Nano Lett. 2025 Apr 9. doi: 10.1021/acs.nanolett.5c01435. Online ahead of print.

ABSTRACT

Point-of-care testing (POCT) with multiplexed capability, ultrahigh sensitivity, affordable smart devices, and user-friendly operation is critically needed for clinical diagnostics and food safety. This study presents a deep-learning-assisted microfluidic immunoassay platform that uses a smartphone-based imaging transcoding system, polystyrene microsphere-based encoding, and artificial-intelligence-assisted decoding. Microspheres of varying sizes act as multiprobes, with their quantities correlating to target concentrations after an immunoreaction and separation-filtration within the microfluidic chip. A smartphone with intelligent decoding software captures images of multiprobes from the chip and performs classification, counting, and concentration calculations. The "encoding-decoding" strategy and integrated microfluidic chip design allow these processes to be completed in simple steps, eliminating the need for additional immunomagnetic separation. As a proof of concept, this platform successfully detected multiple respiratory viruses and antibiotics in various real samples with high sensitivity within 30 min, demonstrating great potential as a smart, universal toolkit for next-generation POCT applications.

PMID:40203242 | DOI:10.1021/acs.nanolett.5c01435

PLoS One. 2025 Apr 9;20(4):e0320184. doi: 10.1371/journal.pone.0320184. eCollection 2025.

ABSTRACT

BACKGROUND: Head and neck cancer (HNC) becomes a vital global health burden. Accurate assessment of the disease burden plays an essential role in setting health priorities and guiding decision-making.

METHODS: This study explores data from the Global Burden of Disease (GBD) 2021 study, involving totally 204 countries during the period from 1980 to 2021. The analysis focuses on age-standardized incidence, mortality, and disability-adjusted life years (DALYs) for HNC. A Transformer-based model, HNCP-T, is used for the prediction of future trends from 2022 to 2030, quantified based on the estimated annual percentage change (EAPC).

RESULTS: The global age-standardized incidence rate (ASIR) for HNC has escalated between 1980 and 2021, with men bearing a higher burden than women. In addition, the burden rises with age and exhibits regional disparities, with the greatest impact on low-to-middle sociodemographic index (SDI) regions. Additionally, the model predicts a continued rise in ASIR (EAPC = 0.22), while the age-standardized death rate (ASDR) is shown to decrease more sharply for women (EAPC = -0.92) than men (EAPC = -0.54). The most rapid increase in ASIR is projected for low-to-middle SDI countries, while ASDR and DALY rates are found to decrease in different degrees across regions.

CONCLUSIONS: The current work offers a detailed analysis of the global burden of HNC based on the GBD 2021 dataset and demonstrates the accuracy of the HNCP-T model in predicting future trends. Significant regional and gender-based differences are found, with incidence rates rising, especially among women and in low-middle SDI regions. Furthermore, the results underscore the value of deep learning models in disease burden prediction, which can outperform traditional methods.

PMID:40203229 | DOI:10.1371/journal.pone.0320184

PLoS One. 2025 Apr 9;20(4):e0320816. doi: 10.1371/journal.pone.0320816. eCollection 2025.

ABSTRACT

Advancements in robotics and other technological innovations have accelerated the development of surgical procedures, increasing the demand for training environments that accurately replicate human anatomy. This study developed a system that utilizes the AutoSegmentator extension of 3D Slicer, based on nnU-Net, a state-of-the-art deep learning framework for automatic organ extraction, to import automatically extracted organ surface data into CAD software along with original DICOM-derived images. This system allows medical experts to manually refine the automatically extracted data, making it more accurate and closer to the ideal dataset. First, Python programming is used to automatically generate and save JPEG-format image data from DICOM data for display in Blender. Next, DICOM data imported into 3D Slicer is processed by AutoSegmentator to extract surface data of 104 organs in bulk, which is then exported in STL format. In Blender, a custom-developed Python script aligns the image data and organ surface data within the same 3D space, ensuring accurate spatial coordinates. By using Blender's CAD functionality within this space, the automatically extracted organ boundaries can be manually adjusted based on the image data, resulting in more precise organ surface data. Additionally, organs and blood vessels that cannot be automatically extracted can be newly created and added by referencing the image data. Through this process, a comprehensive anatomical dataset encompassing all required organs and blood vessels can be constructed. The dataset created with this system is easily customizable and can be applied to various surgical simulations, including 3D-printed simulators, hybrid simulators that incorporate animal organs, and surgical simulators utilizing augmented reality (AR). Furthermore, this system is built entirely using open-source, free software, providing high reproducibility, flexibility, and accessibility. By using this system, medical professionals can actively participate in the design and data processing of surgical simulation systems, leading to shorter development times and reduced costs.

