pHusion: A robust and versatile toolset for automated detection and analysis of exocytosis.

Exocytosis is a fundamental process used by eukaryotes to regulate the composition of the plasma membrane and facilitate cell-cell communication. To investigate exocytosis in neuronal morphogenesis, previously we developed computational tools with a graphical user interface to enable the automatic detection and analysis of exocytic events from fluorescence timelapse images. Though these tools were useful, we found the code was brittle and not easily adapted to different experimental conditions. Here we developed and validated a robust and versatile toolkit, named pHusion, for the analysis of exocytosis written in ImageTank, a graphical programming language that combines image visualization and numerical methods. We tested this method using a variety of imaging modalities and pH-sensitive fluorophores, diverse cell types, and various exocytic markers to generate a flexible and intuitive package. We show that VAMP3-mediated exocytosis occurs 30-times more frequently in melanoma cells compared with primary oligodendrocytes, that VAMP2-mediated fusion events in mature rat hippocampal neurons are longer lasting than those in immature murine cortical neurons, and that exocytic events are clustered in space yet random in time in developing cortical neurons.

Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties.

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

Segmentation and Volume Estimation of the Habenula Using Deep Learning in Patients With Depression.

Background: The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods: This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results: A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10-7, r = -0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η2 = 0.099). Conclusions: Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.

Accurate segmentation of the habenula, a brain region implicated in depression, is challenging. In this study, we developed an automated human habenula segmentation model using deep learning techniques. The model was confirmed to be reproducible and generalizable at various spatial resolutions. Application of this model to a multicenter dataset confirmed that habenula volume decreased with age in healthy volunteers, an association that was more pronounced in individuals with depression. In addition, habenula volume decreased with the severity of depression in women. This novel model for habenula segmentation enables further study of the role of the habenula in depression.

A three-dimensional algorithm for precise measurement of human auricle parameters.

Measurement of auricle parameters for planning and post-operative evaluation presents substantial challenges due to the complex 3D structure of the human auricle. Traditional measurement methods rely on manual techniques, resulting in limited precision. This study introduces a novel automated surface-based three-dimensional measurement method for quantifying human auricle parameters. The method was applied to virtual auricles reconstructed from Computed Tomography (CT) scans of a cadaver head and subsequent measurement of important clinically relevant aesthetical auricular parameters (length, width, protrusion, position, auriculocephalic angle, and inclination angle). Reference measurements were done manually (using a caliper and using a 3D landmarking method) and measurement precision was compared to the automated method. The CT scans were performed using both a contemporary high-end and a low-end CT scanner. Scans were conducted at a standard scanning dose, and at half the dose. The automatic method demonstrated significantly higher precision in measuring auricle parameters compared to manual methods. Compared to traditional manual measurements, precision improved for auricle length (9×), width (5×), protrusion (5×), Auriculocephalic Angle (5-54×) and posteroanterior position (23×). Concerning parameters without comparison with a manual method, the precision level of supero-inferior position was 0.489 mm; and the precisions of the inclination angle measurements were 1.365 mm and 0.237 mm for the two automated methods investigated. Improved precision of measuring auricle parameters was associated with using the high-end scanner. A higher dose was only associated with a higher precision for the left auricle length. The findings of this study emphasize the advantage of automated surface-based auricle measurements, showcasing improved precision compared to traditional methods. This novel algorithm has the potential to enhance auricle reconstruction and other applications in plastic surgery, offering a promising avenue for future research and clinical application.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model's effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model's focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools.

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification.

Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI .

Learning Morphological, Spatial, and Dynamic Models of Cellular Components.

In this chapter, we describe protocols for using the CellOrganizer software on the Jupyter Notebook platform to analyze and model cell and organelle shape and spatial arrangement. CellOrganizer is an open-source system for using microscope images to learn statistical models of the structure of cell components and how those components are organized relative to each other. Such models capture the statistical variation in the organization of cellular components by jointly modeling the distributions of their number, shape, and spatial distributions. These models can be created for different cell types or conditions and compared to reflect differences in their spatial organizations. The models are also generative, in that they can be used to synthesize new cell instances reflecting what a model learned and to provide well-structured cell geometries that can be used for biochemical simulations.

Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.

