Int J Neonatal Screen. 2025 May 20;11(2):39. doi: 10.3390/ijns11020039.

ABSTRACT

The publication of Cystic Fibrosis Newborn Screening: A Systematic Review-Driven Consensus Guideline from the United States Cystic Fibrosis Foundation (CFF) presents the challenge of implementation. CFF is prepared to partner with stakeholders to enhance newborn screening (NBS) practices. Through funding provided to the Center for Public Health Innovation (CPHI), the CFF has helped establish two genetic testing resource centers to help states implement CFTR sequencing within the NBS algorithm. CPHI, with CFF funding, is facilitating quality improvement collaboratives that unite CF clinicians and NBS staff nationwide to share best practices in laboratory methods, communication, and education. CFF continues to fund the Screening Improvement Program Award for Optimizing the Diagnosis of Infants and has developed a toolkit to help CF care teams collaborate with NBS programs on guideline implementation. Together, these initiatives aim to support states and CF providers in adapting their algorithms and processes. By identifying current best practices to improve timeliness, sensitivity, and equity in CF NBS, CFF seeks to promote better outcomes for all individuals with CF. Recognizing the competing demands on state public health departments, CFF is committed to partnering with stakeholders to ensure meaningful improvements in CF NBS.

PMID:40407522 | DOI:10.3390/ijns11020039

Respirology. 2025 May 23. doi: 10.1111/resp.70047. Online ahead of print.

ABSTRACT

Treatment targets in severe asthma have evolved towards a remission-focused paradigm guided by precision medicine. This novel concept requires a shift from evaluating the efficacy of therapies based on a single outcome at a single time point to an outcome that captures the complexity of asthma remission involving several domains assessed over a sustained period. Since the concept is still emerging, multiple definitions have been proposed, ranging from symptom control and exacerbation-free to resolution of underlying pathobiology, with varying rigour in each parameter. Understanding the strengths and weaknesses of the current construct is needed to progress further. We conducted a roundtable discussion with 27 asthma experts to address this issue, and discussions were narratively synthesised and summarised. The participants observed that between one in three and one in five people treated with targeted biological therapies or macrolides experience low disease activity over a sustained period. They unanimously agreed that labelling the attained clinical state as clinical remission is useful as a clinical (e.g., facilitating a treat-to-target approach), policy (e.g., widening eligibility criteria for biologics), and scientific (e.g., a path to understanding cure) tool. Current remission rates vary significantly due to definition variability. When assessing remission, it is essential to consider confounding factors (e.g., steroid use for adrenal insufficiency). More research is required to reach an acceptable definition, and including the patient's voice in such research is essential. In conclusion, the concept of treatment-induced clinical remission is possible and valuable in asthma. However, further refinement of the definition is required.

PMID:40407301 | DOI:10.1111/resp.70047

Int J Comput Assist Radiol Surg. 2025 May 23. doi: 10.1007/s11548-025-03412-2. Online ahead of print.

ABSTRACT

PURPOSE: Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT), a revolutionary intervention for ischemic stroke, is limited by its reliance on 2D fluoroscopic imaging, which lacks depth and comprehensive vascular detail. We propose a novel AI-driven pipeline for 3D CTA to 2D DSA cross-modality registration, termed DeepIterReg.

METHODS: The proposed pipeline integrates neural network-based initialization with iterative optimization to align pre-intervention and peri-intervention data. Our approach addresses the challenges of cross-modality alignment, particularly in scenarios involving limited shared vascular structures, by leveraging synthetic data, vein-centric anchoring, and differentiable rendering techniques.

RESULTS: We assess the efficacy of DeepIterReg through quantitative analysis of capture ranges and registration accuracy. Results show that our method can accurately register 70% of a test set of 20 patients and can improve capture ranges when performing an initial pose estimation using a convolutional neural network.

CONCLUSIONS: DeepIterReg demonstrates promising performance for 3D-to-2D stroke intervention image registration, potentially aiding clinicians by improving spatial understanding during EVT and reducing dependence on manual adjustments.

PMID:40407997 | DOI:10.1007/s11548-025-03412-2

Magn Reson Med. 2025 May 23. doi: 10.1002/mrm.30577. Online ahead of print.

