ACS Synth Biol. 2025 May 19. doi: 10.1021/acssynbio.5c00142. Online ahead of print.

ABSTRACT

Optogenetic systems offer precise control over gene expression, but leaky activity in the dark limits their dynamic range and, consequently, their applicability. Here, we enhanced an optogenetic system based on a split T7 RNA polymerase fused to blue-light-inducible Magnets by incorporating a tet-controlled riboregulatory module. This module exploits the photosensitivity of anhydrotetracycline and the designability of synthetic small RNAs to digitize light-controlled gene expression, implementing a repressive action over the translation of a polymerase fragment gene that is relieved with blue light. Our engineered system exhibited 13-fold improvement in dynamic range upon blue light exposure, which even raised to 23-fold improvement when using cells preadapted to chemical induction. As a functional demonstration, we implemented light-controlled antibiotic resistance in bacteria. Such integration of regulatory layers represents a suitable strategy for engineering better circuits for light-based biotechnological applications.

PMID:40384364 | DOI:10.1021/acssynbio.5c00142

Trop Med Int Health. 2025 May 19. doi: 10.1111/tmi.14123. Online ahead of print.

ABSTRACT

OBJECTIVES: To determine, in a randomised placebo-controlled trial, if cutaneous adverse reactions during treatment (CARDT) with Benznidazole occur as often as with Nifurtimox, and whether the dose and duration of treatment change that frequency.

METHODS: We conducted the EQUITY trial (NCT02369978), allocating Trypanosoma cruzi seropositive adults with no apparent clinical disease to a 120-day, blinded treatment with Benznidazole, Nifurtimox, or Placebo (ratio 2:2:1). Active treatment groups included either 60-day conventional-dose (60CD) regimens (Benznidazole 300 mg/day or Nifurtimox 480 mg/day, followed or preceded by, 60 days of placebo) or 120-day half-dose (120HD) regimens (Benznidazole 150 mg/day or Nifurtimox 240 mg/day). CARDT had blinded adjudication as moderate to severe during the follow-up visits.

RESULTS: Among 307 participants, 42 CARDT (17.1%, 95% confidence interval [CI] 12.6-22.4) occurred in 246 receiving active treatment, compared to two CARDT (3.3%, 95% CI 0.0-11.3) in 61 participants receiving placebo. In 122 patients treated with Benznidazole, there were 31 CARDT (25.4%, including eight severe), compared to 11 CARDT (8.9%, including four severe) in 124 individuals treated with Nifurtimox (p < 0.001). Among the 125 participants assigned to the 120HD regimen, there were 26 CARDT (20.8%, including six severe), compared to 16 CARDT (13.2%, including six severe) among 121 in the 60CD group (p = 0.005). The agent-regime interaction was not significant (p = 0.443). Eleven participants (25%) with CARDT did not complete their treatment.

CONCLUSION: CARDT occurred more frequently with Benznidazole treatment, particularly with longer exposure despite the half-dose regimen. Clinicians should consider these differences when discussing treatment options with patients receiving nitro derivative agents.

PMID:40384408 | DOI:10.1111/tmi.14123

Carbohydr Polym. 2025 Jun 15;358:123478. doi: 10.1016/j.carbpol.2025.123478. Epub 2025 Mar 6.

ABSTRACT

Acute myeloid leukaemia (AML) accounts for 30 % of adult leukaemia cases, predominantly affecting individuals over 60. The standard "7 + 3" intensive chemotherapy regimen is unsuitable for many elderly patients, contributing to AML's poor prognosis. While progress in drug therapies has been made, breakthroughs remain limited, indication-specific, and slow to expand. Drug repurposing offers a faster route to therapy development, while nanocarrier encapsulation broadens the scope of viable drug candidates. Chlorpromazine (CPZ) is an antipsychotic which has been identified as a potential anti-leukaemic agent. Due to its ability to cross the blood-brain barrier, it is likely to cause central nervous system (CNS) effects. The drug-in-cyclodextrin-in-liposome (DCL) nanocarrier platform enables the formulation of CPZ encapsulated with cyclodextrins (CDs) such as HP-γ-CD, SBE-β-CD, and Sugammadex. The CD/CPZ formulations were equally, or more efficient than free CPZ in inducing AML cell death. Uptake of the DCL in AML cells quickly reached saturation, with minimal differences among formulations, except for SBE-β-CD. When injected intravenously in zebrafish larvae, the different DCLs did not differ in biodistribution, and no brain accumulation was observed at two days post-injection. These DCL-based CPZ formulations maintain anti-leukaemic activity, avoid CNS accumulation, and allow drug availability adjustments based on the included CD.