High-throughput microscopy has enabled screening of cell phenotypes at unprecedented scale. Systematic identification of cell phenotype changes (such as cell morphology and protein localization changes) is a major analysis goal. Because cell phenotypes are high-dimensional, unbiased approaches to detect and visualize the changes in phenotypes are still needed. Here, we suggest that changes in cellular phenotype can be visualized in reduced dimensionality representations of the image feature space. We describe a freely available analysis pipeline to visualize changes in protein localization in feature spaces obtained from deep learning. As an example, we use the pipeline to identify changes in subcellular localization after the yeast GFP collection was treated with hydroxyurea.

Comparison of Child and Adult Mastication of a Sticky Processed Cream Cheese and Simulation with a Masticator.

An advantage of masticators is the calibration and possible standardization of intra- and inter-individual mastication variability. However, mastication of soft, sticky and melting products, such as processed cream cheeses, is challenging to reproduce with a masticator. The objectives of this work were, for the cheese studied: (1) to compare child and adult mastication and (2) to find in vitro parameters which best reproduce their in vivo chewing. Five parameters influencing mastication (mouth volume, quantity consumed, saliva volume, mastication time and number of tongue-palate compressions) were measured in 30 children (5-12 years old) and 30 adults (18-65 years old) and compared between the two populations. They were then transposed to a masticator (Oniris device patent). The initial cheese, a homogeneous white paste, was surface-colored to investigate its in-mouth destructuring. In vivo boli were collected at three chewing stages (33, 66 and 99% of mastication time) and in vitro boli were obtained by varying the number of tongue-palate compressions and the rotation speed. In vivo and in vitro boli were compared by both image and texture analysis. Child masticatory parameters were proportionally smaller than those of adults. The in vivo child boli were less homogeneous and harder than adult ones. Comparison of in vivo and in vitro bolus color and texture enabled the successful determination of two in vitro settings that closely represented the mastication of the two populations studied.

Effect of Different Hydrocolloids on the Qualitative Characteristics of Fermented Gluten-Free Quinoa Dough and Bread.

The aim of this research was to optimize the production process of fermented gluten-free quinoa bread. To this end, the effect of different hydrocolloids on the technological, fermentative, and nutritional properties of quinoa-based gluten-free doughs and breads was evaluated. For this purpose, 3% of four different hydrocolloids (sodium alginate, k-carrageenan, xanthan gum, and hydroxypropyl methylcellulose (HPMC)) were used in gluten-free doughs composed of 50% quinoa flour, 20% rice flour, and 30% potato starch. The rheological and fermentative properties of the doughs were evaluated, as well as the chemical composition, specific volume, crust and crumb color, and alveolar structure profile of gluten-free breads. The results highlighted the differences in dough rheology during mixing and fermentation of the doughs. In particular, HPMC showed a good gas retention (93%) during the fermentation of quinoa dough by registering the highest maximum dough development height (Hm). The gluten-free quinoa breads obtained were characterized by significantly different quality parameters (p < 0.05). The use of 3% HPMC resulted in breads with the lowest baking loss, the highest volume, and the most open crumb structure.

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction.

Surpassing the diffraction barrier revolutionized modern fluorescence microscopy. However, intrinsic limitations in statistical sampling, the number of simultaneously analyzable channels, hardware requirements, and sample preparation procedures still represent an obstacle to its widespread diffusion in applicative biomedical research. Here, we present a novel pipeline based on automated multimodal microscopy and super-resolution techniques employing easily available materials and instruments and completed with open-source image-analysis software developed in our laboratory. The results show the potential impact of single-molecule localization microscopy (SMLM) on the study of biomolecules' interactions and the localization of macromolecular complexes. As a demonstrative application, we explored the basis of p53-53BP1 interactions, showing the formation of a putative macromolecular complex between the two proteins and the basal transcription machinery in situ, thus providing visual proof of the direct role of 53BP1 in sustaining p53 transactivation function. Moreover, high-content SMLM provided evidence of the presence of a 53BP1 complex on the cell cytoskeleton and in the mitochondrial space, thus suggesting the existence of novel alternative 53BP1 functions to support p53 activity.

Spatial Distribution of Macrophage and Lymphocyte Subtypes within Tumor Microenvironment to Predict Recurrence of Non-Muscle-Invasive Papillary Urothelial Carcinoma after BCG Immunotherapy.

