Proc Natl Acad Sci U S A. 2025 May 13;122(19):e2421747122. doi: 10.1073/pnas.2421747122. Epub 2025 May 6.

ABSTRACT

Infiltrating neutrophils are key effector cells in inflammatory bowel disease (IBD) while providing antimicrobial defense and tissue restitution in the intestine. The complexity of neutrophil functions in local environments underscores our limited understanding of how their adaptation in tissues influences disease progression. Here, we demonstrate that neutrophils recruited in murine colitis and infection models, idiopathic IBD, and chronic granulomatous disease-associated IBD undergo extensive transcriptional reprogramming, resulting in the emergence of neutrophil populations that feature unique DUOX2 NADPH oxidase expression. Functional studies utilizing mice with myeloid and neutrophil specific DUOX2 inactivation reveal a vital and dichotomous role for this NADPH oxidase in both colitis and intestinal infection. Niche-directed reprogramming promoted a DUOX2-dependent chemokine and cytokine-rich intestinal environment that amplified and prolonged inflammatory responses, suggesting that selectively suppressing DUOX2 may constitute an anti-inflammatory strategy for IBD treatment. Altering spatiotemporal redox signaling by de novo expression of a ROS-generating enzyme represents an important feature for functional neutrophil diversification in disease, with implications for other neutrophil-driven diseases in specialized niches.

PMID:40327691 | DOI:10.1073/pnas.2421747122

Bioinformatics. 2025 May 6:btaf280. doi: 10.1093/bioinformatics/btaf280. Online ahead of print.

ABSTRACT

MOTIVATION: Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram describes this interplay in a discrete analog of Waddington's epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. Efficient computational tools for studying succession diagrams are essential for the understanding of Boolean attractor landscapes and connecting them to their biological functions.

RESULTS: We present a new approach to succession diagram construction for asynchronously updated Boolean networks, implemented in the biologist's Boolean attractor landscape mapper, biobalm. We compare biobalm to similar tools and find a substantial performance increase in succession diagram construction, attractor identification, and attractor control. We perform the most comprehensive comparative analysis to date of the succession diagram structure in experimentally-validated Boolean models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small succession diagrams, indicating simple decision structures. In contrast, non-random models from the literature are enriched in extremely large succession diagrams, indicating an abundance of decision points and suggesting the presence of complex Waddington landscapes in nature.

AVAILABILITY AND IMPLEMENTATION: The tool biobalm is available online at https://github.com/jcrozum/biobalm. Further data, scripts for testing, analysis and figure generation are available online at https://github.com/jcrozum/biobalm-analysis and in the reproducibility artefact at https://doi.org/10.5281/zenodo.13854760.

CONTACT AND SUPPLEMENTARY INFORMATION: giang.trinh91@gmail.com (V.G.T.), xpastva@fi.muni.cz (S.P.), jrozum@binghamton.edu (J.C.R.); The Supplementary Text is available online through Bioinformatics.

PMID:40327535 | DOI:10.1093/bioinformatics/btaf280

Naunyn Schmiedebergs Arch Pharmacol. 2025 May 6. doi: 10.1007/s00210-025-04233-5. Online ahead of print.

ABSTRACT

Since the onset of the COVID-19 pandemic in March 2020, researchers worldwide have sought effective drugs to prevent and manage SARS-CoV-2 and its spectrum of symptoms. Ivermectin, originally developed as an anthelmintic for controlling parasitic infections in humans and animals, has drawn attention based on the hypothesis that it inhibits viral replication. In Austria, ivermectin usage peaked in November 2021, following promotion by the right-wing Freedom Party of Austria (FPÖ) as an alternative treatment to vaccination, resonating strongly within anti-vaccine and skeptical communities. The topic is also very present in the United States of America due to the re-election of D. Trump as US President and the designation of R. Kennedy as the United States' Secretary of Health and Human Services. To critically examine the controversial use of ivermectin for COVID-19 and publication trends during the pandemic, this study analysed all publications listed in PubMed from 1 January 2020 to 31 December 2022 using the keywords 'ivermectin' and 'COVID-19', resulting in a dataset of 353 publications. These publications were assessed for scientific quality, methodological rigour and bias, with particular focus on the influence of social and political dynamics on publication practices, as well as the prevalence of preprints, citation trends and the role of funding sources. Our study shows that many highly cited studies on ivermectin display methodological weaknesses and data gaps, contributing to the propagation of hypotheses lacking substantial empirical support. This analysis underscores the necessity of rigorous quality control during crises and highlights the long-term risks posed to scientific databases and public health by methodologically deficient research.