ABSTRACT

PURPOSE: We aimed to develop a fully automatic, referenceless method for correcting distortions in echo-planar imaging (EPI) data sets, specifically designed for applications in retrospective studies lacking reference field maps or reversed-gradient scans. This work primarily targets data sets acquired with anterior-posterior or posterior-anterior phase-encoding protocols.

METHODS: Our approach used a generative adversarial network to generate a displacement map. The network model took a three-dimensional raw b0 volume from a diffusion-tensor data set as input and reproduced a displacement map, similar to that originally derived using a reversed-gradient correction method. This generative displacement map was used to correct echo-planar images across an entire diffusion data set.

RESULTS: The performance of our method was evaluated across multiple institutions using large-scale databases. We found that it effectively reduced geometric distortions in EPI data sets and improved the accuracy of diffusion indices. Moreover, it significantly enhanced the coregistration between EPI and high-resolution T1-weighted images (p < 0.01).

CONCLUSIONS: Our referenceless EPI distortion correction method has been publicly shared as a standalone application and offers a practical solution for enhancing the quality of EPI data sets in retrospective studies. It effectively reduces distortions and increases the accuracy of diffusion measures, making it a valuable tool for studies where EPI data contain no distortion calibration scan.

PMID:40407812 | DOI:10.1002/mrm.30577

Antibodies (Basel). 2025 May 14;14(2):41. doi: 10.3390/antib14020041.

ABSTRACT

BACKGROUND/OBJECTIVES: Venezuelan equine encephalitis virus (VEEV) represents a significant biothreat with no FDA-approved vaccine currently available, highlighting the need for alternative therapeutic strategies. Single-domain antibodies (sdAbs) present a potential alternative to conventional antibodies, due to their small size and ability to recognize cryptic epitopes.

METHODS: This research describes the development and preliminary evaluation of VEEV-binding sdAbs generated using a generative artificial intelligence (AI) platform. Using a dataset of known alphavirus-binding sdAbs, the AI model produced sequences with predicted affinity for the E2 glycoprotein of VEEV. These candidate sdAbs were expressed in a bacterial periplasmic system and purified for initial assessment.

RESULTS: Enzyme-linked immunosorbent assays (ELISAs) indicated binding activity of the sdAbs to VEEV antigens. In vitro neutralization tests suggested inhibition of VEEV infection in cultured cells for some of the candidates.

CONCLUSIONS: This study demonstrates how generative AI can expedite antiviral therapeutic development and establishes a framework for quick responses to emerging viral threats when extensive example databases are unavailable. Additional refinement and validation of AI-generated sdAbs could establish effective VEEV therapeutics.

PMID:40407693 | DOI:10.3390/antib14020041

Audiol Res. 2025 May 8;15(3):56. doi: 10.3390/audiolres15030056.

ABSTRACT

Background/Objectives: Cochlear implantation is an advantageous procedure for individuals with severe to profound hearing loss in many aspects related to auditory performance, social communication and quality of life. As machine learning applications have been used in the field of Otorhinolaryngology and Audiology in recent years, signal processing, speech perception and personalised optimisation of cochlear implantation are discussed. Methods: A comprehensive literature review was conducted in accordance with the PRISMA guidelines. PubMed, Scopus, Web of Science, Google Scholar and IEEE databases were searched for studies published between 2010 and 2025. We analyzed 59 articles that met the inclusion criteria. Rayyan AI software was used to classify the studies so that the risk of bias was reduced. Study design, machine learning algorithms, and audiological measurements were evaluated in the data analysis. Results: Machine learning applications were classified as preoperative evaluation, speech perception, and speech understanding in noise and other studies. The success rates of the articles are presented together with the number of articles changing over the years. It was observed that Random Forest, Decision Trees (96%), Bayesian Linear Regression (96.2%) and Extreme machine learning (99%) algorithms reached high accuracy rates. Conclusions: In cochlear implantation applications in the field of audiology, it has been observed that studies have been carried out with a variable number of people and data sets in different subfields. In machine learning applications, it is seen that a high amount of data, data diversity and long training times contribute to achieving high performance. However, more research is needed on deep learning applications in complex problems such as comprehension in noise that require time series processing. Funding and other resources: This study was not funded by any institution or organization. No registration was performed for this study.