PMID:40203219 | DOI:10.1371/journal.pone.0320816

Langmuir. 2025 Apr 9. doi: 10.1021/acs.langmuir.4c04969. Online ahead of print.

ABSTRACT

Geopolymer is regarded as a novel type of eco-friendly material that may replace cement. To improve the prediction accuracy of mechanical properties of fly ash-slag-based geopolymer (FASGG), as well as optimize material composition and mix design, this study utilizes seven key parameters as variables, and compressive and flexural strengths were as outputs. Deep learning techniques were applied to train and predict 600 sets of experimental data, developing a novel predictive model of MK-CNN-GRU, which integrated Maximal Information Coefficient-K-median algorithm, Convolutional Neural Network, and Gated Recurrent Unit algorithms. Results indicated that the ranking of input parameters which were related with compressive strength was curing age, Ca/Si ratio, fly ash-to-slag ratio, Si/Al ratio, water-to-binder ratio, alkali activator modulus, and alkali equivalent. Three classical models were selected as benchmarks for predicting compressive strength at different curing ages. The MK-CNN-GRU model could fully exploit the internal features of experimental data and learn its variation patterns, resulting in more stable predictive performance. An ablation study of the submodels confirms that MK-CNN-GRU model considers temporal dependencies, long- and short-term features, as well as local dependencies and hierarchical feature representations within the data. Experimental data suggested an exponential relationship between flexural and compressive strengths of FASGG. The predictions for flexural strength indicated that the MK-CNN-GRU model effectively captured variations, demonstrating good generalization ability and applicability. This model enhances the estimation accuracy regarding the mechanical behavior of FASGG, offering a theoretical framework for refining its composition and mix design.

PMID:40203137 | DOI:10.1021/acs.langmuir.4c04969

PLoS Comput Biol. 2025 Apr 9;21(4):e1012997. doi: 10.1371/journal.pcbi.1012997. Online ahead of print.

ABSTRACT

In the mouse distal terminal phalanx (P3), it remains mystery why amputation at less than 33% of the digit results in regeneration, while amputation exceeding 67% leads to non-regeneration. Unraveling the molecular mechanisms underlying this disparity could provide crucial insights for regenerative medicine. In this study, we aim to investigate the tissues within the wound bed to understand the tissue microenvironment associated with regenerative versus non-regenerative outcomes. We employed a P3-specific amputation model in mice, integrated with time-series RNA-seq and a macrophage assay challenged with pro- and anti-inflammatory cytokines, to explore these mechanisms. Our findings revealed that non-regenerative digits exhibit a greater intense early transcriptional response in the wound bed compared to regenerative ones. Furthermore, early macrophage phenotypes differ distinctly between regenerative and non-regenerative outcomes. Regenerative digits also display unique co-expression modules related to Bone Morphogenetic Protein 2 (Bmp2). The differentially expressed genes (DEGs) between regenerative and non-regenerative digits are enriched in targets of several transcription factors, such as HOXA11 and HOXD11 from the HOX gene family, showing a time-dependent pattern of enrichment. These transcription factors, known for their roles in bone regeneration, skeletal patterning, osteoblast activity, fracture healing, angiogenesis, and key signaling pathways, may act as master regulators of the regenerative gene signatures. Additionally, we developed a deep learning AI model capable of predicting post-amputation time and level from RNA-seq data, indicating that the regenerative probability may be "encoded" in the transcriptomic response to amputation.

