Parkinsonism Relat Disord. 2025 Mar 14:107774. doi: 10.1016/j.parkreldis.2025.107774. Online ahead of print.

NO ABSTRACT

PMID:40118710 | DOI:10.1016/j.parkreldis.2025.107774

Am J Vet Res. 2025 Mar 21:1-9. doi: 10.2460/ajvr.24.12.0377. Online ahead of print.

ABSTRACT

OBJECTIVE: To determine the population variability in dextromethorphan metabolism by cytochrome (CY) P450 2D15 (CYP2D15) in dogs.

METHODS: Healthy pet dogs were recruited from 2018 through 2024 from the Inland Pacific Northwest and phenotyped by orally administering the Program in Individualized Medicine cocktail, which contains dextromethorphan, a CYP2D15-specific probe drug. Glucuronidase-treated urine samples collected 6 hours after dosing were assayed for dextromethorphan and dextrorphan concentrations. Log-transformed metabolic ratios of dextrorphan divided by dextromethorphan (DOR/DXM Log MRs) were calculated. Dogs were genotyped for 5 missense CYP2D15 variants. Univariate and multivariate statistical approaches were used to evaluate associations between DOR/DXM Log MRs and demographic variables.

RESULTS: 105 dogs, including 34 mixed breeds and 71 dogs from 20 different owner-designated breeds, were enrolled and completed the study. There was a wide distribution of DOR/DXM Log MRs, from 0.97 to 2.76, representing a log unit range of 1.8 (63-fold variation DOR/DXM Log MRs). Log-transformed metabolic ratios of dextrorphan divided by dextromethorphan were normally distributed and unimodal. The mean (± SD) DOR/DXM Log MR was 2.04 ± 0.37. Multiple linear regression analysis showed significant association (R2 = 0.16) between DOR/DXM Log MRs and dog breed for Golden Retrievers (2.26 ± 0.29; N = 23) and Pugs (1.47 ± 0.29; N = 3). Log-transformed metabolic ratios of dextrorphan divided by dextromethorphan were not associated with dog sex, age, weight, or genotype.

CONCLUSIONS: There is substantial variability in DOR/DXM Log MR values among individuals, which can be partially attributed to differences between breeds.

CLINICAL RELEVANCE: These findings predict high variability in the metabolism of drugs by CYP2D15 associated with differences between dog breeds.

PMID:40118021 | DOI:10.2460/ajvr.24.12.0377

J Cyst Fibros. 2025 Mar 20:S1569-1993(25)00074-8. doi: 10.1016/j.jcf.2025.03.006. Online ahead of print.

ABSTRACT

We present a case of a carrier mother treated with elexacaftor/tezacaftor/ivacaftor (ETI) for in-utero management of meconium ileus in a fetus diagnosed with cystic fibrosis (CF), homozygous for the F508del variant. Following multidisciplinary discussion and shared decision-making involving the parents, ETI was initiated at 27 weeks of gestation. At 38+4 weeks, the infant was delivered. Despite the treatment, the newborn developed meconium ileus, necessitating emergency surgery after birth. We explore potential factors contributing to the lack of success in our case compared to previously reported successful cases in USA and Spain. Drug levels measured in neonatal blood and in maternal breast milk indicated minimal drug exposure, raising questions about whether variability in placental transfer and excretion in breast milk or suboptimal ETI dosing in the overweight mother impacted the outcome. Additionally, the natural variability in meconium ileus outcome, which can range from spontaneous resolution to severe complications must be considered. In our case, ETI may have mitigated the severity of the condition, preventing serious complications like bowel perforation or peritonitis. However, given that about 20 % of all fetal bowel dilation resolves spontaneously, it remains uncertain whether the positive outcomes in prior cases were attributable to ETI or the natural course of the disease. We emphasize the need for more evidence on in utero ETI exposure by advocating for the collection of cases involving ETI treatment for fetal meconium ileus, regardless of outcomes. Developing guidelines will be essential to optimize benefits for both mother and fetus while minimizing risks.

