Comput Biol Med. 2025 May 17;192(Pt B):110322. doi: 10.1016/j.compbiomed.2025.110322. Online ahead of print.

ABSTRACT

The application of AI for predicting critical heart failure endpoints using echocardiography is a promising avenue to improve patient care and treatment planning. However, fully supervised training of deep learning models in medical imaging requires a substantial amount of labelled data, posing significant challenges due to the need for skilled medical professionals to annotate image sequences. Our study addresses this limitation by exploring the potential of self-supervised learning, emphasising interpretability, robustness, and safety as crucial factors in cardiac imaging analysis. We leverage self-supervised learning on a large unlabelled dataset, facilitating the discovery of features applicable to a various downstream tasks. The backbone model not only generates informative features for training smaller models using simple techniques but also produces features that are interpretable by humans. The study employs a modified Self-supervised Transformer with Energy-based Graph Optimisation (STEGO) network on top of self-DIstillation with NO labels (DINO) as a backbone model, pre-trained on diverse medical and non-medical data. This approach facilitates the generation of self-segmented outputs, termed "parcels", which identify distinct anatomical sub-regions of the heart. Our findings highlight the robustness of these self-learned parcels across diverse patient profiles and phases of the cardiac cycle phases. Moreover, these parcels offer high interpretability and effectively encapsulate clinically relevant cardiac substructures. We conduct a comprehensive evaluation of the proposed self-supervised approach on publicly available datasets, demonstrating its adaptability to a wide range of requirements. Our results underscore the potential of self-supervised learning to address labelled data scarcity in medical imaging, offering a path to improve cardiac imaging analysis and enhance the efficiency and interpretability of diagnostic procedures, thus positively impacting patient care and clinical decision-making.

PMID:40383057 | DOI:10.1016/j.compbiomed.2025.110322

Epilepsy Behav. 2025 May 17;170:110470. doi: 10.1016/j.yebeh.2025.110470. Online ahead of print.

ABSTRACT

This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EEG findings using artificial intelligence models. The study included 575 patients followed in the Neurology Epilepsy Polyclinics of Adana City Training and Research Hospital between June 2021 and July 2024. Patient history, examination findings, seizure characteristics and EEG results were retrospectively reviewed to create a comprehensive database. Initially, machine learning architectures were applied to differentiate generalized and focal epilepsy. Subsequently, EEG findings were categorized into eight subgroups, and machine learning methods were utilized for classification. Three AI models-Multilayer Perceptron (MLP), Random Forest, and Support Vector Machine (SVM)-were employed. The dataset was further improved through data augmentation with SMOTE. The initial deep learning model achieved 89 % accuracy, recall, and F1 scores. Then, Optuna framework was incorporated into model to optimize hyperparameters, thus the accuracy reached 96 %. In comparison, the proposed ensemble model combining MLP, SVM and XGBoost achieved the highest accuracy of 98 %. The study demonstrates that data augmentation and ensemble AI models can provide robust decision support for physicians in classifying epilepsy types.

PMID:40382997 | DOI:10.1016/j.yebeh.2025.110470

Neural Netw. 2025 May 16;189:107573. doi: 10.1016/j.neunet.2025.107573. Online ahead of print.

ABSTRACT

In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in clinical applications. To address this issue, we construct a framework (EScarcityS) aimed at enhancing the success rate of disease diagnosis in scarcity of trainable medical image scenarios. Firstly, considering that Transformer-based deep learning networks rely on a large amount of trainable data, this study takes into account the unique characteristics of pathological regions. By extracting the feature representations of all particles in medical images at different granularities, a multi-granularity Transformer network (MGVit) is designed. This network leverages additional prior knowledge to assist the Transformer network during training, thereby reducing the data requirement to some extent. Next, the importance maps of particles at different granularities, generated by MGVit, are fused to construct disease probability maps corresponding to the images. Based on these maps, a disease probability map-guided diffusion generation model is designed to generate more realistic and interpretable synthetic data. Subsequently, authentic and synthetical data are mixed and used to retrain MGVit, aiming to enhance the accuracy of medical image classification in scarcity of trainable medical image scenarios. Finally, we conducted detailed experiments on four real medical image datasets to validate the effectiveness of EScarcityS and its specific modules.