PMID:40407670 | DOI:10.3390/audiolres15030056

Cancer Control. 2025 Jan-Dec;32:10732748251343245. doi: 10.1177/10732748251343245. Epub 2025 May 23.

ABSTRACT

IntroductionThe use of artificial intelligence (AI) in oncology has increased rapidly, transforming various healthcare areas such as pathology, radiology, diagnostics, prognosis, genomics, treatment planning, and clinical trials. However, perspectives, comfort levels, and concerns about AI in cancer care remain largely unexplored.Materials and MethodsThis prospective, descriptive cross-sectional survey study was conducted between May 20, 2024 and October 22, 2024, among 363 patients with cancer from two different hospitals affiliated with Ankara University, a tertiary care center in Türkiye. The survey included three distinct sections: (1) Perceptions: Patients' general views on AI's impact in oncology; (2) Attitudes: Comfort level with AI performing medical tasks; (3) Concerns: Specific fears related to AI implementation (eg, diagnostic errors, data privacy, healthcare costs). Survey responses were summarized descriptively, and differences by age, gender, and education were analyzed using chi-square tests.ResultsA majority (50.7%) believed AI would somewhat (32%) or significantly (18.7%) improve healthcare. However, one-third of patients (33.1%) were very uncomfortable with AI diagnosing cancer, with higher discomfort among less-educated participants (P < .005). Top patient concerns included AI making incorrect diagnoses (31.1%), increasing healthcare costs (27.5%), and not keeping data private (19.6%). Patients with higher education levels expressed less discomfort and fewer concerns.ConclusionsPatients' perceptions and attitudes on AI varied significantly based on education, highlighting the need for targeted educational initiatives. While AI holds potential to revolutionize cancer care, addressing concerns about accuracy, security, and transparency is critical to enhance its acceptance and effectiveness in clinical practice.

PMID:40407404 | DOI:10.1177/10732748251343245

Rev Sci Instrum. 2025 May 1;96(5):051501. doi: 10.1063/5.0252130.

ABSTRACT

Since the establishment of global goals for carbon neutrality and peak carbon emissions, optimizing renewable energy use has become a global priority. Wind turbine blades, as core components of wind power systems, require effective health monitoring and damage identification to ensure stable turbine operation and enhance economic efficiency. This paper applies bibliometric analysis to classify existing blade damage detection methods, comparing major non-destructive testing techniques, including strain data monitoring, vibration data monitoring, acoustic measurement, ultrasonic testing, thermal imaging, and image recognition. This paper discusses the application scenarios, strengths, and limitations of each technique, with an emphasis on future trends, and includes damage assessment through multi-method integration, advancements in online and non-destructive damage detection technologies, and the application of intelligent algorithms, such as deep learning. This study aims to guide wind power professionals in selecting blade health monitoring technologies, thereby promoting sustainability and efficiency in the wind power industry.

PMID:40407392 | DOI:10.1063/5.0252130

Brief Bioinform. 2025 May 1;26(3):bbaf210. doi: 10.1093/bib/bbaf210.

ABSTRACT

Spatial omics technologies, generating high-throughput and multimodal data, have necessitated the development of advanced data integration methods to facilitate comprehensive biological and clinical treatment discoveries. Based on the cross-attention concept, we developed an AI learning based toolchain called StereoMM, a graph based fusion model that can incorporate omics data such as gene expression, histological images, and spatial location. StereoMM uses an attention module for omics data interaction and a graph autoencoder to integrate spatial positions and omics data in a self-supervised manner. Applying StereoMM across various cancer types and platforms has demonstrated its robust capability. StereoMM outperforms competitors in identifying spatial regions reflecting tumour progression and shows promise in classifying colorectal cancer patients into deficient mismatch repair and proficient mismatch repair groups. The comprehensive inter-modal integration and efficiency of StereoMM enable researchers to construct spatial views of integrated multimodal features efficiently, advancing thorough tissue and patient characterization.

PMID:40407386 | DOI:10.1093/bib/bbaf210

Bioinformatics. 2025 May 23:btaf317. doi: 10.1093/bioinformatics/btaf317. Online ahead of print.

