Stud Health Technol Inform. 2025 May 12;326:27-32. doi: 10.3233/SHTI250230.

ABSTRACT

We evaluated the performance of Semantic Clinical Artificial Intelligence (SCAI, pronounced Sky), a large language model (LLM) knowledge resource through usability testing. This pretest-intervention-posttest mixed-methods user interface (UI) design study investigates usability to determine whether the LLM provides a more comprehensive, efficient, and enhanced user-friendly means of delivering end user information as compared to using primary sources of information from the Internet (Web). Our analysis focused on assessing the LLM's efficiency and usability in helping users arrive at accurate and reliable outcomes, to ultimately determine its value as an innovative tool for packaging and presenting information. Usability test sessions were conducted using the cognitive walkthrough approach, via Zoom. Participants were asked to respond to scenarios using only the LLM, and then only the web, and vice versa. These sessions were followed by user feedback sessions where participants rated their experiences and responded to open-ended questions related to the overall usability and satisfaction with SCAI.

PMID:40357596 | DOI:10.3233/SHTI250230

Front Pharmacol. 2025 Apr 28;16:1539870. doi: 10.3389/fphar.2025.1539870. eCollection 2025.

ABSTRACT

BACKGROUND: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have emerged as promising therapeutics for heart failure (HF). Nevertheless, evidence supporting the mechanism of SGLT2i efficacy in HF patients is currently limited. Genetic variation in SLC5A2 (encoding SGLT2) may influence HF progression and SGLT2i response, as well as inform potential SGLT2i mechanisms. Thus, this study investigated associations between SLC5A2 variation and clinical outcomes in SGLT2i-naïve and dapagliflozin-treated HF cohorts.

METHODS: We analyzed two HF cohorts to identify variants associated with SGLT2i response pathways. Adjusted Cox proportional-hazard regression models were used to assess the effect of SLC5A2 variation on a primary composite outcome of cardiovascular (CV) hospitalization or all-cause mortality in SGLT2i-naïve patients, and HF hospitalization or CV death in dapagliflozin-treated patients. The initial cohort comprised 327 American HF patients naïve to SGLT2i throughout the study. Subsequently, a prospective cohort study of 190 Egyptian SGLT2i-naïve HF patients treated with dapagliflozin was analyzed. In this cohort, SNPs in UGT2B4 and SLC2A1 were also investigated. Changes in NT-proBNP levels, KCCQ-12 scores, echocardiographic parameters, and eGFR throughout 6-month follow-up were tested with linear regression models as secondary outcomes.

RESULTS: In SGLT2i-naïve patients, rs3813008 (SLC5A2) was significantly associated with reduced risk of the composite outcome of all-cause death or hospitalization (HR = 0.65, 95% CI: 0.47-0.89, P = 0.008). In the dapagliflozin-treated cohort, rs3813008 was also associated with death or hospitalization, but with increased risk in treated patients (HR = 3.38, 95% CI: 1.35-8.42, P = 0.008).

CONCLUSION: Our study suggests that SLC5A2 variation is associated with clinical outcomes in SGLT2i-naïve and treated HF patients, warranting further investigation of SLC5A2 and SGLT2i interactions.

PMID:40356983 | PMC:PMC12066643 | DOI:10.3389/fphar.2025.1539870

Future Oncol. 2025 May 13:1-14. doi: 10.1080/14796694.2025.2501517. Online ahead of print.

ABSTRACT

AIMS: This study identifies single-nucleotide polymorphisms (SNPs) associated with cellular response to cyclophosphamide (CTX) using phosphoramide mustard (PM), its primary cytotoxic metabolite, and explores the downstream consequences for breast cancer (BC) patients.

METHODS: We analyzed 1,978,545 SNPs from EBV-transformed lymphoblastic cell lines (LCLs) derived from 53 unrelated European individuals, in a genome-wide association study using cellular PM sensitivity data. We filtered SNPs associated with PM sensitivity (p < 5 × 10-5) predicted to overlap with regulatory elements in breast tissue using a chromatin state prediction model. We then assessed the consequences using LCL transcriptomic data and data from BC patients treated with (ACT-BC; N = 155) and without CTX.

