Sci Rep. 2025 Aug 11;15(1):29365. doi: 10.1038/s41598-025-09717-1.

ABSTRACT

Representation learning via pre-trained deep learning models is emerging as an integral method for studying the molecular structure-property relationship, which is then leveraged to predict molecular properties or design new molecules with desired attributes. We propose an unsupervised method to localize and characterize representations of pre-trained models through the lens of non-parametric property-driven subset scanning (PDSS), to improve the interpretability of deep molecular representations. We assess its detection capabilities on diverse molecular benchmarks (ZINC-250K, MOSES, MoleculeNet, FlavorDB, M2OR) across predictive chemical language models (MoLFormer, ChemBERTa) and molecular graph generative models (GraphAF, GCPN). We further study how representations evolve due to domain adaptation, and we evaluate the usefulness of the extracted property-driven elements in the embeddings as lower-dimension representations for downstream tasks. Experiments reveal notable information condensation in the pre-trained embeddings upon task-specific fine-tuning. For example, among the property-driven elements found in the embedding (out of [Formula: see text]), only 11 are shared between three distinct tasks (BACE, BBBP, and HIV), while [Formula: see text]-80 of those are unique to each task. Similar patterns are found for flavor and odor detection tasks. When we use the discovered property-driven elements as features for a new task, we find the same or improved performance (3 points up) while reducing the dimensions by 75% without fine-tuning required, thus indicating information localization.

PMID:40790135 | DOI:10.1038/s41598-025-09717-1

Radiother Oncol. 2025 Aug 9:111095. doi: 10.1016/j.radonc.2025.111095. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Accurate delineation of the Gross Tumor Volume (GTV) and the Internal Target Volume (ITV) in early-stage lung tumors is crucial in Stereotactic Body Radiation Therapy (SBRT). Traditionally, the ITVs, which account for breathing motion, are generated by manually contouring GTVs across all breathing phases (BPs), a time-consuming process. This research aims to streamline this workflow by developing a deep learning algorithm to automatically delineate GTVs in all four-dimensional computed tomography (4D-CT) BPs for early-stage Non-Small Cell Lung Cancer Patients (NSCLC).

METHODS: A dataset of 214 early-stage NSCLC patients treated with SBRT was used. Each patient had a 4D-CT scan containing ten reconstructed BPs. The data were divided into a training set (75 %) and a testing set (25 %). Three models SwinUNetR and Dynamic UNet (DynUnet), and a hybrid model combining both (Swin + Dyn)were trained and evaluated using the Dice Similarity Coefficient (DSC), 3 mm Surface Dice Similarity Coefficient (SDSC), and the 95th percentile Hausdorff distance (HD95). The best performing model was used to delineate GTVs in all test set BPs, creating the ITVs using two methods: all 10 phases and the maximum inspiration/expiration phases. The ITVs were compared to the ground truth ITVs.

RESULTS: The Swin + Dyn model achieved the highest performance, with a test set SDSC of 0.79 ± 0.14 for GTV 50 %. For the ITVs, the SDSC was 0.79 ± 0.16 using all 10 BPs and 0.77 ± 0.14 using 2 BPs. At the voxel level, the Swin + DynNet network achieved a sensitivity of 0.75 ± 0.14 and precision of 0.84 ± 0.10 for the ITV 2 breathing phases, and a sensitivity of 0.79 ± 0.12 and precision of 0.80 ± 0.11 for the 10 breathing phases.

CONCLUSION: The Swin + Dyn Net algorithm, trained on the maximum expiration CT-scan effectively delineated gross tumor volumes in all breathing phases and the resulting ITV showed a good agreement with the ground truth (surface DSC = 0.79 ± 0.16 using all 10 BPs and 0.77 ± 0.14 using 2 BPs.). The proposed approach could reduce delineation time and inter-performer variability in the tumor contouring process for NSCLC SBRT workflows.

PMID:40789427 | DOI:10.1016/j.radonc.2025.111095

Neuroscience. 2025 Aug 9:S0306-4522(25)00842-5. doi: 10.1016/j.neuroscience.2025.08.009. Online ahead of print.

