J Parkinsons Dis. 2025 Mar 16:1877718X251323922. doi: 10.1177/1877718X251323922. Online ahead of print.

ABSTRACT

BackgroundImpulsive-compulsive disorders (ICDs) are commonly acknowledged as side effects of dopaminergic therapy in Parkinson's disease (PD). While many large-scale studies have focused on prevalences and high-risk treatments, little is known about practical management of ICDs in clinical care and patients' experiences.ObjectiveTo investigate how ICDs are recognized in clinical PD care, clinical features of patients with ICDs, and how patients are impacted by their ICD.MethodsQuestionnaires were sent to all patients who reported ICD symptoms in the Swedish quality register for PD in Skåne County (n = 170) and patients' medical records were screened for mention of ICDs. Core subjects were communication between clinician and patient, course and management of ICDs, and impact on different life domains.ResultsDespite sufficient awareness of the ICD risk during PD treatment, there was limited communication between clinical care staff and patients regarding ICDs. Only 49% of patients had reported their ICD as part of clinical care, and only 14% had been asked about it. Additionally, collaboration with psychiatry was rare (12%). ICD severity increased over time with ongoing PD treatment, and most patients reported a mild to moderate impact of their ICD on close relationships, family, mental and physical health.ConclusionsThis study identified insufficient communication about ICDs as part of clinical care in PD and a very limited involvement of mental health services. Thus, to improve prevention and treatment, ICDs should be recognized, monitored and treated more systematically in routine clinical care, and collaboration with mental health services should be increased.

PMID:40091420 | DOI:10.1177/1877718X251323922

CNS Neurosci Ther. 2025 Mar;31(3):e70330. doi: 10.1111/cns.70330.

ABSTRACT

BACKGROUND: Gliomas represent the most aggressive malignancies of the central nervous system, with posttranslational modifications (PTMs) emerging as critical regulators of oncogenic processes through dynamic protein functional modulation. Despite their established role in tumor biology, the systematic characterization of PTM-mediated molecular mechanisms driving glioma progression remains unexplored. This study aims to uncover the molecular mechanisms of glioma, with a focus on the role of PTMs.

METHODS: We analyzed the PTM pathway to classify glioma patients into distinct clusters. Comprehensive analyses compared intercluster differences in clinical outcomes, mutational landscapes, and immune microenvironment profiles. Differentially expressed genes (DEGs) were identified to construct a robust prognostic prediction model with machine learning approaches. Among the genes included in the model, TOM1L1 (Target of Myb1 Like 1 Membrane Trafficking Protein) was selected for in vitro experimental validation to assess its role in glioma progression.

RESULTS: PTMs were found to influence glioma prognosis significantly. Dysregulation in specific pathways, such as glutathionylation and citrullination, was correlated with more aggressive clinical features. The prognostic model, comprising DEGs such as TOM1L1, demonstrated high predictive accuracy (c-index = 0.867)-the scores derived from the model strongly correlated with glioma progression indicators. In vitro experiments revealed that TOM1L1 facilitates malignant progression by modulating PTM pathways, confirming its functional role in glioma.

CONCLUSION: Our study establishes the first comprehensive PTM atlas in gliomas, revealing subtype-specific modification patterns with clinical and therapeutic implications. TOM1L1 emerges as a promising prognostic biomarker and a potential therapeutic intervention target. Targeting PTM pathways may offer novel strategies for glioma treatment, enhancing patient outcomes.

PMID:40090864 | DOI:10.1111/cns.70330

Am J Med. 2025 Mar 14:S0002-9343(25)00166-4. doi: 10.1016/j.amjmed.2025.03.011. Online ahead of print.

NO ABSTRACT

PMID:40090392 | DOI:10.1016/j.amjmed.2025.03.011

Respir Med. 2025 Mar 14:108042. doi: 10.1016/j.rmed.2025.108042. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic pulmonary infection with pathogens such as Pseudomonas aeruginosa is associated with lung function decline and increased mortality in people with cystic fibrosis (CF). The relationship between sputum bacterial load and the severity of pulmonary exacerbations remains unclear. This study aimed to explore the relationship between sputum bacterial load and clinical response to antibiotic treatment of pulmonary exacerbations in children with CF.

METHODS: Multicentre prospective longitudinal study of children with CF receiving IV tobramycin for a pulmonary exacerbation and who had prior isolation of Gram-negative bacteria and able to expectorate sputum. Lung function (FEV1) and sputum bacterial load were assessed. Bacterial load was performed using quantitative PCR on either intact (live) bacterial cells or all bacterial DNA (live+dead) and targeted either P. aeruginosa only or all bacteria.