RESULTS: Twenty SNPs were filtered out including rs12408401, which was associated with PM resistance (p = 3.89 × 10-5), potentially disrupted a CTCF-loop, and was associated with increased RFX5 expression (p = 0.036), which was associated with poor disease-free interval in ACT-BC patients (HR = 5.32; p = 0.028); and rs784562, which was associated with improved PM sensitivity (p = 6.41 × 10-6), potentially altered nearby enhancer functionality, and reduced expression of KRT72 which was associated with poor progression-free survival in ACT-BC patients (HR = 3.61; p = 0.040).

CONCLUSION: Our study identifies SNPs significantly associated with cellular CTX response with potential mechanistic and clinical relevance, thereby providing insights toward optimized CTX treatment strategies.

PMID:40356407 | DOI:10.1080/14796694.2025.2501517

Front Cell Infect Microbiol. 2025 Apr 28;15:1560634. doi: 10.3389/fcimb.2025.1560634. eCollection 2025.

ABSTRACT

INTRODUCTION: Rarely does Pandoraea occur in bloodstream infections (BSI), although it's typically found in cystic fibrosis. This study aims to decipher the genetic map and obtain insights of clinical symptoms into Pandoraea from BSI patients.

METHODS: 30 suspected BSI patients' diagnostic records and medical histories were recorded. Pandoraea spp. isolates were collected and subjected to antimicrobial susceptibility testing, Sanger sequencing and Whole-genome sequencing (WGS).

RESULTS: Of the 30 clinical cases, five (16.67%) ultimately died, whereas 25 (83.33%) are alive. 30 purified Pandoraea isolates showed high degree of MIC values to Meropenem, Amoxicillin and Potassium Clavulanate, Gentamicin, and Ceftazidime. Then, all isolates were identified as P. pneumonica based on the 16S rRNA-based phylogenetic analysis. Among 28 genomes of them, the average genome size and average GC contents were 5,397,568 bp, and 62.43%, respectively. However, WP1 displayed high similarity (90.6%) to reference Pandoraea sp. LMG 31114. Genetic differences between the tested isolates and LMG 31114 suggested that the outbreak's causative pathogen could be a novel cluster of P. pneumonica. The genomes accumulated mutations at an estimated rate of 1.3 × 10-7 mutations/year/site. Moreover, 26 clinical isolates within the P. pneumonica cluster were formed in July 2014, revealing a tendency to develop regional endemic patterns.

CONCLUSION: BSI caused by this novel cluster of P. pneumonica is linked to significant morbidity and mortality. Such cluster remains a critical public health challenge due to their regional epidemiological patterns and antibiotic treatment risk. This study contributed to the basis on pathogen identification, disease diagnosis, and BSI treatment.

PMID:40357401 | PMC:PMC12066476 | DOI:10.3389/fcimb.2025.1560634

Front Cell Infect Microbiol. 2025 Apr 28;15:1566495. doi: 10.3389/fcimb.2025.1566495. eCollection 2025.

ABSTRACT

The leading cause of death for people with cystic fibrosis (pwCF) continues to be due to respiratory-related illnesses. Both wound repair and immune cell responses are dysregulated in the CF airways, creating a cycle of unresolved injury and perpetuating inflammation. PwCF are predisposed to colonization and infections with opportunistic bacteria like Pseudomonas aeruginosa (Pa), the most common adult pathogen in CF. Pa possesses key virulence factors that can exacerbate chronic inflammation and lung injury. With the approval of highly effective modulator therapies like elexacaftor/tezacaftor/ivacaftor (ETI), pwCF eligible for ETI have seen drastic improvements in lung function and clinical outcomes, including an increased life expectancy. While modulator therapies are improving bronchial epithelial cellular processes in wound repair and some areas of immunity, many of these processes do not reach a non-CF baseline state or have not been thoroughly studied. The effect of modulator therapy on Pa may lead to a reduction in infection, but in more longitudinal studies, there is not always eradication of Pa, and colonization and infection frequency can return to pre-modulator levels over time. Finally, in this review we explore the current state of additional treatments for CF lung disease, independent of CFTR genotype, including anti-inflammatories, phage-therapies, and Pa vaccines.

PMID:40357395 | PMC:PMC12066499 | DOI:10.3389/fcimb.2025.1566495

Front Med (Lausanne). 2025 Apr 28;12:1571506. doi: 10.3389/fmed.2025.1571506. eCollection 2025.