ABSTRACT

MOTIVATION: Modeling genetic perturbations and their effect on the transcriptome is a key area of pharmaceutical research. Due to the complexity of the transcriptome, there has been much excitement and development in deep learning (DL) because of its ability to model complex relationships. In particular, the transformer-based foundation model paradigm emerged as the gold-standard of predicting post-perturbation responses. However, understanding these increasingly complex models and evaluating their practical utility is lacking, along with simple but appropriate benchmarks to compare predictive methods.

RESULTS: Here, we present a simple baseline method that outperforms both state of the art (SOTA) in DL and other proposed simpler neural architectures, setting a necessary benchmark to evaluate in the field of post-perturbation prediction. We also elucidate the utility of foundation models for the task of post-perturbation prediction via generalizable fine-tuning experiments that can be translated to different applications of transformer-based foundation models to tasks of interest. Furthermore, we provide a corrected version of a popular dataset used for benchmarking perturbation prediction models. Our hope is that this work will properly contextualize further development of DL models in the perturbation space with necessary control procedures.

AVAILABILITY AND IMPLEMENTATION: All source code is available at: https://github.com/pfizer-opensource/perturb_seq. The DOI is 10.5281/zenodo.15352937.

CONTACT: daniel.wong@pfizer.com.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40407144 | DOI:10.1093/bioinformatics/btaf317

Spine (Phila Pa 1976). 2025 May 23. doi: 10.1097/BRS.0000000000005381. Online ahead of print.

ABSTRACT

STUDY DESIGN: Retrospective image analysis study.

OBJECTIVE: To propose a novel classification system for adolescent idiopathic scoliosis (AIS) curvature using unsupervised machine learning and evaluate its reliability and clinical implications.

SUMMARY OF BACKGROUND DATA: Existing AIS classification systems, such as King and Lenke, have limitations in accurately describing curve variations, particularly long C-shaped curves or curves with distinct characteristics. Unsupervised machine learning offers an opportunity to refine classification and enhance clinical decision-making.

METHODS: A total of 1,156 AIS patients who underwent deformity correction surgery were analyzed. Standard posteroanterior radiographs were segmented using U-net algorithms. Contrastive clustering was employed for automatic grouping, with the number of clusters ranging from three to 10. Cluster quality was assessed using t-SNE and Silhouette scores. Clusters were defined based on consensus among spine surgeons. Interobserver reliability was evaluated using kappa coefficients.

RESULTS: Six clusters were identified, reflecting variations in structural curve location, single (C-shaped) versus double (S-shaped) curves, and thoracolumbar curve characteristics. Cluster reliability was moderate (kappa = 0.701-0.731). The silhouette score was 0.308, with t-SNE demonstrating distinct clustering patterns. The classification highlighted differences not captured by the Lenke classification, such as thoracic curves confined to the thoracic spine versus those extending to the lumbar spine.

CONCLUSION: Unsupervised machine learning successfully categorized AIS curvatures into six distinct clusters, revealing meaningful patterns such as unique variations in thoracic and lumbar curves. These findings could potentially inform surgical planning and prognostic assessments. However, further studies are needed to validate clinical applicability and improve clustering quality.

LEVEL OF EVIDENCE: 3.

PMID:40407029 | DOI:10.1097/BRS.0000000000005381

Br J Dermatol. 2025 May 23:ljaf195. doi: 10.1093/bjd/ljaf195. Online ahead of print.

ABSTRACT

BACKGROUND: Epidermolysis bullosa simplex (EBS) is a hereditary skin fragility disorder caused by missense pathogenic variants in KRT5 or KRT14. These variants trigger the collapse of the cytoskeleton into cytoplasmic protein aggregates, which renders the epidermis highly susceptible to mechanical stress and leads to intraepidermal blistering and loss of intercellular cohesion. Currently, no molecular therapies for EBS exist.

OBJECTIVES: Characterization of K5 or K14 mutant keratinocytes, from patients with EBS, in response to PKC412 treatment in monolayer culture and in epidermal equivalents. This clarifies the potential of PKC412 as a drug repurposing therapy approach in EBS.