PMID:40382989 | DOI:10.1016/j.neunet.2025.107573

Water Res. 2025 May 13;283:123801. doi: 10.1016/j.watres.2025.123801. Online ahead of print.

ABSTRACT

Microalgae-driven nutrient recovery represents a promising technology for phosphorus removal from wastewater while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based dense neural network (DNN) models. Across a two-year study period, Scenedesmus spp. appeared most important for phosphorus removal, and were negatively impacted by elevated temperatures and increase in nitrite/nitrate concentrations. Chlorella and Monoraphidium also played an important role in phosphorus removal. For both Scenedesmus and Chlorella, smaller morphological types were more often associated with better system performance, whereas larger morphotypes likely associated with stress response(s) correlated with poor phosphorus recovery rates. These results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes.

PMID:40382876 | DOI:10.1016/j.watres.2025.123801

Comput Methods Programs Biomed. 2025 May 12;268:108812. doi: 10.1016/j.cmpb.2025.108812. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of individuals at heightened risk of such complications or their exacerbation can be crucial to set a correct course of treatment. However, there are currently no widely accepted predictive tools for this task and, additionally, most of these models rely only on information at a single baseline visit. Considering this, we investigate the potential predictive role of patients' clinical history over multiple levels of renal disease severity while, at the same time, developing an effective predictive model.

METHODS: From the data collected in the DARWIN-Renal (DApagliflozin Real-World evIdeNce-Renal) study, a nationwide multicentre retrospective real-world study, we develop four different types of machine learning models, namely, logistic regression, random forest, Cox proportional hazards regression, and a deep learning model based on recurrent neural network to predict the crossing of 5 clinically relevant glomerular filtration rate thresholds for patients with type 2 diabetes.

RESULTS: The predictive performance of all models is satisfactory for all outcomes, even without the introduction of information referring to past visits, with AUROC and C-index between 0.69 and 0.98 and average precision well above the random model. The introduction of past information results into a clear improvement in performance for all the models, with percentage increases of up to 12% for both AUROC and C-index and 300% for average precision. The usefulness of past information is further corroborated by a feature importance analysis.

CONCLUSIONS: Incorporating data from the patients' clinical history into the predictive models greatly improves their performance, particularly for recurrent neural network where the full sequence of values for dynamic variables is provided compared to synthetic indicators of past history.

PMID:40382871 | DOI:10.1016/j.cmpb.2025.108812

J Control Release. 2025 May 16:113862. doi: 10.1016/j.jconrel.2025.113862. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) exhibits extremely high mortality rates. Targeted therapy, which utilizes specific drugs or other substances to identify and attack specific molecular targets in the lesion, holds promise as a potent means of treating IPF. M2 macrophages have been shown to express high levels of osteopontin (OPN) early in the onset of IPF and sustain this high expression to promote the progression of IPF. Intervention in OPN expression can effectively impede the development of fibrosis. While the technology for targeting proteins with siRNA has become increasingly mature, the targeted delivery of siRNA to resident M2 macrophages in the lungs remains challenging. In this study, we developed an engineered self-assembling OPN siRNA carrier complex based on a metal-polyphenol network (luteolin-Zr) and PEG conjugated with an M2 macrophage-targeting peptide (Pery-PEG-M2), termed siOPN@LuZ-M2, for the treatment of pulmonary fibrosis. Consequently, significant therapeutic effects were observed in both bleomycin-induced pulmonary fibrosis mouse models and human precision-cut lung slices (hPCLS) models. Importantly, luteolin, which is slowly released from siOPN@LuZ-M2 within cells, can gradually accumulate in fibrotic lung tissue, exerting an anti-inflammatory effect and further enhancing the treatment of IPF. It is worth mentioning that siOPN@LuZ-M2 can be labeled with 89Zr, allowing for the detection of its in vivo distribution and metabolic behavior via PET-CT. This study presents a promising new image-guided molecular targeting strategy for the treatment of fibrosis.