ABSTRACT

Tannerella forsythia infection was common in oral diseases but less reported in lung diseases. This report presents a patient with bronchiectasis who was infected by Tannerella forsythia and subsequently hospitalized with symptoms including fever, progressive cough, sputum production, and shortness of breath. A chest computed tomography (CT) scan revealed multiple bilateral pulmonary bronchiectasis with signs of infection. Metagenomic next-generation sequencing (mNGS) of the bronchoalveolar lavage fluid primarily detected Tannerella forsythia. Treatment with Piperacillin-tazobactam and ornidazole resulted in a favorable outcome. This case first reported a patient with extensive bronchiectasis infected by Tannerella forsythia and provided an effective treatment.

PMID:40357303 | PMC:PMC12066332 | DOI:10.3389/fmed.2025.1571506

J Am Heart Assoc. 2025 May 13:e040641. doi: 10.1161/JAHA.124.040641. Online ahead of print.

ABSTRACT

BACKGROUND: Atrial fibrillation (AF) can cause a reduction in left ventricular ejection fraction (LVEF) that resolves rapidly upon restoration of sinus rhythm. We used artificial intelligence to understand (1) how often transient LVEF reduction during AF is from mismeasurement due to AF's beat-to-beat variability and (2) whether true transient AF-LVEF reduction has prognostic significance.

METHODS: In this observational study, we analyzed all patients at a large academic center with a transthoracic echocardiogram in AF and subsequent transthoracic echocardiogram in sinus rhythm within 90 days. We classified patients by their clinically reported LVEFs: no AF-LVEF reduction, transient AF-LVEF reduction that recovered after conversion to sinus rhythm, or persistent AF-LVEF reduction that did not recover. We evaluated how automated multicycle AF-LVEF measurement using a validated artificial intelligence algorithm affected AF-LVEF and reclassified patients. We used Fine-Gray hazard modeling to analyze 1-year heart failure hospitalization risk.

RESULTS: In 810 patients (mean age 74.1 years, 34.3% female), 459 (56.7%) had no reduced AF-LVEF, 71 (8.8%) had transient AF-LVEF reduction, and 280 (34.6%) had persistent AF-LVEF reduction. In the group with transient AF-LVEF reduction, LVEF increased by 19.5% (95% CI, 12.0%-22.1%) upon conversion to sinus rhythm. AI reassessment increased AF-LVEF by 8.2% (95% CI, 6.0%-10.4%), reclassifying 20 (28.2%) patients as no longer having reduced AF-LVEF. The group with transient AF-LVEF reduction, as determined by AI, had significantly higher 1-year heart failure hospitalization risk (hazard ratio, 2.28 [95% CI, 1.23-4.21], P=0.003).

CONCLUSION: Artificial intelligence may decrease misdiagnosis of reduced LVEF during AF and more accurately identify true transient AF-LVEF reduction, a potentially high-risk phenotype.

PMID:40357662 | DOI:10.1161/JAHA.124.040641

Clin Nucl Med. 2025 May 9. doi: 10.1097/RLU.0000000000005942. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the diagnostic performance of the PET Assisted Reporting System (PARS) in nasopharyngeal carcinoma (NPC) patients without distant metastasis, and to investigate the prognostic significance of the metabolic parameters.

PATIENTS AND METHODS: Eighty-three NPC patients who underwent pretreatment 18F-FDG PET/CT were retrospectively collected. First, the sensitivity, specificity, and accuracy of PARS for diagnosing malignant lesions were calculated, using histopathology as the gold standard. Next, metabolic parameters of the primary tumor were derived using both PARS and manual segmentation. The differences and consistency between the 2 methods were analyzed. Finally, the prognostic value of PET metabolic parameters was evaluated. Prognostic analysis of progression-free survival (PFS) and overall survival (OS) was conducted.

RESULTS: PARS demonstrated high patient-based accuracy (97.2%), sensitivity (88.9%), and specificity (97.4%), and 96.7%, 84.0%, and 96.9% based on lesions. Manual segmentation yielded higher metabolic tumor volume (MTV) and total lesion glycolysis (TLG) than PARS. Metabolic parameters from both methods were highly correlated and consistent. ROC analysis showed metabolic parameters exhibited differences in prognostic prediction, but generally performed well in predicting 3-year PFS and OS overall. MTV and age were independent prognostic factors; Cox proportional-hazards models incorporating them showed significant predictive improvements when combined. Kaplan-Meier analysis confirmed better prognosis in the low-risk group based on combined indicators (χ² = 42.25, P < 0.001; χ² = 20.44, P < 0.001).

