Transl Vis Sci Technol. 2025 May 1;14(5):18. doi: 10.1167/tvst.14.5.18.

ABSTRACT

PURPOSE: This study aimed to develop and evaluate a deep learning model for grading foveal hypoplasia using retinal fundus images.

METHODS: This retrospective study included patients with foveal developmental disorders, using color fundus images and optical coherence tomography scans taken between January 1, 2001, and August 31, 2021. In total, 605 retinal fundus images were obtained from 303 patients (male, 55.1%; female, 44.9%). After augmentation, the training, validation, and testing data sets comprised 1229, 527, and 179 images, respectively. A deep learning model was developed for binary classification (normal vs. abnormal foveal development) and six-grade classification of foveal hypoplasia. The outcome was compared with those by senior and junior clinicians.

RESULTS: Higher grade of foveal hypoplasia showed worse visual outcomes (P < 0.001). The binary classification achieved a best testing accuracy of 84.36% using the EfficientNet_b1 model, with 84.51% sensitivity and 84.26% specificity. The six-grade classification achieved a best testing accuracy of 78.21% with the model. The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.9441 and an area under the precision-recall curve (AUPRC) of 0.9654 (both P < 0.0001) in the validation set and an AUROC of 0.8777 and an AUPRC of 0.8327 (both P < 0.0001) in the testing set. Compared to junior and senior clinicians, the EfficientNet_b1 model exhibited a superior performance in both binary and six-grade classification (both P < 0.00001).

CONCLUSIONS: The deep learning model in this study proved more efficient and accurate than assessments by junior and senior clinicians for identifying foveal developmental diseases in retinal fundus images. With the aid of the model, we were able to accurately assess patients with foveal developmental disorders.

TRANSLATIONAL RELEVANCE: This study strengthened the importance for a pediatric deep learning system to support clinical evaluation, particularly in cases reliant on retinal fundus images.

PMID:40402544 | DOI:10.1167/tvst.14.5.18

Clin Transl Oncol. 2025 May 22. doi: 10.1007/s12094-025-03948-4. Online ahead of print.

ABSTRACT

Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planning, and prognosis prediction. AI-driven approaches-such as CNNs and deep learning-have improved the detection and classification of brain tumors through advanced imaging techniques and genomic analysis. Explainable AI methods mitigate the "black box" problem, promoting model transparency and clinical trust. Mechanistic models complement AI by integrating biological principles, enabling precise tumor growth predictions and treatment response assessments. AI applications also include the creation of digital twins for personalized therapy optimization, virtual clinical trials, and predictive modeling for estimation of tumor resection and pattern of recurrence. However, challenges such as data bias, ethical concerns, and regulatory compliance persist. The European Artificial Intelligence Act and the Health Data Space Regulation impose strict data protection and transparency requirements. This review explores AI's methodological foundations, clinical applications, and ethical challenges in neuro-oncology, emphasizing the need for interdisciplinary collaboration and regulatory adaptation.

PMID:40402414 | DOI:10.1007/s12094-025-03948-4

Eur Radiol. 2025 May 22. doi: 10.1007/s00330-025-11689-9. Online ahead of print.

ABSTRACT

OBJECTIVES: To investigate whether a content-based image retrieval (CBIR) of similar chest CT images can help usual interstitial pneumonia (UIP) CT pattern classifications among readers with varying levels of experience.

MATERIALS AND METHODS: This retrospective study included patients who underwent high-resolution chest CT between 2013 and 2015 for the initial workup for fibrosing interstitial lung disease. UIP classifications were assigned to CT images by three thoracic radiologists, which served as the ground truth. One hundred patients were selected as queries. The CBIR retrieved the top three similar CT images with UIP classifications using a deep learning algorithm. The diagnostic accuracies and inter-reader agreement of nine readers before and after CBIR were evaluated.