PMID:40383161 | DOI:10.1016/j.jconrel.2025.113862

Heart Lung. 2025 May 17;73:114-122. doi: 10.1016/j.hrtlng.2025.05.008. Online ahead of print.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, potentially fatal lung disorder characterized by scarring, leading to reduced lung function and respiratory failure. Nintedanib, a tyrosine kinase inhibitor, shows potential in slowing IPF progression, but uncertainties remain about its long-term efficacy and safety.

OBJECTIVE: To evaluate the efficacy and safety of Nintedanib in idiopathic pulmonary fibrosis.

METHODS: A comprehensive literature search was conducted across PubMed, Cochrane, Scopus, Embase, and ClinicalTrials.gov from inception to August 2024, selecting studies based on predefined eligibility criteria. Dichotomous outcomes were pooled as risk ratios (RR) and continuous outcomes as mean differences (MD), both with 95% confidence intervals (CI), using random-effects models to account for potential heterogeneity. Heterogeneity was assessed using I² and X² statistics, with a p-value of <0.05 considered statistically significant. All calculations were performed using RevMan 5.4.

RESULTS: This meta-analysis included 4 randomized controlled trials with 1,665 patients, 79.7% of whom were male smokers. There was no significant difference in IPF progression (RR=0.61, 95% CI 0.34-1.08, I²=41%) or in nasopharyngitis (RR=0.82, 95% CI 0.62-1.08, I²=0%), cough (RR=0.98, 95% CI 0.71-1.33, I²=0%), bronchitis (RR=0.90, 95% CI 0.63-1.28, I²=0%), respiratory infections (RR=1.01, 95% CI 0.59-1.75, I²=0%), dyspnea (RR=0.68, 95% CI 0.46-1.00, I²=0%), or cardiac disorders (RR=0.89, 95% CI 0.50-1.58, I²=0%), indicating nintedanib does not notably alter these risks. However, nintedanib significantly increased gastrointestinal adverse events, potentially affecting adherence and quality of life.

CONCLUSION: Nintedanib shows promise in slowing disease progression but carries a higher risk of adverse events. Limited sample sizes and short follow-up necessitate larger studies to confirm its efficacy and safety.

PMID:40382966 | DOI:10.1016/j.hrtlng.2025.05.008

Am J Ophthalmol. 2025 May 16:S0002-9394(25)00239-9. doi: 10.1016/j.ajo.2025.05.007. Online ahead of print.

ABSTRACT

PURPOSE: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are important therapeutic options for type 2 diabetes and obesity; however, concerns about ophthalmic safety persist. This study examined associations between GLP-1 RAs and ocular adverse events (AEs).

DESIGN: Global observational pharmacovigilance study.

METHODS: We searched the US FAERS database (via OpenVigil 2.1) and WHO's VigiBase (via VigiAccess) for optic nerve and retinal AEs associated with semaglutide and tirzepatide, covering the period from their respective approval dates-December 2017 for semaglutide and May 2022 for tirzepatide-through September 2024. In FAERS, all other drugs were compared, while in VigiBase, metformin, empagliflozin, dulaglutide, and insulin served as controls. Disproportionality metrics included reporting odds ratios (RORs) with 95% confidence intervals.