PMID:40327060 | DOI:10.1007/s00210-025-04233-5

Anal Chem. 2025 May 6. doi: 10.1021/acs.analchem.5c00390. Online ahead of print.

ABSTRACT

The interpretation of proteomics data often relies on functional enrichment analysis, such as Gene Ontology (GO) enrichment, to uncover the biological functions of proteins, as well as the examination of protein expression patterns across data sets like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. However, conventional approaches to functional enrichment frequently produce extensive and redundant term lists, complicating interpretation and synthesis. Moreover, the absence of specialized tools tailored to proteomics researchers limits the efficient exploration of protein expression within specific biological contexts. To address these challenges, we developed Ontolomics-P, a user-friendly web-based tool designed to advance proteomics data interpretation. Ontolomics-P integrates topic modeling using latent Dirichlet allocation (LDA) with GO semantic similarity analysis, enabling the consolidation of redundant terms into coherent topics. These topics are further refined and reannotated using the GPT-4o language model, creating a novel topics database that provides precise and interpretable insights into shared biological functions. Additionally, Ontolomics-P incorporates quantitative proteomic data from 10 diverse cancer types archived in the CPTAC database, allowing for a comprehensive exploration of protein expression profiles from a data-driven perspective. Through detailed case studies, we demonstrate the tool's capacity to streamline workflows, simplify interpretation, and provide actionable biological insights. Ontolomics-P represents a significant advancement in proteomics data analysis, offering innovative solutions for functional annotation, quantitative exploration, and visualization, ultimately empowering researchers to accelerate discoveries in systems biology and beyond.

PMID:40326493 | DOI:10.1021/acs.analchem.5c00390

Pharmacogenet Genomics. 2025 May 6. doi: 10.1097/FPC.0000000000000568. Online ahead of print.

ABSTRACT

OBJECTIVES: Evaluate the feasibility of implementing a multigene pharmacogenetic (PGx) test and genotype-guided pharmacist recommendations into gynecologic perioperative workflows and fidelity to pharmacist genotype-guided postoperative analgesic recommendations.

METHODS: A randomized, prospective, open-label pilot study was conducted in gynecologic patients undergoing abdominal surgery. Participants received multigene PGx testing and were randomized to the PGx-guided group where results were returned to the electronic health record with pharmacist genotype-guided postoperative analgesic recommendations or usual care. Primary outcomes included the proportion of PGx results and pharmacist recommendations completed before surgery, the number of prescriptions in alignment with pharmacist recommendations, and the proportion of analgesics prescribed differing from usual care.

RESULTS: Of the 101 participants analyzed, all were female, 50 ± 14 years old, 49% were Black, 48% were White, 60% were treated by gynecologic oncology, and 76% underwent minimally invasive surgery. PGx results were returned to the genomics results portal a median of 7 (interquartile range: 6-9) business days after ordering the test. A majority (85%) of results were returned before the participant's surgery. Pharmacist genotype-guided analgesic recommendations were completed for 35 (73%) of the 48 participants in the PGx-guided group. And, 32 (91%) of the prescribed nonsteroidal anti-inflammatory drugs and 23 (66%) of the prescribed opioids matched the pharmacist's recommendations. Barriers included missed pharmacist notes when surgery dates were moved and low use of study-specific order set.

CONCLUSION: PGx test results were available before most surgeries, but the pharmacist recommendations were not always followed. Enhanced implementation strategies will need to be developed in future genotype-guided protocols.

PMID:40327052 | DOI:10.1097/FPC.0000000000000568

J Patient Rep Outcomes. 2025 May 6;9(1):48. doi: 10.1186/s41687-025-00879-0.