ABSTRACT

The serotonin transporter (SERT) plays a key role in the regulation of serotonin levels in synapses and is significantly involved in the pathophysiology of major depressive disorder (MDD). In this review, molecular pathways connecting SERT dysfunction associated with depression are defined, including genetic polymorphisms (e.g., 5-HTTLPR), epigenetics (e.g., SLC6A4 methylation), and environmental interactions through stress and inflammation. Using a serotonergic pharmacophore, oxidative/nitrosative stress, cytokines, and neuroendocrine factors act via the hypothalamic-pituitary-adrenal (HPA) axis on SERT, whereas the crosstalk between SERT and brain-derived neurotrophic factor (BDNF) and cAMP response element-binding protein (CREB) signaling both bring about changes in mood and influence the response to treatment. Pharmacological treatments, such as SSRIs and SNRIs, aim at SERT; however, their effectiveness is limited due to interindividual variability. New treatments consist of allosteric modulators, multimodal antidepressants, and non-pharmacological treatments, involving exercise, diet, and microbiome manipulation. Tailor-made treatments involving the utilization of pharmacogenomics and neurobiological profiling can improve clinical outcomes. This review highlights SERT as a complex target in MDD and provides an argument in support of integrative, precision-focused interventions that aim to target the affective and cognitive symptoms of MDD.

PMID:40789515 | DOI:10.1016/j.neuroscience.2025.08.009

Sci Rep. 2025 Aug 11;15(1):29407. doi: 10.1038/s41598-025-15451-5.

ABSTRACT

Idiopathic osteosclerosis (IOS) and condensing osteitis (CO) represent radiopaque lesions often detected incidentally within the jaws, posing substantial diagnostic challenges due to their overlapping radiographic characteristics. The objective of this study was to assess the diagnostic efficacy of YOLOv8 and YOLOv11 deep learning algorithms in the identification of IOS and CO lesions on panoramic radiographs. A comprehensive collection of 1,000 panoramic images was retrospectively gathered and meticulously annotated utilizing a bounding box approach by two proficient oral and maxillofacial radiologists. All images were standardized to a resolution of 640 × 640 pixels and segregated into training (70%), validation (15%), and testing (15%) subsets. The performance of the models was evaluated based on metrics including accuracy, sensitivity, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). YOLOv11 achieved notable precision scores of 98.8% for IOS and 97.1% for CO, alongside F1 scores of 96.8% and 95.6%, respectively. Conversely, YOLOv8 produced precision scores of 96.6% for IOS and 91.4% for CO, with F1 scores of 94% and 90%. These findings illustrate that AI-enhanced deep learning models possess the capability to accurately identify IOS and CO lesions, thereby presenting opportunities to improve diagnostic consistency, avert unnecessary invasive procedures, and facilitate more effective treatment planning within clinical practice.

PMID:40790082 | DOI:10.1038/s41598-025-15451-5

Sci Rep. 2025 Aug 12;15(1):29452. doi: 10.1038/s41598-025-14726-1.

ABSTRACT

Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstanding potential for image classification. This study presents Maize Net, a convolutional neural network model that precisely identifies diseases in maize leaves. Maize Net uses an attention mechanism to increase the model's efficiency by focusing on the relevant features and residual learning to improve the gradient flow. This also addresses the vanishing gradient problem while training deeper neural networks. A five-fold cross-validation test is conducted for generalization across the dataset, generating five models based on distinct training and testing sets. The macro-average of all evaluation metrics is considered to address the dataset's class imbalance problem. Maize Net achieved an average F1-score of 0.9509, recall of 0.9497, precision of 0.9525, and classification accuracy of 0.9595. These outcomes demonstrate MaizeNet's robustness and reliability in automated plant disease classification.

PMID:40790074 | DOI:10.1038/s41598-025-14726-1

Med Oncol. 2025 Aug 11;42(9):425. doi: 10.1007/s12032-025-02982-0.