PMID:40383608 | DOI:10.1016/j.carbpol.2025.123478

Sci Data. 2025 May 18;12(1):814. doi: 10.1038/s41597-025-05177-4.

ABSTRACT

Understanding human fertility requires dynamic and three-dimensional (3D) analysis of sperm movement, which extends beyond the capabilities of traditional datasets focused primarily on two-dimensional sperm motility or static morphological characteristics. To address this limitation, we introduce the 3D+t Multifocal Imaging Dataset of Human Sperm (3D-SpermVid), a repository comprising 121 multifocal video-microscopy hyperstacks of freely swimming sperm cells, incubated under non-capacitating conditions (NCC) and capacitating conditions (CC). This collection enables detailed observation and analysis of 3D sperm flagellar motility patterns over time, offering novel insights into the capacitation process and its implications for fertility. Data were captured using a multifocal imaging (MFI) system based on an optical microscope equipped with a piezoelectric device that adjusts focus at various heights, recording sperm movement in a volumetric space. By making this data publicly available, we aim to enable applications in deep learning and pattern recognition to uncover hidden flagellar motility patterns, fostering significant advancements in understanding 3D sperm morphology and dynamics, and developing new diagnostic tools for assessing male fertility, as well as assisting in the self-organizaton mechanisms driving spontaneous motility and navigation in 3D.

PMID:40383860 | DOI:10.1038/s41597-025-05177-4

Sci Rep. 2025 May 18;15(1):17263. doi: 10.1038/s41598-025-99858-0.

ABSTRACT

The widespread availability of miniaturized wearable fitness trackers has enabled the monitoring of various essential health parameters. Utilizing wearable technology for precise emotion recognition during human and computer interactions can facilitate authentic, emotionally aware contextual communication. In this paper, an emotion recognition system is proposed for the first time to conduct an experimental analysis of both discrete and dimensional models. An ensemble deep learning architecture is considered that consists of Long Short-Term Memory and Gated Recurrent Unit models to capture dynamic temporal dependencies within emotional data sequences effectively. The publicly available wearable devices EMOGNITION database is utilized to facilitate result reproducibility and comparison. The database includes physiological signals recorded using the Samsung Galaxy Watch, Empatica E4 wristband, and MUSE 2 Electroencephalogram (EEG) headband devices for a comprehensive understanding of emotions. A detailed comparison of all three dedicated wearable devices has been carried out to identify nine discrete emotions, exploring three different bio-signal combinations. The Samsung Galaxy and MUSE 2 devices achieve an average classification accuracy of 99.14% and 99.41%, respectively. The performance of the Samsung Galaxy device is examined for the 2D Valence-Arousal effective dimensional model. Results reveal average classification accuracy of 97.81% and 72.94% for Valence and Arousal dimensions, respectively. The acquired results demonstrate promising outcomes in emotion recognition when compared with the state-of-the-art methods.

PMID:40383809 | DOI:10.1038/s41598-025-99858-0

Sci Rep. 2025 May 18;15(1):17229. doi: 10.1038/s41598-025-02593-9.