RESULTS: Of 587 patients (mean age, 63 years; 356 men), 100 query cases (26 UIP patterns, 26 probable UIP patterns, 5 indeterminate for UIP, and 43 alternative diagnoses) were selected. After CBIR, the mean accuracy (61.3% to 67.1%; p = 0.011) and inter-reader agreement (Fleiss Kappa, 0.400 to 0.476; p = 0.003) were slightly improved. The accuracies of the radiologist group for all CT patterns except indeterminate for UIP increased after CBIR; however, they did not reach statistical significance. The resident and pulmonologist groups demonstrated mixed results: accuracy decreased for UIP pattern, increased for alternative diagnosis, and varied for others.

CONCLUSION: CBIR slightly improved diagnostic accuracy and inter-reader agreement in UIP pattern classifications. However, its impact varied depending on the readers' level of experience, suggesting that the current CBIR system may be beneficial when used to complement the interpretations of experienced readers.

KEY POINTS: Question CT pattern classification is important for the standardized assessment and management of idiopathic pulmonary fibrosis, but requires radiologic expertise and shows inter-reader variability. Findings CBIR slightly improved diagnostic accuracy and inter-reader agreement for UIP CT pattern classifications overall. Clinical relevance The proposed CBIR system may guide consistent work-up and treatment strategies by enhancing accuracy and inter-reader agreement in UIP CT pattern classifications by experienced readers whose expertise and experience can effectively interact with CBIR results.

PMID:40402291 | DOI:10.1007/s00330-025-11689-9

Eur Radiol. 2025 May 22. doi: 10.1007/s00330-025-11707-w. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to compare the diagnostic performance of CT-derived fractional flow reserve (CT-FFR) using model-based iterative reconstruction (MBIR) and high-resolution deep learning reconstruction (HR-DLR) images to detect functionally significant stenosis with invasive FFR as the reference standard.

MATERIALS AND METHODS: This single-center retrospective study included 79 consecutive patients (mean age, 70 ± 11 [SD] years; 57 male) who underwent coronary CT angiography followed by invasive FFR between February 2022 and March 2024. CT-FFR was calculated using a mesh-free simulation. The cutoff for functionally significant stenosis was defined as FFR ≤ 0.80. CT-FFR was compared with MBIR and HR-DLR using receiver operating characteristic curve analysis.

RESULTS: The mean invasive FFR value was 0.81 ± 0.09, and 46 of 98 vessels (47%) had FFR ≤ 0.80. The mean noise of HR-DLR was lower than that of MBIR (14.4 ± 1.7 vs 23.5 ± 3.1, p < 0.001). The area under the receiver operating characteristic curve for the diagnosis of functionally significant stenosis of HR-DLR (0.88; 95% CI: 0.80, 0.95) was higher than that of MBIR (0.76; 95% CI: 0.67, 0.86; p = 0.003). The diagnostic accuracy of HR-DLR (88%; 86 of 98 vessels; 95% CI: 80, 94) was higher than that of MBIR (70%; 69 of 98 vessels; 95% CI: 60, 79; p < 0.001).

CONCLUSIONS: HR-DLR improves image quality and the diagnostic performance of CT-FFR for the diagnosis of functionally significant stenosis.

KEY POINTS: Question The effect of HR-DLR on the diagnostic performance of CT-FFR has not been investigated. Findings HR-DLR improved the diagnostic performance of CT-FFR over MBIR for the diagnosis of functionally significant stenosis as assessed by invasive FFR. Clinical relevance HR-DLR would further enhance the clinical utility of CT-FFR in diagnosing the functional significance of coronary stenosis.

PMID:40402290 | DOI:10.1007/s00330-025-11707-w

Int J Legal Med. 2025 May 22. doi: 10.1007/s00414-025-03497-z. Online ahead of print.