RESULTS: Twelve children (14 admissions) were enrolled and each provided ≥2 sputum samples; 11 children (13 admissions) also had ≥2 FEV1 measurements. In 10 admissions where FEV1 improved, five showed a reduction in all live bacteria, with a median reduction by 8.65×106 copies/g (73% reduction). Live P. aeruginosa was detected in 8/10 children and in seven, a median reduction of 2.99×107 copies/g (90% reduction) was observed. Improved FEV1 correlated with greater reductions in live+dead P. aeruginosa (ρ = -0.63, p = 0.04).

CONCLUSION: A greater reduction in total sputum P. aeruginosa bacterial load (live+dead) was associated with improved lung function (FEV1) in children with CF receiving tobramycin.

PMID:40090524 | DOI:10.1016/j.rmed.2025.108042

J Immunother Cancer. 2025 Mar 15;13(3):e011149. doi: 10.1136/jitc-2024-011149.

ABSTRACT

BACKGROUND: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial for tailoring patient care in esophageal squamous cell carcinoma (ESCC). This study aimed to develop and validate a deep learning model using a novel voxel-level radiomics approach to predict pCR based on preoperative CT images.

METHODS: In this multicenter, retrospective study, 741 patients with ESCC who underwent nICT followed by radical esophagectomy were enrolled from three institutions. Patients from one center were divided into a training set (469 patients) and an internal validation set (118 patients) while the data from the other two centers was used as external validation sets (120 and 34 patients, respectively). The deep learning model, Vision-Mamba, integrated voxel-level radiomics feature maps and CT images for pCR prediction. Additionally, other commonly used deep learning models, including 3D-ResNet and Vision Transformer, as well as traditional radiomics methods, were developed for comparison. Model performance was evaluated using accuracy, area under the curve (AUC), sensitivity, specificity, and prognostic stratification capabilities. The SHapley Additive exPlanations analysis was employed to interpret the model's predictions.

RESULTS: The Vision-Mamba model demonstrated robust predictive performance in the training set (accuracy: 0.89, AUC: 0.91, sensitivity: 0.82, specificity: 0.92) and validation sets (accuracy: 0.83-0.91, AUC: 0.83-0.92, sensitivity: 0.73-0.94, specificity: 0.84-1.0). The model outperformed other deep learning models and traditional radiomics methods. The model's ability to stratify patients into high and low-risk groups was validated, showing superior prognostic stratification compared with traditional methods. SHAP provided quantitative and visual model interpretation.

CONCLUSIONS: We present a voxel-level radiomics-based deep learning model to predict pCR to neoadjuvant immunotherapy combined with chemotherapy based on pretreatment diagnostic CT images with high accuracy and robustness. This model could provide a promising tool for individualized management of patients with ESCC.

PMID:40090670 | DOI:10.1136/jitc-2024-011149

Environ Pollut. 2025 Mar 14:125993. doi: 10.1016/j.envpol.2025.125993. Online ahead of print.

ABSTRACT

Airborne microplastics (AMPs) are prevalent in both indoor and outdoor environments, posing potential health risks to humans. Automating the process of spotting them in micrographs can significantly enhance research and monitoring. Although deep learning has shown substantial promise in microplastic analysis, existing studies have primarily focused on high-resolution images of samples collected from marine and freshwater environments. In contrast, this work introduces a novel approach by employing enhanced U-Net models (Attention U-Net and Dynamic RU-NEXT) along with the Mask Region Convolutional Neural Network (Mask R-CNN) to identify and classify AMPs in lower-resolution micrographs (256 × 256 pixels) obtained from outdoor environments. A key innovation involves integrating classification directly within the U-Net-based segmentation frameworks, thereby streamlining the workflow and improving computational efficiency which is an advancement over previous work where segmentation and classification were performed separately. The enhanced U-Net models attained average classification F1-scores exceeding 85% and segmentation scores above 77%. Additionally, the Mask R-CNN model achieved an average bounding box precision of 73.32% on the test set, a classification F1-score of 84.29%, and a mask precision of 71.31%, demonstrating robust performance. The proposed method provides a faster and more accurate means of identifying AMPs compared to thresholding techniques. It also functions effectively as a pre-screening tool, substantially reducing the number of particles requiring labour-intensive chemical analysis. By integrating advanced deep learning strategies into AMPs research, this study paves the way for more efficient monitoring and characterisation of microplastics.