ABSTRACT

Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer. In this study, we developed deep radiomic models to enhance the prediction of chemotherapy response after the first treatment cycle. 18F-Fludeoxyglucose PET/CT imaging data and clinical record from 60 breast cancer patients were retrospectively obtained from the Cancer Imaging Archive. PET/CT scans were conducted at three distinct stages of treatment; prior to the initiation of chemotherapy (T1), following the first cycle of chemotherapy (T2), and after the full chemotherapy regimen (T3). The patient's primary gross tumor volume (GTV) was delineated on PET images using a 40% threshold of the maximum standardized uptake value (SUVmax). Radiomic features were extracted from the GTV based on the PET/CT images. In addition, a squeeze-and-excitation network (SENet) deep learning model was employed to generate additional features from the PET/CT images for combined analysis. A XGBoost machine learning model was developed and compared with the conventional machine learning algorithm [random forest (RF), logistic regression (LR) and support vector machine (SVM)]. The performance of each model was assessed using receiver operating characteristics area under the curve (ROC AUC) analysis, and prediction accuracy in a validation cohort. Model performance was evaluated through fivefold cross-validation on the entire cohort, with data splits stratified by treatment response categories to ensure balanced representation. The AUC values for the machine learning models using only radiomic features were 0.85(XGBoost), 0.76 (RF), 0.80 (LR), and 0.59 (SVM), with XGBoost showing the best performance. After incorporating additional deep learning-derived features from SENet, the AUC values increased to 0.92, 0.88, 0.90, and 0.61, respectively, demonstrating significant improvements in predictive accuracy. Predictions were based on pre-treatment (T1) and post-first-cycle (T2) imaging data, enabling early assessment of chemotherapy response after the initial treatment cycle. Integrating deep learning-derived features significantly enhanced the performance of predictive models for chemotherapy response in breast cancer patients. This study demonstrated the superior predictive capability of the XGBoost model, emphasizing its potential to optimize personalized therapeutic strategies by accurately identifying patients unlikely to respond to chemotherapy after the first treatment cycle.

PMID:40790010 | DOI:10.1007/s12032-025-02982-0

Light Sci Appl. 2025 Aug 11;14(1):267. doi: 10.1038/s41377-025-01971-2.

ABSTRACT

We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction, while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica to ensure optical transparency and thermal stability. Our design achieves unidirectional imaging across three visible wavelengths (covering red, green, and blue parts of the spectrum), and we experimentally validated this broadband unidirectional imager by creating high-fidelity images in the forward direction and generating weak, distorted output patterns in the backward direction, in alignment with our numerical simulations. This work demonstrates wafer-scale production of diffractive optical processors, featuring 16 levels of nanoscale phase features distributed across two axially aligned diffractive layers for visible unidirectional imaging. This approach facilitates mass-scale production of ~0.5 billion nanoscale phase features per wafer, supporting high-throughput manufacturing of hundreds to thousands of multi-layer diffractive processors suitable for large apertures and parallel processing of multiple tasks. Beyond broadband unidirectional imaging in the visible spectrum, this study establishes a pathway for artificial-intelligence-enabled diffractive optics with versatile applications, signaling a new era in optical device functionality with industrial-level, massively scalable fabrication.

PMID:40789836 | DOI:10.1038/s41377-025-01971-2

Annu Rev Biomed Data Sci. 2025 Aug;8(1):149-171. doi: 10.1146/annurev-biodatasci-103123-095814.

ABSTRACT

The rapid development of artificial intelligence (AI) has had a significant impact on medical research, introducing new possibilities for pathology studies. There is a recent trend of applying large-scale AI models to many fields, and this trend has given rise to the pathology foundation models and pathology ensemble models. Large models in pathology are not standalone innovations; they build upon a legacy where AI has consistently played a vital role in pathology studies long before their advent. Numerous pathology datasets and AI models have been developed to support advancements in the field, with these combined efforts paving the way for the emergence of large models in pathology. AI greatly enhances pathology studies, yet its widespread use in sensitive applications also raises significant ethical concerns, including privacy risks. In this review, we summarize the datasets and models that are useful to pathology studies, with a particular focus on how they illuminate the path toward large-scale applications.