PMID:40203060 | DOI:10.1371/journal.pcbi.1012997

PLoS One. 2025 Apr 9;20(4):e0315340. doi: 10.1371/journal.pone.0315340. eCollection 2025.

ABSTRACT

Deep learning, as a high-performance data analysis method, has demonstrated superior efficiency and accuracy in side-channel attacks compared to traditional methods. However, many existing models enhance accuracy by stacking network layers, leading to increased algorithmic and computational complexity, overfitting, low training efficiency, and limited feature extraction capabilities. Moreover, deep learning methods rely on data correlation, and the presence of noise tends to reduce this correlation, increasing the difficulty of attacks. To address these challenges, this paper proposes the application of an InceptionNet-based network structure for side-channel attacks. This network utilizes fewer training parameters. achieves faster convergence and demonstrates improved attack efficiency through parallel processing of input data. Additionally, a LU-Net-based network structure is proposed for denoising side-channel datasets. This network captures the characteristics of input signals through an encoder, reconstructs denoised signals using a decoder, and utilizes LSTM layers and skip connections to preserve the temporal coherence and spatial details of the signals, thereby achi-eving the purpose of denoising. Experimental evaluations were conducted on the ASCAD dataset and the DPA Contest v4 dataset for comparative studies. The results indicate that the deep learning attack model proposed in this paper effectively enhances side-channel attack performance. On the ASCAD dataset, the recovery of keys requires only 30 traces, and on the DPA Contest v4 dataset, only 1 trace is needed for key recovery. Furthermore, the proposed deep learning denoising model significantly reduces the impact of noise on side-channel attack performance, thereby improving efficiency.

PMID:40203055 | DOI:10.1371/journal.pone.0315340

PLoS One. 2025 Apr 9;20(4):e0320085. doi: 10.1371/journal.pone.0320085. eCollection 2025.

ABSTRACT

An enhancer is a specific DNA sequence typically located within a gene at upstream or downstream position and serves as a pivotal element in the regulation of eukaryotic gene transcription. Therefore, the recognition of enhancers is highly significant for comprehending gene expression regulatory systems. While some useful predictive models have been proposed, there are still deficiencies in these models. To address current limitations, we propose a model, DNABERT2-Enhancer, based on transformer architecture and deep learning, designed for the recognition of enhancers (classified as either enhancer or non-enhancer) and the identification of their activity (strong or weak enhancers). More specifically, DNABERT2-Enhancer is composed of a BERT model for extracting features and a CNN model for enhancers classification. Parameters of the BERT model are initialized by a pre-training DNABERT-2 language model. The enhancer recognition task is then fine-tuned through transfer learning to convert the original sequence into feature vectors. Subsequently, the CNN network is employed to learn the feature vector generated by BERT and produce the prediction results. In comparison with existing predictors utilizing the identical dataset, our approach demonstrates superior performance. This suggests that the model will be a useful instrument for academic research on the enhancer recognition.

PMID:40203028 | DOI:10.1371/journal.pone.0320085

PLoS One. 2025 Apr 9;20(4):e0321028. doi: 10.1371/journal.pone.0321028. eCollection 2025.

ABSTRACT

Campus walking environments significantly influence college students' daily lives and shape their subjective perceptions. However, previous studies have been constrained by limited sample sizes and inefficient, time-consuming methodologies. To address these limitations, we developed a deep learning framework to evaluate campus walking perceptions across four universities in China's Yangtze River Delta region. Utilizing 15,596 Baidu Street View Images (BSVIs), and perceptual ratings from 100 volunteers across four dimensions-aesthetics, security, depression, and vitality-we employed four machine learning models to predict perceptual scores. Our results demonstrate that the Random Forest (RF) model outperformed others in predicting aesthetics, security, and vitality, while linear regression was most effective for depression. Spatial analysis revealed that perceptions of aesthetics, security, and vitality were concentrated in landmark areas and regions with high pedestrian flow. Multiple linear regression analysis indicated that buildings exhibited stronger correlations with depression (β = 0.112) compared to other perceptual aspects. Moreover, vegetation (β = 0.032) and meadows (β = 0.176) elements significantly enhanced aesthetics. This study offers actionable insights for optimizing campus walking environments from a student-centered perspective, emphasizing the importance of spatial design and visual elements in enhancing students' perceptual experiences.