METHODS: We conducted a comprehensive characterization of K5 and K14 mutant keratinocytes in response to PKC412, examining its effects on proliferation, wound closure, and apoptosis. Additionally, we evaluated the improvement of intercellular cohesion through stretch assays, epithelial sheet assays, and assessment of desmosomal organization. Finally, we investigated the efficacy of PKC412 application in both skin explants and EBS-derived epidermal equivalent cultures.

RESULTS: We demonstrated that PKC412 is effective in various keratinocytes carrying pathogenic variants associated with localized, generalized, or intermediate forms of EBS. PKC412 enhanced intercellular adhesion in both immortalized normal and EBS keratinocytes, as well as normal primary keratinocytes, and under stretch conditions. Immunoblot analyses revealed a concentration-dependent reduction in desmoplakin phosphorylation, which remained stable over the course of three days at the sites investigated. Additionally, application of PKC412 in epidermal equivalent cultures restored desmoplakin distribution in the epidermal basal layer.

CONCLUSIONS: PKC412 markedly enhanced intercellular cohesion and stress resilience in patient-derived EBS keratinocytes, both in monolayer and 3D culture systems. These findings highlight PKC412 as a promising therapeutic candidate for the treatment of EBS.

PMID:40406951 | DOI:10.1093/bjd/ljaf195

Front Immunol. 2025 May 8;16:1540018. doi: 10.3389/fimmu.2025.1540018. eCollection 2025.

ABSTRACT

BACKGROUND: Breast cancer is a heterogeneous malignancy with complex molecular characteristics, making accurate prognostication and treatment stratification particularly challenging. Emerging evidence suggests that lactylation, a novel post-translational modification, plays a crucial role in tumor progression and immune modulation.

METHODS: To address breast cancer heterogeneity, we developed a machine learning-derived lactylation signature (MLLS) using lactylation-related genes selected through random survival forest (RSF) and univariate Cox regression analyses. A total of 108 algorithmic combinations were applied across multiple datasets to construct and validate the model. Immune microenvironment characteristics were analyzed using multiple immune infiltration algorithms. Computational drug-repurposing analyses were conducted to identify potential therapeutic agents for high-risk patients.

RESULTS: The MLLS effectively stratified patients into low- and high-risk groups with significantly different prognoses. The model demonstrated robust predictive power across multiple cohorts. Immune infiltration analysis revealed that the low-risk group exhibited higher levels of immune checkpoints (e.g., PD-1, PD-L1) and greater infiltration of B cells, CD4+ T cells, and CD8+ T cells, suggesting better responsiveness to immunotherapy. In contrast, the high-risk group showed immune suppression features associated with poor prognosis. Methotrexate was computationally predicted as a potential therapeutic candidate for high-risk patients, although experimental validation remains necessary.

CONCLUSION: The MLLS represents a promising prognostic biomarker and may support personalized treatment strategies in breast cancer, particularly for identifying candidates who may benefit from immunotherapy.

PMID:40406140 | PMC:PMC12095166 | DOI:10.3389/fimmu.2025.1540018

Front Immunol. 2025 May 8;16:1587705. doi: 10.3389/fimmu.2025.1587705. eCollection 2025.

ABSTRACT

BACKGROUND: Psoriasis and Crohn's disease (CD) are chronic inflammatory diseases that involve complex immune-mediated mechanisms. Despite clinical overlap and shared genetic predispositions, the molecular pathways connecting these diseases remain incompletely understood. The present study seeks to identify shared biomarkers and therapeutic targets for psoriasis and CD.

METHODS: Differentially expressed genes (DEGs) were identified from publicly available transcriptomic datasets related to psoriasis and CD. Simultaneously, weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules associated with the clinical traits of psoriasis and CD. Subsequently, biomarkers were prioritized from shared key genes by integrating protein-protein interaction (PPI) networks with machine learning models. Gene Set Enrichment Analysis (GSEA), along with Gene Ontology (GO) and KEGG pathway analyses, were performed to determine the biological significance of the identified genes. Immune infiltration analysis underscored the involvement of hub genes in immune regulation, while single-cell transcriptomic analysis revealed the cellular localization of these hub genes. Additional targeted molecular biology experiments validated the shared biomarkers. DSigDB predictions were employed to identify potential therapeutic compounds. Molecular docking simulations were performed to assess the binding affinity of the drugs to key target proteins. Finally, additional in vitro experiments were conducted to validate the therapeutic effects of the identified compounds.