PMID:40090454 | DOI:10.1016/j.envpol.2025.125993

J Dent. 2025 Mar 14:105689. doi: 10.1016/j.jdent.2025.105689. Online ahead of print.

ABSTRACT

OBJECTIVES: To develop and validate an explainable Artificial Intelligence (AI) model for classifying and quantifying upper airway obstruction related to adenoid hypertrophy using three-dimensional (3D) shape analysis of cone-beam computed tomography (CBCT) scans.

METHODS: 400 CBCT scans of patients aged 5-18 years were analyzed. Nasopharyngeal airway obstruction (NAO) ratio was calculated to label scans into four grades of obstruction severity, used as the ground truth. Upper airway surface meshes were used to train a deep learning model combining multiview and point-cloud approaches for 3D shape analysis and obstruction severity classification and quantification. Surface Gradient-weighted Class Activation Mapping (SurfGradCAM) generated explainability heatmaps. Performance was evaluated using area under the curve (AUC), precision, recall, F1-score, mean absolute error, root mean squared error, and correlation coefficients.

RESULTS: The explainable AI model demonstrated strong performance in both classification and quantification tasks. The AUC values for the classification task ranged from 0.77 to 0.94, with the highest values of 0.88 and 0.94 for Grades 3 and 4, respectively, indicating excellent discriminative ability for identifying more severe cases of obstruction. The SurfGradCAM-generated heatmaps consistently highlighted the most relevant regions of the upper airway influencing the AI's decision-making process. In the quantification task, the regression model successfully predicted the NAO ratio, with a strong correlation coefficient of 0.854 (p<0.001) and R2= 0.728, explaining a substantial proportion of the variance in NAO ratios.

CONCLUSIONS: The proposed explainable AI model, using 3D shape analysis, demonstrated strong performance in classifying and quantifying adenoid hypertrophy-related upper airway obstruction in CBCT scans.

CLINICAL SIGNIFICANCE: This AI model provides clinicians with a reliable, automated tool for standardized adenoid hypertrophy assessment. The model's explainable nature enhances clinical confidence and patient communication, potentially improving diagnostic workflow and treatment planning.

PMID:40090403 | DOI:10.1016/j.jdent.2025.105689

Can J Ophthalmol. 2025 Mar 13:S0008-4182(25)00070-5. doi: 10.1016/j.jcjo.2025.02.013. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the association between localized features of diabetic macular edema (DME) and point-wise retinal sensitivity (RS) assessed with microperimetry (MP) using deep learning (DL)-based automated quantification on optical coherence tomography (OCT) scans.

DESIGN: Cross-sectional study.

PARTICIPANTS: Twenty eyes of 20 subjects with clinically significant DME were included in this study.

METHODS: Patients with DME visible on OCT scans (Spectralis HRA+OCT) completed 2 MP examinations using a custom 45 stimuli grid on MAIA (CenterVue). MP stimuli were coregistered with the corresponding OCT location using image registration algorithms. DL-based algorithms were used to quantify intraretinal fluid (IRF) and ellipsoid zone (EZ) thickness. Hard exudates (HEs) were quantified semiautomatically. Multivariable mixed-effect models were calculated to investigate the association between DME-specific OCT features and point-wise RS. As EZ thickness values below HEs were excluded, the models included either EZ thickness or HEs.

RESULTS: A total of 1800 MP stimuli from 20 eyes of 20 patients were analyzed. Stimuli with IRF (n = 568) showed significantly decreased RS compared to areas without (estimate [95% CI]: -1.11 dB [-1.69, -0.52]; p = 0.0002). IRF volume was significantly negatively (-0.45 dB/nL [-0.71; -0.18]; p = 0.001) and EZ thickness positively (0.14 dB/µm [0.1; 0.19]; p < 0.0001) associated with localized point-wise RS. In the multivariable mixed model, including HE volume instead of EZ thickness, a negative impact on RS was observed (-0.43/0.1 nL [-0.81; -0.05]; p = 0.027).

CONCLUSIONS: DME-specific features, as analyzed on OCT, have a significant impact on point-wise RS. IRF and HE volume showed a negative and EZ thickness, a positive association with localized RS.

PMID:40090368 | DOI:10.1016/j.jcjo.2025.02.013

Comput Biol Med. 2025 Mar 15;189:109957. doi: 10.1016/j.compbiomed.2025.109957. Online ahead of print.

