and quantitative HRCT scores was assessed by univariate and multivariate analyses.ResultsQLF scores correlated weakly with the extent of QGG, suggesting two distinct processes. In a univariate analysis, 25 proteins from several biologic pathways correlated with QLF scores, including pro-fibrotic factors, tissue remodeling proteins, proteins involved in monocyte/macrophage migration and activation, and proteins linked to inflammation and immune regulation. In contrast, QGG scores correlated with only 6 proteins of which four were unique and related to granulocyte activation, mobilization of bone marrow mesenchymal stem cells, and activation of T cells, B cells, macrophages and eosinophils. In the multivariate models, IL-4, CCL7, receptor activator of NF-κB, and tumor necrosis factor alpha were independently associated with QLF, whereas, IFN-γ was independently associated with QGG.InterpretationQLF and QGG represent distinct radiologic features of SSc-ILD, a conclusion reinforced by the presence of different biologic pathways present within BAL fluid that associate with each. The identified proteins and related biologic pathways may represent important therapeutic targets.

Evaluating response to radium-223 using 68Ga-PSMA PET/CT imaging in patients with metastatic castration-resistant prostate cancer.

Conventional imaging techniques and prostate-specific antigen (PSA) values are not useful to follow-up patients during Radium-223 treatment. The study aimed to evaluate the predictive value of prostate-specific membrane antigen PSMA PET/CT-based response in patients with metastatic castration-resistant prostate cancer (mCRPC) receiving Radium-223 dichloride treatment. Patients treated with radium-223, having performed two 68Ga-PSMA-11 PET/CT scans (baseline 1month before treatment initiation and follow-up 2weeks after the third cycle), were retrospectively evaluated. Visual and quantitative PET image analyses were performed, and patients were dichotomized into progressive (PD) and non-PD according to Response Evaluation Criteria in PSMA‑imaging (RECIP1.0) and PSMA-PET Progression criteria (PPP). The primary endpoint was overall survival (OS). Cohen's Kappa (κ) was used to test the agreement between the two criteria. The Cox regression hazard model and Kaplan-Meier method were used for survival analyses. Twenty-eight mCRPC patients were evaluated. Sixteen (43%) and 18 (64%) patients had PD according to RECIP1.0 and PPP, respectively; κ = 0.85 (95% CI 0.65-1.00). After a median follow-up of 16months (interquartile IQR 9-33), 20 (71%) patients died. Patients with PSMA PD showed a higher risk of death than non-PD according to RECIP1.0 (HR = 2.9; 95% CI 1.14-7.46; p = 0.029) and PPP (HR = 2.8; 95% CI 1.04-7.64; p = 0.042). For both criteria, the median OS was shorter for PD than non-PD (37 vs. 12months, Log-rank; p < 0.05). The C-index for RECIP1.0 and PPP were almost equal (0.66 and 0.63; respectively). This study demonstrated that PSMA-PET/CT imaging is valuable for monitoring radium-223 treatment. Both PSMA PET/CT response criteria (RECIP1.0 and PPP) perform similarly predicting OS at follow-up after three cycles of radium-223. These findings urge further validation in prospective trials.

B-083 Sensitivity of the Sebia Hydragel 6 electrophoresis/immunofixation assay for detection of beta-2 transferrin in dilute cerebrospinal fluid (CSF): assessment by image analysis

Abstract Background Beta-2 transferrin (asialo transferrin) greater than 2-sialo transferrin is characteristic of cerebrospinal fluid (CSF). Our neurosurgery department recently requested availability of an in-house assay to evaluate post-surgical drainage fluids for presence of CSF. The Sebia Hydragel 6 beta-2 transferrin electrophoresis/immunofixation profile can be used as a qualitative (positive/negative) assay for this purpose. As a starting point for evaluation of the assay, we examined the sensitivity of the assay for detection of the beta-2 transferrin band in dilute CSF. This was performed by quantitative image analysis. Methods Assays were conducted according to manufacturer's instructions. Our proband sample was pooled CSF. Successive dilutions were by factors of 2 (1, 1/2, 1/4, 1/8, 1/16, 1/32). Electrophoresis/ immunofixation was performed simultaneously for these samples across lanes in 6-lane gels. We performed quantitative assessment of the electrophoretic bands for beta-2 transferrin by image analysis. The gel image was scanned to an electronic file (600 dpi png) using a desktop scanner. Grayscale pixel data were obtained using Python and the Python Image Library (PIL). Total grayscale integrals for staining of beta-2 transferrin bands were computed and compared across dilutions of the proband sample. Results For CSF dilutions, the assay was invariably visually positive down to dilutions of 1/16. By image analysis, this cutoff had signal/noise ratio (S/N) of 3.5. Across dilutions, band integrals were dependent on dilutions according to a simple hyperbola (representative example: see Figure). Conclusions The Sebia Hydragel 6 electrophoresis/immunofixation assay for qualitative assessment of presence of beta-2 transferrin was found to be suitable for unambiguous detection of beta-2 transferrin in CSF down to 1/16 dilution of CSF. By image analysis, the dose vs. response curve (density of staining for beta-2 transferrin across dilutions of CSF) was non-linear.

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.

Biochar–water–soil interactions: Implications for soil desiccation cracking behavior in subtropical regions

In subtropical regions, soil desiccation cracking often exerts a significant impact on the interactions between soil water and the atmosphere, making it a subject of great interest in the fields of geotechnical and geoenvironmental engineering. Despite the growing utilization of biochar as a sustainable soil amendment, there remains a lack of in-depth understanding of biochar–water–soil interactions, as well as its impact on soil desiccation cracking behavior. To address this gap, this study investigated the influence and mechanism of woody biochar dosages and particle sizes on the cracking behavior of three typical clayey soils in subtropical regions in China, namely Pukou expansive soil (PKE), Xiashu soil (XS), and Zhongshan lateritic soil (ZSL). The quantitative analysis of crack images revealed that the use of biochar was not consistently effective in preventing soil cracking. The application of biochar reduced the crack ratio in PKE and XS by up to 24.03% and 53.89%, respectively. In contrast, ZSL exhibited a 74.57% increase in crack ratio with the addition of 10% biochar. This influence can be further enhanced by increasing the dosage and reducing the particle size of biochar. The microstructural analysis demonstrated that biochar exerts an inhibitory effect on PKE and XS primarily through direct replacement, direct barrier, and indirect physical mechanisms. Moreover, an indirect chemical effect between biochar and clay particles was proposed to explain the exacerbated cracking observed in biochar-amended ZSL. To effectively utilize biochar for soil cracking mitigation in subtropical regions, it is essential to evaluate the initial mineral composition and cation type of the soil.

Quantitative Image Analysis Reveals the Benefits of Dapagliflozin on Glomerulosclerosis, Podocyte Effacement, and Kidney Fibrosis in the Spontaneously Diabetic Torii (SDT) Fatty Rat Model of Diabetic Nephropathy

Quantitative Image Analysis Reveals the Benefits of Dapagliflozin on Glomerulosclerosis, Podocyte Effacement, and Kidney Fibrosis in the Spontaneously Diabetic Torii (SDT) Fatty Rat Model of Diabetic Nephropathy

Quantitative Image Analysis Uncovers the Efficacy of the Selective Endothelin A Receptor Antagonist Atrasentan in a Rat Model of IgA Nephropathy

Quantitative Image Analysis Uncovers the Efficacy of the Selective Endothelin A Receptor Antagonist Atrasentan in a Rat Model of IgA Nephropathy