PMID:40789736 | DOI:10.1146/annurev-biodatasci-103123-095814

Annu Rev Biomed Data Sci. 2025 Aug;8(1):101-124. doi: 10.1146/annurev-biodatasci-103023-050856.

ABSTRACT

Deep learning and artificial intelligence (AI) have seen explosive growth and success in biomedical applications in the last decade, largely due to the rapid development of deep neural networks and their underlying neural network (NN) architectures. Here, we explore biomedical deep learning and AI from the specific perspective of NN architectures. We discuss widely varying design principles of NN architectures, their use in particular biomedical applications, and the assumptions (often hidden) built into them. We explore neural architecture search techniques that automate the design of NN topology to optimize task performance. Advanced neural architectures are being developed for both molecular and healthcare applications, employing elements of graph networks, transformers, and interpretable NNs, and we discuss and summarize the design considerations and unique advantages of each architecture. Future advances will include the employment of multimodal language models and smaller highly focused mechanistic models that build on the success of today's large models.

PMID:40789735 | DOI:10.1146/annurev-biodatasci-103023-050856

SLAS Technol. 2025 Aug 9:100338. doi: 10.1016/j.slast.2025.100338. Online ahead of print.

ABSTRACT

Optic disc and cup segmentation is a crucial subfield of computer vision, playing a pivotal role in automated pathological image analysis. It enables precise, efficient, and automated diagnosis of ocular conditions, significantly aiding clinicians in real-world medical applications. However, due to the scarcity of medical segmentation data and the insufficient integration of global contextual information, the segmentation accuracy remains suboptimal. This issue becomes particularly pronounced in optic disc and cup cases with complex anatomical structures and ambiguous boundaries.In order to address these limitations, this paper introduces a self-supervised training strategy integrated with a newly designed network architecture to improve segmentation accuracy.Specifically,we initially propose a non-local dual deformable convolutional block,which aims to capture the irregular image patterns(i.e. boundary).Secondly,we modify the traditional vision transformer and design an adaptive K-Nearest Neighbors(KNN) transformation block to extract the global semantic context from images. Finally,an initialization strategy based on self-supervised training is proposed to reduce the burden on the network on labeled data.Comprehensive experimental evaluations demonstrate the effectiveness of our proposed method, which outperforms previous networks and achieves state-of-the-art performance,with IOU scores of 0.9577 for the optic disc and 0.8399 for the optic cup on the REFUGE dataset.

PMID:40789537 | DOI:10.1016/j.slast.2025.100338

Ann Vasc Surg. 2025 Aug 9:S0890-5096(25)00526-6. doi: 10.1016/j.avsg.2025.08.001. Online ahead of print.

ABSTRACT

INTRODUCTION: Decreased kidney volume is a sign of renal aging and/or decreased vascularization. The aim of this study was to determine whether renal volume changes 24 months after exclusion of an abdominal aortic aneurysm (AAA), and to compare fenestrated (FEVAR) and subrenal (EVAR) stents.

METHODS: Retrospective single-center study from a prospective registry, including patients between 60 and 80 years with normal preoperative renal function (eGFR≥60 ml/min/1.73 m-2) who underwent fenestrated (FEVAR) or infrarenal (EVAR) stent grafts between 2015 and 2021. Patients had to have had an CT scan at 24 months of the study to be included. Exclusion criteria were renal branches, the presence of preoperative renal insufficiency, a single kidney, embolization or coverage of an accessory renal artery, occlusion of a renal artery during follow-up and mention of AAA rupture. Renal volume was measured using sizing software (EndoSize, therenva) based on fully automatic deep-learning segmentation of several anatomical structures (arterial lumen, bone structure, thrombus, heart, etc.), including the kidneys. In the presence of renal cysts, these were manually excluded from the segmentation.

