JAMA Netw Open. 2025 Aug 1;8(8):e2526714. doi: 10.1001/jamanetworkopen.2025.26714.

NO ABSTRACT

PMID:40779270 | DOI:10.1001/jamanetworkopen.2025.26714

Pediatr Pulmonol. 2025 Aug;60(8):e71229. doi: 10.1002/ppul.71229.

ABSTRACT

Cystic fibrosis (CF) was once a fatal disease of childhood, but with advances in combination CFTR modulator therapies, life expectancy for persons with CF (PwCF) has increased. Despite remarkable improvements in life expectancy, CF is a chronic multiple organ system disease and comorbidities characterized by recurrent respiratory infections, pancreatic insufficiency, diabetes, liver disease, depression, anxiety, and bone disease resulting in exposure to many drugs. The Clinical Pharmacogenetics (PGx) Implementation Consortium (CPIC) publishes evidence-based guidelines for use of PGx to guide dosing for drug-gene interactions. This study aimed to assess the current use of PGx testing in CF care at CF Foundation-accredited care centers and affiliate programs (CFF-ACCAP) across the United States. A 14-item survey was distributed electronically to CF Foundation-accredited care centers and affiliate programs in the United States using the CF Foundation email exchange. Overall, 74 responses were received from a potential of the 287 CFF-ACCAP. Since each individual CFF-ACCAP may have had multiple team members who could have received and responded to the survey, it is possible that these responses include multiple respondents from a single center. Only eight (4%) respondents affirmed they were obtaining PGx testing beyond cystic fibrosis transmembrane conductance regulator (CFTR) and 66 (89%) respondents answered that they were not currently doing PGx for drug-gene pairs beyond CFTR. Based on this landscape survey, PGx is not commonly implemented in US CFF-ACCAP, but providers are open to using PGx to improve care in PwCF. Several barriers limit the implementation of PGx in CFF-ACCAP, which calls for guidance on how to effectively integrate PGx into CF clinical care.

PMID:40778643 | DOI:10.1002/ppul.71229

Pharmacotherapy. 2025 Aug 8. doi: 10.1002/phar.70048. Online ahead of print.

ABSTRACT

Succinylcholine, a commonly used neuromuscular blocker, is hydrolyzed by the pseudocholinesterase (also known as butyrylcholinesterase) enzyme in the plasma to inactive metabolites. Individuals who have inherited genetic variants in the BCHE gene that result in decreased or no pseudocholinesterase enzyme activity are at increased risk of prolonged neuromuscular blockade with succinylcholine. Although succinylcholine/BCHE is one of the earliest identified pharmacogenomic drug/gene associations, clinical implementation remains the exception rather than the norm today. This review will explore the historical roots of pseudocholinesterase deficiency, its therapeutic implications for succinylcholine use, and future considerations for BCHE genetic testing to minimize the occurrence of prolonged neuromuscular blockade that can cause serious physical (i.e., apnea) and psychological (i.e., post-traumatic stress) consequences for patients. A summary and critical examination of the published literature that includes BCHE genetic testing in relation to succinylcholine response is also provided. Prolonged paralysis with succinylcholine may be prevented with preemptive BCHE genetic testing.

PMID:40778538 | DOI:10.1002/phar.70048

Pediatr Pulmonol. 2025 Aug;60(8):e71230. doi: 10.1002/ppul.71230.

ABSTRACT

BACKGROUND: Medication adherence is essential in managing cystic fibrosis (CF). The role of socioeconomic factors for medication adherence in people with CF is poorly understood, and their differential impact across the life course is underexplored. This study investigates associations between measures of socioeconomic status (SES)-educational attainment, household income, and health insurance type-and adherence to CF medications across age groups.

METHODS: We conducted a cross-sectional analysis of data collected during the validation of the Daily Care Check-In, a measure of adherence barriers in people with CF. Adherence was measured as a composite medication possession ratio (cMPR) averaged across five CF-specific medications, with data collected from pharmacy records. Sociodemographic and clinical data were collected through self-report and medical record review.