ABSTRACT

The epiphyses of the hand and wrist serve as crucial indicators for assessing skeletal maturity in adolescents. This study aimed to develop a deep learning (DL) model for bone age (BA) assessment using hand and wrist X-ray images, addressing the challenge of classifying BA in adolescents. The results of this DL-based classification were then compared and analyzed with those obtained from manual assessment. A retrospective analysis was conducted on 688 hand and wrist X-ray images of adolescents aged 11.00-23.99 years from western China, which were randomly divided into training set, validation set and test set. The BA assessment results were initially analyzed and compared using four DL network models: InceptionV3, InceptionV3 + SE + Sex, InceptionV3 + Bilinear and InceptionV3 + Bilinear. + SE + Sex, to identify the DL model with the best classification performance. Subsequently, the results of the top-performing model were compared with those of manual classification. The study findings revealed that the InceptionV3 + Bilinear + SE + Sex model exhibited the best performance, achieving classification accuracies of 96.15% and 90.48% for the training and test set, respectively. Furthermore, based on the InceptionV3 + Bilinear + SE + Sex model, classification accuracies were calculated for four age groups (< 14.0 years, 14.0 years ≤ age < 16.0 years, 16.0 years ≤ age < 18.0 years, ≥ 18.0 years), with notable accuracies of 100% for the age groups 16.0 years ≤ age < 18.0 years and ≥ 18.0 years. The BA classification, utilizing the feature fusion DL network model, holds significant reference value for determining the age of criminal responsibility of adolescents, particularly at the critical legal age boundaries of 14.0, 16.0, and 18.0 years.

PMID:40402226 | DOI:10.1007/s00414-025-03497-z

J Neurooncol. 2025 May 22. doi: 10.1007/s11060-025-05073-2. Online ahead of print.

ABSTRACT

BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperative evaluation. Therefore, we aim to propose an innovative, machine learning- and deep learning-based framework for the rapid and non-invasive preoperative assessment of GAE in pediatric patients using magnetic resonance imaging (MRI).

METHODS: In this study, we propose a novel radiomics-based approach that integrates tumor and peritumoral features extracted from preoperative multiparametric MRI scans to accurately and non-invasively predict the occurrence of tumor-related epilepsy in pediatric patients.

RESULTS: Our study developed a multimodal MRI radiomics model to predict epilepsy in pLGGs patients, achieving an AUC of 0.969. The integration of multi-sequence MRI data significantly improved predictive performance, with Stochastic Gradient Descent (SGD) classifier showing robust results (sensitivity: 0.882, specificity: 0.956).

CONCLUSION: Our model can accurately predict whether pLGGs patients have tumor-related epilepsy, which could guide surgical decision-making. Future studies should focus on similarly standardized preoperative evaluations in pediatric epilepsy centers to increase training data and enhance the generalizability of the model.

PMID:40402200 | DOI:10.1007/s11060-025-05073-2

J Comput Biol. 2025 May 22. doi: 10.1089/cmb.2024.0632. Online ahead of print.

ABSTRACT

Accurate triage in emergency rooms is crucial for efficient patient care and resource allocation. We developed methods to predict triage levels using several traditional machine learning methods (logistic regression, random forest, XGBoost) and neural network deep learning-based approaches. These models were tested on a dataset from emergency department visits of patients at a local Turkish hospital; this dataset consists of both structured and unstructured data. Compared with previous work, our challenge was to build a predictive model that uses documents written in the Turkish language and that handles specific aspects of the Turkish medical system. Text embedding techniques such as Bag of Words, Word2Vec, and BERT-based embedding were used to process the unstructured patient complaints. We used a comprehensive set of features including patient history data and disease diagnosis within our predictive models, which included advanced neural network architectures such as convolutional neural networks, attention mechanisms, and long-short-term memory networks. Our results revealed that BERT embeddings significantly enhanced the performance of neural network models, while Word2Vec embeddings showed slight better results in traditional machine learning models. The most effective model was XGBoost combined with Word2Vec embeddings, achieving 86.7% AUC, 81.5% accuracy, and 68.7% weighted F1 score. We conclude that text embedding methods and machine learning methods are effective tools to predict emergency room triage levels. The integration of patient history into the models, alongside the strategic use of text embeddings, significantly improves predictive accuracy.

PMID:40401726 | DOI:10.1089/cmb.2024.0632

Pharmacogenomics. 2025 May 22:1-12. doi: 10.1080/14622416.2025.2504863. Online ahead of print.