RESULTS: Forty-eight patients were included (24 EVAR vs. 24 FEVAR), 96 kidneys were segmented. There was no difference between groups in age (78.9±6.7 years vs. 69.4±6.8, p=0.89), eGFR 85.8 ± 12.4 [62-107] ml/min/1.73 m-2 vs. 81 ± 16.2 [42-107] (p=0.36), and renal volume 170.9 ± 29.7 [123-276] mL vs. 165.3 ± 37.4 [115-298] (p=0.12). At 24 months in the EVAR group, there was a non-significant reduction in eGFR 84.1 ± 17.2 [61-128] ml/min/1.73 m-2 vs. 81 ± 16.2 [42-107] (p=0.36) or renal volume 170.9 ± 29.7 [123-276] mL vs. 165.3 ± 37.4 [115-298] (p=0.12). In the FEVAR group, at 24 months there was a non-significant fall in eGFR 84.1 ± 17.2 [61-128] ml/min/1.73 m-2 vs. 73.8 ± 21.4 [40-110] (p=0.09), while renal volume decreased significantly 182 ± 37.8 [123-293] mL vs. 158.9 ± 40.2 [45-258] (p=0.007).

CONCLUSION: In this study, there appears to be a significant decrease in renal volume without a drop in eGFR 24 months after fenestrated stenting. This decrease may reflect changes in renal perfusion and could potentially be predictive of long-term renal impairment, although this cannot be confirmed within the limits of this small sample. Further studies with long-term follow-up are needed.

PMID:40789507 | DOI:10.1016/j.avsg.2025.08.001

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Aug 9:S2451-9022(25)00247-2. doi: 10.1016/j.bpsc.2025.07.012. Online ahead of print.

ABSTRACT

BACKGROUND: Research has demonstrated a broad network of dysfunction across the brain in Attention Deficit/Hyperactivity Disorder (ADHD), suggesting the potential role of white matter (WM) organization. This study sought to estimate the developmental trajectories of brain WM myelination in children with ADHD.

METHODS: Neuroimaging and clinical data were collected as part of a longitudinal community-based pediatric cohort (Nscans=400; 195 with ADHD; age range, 9-14 years). Brain WM myelin was examined for 71 WM tracts across 3 time points using the T1w/T2w-ratio. Tracts were defined via a deep-learning based automated tractography method, performed on participant diffusion-weighted imaging. Linear and non-linear regression was conducted to examine group differences in T1w/T2w-ratio values. In addition to this, voxel-wise analysis was undertaken at each time point.

RESULTS: Brain-wide, children with ADHD were found to exhibit the same developmental profile as those without ADHD for WM myelin. No group effects were seen at a cross-sectional or longitudinal level. In agreement with previous work, modelling suggests non-linear developmental increases with age across most tract. This non-linear relationship was characterized by a positive parabolic, or U-shaped developmental trajectory.

CONCLUSIONS: These findings indicate that there may not be distinct difference in the development of brain white matter myelination between children with and without ADHD. However, this suggests that previously reported differences in ADHD brain WM development may be attributable to properties other than myelin, such as fiber architecture and axon diameter. This further informs the understanding of brain development and highlights the need for further multi-modal longitudinal work.

PMID:40789484 | DOI:10.1016/j.bpsc.2025.07.012

AAPS J. 2025 Aug 11;27(5):129. doi: 10.1208/s12248-025-01119-5.

ABSTRACT

Nintedanib (Nint) is a potent tyrosine kinase inhibitor recently approved by the US FDA to treat idiopathic pulmonary fibrosis (IPF). Delivery of Nint through available approaches is highly challenging because of its poor solubility and rapid metabolic degradation via hydrolytic ester cleavage, thereby reflecting poor oral bioavailability (< 5%). Hence, the current study was focused on formulating a Nint-loaded microemulsion (Nint-ME) and investigating its therapeutic potential in experimental animals to overcome the constraints of available therapies. Nint-ME was prepared via low-energy O/W emulsification aqueous titration techniques and optimized using QbD approach. Optimized ME subjected to screen for globule size, polydispersity index, encapsulation efficiency, transmittance, surface charge, and viscosity and were found to be 23.8 ± 1.4 nm, 0.18 ± 0.03, 99.8 ± 2.4%, 99.4 ± 0.1%, -0.7 ± 0.01 mV, and 1.5 ± 0.3 cP, respectively. Additionally, 94.5 ± 3.1% Nint was released from Nint-ME through the dialysis cassette within 72 h, demonstrating first-order kinetics with R2 of 0.966. First-order and Higuchi release kinetic patterns support concentration-dependent release and Fickian diffusion from the matrix of Nint-ME. In-vitro permeation study of Nint across Caco2 colon epithelial cell monolayer depicted 48.1 ± 1.5 µg of cellular permeation out of 50 µg, ensuring the permeation potential of Nint-ME. Concurrently, an in-vivo pharmacokinetic study for optimized Nint-ME against Nint suspension reflected 41.0 ± 12.5% oral bioavailability, a 2-fold enhancement compared to plain Nint suspension. Existing work demonstrated the successful development of oral Nint-ME as a novel formulation for safe and effective delivery of Nint in IPF.