RESULTS: A total of 405 participants completed the study, with an overall cMPR of 45.6%, lowest (38.5%) among young adults (aged 18-26 years) and highest (53.0%) among adolescents (aged 13-17 years). Lower household income and lack of college degree were associated with lower cMPR, more interference from adherence barriers, and decreased self-efficacy, as well as with increased depressive and anxiety symptoms. Similar associations, but less consistent, were observed for public health insurance. When stratified by age, associations between SES measures and adherence were most evident in adolescents, followed by adults, but absent in young adults, bringing into focus challenges with measuring SES in the 18-25 years age group.

CONCLUSION: Lower SES is associated with worse medication adherence, more interference from adherence barriers, and lower self-efficacy. Associations vary by SES measure and age group, calling for a nuanced approach to adherence interventions in this population.

PMID:40778648 | DOI:10.1002/ppul.71230

Pediatr Pulmonol. 2025 Aug;60(8):e71229. doi: 10.1002/ppul.71229.

ABSTRACT

Cystic fibrosis (CF) was once a fatal disease of childhood, but with advances in combination CFTR modulator therapies, life expectancy for persons with CF (PwCF) has increased. Despite remarkable improvements in life expectancy, CF is a chronic multiple organ system disease and comorbidities characterized by recurrent respiratory infections, pancreatic insufficiency, diabetes, liver disease, depression, anxiety, and bone disease resulting in exposure to many drugs. The Clinical Pharmacogenetics (PGx) Implementation Consortium (CPIC) publishes evidence-based guidelines for use of PGx to guide dosing for drug-gene interactions. This study aimed to assess the current use of PGx testing in CF care at CF Foundation-accredited care centers and affiliate programs (CFF-ACCAP) across the United States. A 14-item survey was distributed electronically to CF Foundation-accredited care centers and affiliate programs in the United States using the CF Foundation email exchange. Overall, 74 responses were received from a potential of the 287 CFF-ACCAP. Since each individual CFF-ACCAP may have had multiple team members who could have received and responded to the survey, it is possible that these responses include multiple respondents from a single center. Only eight (4%) respondents affirmed they were obtaining PGx testing beyond cystic fibrosis transmembrane conductance regulator (CFTR) and 66 (89%) respondents answered that they were not currently doing PGx for drug-gene pairs beyond CFTR. Based on this landscape survey, PGx is not commonly implemented in US CFF-ACCAP, but providers are open to using PGx to improve care in PwCF. Several barriers limit the implementation of PGx in CFF-ACCAP, which calls for guidance on how to effectively integrate PGx into CF clinical care.

PMID:40778643 | DOI:10.1002/ppul.71229

Pediatr Pulmonol. 2025 Aug;60(8):e71231. doi: 10.1002/ppul.71231.

ABSTRACT

INTRODUCTION: Medication adherence in cystic fibrosis (CF) has been shown to slow disease progression. Integrated pharmacy services can help increase medication access. The objective of this study was to compare the medication possession ratio (MPR) of dornase alfa between people with cystic fibrosis (PwCF) filling maintenance CF medications at one integrated health system specialty pharmacy (IHSSP) to those filling at multiple pharmacies.

METHODS: This retrospective study included PwCF < 18 years of age who were prescribed dornase alfa from January 1 to December 31, 2019. The primary endpoint was the MPR for dornase alfa. Subgroup analyses were performed for those with Medicaid and those with private insurance.

RESULTS: A total of 85 patients were included, with 29 (34.1%) filling all medications at IHSSP and 56 (65.9%) filling at multiple pharmacies. The median MPR of dornase alfa was 0.98 (IQR: 0.76-1) and 0.64 (IQR: 0.34-0.85), (p < 0.001), and ppFEV1 changed by -1% (IQR: -7% to 5%) compared to -5% (IQR: -10% to -1%), (p = 0.03) for the IHSSP group and multiple pharmacies groups, respectively. There was no difference in the number of hospitalizations or length of stay. Improved MPR for PwCF in the IHSSP group was sustained in the Medicaid and private insurance subgroups.