ABSTRACT

Pharmacogenomic variations in genes involved in drug disposition and in drug targets is a major determinant of inter-individual differences in drug response and toxicity. While the effects of common variants are well established, millions of rare variations remain functionally uncharacterized, posing a challenge for the implementation of precision medicine. Recent advances in machine learning (ML) have significantly enhanced the prediction of variant effects by considering DNA as well as protein sequences, as well as their evolutionary conservation and haplotype structures. Emerging deep learning models utilize techniques to capture evolutionary conservation and biophysical properties, and ensemble approaches that integrate multiple predictive models exhibit increased accuracy, robustness, and interpretability. This review explores the current landscape of ML-based variant effect predictors. We discuss key methodological differences and highlight their strengths and limitations for pharmacogenomic applications. We furthermore discuss emerging methodologies for the prediction of substrate-specificity and for consideration of variant epistasis. Combined, these tools improve the functional effect prediction of drug-related variants and offer a viable strategy that could in the foreseeable future translate comprehensive genomic information into pharmacogenetic recommendations.

PMID:40401639 | DOI:10.1080/14622416.2025.2504863

JCI Insight. 2025 May 22;10(10):e179246. doi: 10.1172/jci.insight.179246. eCollection 2025 May 22.

ABSTRACT

Dry age-related macular degeneration (AMD) is a leading cause of untreatable vision loss. In advanced cases, retinal pigment epithelium (RPE) cell loss occurs alongside photoreceptor and choriocapillaris degeneration. We hypothesized that an RPE-patch would mitigate photoreceptor and choriocapillaris degeneration to restore vision. An induced pluripotent stem cell-derived RPE (iRPE) patch was developed using a clinically compatible manufacturing process by maturing iRPE cells on a biodegradable poly(lactic-co-glycolic acid) (PLGA) scaffold. To compare outcomes, we developed a surgical procedure for immediate sequential delivery of PLGA-iRPE and/or PLGA-only patches in the subretinal space of a pig model of laser-induced outer retinal degeneration. Deep learning algorithm-based optical coherence tomography (OCT) image segmentation verified preservation of the photoreceptors over the areas of PLGA-iRPE-transplanted retina and not in laser-injured or PLGA-only-transplanted retina. Adaptive optics imaging of individual cone photoreceptors further supported this finding. OCT-angiography revealed choriocapillaris regeneration in PLGA-iRPE- and not in PLGA-only-transplanted retinas. Our data, obtained using clinically relevant techniques, verified that PLGA-iRPE supports photoreceptor survival and regenerates choriocapillaris in a laser-injured pig retina. Sequential delivery of two 8 mm2 transplants allows for testing of surgical feasibility and safety of the double dose. This work allows one surgery to treat larger and noncontiguous retinal degeneration areas.

PMID:40401519 | DOI:10.1172/jci.insight.179246

Eur Radiol. 2025 May 22. doi: 10.1007/s00330-025-11689-9. Online ahead of print.

ABSTRACT

OBJECTIVES: To investigate whether a content-based image retrieval (CBIR) of similar chest CT images can help usual interstitial pneumonia (UIP) CT pattern classifications among readers with varying levels of experience.

MATERIALS AND METHODS: This retrospective study included patients who underwent high-resolution chest CT between 2013 and 2015 for the initial workup for fibrosing interstitial lung disease. UIP classifications were assigned to CT images by three thoracic radiologists, which served as the ground truth. One hundred patients were selected as queries. The CBIR retrieved the top three similar CT images with UIP classifications using a deep learning algorithm. The diagnostic accuracies and inter-reader agreement of nine readers before and after CBIR were evaluated.

RESULTS: Of 587 patients (mean age, 63 years; 356 men), 100 query cases (26 UIP patterns, 26 probable UIP patterns, 5 indeterminate for UIP, and 43 alternative diagnoses) were selected. After CBIR, the mean accuracy (61.3% to 67.1%; p = 0.011) and inter-reader agreement (Fleiss Kappa, 0.400 to 0.476; p = 0.003) were slightly improved. The accuracies of the radiologist group for all CT patterns except indeterminate for UIP increased after CBIR; however, they did not reach statistical significance. The resident and pulmonologist groups demonstrated mixed results: accuracy decreased for UIP pattern, increased for alternative diagnosis, and varied for others.