PMID:40789799 | DOI:10.1208/s12248-025-01119-5

NPJ Vaccines. 2025 Aug 11;10(1):188. doi: 10.1038/s41541-025-01121-0.

ABSTRACT

Given the local and systemic adverse reactions associated with whole-cell pertussis vaccines combined with diphtheria and tetanus toxoids (DTP), acellular pertussis vaccines combined with the same toxoids (DTaP) were developed in the 1990s. In comparison to DTP, DTaP vaccines demonstrated reduced reactogenicity and equivalent or improved immunogenicity and efficacy. However, there has been a resurgence of pertussis disease, particularly in DTaP-vaccinated children, suggesting that immunity wanes more quickly with DTaP vaccination. To elucidate the differences in immune responses to DTP and DTaP vaccines, we employed a systems biology-based strategy to compare global changes in gene expression following primary vaccination with either DTP or DTaP. We used RNA-Seq and ribosome profiling (RP) to identify transcriptional and translational signatures, respectively, in peripheral blood mononuclear cells (PBMCs) collected from 50 infant recipients of DTP or DTaP at two time-points (baseline (pre-vaccination at Day 1) and either Day 2 or 8 post-vaccination). We also used standard serologic methods to assess immunogenicity, and correlated these results with transcriptional and translational signatures. Here, we provide a detailed description of the rationale, experimental design, methodology, and enrollment procedures used. Given the technical complexity of our approach, our objective is to fill knowledge gaps, describe key quality metrics, and support future publications. In brief, we recovered 4-12 million PBMCs (average 8.9 million) with 99% viability per 2.5 mL blood sample, enabling excellent nucleic acid recovery yields for the preparation of high-quality sequencing libraries. In turn, these generated RNA-Seq and RP datasets with sufficient genome coverage breadth and depth to enable differential gene expression analyses, demonstrating the validity of this approach to study pertussis vaccine immunology specifically, and its utility to characterize mechanisms of the human immune response to vaccination generally.

PMID:40789865 | DOI:10.1038/s41541-025-01121-0

Annu Rev Biomed Data Sci. 2025 Aug;8(1):605-632. doi: 10.1146/annurev-biodatasci-120924-091033.

ABSTRACT

DNA methylation, a covalent modification, fundamentally shapes mammalian gene regulation and cellular identity. This review examines methylation's biochemical underpinnings, genomic distribution patterns, and analytical approaches. We highlight three distinctive aspects that separate methylation from other epigenetic marks: its remarkable stability as a silencing mechanism, its capacity to maintain distinct states independently of DNA sequence, and its effectiveness as a quantitative trait linking genotype to disease risk. We also explore the phenomenon of methylation clocks and their biological significance. The review addresses technical considerations across major assay types-both array-based technologies and sequencing approaches-with emphasis on data normalization, quality control, cell proportion inference, and the specialized statistical models required for next-generation sequencing analysis.

PMID:40789737 | DOI:10.1146/annurev-biodatasci-120924-091033

Annu Rev Biomed Data Sci. 2025 Aug;8(1):227-250. doi: 10.1146/annurev-biodatasci-111224-124530.

