Respir Med. 2025 May 23:108177. doi: 10.1016/j.rmed.2025.108177. Online ahead of print.

ABSTRACT

BACKGROUND: The role of Cystic fibrosis transmembrane conductance regulator (CFTR) dysfunction in non-cystic fibrosis lung diseases, including COPD, is not well understood. The objective of this study was to assess the prevalence of intermediate sweat chloride levels, 30-59 mmol/L, in healthy young adults and the relationship between sweat chloride and pulmonary function.

METHODS: Healthy volunteers > 18 years of age were enrolled in this single center, prospective, cross-sectional pilot study. Sweat chloride testing was performed by pilocarpine iontophoresis. Study participants completed the ATS-DLD LHS-III modified general respiratory symptom questionnaire, spirometry pre- and post- inhaled bronchodilator, and Lung Clearance Index.

RESULTS: 93 subjects were enrolled. 1 subject withdrew and 2 had insufficient sweat volumes collected. Median (IQR) age was 27 years (25, 33) and 40% were male. Median (IQR) sweat chloride was 21 mmol/L (12, 29). 25/90 subjects (28%) had intermediate sweat chloride values, median 37 (33, 40) mmol/L. 60% of individuals with intermediate sweat chloride values were male as compared to 34% of individuals with normal sweat chloride values, p<0.001. Median FEV1 (% predicted) was 100 (90, 109), FEV1/FVC 0.83 (0.81, 0.86), and LCI was 6.01 (5.38, 6.98). There were no differences in pulmonary function between those with normal and intermediate sweat chloride values.

CONCLUSIONS: A significant number of healthy young adults have intermediate sweat chloride levels, but no differences in spirometry and LCI were found. Larger studies, including genetic analyses, are needed to determine if mild CFTR dysfunction impacts respiratory health, especially in older individuals with respiratory co-morbidities.

PMID:40414315 | DOI:10.1016/j.rmed.2025.108177

Fetal Diagn Ther. 2025 May 23:1-12. doi: 10.1159/000546547. Online ahead of print.

ABSTRACT

INTRODUCTION: Cystic fibrosis (CF) is a severe genetic disorder with a carrier frequency of 1 in 30. In Denmark, prenatal testing is offered when there is a family history of CF or ultrasound anomalies suggest an increased risk of the disease. This study evaluates indications for prenatal CF testing and their outcomes.

METHODS: Clinical indications and genetic results were analyzed for pregnancies undergoing CF testing in the Central Denmark Region between August 2012 to 2023. The data were categorized according to clinical indication.

RESULTS: Among 302 prenatal CF tests, echogenic bowel was the most frequent (44.7%, N = 135), leading to identification of one CF-affected fetus (0.7%). The second most common indication was EB together with non-CF-associated ultrasound findings (29.5%, N = 89), with no CF-affected fetuses. Known CF predisposition due to family history (15.6%, N = 47) identified five affected fetuses (10.6%) and 25 carriers (53.2%). No CF cases were detected in other included groups (N = 31).

CONCLUSION: This data shows that echogenic bowel has a low positive predictive value for cystic fibrosis in the fetus (1:230) in a country with combined first trimester screening but no systematic pre-conception or prenatal screening program for cystic fibrosis. Although the relative risk is increased compared to the general population (1:2,500), echogenic bowel appears to be a marker of limited clinical utility. In settings without dedicated CF screening strategies, this underscores the importance of considering the most effective allocation of diagnostic resources.

PMID:40414202 | DOI:10.1159/000546547

Clin Oral Investig. 2025 May 26;29(6):311. doi: 10.1007/s00784-025-06397-z.

ABSTRACT

OBJECTIVE: The determination of the mid-sagittal plane (MSP) on three-dimensional (3D) head imaging is key to the assessment of facial asymmetry. The aim of this study was to evaluate the reliability of an automated landmark-based MSP to quantify mandibular asymmetry on head computed tomography (CT) scans.

MATERIALS AND METHODS: A dataset of 368 CT scans, including orthognathic surgery patients, was automatically annotated with 3D cephalometric landmarks via a previously published deep learning-based method. Five of these landmarks were used to automatically construct an MSP orthogonal to the Frankfurt horizontal plane. The reliability of automatic MSP construction was compared with the reliability of manual MSP construction based on 6 manual localizations by 3 experienced operators on 19 randomly selected CT scans. The mandibular asymmetry of the 368 CT scans with respect to the MSP was calculated and compared with clinical expert judgment.