Non-muscle-invasive papillary urothelial carcinoma (NMIPUC) of the urinary bladder is the most common type of bladder cancer. Intravesical Bacille Calmette-Guerin (BCG) immunotherapy is applied in patients with a high risk of recurrence and progression of NMIPUC to muscle-invasive disease. However, the tumor relapses in about 30% of patients despite the treatment, raising the need for better risk stratification. We explored the potential of spatial distributions of immune cell subtypes (CD20, CD11c, CD163, ICOS, and CD8) within the tumor microenvironment to predict NMIPUC recurrence following BCG immunotherapy. Based on analyses of digital whole-slide images, we assessed the densities of the immune cells in the epithelial-stromal interface zone compartments and their distribution, represented by an epithelial-stromal interface density ratio (IDR). While the densities of any cell type did not predict recurrence, a higher IDR of CD11c (HR: 0.0012, p-value = 0.0002), CD8 (HR: 0.0379, p-value = 0.005), and ICOS (HR: 0.0768, p-value = 0.0388) was associated with longer recurrence-free survival (RFS) based on the univariate Cox regression. The history of positive repeated TUR (re-TUR) (HR: 4.93, p-value = 0.0001) and T1 tumor stage (HR: 2.04, p-value = 0.0159) were associated with shorter RFS, while G3 tumor grade according to the 1973 WHO classification showed borderline significance (HR: 1.83, p-value = 0.0522). In a multivariate analysis, the two models with a concordance index exceeding 0.7 included the CD11c IDR in combination with either a history of positive re-TUR or tumor stage. We conclude that the CD11c IDR is the most informative predictor of NMIPUC recurrence after BCG immunotherapy. Our findings highlight the importance of assessment of the spatial distribution of immune cells in the tumor microenvironment.

The politics of allyship: Multiethnic coalitions and mass attitudes toward protest.

Recent work finds that nonviolent resistance by ethnic minorities is perceived as more violent and requiring more policing than identical resistance by ethnic majorities, reducing its impact and effectiveness. We ask whether allies-advantaged group participants in disadvantaged group movements-can mitigate these barriers. On the one hand, allies can counter negative stereotypes and defuse threat perceptions among advantaged group members, while raising expectations of success and lowering expected risks among disadvantaged group members. On the other hand, allies can entail significant costs, carrying risks of cooptation, replication of power hierarchies, and marginalization of core constituencies. To shed light on this question we draw on the case of the Black Lives Matter (BLM) movement, which, in 2020, attracted unprecedented White participation. Employing a national survey experiment, we find that sizeable White presence at racial justice protests increases protest approval, reduces perceptions of violence, and raises the likelihood of participation among White audiences, while not causing significant backlash among Black audiences. Black respondents mostly see White presence as useful for advancing the movement's goals, and predominant White presence reduces expectations that protests will be forcefully repressed. We complement these results with analysis of tens of thousands of images shared on social media during the 2020 BLM protests, finding a significant association between the presence of Whites in the images and user engagement and amplification. The findings suggest that allyship can be a powerful tool for promoting sociopolitical change amid deep structural inequality.

Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics.

OBJECTIVE: Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the diagnostic quality of reconstructed images. MATERIALS AND METHODS: A retrospective multisite study of 1535 patients assessed biparametric prostate MRI between 2016 and 2020. Likely clinically significant prostate cancer (csPCa) lesions (PI-RADS ≥ 4) were delineated by expert radiologists. T2-weighted scans were retrospectively undersampled, simulating accelerated protocols. DL reconstruction (DLRecon) and diagnostic DL detection (DLDetect) were developed. The effect on the partial area under (pAUC), the Free-Response Operating Characteristic (FROC) curve, and the structural similarity (SSIM) were compared as metrics for diagnostic and visual quality, respectively. DLDetect was validated with a reader concordance analysis. Statistical analysis included Wilcoxon, permutation, and Cohen's kappa tests for visual quality, diagnostic performance, and reader concordance. RESULTS: DLRecon improved visual quality at 4- and 8-fold (R4, R8) subsampling rates, with SSIM (range: -1 to 1) improved to 0.78 ± 0.02 (p < 0.001) and 0.67 ± 0.03 (p < 0.001) from 0.68 ± 0.03 and 0.51 ± 0.03, respectively. However, diagnostic performance at R4 showed a pAUC FROC of 1.33 (CI 1.28-1.39) for DL and 1.29 (CI 1.23-1.35) for naive reconstructions, both significantly lower than fully sampled pAUC of 1.58 (DL: p = 0.024, naïve: p = 0.02). Similar trends were noted for R8. CONCLUSION: DL reconstruction produces visually appealing images but may reduce diagnostic accuracy. Incorporating diagnostic AI into the assessment framework offers a clinically relevant metric essential for adopting reconstruction models into clinical practice. CLINICAL RELEVANCE STATEMENT: In clinical settings, caution is warranted when using DL reconstruction for MRI scans. While it recovered visual quality, it failed to match the prostate cancer detection rates observed in scans not subjected to acceleration and DL reconstruction.