ABSTRACT

BACKGROUND: Elexacaftor/Tezacaftor/Ivacaftor (ETI) is a therapy approved for cystic fibrosis (CF) that has given improved clinical outcomes in patients carrying the F508del mutation. There are few published data regarding ETI's effects on patients' quality of life (QoL). This study aims to (fill the data gap in current literature by assessing) evaluate the long-term effects of ETI on QoL.

METHODOLOGY: A prospective observational study was conducted with thirty-seven severe patients that received ETI for compassionate use (group A), 184 received it for on-label use (group B). All carried one F508del mutation. Patients were assessed using the CFQ-R (Cystic Fibrosis Questionnaire-Revised). The evaluation time-points were pre-treatment (T0), and after 12 (T1) and 24 months (T2); group A was also assessed after 36 months (T3). Twenty-five patients completed 3 years of treatment and 65 patients completed 2 years of treatment, in groups A and B respectively.

RESULTS: At T1, median values for almost all areas of CFQ-R statistically significant increased in group A, particularly Physical Functioning (+ 25.0), Respiratory (+ 22.2) and Health Perception (+ 22.2).The Social Functioning area statistically significant increased at T2 (+ 5.6). At T3, these improvements remained stable. At T1, all areas of CFQ-R statistically significant increased in group B, particularly the Health Perception (+ 22,2) heading. At T2, these improvements remained stable. For both groups, the changes identified at the last follow-up showed no major differences by gender, age or genetic status.

CONCLUSIONS: Treatment with ETI significantly improved patients' QoL in both groups at 12-24 months, these improvements remaining stable in patients tested at 36 months.

PMID:40327240 | DOI:10.1186/s41687-025-00879-0

Allergy. 2025 May 6. doi: 10.1111/all.16581. Online ahead of print.

NO ABSTRACT

PMID:40326788 | DOI:10.1111/all.16581

Pediatr Pulmonol. 2025 May;60(5):e71110. doi: 10.1002/ppul.71110.

ABSTRACT

BACKGROUND: There have been significant improvements in the health of infants and children with cystic fibrosis (CF) since universal newborn screening (NBS) was implemented in the United States (US). However, a significant proportion of infants with CF are not evaluated in a timely manner, and delays disproportionately affect children from minoritized racial/ethnic groups. The aim of this study was to understand experiences of NBS in caregivers of young children with CF in the United States.

METHODS: We recruited caregivers of children (0-13 years) with CF through listservs and social media of CF organizations. The survey was administered online in 2023 and included questions about their recollections of their child's NBS and the process of getting a CF diagnosis.

RESULTS: Of 383 caregiver respondents, 43% reported being informed that their child's race or ethnicity was a predictor of the chances of their child having CF. Most reported that after they were notified of a positive NBS test, the initial evaluation for CF was scheduled ≥ 4 days later, 45% reported a delay of ≥ 8 days, and 5% reporting a delay of ≥ 15 days. Most (91%) felt the initial evaluation for CF was thorough, but 35% reported delays in getting information about their child's diagnosis.

CONCLUSIONS: Caregivers report delays in evaluation after a positive NBS. A significant proportion reported delays in receiving information about their child's diagnosis or being told that race or ethnicity were related to risk of CF. These findings show the need for education and practice changes in both primary care and CF center settings.

PMID:40326644 | DOI:10.1002/ppul.71110

Acta Physiol (Oxf). 2025 Jun;241(6):e70051. doi: 10.1111/apha.70051.

ABSTRACT

AIM: Amino acids, sugars, short-chain fatty acids (SCFA), vitamins, and other small molecules compose the extracellular metabolome on the airway lumen surface, but how the airway epithelium deals with these molecules has not been deeply studied. Due to the broad spectrum of metabolites transported by SLC5A8 and SLC5A12, we aim to determine if they are functionally expressed and participate in the absorption of Na+, short-chain fatty acids, and monocarboxylates in mouse and human airway epithelium.