ABSTRACT

The availability of large-scale electrocardiogram (ECG) databases and advancements in machine learning have facilitated the development of automated diagnostic systems for cardiac arrhythmias. Deep learning models, despite their potential for high accuracy, have had limited clinical adoption due to their inherent lack of interpretability. This study bridges this gap by proposing a feature-based approach that maintains comparable performance to deep learning while providing enhanced interpretability for clinical utility. The method extracts a total of 654 individual features, classified into 60 feature types from each ECG. The features use mathematical techniques such as the Fourier transform, wavelet transform, and cross-correlation for rigorous evaluation of ECG characteristics. The eXtreme Gradient Boosting framework was employed to classify each ECG into one of nine diagnostic classes. Shapley Additive Explanations (SHAP) value analysis was used to downselect the feature set to the minimal set without incurring a performance reduction (159 features). Overall, the proposed method demonstrated performance comparable to state-of-the-art deep learning classifiers, achieving a weighted F1 score of 81% during cross-validation and 68% on the external test dataset, while offering greater ease of implementation and adaptability to diverse clinical applications. Notably, the proposed method demonstrated superior accuracy in identifying atrial fibrillation and block-related abnormalities, achieving overall F1 scores of 89% and 87% during cross-validation and 79% and 75% on the external test dataset, respectively. SHAP value analysis of the testing results revealed the top-performing features for each diagnostic class aligned with standard clinical diagnostic processes, highlighting the clinical interpretability of our approach.

PMID:40090185 | DOI:10.1016/j.compbiomed.2025.109957

Contemp Clin Trials. 2025 Mar 14:107883. doi: 10.1016/j.cct.2025.107883. Online ahead of print.

ABSTRACT

BACKGROUND: Pulmonary fibrosis is a major complication of the Acute Respiratory Distress Syndrome (ARDS). Pirfenidone is an approved treatment for idiopathic pulmonary fibrosis. It may attenuate ARDS-related fibrosis and decrease the need for prolonged ventilation. Accordingly, we aimed to evaluate the effect of pirfenidone on ventilator-free days in patients with ARDS.

METHODS: In a multi-center, randomized, double-blind, placebo-controlled trial, we plan to randomly assign 130 adults invasively ventilated for ARDS to receive pirfenidone or placebo for up to 28 days. The primary outcome is days alive and ventilator free at 28 days. Secondary outcomes include ICU-free days, hospital free days all at 28 day, ICU mortality and hospital mortality. We will also assess fibroproliferative changes on high-resolution CT scans at ICU discharge and quality of life. Data analysis will be on an intention-to-treat basis.

DISCUSSION: The trial is ongoing and currently recruiting. It will be the first randomized controlled study to investigate whether, compared to placebo, pirfenidone reduces the number of days alive and ventilator-free in patients with ARDS. Its double-blind multicenter design will provide internal validity, minimal bias, and a degree of external validity. If our hypothesis is confirmed, this treatment would justify larger trials of this intervention.

TRIAL REGISTRATION: This trial was registered on ClinicalTrials.gov with the trial identification NCT05075161.

PMID:40090666 | DOI:10.1016/j.cct.2025.107883

SLAS Discov. 2025 Mar 14:100226. doi: 10.1016/j.slasd.2025.100226. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive age-related lung disease with an average survival of 3-5 years post-diagnosis if left untreated. It is characterized by lung fibrosis, inflammation, and destruction of lung architecture, leading to worsening respiratory symptoms and physiological impairment, ultimately culminating in progressive respiratory failure. The development of novel therapeutics for the treatment of IPF represents a significant unmet medical need. Fibroblast to myofibroblast transition (FMT) in response to fibrogenic mediators such as transforming growth factor beta 1 (TGF-β1) has been identified as a key cellular phenotype driving the formation of myofibroblasts and lung fibrosis in IPF. Establishing a robust and high-throughput in vitro human FMT assay is crucial for uncovering new disease targets and for efficiently screening compounds for the advancement of novel therapeutics aimed at targeting myofibroblast activity. However, creating a robust FMT assay suitable for high-throughput drug screening has proven challenging due to the requisite level of automation. In this study, we focus on evaluating different automation approaches for liquid exchange and compound dosing in the human FMT assay. A semi-automated assay, capable of screening a large number of compounds that inhibit TGF-β1-induced FMT in both Normal Human Lung Fibroblasts (NHLF) and IPF-patient derived Disease Human Lung Fibroblasts (IPF-DHLF), has been successfully developed and optimized. We demonstrate that the optimized FMT assay using liquid handling automation exhibits great assay reproducibility, shows good assay translation using human lung fibroblasts from normal healthy versus IPF-patients, and demonstrates acceptable human primary donor variability. This allows for the standardization of comparisons of compound anti-fibrotic potency across IPF projects.