PMID:40118755 | DOI:10.1016/j.jcf.2025.03.006

Radiother Oncol. 2025 Mar 19:110852. doi: 10.1016/j.radonc.2025.110852. Online ahead of print.

ABSTRACT

BACKGROUND: In the HECKTOR 2022 challenge set [1], several state-of-the-art (SOTA, achieving best performance) deep learning models were introduced for predicting recurrence-free period (RFP) in head and neck cancer patients using PET and CT images.

PURPOSE: This study investigates whether a conventional DenseNet architecture, with optimized numbers of layers and image-fusion strategies, could achieve comparable performance as SOTA models.

METHODS: The HECKTOR 2022 dataset comprises 489 oropharyngeal cancer (OPC) patients from seven distinct centers. It was randomly divided into a training set (n = 369) and an independent test set (n = 120). Furthermore, an additional dataset of 400 OPC patients, who underwent chemo(radiotherapy) at our center, was employed for external testing. Each patients' data included pre-treatment CT- and PET-scans, manually generated GTV (Gross tumour volume) contours for primary tumors and lymph nodes, and RFP information. The present study compared the performance of DenseNet against three SOTA models developed on the HECKTOR 2022 dataset.

RESULTS: When inputting CT, PET and GTV using the early fusion (considering them as different channels of input) approach, DenseNet81 (with 81 layers) obtained an internal test C-index of 0.69, a performance metric comparable with SOTA models. Notably, the removal of GTV from the input data yielded the same internal test C-index of 0.69 while improving the external test C-index from 0.59 to 0.63. Furthermore, compared to PET-only models, when utilizing the late fusion (concatenation of extracted features) with CT and PET, DenseNet81 demonstrated superior C-index values of 0.68 and 0.66 in both internal and external test sets, while using early fusion was better in only the internal test set.

CONCLUSIONS: The basic DenseNet architecture with 81 layers demonstrated a predictive performance on par with SOTA models featuring more intricate architectures in the internal test set, and better performance in the external test. The late fusion of CT and PET imaging data yielded superior performance in the external test.

PMID:40118186 | DOI:10.1016/j.radonc.2025.110852

Food Chem Toxicol. 2025 Mar 19:115401. doi: 10.1016/j.fct.2025.115401. Online ahead of print.

ABSTRACT

Acrylamide (AA), a food hazard generated during thermal processing, poses significant safety risks due to its toxicity. Conventional methods for AA toxicology are time-consuming and inadequate for analyzing cellular morphology. This study developed a novel approach combining deep learning models (U-Net and ResNet34) with cell fluorescence imaging. U-Net was used for cell segmentation, generating a single-cell dataset, while ResNet34 trained the dataset over 200 epochs, achieving an 80% validation accuracy. This method predicts AA concentration ranges by matching cell fluorescence features with the dataset and analyzes cellular phenotypic changes under AA exposure using k-means clustering and CellProfiler. The approach overcomes the limitations of traditional toxicological methods, offering a direct link between cell phenotypes and hazard toxicology. It provides a high-throughput, accurate solution to evaluate AA toxicology and refines the understanding of its cellular impacts.

PMID:40118138 | DOI:10.1016/j.fct.2025.115401

Cell Rep Med. 2025 Mar 14:102032. doi: 10.1016/j.xcrm.2025.102032. Online ahead of print.

ABSTRACT

Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evaluation. We present SALSA (system for automatic liver tumor segmentation and detection), a fully automated tool for liver tumor detection and delineation. Developed on 1,598 computed tomography (CT) scans and 4,908 liver tumors, SALSA demonstrates superior accuracy in tumor identification and volume quantification, outperforming state-of-the-art models and inter-reader agreement among expert radiologists. SALSA achieves a patient-wise detection precision of 99.65%, and 81.72% at lesion level, in the external validation cohorts. Additionally, it exhibits good overlap, achieving a dice similarity coefficient (DSC) of 0.760, outperforming both state-of-the-art and the inter-radiologist assessment. SALSA's automatic quantification of tumor volume proves to have prognostic value across various solid tumors (p = 0.028). SALSA's robust capabilities position it as a potential medical device for automatic cancer detection, staging, and response evaluation.