RESULTS: Semaglutide and tirzepatide accounted for 76,444 cases (0.59%) in FAERS (n=12,936,341) and 118,639 cases (0.34%) in VigiBase (n>35,000,000). Semaglutide showed significantly higher odds of ischemic optic neuropathy (ION) (FAERS: ROR=11.12, 95%CI=8.15-15.16; VigiBase: ROR=68.58, 95%CI=16.75-280.67), diabetic retinopathy (DR) (FAERS: ROR=17.28, 95%CI=13.62-21.91; VigiBase: ROR=7.81, 95%CI=5.60-10.90), as well as retinal/vitreous detachment, retinal/vitreous hemorrhage, and retinal tear (FAERS: ROR=2.44-5.89, 95%CI=1.70-8.97, all p<0.001, IC025=0.49, compared to all other drugs. VigiBase: ROR=5.49-20.91, 95%CI=2.71-90.11, all p≤0.0001, IC025≥0.53, compared to metformin). Unique to VigiBase were macular edema (ROR=3.87, 95%CI=1.89-7.92), macular hole (ROR=20.90, 95%CI=2.65-165.01), and papilledema (ROR=6.97, 95%CI=2.53-19.17) (all p≤0.004, IC025≥0.27, compared to metformin). Sensitivity analyses using empagliflozin and dulaglutide revealed significant associations with ION and DR, while vitreous detachment and hemorrhage were significant when compared to dulaglutide. Additionally, when insulin was used as a comparator, semaglutide showed a higher ROR for ION (ROR=9.84, 95%CI=4.25-22.81, P<0.0001, IC025=0.42). However, tirzepatide was only significantly associated with DR in FAERS.

CONCLUSIONS: Given the widespread use of semaglutide, its association with ocular AEs highlight the need for global pharmacovigilance and post-marketing surveillance.

PMID:40383360 | DOI:10.1016/j.ajo.2025.05.007

Cell Rep. 2025 May 17;44(6):115715. doi: 10.1016/j.celrep.2025.115715. Online ahead of print.

ABSTRACT

Plants are one of the major reservoirs of medicinal compounds, serving as a cornerstone of both traditional and modern medicine. However, despite their importance, the complex biosynthetic pathways of many plant-derived compounds remain only partially understood, hindering their full potential in therapeutic applications. This review paper summarizes the advances in systems and synthetic biology utilized in the characterization and engineering of plant metabolic pathways. We discuss various strategies such as (1) co-expression analysis, (2) gene cluster identification, (3) metabolite profiling, (4) deep learning approaches, (5) genome-wide association studies, and (6) protein complex identification. Through case studies on several biosynthesis pathways, we highlight how these methods are applied to unravel complex pathways and enhance the production of important natural products. Finally, we discuss future directions in the context of metabolic engineering, including metabolon engineering, AI integration, and sustainable production strategies, underscoring the potential for cheaper and greener production of plant natural products.

PMID:40382775 | DOI:10.1016/j.celrep.2025.115715

Insights Imaging. 2025 May 18;16(1):108. doi: 10.1186/s13244-025-01988-6.

ABSTRACT

OBJECTIVES: Lymphovascular invasion significantly impacts the prognosis of urothelial carcinoma of the bladder. Traditional lymphovascular invasion detection methods are time-consuming and costly. This study aims to develop a deep learning-based model to preoperatively predict lymphovascular invasion status in urothelial carcinoma of bladder using CT images.

METHODS: Data and CT images of 577 patients across four medical centers were retrospectively collected. The largest tumor slices from the transverse, coronal, and sagittal planes were selected and used to train CNN models (InceptionV3, DenseNet121, ResNet18, ResNet34, ResNet50, and VGG11). Deep learning features were extracted and visualized using Grad-CAM. Principal Component Analysis reduced features to 64. Using the extracted features, Decision Tree, XGBoost, and LightGBM models were trained with 5-fold cross-validation and ensembled in a stacking model. Clinical risk factors were identified through logistic regression analyses and combined with DL scores to enhance lymphovascular invasion prediction accuracy.

RESULTS: The ResNet50-based model achieved an AUC of 0.818 in the validation set and 0.708 in the testing set. The combined model showed an AUC of 0.794 in the validation set and 0.767 in the testing set, demonstrating robust performance across diverse data.

CONCLUSION: We developed a robust radiomics model based on deep learning features from CT images to preoperatively predict lymphovascular invasion status in urothelial carcinoma of the bladder. This model offers a non-invasive, cost-effective tool to assist clinicians in personalized treatment planning.

CRITICAL RELEVANCE STATEMENT: We developed a robust radiomics model based on deep learning features from CT images to preoperatively predict lymphovascular invasion status in urothelial carcinoma of the bladder.