CONCLUSIONS: Preliminary validation of PARS in NPC patients without distant metastasis shows high diagnostic sensitivity and accuracy for lesion identification and classification, and metabolic parameters correlate well with manual. MTV reflects prognosis, and its combination with age enhances prognostic prediction and risk stratification.

PMID:40357637 | DOI:10.1097/RLU.0000000000005942

Stud Health Technol Inform. 2025 May 12;326:147-151. doi: 10.3233/SHTI250257.

ABSTRACT

INTRODUCTION: Artificial Intelligence (AI) is playing an increasing role in advancing diagnostic processes and decision-making in healthcare. In the early detection of oral cancer and oral potentially malignant disorders (OPMDs), its role is still being explored. This paper evaluates advancements in AI applications for the early detection of oral cancer and OPMDs.

METHODS: A narrative umbrella review was performed on reviews that explicitly evaluated non-invasive diagnostic techniques combined with AI-modalities or machine learning techniques in the early detection of oral cancer and OPMDs.

RESULTS: Key findings of eight studies published between 2015 and 2024 demonstrate various AI-modalities and their diagnostic accuracy, accessibility and affordability, limitations and challenges and ethical and regulatory needs.

CONCLUSION: AI- and deep learning models hold promise in improving the early detection of oral cancer and OPMDs, offering high diagnostic accuracy that can significantly enhance patient outcomes. Challenges such as limited explainability and ethical concerns must be addressed to fully integrate these technologies into daily clinical practice.

PMID:40357619 | DOI:10.3233/SHTI250257

Circ Cardiovasc Imaging. 2025 May 13:e018188. doi: 10.1161/CIRCIMAGING.124.018188. Online ahead of print.

ABSTRACT

BACKGROUND: Computed tomography (CT) attenuation correction scans are an intrinsic part of positron emission tomography (PET) myocardial perfusion imaging using PET/CT, but anatomic information is rarely derived from these ultralow-dose CT scans. We aimed to assess the association between deep learning-derived cardiac chamber volumes (right atrial, right ventricular, left ventricular, and left atrial) and mass (left ventricular) from these scans with myocardial flow reserve and heart failure hospitalization.

METHODS: We included 18 079 patients with consecutive cardiac PET/CT from 6 sites. A deep learning model estimated cardiac chamber volumes and left ventricular mass from computed tomography attenuation correction imaging. Associations between deep learning-derived CT mass and volumes with heart failure hospitalization and reduced myocardial flow reserve were assessed in a multivariable analysis.

RESULTS: During a median follow-up of 4.3 years, 1721 (9.5%) patients experienced heart failure hospitalization. Patients with 3 or 4 abnormal chamber volumes were 7× more likely to be hospitalized for heart failure compared with patients with normal volumes. In adjusted analyses, left atrial volume (hazard ratio [HR], 1.25 [95% CI, 1.19-1.30]), right atrial volume (HR, 1.29 [95% CI, 1.23-1.35]), right ventricular volume (HR, 1.25 [95% CI, 1.20-1.31]), left ventricular volume (HR, 1.27 [95% CI, 1.23-1.35]), and left ventricular mass (HR, 1.25 [95% CI, 1.18-1.32]) were independently associated with heart failure hospitalization. In multivariable analyses, left atrial volume (odds ratio, 1.14 [95% CI, 1.0-1.19]) and ventricular mass (odds ratio, 1.12 [95% CI, 1.6-1.17]) were independent predictors of reduced myocardial flow reserve.

CONCLUSIONS: Deep learning-derived chamber volumes and left ventricular mass from computed tomography attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients undergoing cardiac PET perfusion imaging. This anatomic data can be routinely reported along with other PET/CT parameters to improve risk prediction.

PMID:40357553 | DOI:10.1161/CIRCIMAGING.124.018188

Front Med (Lausanne). 2025 Apr 28;12:1530361. doi: 10.3389/fmed.2025.1530361. eCollection 2025.

ABSTRACT

OBJECTIVE: Accurate anterior visual pathway (AVP) segmentation is vital for clinical applications, but manual delineation is time-consuming and resource-intensive. We aim to explore the feasibility of automatic AVP segmentation and volume measurement in brain T1-weighted imaging (T1WI) using the 3D UX-Net deep-learning model.