PMID:40118052 | DOI:10.1016/j.xcrm.2025.102032

Neural Netw. 2025 Mar 15;187:107414. doi: 10.1016/j.neunet.2025.107414. Online ahead of print.

ABSTRACT

The identification of early micro-lesions and adjacent blood vessels in CT scans plays a pivotal role in the clinical diagnosis of pancreatic cancer, considering its aggressive nature and high fatality rate. Despite the widespread application of deep learning methods for this task, several challenges persist: (1) the complex background environment in abdominal CT scans complicates the accurate localization of potential micro-tumors; (2) the subtle contrast between micro-lesions within pancreatic tissue and the surrounding tissues makes it challenging for models to capture these features accurately; and (3) tumors that invade adjacent blood vessels pose significant barriers to surgical procedures. To address these challenges, we propose LUNETR (Language-Infused UNETR), an advanced multimodal encoder model that combines textual and image information for precise medical image segmentation. The integration of an autoencoding language model with cross-attention enabling our model to effectively leverage semantic associations between textual and image data, thereby facilitating precise localization of potential pancreatic micro-tumors. Additionally, we designed a Multi-scale Aggregation Attention (MSAA) module to comprehensively capture both spatial and channel characteristics of global multi-scale image data, enhancing the model's capacity to extract features from micro-lesions embedded within pancreatic tissue. Furthermore, in order to facilitate precise segmentation of pancreatic tumors and nearby blood vessels and address the scarcity of multimodal medical datasets, we collaborated with Zhuzhou Central Hospital to construct a multimodal dataset comprising CT images and corresponding pathology reports from 135 pancreatic cancer patients. Our experimental results surpass current state-of-the-art models, with the incorporation of the semantic encoder improving the average Dice score for pancreatic tumor segmentation by 2.23 %. For the Medical Segmentation Decathlon (MSD) liver and lung cancer datasets, our model achieved an average Dice score improvement of 4.31 % and 3.67 %, respectively, demonstrating the efficacy of the LUNETR.

PMID:40117980 | DOI:10.1016/j.neunet.2025.107414

Mar Pollut Bull. 2025 Mar 19;215:117813. doi: 10.1016/j.marpolbul.2025.117813. Online ahead of print.

ABSTRACT

Efficiently monitoring red tide via satellite remote sensing is pivotal for marine disaster monitoring and ecological early warning systems. Traditional remote sensing methods for monitoring red tide typically rely on ocean colour sensors with low spatial resolution and high spectral resolution, making it difficult to monitor small events and detailed distribution of red tide. Furthermore, traditional methods are not applicable to satellite sensors with medium to high spatial resolution and low spectral resolution, significantly limiting the ability to detect red tide outbreaks in their early stages. Therefore, this study proposes a Residual Neural Network Red Tide Monitoring Model based on Spectral Information Channel Constraints (SIC-RTNet) using HY-1C/D CZI satellite data. SIC-RTNet improves monitoring accuracy through adding three key steps compared to basic deep learning methods. First, the SIC-RTNet introduces residual blocks to enhance the effective retention and transmission of weak surface signal features of red tides. Second, three spectral information channels are calculated using the four wideband channels of the images to amplify the spectral differences between red tide and seawater. Finally, an improved loss function is employed to address the issue of sample imbalance between red tides and seawater. Compared to other models, SIC-RTNet demonstrates superior performance, achieving precision and recall rates of 85.5 % and 95.4 % respectively. The F1-Score is 0.90, and the Mean Intersection over Union (MoU) is 0.90. The results indicate that the SIC-RTNet can automatically identify red tides using high spatial resolution and wideband remote sensing data, which can help the monitoring of marine ecological disasters.

PMID:40117936 | DOI:10.1016/j.marpolbul.2025.117813

Food Chem. 2025 Mar 17;480:143854. doi: 10.1016/j.foodchem.2025.143854. Online ahead of print.