RESULTS: The construction of the MSP was found to be highly reliable, both manually and automatically. The manual reproducibility 95% limit of agreement was less than 1 mm for -y translation and less than 1.1° for -x and -z rotation, and the automatic measurement lied within the confidence interval of the manual method. The automatic MSP construction was shown to be clinically relevant, with the mandibular asymmetry measures being consistent with the expertly assessed levels of asymmetry.

CONCLUSION: The proposed automatic landmark-based MSP construction was found to be as reliable as manual construction and clinically relevant in assessing the mandibular asymmetry of 368 head CT scans.

CLINICAL RELEVANCE: Once implemented in a clinical software, fully automated landmark-based MSP construction could be clinically used to assess mandibular asymmetry on head CT scans.

PMID:40415151 | DOI:10.1007/s00784-025-06397-z

Sci Rep. 2025 May 25;15(1):18151. doi: 10.1038/s41598-025-94772-x.

ABSTRACT

The burgeoning problem of electronic waste (e-waste) management necessitates sophisticated, efficient, and precise classification techniques for recycling and repurposing. To address these critical environmental and health implications, this research delves into a comprehensive analysis of three cutting-edge object detection models: YOLOv5, YOLOv7, and YOLOv8. These models are examined through the lens of efficient e-waste classification, a pivotal step in recycling and repurposing efforts. The 'You Only Look Once' (YOLO) methodology underpins our research, highlighting the distinctive architectural features of each model, including the CSPDarknet53 backbone, PANet, and advanced anchor-free detection. This research approach involved the creation of a specialized image dataset encompassing seven distinct e-waste categories to facilitate the training and validation of these models. The performance of improved and customizable YOLOv5, YOLOv7, and YOLOv8 was meticulously evaluated across various parameters such as precision, recall, speed, and training efficiency. This evaluation explores the architectural nuances of each model and its efficacy in accurately detecting diverse e-waste components. The standout performer, YOLOv8, demonstrated exceptional capabilities with its enhanced feature pyramid networks and improved CSPDarknet53 backbone with 53 convolutional layers, achieving superior precision and accuracy. Notably, this model showcased a significant reduction in training time while leveraging the computational power of the Tesla T4 GPU on Google Colab. However, the research also identified challenges, particularly in object orientation detection, suggesting avenues for future refinement. This study underscores the vital role of advanced YOLO architectures in e-waste management, providing critical insights into their practical viability, applicability in real-world scenarios, and potential limitations. By setting a benchmark in real-time object detection, our work paves the way for future innovations and improvements in environmental management technologies, specifically tailored to meet the escalating challenge of e-waste management.

PMID:40415121 | DOI:10.1038/s41598-025-94772-x

Sci Rep. 2025 May 25;15(1):18137. doi: 10.1038/s41598-025-03677-2.

ABSTRACT

This study presents StarEye, a lightweight deep learning model designed for underwater human body detection (UHBD) that addresses the challenges of complex underwater environments. The proposed model incorporates several innovative components: a comprehensive underwater dataset construction methodology, a StarBlock-based backbone structure for efficient feature extraction, a Context Anchor Attention (CAA) mechanism integrated into both backbone and neck components, and a Shared Convolution Batch Normalization (SCBN) detection head. Extensive experiments demonstrate that StarEye achieves 91.1% precision, 88.6% recall, and 95.1% mAP50 while reducing the model size to 3.8MB (16.9% of the original size). The model maintains robust performance across various underwater conditions, including poor visibility, varying illumination, and biological interference. The results indicate that StarEye effectively balances model efficiency and detection accuracy, making it particularly suitable for mobile device deployment in underwater scenarios.

PMID:40415110 | DOI:10.1038/s41598-025-03677-2

Sci Rep. 2025 May 25;15(1):18262. doi: 10.1038/s41598-025-03141-1.