CONCLUSIONS: The MPR of dornase alfa was higher, and pulmonary function was maintained in PwCF who were able to use the IHSSP for CF medications. Some insurance policies require specific pharmacies for specialty medications, requiring PwCF to fill prescriptions at multiple pharmacies and potentially worsening adherence and clinical outcomes.

PMID:40778618 | DOI:10.1002/ppul.71231

Pediatr Pulmonol. 2025 Aug;60(8):e71222. doi: 10.1002/ppul.71222.

ABSTRACT

In 2024, important advances for people with cystic fibrosis (CF) were published. Important guidelines for newborn screening and care of infants diagnosed with CF transmembrane conductance regulator (CFTR)-Related Metabolic Syndrome/Cystic Fibrosis Screen Positive Inconclusive Diagnosis (CRMS/CFSPID) were published alongside related key lessons from individual programs. Work continues to improve growth and nutrition and treat pulmonary exacerbations. New position papers on care delivery and the care team in the post-CFTR modulator era were developed next to continued information related to CFTR modulator use on treatment burden simplification and side effects, such as mental health and use during pregnancy. The aim of this review is to provide high-level information that may lead to changes in clinical care.

PMID:40778614 | DOI:10.1002/ppul.71222

Clin Transl Oncol. 2025 Aug 8. doi: 10.1007/s12094-025-04014-9. Online ahead of print.

ABSTRACT

BACKGROUND: This study aimed to develop and validate a hybrid deep learning (DL) model that integrates convolutional neural network (CNN) and vision transformer (ViT) architectures to predict distant metastasis (DM) in patients with non-small cell lung cancer (NSCLC) using 18F-FDG PET/CT images.

METHODS: A retrospective analysis was conducted on a cohort of consecutively registered patients who were newly diagnosed and untreated for NSCLC. A total of 167 patients with available PET/CT images were included in the analysis. DL features were extracted using a combination of CNN and ViT architectures, followed by feature selection, model construction, and evaluation of model performance using the receiver operating characteristic (ROC) and the area under the curve (AUC).

RESULTS: The ViT-based DL model exhibited strong predictive capabilities in both the training and validation cohorts, achieving AUCs of 0.824 and 0.830 for CT features, and 0.602 and 0.694 for PET features, respectively. Notably, the model that integrated both PET and CT features demonstrated a notable AUC of 0.882 in the validation cohort, outperforming models that utilized either PET or CT features alone. Furthermore, this model outperformed the CNN model (ResNet 50), which achieved an AUC of 0.752 [95% CI 0.613, 0.890], p < 0.05. Decision curve analysis further supported the efficacy of the ViT-based DL model.

CONCLUSION: The ViT-based DL developed in this study demonstrates considerable potential in predicting DM in patients with NSCLC, potentially informing the creation of personalized treatment strategies. Future validation through prospective studies with larger cohorts is necessary.

PMID:40779149 | DOI:10.1007/s12094-025-04014-9

Radiol Phys Technol. 2025 Aug 8. doi: 10.1007/s12194-025-00938-7. Online ahead of print.