ABSTRACT

Adverse drug events (ADEs) in pediatric populations pose significant public health challenges, yet research on their detection and monitoring remains limited. This scoping review evaluates the use of unstructured data from electronic health records (EHRs) to identify ADEs in children. We searched six databases, including MEDLINE, Embase, and IEEE Xplore, in September 2024. From 984 records, only nine studies met our inclusion criteria, indicating a significant gap in research toward identifying ADEs in children. We found that unstructured data in EHRs can indeed be of value and enhance pediatric pharmacovigilance, although their use has been so far very limited. Traditional natural language processing methods have been employed to extract ADEs, but the approaches utilized face challenges in generalizability and context interpretation. These challenges could be addressed with recent advances in transformer-based models and large language models, unlocking the use of EHR data at scale for pediatric pharmacovigilance.

PMID:40789734 | DOI:10.1146/annurev-biodatasci-111224-124530

Clin Transl Sci. 2025 Aug;18(8):e70300. doi: 10.1111/cts.70300.

ABSTRACT

Pharmacogenomics (PGx) can potentially tailor medication prescriptions to the genetic profiles of individuals, enhancing treatment outcomes and minimizing adverse drug reactions. This study assessed cardiovascular disease (CVD) patients' knowledge and views toward PGx testing in the United Arab Emirates (UAE). A cross-sectional study was conducted among CVD patients attending multiple clinics using a validated, culturally adapted, and piloted bilingual questionnaire. Participants were invited via phone calls or in-person contact at clinics. Data analysis was conducted using SPSS V.29, incorporating descriptive statistics and multivariable logistic regression. A total of 425 responses were analyzed; 67.5% were over 50 years old, and 67.5% held a bachelor's degree. Chronic diseases, excluding CVD, affected 65.2%, with 58.1% reporting medication side effects and 36.5% was hospitalized due to these effects. Knowledge varied, with 55.3% demonstrating good knowledge; 75.3% recognized DNA as gene-based, while 47.5% understood PGx for predicting medication responses. Participants were grouped into three PGx perception clusters: Cluster 1 (33.17%) concerned about risks but valued PGx, Cluster 2 (40.23%) worried about privacy/costs, and Cluster 3 (26.58%) confident in PGx benefits. Safety was the top priority for 60.2% of respondents, 34.8% would not pay for PGx tets, and 35.3% preferred preemptive testing. Regression linked higher PGx knowledge to females, non-healthcare workers, those with genetic diseases, and those hospitalized for side effects (p < 0.05). The study highlights a need for educational initiatives in the UAE to improve PGx literacy among CVD patients. The findings suggest that targeted awareness campaigns, policy interventions addressing privacy, and financial support could promote PGx wider adoption.

PMID:40788819 | DOI:10.1111/cts.70300

Am J Respir Crit Care Med. 2025 Aug 11. doi: 10.1164/rccm.202411-2312OC. Online ahead of print.

ABSTRACT

RATIONALE: Abnormal glucose metabolism is a common complication in people with cystic fibrosis (CF), and those with impaired glucose tolerance (IGT) or CF-related diabetes (CFRD) have increased disease burden. Elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) is safe and effective for people with CF aged ≥2 years with ELX/TEZ/IVA-responsive CFTR mutations; however, efficacy on glycemic control has not been studied.

OBJECTIVES: To evaluate the impact of ELX/TEZ/IVA on glucose tolerance in people with CF who have IGT or CFRD.

METHODS: This phase 3b, open-label study, ELX/TEZ/IVA was administered for 48weeks to participants aged ≥12 years, heterozygous for F508del and a minimal function CFTR mutation, and with either IGT or CFRD.

MEASUREMENTS AND MAIN RESULTS: Sixty-nine participants received ELX/TEZ/IVA. The primary endpoint was change in blood glucose levels following 2-hour oral glucose tolerance test (OGTT) from baseline to the average of Week36 and Week48; participants had a mean change of -35.0 mg/dL (95%CI:-49.2,-20.7;P<0.0001) (-1.9 mmol/L [95%CI: 2.7, 1.2]). Secondary endpoints were the proportion of participants with improvement in dysglycemia categorization (CFRD, IGT, normal glucose tolerance) at Week48 and safety. Among participants with abnormal glucose tolerance at baseline, 37.7% (95%CI:24.8,52.1) had improvements in dysglycemia categorization at Week48. Overall, 35.5% of participants had normal glucose tolerance at Week48 compared to 13.0% at baseline. Safety was consistent with the established safety profile of ELX/TEZ/IVA.