KEY POINTS: We developed a deep learning feature-based stacking model to predict lymphovascular invasion in urothelial carcinoma of the bladder patients using CT. Max cross sections from three dimensions of the CT image are used to train the CNN model. We made comparisons across six CNN networks, including ResNet50.

PMID:40382748 | DOI:10.1186/s13244-025-01988-6

ACS Sens. 2025 May 18. doi: 10.1021/acssensors.4c03397. Online ahead of print.

ABSTRACT

Surface-enhanced Raman spectroscopy (SERS) is a transformative tool for infectious disease diagnostics, offering rapid and sensitive species identification. However, background spectra in biological samples complicate analyte peak detection, increase the limit of detection, and hinder data augmentation. To address these challenges, we developed a deep learning framework utilizing dual neural networks to extract true virus SERS spectra and estimate concentration coefficients in water for 12 different respiratory viruses. The extracted spectra showed a high similarity to those obtained at the highest viral concentration, validating their accuracy. Using these spectra and the derived concentration coefficients, we augmented spectral data sets across varying virus concentrations in water. XGBoost models trained on these augmented data sets achieved overall classification and concentration prediction accuracy of 92.3% with a coefficient of determination (R2) > 0.95. Additionally, the extracted spectra and coefficients were used to augment data sets in saliva backgrounds. When tested against real virus-in-saliva spectra, the augmented spectra-trained XGBoost models achieved 91.9% accuracy in classification and concentration prediction with R2 > 0.9, demonstrating the robustness of the approach. By delivering clean and uncontaminated spectra, this methodology can significantly improve species identification, differentiation, and quantification and advance SERS-based detection and diagnostics.

PMID:40382719 | DOI:10.1021/acssensors.4c03397

Arch Iran Med. 2025 Apr 1;28(4):198-206. doi: 10.34172/aim.32070. Epub 2025 Apr 1.

ABSTRACT

BACKGROUND: To investigate whether the bone age (BA) of Iranian children could be accurately assessed via an artificial intelligence (AI) system. Accurate assessment of skeletal maturity is crucial for diagnosing and treating various musculoskeletal disorders, and is traditionally achieved through manual comparison with the Greulich-Pyle atlas. This process, however, is subjective and time-consuming. Recent advances in deep learning offer more efficient and consistent BA evaluations.

METHODS: From left-hand radiographs of children aged 1-18 years who presented to a tertiary research hospital, 555 radiographs (220 boys and 335 girls) were collected. The reference BA was determined via the Greulich and Pyle (GP) method by two radiologists in consensus. The BA was then estimated to use a deep learning model specifically developed for this population. Model performance was evaluated using multiple metrics: Mean square error (MSE), mean absolute error (MAE), intra-class correlation coefficient (ICC), and 95% limits of agreement (LoA). Gender-specific results were analyzed separately.

RESULTS: The model demonstrated acceptable accuracy. For boys, MSE was 0.55 years, MAE was 0.59 years, ICC was 0.74, and the 95% LoA ranged from -0.8 to 1.2 years. For girls, MSE was 0.59 years, MAE was 0.61 years, ICC was 0.82, and the 95% LoA ranged from -0.6 to 1.0 years. These results indicate stronger predictive accuracy for girls compared to boys.

CONCLUSION: Our findings demonstrate that the proposed deep learning model achieves reasonable accuracy in BA assessment, with stronger performance in girls compared to boys. However, the relatively wide 95% LoA, particularly for boys, and prediction errors at the extremes of the age range highlight the need for further refinement and validation. While the model shows potential as a supplementary tool for clinicians, future studies should focus on improving prediction accuracy, reducing variability, and validating the model on larger, more diverse datasets before considering widespread clinical implementation. Additionally, addressing edge cases and specific conditions that a human reviewer may detect but the model might overlook, will be essential for enhancing its clinical reliability.

PMID:40382691 | DOI:10.34172/aim.32070

Pharmacogenomics J. 2025 May 17;25(3):14. doi: 10.1038/s41397-025-00374-1.