ABSTRACT

Cereals are a primary source of sustenance for humanity. Monitoring, controlling, and preventing mycotoxins in cereals are vital for ensuring the safety of the cereals and their derived products. This study introduces transfer learning strategies into chemometrics to improve deep learning models applied to spectral data from different grains or toxins. Three transfer learning methods were explored for their potential to quantitatively detect fungal toxins in cereals. The feasibility of transfer learning was demonstrated by predicting wheat zearalenone (ZEN) and peanut aflatoxin B1 (AFB1) sample sets on different instruments. The results indicated that the second transfer method is effective in detecting toxins. For FT-NIR spectrometry, the transfer model achieved an R2 of 0.9356, a relative prediction deviation (RPD) of 3.9497 for wheat ZEN prediction, and an R2 of 0.9419 with an RPD of 4.1551 for peanut AFB1 detection. With NIR spectrometry, effective peanut AFB1 detection was also achieved, yielding an R2 of 0.9386 and an RPD of 4.0434 in the prediction set. These results suggest that the proposed transfer learning approach can successfully update a source domain model into one that is suitable for tasks in the target domain. This study provides a viable solution to the problem of poor adaptability of single-source models, presenting a more universally applicable method for spectral detection of fungal toxins in cereals.

PMID:40117813 | DOI:10.1016/j.foodchem.2025.143854

Comput Biol Med. 2025 Mar 20;190:110007. doi: 10.1016/j.compbiomed.2025.110007. Online ahead of print.

ABSTRACT

Skin diseases affect over a third of the global population, yet their impact is often underestimated. Automating the classification of these diseases is essential for supporting timely and accurate diagnoses. This study leverages Vision Transformers, Swin Transformers, and DinoV2, introducing DinoV2 for the first time in dermatology tasks. On a 31-class skin disease dataset, DinoV2 achieves state-of-the-art results with a test accuracy of 96.48 ± 0.0138% and an F1-Score of 97.27%, marking a nearly 10% improvement over existing benchmarks. The robustness of DinoV2 is further validated on the HAM10000 and Dermnet datasets, where it consistently surpasses prior models. Comparative analysis also includes ConvNeXt and other CNN architectures, underscoring the benefits of transformer models. Additionally, explainable AI techniques like GradCAM and SHAP provide global heatmaps and pixel-level correlation plots, offering detailed insights into disease localization. These complementary approaches enhance model transparency and support clinical correlations, assisting dermatologists in accurate diagnosis and treatment planning. This combination of high performance and clinical relevance highlights the potential of transformers, particularly DinoV2, in dermatological applications.

PMID:40117795 | DOI:10.1016/j.compbiomed.2025.110007

Ultrasonics. 2025 Mar 14;152:107641. doi: 10.1016/j.ultras.2025.107641. Online ahead of print.

ABSTRACT

This study aims to explore the feasibility of a deep learning approach to correct the distortion caused by the skull, thereby developing a transcranial adaptive focusing method for safe ultrasonic treatment in opening of the blood-brain barrier (BBB). However, aberration correction often requires significant computing power and time to ensure the accuracy of phase correction. This is due to the need to solve the evolution procedure of the sound field represented by numerous discretized grids. A combined method is proposed to train the phase prediction model for correcting the phase accurately and quickly. The method comprises pre-segmentation, k-Wave simulation, and a 3D U-net-based network. We use the k-Wave toolbox to construct a nonlinear simulation environment consisting of a 256-element phased array, a small piece of skull, and water. The skull sound speed sample combining with the phase delay serves as input for the model training. The focus volume and grating lobe level obtained by the proposed approach were the closest to those obtained by the time reversal method in all relevant approaches. Furthermore, the mean peak value obtained by the proposed approach was no less than 77% of that of the time reversal method. In this study, the computational cost of each sample's phase delay was no more than 0.05 s, which was 1/200th of the time reversal method. The proposed method eliminates the complexity of numerical calculation processes requiring consideration of more acoustic parameters, while circumventing the substantial computational resource demands and time-consuming challenges to traditional numerical approaches. The proposed method enables rapid, precise, and adaptive transcranial aberration correction on the 3D skull-based conditions, overcoming the potential inaccuracies in predicting the focal position or the acoustic energy distribution from 2D simulations. These results show the possibility of the proposed approach enabling near-real-time correction of skull-induced phase aberrations to achieve transcranial focus, thereby offering a novel option for treating brain diseases through temporary BBB opening.