RESULTS: The study identified KIF4A, DLGAP5, NCAPG, CCNB1, and CEP55 as key regulatory molecules and shared biomarkers for both diseases. GSEA and pathway analysis highlighted the importance of cell cycle regulation and immune response pathways in the comorbidities of psoriasis and CD. Immune infiltration analysis emphasized the role of hub genes in immune regulation. Furthermore, DSigDB predictions and molecular docking simulations indicated strong therapeutic potential for Etoposide, Lucanthone, and Piroxicam, with Etoposide showing the highest affinity for key targets. In cellular models, Etoposide demonstrated promising therapeutic effects by significantly downregulating the expression of psoriasis-related keratinocytes marker genes (KRT6, KRT16) and CD-related inflammatory cytokines (IL6, IL8, TNF-α), highlighting its potential in treating psoriasis and CD.

DISCUSSION: This study integrates bioinformatics, machine learning, and molecular validation to identify the shared molecular mechanisms of psoriasis and CD, uncovering novel biomarkers and potential combined therapeutic candidates. These findings provide valuable insights into potential treatment strategies for these diseases.

PMID:40406126 | PMC:PMC12095375 | DOI:10.3389/fimmu.2025.1587705

JAMA Neurol. 2025 May 23. doi: 10.1001/jamaneurol.2025.1522. Online ahead of print.

ABSTRACT

IMPORTANCE: Nontraumatic subarachnoid hemorrhage (SAH) represents the third most common stroke type with unique etiologies, risk factors, diagnostics, and treatments. Nevertheless, epidemiological studies often cluster SAH with other stroke types leaving its distinct burden estimates obscure.

OBJECTIVE: To estimate the worldwide burden of SAH.

DESIGN, SETTING, AND PARTICIPANTS: Based on the repeated cross-sectional Global Burden of Disease (GBD) 2021 study, the global burden of SAH in 1990 to 2021 was estimated. Moreover, the SAH burden was compared with other diseases, and its associations with 14 individual risk factors were investigated with available data in the GBD 2021 study. The GBD study included the burden estimates of nontraumatic SAH among all ages in 204 countries and territories between 1990 and 2021.

EXPOSURES: SAH and 14 modifiable risk factors.

MAIN OUTCOMES AND MEASURES: Absolute numbers and age-standardized rates with 95% uncertainty intervals (UIs) of SAH incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) as well as risk factor-specific population attributable fractions (PAFs).

RESULTS: In 2021, the global age-standardized SAH incidence was 8.3 (95% UI, 7.3-9.5), prevalence was 92.2 (95% UI, 84.1-100.6), mortality was 4.2 (95% UI, 3.7-4.8), and DALY rate was 125.2 (95% UI, 110.5-142.6) per 100 000 people. The highest burden estimates were found in Latin America, the Caribbean, Oceania, and high-income Asia Pacific. Although the absolute number of SAH cases increased, especially in regions with a low sociodemographic index, all age-standardized burden rates decreased between 1990 and 2021: the incidence by 28.8% (95% UI, 25.7%-31.6%), prevalence by 16.1% (95% UI, 14.8%-17.7%), mortality by 56.1% (95% UI, 40.7%-64.3%), and DALY rate by 54.6% (95% UI, 42.8%-61.9%). Of 300 diseases, SAH ranked as the 36th most common cause of death and 59th most common cause of DALY in the world. Of all worldwide SAH-related DALYs, 71.6% (95% UI, 63.8%-78.6%) were associated with the 14 modeled risk factors of which high systolic blood pressure (population attributable fraction [PAF] = 51.6%; 95% UI, 38.0%-62.6%) and smoking (PAF = 14.4%; 95% UI, 12.4%-16.5%) had the highest attribution.