METHODS: Clinical data and brain 3D T1WI from 119 adults were retrospectively collected. Two radiologists annotated the AVP course in each participant's images. The dataset was randomly divided into training (n = 89), validation (n = 15), and test sets (n = 15). A 3D UX-Net segmentation model was trained on the training data, with hyperparameters optimized using the validation set. Model accuracy was evaluated on the test set using Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). The 3D UX-Net's performance was compared against 3D U-Net, Swin UNEt TRansformers (UNETR), UNETR++, and Swin Soft Mixture Transformer (Swin SMT). The AVP volume in the test set was calculated using the model's effective voxel volume, with volume difference (VD) assessing measurement accuracy. The average AVP volume across all subjects was derived from 3D UX-Net's automatic segmentation.

RESULTS: The 3D UX-Net achieved the highest DSC (0.893 ± 0.017), followed by Swin SMT (0.888 ± 0.018), 3D U-Net (0.875 ± 0.019), Swin UNETR (0.870 ± 0.017), and UNETR++ (0.861 ± 0.020). For surface distance metrics, 3D UX-Net demonstrated the lowest median ASSD (0.234 mm [0.188-0.273]). The VD of Swin SMT was significantly lower than that of 3D U-Net (p = 0.008), while no statistically significant differences were observed among other groups. All models exhibited identical HD95 (1 mm [1-1]). Automatic segmentation across all subjects yielded a mean AVP volume of 1446.78 ± 245.62 mm3, closely matching manual segmentations (VD = 0.068 ± 0.064). Significant sex-based volume differences were identified (p < 0.001), but no age correlation was observed.

CONCLUSION: We provide normative values for the automatic MRI measurement of the AVP in adults. The 3D UX-Net model based on brain T1WI achieves high accuracy in segmenting and measuring the volume of the AVP.

PMID:40357297 | PMC:PMC12066431 | DOI:10.3389/fmed.2025.1530361

Front Med (Lausanne). 2025 Apr 28;12:1564446. doi: 10.3389/fmed.2025.1564446. eCollection 2025.

ABSTRACT

BACKGROUND: Transperineal ultrasound (TPUS) is widely utilized for the evaluation of female stress urinary incontinence (SUI). However, the diagnostic accuracy of parameters related to urethral mobility and morphology remains limited and requires further optimization.

OBJECTIVE: This study aims to develop and validate an optimized deep learning (DL) model based on TPUS images to improve the precision and reliability of female SUI diagnosis.

METHODS: This retrospective study analyzed TPUS images from 464 women, including 200 patients with SUI and 264 controls, collected between 2020 and 2024. Three DL models (ResNet-50, ResNet-152, and DenseNet-121) were trained on resting-state and Valsalva-state images using an 8:2 training-to-testing split. Model performance was assessed using diagnostic metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity. A TPUS-index model, constructed using measurement parameters assessing urethral mobility, was used for comparison. Finally, the best-performing DL model was selected to evaluate its diagnostic advantages over traditional methods.

RESULTS: Among the three developed DL models, DenseNet-121 demonstrated the highest diagnostic performance, achieving an AUC of 0.869, an accuracy of 0.87, a sensitivity of 0.872, a specificity of 0.761, a negative predictive value (NPV) of 0.788, and a positive predictive value (PPV) of 0.853. When compared to the TPUS-index model, the DenseNet-121 model exhibited significantly superior diagnostic performance in both the training set (z = -2.088, p = 0.018) and the testing set (z = -1.997, p = 0.046).

CONCLUSION: This study demonstrates the potential of DL models, particularly DenseNet-121, to enhance the diagnosis of female SUI using TPUS images, providing a reliable and consistent diagnostic tool for clinical practice.

PMID:40357276 | PMC:PMC12066636 | DOI:10.3389/fmed.2025.1564446

Front Med (Lausanne). 2025 Apr 28;12:1567119. doi: 10.3389/fmed.2025.1567119. eCollection 2025.