PMID:40090552 | DOI:10.1016/j.slasd.2025.100226

Semin Arthritis Rheum. 2025 Mar 15;72:152708. doi: 10.1016/j.semarthrit.2025.152708. Online ahead of print.

ABSTRACT

OBJECTIVE: Genome-wide association studies (GWAS) facilitate construction of polygenic risk scores (PRSs) for rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF). We investigated associations of RA and IPF PRSs with RA and high-resolution chest computed tomography (HRCT) parenchymal lung abnormalities.

METHODS: Participants in COPDGene, a prospective multicenter cohort of current/former smokers, had chest HRCT at study enrollment. Using genome-wide genotyping, RA and IPF PRSs were constructed using GWAS summary statistics. HRCT imaging underwent visual inspection for interstitial lung abnormalities (ILA) and quantitative CT (QCT) analysis using a machine-learning algorithm that quantified percentage of normal lung, interstitial abnormalities, and emphysema. RA was identified through self-report and DMARD use. We investigated associations of RA and IPF PRSs with RA, ILA, and QCT features using multivariable logistic and linear regression.

RESULTS: We analyzed 9,230 COPDGene participants (mean age 59.6 years, 46.4 % female, 67.2 % non-Hispanic White, 32.8 % Black/African American). In non-Hispanic White participants, RA PRS was associated with RA diagnosis (OR 1.32 per unit, 95 %CI 1.18-1.49) but not ILA or QCT features. Among non-Hispanic White participants, IPF PRS was associated with ILA (OR 1.88 per unit, 95 %CI 1.52-2.32) and quantitative interstitial abnormalities (adjusted β=+0.50 % per unit, p = 7.3 × 10-8) but not RA. There were no statistically significant associations among Black/African American participants.

CONCLUSIONS: RA and IPF PRSs were associated with their intended phenotypes among non-Hispanic White participants but performed poorly among Black/African American participants. PRS may have future application to risk stratify for RA diagnosis among patients with ILD or for ILD among patients with RA.

PMID:40090204 | DOI:10.1016/j.semarthrit.2025.152708

Phytomedicine. 2025 Mar 8;140:156628. doi: 10.1016/j.phymed.2025.156628. Online ahead of print.

ABSTRACT

BACKGROUND: The Active Ingredient Composition of Jinshui Huanxian Formula (ECC-JHF) consists of five active ingredients: icariin, isoliquiritigenin, nobiletin, peimine, and paeoniflorin, which demonstrate notable therapeutic effects on pulmonary fibrosis.

PURPOSE: Inhibition of glycolysis has been demonstrated to be effective in treating experimental idiopathic pulmonary fibrosis (IPF). This research seeks to explore the impact of aerobic glycolysis on the mitigation of pulmonary fibrosis through ECC-JHF.

METHODS: A pulmonary fibrosis mouse model was generated through the administration of bleomycin (Bleomycin). The degree of pulmonary fibrosis was analyzed through hematoxylin and eosin (H&E) staining as well as Masson's trichrome staining. Western Blot (WB), Immunofluorescence (IF), and real-time quantitative PCR (Q-PCR) assay for fibroblast activation markers and glycolysis-related genes in lung tissues. The Lactic Acid (LA) Content Assay Kit was employed to quantify lactate concentrations in lung tissues and fibroblast cultures. Immunoprecipitation (IP) was applied to detect lactylated modified protein levels, and mass spectrometry (MS) was used to analyze lactate substrate profiles in fibroblasts. WB was employed to detect the lactate modification level of histone H3K18 (H3K18la). The targets of ECC-JHF were analyzed using network pharmacology, while molecular docking and cellular enthusiasm transfer analysis (CETSA) examined the binding of ECC-JHF to SRC. The influence of ECC-JHF on SRC activation was assessed using WB. SRC small interfering RNA (siSRC) was designed and transfected into L929 cells to validate the function of SRC in the inhibition of fibroblast activation by ECC-JHF.

RESULTS: In BLM-induced pulmonary fibrosis mice, ECC-JHF significantly reduced alveolar inflammation and collagen deposition. In lung tissues and fibroblasts, ECC-JHF notably inhibited the expression of HK2, lactate levels, and lactylated modifying proteins. IP-MS and WB analyses showed that ECC-JHF significantly reduced H3K18la levels. Network pharmacology analysis, molecular docking and CETSA results indicated that SRC serves as a key target for ECC-JHF. siSRC effectively mitigated the impact of ECC-JHF on the expression of HK2, levels of H3K18la, and the activation of fibroblasts.