METHODS: Tracheas isolated from male or female mice and human bronchial epithelial cells (HBECs) were used for electrophysiological studies in the Ussing chamber and to detect members of the SLC16 family by RT-PCR and bulk RNAseq. Additionally, cell lines expressing the human and murine SLC5A8 transporter were employed for uptake studies using a fluorescent lactate probe.

RESULTS: We showed for the first time that human and murine airway epithelium express a functional SLC5A8 transporter, facilitating the absorption of glucose metabolites and SCFAs. The Na+-coupled monocarboxylate transport was not additive with ENaC-mediated Na+ absorption in mouse trachea. We observed that valproate acts as an inhibitor of the murine but not of the human SLC5A8 transporter.

CONCLUSIONS: Our results demonstrate that several metabolites derived from bacterial and cellular metabolism can be transported from the airway lumen into the epithelial cells, participating in a homeostatic relation of the tissue with its environment.

PMID:40326639 | DOI:10.1111/apha.70051

Pediatr Pulmonol. 2025 May;60(5):e71117. doi: 10.1002/ppul.71117.

ABSTRACT

BACKGROUND: Previous research showed that lung function abnormalities are common in infants with cystic fibrosis (IwCF) but real-world data are missing.

METHODS: This single-center retrospective study analyzed infant lung function results from IwCF born in 2012-2018. The tests were conducted at Great Ormond Street Hospital, London, as part of routine care at 3 months, 1 year, and 2 years of age. Z-scores for SF6 Lung Clearance Index (zLCI), plethysmographic FRC (zFRCpleth) and FEV0.5 were derived. Microbiology and antibiotics prescription from 3 months before lung function assessments, up to the closest medical review following the lung function encounter, were analyzed, along with changes in management advised by the physician.

RESULTS: A total of 126 lung function encounters (n = 43 at 3 months, 46 at 1 year, 37 at 2 years) from 60 IwCF were included. LCI was abnormal (zLCI > 1.96) in 31% (12/39) of 3-month-olds (mean± zLCI 1.21 ± 1.08), 28% (12/43) of 1-year-olds and 19% (7/36) of 2-year-olds (mean± zLCI 1.13 ± 1.10). Among 74 cases with recent positive microbiology or abnormal chest findings at medical review, 100% (31/31) of those with abnormal lung function and 86% (37/43) of those with normal lung function (p = 0.04) had a recent antibiotic prescription or a change in clinical management. Conversely, in encounters with abnormal lung function but normal clinical findings, management changes occurred in only 12% (2/16) of cases.

CONCLUSION: In this real-word cohort of IwCF, clinical management was mainly influenced by clinical findings and only marginally by abnormal lung function (elevated FRC or LCI).

PMID:40326637 | DOI:10.1002/ppul.71117

JMIR Med Inform. 2025 May 6;13:e66556. doi: 10.2196/66556.

NO ABSTRACT

PMID:40327366 | DOI:10.2196/66556

Mol Biol Cell. 2025 May 6:mbcE25010009. doi: 10.1091/mbc.E25-01-0009. Online ahead of print.

ABSTRACT

The life cycle of eukaryotic microorganisms involves complex transitions between states such as dormancy, mating, meiosis, and cell division, which are often studied independently from each other. Therefore, most microbial life cycles are theoretical reconstructions from partial observations of cellular states. Here we show that complete microbial life cycles can be directly and continuously studied by combining microfluidic culturing, life cycle stage-specific segmentation of micrographs, and a novel cell tracking algorithm, FIEST, based on deep learning video frame interpolation. As proof of principle, we quantitatively imaged and compared cell growth and the activity state of the cell division kinase, Cdk1, across the life cycle of Saccharomyces cerevisiae for up to three sexually reproducing generations. Our analysis of S. cerevisiae's life cycle provided the following new insights: (1) the accumulation of cell cycle regulators, such as Whi5, is tailored to each life cycle stage; (2) cell growth always preceded exit from non-proliferative states in our conditions; (3) the temporal coordination of meiotic events is the same across sexually reproducing populations when each generation is exposed to same conditions; (4) information such as cell size and morphology resets after each sexual reproduction cycle. Image processing and tracking algorithms are available as the Python package Yeastvision, which could be used study pathogens such as Candida glabrata, Cryptococcus neoformans, Colletotrichum acutatum, and other unicellular systems. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].