ABSTRACT

Personalized recommendation systems are vital for enhancing user satisfaction and reducing information overload, especially in data-sparse environments like e-commerce platforms. This paper introduces a novel hybrid framework that combines Long Short-Term Memory (LSTM) with a modified Split-Convolution (SC) neural network (LSTM-SC) and an advanced sampling technique-Self-Inspected Adaptive SMOTE (SASMOTE). Unlike traditional SMOTE, SASMOTE adaptively selects "visible" nearest neighbors and incorporates a self-inspection strategy to filter out uncertain synthetic samples, ensuring high-quality data generation. Additionally, Quokka Swarm Optimization (QSO) and Hybrid Mutation-based White Shark Optimizer (HMWSO) are employed for optimizing sampling rates and hyperparameters, respectively. Experiments conducted on the goodbooks-10k and Amazon review datasets demonstrate significant improvements in RMSE, MAE, and R² metrics, proving the superiority of the proposed model over existing deep learning and collaborative filtering techniques. The framework is scalable, interpretable, and applicable across diverse domains, particularly in e-commerce and electronic publishing.

PMID:40383722 | DOI:10.1038/s41598-025-02593-9

Clin Nutr ESPEN. 2025 May 16:S2405-4577(25)00319-5. doi: 10.1016/j.clnesp.2025.05.022. Online ahead of print.

ABSTRACT

Enteral feeding needs secure access to the upper gastrointestinal tract, an evaluation of the gastric function to detect gastrointestinal intolerance, and a nutritional target to reach the patient's needs. Only in the last decades has progress been accomplished in techniques allowing an appropriate placement of the nasogastric tube, mainly reducing pulmonary complications. These techniques include point-of-care ultrasound (POCUS), electromagnetic sensors, real-time video-assisted placement, impedance sensors, and virtual reality. Again, POCUS is the most accessible tool available to evaluate gastric emptying, with antrum echo density measurement. Automatic measurements of gastric antrum content supported by deep learning algorithms and electric impedance provide gastric volume. Intragastric balloons can evaluate motility. Finally, advanced technologies have been tested to improve nutritional intake: Stimulation of the esophagus mucosa inducing contraction mimicking a contraction wave that may improve enteral nutrition efficacy, impedance sensors to detect gastric reflux and modulate the rate of feeding accordingly have been clinically evaluated. Use of electronic health records integrating nutritional needs, target, and administration is recommended.

PMID:40383254 | DOI:10.1016/j.clnesp.2025.05.022

J Cardiovasc Magn Reson. 2025 May 16:101913. doi: 10.1016/j.jocmr.2025.101913. Online ahead of print.

ABSTRACT

BACKGROUND: Image reconstruction from highly undersampled 4D flow MRI data can be very time consuming and may result in significant underestimation of velocities depending on regularization, thereby limiting the applicability of the method. The objective of the present work was to develop a generalizable self-supervised deep learning-based framework for fast and accurate reconstruction of highly undersampled 4D flow MRI and to demonstrate the utility of the framework for aortic and cerebrovascular applications.

METHODS: The proposed deep-learning-based framework, called FlowMRI-Net, employs physics-driven unrolled optimization using a complex-valued convolutional recurrent neural network and is trained in a self-supervised manner. The generalizability of the framework is evaluated using aortic and cerebrovascular 4D flow MRI acquisitions acquired on systems from two different vendors for various undersampling factors (R=8,16,24) and compared to compressed sensing (CS-LLR) reconstructions. Evaluation includes an ablation study and a qualitative and quantitative analysis of image and velocity magnitudes.

RESULTS: FlowMRI-Net outperforms CS-LLR for aortic 4D flow MRI reconstruction, resulting in significantly lower vectorial normalized root mean square error and mean directional errors for velocities in the thoracic aorta. Furthermore, the feasibility of FlowMRI-Net's generalizability is demonstrated for cerebrovascular 4D flow MRI reconstruction. Reconstruction times ranged from 3 to 7minutes on commodity CPU/GPU hardware.

CONCLUSION: FlowMRI-Net enables fast and accurate reconstruction of highly undersampled aortic and cerebrovascular 4D flow MRI, with possible applications to other vascular territories.

PMID:40383184 | DOI:10.1016/j.jocmr.2025.101913

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