CONCLUSION: ECC-JHF may improve pulmonary fibrosis by inhibiting SRC activation, blocking HK2-mediated lactate production, down-regulating H3K18la levels, and inhibiting fibroblast activation. Our results serve as a significant reference for the advancement of ECC-JHF and the exploration of IPF.

PMID:40090047 | DOI:10.1016/j.phymed.2025.156628

Med Phys. 2025 Mar 16. doi: 10.1002/mp.17759. Online ahead of print.

ABSTRACT

BACKGROUND: Magnetic resonance-guided adaptive radiation therapy (MRgART) systems combine Magnetic resonance imaging (MRI) technology with linear accelerators (LINAC) to enhance the precision and efficacy of cancer treatment. These systems enable real-time adjustments of treatment plans based on the latest patient anatomy, creating an urgent need for accurate and rapid dose calculation algorithms. Traditional CT-based dose calculations and ray-tracing (RT) processes are time-consuming and may not be feasible for the online adaptive workflow required in MRgART. Recent advancements in deep learning (DL) offer promising solutions to overcome these limitations.

PURPOSE: This study aims to develop a DL-based dose calculation engine for MRgART that relies solely on MR images. This approach addresses the critical need for accurate and rapid dose calculations in the MRgART workflow without relying on CT images or time-consuming RT processes.

METHODS: We used a deep residual network inspired by U-Net to establish a direct connection between distance-corrected conical (DCC) fluence maps and dose distributions in the image domain. The study utilized data from 30 prostate cancer patients treated with fixed-beam Intensity-Modulated Radiation Therapy (IMRT) on an MR-guided LINAC system. We trained, validated, and tested the model using a total of 120 online treatment plans, which encompassed 1080 individual beams. We extensively evaluated the network's performance by comparing its dose calculation accuracy against Monte Carlo (MC)-based methods using metrics such as mean absolute error (MAE) of pixel-wise dose differences, 3D gamma analysis, dose-volume histograms (DVHs), dosimetric indices, and isodose line similarity.

RESULTS: The proposed DL model demonstrated high accuracy in dose calculations. The median MAE of pixel-wise dose differences was 1.2% for the whole body, 1.9% for targets, and 1.1% for organs at risk (OARs). The median 3D gamma passing rates for the 3%/3 mm criterion were 94.8% for the whole body, 95.7% for targets, and 98.7% for OARs. Additionally, the Dice similarity coefficient (DSC) of isodose lines between the DL-based and MC-based dose calculations averaged 0.94 ± 0.01. There were no big differences between the DL-based and MC-based calculations in the DVH curves and clinical dosimetric indices. This proved that the two methods were clinically equivalent.

CONCLUSION: This study presents a novel MR-only dose calculation engine that eliminates the need for CT images and complex RT processes. By leveraging DL, the proposed method significantly enhances the efficiency and accuracy of the MRgART workflow, particularly for prostate cancer treatment. This approach holds potential for broader applications across different cancer types and MR-linac systems, paving the way for more streamlined and precise radiation therapy planning.

PMID:40089982 | DOI:10.1002/mp.17759

Med Phys. 2025 Mar 16. doi: 10.1002/mp.17747. Online ahead of print.

ABSTRACT

BACKGROUND: Quantitative susceptibility mapping (QSM) is a post-processing magnetic resonance imaging (MRI) technique that extracts the distribution of tissue susceptibilities and holds significant promise in the study of neurological diseases. However, the ill-conditioned nature of dipole inversion often results in noise and artifacts during QSM reconstruction from the tissue field. Deep learning methods have shown great potential in addressing these issues; however, most existing approaches rely on basic U-net structures, leading to limited performances and reconstruction artifacts sometimes.

PURPOSE: This study aims to develop a novel deep learning-based method, IR2QSM, for improving QSM reconstruction accuracy while mitigating noise and artifacts by leveraging a unique network architecture that enhances latent feature utilization.

METHODS: IR2QSM, an advanced U-net architecture featuring four iterations of reverse concatenations and middle recurrent modules, was proposed to optimize feature fusion and improve QSM accuracy, and comparative experiments based on both simulated and in vivo datasets were carried out to compare IR2QSM with two traditional iterative methods (iLSQR, MEDI) and four recently proposed deep learning methods (U-net, xQSM, LPCNN, and MoDL-QSM).