ABSTRACT

Clinical magnetic resonance imaging (MRI) is a high-resolution tool widely used for detailed anatomical imaging. However, prolonged scan times often lead to motion artefacts and patient discomfort. Fast acquisition techniques can reduce scan times but often produce noisy, low-contrast images, compromising segmentation accuracy essential for diagnosis and treatment planning. To address these limitations, we developed an end-to-end framework that incorporates BIDS-based data organiser and anonymizer, a GAN-based MR image enhancement model (GAN-MRI), AssemblyNet for brain region segmentation, and an attention-residual U-Net with Guided loss for abdominal and thigh segmentation. Thirty brain scans (5,400 slices) and 32 abdominal (1,920 slices) and 55 thigh scans (2,200 slices) acquired from multiple MRI scanners (GE, Siemens, Toshiba) underwent evaluation. Image quality improved significantly, with SNR and CNR for brain scans increasing from 28.44 to 42.92 (p < 0.001) and 11.88 to 18.03 (p < 0.001), respectively. Abdominal scans exhibited SNR increases from 35.30 to 50.24 (p < 0.001) and CNR from 10,290.93 to 93,767.22 (p < 0.001). Double-blind evaluations highlighted improved visualisations of anatomical structures and bias field correction. Segmentation performance improved substantially in the thigh (muscle: + 21%, IMAT: + 9%) and abdominal regions (SSAT: + 1%, DSAT: + 2%, VAT: + 12%), while brain segmentation metrics remained largely stable, reflecting the robustness of the baseline model. Proposed framework is designed to handle data from multiple anatomies with variations from different MRI scanners and centres by enhancing MRI scan and improving segmentation accuracy, diagnostic precision and treatment planning while reducing scan times and maintaining patient comfort.

PMID:40779148 | DOI:10.1007/s12194-025-00938-7

Methods Mol Biol. 2025;2932:291-301. doi: 10.1007/978-1-0716-4566-6_16.

ABSTRACT

Somatic variant detection is an important step in the analysis of cancer genomes for basic research as well as precision oncology. Here, we review existing computational methods for identifying somatic mutations from tissue as well as liquid biopsy samples. We then describe steps to run VarNet (Krishnamachari et al., Nat Commun 13:4248, 2022), a variant caller using deep learning, to accurately identify single nucleotide variants (SNVs) and short insertion-deletion (indels) mutations from next-generation sequencing (NGS) of tumor tissue samples.

PMID:40779117 | DOI:10.1007/978-1-0716-4566-6_16

Methods Mol Biol. 2025;2932:273-289. doi: 10.1007/978-1-0716-4566-6_15.

ABSTRACT

Cancer precision medicine aims to identify the best course of treatment for an individual. To achieve this goal, two important questions include predicting the response of an individual to a treatment strategy and identifying molecular markers that determine the response. The rapid growth of large publicly available databases containing clinical and molecular characteristics of cancer-derived samples paired with their response to single or multiple drugs, has enabled the development of computational models to answer these questions. In recent years, various deep learning models have been proposed to predict the response to polytherapy and monotherapies. However, selecting among all available options or developing new models for a particular study requires careful considerations and best practices to avoid various pitfalls. In this chapter, and drawing from our own studies, we will discuss various important points for choosing, utilizing, and developing such deep learning tools.

PMID:40779116 | DOI:10.1007/978-1-0716-4566-6_15

Methods Mol Biol. 2025;2932:259-271. doi: 10.1007/978-1-0716-4566-6_14.

ABSTRACT

Over the past decade, convolutional neural networks (CNNs) have revolutionized predictive modeling of data containing spatial correlations, specifically excelling at image analysis tasks due to their embedded feature extraction and improved generalization. However, outside of image or sequence data, datasets typically lack the structural correlation needed to exploit the benefits of CNN modeling. This is especially true regarding anticancer drug sensitivity prediction tasks, as the data used is often tabular without any embedded information in the ordering or locations of the features when utilizing data other than DNA or RNA sequences. This chapter provides a computational procedure, REpresentation of Features as Images with NEighborhood Dependencies (REFINED), that maps high-dimensional feature vectors into compact 2D images suitable for CNN-based deep learning. The pairing of REFINED mappings with CNNs enables enhanced predictive performance through reduced model parameterization and improved embedded feature extraction as compared to fully connected alternatives utilizing the high-dimensional feature vectors.