PMID:40203019 | DOI:10.1371/journal.pone.0321028

PLoS One. 2025 Apr 9;20(4):e0321233. doi: 10.1371/journal.pone.0321233. eCollection 2025.

NO ABSTRACT

PMID:40203015 | DOI:10.1371/journal.pone.0321233

Mol Biol Evol. 2025 Apr 9:msaf084. doi: 10.1093/molbev/msaf084. Online ahead of print.

ABSTRACT

Ancestral sequence reconstruction (ASR) is typically performed using homogeneous evolutionary models, which assume that the same substitution propensities affect all sites and lineages. These assumptions are routinely violated: heterogeneous structural and functional constraints favor different amino acids at different sites, and these constraints often change among lineages as epistatic substitutions accrue at other sites. To evaluate how violations of the homogeneity assumption affect ASR under realistic conditions, we developed site-specific substitution models and parameterized them using data from deep mutational scanning experiments on three protein families; we then used these models to perform ASR on the empirical alignments and on alignments simulated under heterogeneous conditions derived from the experiments. Extensive among-site and -lineage heterogeneity is present in these datasets, but the sequences reconstructed from empirical alignments are almost identical when heterogeneous or homogeneous models are used for ASR. Using models fit to DMS data from distantly related proteins in which mutational effects are very different also has a minimal impact on ASR. The rare differences occur primarily where phylogenetic signal is weak - at fast-evolving sites and nodes connected by long branches. When ASR is performed on simulated data, errors in the reconstructed sequences become more likely as branch lengths increase, but incorporating heterogeneity into the model does not improve accuracy. These data establish that ASR is robust to unincorporated realistic forms of evolutionary heterogeneity, because the primary determinant of ASR is phylogenetic signal, not the substitution model. The best way to improve accuracy is therefore not to develop more elaborate models but to apply ASR to densely sampled alignments that maximize phylogenetic signal at the nodes of interest.

PMID:40203289 | DOI:10.1093/molbev/msaf084

Proc Natl Acad Sci U S A. 2025 Apr 15;122(15):e2403083122. doi: 10.1073/pnas.2403083122. Epub 2025 Apr 9.

ABSTRACT

Wetting of liquid droplets on passive surfaces is ubiquitous in our daily lives, and the governing physical laws are well understood. When surfaces become active, however, the governing laws of wetting remain elusive. Here, we propose chemically active wetting as a class of active systems where the surface is active due to a binding process that is maintained away from equilibrium. We derive the corresponding nonequilibrium thermodynamic theory and show that active binding fundamentally changes the wetting behavior, leading to steady, nonequilibrium states with droplet shapes reminiscent of a pancake or a mushroom. The origin of such anomalous shapes can be explained by mapping to electrostatics, where pairs of binding sinks and sources correspond to electrostatic dipoles along the triple line. This is an example of a more general analogy, where localized chemical activity gives rise to a multipole field of the chemical potential. The underlying physics is relevant for cells, where droplet-forming proteins can bind to membranes accompanied by the turnover of biological fuels.

PMID:40203039 | DOI:10.1073/pnas.2403083122

STAR Protoc. 2025 Apr 8;6(2):103747. doi: 10.1016/j.xpro.2025.103747. Online ahead of print.

ABSTRACT

Activity tagging of neuronal ensembles has become an important tool in neuroscience. Here, we present a protocol for visualizing separate neuronal ensembles active during three distinct phases of a memory in transgenic mice. We describe steps to label active neurons using viral microinjection, inducing GFP expression under the robust activity marker (RAM) promoter, and transgenic mice, inducing tdTomato (TdT) expression, and immunohistochemical (IHC) visualization of endogenous cFos expression. We then detail procedures for preparation of tissue, imaging, and quantification of memory events. For complete details on the use and execution of this protocol, please refer to Lesuis et al.1.

PMID:40202842 | DOI:10.1016/j.xpro.2025.103747

STAR Protoc. 2025 Apr 8;6(2):103705. doi: 10.1016/j.xpro.2025.103705. Online ahead of print.