PMID:40117699 | DOI:10.1016/j.ultras.2025.107641

Neurotherapeutics. 2025 Mar 20:e00575. doi: 10.1016/j.neurot.2025.e00575. Online ahead of print.

ABSTRACT

Neuroblastoma is an aggressive childhood cancer that arises from the sympathetic nervous system. Despite advances in treatment, high-risk neuroblastoma remains difficult to manage due to its heterogeneous nature and frequent development of drug resistance. Drug repurposing guided by single-cell analysis presents a promising strategy for identifying new therapeutic options. Here, we aim to characterize high-risk neuroblastoma subpopulations and identify effective repurposed drugs for targeted treatment. We performed single-cell transcriptomic analysis of neuroblastoma samples, integrating bulk RNA-seq data deconvolution with clinical outcomes to define distinct malignant cell states. Using a systematic drug repurposing pipeline, we identified and validated potential therapeutic agents targeting specific high-risk neuroblastoma subpopulations. Single-cell analysis revealed 17 transcriptionally distinct neuroblastoma subpopulations. Survival analysis identified a highly aggressive subpopulation characterized by elevated UBE2C/PTTG1 expression and poor patient outcomes, distinct from a less aggressive subpopulation with favorable prognosis. Drug repurposing screening identified Adavosertib as particularly effective against the aggressive subpopulation, validated using SK-N-DZ cells as a representative model. Mechanistically, Adavosertib suppressed cell proliferation through AKT/mTOR pathway disruption, induced G2/M phase cell cycle arrest, and promoted apoptosis. Further analysis revealed UBE2C and PTTG1 as key molecular drivers of drug resistance, where their overexpression enhanced proliferation, Adavosertib resistance, and cell migration. This study establishes a single-cell-based drug repurposing strategy for high-risk neuroblastoma treatment. Our approach successfully identified Adavosertib as a promising repurposed therapeutic agent for targeting specific high-risk neuroblastoma subpopulations, providing a framework for developing more effective personalized treatment strategies.

PMID:40118716 | DOI:10.1016/j.neurot.2025.e00575

Med Mycol. 2025 Mar 21:myaf025. doi: 10.1093/mmy/myaf025. Online ahead of print.

ABSTRACT

Candida albicans is a major human fungal pathogen; however, limited antifungal agents, undesirable drug side effects, and ineffective prevention of drug-resistant strains have become serious problems. Chitosan is a nontoxic, biodegradable, and biocompatible linear polysaccharide made from the deacetylation of chitin. In this study, a ZCF31 putative transcription factor gene was selected from a previous mutant library screen, as zcf31Δ strains exhibited defective cell growth in response to chitosan. Furthermore, chitosan caused notable damage to zcf31Δ cells; however, ZCF31 expression was not significantly changed by chitosan, suggesting that zcf31Δ is sensitive to chitosan could be due to changes in the physical properties of C. albicans. Indeed, zcf31Δ cells displayed significant increases in cell wall thickness. Consistent to the previous study, zcf31Δ strains were resistant to calcofluor white but highly susceptible to SDS. These results implied that chitosan mainly influences membrane function, as zcf31Δ strengthens the stress resistance of the fungal cell wall but lessens cell membrane function. Interestingly, this effect on the cell surface mechanics of the C. albicans zcf31Δ strains was not responsible for the virulence-associated function. RNA-seq analysis further revealed that six mannosyltransferase-related genes were upregulated in zcf31Δ. Although five mannosyltransferase-related mutant strains in the zcf31Δ background partially reduced the cell wall thickness, only zcf31Δ/mnn45Δ showed the recovery of chitosan resistance. Our findings suggest that Zcf31 mediates a delicate and complicated dynamic balance between the cell membrane and cell wall architectures through the mannosyltransferase genes in C. albicans, leading to altered chitosan susceptibility.