CONCLUSION: CBIR slightly improved diagnostic accuracy and inter-reader agreement in UIP pattern classifications. However, its impact varied depending on the readers' level of experience, suggesting that the current CBIR system may be beneficial when used to complement the interpretations of experienced readers.

KEY POINTS: Question CT pattern classification is important for the standardized assessment and management of idiopathic pulmonary fibrosis, but requires radiologic expertise and shows inter-reader variability. Findings CBIR slightly improved diagnostic accuracy and inter-reader agreement for UIP CT pattern classifications overall. Clinical relevance The proposed CBIR system may guide consistent work-up and treatment strategies by enhancing accuracy and inter-reader agreement in UIP CT pattern classifications by experienced readers whose expertise and experience can effectively interact with CBIR results.

PMID:40402291 | DOI:10.1007/s00330-025-11689-9

Bot Stud. 2025 May 22;66(1):14. doi: 10.1186/s40529-025-00462-2.

ABSTRACT

AlphaFold 3 (AF3), an artificial intelligence (AI)-based software for protein complex structure prediction, represents a significant advancement in structural biology. Its flexibility and enhanced scalability have unlocked new applications in various fields, specifically in plant science, including improving crop resilience and predicting the structures of plant-specific proteins involved in stress responses, signalling pathways, and immune responses. Comparisons with existing tools, such as ClusPro and AlphaPulldown, highlight AF3's unique strengths in sequence-based interaction predictions and its greater adaptability to various biomolecular structures. However, limitations persist, including challenges in modelling large complexes, protein dynamics, and structures from underrepresented plant proteins with limited evolutionary data. Additionally, AF3 encounters difficulties in predicting mutation effects on protein interactions and DNA binding, which can be improved with molecular dynamics and experimental validation. This review presents an overview of AF3's advancements, using examples in plant and fungal research, and comparisons with existing tools. It also discusses current limitations and offers perspectives on integrating molecular dynamics and experimental validation to enhance its capabilities.

PMID:40402396 | DOI:10.1186/s40529-025-00462-2

Crit Care Med. 2025 May 22. doi: 10.1097/CCM.0000000000006716. Online ahead of print.

ABSTRACT

OBJECTIVES: Soluble ST2 (sST2), a decoy receptor for the alarmin interleukin-33 (IL-33), has been implicated in adverse clinical outcomes in acute respiratory failure (ARF). We evaluated sST2 distribution across diverse cohorts of patients with different etiologies of ARF, compared plasma and lower respiratory tract (LRT) concentrations, and examined associations with individual organ dysfunction, biological subphenotypes, and outcomes.

DESIGN: Observational study.

SETTING: Multicenter cohorts of ARF patients.

PATIENTS: A total of 1432 ARF patients, including 863 non-COVID and 569 COVID-19 cases, from five cohorts.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: sST2 levels were measured in plasma and LRT specimens (when available) and analyzed for associations with ARF etiology, severity, organ dysfunction, systemic host response, subphenotypes, and 30-day mortality. Plasma sST2 levels were higher in non-COVID ARF patients compared with COVID-19 patients (p < 0.05) and were markedly elevated compared with LRT levels (> 19-fold), with weak intercompartmental correlation. Elevated plasma sST2 levels were associated with extrapulmonary organ dysfunction and a hyperinflammatory ARF subphenotype but not with respiratory indices, including hypoxemia. Plasma sST2 independently predicted 30-day mortality in pooled cohort data, adjusted for age, sex, and illness severity. In longitudinal measurements, nonsurvivors had persistently elevated plasma sST2 levels in the first 2 weeks of critical illness compared with survivors.

CONCLUSIONS: Plasma sST2 levels independently predict outcomes in ARF and are strongly associated with extrapulmonary organ dysfunction. The weak correlation between plasma and LRT sST2 levels suggests a predominantly systemic source. These findings highlight the potential of the IL-33/ST2 axis as a therapeutic target and warrant further investigation into its role in multiple organ dysfunction in ARF.