ABSTRACT

The early diagnosis and accurate classification of lung cancer have a critical impact on clinical treatment and patient survival. The rise of artificial intelligence technology has led to breakthroughs in medical image analysis. The Lung-PET-CT-Dx public dataset was used for the model training and evaluation. The performance of the You Only Look Once (YOLO) series of models in the lung CT image object detection task is compared in terms of algorithms, and different versions of YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 are examined for lung cancer detection and classification. The experimental results indicate that the prediction results of YOLOv8 are better than those of the other YOLO versions, with a precision rate of 90.32% and a recall rate of 84.91%, which proves that the model can effectively assist physicians in lung cancer diagnosis and improve the accuracy of disease localization and identification.

PMID:40357272 | PMC:PMC12067791 | DOI:10.3389/fmed.2025.1567119

Front Med (Lausanne). 2025 Apr 28;12:1587307. doi: 10.3389/fmed.2025.1587307. eCollection 2025.

ABSTRACT

INTRODUCTION: Aortic stenosis (AS) is a valvular heart disease that obstructs normal blood flow from the left ventricle to the aorta due to pathological changes in the valve, leading to impaired cardiac function. Echocardiography is a key diagnostic tool for AS; however, its accuracy is influenced by inter-observer variability, operator experience, and image quality, which can result in misdiagnosis. Therefore, alternative methods are needed to assist healthcare professionals in achieving more accurate diagnoses.

METHODS: We proposed a deep learning model, RSMAS-Net, for the automated identification and diagnosis of AS using echocardiography. The model enhanced the ResNet50 backbone by replacing Stage 4 with Spatial and Channel Reconstruction Convolution (SCConv) and Multi-Dconv Head Transposed Attention (MDTA) modules, aiming to reduce redundant computations and improve feature extraction capabilities.

RESULTS: The proposed method was evaluated on the TMED-2 echocardiography dataset, achieving an accuracy of 94.67%, an F 1-score of 94.37%, and an AUC of 0.95 for AS identification. Additionally, the model achieved an AUC of 0.93 for AS severity classification on TMED-2. RSMAS-Net outperformed multiple baseline models in recall, precision, parameter efficiency, and inference time. It also achieved an AUC of 0.91 on the TMED-1 dataset.

CONCLUSION: RSMAS-Net effectively diagnoses and classifies the severity of AS in echocardiographic images. The integration of SCConv and MDTA modules enhances diagnostic accuracy while reducing model complexity compared to the original ResNet50 architecture. These results highlight the potential of RSMAS-Net in improving AS assessment and supporting clinical decision-making.

PMID:40357270 | PMC:PMC12066763 | DOI:10.3389/fmed.2025.1587307

BMJ Open Sport Exerc Med. 2025 Apr 15;11(2):e002388. doi: 10.1136/bmjsem-2024-002388. eCollection 2025.

ABSTRACT

BACKGROUND: The most talented young athletes often face challenges related to sports health problems (ie, injury and illness), largely due to inappropriate training, condensed competition schedules and high demands. Previous preventive measures in Norway have lacked successful integration into young athletes' routines, highlighting the need for a systematic approach to safeguarding their health.

OBJECTIVE: To document the development of Prep to be PRO, an educational module-based programme, designed to support the development and protect the health of young athletes enrolled in sports junior high schools and sports academy high schools. Prep to be PRO aims to empower athletes with the relevant knowledge and skills to prevent health problems.

METHODS: The development process, guided by the Translating Research into Injury Prevention Practice framework, involved extensive collaboration with school leaders, coaches and athletes. From June 2019 to June 2023, the process incorporated multidisciplinary input from more than 40 stakeholders, including health personnel, as well as experts in sports science, nutrition and sports psychology.

RESULTS: Prep to be PRO consists of 10 modules tailored for both sports Junior high schools and sports academy high schools. The modules cover a range of topics, including performance training, growth and maturation, load progression, recovery, total load, nutrition and sports psychology. The programme is athlete-centred, but coach-driven, including student-active approaches, collaboration, use of digital tools and deep learning. Prep to be PRO is anchored in the National High School Curriculum, ensuring relevance and alignment with educational standards. Specific competence goals and learning objectives from the curriculum are addressed and linked to each individual module.

CONCLUSIONS: This educational programme appears to be a notable step forward in the Norwegian sports school's approach. Specifically, it may enhance the focus on overall health, introduce an individualised approach and foster long-term athlete development. The integration into the national curriculum and the involvement of school staff in its delivery is expected to facilitate implementation. Future work will focus on the next phases of implementation, as systematic data collection from coaches and athletes, ongoing stakeholder engagement, continuous adaptation and support for educators to ensure fidelity and relevance. Updates and analyses from all evaluations will examine the programme's effectiveness. Long-term sustainability will be secured by organisational commitment, resource alignment and integrating the initiative into existing structures.