ABSTRACT

We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps for data preparation, subSCOPE setup, and running subSCOPE inference on prepared data. The protocol supports five -omics data types as input (DNA methylation, gene expression, microRNA [miRNA] expression, point mutations, and copy-number variants) and allows individual cancer type and data type selection. For non-The Cancer Genome Atlas (TCGA) cancer samples, it provides subtype-level classification across 26 different TCGA cancer cohorts and 106 subtypes. For complete details on the use and execution of this protocol, please refer to Ellrott et al.1.

PMID:40202838 | DOI:10.1016/j.xpro.2025.103705

Mol Biol Cell. 2025 Apr 9:mbcE24090394. doi: 10.1091/mbc.E24-09-0394. Online ahead of print.

ABSTRACT

The tumor suppressor protein p53 plays a key role in the cellular response to DNA damage. In response to DNA double strand breaks (DSB), cultured cells exhibit oscillations of p53 levels, which impact gene expression and cell fate. The dynamics of p53 in-vivo have only been studied in fixed tissues or using reporters for p53's transcriptional activity. Here we established breast tumors expressing a fluorescent reporter for p53 levels and employed intravital imaging to quantify its dynamics in response to DSB in-vivo. Our findings revealed large heterogeneity among individual cells, with most cells exhibiting a single prolonged pulse. We then tested how p53 dynamics might change under high cell confluency, one factor that differs between cell culture and tissues. We revealed that highly confluent cultured breast cancer cells also show one broad p53 pulse instead of oscillations. Through mathematical modeling, sensitivity analysis and live cell imaging we identified low levels of the phosphatase Wip1, a transcriptional target and negative regulator of p53, as a key contributor to these dynamics. Since high cell confluency better reflects the microenvironment of tissues, the impact of cell confluency on p53 dynamics may have important consequences for cancerous tissues responding to DNA damage inducing therapies.

PMID:40202833 | DOI:10.1091/mbc.E24-09-0394

Mol Biol Cell. 2025 Apr 9:mbcE25030119. doi: 10.1091/mbc.E25-03-0119. Online ahead of print.

ABSTRACT

Pyroptosis represents one type of Programmed Cell Death (PCD). It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and Gasdermin-mediated membrane pore formation. Caspase-1 initiates the inflammatory response (through IL-1β processing), and the N-terminal cleaved fragment of Gasdermin D polymerizes at the cell periphery forming pores to secrete pro-inflammatory markers. Cell morphology also changes in pyroptosis, with nuclear condensation and membrane rupture. However, recent research challenges canon, revealing a more complex secretome and morphological response in pyroptosis, including overlapping molecular characterization with other forms of cell death, such as apoptosis. Here, we take a multimodal, systems biology approach to characterize pyroptosis. We treated human Peripheral Blood Mononuclear Cells (PBMCs) with 36 different combinations of stimuli to induce pyroptosis or apoptosis. We applied both secretome profiling (nELISA) and high-content fluorescence microscopy (Cell Painting). To differentiate apoptotic, pyroptotic and control cells, we used canonical secretome markers and modified our Cell Painting assay to mark the N-terminus of Gasdermin-D. We trained hundreds of machine learning (ML) models to reveal intricate morphology signatures of pyroptosis that implicate changes across many different organelles and predict levels of many pro-inflammatory markers. Overall, our analysis provides a detailed map of pyroptosis which includes overlapping and distinct connections with apoptosis revealed through a mechanistic link between cell morphology and cell secretome.

PMID:40202832 | DOI:10.1091/mbc.E25-03-0119

Mol Biol Rep. 2025 Apr 9;52(1):373. doi: 10.1007/s11033-025-10474-7.

ABSTRACT

BACKGROUND: Gastric cancer is a highly heterogeneous and aggressive disease with limited treatment options, necessitating innovative therapeutic strategies. Drug repurposing, a cost-effective approach, offers opportunities to identify new applications for existing medications. This study systematically investigated the apoptotic effects of serotonergic drugs on MKN-45 gastric cancer cells, providing a novel perspective on serotonin signaling in cancer therapy.