PMID:40118513 | DOI:10.1093/mmy/myaf025

Cell Syst. 2025 Mar 17:101239. doi: 10.1016/j.cels.2025.101239. Online ahead of print.

ABSTRACT

A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing (RNA-seq) to monitor the dynamics of thousands of growth- and survival-related signals over the first minutes, hours, and days of oncogenic BRAF inhibition in human melanoma cells. We observed sustained inhibition of the BRAF-ERK axis, gradual downregulation of cell cycle signaling, and three distinct, reversible phase transitions toward quiescence. Statistical inference of kinetically defined regulatory modules revealed a dominant compensatory induction of SRC family kinase (SFK) signaling, promoted in part by excess reactive oxygen species, rendering cells sensitive to co-treatment with an SFK inhibitor in vitro and in vivo, underscoring the translational potential for assessing early drug-induced adaptive signaling. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:40118060 | DOI:10.1016/j.cels.2025.101239

Mol Divers. 2025 Mar 21. doi: 10.1007/s11030-025-11168-9. Online ahead of print.

ABSTRACT

Vitamin D receptor (VDR) agonists play a pivotal role in modulating immune responses and promoting melanocyte survival, making them potential candidates for vitiligo treatment. The VDR gene is integral to mediating the effects of vitamin D in the immune system, and disruptions in its structure due to missense mutations may significantly contribute to the pathogenesis of vitiligo. Missense single-nucleotide polymorphisms (SNPs) can alter the amino acid sequence of the VDR protein, potentially affecting its ligand-binding affinity and downstream signaling. Investigating these missense SNPs provides critical insights into the genetic underpinnings of vitiligo and may help identify biomarkers for early detection and precision-targeted therapies. This study explored the therapeutic potential of vitamin D analogs in vitiligo management, with a particular focus on their binding interactions and molecular efficacy. Using molecular docking and virtual screening, 24 vitamin D analogs were evaluated. Calcipotriol exhibited the highest binding affinity (-11.4 kcal/mol) and unique interactions with key residues in the VDR ligand-binding domain. Additionally, an analysis of structural variations stemming from missense mutations in the VDR gene highlighted potential impacts on receptor-ligand interactions, further emphasizing the importance of genetic factors in treatment response. These findings underscore the potential of calcipotriol to promote melanogenesis and modulate pigmentation in vitiligo. A comparative analysis identified structural variations influencing the efficacy of other analogs, such as calcitriol and tacalcitol. Although the in silico methods provided valuable insights, the study acknowledges the limitations of excluding dynamic cellular environments and emphasizes the need for experimental validation. Overall, this study enhances our understanding of VDR-targeted therapies, and calcipotriol is a promising candidate for further development in the management of vitiligo.

PMID:40117094 | DOI:10.1007/s11030-025-11168-9

Int J Clin Pharm. 2025 Mar 21. doi: 10.1007/s11096-025-01899-y. Online ahead of print.

ABSTRACT

BACKGROUND: Achieving optimal tacrolimus dosing is vital for effectively balancing therapeutic efficacy and safety, as CYP3A5 genetic variants and inter-patient variability emphasize the need for precision strategies.

AIM: This study aimed to optimize tacrolimus dosage prediction for renal transplant recipients by incorporating genetic polymorphisms, specifically profiling CYP3A5 genetic variants, within the DoseOptimal framework to enhance interpretability and accuracy of dosing decisions.

METHOD: The dataset comprised clinical, demographic, and CYP3A5 genetic variants information from 1045 stable tacrolimus-treated patients. The DoseOptimal framework was developed by integrating the strengths of the most effective algorithms from fifteen machine learning models. SHapley Additive exPlanations (SHAP) and decision tree insights were incorporated to enhance the framework's interpretability. The framework's performance was assessed using mean absolute error (MAE) and the coefficient of determination (R2 score). The F-statistic and p value were calculated to validate the framework's statistical significance.