ABSTRACT

Bladder cancer (BC) is a highly prevalent form of cancer worldwide, and cisplatin (CDDP) resistance poses a major challenge to patients. Cytoplasmic linker-associated protein 2 (CLASP2) is a member of the microtubule plus-end tracking protein family and is involved in the regulation of microtubule dynamics. In this study, we evaluated the influence of CLASP2 on BC progression and cisplatin resistance. Levels of CLASP2, HNRNPA1, NONO, ZRANB2, FUS, KHSRP and QKI in BC tissues and cells were tested by RT-qPCR. Protein levels of CLASP2 and KHSRP were detected by Western blot. Cell viability and IC50 of cisplatin-treated BC cells were measured by CCK-8. Cell proliferation and apoptosis were determined using colony formation assay and flow cytometry, respectively. RNA immunoprecipitation (RIP) and Co-immunoprecipitation (Co-IP) experiments were adopted to verify target genes of CLASP2. Cellular localization of CLASP2 and MAPRE1 was detected utilizing immunofluorescence staining. The xenograft tumor model was established in BALB/c nude mice. We found that iCLASP2 levels were increased in CDDP-resistant BC tissues and cells. Suppression of CLASP2 impeded BC cell proliferation and alleviated their resistance to CDDP. KHSRP positively influenced the stability of CLASP2 mRNA. There was a protein interaction between CLASP2 and MAPRE1. Silencing KHSRP or MAPRE1 reversed the effect exerted of CLASP2 on BC cells. CLASP2 decreased the sensitivity of BC to CDDP in vivo. Our results imply that CLASP2 contributes to tumorigenesis and cisplatin resistance in BC via targeting MAPRE1, thereby promoting BC progression and providing a new therapeutic target for BC treatment.

PMID:40382315 | DOI:10.1038/s41397-025-00374-1

Gastroenterology. 2025 May 15:S0016-5085(25)00755-3. doi: 10.1053/j.gastro.2025.04.025. Online ahead of print.

ABSTRACT

There is a large heterogeneity among individuals in their therapeutic responses to the same drug and in the occurrence of adverse events. A key factor increasingly recognized to contribute to this variability is the gut microbiome. The gut microbiome can be regarded as a second genome, holding significant metabolic capacity. Consequently, the field of pharmacomicrobiomics has emerged as a natural extension of pharmacogenomics for studying variations in drug responses. Pharmacomicrobiomics explores the interaction of microbiome variation with drug response and disposition. The interaction between microbes and drugs is, however, complex and bidirectional. While drugs can directly alter microbial growth or influence gut microbiome composition and functionality, the gut microbiome also modulates drug responses directly through enzymatic activities and indirectly via host-mediated immune and metabolic mechanisms. Here we review recent studies that demonstrate the interaction between drugs and the gut microbiome, focusing on cancer immunotherapy and immunomodulation in the context of inflammatory bowel disease and solid organ transplantation. Since the gut microbiome is modifiable, pharmacomicrobiomics presents promising opportunities for optimizing therapeutic outcomes, with recent clinical trials highlighting fecal microbiota transplantation as a strategy to enhance the efficacy of immune checkpoint blockade. We also shed light on the future perspectives for patients arising from this field. While multiple lines of evidence already demonstrate that the gut microbiome interacts with drugs, and vice versa, thereby affecting treatment efficacy and safety, well-designed clinical studies and integrated in vivo and ex vivo models are necessary to obtain consistent results, improve clinical translation and further unlock the gut microbiome's potential to improve drug responses.

PMID:40381958 | DOI:10.1053/j.gastro.2025.04.025

Curr Probl Cardiol. 2025 May 15:103076. doi: 10.1016/j.cpcardiol.2025.103076. Online ahead of print.