CONCLUSIONS: ELX/TEZ/IVA treatment led to clinically meaningful improvements in blood glucose regulation with significant within-group decreases in blood glucose levels following OGTT and improved dysglycemia categorization in people with CF with early IGT or CFRD. Clinical trial registration available at www.

CLINICALTRIALS: gov, ID: NCT04599465.

PMID:40788823 | DOI:10.1164/rccm.202411-2312OC

Comput Biol Med. 2025 Aug 10;196(Pt C):110774. doi: 10.1016/j.compbiomed.2025.110774. Online ahead of print.

ABSTRACT

Cardiac anomalies are severe and life-threatening, making early detection essential to reducing health risks and mortality. According to the European Society of Cardiology, over 13 million people suffer from heart valve diseases annually, often identified by heartbeat anomalies. Traditional diagnostic methods depend on specialized expertise and advanced equipment. This paper proposes SpectroNet-LSTM, an automated framework for detecting cardiac anomalies from a comprehensive dataset of heartbeat sound recordings. Mel-frequency cepstral coefficients (MFCCs) and spectrogram analysis are used to capture critical acoustic features. These features are then extracted and employed to train state-of-the-art deep learning models, including ResNet101, VGG16, and Inception V3. The core architecture is trained on the extracted features and optimized for improved performance. The model outperforms benchmarks on various evaluation metrics for detecting heart anomalies. To ensure system interpretability, the study integrates two Explainable AI (XAI) techniques, namely SHAP and LIME. These techniques enable clinicians and patients to visualize and understand the model's decision-making process. The novelty of SpectroNet-LSTM lies in its integrated use of advanced feature extraction, deep learning fusion and explainable AI to create a fully automated and interpretable cardiac anomaly detection system. This research underscores the potential of automation in transforming cardiovascular diagnostics, paving the way for accessible healthcare solutions and efficient patient outcomes worldwide.

PMID:40789236 | DOI:10.1016/j.compbiomed.2025.110774

Comput Biol Med. 2025 Aug 10;196(Pt C):110902. doi: 10.1016/j.compbiomed.2025.110902. Online ahead of print.

ABSTRACT

Cervical cancer is a significant global health issue, and high-dose-rate brachytherapy (HDR-BT) is crucial for its treatment. However, manually creating HDR-BT plans is time-consuming and heavily relies on the planner's expertise, making standardization difficult. This study introduces two advanced deep learning models to address this need: Bi-branch Cross-Attention UNet (BiCA-UNet) and Dendrite Cross-Attention UNet (DCA-UNet). BiCA-UNet enhances the correlation between the CT scan and segmentation maps of the clinical target volume (CTV), applicator, bladder, and rectum. It uses two branches: one processes the stacked input of CT scans and segmentations, and the other focuses on the CTV segmentation. A cross-attention mechanism integrates these branches, improving the model's understanding of the CTV region for accurate dose predictions. Building on BiCA-UNet, DCA-UNet further introduces a primary branch of stacked inputs and three secondary branches for CTV, bladder, and rectum segmentations forming a dendritic structure. Cross attention with bladder and rectum segmentation helps the model understand the regions of organs at risk (OAR), refining dose prediction. Evaluation of these models using multiple metrics indicates that both BiCA-UNet and DCA-UNet significantly improve HDR-BT dose prediction accuracy for various applicator types. The cross-attention mechanisms enhance the feature representation of critical anatomical regions, leading to precise and reliable treatment plans. This research highlights the potential of BiCA-UNet and DCA-UNet in advancing HDR-BT planning, contributing to the standardization of treatment plans, and offering promising directions for future research to improve patient outcomes in the source data.

PMID:40789235 | DOI:10.1016/j.compbiomed.2025.110902