RESULTS: The DoseOptimal framework demonstrated robust performance with an R2 score of 0.884 in the training set and 0.830 in the testing set. The MAE was 0.40 mg/day (95% CI 0.38-0.43) in the training set and 0.41 mg/day (95% CI 0.38-0.45) in the testing set. The framework predicted the ideal tacrolimus dosage in 87.6% (n = 275) of the test cohort, with 3.2% (n = 10) underestimation and 9.2% (n = 29) overestimation. The framework's statistical significance was confirmed with an F-statistic of 266.095 and a p value < 0.001.

CONCLUSION: The framework provides precision medicine-based dosing solutions tailored to individual genetic profiles, minimizing dosing errors and enhancing patient outcomes.

PMID:40117041 | DOI:10.1007/s11096-025-01899-y

Eur J Clin Pharmacol. 2025 Mar 21. doi: 10.1007/s00228-025-03821-x. Online ahead of print.

ABSTRACT

PURPOSE: Direct oral anticoagulants (DOACs) are used to prevent and treat thromboembolic events in adults. We aimed to investigate whether pharmacogenomic variation contributes to the risk of bleeding during DOAC treatment.

METHODS: Cases were recruited from reports of bleeding sent to the Swedish Medical Products Agency (n = 129, 60% men, 93% Swedish, 89% on factor Xa inhibitors) and compared with population controls (n = 4891) and a subset matched for exposure to DOACs (n = 353). We performed a genome-wide association study, with analyses of candidate single nucleotide polymorphisms (SNPs) and candidate gene set analyses.

RESULTS: Forty-four cases had major, 37 minor, and 48 clinically relevant non-major (CRNM) bleeding. When cases were compared with matched controls, BAIAP2L2 rs142001534 was significantly associated with any bleeding and major/CRNM bleeding (P = 4.66 × 10-8 and P = 3.28 × 10-8, respectively). The candidate SNP CYP3A5 rs776746 was significantly associated with major and major/CRNM bleeding (P = 0.00020 and P = 0.00025, respectively), and ABCG2 rs2231142 was nominally associated with any bleeding (P = 0.01499). Rare coding variants in the candidate gene VWF were significantly associated with any bleeding (P = 0.00296).

CONCLUSION: BAIAP2L2, CYP3A5, ABCG2, and VWF may be associated with bleeding in DOAC-treated patients. The risk estimates of the candidate variants in CYP3A5 and ABCG2 were in the same direction as in previous studies. The Von Willebrand Factor gene (VWF) is linked to hereditary bleeding disorders, while there is no previous evidence of bleeding associated with BAIAP2L2.

PMID:40116934 | DOI:10.1007/s00228-025-03821-x

Pharmacogenomics. 2025 Mar 21:1-20. doi: 10.1080/14622416.2025.2481025. Online ahead of print.

ABSTRACT

AIM: To systematically assess clinical studies involving patients undergoing drug therapy, comparing different genotypes to assess the relationship with changes in QT intervals, with no limitations on study design, setting, population, dosing regimens, or duration.

METHODS: This systematic review followed PRISMA guidelines and a pre-registered protocol. Clinical human studies on PGx markers of diQTP were identified, assessed using standardized tools, and categorized by design. Gene associations were classified as pharmacokinetic or pharmacodynamic. Identified genes underwent pathway enrichment analyses. Drugs were classified by third-level Anatomical Therapeutic Chemical (ATC) codes. Descriptive statistics were computed by study category and drug classes.

RESULTS: Of 4,493 reports, 84 studies were included, identifying 213 unique variants across 42 drug classes, of which 10% were replicated. KCNE1-Asp85Asn was the most consistent variant. Most findings (82%) were derived from candidate gene studies, suggesting bias toward known markers. The diQTP-associated genes were mainly linked to "cardiac conduction" and "muscle contraction" pathways (false discovery rate = 4.71 × 10-14). We also found an overlap between diQTP-associated genes and congenital long QT syndrome genes.