Imaging Characteristics and Clinical Utility of Half-Dose vs Full-Dose Ultra-Widefield Fluorescein Angiography.

PURPOSE: In early 2022, a fluorescein shortage occurred in the United States. To meet the standard of care for patients who required ultra-wide fundus fluorescein angiography (UWFFA), a regimen of half-dose (250mg) sodium fluorescein (10%) was adopted instead of the full-dose (500mg) at the Cole Eye Institute (CEI). In this paper, we compare the image quality, clinical utility, and the side effect profile of half-dose vs full-dose fluorescein in UWFFA for a cohort of stable patients. DESIGN: Retrospective chart review. SUBJECTS, PARTICIPANTS, AND/OR CONTROLS: Patients with retinal vascular disease were included if they received half-dose and full-dose UWFFA (Optos California, Dunfermline, UK) within 6 months at the CEI. Eyes were excluded if they received intraocular injections, laser procedures, new immunosuppression, and worsened or improved inflammation on clinical examination. METHODS, INTERVENTION, OR TESTING: Quantitative assessment of vascular leakage was performed using a machine-learning enhanced automated segmentation platform. Leakage from late-phase UWFFA images was compared between half-dose and full-dose images. Qualitative assessment of image quality and relative vascular leakage was performed by 2 masked independent reviewers. Side effects after fluorescein administration were recorded for each patient. MAIN OUTCOME MEASURES: Masked leakage grading and automated leakage scores. RESULTS: There were 52 eyes of 35 patients, 42 (81%) uveitic, 5 (9%) diabetic, and 4 (8%) normal controls. Patients had no change to their visual acuity (LogMAR mean 0.3±0.6), anterior chamber and vitreous cell between UFFWA's. UWFFA images were deemed of equal quality and leakage by both masked reviewers (78-87% agreement, kappa 0.642). Automated leakage analysis showed mildly increased leakage in half-dose images overall (3.8% vs 2.8%, p=0.01), and in the macula (1.5% vs 0.6%, p=0.01). Side effects included nausea (half (n=3, 9%) vs full (n=2, 6%), p=0.69) and urticaria (n=0, 0% vs n=1, 2%, p=0.99) and were not different between doses. CONCLUSIONS: In this cohort, half dose UWFFA produced images that were of similar quality, clinical utility and with a similar side effect profile compared to full dose. Half dose UWFFA can be used to accurately assess the retinal vasculature and could be used primarily as a method to save cost and prevent waste.

Finding and following: a deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivo.

The last decade has seen increasing use of advanced imaging techniques in platelet research. However, there has been a lag in the development of image analysis methods, leaving much of the information trapped in images. Herein, we present a robust analytical pipeline for finding and following individual platelets over time in growing thrombi. Our pipeline covers four steps: detection, tracking, estimation of tracking accuracy, and quantification of platelet metrics. We detect platelets using a deep learning network for image segmentation, which we validated with proofreading by multiple experts. We then track platelets using a standard particle tracking algorithm and validate the tracks with custom image sampling - essential when following platelets within a dense thrombus. We show that our pipeline is more accurate than previously described methods. To demonstrate the utility of our analytical platform, we use it to show that in vivo thrombus formation is much faster than that ex vivo. Furthermore, platelets in vivo exhibit less passive movement in the direction of blood flow. Our tools are free and open source and written in the popular and user-friendly Python programming language. They empower researchers to accurately find and follow platelets in fluorescence microscopy experiments.

In this paper we describe computational tools to find and follow individual platelets in blood clots recorded with fluorescence microscopy. Our tools work in a diverse range of conditions, both in living animals and in artificial flow chamber models of thrombosis. Our work uses deep learning methods to achieve excellent accuracy. We also provide tools for visualizing data and estimating error rates, so you don't have to just trust the output. Our workflow measures platelet density, shape, and speed, which we use to demonstrate differences in the kinetics of clotting in living vessels versus a synthetic environment. The tools we wrote are open source, written in the popular Python programming language, and freely available to all. We hope they will be of use to other platelet researchers.

Integrated transcriptomics and histopathology approach identifies a subset of rejected donor livers with potential suitability for transplantation.