PMID:40327364 | DOI:10.1091/mbc.E25-01-0009

JMIR Mhealth Uhealth. 2025 May 6;13:e70473. doi: 10.2196/70473.

ABSTRACT

BACKGROUND: Encouraging physical activity improves mental health and is recommended in workplace mental health guidelines. Although mobile health (mHealth) interventions are promising for physical activity promotion, their impact on mental health outcomes is inconsistent. Furthermore, poor user retention rates of mHealth apps pose a major challenge.

OBJECTIVE: This study aimed to examine the effectiveness and implementation outcomes of the smartphone app ASHARE in Japanese workplace settings, leveraging a deep learning model to monitor depression and anxiety through physical activity.

METHODS: This hybrid effectiveness-implementation trial was a 3-month nonrandomized controlled trial conducted from October 2023 to September 2024. Work units and employees were recruited and allocated to the intervention or active control group based on preference. The intervention group installed the ASHARE app, whereas the control group participated in an existing multicomponent workplace program promoting physical activity. Changes in physical activity and psychological distress levels were compared between the groups. User retention rates, participation rates, acceptability, appropriateness, feasibility, satisfaction, and potential harm were also assessed.

RESULTS: A total of 84 employees from 7 work units participated (67 from 5 units in the intervention group and 17 from 2 units in the control group). In total, 78 employees completed the 3-month follow-up survey (follow-up rate: 93%). Both groups showed increased physical activity, and the intervention group showed reduced psychological distress; however, the differences between groups were not statistically significant (P=.20; P=.36). In a sensitivity analysis of protocol-compliant employees (n=21), psychological distress levels were significantly reduced in the intervention group compared with the control group (coefficient=-3.68, SE 1.65; P=.03). The app's 3-month user retention rate was 20% (12/61), which was lower than the participation rate in each component of the control programs. Implementation outcomes evaluated by employees were less favorable in the intervention group than in the control group, whereas health promotion managers found them to be similar.

CONCLUSIONS: The ASHARE app did not show superior effectiveness compared with an existing multicomponent workplace program for promoting physical activity. An implementation gap may exist between health promotion managers and employees, possibly contributing to the app's low user retention rate. Future research should focus on examining the effectiveness of strategies to get engagement from managers and from segments of employees with favorable responses in the workplace at an early stage.

PMID:40327360 | DOI:10.2196/70473

Med Biol Eng Comput. 2025 May 6. doi: 10.1007/s11517-025-03357-3. Online ahead of print.

ABSTRACT

The corticospinal tract (CST) is the primary neural pathway responsible for voluntary motor functions, and preoperative CST reconstruction is crucial for preserving nerve functions during neurosurgery. Diffusion magnetic resonance imaging-based tractography is the only noninvasive method to preoperatively reconstruct CST in clinical practice. However, for the largesize bundle CST with complex fiber geometry (fanning fibers), reconstructing its full extent remains challenging with local-derived methods without incorporating global information. Especially in the presence of tumors, the mass effect and partial volume effect cause abnormal diffusion signals. In this work, a CST reconstruction tractography method based on a novel direction filter was proposed, designed to ensure robust CST reconstruction in the clinical dataset with tumors. A direction filter based on a fourth-order differential equation was introduced for global direction estimation. By considering the spatial consistency and leveraging anatomical prior knowledge, the direction filter was computed by minimizing the energy between the target directions and initial fiber directions. On the basis of the new directions corresponding to CST obtained by the direction filter, the fiber tracking method was implemented to reconstruct the fiber trajectory. Additionally, a deep learning-based method along with tractography template prior information was employed to generate the regions of interest (ROIs) and initial fiber directions. Experimental results showed that the proposed method yields higher valid connections and lower no connections and exhibits the fewest broken fibers and short-connected fibers. The proposed method offers an effective tool to enhance CST-related surgical outcomes by optimizing tumor resection and preserving CST.