PMID:40402026 | DOI:10.1097/CCM.0000000000006716

J Toxicol Environ Health A. 2025 May 22:1-12. doi: 10.1080/15287394.2025.2509761. Online ahead of print.

ABSTRACT

Saxitoxin (STX), a potent neurotoxin produced by cyanobacteria, has not been comprehensively investigated with respect to genotoxic potential, especially in freshwater environments. This study aimed to characterize the genotoxic potential of STX obtained from Raphidiopsis. raciborskii cultures using in vitro and in silico approaches. Mutagenic potential was determined through the Ames test with Salmonella typhimurium strains TA98, TA100, and TA102. DNA damage and chromosomal instability were assessed in human glioblastoma U87-MG cells using the comet and cytokinesis-block micronucleus cytome (CBMN-Cyt) assay, respectively. In addition, systems biology tools were applied to explore STX interactions with genes involved in DNA damage response pathways. Data demonstrated no marked mutagenic activity in the Ames test across tested concentrations (0.625-10 µg/L). However, significant DNA damage and increased micronucleus (MN) formation were observed at 2.5, 5, or 10 µg/L in U87-MG cells, without accompanying cytotoxicity. In silico analysis identified interactions between STX and key proteins, including P53, CDK5, and GSK3B, indicating pathways related to DNA damage, cell cycle regulation, and neurogenesis. These findings suggest that STX from freshwater cyanobacteria might induce genotoxic effects at environmentally relevant concentrations. The integration of in vitro and computational data supports the need for regulatory monitoring of STX in drinking water and emphasizes the relevance of neural cell-based models in assessing cyanotoxin-related adverse risks.

PMID:40401712 | DOI:10.1080/15287394.2025.2509761

Expert Opin Investig Drugs. 2025 May 22. doi: 10.1080/13543784.2025.2510664. Online ahead of print.

ABSTRACT

BACKGROUND: BI 764,198 is a selective, oral transient receptor potential cation channel, subfamily C, member 6 inhibitor under investigation for focal segmental glomerulosclerosis.

RESEARCH DESIGN AND METHODS: Phase I study in 44 Japanese male volunteers. Single dose part: BI 764,198 20 mg (n = 6) vs. placebo (n = 2); multiple dose part: BI 764,198 40, 80, or 160 mg (n = 9 each) or placebo (n = 9) as a single dose then multiple daily dosing for 2 weeks. Primary endpoint: participants with drug-related adverse events (DRAEs); secondary endpoints: pharmacokinetic.

RESULTS: DRAEs were reported in 20.5% (9/44) of participants (total BI 764,198 21.2% [7/33]; placebo 18.2% [2/11]), mostly diarrhea (total BI 764,198 15.2% [5/33]; placebo 18.2% [2/11]) and headache (BI 764,198 80 mg 11.1% [1/9]; BI 764,198 160 mg 33.3% [3/9]). BI 764198 exposure increased near dose proportionally to 80 mg and was slightly higher than anticipated with 160 mg. Pharmacokinetics were similar in Asians and non-Asians after accounting for body weight. Limitations include small sample size per dose and short trial duration.

CONCLUSIONS: BI 764,198 was well tolerated; exposure increased near dose proportionally to 80 mg, as previously observed in predominantly White volunteers.

CLINICAL TRIAL REGISTRATION: This study was registered on Clinical Trials.gov, identifier NCT04665700.

PMID:40402558 | DOI:10.1080/13543784.2025.2510664

Front Cell Neurosci. 2025 May 7;19:1582902. doi: 10.3389/fncel.2025.1582902. eCollection 2025.