PMID:40357054 | PMC:PMC12067783 | DOI:10.1136/bmjsem-2024-002388

Oral (Basel). 2025 Mar;5(1):2. doi: 10.3390/oral5010002. Epub 2025 Jan 6.

ABSTRACT

Artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) techniques such as convolutional neural networks (CNNs) and natural language processing (NLP), has shown remarkable promise in image analysis and clinical documentation in oral pathology. In order to explore the transformative potential of artificial intelligence (AI) in oral pathology, this review highlights key studies demonstrating current AI's improvement in oral pathology, such as detecting oral diseases accurately and streamlining diagnostic processes. However, several limitations, such as data quality, generalizability, legal and ethical considerations, financial constraints, and the need for paradigm shifts in practice, are critically examined. Addressing these challenges through collaborative efforts, robust validation, and strategic integration can pave the way for AI to revolutionize oral pathology, ultimately improving patient outcomes and advancing the field.

PMID:40357025 | PMC:PMC12068879 | DOI:10.3390/oral5010002

Circ Res. 2025 May 13. doi: 10.1161/CIRCRESAHA.125.326173. Online ahead of print.

ABSTRACT

BACKGROUND: Pulmonary hypertension (PH) is a lethal disease characterized in part by progressive pulmonary arteriole (PA) remodeling. Excessive PA fibrosis and macrophage infiltration are often present in PH, but the potential associations are obscure. We investigated the link between interstitial macrophage (iMΦ) infiltration and PA fibrosis in PH and idiopathic pulmonary arterial hypertension.

METHODS: Lung tissue samples from patients with idiopathic pulmonary arterial hypertension and experimental PH animals were obtained to analyze the extent of fibrosis and iMΦ infiltration in the different layers of PAs and their correlation with disease severity. Single-cell RNA sequencing, lineage tracing, histological analyses, iMΦ and PA smooth muscle cell coculture, and transgenic animal experiments were used to investigate the cell heterogeneity and origins and molecular mechanisms by which iMΦs promote PA fibrosis.

RESULTS: We found that increased collagen deposition and fibrosis in the PA media were most strongly related to the severity of PH, and medial iMΦ infiltration may be involved in these pathological processes. Single-cell transcriptomics revealed that MHCIIhiLYVE1loCCR2hi iMΦs were the major type of iMΦ that expanded upon Sugen-5416 and hypoxia plus normoxia stimulation and were responsible for PA medial fibrosis. Lineage tracing experiments suggested that these medial iMΦs were largely from recruited monocytes. Mechanistically, MHCIIhiLYVE1loCCR2hi iMΦs promoted the transition of PA smooth muscle cells to a fibroblast-like phenotype through the WNT11 (wingless member 11)/planar cell polarity (PCP) pathway. Wnt11 deletion in iMΦs from PH rats normalized the fibrotic PA smooth muscle cell phenotype and decreased PA medial fibrosis, thereby improving vascular compliance and protecting against PH. Moreover, myeloid-specific Ccr2 deficiency in PH-PAs inhibited the medial infiltration of MHCIIhiLYVE1loCCR2hi iMΦs, which also relieved PH.

CONCLUSIONS: This study demonstrates that the recruitment of MHCIIhiLYVE1loCCR2hi iMΦs leads to medial fibrosis in PH-PAs associated with PH severity and that inhibition of their pathogenicity or recruitment reverses PA medial fibrosis and PH.

PMID:40357547 | DOI:10.1161/CIRCRESAHA.125.326173

Front Pharmacol. 2025 Apr 28;16:1530697. doi: 10.3389/fphar.2025.1530697. eCollection 2025.

ABSTRACT

INTRODUCTION: To date, only two drugs, pirfenidone and nintedanib, are approved for the treatment of patients with idiopathic pulmonary fibrosis (IPF). In addition, very few studies have reported on the safety profile of either drug in large populations. This study aims to identify and compare adverse drug events (ADEs) associated with pirfenidone and nintedanib in real-world settings by analyzing data from the US Food and Drug Administration Adverse Event Reporting System (FAERS). In addition, we utilized data from the Japanese Adverse Drug Event Report (JADER) database for external validation.