BACKGROUND: Liver transplantation is an effective treatment for liver failure. There is a large unmet demand, even as not all donated livers are transplanted. The clinical selection criteria for donor livers based on histopathological evaluation and liver function tests are variable. We integrated transcriptomics and histopathology to characterize donor liver biopsies obtained at the time of organ recovery. We performed RNA sequencing as well as manual and artificial intelligence-based histopathology (10 accepted and 21 rejected for transplantation). RESULTS: We identified two transcriptomically distinct rejected subsets (termed rejected-1 and rejected-2), where rejected-2 exhibited a near-complete transcriptomic overlap with the accepted livers, suggesting acceptability from a molecular standpoint. Liver metabolic functional genes were similarly upregulated, and extracellular matrix genes were similarly downregulated in the accepted and rejected-2 groups compared to rejected-1. The transcriptomic pattern of the rejected-2 subset was enriched for a gene expression signature of graft success post-transplantation. Serum AST, ALT, and total bilirubin levels showed similar overlapping patterns. Additional histopathological filtering identified cases with borderline scores and extensive molecular overlap with accepted donor livers. CONCLUSIONS: Our integrated approach identified a subset of rejected donor livers that are likely suitable for transplantation, demonstrating the potential to expand the pool of transplantable livers.

Colour-analyzer: a new dual colour model-based imaging tool to quantify plant disease.

BACKGROUND: Despite major efforts over the last decades, the rising demands of the growing global population makes it of paramount importance to increase crop yields and reduce losses caused by plant pathogens. One way to tackle this is to screen novel resistant genotypes and immunity-inducing agents, which must be conducted in a high-throughput manner. RESULTS: Colour-analyzer is a free web-based tool that can be used to rapidly measure the formation of lesions on leaves. Pixel colour values are often used to distinguish infected from healthy tissues. Some programs employ colour models, such as RGB, HSV or L*a*b*. Colour-analyzer uses two colour models, utilizing both HSV (Hue, Saturation, Value) and L*a*b* values. We found that the a* b* values of the L*a*b* colour model provided the clearest distinction between infected and healthy tissue, while the H and S channels were best to distinguish the leaf area from the background. CONCLUSION: By combining the a* and b* channels to determine the lesion area, while using the H and S channels to determine the leaf area, Colour-analyzer provides highly accurate information on the size of the lesion as well as the percentage of infected tissue in a high throughput manner and can accelerate the plant immunity research field.

Resting-state fMRI network efficiency as a mediator in the relationship between the glymphatic system and cognitive function in obstructive sleep apnea hypopnea syndrome: Insights from a DTI-ALPS investigation.

INTRODUCTION: Obstructive sleep apnea hypopnea syndrome (OSAHS) is associated with cognitive impairment and physiological complications, necessitating further understanding of its mechanisms. This study investigates the relationship between glymphatic system function, brain network efficiency, and cognitive impairment in OSAHS patients using diffusion tensor image analysis along the perivascular space (DTI-ALPS) and resting-state fMRI. MATERIALS AND METHODS: This study included 31 OSAHS patients and 34 age- and gender-matched healthy controls (HC). All participants underwent GE 3.0T magnetic resonance imaging (MRI) with diffusion tensor image (DTI) and resting-state fMRI scans. The DTI-ALPS index and brain functional networks were assessed. Differences between groups and correlations with clinical characteristics were analyzed. Additionally, the mediating role of brain network efficiency was explored. Finally, receiver operating characteristics (ROC) analysis assessed diagnostic performance. RESULTS: OSAHS patients had significantly lower ALPS-index (1.268 vs. 1.431, p < 0.0001) and moderate negative correlation with Apnea Hypopnea Index (AHI) (r = -0.389, p = 0.031), as well as moderate positive correlation with Montreal Cognitive Assessment (MoCA) (r = 0.525, p = 0.002). Moreover, global efficiency (Eg) of the brain network was positively correlated with the ALPS-index and MoCA scores in OSAHS patients (r = 0.405, p = 0.024; r = 0.56, p = 0.001, respectively). Furthermore, mediation analysis showed that global efficiency partially mediated the impact of glymphatic system dysfunction on cognitive impairment in OSAHS patients (indirect effect = 4.58, mediation effect = 26.9 %). The AUROC for identifying OSAHS and HC was 0.80 (95 % CI 0.69 to 0.91) using an ALPS-index cut-off of 1.35. CONCLUSIONS: OSAHS patients exhibit decreased ALPS-index, indicating impaired glymphatic system function. Dysfunction of the glymphatic system can affect cognitive function in OSAHS by disrupting brain functional network, suggesting a potential underlying pathological mechanism. Additionally, preliminary findings suggest that the ALPS-index may offer promise as a potential indicator for OSAHS.