PMID:40779115 | DOI:10.1007/978-1-0716-4566-6_14

J Phys Chem Lett. 2025 Aug 8:8418-8426. doi: 10.1021/acs.jpclett.5c01753. Online ahead of print.

ABSTRACT

Single-molecule detection of post-translational modifications (PTMs) such as phosphorylation plays a crucial role in early diagnosis of diseases and therapeutics development. Although single-molecule surface-enhanced Raman spectroscopy (SM-SERS) detection of PTMs has been demonstrated, the data analysis and detection accurracies were hindered by interference from citrate signals and lack of reference databases. Previous reports required complete coverage of the nanoparticle surface by analyte molecules to replace citrates, hampering the detection limit. Here, we developed a high-accuracy SM-SERS approach by combining a plasmonic particle-in-pore sensor to collect SM-SERS spectra of phosphorylation at Serine and Tyrosine, k-means-based clustering for citrate signal removal, and a one-dimensional convolutional neural network (1D-CNN) for phosphorylation identification. Significantly, we collected SM-SERS data with submonolayer analyte coverage of the particle surface and discriminated the phosphorylation in Serine and Tyrosine with over 95% and 97% accuracy, respectively. Finally, the 1D-CNN features were interpreted by a one-dimensional gradient feature weight and SM-SERS peak occurrence frequencies.

PMID:40778942 | DOI:10.1021/acs.jpclett.5c01753

JACC Cardiovasc Imaging. 2025 Aug 1:S1936-878X(25)00331-6. doi: 10.1016/j.jcmg.2025.06.010. Online ahead of print.

ABSTRACT

BACKGROUND: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) uses computed tomography (CT)-based attenuation correction (AC) to improve diagnostic accuracy. Deep learning (DL) has the potential to generate synthetic AC images, as an alternative to CT-based AC.

OBJECTIVES: This study evaluated whether DL-generated synthetic SPECT images could enhance accuracy of conventional SPECT MPI.

METHODS: Study investigators developed a DL model in a multicenter cohort of 4,894 patients from 4 sites to generate simulated SPECT AC images (DeepAC). The model was externally validated in 746 patients from 72 sites in a clinical trial (A Phase 3 Multicenter Study to Assess PET Imaging of Flurpiridaz F 18 Injection in Patients With CAD; NCT01347710) and in 320 patients from another external site. In the first external cohort, the study assessed the diagnostic accuracy for obstructive coronary artery disease (CAD)-defined as left main coronary artery stenosis ≥50% or ≥70% in other vessels-for total perfusion deficit (TPD). In the latter, the study completed change analysis and compared quantitative scores for AC, DeepAC, and nonattenuation correction (NC) with clinical scores.

RESULTS: In the first external cohort (mean age, 63 ± 9.5 years; 69.0% male), 206 patients (27.6%) had obstructive CAD. The area under the receiver-operating characteristic curve (AUC) of DeepAC TPD (0.77; 95% CI: 0.73-0.81) was higher than the NC TPD (AUC: 0.73; 95% CI: 0.69-0.77; P < 0.001). In the second external cohort, DeepAC quantitative scores had closer agreement with actual AC scores compared with NC.

CONCLUSIONS: In a multicenter external cohort, DeepAC improved prediction performance for obstructive CAD. This approach could enhance diagnostic accuracy in facilities using conventional SPECT systems without requiring additional equipment, imaging time, or radiation exposure.

PMID:40778900 | DOI:10.1016/j.jcmg.2025.06.010

JASA Express Lett. 2025 Aug 1;5(8):086001. doi: 10.1121/10.0038764.

ABSTRACT

Model-based deep learning approaches provide an alternative scheme to address the problem of the shortage of training data. However, performance degradation caused by sound speed profile (SSP) mismatch remains a critical challenge, particularly in shallow-water environments influenced by internal waves. In this paper, a simple range-dependent SSP model is integrated into the deep learning approach for source localization. The network trained on simulated data generated with the range-dependent SSP model performs well on validation data and generalizes to experimental test data after transfer learning with limited experimental samples.