METHODS: The ADE reports on both drugs from 2014 Q3 to 2024 Q2 in FAERS and from 2008 Q1 to 2024 Q1 in JADER were collected. After deduplication, Bayesian and non-Bayesian methods for disproportionality analysis, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multiple Gamma Poisson Shrinkers (MGPS), were used for signal detection. Additionally, time to onset (TTO) analysis were performed.

RESULTS: In total, 35,804 and 20,486 ADE reports were identified from the FAERS database for pirfenidone and nintedanib, respectively. At the system organ class (SOC) level, both drugs have a positive signal value for "gastrointestinal disorders," "respiratory, thoracic, and mediastinal disorders," and "metabolism and nutrition disorders." Other positive signals for pirfenidone include "general disorders and administration site conditions," and "skin and subcutaneous tissue disorders," while for nintedanib, they were "investigations," "infections and infestations," and "hepatobiliary disorders." Some positive signals were consistent with the drug labels, including nausea, decreased appetite, and weight decreased identified in pirfenidone, as well as diarrhea, decreased appetite, abdominal pain upper, and epistaxis identified in nintedanib. We also identified unexpected signals not listed on the drug label, such as decreased gastric pH, and pneumothorax for pirfenidone, and constipation, flatulence for nintedanib. The median onset time for ADEs was 146 days for pirfenidone and 45 days for nintedanib, respectively. Although the two antifibrotics differed in the proportion of periods in which the ADEs occurred, these ADEs were likely to continue even after a year of treatment. In the external validation of JADER, the number of reports for pirfenidone and nintedanib were 265, and 1,327, respectively. The disproportionality analysis at the SOC and preferred term (PT) levels supports the FAERS results.

CONCLUSION: This study systematically investigates and compares the ADEs and their onset times at the SOC and specific PT levels for pirfenidone and nintedanib. Our results provide valuable pharmacological insights for the similarities and differences between the safety profiles of the two drugs and highlight the importance of monitoring and managing the toxicity profile associated with antifibrotic drugs.

PMID:40356972 | PMC:PMC12067420 | DOI:10.3389/fphar.2025.1530697

Nucleic Acids Res. 2025 May 13:gkaf402. doi: 10.1093/nar/gkaf402. Online ahead of print.

ABSTRACT

BioPortal (https://bioportal.bioontology.org) is the world's most comprehensive repository of biomedical ontologies. It provides infrastructure for finding, sharing, searching, and utilizing biomedical ontologies. Launched in 2005, BioPortal now includes 1549 ontologies (1182 of them public). Its open, freely accessible website enables anyone (i) to browse the ontology library, (ii) to search for terms across ontologies, (iii) to browse mappings between terms, (iv) to see popularity ratings and recommendations on which ontologies are most relevant to their use cases, (v) to annotate text with ontology terms, (vi) to submit an ontology, and (vii) to request ontology changes. The library of ontologies can be accessed programmatically via a REST application programming interface (API). Recent enhancements include a BioPortal knowledge graph that integrates knowledge from multiple ontologies; a unified data model for interoperability with other knowledge sources; ontology popularity ratings and recommendations for relevant ontologies; and the ability to request ontology changes via a simple user interface that automatically converts user change requests to GitHub Pull Requests that specify the edits that will be made to the ontology upon approval.

PMID:40357648 | DOI:10.1093/nar/gkaf402

Front Neurosci. 2025 Apr 28;19:1534061. doi: 10.3389/fnins.2025.1534061. eCollection 2025.

ABSTRACT

Bridge-like lipid transporters (BLTPs) have recently been revealed as key regulators of intraorganellar lipid trafficking, with their loss being associated with defective synaptic signalling and congenital neurological diseases. This group consists of five protein subfamilies [BLTP1-3, autophagy-related 2 (ATG2), and vacuolar protein sorting 13 (VPS13)], which mediate minimally selective lipid transfer between cellular membranes. Deceptively simple in both structure and presumed function, this review addresses open questions as to how bridge-like transporters work, the functional consequences of bulk lipid transfer on cellular signalling, and summarises some recent studies that have shed light on the surprising level of regulation and specificity found in this family of transporters.

PMID:40356703 | PMC:PMC12066543 | DOI:10.3389/fnins.2025.1534061