PMID:40327206 | DOI:10.1007/s11517-025-03357-3

Med Biol Eng Comput. 2025 May 6. doi: 10.1007/s11517-025-03372-4. Online ahead of print.

ABSTRACT

Rehabilitation exercises are critical for recovering from motor dysfunction caused by neurological conditions like stroke, back pain, Parkinson's disease, and spinal cord injuries. Traditionally, these exercises require constant monitoring by therapists, which is time-consuming and costly, often leading to therapist shortages. This paper introduces a deep learning model, convolutional neural network - squeeze excitation (CNN-SE), to automate rehabilitation exercise assessment. By optimizing its parameters with the grey wolf optimization algorithm, the model was fine-tuned for optimal performance. The model's effectiveness was tested on both healthy and unhealthy participants with motor dysfunction, providing a comprehensive evaluation of its capabilities. To interpret the model's decisions and understand its inner workings, we employed Shapley additive explanations (SHAP) to analyze feature importance at each time step. Our CNN-SE model achieved a state-of-the-art mean absolute deviation of 0.127 on the KIMORE dataset and a comparable MAD of 0.014 on the UI-PRMD dataset across various exercises, demonstrating its potential to provide a cost-effective, efficient alternative to traditional therapist-led evaluations.

PMID:40327204 | DOI:10.1007/s11517-025-03372-4

EJNMMI Phys. 2025 May 6;12(1):43. doi: 10.1186/s40658-025-00756-1.

ABSTRACT

PURPOSE: Dopamine transporter (DAT) SPECT is an effective tool for early Parkinson's disease (PD) detection and heavily hampered by attenuation. Attenuation correction (AC) is the most important correction among other corrections. Transfer learning (TL) with fine-tuning (FT) a pre-trained model has shown potential in enhancing deep learning (DL)-based AC methods. In this study, we investigate leveraging realistic Monte Carlo (MC) simulation data to create a pre-trained model for TL-based AC (TLAC) to improve AC performance for DAT SPECT.

METHODS: A total number of 200 digital brain phantoms with realistic 99mTc-TRODAT-1 distribution was used to generate realistic noisy SPECT projections using MC SIMIND program and an analytical projector. One hundred real clinical 99mTc-TRODAT-1 brain SPECT data were also retrospectively analyzed. All projections were reconstructed with and without CT-based attenuation correction (CTAC/NAC). A 3D conditional generative adversarial network (cGAN) was pre-trained using 200 pairs of simulated NAC and CTAC SPECT data. Subsequently, 8, 24, and 80 pairs of clinical NAC and CTAC DAT SPECT data were employed to fine-tune the pre-trained U-Net generator of cGAN (TLAC-MC). Comparisons were made against without FT (DLAC-MC), training on purely limited clinical data (DLAC-CLI), clinical data with data augmentation (DLAC-AUG), mixed MC and clinical data (DLAC-MIX), TL using analytical simulation data (TLAC-ANA), and Chang's AC (ChangAC). All datasets used for DL-based methods were split to 7/8 for training and 1/8 for validation, and a 1-/2-/5-fold cross-validation were applied to test all 100 clinical datasets, depending on the numbers of clinical data used in the training model.

RESULTS: With 8 available clinical datasets, TLAC-MC achieved the best result in Normalized Mean Squared Error (NMSE) and Structural Similarity Index Measure (SSIM) (TLAC-MC; NMSE = 0.0143 ± 0.0082/SSIM = 0.9355 ± 0.0203), followed by DLAC-AUG, DLAC-MIX, TLAC-ANA, DLAC-CLI, DLAC-MC, ChangAC and NAC. Similar trends exist when increasing the number of clinical datasets. For TL-based AC methods, the fewer clinical datasets available for FT, the greater the improvement as compared to DLAC-CLI using the same number of clinical datasets for training. Joint histograms analysis and Bland-Altman plots of SBR results also demonstrate consistent findings.

CONCLUSION: TLAC is feasible for DAT SPECT with a pre-trained model generated purely based on simulation data. TLAC-MC demonstrates superior performance over other DL-based AC methods, particularly when limited clinical datasets are available. The closer the pre-training data is to the target domain, the better the performance of the TLAC model.