METHODS AND RESULTS: MKN-45 cells were treated with concentrations of Tropisetron, Imipramine, Ketanserin, Citalopram, and Cyproheptadine. The IC50 values were determined using an MTT assay, while acridine orange/ethidium bromide staining and Annexin V/PI flow cytometry assessed apoptotic activity. Gene expression related to serotonin receptors (HTR2A, HTR2B, HTR3A), Serotonin transporter (SLC6A4), apoptosis (Bcl-2, Bax), and proliferation (PCNA) was evaluated via real-time PCR. Tropisetron, Imipramine, Ketanserin, and Cyproheptadine demonstrated statistically significant apoptotic induction compared to untreated cells. These treatments significantly reduced anti-apoptotic Bcl-2 and PCNA, proliferation marker, expression, while pro-apoptotic Bax expression was markedly elevated (p < 0.05).

CONCLUSIONS: This study highlights the potential of Tropisetron, Imipramine, Ketanserin, and Cyproheptadine as repurposed drugs for gastric cancer therapy, with Tropisetron and Imipramine showing particularly promising apoptotic effects. These findings pave the way for further preclinical and clinical investigations, offering a foundation for personalized therapeutic strategies in gastric cancer management.

PMID:40202572 | DOI:10.1007/s11033-025-10474-7

Am J Health Syst Pharm. 2025 Apr 9:zxaf090. doi: 10.1093/ajhp/zxaf090. Online ahead of print.

ABSTRACT

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

PURPOSE: Expanding access to pharmacogenomics (PGx) testing to veterans has been an emphasis in the Veterans Health Administration (VHA); using population dashboard tools (PDTs) may identify additional patients who qualify for testing. Involving pharmacy residents in PGx can help prepare them for precision medicine practice and more efficiently provide PGx care to patients.

METHODS: Veterans treated in the outpatient setting at the Lt. Col. Luke Weathers, Jr. Veterans Affairs (VA) Medical Center from March 2023 to June 2024 were included in this study. Upon creation of a virtual PGx clinic, a PGx PDT was used to identify patients newly prescribed medications on the VA PGx gene testing panel. The clinic was driven by a postgraduate year 2 pharmacy resident with a preceptor overseeing the practice, and patients were contacted for consent and testing. The number and type of PGx gene variants identified were assessed, with results discussed with patients and recommendations made to providers.

RESULTS: A total of 130 patients were screened, of whom 104 had PGx testing, corresponding to an 80% consent rate. Overall, 247 PGx gene variants were identified, including 149 informational and 78 actionable drug-gene variants, 18 variants indicating inheritable conditions, and 17 variants corresponding to phenoconversion. A total of 90 recommendations were made to providers, and patients had an average of 2.3 PGx-impacted medications prescribed. Of the actionable drug-gene variants, the majority were related to use of clopidogrel, statins, sertraline, and proton pump inhibitors.

CONCLUSION: Novel use of a PDT was helpful in identifying patients qualifying for PGx testing. Creation of the resident-driven clinic resulted in PGx interventions for the majority of patients who underwent testing.

PMID:40202453 | DOI:10.1093/ajhp/zxaf090

Expert Opin Drug Discov. 2025 Apr 9. doi: 10.1080/17460441.2025.2490250. Online ahead of print.

ABSTRACT

INTRODUCTION: The advent of variant-specific disease-modifying drugs into clinical practice has provided remarkable benefits for people with cystic fibrosis (PwCF), a multi-organ life-limiting inherited disease. However, further efforts are needed to maximize therapeutic benefits as well as to increase the number of PwCF taking CFTR modulators.

AREA COVERED: The authors discuss some of the key limitations of the currently available CFTR modulator therapies (e.g. adverse reactions) and strategies in development to increase the number of available therapeutics for CF. These include novel methods to accelerate theratyping and identification of novel small molecules and cellular targets representing alternative or complementary therapies for CF.