ABSTRACT

Dilated cardiomyopathy (DCM) is a prevalent cardiac disorder affecting 1 in 250-500 individuals, characterized by ventricular dilation and impaired systolic function, leading to heart failure and increased mortality, including sudden cardiac death. DCM arises from genetic and environmental factors, such as drug-induced, inflammatory, and viral causes, resulting in diverse yet overlapping phenotypes. Advances in precision medicine are revolutionizing DCM management by leveraging genetic and molecular profiling for tailored diagnostic and therapeutic approaches. This review highlights comprehensive diagnostic evaluations, genetic discoveries, and multi-omics approaches integrating genomic, transcriptomic, proteomic, and metabolomic data to enhance understanding of DCM pathophysiology. Innovative risk stratification methods, including machine learning, are improving predictions of disease progression. Despite these advancements, the current one-size-fits-all management strategy contributes to persistently high morbidity and mortality. Emerging targeted therapies, such as CRISPR/Cas9 genome editing, aetiology-specific interventions, and pharmacogenomics, are reshaping treatment paradigms. Precision medicine holds promise for optimizing DCM diagnosis, treatment, and outcomes, aiming to reduce the burden of this debilitating condition.

PMID:40381754 | DOI:10.1016/j.cpcardiol.2025.103076

J Cyst Fibros. 2025 May 16:S1569-1993(25)00771-4. doi: 10.1016/j.jcf.2025.04.005. Online ahead of print.

ABSTRACT

BACKGROUND: Advanced cystic fibrosis (CF) liver disease can necessitate liver transplantation. This study aims to investigate characteristics, waiting list dynamics, and waiting list mortality of people with CF (pwCF) registered for liver transplantation within Eurotransplant countries, to understand and improve transplant outcomes for this group.

METHODS: We analysed Eurotransplant liver transplantation registration data (January 2007-December 2022), comparing pwCF to people with no CF (pwnoCF) and non-CF age/liver disease severity score (Lab-MELD) matched controls, with subgroup comparisons between pwCF receiving isolated liver transplantation (LiverTx) and combined liver-lung transplantation (Liver+LungTx).

RESULTS: 215 out of 38,125 liver transplantation registrations were for pwCF. 65.1 % of the pwCF were listed for LiverTx and 34.9 % for Liver+LungTx. At registration, pwCF were younger (17.9 ± 0.8 vs. 55.0 ± 0.1; P < 0.05) and had lower Lab-MELD scores (10.0 ± 0.4 vs. 15.0 ± 0.0; P < 0.05) compared to pwnoCF. PwCF listed for LiverTx were younger (15.1 ± 0.9 vs. 26.0 ± 1.1; P < 0.05) and had higher Lab-MELD scores at transplantation (12.0 ± 1.0 vs. 9.0 ± 0.5; P < 0.05) compared to Liver+LungTx. PwCF had a higher 2-year waiting list mortality than non-CF matched controls (14.9 % vs. 0.0 %; P < 0.05). Within pwCF, mortality was higher for pwCF listed for Liver+LungTx than for LiverTx (30.7 % vs. 6.4 %; P < 0.05).

CONCLUSIONS: CF represents a distinct indication for liver transplantation, with increased varying waiting list mortality risks across the different CF-groups, not adequately captured by standard liver disease severity scores. PwCF considered for liver transplantation require close monitoring as their disease severity and mortality risk are not well represented in the current allocation algorithm, necessitating adaptations to the organ allocation rules.

PMID:40382307 | DOI:10.1016/j.jcf.2025.04.005

J Cyst Fibros. 2025 May 16:S1569-1993(25)01468-7. doi: 10.1016/j.jcf.2025.05.001. Online ahead of print.

ABSTRACT

Airway clearance to clear excessive sputum has long been a key part of cystic fibrosis care, however the introduction of highly effective modulator medications where many people with CF are experiencing reduced sputum loads, has raised a question about its necessity. Specifically, questions are being asked as to if exercise, historically an adjunct to airway clearance, could become a replacement. This short communication summarizes a debate that was held at the 2024 North American Cystic Fibrosis Conference, summarizing the key arguments for and against the replacement of traditional airway clearance with exercise.

PMID:40382306 | DOI:10.1016/j.jcf.2025.05.001

Drug Discov Today. 2025 May 15:104380. doi: 10.1016/j.drudis.2025.104380. Online ahead of print.