CONCLUSIONS AND RELEVANCE: Although the global age-standardized burden rates of SAH more than halved over the last 3 decades, SAH remained one of the most common cardiovascular and neurological causes of death and disabilities in the world, with increasing absolute case numbers. These findings suggest evidence for the potential health benefits of proactive public health planning and resource allocation toward the prevention of SAH.

PMID:40406922 | DOI:10.1001/jamaneurol.2025.1522

Hosp Pharm. 2025 May 20:00185787251339360. doi: 10.1177/00185787251339360. Online ahead of print.

ABSTRACT

Background: Opioids are utilized for acute pain in hospitalized patients and carry the risk of unintentional toxicity. The relationship between unintentional toxicity within a hospital setting and genetic polymorphisms has not been fully evaluated within the literature to date. Assessment and utilization of pharmacogenetic data may be a way to prevent unintentional toxicity in hospitalized patients and reduce the need for naloxone administration. Objective: This study aimed to provide proof of concept for the comparison of allele frequencies of hospitalized patients who received naloxone for opioid reversal with lab control data allele frequencies to identify variations between groups. Methods: This single-center, exploratory, pilot study enrolled 15 patients. Genotype samples were collected via buccal swab and analyzed using a custom 13 gene panel of genes which impact opioid metabolism. Genes assessed include CYP1A2, CYP3A4/A5, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, UGT2B7, UGT1A3, ABCB1, COMT, OPRM1. The 15 patients were compared to an internal lab control group of 100 patients and separated into preventable and not preventable events for further analysis. Results: CYP3A5 genotype was found to be statistically significantly different between the experimental and control groups (P = .004). This statistically significant difference was also seen in CYP3A5 phenotypes (P = .038). When comparing preventable and not preventable events, a statistically significant difference was found in both the genotype (P = .030) and phenotype (P = .029) of CYP2C19. Other assessed risk factors included mean MME in the 24 hours preceding naloxone being higher among preventable events and hospital or emergency department admission percent risk being higher among not preventable events. Conclusion: Factors other than pharmacogenetics, including opioid route of administration, medication formulation, and overall hospital admission risk, may play an additive role in unintentional toxicity risk. Future research of genotype-guided opioid dosing in pain management services further adds to calculating the risk of unintentional opioid-related adverse effects with standard dosing of this drug class.

PMID:40406364 | PMC:PMC12092407 | DOI:10.1177/00185787251339360

Front Immunol. 2025 May 8;16:1493950. doi: 10.3389/fimmu.2025.1493950. eCollection 2025.

ABSTRACT

Mucoinflammatory lung disease in cystic fibrosis (CF) is characterized by airway surface liquid (ASL) layer dehydration and mucins hyperconcentration, which leads to airway obstruction, inflammation, bronchiectasis, and increased susceptibility to recurrent bacterial infections. Epidermal growth factor receptor (EGFR) is known to regulate airway mucous cell metaplasia (MCM) and mucins expression, but the role of EGFR pathway in the pathogenesis of CF-like lung disease remains unclear. Therefore, we hypothesized that airway epithelial cell-specific deficiency of EGFR mitigates mucoinflammatory responses in Scnn1b-transgenic (Tg+) mice that phenocopy human CF-like lung disease. To test this hypothesis, we examined the effect of airway epithelial cell-specific EGFR deficiency on the manifestation of mucoinflammatory outcomes in Tg+ mice. The airway epithelial cell-specific EGFR-deficient wild-type (WT) mice did not exhibit any obvious structural and functional defects in the lungs. The deletion of EGFR in airway epithelial cells in Tg+ mice, however, resulted in increased recruitment of neutrophils and macrophages into the lung airspaces, which was accompanied by significantly increased bronchoalveolar lavage fluid (BALF) levels of inflammatory mediators, including KC, G-CSF, MIP-2, MIP-1α, TNF-α, and MIP-1β. Additionally, as compared with the EGFR-sufficient Tg+ mice, the airway epithelial cell-specific EGFR-deficient Tg+ mice exhibited significantly increased postnatal mortality and compromised bacterial clearance. The deletion of EGFR in the airway epithelial cells of Tg+ mice resulted in an increased degree of mucus obstruction, which was associated with an increase in MCM and MUC5B production. Some of the molecular markers of type 2 inflammation, including Il13, Slc26a4, and Retnla, were significantly increased in airway epithelial cell-specific EGFR-deficient Tg+ mice versus EGFR-sufficient Tg+ mice. Taken together, our data show that EGFR deletion in the airway epithelial cells compromises postnatal survival, delays bacterial clearance, and modulates inflammatory and mucus obstruction-relevant endpoints, i.e., MCM, MUC5B production, and mucus obstruction, in Tg+ mice.