PMID:40778845 | DOI:10.1121/10.0038764

Liver Int. 2025 Sep;45(9):e70274. doi: 10.1111/liv.70274.

ABSTRACT

BACKGROUND AND AIMS: The first variceal haemorrhage (FVH) is a life-threatening complication of liver cirrhosis that requires timely intervention; however, noninvasive tools for accurately predicting FVH remain limited. This study aimed to develop noninvasive, deep learning-enhanced computed tomographic angiography (CTA) models for early and accurate FVH prediction.

METHODS: This multi-centre retrospective study included 184 cirrhotic patients (FVH: n = 107, non-FVH: n = 77) enrolled from December 2014 to May 2022. Patients were randomly divided (7:3) into training and validation cohorts. CTA and clinical data were collected and analysed. A novel Vision-Transformer (ViT) network, combined with reinforcement learning (RL), was applied to CTA images to predict FVH and was compared with convolutional neural networks (CNNs). Models were evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA), and feature importance was determined from model coefficients and gradients.

RESULTS: The ViT + RL* model demonstrated superior diagnostic performance, achieving an AUC of 0.985 (95% CI, 0.955-1.0) in the validation cohort and 0.956 (95% CI, 0.919-0.988) in the training cohort, outperforming traditional CNNs. DCA and the area under the curve confirmed the enhanced clinical utility of the ViT + RL* model compared to CNNs; the ViT + RL* model highlighted critical regions in the liver, spleen, oesophageal lumen, and abdominal vessels. Meanwhile, clinical data identified creatinine and prothrombin time as potential predictive factors, with moderate predictive performance.

CONCLUSIONS: The novel deep learning-enhanced CTA models offer a robust, non-invasive method for predicting FVH, with the ViT + RL* model demonstrating excellent efficacy; thus providing a valuable tool for early risk stratification in cirrhotic patients.

PMID:40778828 | DOI:10.1111/liv.70274

Chemistry. 2025 Aug 8:e02067. doi: 10.1002/chem.202502067. Online ahead of print.

ABSTRACT

The inherently low sensitivity of solution-state Nuclear Magnetic Resonance (NMR) has long limited its ability to characterize transient biomolecular states at atomic resolution. While dissolution dynamic nuclear polarization (dDNP) offers a compensating signal enhancement, its broader use has been hindered by rapid polarization decay, causing severe spectral distortion. Here, we introduce HyperW-Decon, an approach that enables high-sensitivity, high-resolution NMR of biomolecules in solution. HyperW-Decon combines two key aspects: (i) the use of hyperpolarized water (HyperW) to transfer polarization to proteins through rapid proton exchange; and (ii) a theory-driven, machine learning (ML)-based deconvolution method that corrects polarization-induced artifacts without requiring external reference signals. This approach is based on a first-principles understanding of dDNP line shapes and delivers a scalable solution to spectral distortion. Applied to intrinsically disordered proteins (IDPs) involved in biomineralization, HyperW-Decon reveals previously inaccessible, short-lived ion-peptide encounter complexes with residue resolution.

PMID:40778633 | DOI:10.1002/chem.202502067

Liver Int. 2025 Sep;45(9):e70255. doi: 10.1111/liv.70255.

ABSTRACT

BACKGROUND & AIMS: Immune checkpoint inhibitors (ICIs) are therapy for many malignancies including hepatocellular carcinoma (HCC), yet the impact of HCC on immune-mediated liver injury from checkpoint inhibitors (ILICI) remains poorly understood and no direct comparison exists for hepatotoxicity rates between ICI and sorafenib in HCC.

METHODS: In this retrospective cohort study, we extracted data on adult patients treated with five ICI regimens for HCC or non-HCC cancers, and HCC patients who received sorafenib between 2010 and 2020. The primary outcome was grade ≥ 3 ILICI or sorafenib (DILI). Logistic regression estimated adjusted odds ratios (OR) for liver injury.