ABSTRACT

White matter in the central nervous system comprises bundled nerve fibers myelinated by oligodendrocytes. White matter injury, characterized by the loss of oligodendrocytes and myelin, is common after ischemic brain injury, inflammatory demyelinating diseases including multiple sclerosis, and traumatic damage such as spinal cord injury. Currently, no therapies have been confirmed to promote remyelination in these diseases. Over the past decade, various reports have suggested that the anti-muscarinic drug clemastine can stimulate remyelination by oligodendrocytes. Consequently, the repurposing of clemastine as a potential treatment for a variety of neurological disorders has gained significant attention. The therapeutic effects of clemastine have been demonstrated in various animal models, and its mechanisms of action in various neurological disorders are currently being investigated. In this review, we summarize reports relating to clemastine administration for white matter injury and neurological disease and discuss the therapeutic potential of remyelination promotion.

PMID:40400770 | PMC:PMC12092462 | DOI:10.3389/fncel.2025.1582902

Psychol Med. 2025 May 22;55:e158. doi: 10.1017/S0033291725001096.

ABSTRACT

BACKGROUND: Precise stratification of patients into homogeneous disease subgroups could address the heterogeneity of phenotypes and enhance understanding of the pathophysiology underlying specific subtypes. Existing literature on subtyping patients with major depressive disorder (MDD) mainly utilized clinical features only. Genomic and imaging data may improve subtyping, but advanced methods are required due to the high dimensionality of features.

METHODS: We propose a novel disease subtyping framework for MDD by integrating brain structural features, genotype-predicted expression levels in brain tissues, and clinical features. Using a multi-view biclustering approach, we classify patients into clinically and biologically homogeneous subgroups. Additionally, we propose approaches to identify causally relevant genes for clustering.

RESULTS: We verified the reliability of the subtyping model by internal and external validation. High prediction strengths (PS) (average PS: 0.896, minimum: 0.854), a measure of generalizability of the derived clusters in independent datasets, support the validity of our approach. External validation using patient outcome variables (treatment response and hospitalization risks) confirmed the clinical relevance of the identified subgroups. Furthermore, subtype-defining genes overlapped with known susceptibility genes for MDD and were involved in relevant biological pathways. In addition, drug repositioning analysis based on these genes prioritized promising candidates for subtype-specific treatments.

CONCLUSIONS: Our approach successfully stratified MDD patients into subgroups with distinct clinical prognoses. The identification of biologically and clinically meaningful subtypes may enable more personalized treatment strategies. This study also provides a framework for disease subtyping that can be extended to other complex disorders.

PMID:40400388 | DOI:10.1017/S0033291725001096

Br J Clin Pharmacol. 2025 May 21. doi: 10.1002/bcp.70078. Online ahead of print.

ABSTRACT

AIMS: Gene polymorphisms play a critical role in the variability of plasma concentrations of direct-acting oral anticoagulants (DOACs). In this study, we aimed to investigate the effects of genetic variants on the clinical outcomes of Chinese patients treated with DOACs.

METHODS: The retrospective study recruited 720 patients with nonvalvular atrial fibrillation who were receiving dabigatran, rivaroxaban or edoxaban. Cox regression models were employed to compare the clinical outcomes between carriers and noncarriers of the key single nucleotide polymorphisms.

RESULTS: Results revealed that the CES1 rs2244613 C allele significantly reduced bleeding events in patients treated with dabigatran (adjusted hazard ratio 0.33, 95% confidence interval 0.13-0.85, P = .021). The carriage of ABCB1 rs1045642 T allele was associated with a lower risk of thromboembolism in rivaroxaban users (adjusted hazard ratio 0.19, 95% confidence interval 0.07-0.57, P = .003). Additionally, a trend toward statistical significance (P = .052) was observed between the SLCO1B1 rs4149056 C allele and bleeding risk among the edoxaban users.

CONCLUSIONS: Our study showed that the CES1 rs2244613 and ABCB1 rs1045642 alleles were associated with outcome events in Chinese patients taking dabigatran and rivaroxaban, respectively. The findings could help predict clinical outcomes and develop personalized anticoagulation treatment strategies for Chinese patients taking DOACs.

PMID:40400080 | DOI:10.1002/bcp.70078

JCEM Case Rep. 2025 May 21;3(7):luaf114. doi: 10.1210/jcemcr/luaf114. eCollection 2025 Jul.