CONCLUSION: Key genes, drugs, and pathways were identified, but few consistent PGx markers emerged. Extensive, unbiased studies with diverse populations are crucial to advancing the field.

REGISTRATION: A protocol was pre-registered at PROSPERO under registration number CRD42022296097.

DATA DEPOSITION: Data sets generated by this review are available at figshare: DOI: 10.6084/m9.figshare.27959616.

PMID:40116580 | DOI:10.1080/14622416.2025.2481025

J Med Internet Res. 2025 Mar 21;27:e60148. doi: 10.2196/60148.

ABSTRACT

BACKGROUND: Despite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after study completion.

OBJECTIVE: We aimed to estimate the proportion of AI/ML research that reported results through ClinicalTrials.gov or peer-reviewed publications indexed in PubMed or Scopus.

METHODS: Using data from the Clinical Trials Transformation Initiative Aggregate Analysis of ClinicalTrials.gov, we identified studies initiated and completed between January 2010 and December 2023 that contained AI/ML-specific terms in the official title, brief summary, interventions, conditions, detailed descriptions, primary outcomes, or keywords. For 842 completed studies, we searched PubMed and Scopus for publications containing study identifiers and AI/ML-specific terms in relevant fields, such as the title, abstract, and keywords. We calculated disclosure rates within 3 years of study completion and median times to disclosure-from the "primary completion date" to the "results first posted date" on ClinicalTrials.gov or the earliest date of journal publication.

RESULTS: Of 842 completed studies (n=357 interventional; n=485 observational), 5.5% (46/842) disclosed results on ClinicalTrials.gov, 13.9% (117/842) in journal publications, and 17.7% (149/842) through either route within 3 years of completion. Higher disclosure rates were observed for trials: 10.4% (37/357) on ClinicalTrials.gov, 19.3% (69/357) in journal publications, and 26.1% (93/357) through either route. Randomized controlled trials had even higher disclosure rates: 11.3% (23/203) on ClinicalTrials.gov, 24.6% (50/203) in journal publications, and 32% (65/203) through either route. Nevertheless, most study findings (82.3%; 693/842) remained undisclosed 3 years after study completion. Trials using randomization (vs nonrandomized) or masking (vs open label) had higher disclosure rates and shorter times to disclosure. Most trials (85%; 305/357) had sample sizes of ≤1000, yet larger trials (n>1000) had higher publication rates (30.8%; 16/52) than smaller trials (n≤1000) (17.4%; 53/305). Hospitals (12.4%; 42/340), academia (15.1%; 39/259), and industry (13.7%; 20/146) published the most. High-income countries accounted for 82.4% (89/108) of all published studies. Of studies with disclosed results, the median times to report through ClinicalTrials.gov and in journal publications were 505 days (IQR 399-676) and 407 days (IQR 257-674), respectively. Open-label trials were common (60%; 214/357). Single-center designs were prevalent in both trials (83.3%; 290/348) and observational studies (82.3%; 377/458).

CONCLUSIONS: For over 80% of AI/ML studies completed during 2010-2023, study findings remained undisclosed even 3 years after study completion, raising questions about the representativeness of publicly available evidence. While methodological rigor was generally associated with higher publication rates, the predominance of single-center designs and high-income countries may limit the generalizability of the results currently accessible.

PMID:40117574 | DOI:10.2196/60148

Med Sci (Paris). 2025 Mar;41(3):277-280. doi: 10.1051/medsci/2025036. Epub 2025 Mar 21.

ABSTRACT

On October 8, 2024, the Nobel Prize in Physics was awarded to John J. Hopfield, professor at Princeton University, and Geoffrey E. Hinton, professor at the University of Toronto, for their "fundamental discoveries that made possible machine learning through artificial neural networks." According to the Nobel committee, John Hopfield designed an associative memory capable of storing and reconstructing images, while Geoffrey Hinton developed a method enabling tasks such as identifying specific elements within images. This article retraces the career paths of these two researchers and highlights their pioneering contributions.

PMID:40117553 | DOI:10.1051/medsci/2025036