RESULTS: In this work, IR2QSM outperformed all other methods in reducing artifacts and noise in QSM images. It achieved on average the lowest XSIM (84.81%) in simulations, showing improvements of 12.80%, 12.68%, 18.66%, 10.49%, 25.57%, and 19.78% over iLSQR, MEDI, U-net, xQSM, LPCNN, and MoDL-QSM, respectively, and yielded results with the least artifacts on the in vivo data and present the most visually appealing results. In the meantime, it successfully alleviated the over-smoothing and susceptibility underestimation in LPCNN results.

CONCLUSION: Overall, the proposed IR2QSM showed superior QSM results compared to iterative and deep learning-based methods, offering a more accurate QSM solution for clinical applications.

PMID:40089979 | DOI:10.1002/mp.17747

Hepatol Int. 2025 Mar 16. doi: 10.1007/s12072-025-10793-8. Online ahead of print.

ABSTRACT

PURPOSE: Existing prognostic staging systems depend on expensive manual extraction by pathologists, potentially overlooking latent patterns critical for prognosis, or use black-box deep learning models, limiting clinical acceptance. This study introduces a novel deep learning-assisted paradigm that complements existing approaches by generating interpretable, multi-view risk scores to stratify prognostic risk in hepatocellular carcinoma (HCC) patients.

METHODS: 510 HCC patients were enrolled in an internal dataset (SYSUCC) as training and validation cohorts to develop the Hybrid Deep Score (HDS). The Attention Activator (ATAT) was designed to heuristically identify tissues with high prognostic risk, and a multi-view risk-scoring system based on ATAT established HDS from microscopic to macroscopic levels. HDS was also validated on an external testing cohort (TCGA-LIHC) with 341 HCC patients. We assessed prognostic significance using Cox regression and the concordance index (c-index).

RESULTS: The ATAT first heuristically identified regions where necrosis, lymphocytes, and tumor tissues converge, particularly focusing on their junctions in high-risk patients. From this, this study developed three independent risk factors: microscopic morphological, co-localization, and deep global indicators, which were concatenated and then input into a neural network to generate the final HDS for each patient. The HDS demonstrated competitive results with hazard ratios (HR) (HR 3.24, 95% confidence interval (CI) 1.91-5.43 in SYSUCC; HR 2.34, 95% CI 1.58-3.47 in TCGA-LIHC) and c-index values (0.751 in SYSUCC; 0.729 in TCGA-LIHC) for Disease-Free Survival (DFS). Furthermore, integrating HDS into existing clinical staging systems allows for more refined stratification, which enables the identification of potential high-risk patients within low-risk groups.

CONCLUSION: This novel paradigm, from identifying high-risk tissues to constructing prognostic risk scores, offers fresh insights into HCC research. Additionally, the integration of HDS complements the existing clinical staging system by facilitating more detailed stratification in DFS and Overall Survival (OS).

PMID:40089963 | DOI:10.1007/s12072-025-10793-8

Ann Nucl Med. 2025 Mar 16. doi: 10.1007/s12149-025-02038-3. Online ahead of print.

ABSTRACT

OBJECTIVE: To provide fully automatic scanner-independent 5-level categorization of the [123I]FP-CIT uptake in striatal subregions in dopamine transporter SPECT.

METHODS: A total of 3500 [123I]FP-CIT SPECT scans from two in house (n = 1740, n = 640) and two external (n = 645, n = 475) datasets were used for this study. A convolutional neural network (CNN) was trained for the categorization of the [123I]FP-CIT uptake in unilateral caudate and putamen in both hemispheres according to 5 levels: normal, borderline, moderate reduction, strong reduction, almost missing. Reference standard labels for the network training were created automatically by fitting a Gaussian mixture model to histograms of the specific [123I]FP-CIT binding ratio, separately for caudate and putamen and separately for each dataset. The CNN was trained on a mixed-scanner subsample (n = 1957) and tested on one independent identically distributed (IID, n = 1068) and one out-of-distribution (OOD, n = 475) test dataset.

RESULTS: The accuracy of the CNN for the 5-level prediction of the [123I]FP-CIT uptake in caudate/putamen was 80.1/78.0% in the IID test dataset and 78.1/76.5% in the OOD test dataset. All 4 regional 5-level predictions were correct in 54.3/52.6% of the cases in the IID/OOD test dataset. A global binary score automatically derived from the regional 5-scores achieved 97.4/96.2% accuracy for automatic classification of the scans as normal or reduced relative to visual expert read as reference standard.