ABSTRACT

Inhaled antibiotics significantly impact respiratory-disorder management through targeted delivery with reduced systemic side effects. Advances in pharmaceutical formulations, particularly lipid-based nanomedicine, help improve biopharmaceutical performance and therapeutic efficacy. In addition, advancements in inhaler technologies ensure effective lung deposition and minimize systemic exposure. These innovations have further benefited chronic respiratory diseases like cystic fibrosis and COPD, where infections are frequent. For instance, the encapsulation of inhaled antibiotics, particularly the tobramycin liposomal system, has improved efficacy and reduced toxicity, whereas the nebulized colistin nanoformulation effectively targets multidrug-resistant pathogens, including the clinical efficacy of amikacin liposome inhalation in refractory pulmonary infections. Overall, advancements in lipid-based nanoformulation and delivery technologies have significantly enhanced the utility of inhaled antibiotics, providing safer and more-effective options for managing chronic and resistant infections.

PMID:40381726 | DOI:10.1016/j.drudis.2025.104380

Plant Methods. 2025 May 17;21(1):63. doi: 10.1186/s13007-025-01381-w.

ABSTRACT

Pest infestations in rice crops greatly affect yield and quality, making early detection essential. As most rice pests affect leaves and rhizomes, visual inspection of rice for pests is becoming increasingly important. In precision agriculture, fast and accurate automatic pest identification is essential. To tackle this issue, multiple models utilizing computer vision and deep learning have been applied. Owing to its high efficiency, deep learning is now the favored approach for detecting plant pests. In this regard, the paper introduces an effective rice pest detection framework utilizing the Transformer architecture, designed to capture long-range features. The paper enhances the original model by adding the self-developed RepPConv-block to reduce the problem of information redundancy in feature extraction in the model backbone and to a certain extent reduce the model parameters. The original model's CCFM structure is enhanced by integrating the Gold-YOLO neck, improving its ability to fuse multi-scale features. Additionally, the MPDIoU-based loss function enhances the model's detection performance. Using the self-constructed high-quality rice pest dataset, the model achieves higher identification accuracy while reducing the number of parameters. The proposed RP18-DETR and RP34-DETR models reduce parameters by 16.5% and 25.8%, respectively, compared to the original RT18-DETR and RT34-DETR models. With a threshold of 0.5, the average accuracy calculated is 1.2% higher for RP18-DETR than for RT18-DETR.

PMID:40382633 | DOI:10.1186/s13007-025-01381-w

BMC Cardiovasc Disord. 2025 May 17;25(1):371. doi: 10.1186/s12872-025-04739-z.

ABSTRACT

Globally, the high number of deaths are happening due to Myocardial infarction (MI). MI is considered as a life-threatening disease, which leads to an increase number of deaths or damage to the heart, and hence, prompt detection of MI is critical to decrease the mortality rate. Though, numerous works have addressed MI identification, an increased number suffer from over fitting and high computational burden in real-time scenarios. The proposed system introduces a novel MI detection technique using a Deep Residual Network (DRN), where the solution is optimized by the proposed Social Ski-Spider (SSS) Optimization algorithm is the novel combination of both Social Ski-driver (SSD) Optimization and the Spider Monkey Optimization (SMO). This model highly prevents the overfitting and computational burden, which increases the MI detection accuracy. Here, the proposed SSS-DRN performs detection by filtering the electrocardiography (ECG) signals. Later, the signal feature, transform feature, medical feature and statistical feature are extracted by the feature extraction phase followed by data augmentation that consists of permutation, random generation and re-sampling processes and finally, detection is accomplished by the SSS-DRN. Moreover, the developed SSS-DRN is researched for its efficiency considering metrics like accuracy, sensitivity, and specificity and observed 0.916, 0.921, and 0.926. Here, when considering the accuracy metrics, the performance gain observed by the devised model is 13.96%, 12.61%, 10.37%, 7.95%, 5%, 2.21%, and 2% higher than the traditional schemes. This indicates the devised model has high detection accuracy, which could be embedded in real-time clinical settings like hospital ECG machines, wearable ECG monitors, and mobile health applications. This improves the clinical decision-making process with increased patient outcomes.

PMID:40382575 | DOI:10.1186/s12872-025-04739-z