RESULTS: We identified 530 patients, 129 (24%) HCC-ICI, 256 (48%) non-HCC ICI, and 145 (27%) HCC-sorafenib. Compared to non-HCC ICI, HCC-ICI and HCC-sorafenib were more often male (57%, 82%, 77%), Hispanic (14%, 35%, 34%), and cirrhotic (1%, 85%, 88%). Twenty-three patients developed grade ≥ 3 ILICI. ILICI incidence was higher for HCC-ICI (11%, CI 6-18) versus non-HCC ICI (4%, CI 2-6, p = 0.006) and DILI in HCC-sorafenib (3%, CI 1-8, p = 0.02) with incidence highest for ipilimumab-nivolumab (HCC-ICI 42%, CI 15-72 versus non-HCC 10%, CI 3-24; p = 0.02). On multivariable regression, ILICI was associated with HCC (OR 4.5, CI 1.8-11.4, p = 0.002) and treatment with ipilimumab-nivolumab (OR 6.9, CI 2.6-18.3, p < 0.001). Incidence of liver injury in HCC remained elevated for ICI versus sorafenib (OR 3.5, CI 1.2-10.4, p = 0.02).

CONCLUSIONS: We identified an elevated risk of liver injury in HCC patients receiving ICIs compared to ICI-treated non-HCC cancers and sorafenib-treated HCC, with dual ipilimumab-nivolumab therapy carrying the highest risk.

PMID:40778804 | DOI:10.1111/liv.70255

Pharmacol Res Perspect. 2025 Aug;13(4):e70164. doi: 10.1002/prp2.70164.

ABSTRACT

This study assessed whether the completeness of spontaneously reported adverse drug reaction (ADR) reports differs between consumers and healthcare professionals when submitted directly to regulators, and how this compares to reports from pharmaceutical companies. ADR reports (2014-2023) were obtained from public databases in Canada and the United Kingdom (UK), focusing on the medicine classes sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, and dipeptidyl peptidase-4 inhibitors. ADR report completeness was assessed using vigiGrade tool variables. Descriptive statistics and chi-square tests were used for analysis. A total of 17 897 reports were analyzed-13 613 from the UK Yellow Card Scheme and 4284 from Canada. Most Canadian reports were submitted by pharmaceutical companies (55%), while in the UK, healthcare professionals submitted the majority (69%). Few reports were submitted directly by consumers in either Canada (4%) or the UK (7%). In Canada, the average completeness was 82% for consumer and healthcare professional reports and 57% for pharmaceutical companies. In the UK, completeness was 80% (consumers), 82% (healthcare professionals), and 69% (pharmaceutical companies). Canadian pharmaceutical company reports were significantly less complete for age, sex, outcome, dose, indication, and route of administration (all p < 0.001). In the UK, they were less complete for age, sex, and route of administration (all p < 0.001). In conclusion, reports submitted directly to regulators by consumers and healthcare professionals were more complete than those from pharmaceutical companies. The low consumer reporting rate, yet high completeness rate, highlights the need to encourage direct reporting to regulators to improve medicine safety monitoring.

PMID:40778745 | DOI:10.1002/prp2.70164

Stud Health Technol Inform. 2025 Aug 7;329:1942-1943. doi: 10.3233/SHTI251290.

ABSTRACT

This study develops a knowledge graph-based intelligent Q&A system for rare diseases, integrating data from 126 diseases, 2,609 symptoms, and related departments. The Bert-BiLSTM-CRF model achieved 82.13% accuracy in entity extraction, and TextCNN achieved 94.54% accuracy in intent recognition. The system improves access to medical knowledge but requires further optimization to reduce response times and handle a broader range of user queries.

PMID:40776307 | DOI:10.3233/SHTI251290