CONCLUSIONS: Automatic scanner-independent 5-level categorization of the [123I]FP-CIT uptake in striatal subregions by a CNN model is feasible with clinically useful accuracy.

PMID:40089953 | DOI:10.1007/s12149-025-02038-3

BMC Health Serv Res. 2025 Mar 15;25(1):383. doi: 10.1186/s12913-025-12491-5.

ABSTRACT

General practice-based care for Australian children is facing low levels of clinical guideline adherence particularly in three key areas: asthma, type 1 diabetes and antibiotic use. We offer an implementation science-informed position paper, providing a broad overview of how we aim to address this issue. This is the co-designed National Paediatric Applied Research Translation Initiative (N-PARTI), a bespoke, three-phased research solution by deploying mixed methods, simulation and scale-up of evidence into practice.

PMID:40089760 | DOI:10.1186/s12913-025-12491-5

Adv Sci (Weinh). 2025 Mar 16:e2500859. doi: 10.1002/advs.202500859. Online ahead of print.

ABSTRACT

Evolutionary constraints significantly limit the diversity of naturally occurring enzymes, thereby reducing the sequence repertoire available for enzyme discovery and engineering. Recent breakthroughs in protein structure prediction and de novo design, powered by artificial intelligence, now enable to create enzymes with desired functions without solely relying on traditional genome mining. Here, a computational strategy is demonstrated for creating new-to-nature polyethylene terephthalate hydrolases (PET hydrolases) by leveraging the known catalytic mechanisms and implementing multiple deep learning algorithms and molecular computations. This strategy includes the extraction of functional motifs from a template enzyme (here leaf-branch compost cutinase, LCC, is used), regeneration of new protein sequences, computational screening, experimental validation, and sequence refinement. PET hydrolytic activity is successfully replicated with designer enzymes that are at least 30% shorter in sequence length than LCC. Among them, RsPETase1 stands out due to its robust expressibility. It exhibits comparable catalytic efficiency (kcat/Km) to LCC and considerable thermostability with a melting temperature of 56 °C, despite sharing only 34% sequence similarity with LCC. This work suggests that enzyme diversity can be expanded by recapitulating functional motifs with computationally built protein scaffolds, thus generating opportunities to acquire highly active and robust enzymes that do not exist in nature.

PMID:40089854 | DOI:10.1002/advs.202500859

Syst Rev. 2025 Mar 15;14(1):62. doi: 10.1186/s13643-025-02779-2.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) has shown immense potential in the field of medicine, but its actual effectiveness and safety still need to be validated through clinical trials. Currently, the research themes, methodologies, and development trends of AI-related clinical trials remain unclear, and further exploration of these studies will be crucial for uncovering AI's practical application potential and promoting its broader adoption in clinical settings.

OBJECTIVE: To analyze the current status, hotspots, and trends of published clinical research on AI applications.

METHODS: Publications related to AI clinical applications were retrieved from the Web of Science database. Relevant data were extracted using VOSviewer 1.6.17 to generate visual cooperation network maps for countries, organizations, authors, and keywords. Burst citation detection for keywords and citations was performed using CiteSpace 5.8.R3 to identify sudden surges in citation frequency within a short period, and the theme evolution was analyzed using SciMAT to track the development and trends of research topics over time.

RESULTS: A total of 22,583 articles were obtained from the Web of Science database. Seven-hundred and thirty-five AI clinical application research were published by 1764 institutions from 53 countries. The majority of publications were contributed by the United States, China, and the UK. Active collaborations were noted among leading authors, particularly those from developed countries. The publications mainly focused on evaluating the application value of AI technology in the fields of disease diagnosis and classification, disease risk prediction and management, assisted surgery, and rehabilitation. Deep learning and chatbot technologies were identified as emerging research hotspots in recent studies on AI applications.

CONCLUSIONS: A total of 735 articles on AI in clinical research were analyzed, with publication volume and citation counts steadily increasing each year. Institutions and researchers from the United States contributed the most to the research output in this field. Key areas of focus included AI applications in surgery, rehabilitation, disease diagnosis, risk prediction, and health management, with emerging trends in deep learning and chatbots. This study also provides detailed and intuitive information about important articles, journals, core authors, institutions, and topics in the field through visualization maps, which will help researchers quickly understand the current status, hotspots, and trends of artificial intelligence clinical application research. Future clinical trials of artificial intelligence should strengthen scientific design, ethical compliance, and interdisciplinary and international cooperation and pay more attention to its practical clinical value and reliable application in diverse scenarios.

PMID:40089747 | DOI:10.1186/s13643-025-02779-2