PMID:40327202 | DOI:10.1186/s40658-025-00756-1

Eur Spine J. 2025 May 6. doi: 10.1007/s00586-025-08895-w. Online ahead of print.

ABSTRACT

PURPOSE: Scoliosis is a prevalent spine deformity that impacts millions of children globally. The Cobb angle, a crucial and widely-accepted metric, serves as the "gold standard" for assessing scoliosis in patients. However, the traditional manual measurement of spine curvature is time-consuming and labor-intensive. It also comes with issues like intra - and inter-observer variations. Moreover, accurately and robustly evaluating Cobb angles is extremely challenging. This is because it necessitates the correct identification of all the required vertebrae in both the anterior-posterior (AP) and lateral (LAT) views of full-spine digital radiography (DR).

METHODS: To solve these challenges, a deep learning-based framework is developed to fully automatically measure patient Cobb angels from full-spine DR of both AP and LAT views. First, a deep learning network was used to distinguish AP and LAT views. Then the region of interest (ROI) of the whole spine was located and extracted. Subsequently, a detection network was applied to detect and identify the boundaries and locations, the types, and the four corner points of each spinal vertebra. Finally, the Cobb angles was measured automatically. When taking into account the location, recognition, and key points detection of spinal vertebrae, YOLOv8 architecture with CBAM module was adopted as the backbone.

RESULTS: A total of 1,163 AP view and 1,378 LAT view DR images were used to train and evaluate the models. Experimental results in the evaluation testing showed a mean Cobb angle error of 2.56° for AP view and 2.498° for LAT view DR images. The intra-class correlation coefficient (ICC) with 95% confidence interval (CI) was 0.956 (0.932, 0.972) for AP view and 0.925 (0.888, 0.952) for LAT view. The Pearson correlation coefficient was 0.961 for AP view and 0.930 for LAT view. In the comprehensive reader study, for the major curve, a mean Cobb angle error of 3.918°, an ICC of 0.943 (0.912, 0.965), and a high correlation coefficient of 0.960 were obtained.

CONCLUSION: The results showed that the proposed framework had a significant accuracy and consistency advantage in measuring Cobb angle, which not only validated the effectiveness of the algorithm, but also provided strong support for the diagnosis of clinicians.

PMID:40327070 | DOI:10.1007/s00586-025-08895-w

Radiology. 2025 May;315(2):e240714. doi: 10.1148/radiol.240714.

ABSTRACT

Background Four-dimensional (4D) flow MRI provides assessment of thoracic aorta hemodynamic measures that are increasingly recognized as important biomarkers for risk assessment. However, long acquisition times and cumbersome data analysis limit widespread availability. Purpose To evaluate the feasibility and accuracy of a generative artificial intelligence (AI) approach (fluid physics-informed cycle generative adversarial network [FPI-CycleGAN]) in quantifying aorta hemodynamics directly from anatomic input as an alternative to 4D flow MRI. Materials and Methods Patients were retrospectively identified from a dataset of clinical cardiothoracic MRI examinations performed between November 2011 and July 2020. All patients underwent aortic 4D flow MRI, which served as a reference standard for training and testing of FPI-CycleGANs. A three-dimensional (3D) segmentation of the aortic geometry was used as the only input to predict systolic aortic hemodynamics, with separate networks for bicuspid aortic valve (BAV) (994 in the training set and 248 in the test set) and tricuspid aortic valve (TAV) (419 in the training set and 104 in the test set). Voxel-by-voxel and regional analyses were used to quantify and compare (AI vs the reference standard, 4D flow) systolic velocity vector fields, peak velocity, wall shear stress (WSS), and classification of aortic valve stenosis. Results In total, 1765 patients (median age, 53 years [IQR, 41-63 years]; 1242 patients had BAV and 523 had TAV) were included. Mean AI computation time was 0.15 second ± 0.11 (SD), and total training was 1500 and 3600 minutes for the TAV and BAV networks, respectively. The FPI-CycleGAN predicted systolic 3D velocity vector fields accurately, with low bias (<0.01 m/sec) and excellent limits of agreements (±0.06-0.08 m/sec). For peak velocities and WSS, there was strong agreement between FPI-CycleGAN and 4D flow (r2 = 0.930-0.957 [P < .001], with relative differences of 6.2%-9.8%). AI accurately classified aortic valve stenosis severity in 85.8% of patients (302 of 352) (κ = 0.80 [95% CI: 0.71, 0.89]). The FPI-CycleGAN was robust to one- and two-voxel dilation and erosion (bias, -0.05 to 0.1 m/sec) and ±5° rotation (bias, -0.02 to 0.03 m/sec) of the input data. The application of the trained FPI-CycleGAN in an external test set with contrast-enhanced MR angiography (n = 60 patients) as AI input data demonstrated strong to excellent performance for peak velocities and WSS (r2 = 0.944-0.965 [P < .001], with relative differences of 6.2%-9.2%). Conclusion Aorta 3D hemodynamics can be derived from anatomic input in less than 1 second using an FPI-CycleGAN and demonstrate strong agreement with in vivo 4D flow MRI systolic hemodynamics. © RSNA, 2025 Supplemental material is available for this article.