ABSTRACT

Cystic fibrosis (CF), a genetic disorder caused by pathogenic variants in the CFTR gene, is associated with various complications including cystic fibrosis-related diabetes (CFRD). CFRD is an entity distinct from type 1 or type 2 diabetes. We report a rare case of diabetic ketoacidosis (DKA) in a pediatric patient with CFRD, occurring during a significant pulmonary exacerbation. The patient's management involved addressing fluid and electrolyte imbalances, careful monitoring of nutritional status, and correction of hyperglycemia with insulin. This case serves as a reminder to consider DKA in the differential diagnosis of patients with CF presenting with respiratory distress, even in the absence of typical symptoms such as polyuria and polydipsia.

PMID:40401175 | PMC:PMC12093047 | DOI:10.1210/jcemcr/luaf114

Respir Med Case Rep. 2025 Apr 19;55:102214. doi: 10.1016/j.rmcr.2025.102214. eCollection 2025.

ABSTRACT

We report three cases of bronchiectasis caused by homozygous WFDC2 variants. The ages at diagnosis of bronchiectasis were 18, 24, and 16 years, and all patients had a history of chronic sinusitis since childhood. Despite low nasal nitric oxide levels, the radiologic features resembled those of cystic fibrosis, characterized by bronchiectasis predominantly in the upper lobes. All patients experienced frequent exacerbations and respiratory dysfunction, even with long-term macrolide therapy. Consequently, two of the three patients required lung transplantation. Considering the possibility of founder mutations, WFDC2 variants should be included in diagnostic panels for patients with sinopulmonary disease in Asian populations.

PMID:40401042 | PMC:PMC12093231 | DOI:10.1016/j.rmcr.2025.102214

J Thorac Dis. 2025 Apr 30;17(4):1832-1843. doi: 10.21037/jtd-2024-2069. Epub 2025 Apr 27.

ABSTRACT

BACKGROUND: Ex vivo lung perfusion (EVLP) of donor lungs not otherwise acceptable for transplantation can provide outcomes similar to standard-criteria lung transplantation and has been reported to increase transplant volume by approximately 20% in some transplant centers. Evidence to support decisions about use of EVLP is limited, so expert opinion can be a useful decision aid. This study developed expert consensus recommendations for EVLP with acellular perfusate using a modified Delphi method.

METHODS: A panel of 18 physicians with expertise in lung transplantation and EVLP who practice in North America completed three surveys on EVLP: Survey 1 used open-ended questions; Survey 2 used primarily Likert-scale questions; and Survey 3 repeated Survey 2 while providing panelists with the Survey 2 results. A follow-up meeting after Survey 3 probed open questions.

RESULTS: The primary goal for EVLP is expanding the number of donor lungs available for transplant. Lungs that are acceptable after EVLP are equivalent to lungs that met standard criteria initially. Lungs with unclear or marginal quality should be placed on EVLP for evaluation, including lungs received from third party organizations with incomplete or concerning information. Decisions on whether to put lungs on EVLP require nuanced clinical judgement and should consider compliance and deflation, the ratio of PaO2 to fraction of inspired oxygen (P/F ratio), peak inspiratory pressure (PIP), edema on imaging, and bronchoscopy, with additional parameters considered as appropriate if lung quality is unclear. EVLP lungs are appropriate for transplant if all relevant parameters are acceptable and may be appropriate if some parameters are borderline depending on clinical judgment. Decisions about transplanting EVLP lungs should consider radiography, delta PO2, overall movement, STEEN Solution™ loss, bronchoscopy, peak airway pressure, and palpation, along with other parameters as appropriate. Key open areas for research include evidence-based criteria for lung selection and assessment, the role of biomarkers, and enhanced techniques and perfusion solutions. In addition, the role of EVLP is unclear in lungs with pulmonary emboli and lungs procured with normothermic regional perfusion (NRP), as is the maximal duration of cold ischemia time (CIT).

CONCLUSIONS: Decisions about EVLP require nuanced consideration of numerous parameters. Expert opinion from this study may help optimize use of EVLP.

PMID:40400975 | PMC:PMC12090176 | DOI:10.21037/jtd-2024-2069