EXPERT OPINION: While the CF therapy development pipeline continues to grow, there is a critical need to optimize strategies that will accelerate testing and approval of effective therapies for (ultra)rare CFTR variants as traditional assays and trials are not suitable to address such issues. Another major barrier that needs to be solved is the restricted access to currently available modulator therapies, which remains a significant burden for PwCF who are from racial and ethnic minorities or living in underprivileged regions.

PMID:40202089 | DOI:10.1080/17460441.2025.2490250

Invest Ophthalmol Vis Sci. 2025 Apr 1;66(4):22. doi: 10.1167/iovs.66.4.22.

ABSTRACT

PURPOSE: This study aims to develop a new method to quantify nonperfused retinal capillaries (NPCs) and evaluate NPCs in eyes with AMD and diabetic retinopathy (DR).

METHODS: We averaged multiple registered optical coherence tomography (OCT)/OCT angiography (OCTA) scans to create high-definition volumes. The deep capillary plexus slab was defined and segmented. A developed deep learning denoising algorithm removed tissue background noise from capillaries in en face OCT/OCTA. The algorithm segmented NPCs by identifying capillaries from OCT without corresponding flow signals in OCTA. We then investigated the relationships between NPCs and known features in AMD and DR.

RESULTS: The segmented NPC achieved an accuracy of 88.2% compared to manual grading of NPCs in DR. Compared to healthy controls, both the mean number and total length (mm) of NPCs was significantly increased in AMD and DR eyes (P < 0.001, P < 0.001). Compared to early and intermediate AMD, the number and total length of NPCs were significantly higher in advanced AMD (number: P < 0.001, P < 0.001; total length: P = 0.002, P = 0.003). Geography atrophy, macular neovascularization, drusen volume, and extrafoveal avascular area (EAA) significantly correlated with increased NPCs (P < 0.05). In DR eyes, NPCs correlated with the number of microaneurysms and EAA (P < 0.05). The presence of fluid did not significantly correlate with NPCs in AMD and DR.

CONCLUSIONS: A deep learning-based algorithm can segment and quantify retinal capillaries that lack flow using colocalized OCT/OCTA. This new biomarker may be useful in AMD and DR in predicting progression of these diseases.

PMID:40202734 | DOI:10.1167/iovs.66.4.22

Radiol Artif Intell. 2025 Apr 9:e240459. doi: 10.1148/ryai.240459. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the predictive value of deep learning (DL)-based coronary artery disease (CAD) extent analysis for major adverse cardiac events (MACEs) in patients with acute chest pain presenting to the emergency department (ED). Materials and Methods This retrospective multicenter observational study included consecutive patients with acute chest pain who underwent coronary CT angiography (CCTA) at three institutional EDs from January 2018 to December 2022. Patients were classified as having no CAD, nonobstructive CAD, or obstructive CAD using a DL model. The primary outcome was MACEs during follow-up, defined as a composite of cardiac death, nonfatal myocardial infarction, and hospitalization for unstable angina. Cox proportional hazards regression models were used to evaluate the predictors of MACEs. Results The study included 408 patients (224 male; mean age, 59.4 ± 14.6 years). The DL model classified 162 (39.7%) patients as having no CAD, 94 (23%) as having nonobstructive CAD, and 152 (37.3%) as having obstructive CAD. Sixty-three (15.4%) patients experienced MACEs during follow-up. Patients with MACEs had a higher prevalence of obstructive CAD than those without (P < .001). In multivariate analysis model 1 (clinical risk factors), dyslipidemia (Hazard ratio [HR], 2.15 and elevated Troponin-T (HR 2.13) predicted MACEs (all P < .05). In model 2 (clinical risk factors + DL-based CAD extent), obstructive CAD detected by the DL model was the most significant independent predictor of MACEs (HR, 88.07, P < .001). Harrell's C-statistic showed that DL-based CAD extent enhanced the risk stratification beyond clinical risk factors (Harrell's C-statistics: 0.94 versus 0.80, P < .001). Conclusion DL-based detection of obstructive CAD demonstrated stronger predictive value than clinical risk factors for MACEs in patients with acute chest pain presenting to the ED. ©RSNA, 2025.

PMID:40202417 | DOI:10.1148/ryai.240459