PMID:40406132 | PMC:PMC12094982 | DOI:10.3389/fimmu.2025.1493950

J Cardiovasc Electrophysiol. 2025 May 23. doi: 10.1111/jce.16733. Online ahead of print.

ABSTRACT

BACKGROUND: The long-term success rate of atrial fibrillation (AF) ablation remains a significant clinical challenge, particularly in patients with persistent atrial fibrillation (Persistent AF, PeAF). The recurrence risk in PeAF patients is influenced by various factors, which complicates the prediction of ablation outcomes. While clinical characteristics provide important references for risk assessment, the predictive accuracy of existing methods is limited and they fail to fully leverage the rich information contained in electrocardiogram (ECG) signals. Integrating clinical features with ECG signals holds promise for enhancing recurrence prediction accuracy and supporting personalized management.

METHODS: This study conducted a retrospective analysis of PeAF patients who underwent radiofrequency catheter ablation treatment between 2016 and 2019. A multimodal fusion framework based on a residual block network structure was proposed, integrating preprocedural AF rhythm 12-lead ECG signals, clinical scores, and baseline characteristics of the patients to construct a deep learning model for predicting the risk of postablation recurrence in PeAF patients. A fivefold cross-validation method was used to partition the data set for model training and testing.

RESULTS: The fusion model was evaluated on a cohort of 77 PeAF patients, achieving good predictive performance with an average AUC of 0.74, and a maximum of 0.82. It significantly outperformed traditional clinical scoring systems and single-modal models based solely on ECG signals. Additionally, the model demonstrated lower variance (0.08), reflecting its robustness and stability with small sample sizes.

CONCLUSION: This study innovatively combines AF rhythm ECG signals with clinical characteristics to construct a deep learning model for predicting the recurrence risk in PeAF patients after radiofrequency catheter ablation. The results show that this method effectively improves prediction performance and provides support for personalized clinical decision-making, with significant potential for clinical application.

PMID:40406972 | DOI:10.1111/jce.16733

Neuroradiol J. 2025 May 23:19714009251345108. doi: 10.1177/19714009251345108. Online ahead of print.

ABSTRACT

Background and purpose: This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Image Reconstruction (DLIR)-ability to improve image quality and reduce radiation dose compared to the established standard of Adaptive Statistical Iterative Reconstruction-V (ASIR-V), in unenhanced head computed tomography (CT). Materials and methods: A retrospective analysis of 163 consecutive unenhanced head CTs was conducted. Image quality assessment was computed on the objective parameters of Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), derived from 5 regions of interest (ROI). The evaluation of DLIR dose reduction abilities was based on the analysis of the PACS derived parameters of dose length product and computed tomography dose index volume (CTDIvol). Results: Following the application of rigorous criteria, the study comprised 35 patients. Significant image quality improvement was achieved with the implementation of DLIR, as evidenced by up to a 145% and 160% increase in SNR in supra- and infratentorial regions, respectively. CNR measurements further confirmed the superiority of DLIR over ASIR-V, with an increase of 171.5% in the supratentorial region and a 59.3% increase in the infratentorial region. Despite the signal improvement and noise reduction DLIR facilitated radiation dose reduction of up to 44% in CTDIvol. Conclusion: Implementation of DLIR in head CT scans enables significant image quality improvement and dose reduction abilities compared to standard ASIR-V. However, the dose reduction feature was proven insufficient to counteract the lack of gantry angulation in wide-detector scanners.

PMID:40406852 | DOI:10.1177/19714009251345108

Front Plant Sci. 2025 May 8;16:1615289. doi: 10.3389/fpls.2025.1615289. eCollection 2025.

NO ABSTRACT

PMID:40406730 | PMC:PMC12095160 | DOI:10.3389/fpls.2025.1615289