PMID:40326877 | DOI:10.1148/radiol.240714

J Acoust Soc Am. 2025 May 1;157(5):3508-3523. doi: 10.1121/10.0036456.

ABSTRACT

Underwater passive acoustic recognition, which focuses on classifying targets based on ship-radiated noise, is a key challenge in underwater acoustics. Deep learning-based methods have gained popularity in recent years because of their strong performance. However, these methods often fail to generalize well in real-world scenarios. This work reveals one underlying challenge: the characteristics of ship-radiated noise are influenced by factors such as vessel structures and propulsion systems. Although vessels of the same type may exhibit different patterns in these aspects, vessels of different categories share similarities. As a result, data-driven models often tend to overemphasize individual-specific features, leading to "overfitting" and poor generalization. The momentum-based adversarial training (MBAT) framework is proposed to mitigate this challenge. MBAT leverages a momentum adversarial strategy to use category information and individual vessel relationships, helping extract class-discriminative features. A homoscedastic uncertainty algorithm is employed to balance the optimization objectives of category-related and vessel-specific features. These strategies allow the model to capture category-discriminative patterns more effectively and generalize to unseen targets. Experiments on DeepShip and ShipsEar demonstrate that MBAT significantly improves generalization capability on unseen individual vessels, outperforming existing state-of-the-art methods. Visualizations further confirm the efficacy and necessity of the proposed approach.

PMID:40326792 | DOI:10.1121/10.0036456

Bioinformatics. 2025 May 6:btaf277. doi: 10.1093/bioinformatics/btaf277. Online ahead of print.

ABSTRACT

MOTIVATION: The availability of very large numbers of protein structures from accurate computational methods poses new challenges in storing, searching and detecting relationships between these structures. In particular, the new-found abundance of multi-domain structures in the AlphaFold structure database introduces challenges for traditional structure comparison methods.

RESULTS: We address these challenges using a fast, embedding-based structure comparison method called Foldclass which detects structural similarity between protein domains. We demonstrate the accuracy of Foldclass embeddings for homology detection. In combination with a recently developed deep learning-based automatic domain segmentation tool Merizo, we develop Merizo-search, which first segments multi-domain query structures into domains, and then searches a Foldclass embedding database to determine the top matches for each constituent domain. Combining the ability of Merizo to accurately segment complete chains into domains, and Foldclass to embed and detect similar domains, the Merizo-search tool can be used to rapidly detect per-domain similarities for complete chains, taking as little as 2 minutes to search all 365 million domains from the Encyclopedia of Domains. We anticipate that these tools will enable many analyses using the wealth of predicted structural data now available.

AVAILABILITY: Foldclass and Merizo-search are available at https://github.com/psipred/merizo_search. The version used in this publication is archived at https://doi.org/10.5281/zenodo.15120830. Merizo-search is also available on the PSIPRED web server at http://bioinf.cs.ucl.ac.uk/psipred.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40326701 | DOI:10.1093/bioinformatics/btaf277