ABSTRACT

Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strategies using normal saline, Ringer's lactate, and albumin, and to determine which patient factors are associated with improved outcomes. We analyzed septic shock patients from the MIMIC-IV database, categorizing them by the fluid administered: normal saline, Ringer's lactate, albumin, or their combinations. A deep learning-based causal inference model estimated treatment effects on in-hospital mortality and kidney outcomes (defined as a doubling of creatinine or the initiation of kidney replacement therapy). Multivariable logistic regression was then applied to the individual treatment effects to identify patient characteristics linked to better outcomes for Ringer's lactate and additional albumin infusion compared to normal saline alone. Among 13,527 patients, 17.8% experienced in-hospital mortality and 16.2% developed kidney injury. Ringer's lactate reduced mortality by 2.33% and kidney injury by 1.41% compared to normal saline. Adding albumin to normal saline further reduced mortality by 1.20% and kidney outcomes by 0.71%. The combination of Ringer's lactate and albumin provided the greatest benefit (mortality: -3.07%, kidney injury: -3.00%). Patients with high SOFA scores, low albumin, or high lactate levels benefited more from normal saline, whereas those with low eGFR or on vasopressors were less likely to benefit from albumin. Ringer's lactate, particularly when combined with albumin, is superior to normal saline in reducing mortality and kidney injury in septic shock patients, underscoring the need for personalized fluid management based on patient-specific factors.

PMID:40415107 | DOI:10.1038/s41598-025-03141-1

Sci Rep. 2025 May 25;15(1):18202. doi: 10.1038/s41598-025-02846-7.

ABSTRACT

Numerous contemporary computer-aided disease detection methodologies predominantly depend on feature engineering techniques; yet, they possess several drawbacks, including the presence of redundant features and excessive time consumption. Conventional feature engineering necessitates considerable manual effort, resulting in issues from superfluous features that diminish the model's performance potential. In contrast to recent effective deep-learning models, these may address these issues while concurrently obtaining and capturing intricate structures inside extensive medical image datasets. Deep learning models autonomously develop feature extraction abilities but require substantial computational resources and extensive datasets to yield significant abstraction methods. The dimensionality problem is a key challenge in healthcare research. Despite the hopeful advancements in illness identification with deep learning architectures in recent years, attaining high performance remains notably tough, particularly in scenarios with limited data or intricate feature spaces. This research endeavors to elucidate the integration of bio-inspired optimization techniques that improve disease diagnostics through deep learning models. The targeted feature selection of bio-inspired methods enhances computational efficiency and operational efficacy by minimizing model redundancy and computational costs, particularly when data availability is constrained. These algorithms employ natural selection and social behavior models to efficiently explore feature spaces, enhancing the robustness and generalizability of deep learning systems. This paper seeks to elucidate the efficacy of deep learning models in medical diagnostics by employing concepts and strategies derived from biological system ontologies, such as genetic algorithms, particle swarm optimization, ant colony optimization, artificial immune systems, and swarm intelligence. Bio-inspired methodologies have exhibited significant potential in addressing critical challenges in illness detection across many data types. It seeks to tackle the problem by creating bio-inspired optimization methods to enhance efficient and equitable deep learning for illness diagnosis. This work assists researchers in selecting the most effective bio-inspired algorithm for disease categorization, prediction, and the analysis of high-dimensional biomedical data.

PMID:40415068 | DOI:10.1038/s41598-025-02846-7

Sci Rep. 2025 May 25;15(1):18238. doi: 10.1038/s41598-025-01920-4.

ABSTRACT

Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning algorithms, deep learning, and transfer learning models, issues with diagnostic accuracy and minimizing diagnostic errors persist. This paper introduces MobNAS, a model that uses MobileNetV2 and NASNetLarge to sort breast cancer images into benign, malignant, or normal classes. The study employs a multi-class classification design and uses a publicly available dataset comprising 1,578 ultrasound images, including 891 benign, 421 malignant, and 266 normal cases. By deploying MobileNetV2, it is easy to work well on devices with less computational capability than is used by NASNetLarge, which enhances its applicability and effectiveness in other tasks. The performance of the proposed MobNAS model was tested on the breast cancer image dataset, and the accuracy level achieved was 97%, the Mean Absolute Error (MAE) was 0.05, and the Matthews Correlation Coefficient (MCC) was 95%. From the findings of this research, it is evident that MobNAS can enhance diagnostic accuracy and reduce existing shortcomings in breast cancer detection.

PMID:40415060 | DOI:10.1038/s41598-025-01920-4

Sci Rep. 2025 May 25;15(1):18186. doi: 10.1038/s41598-025-02590-y.

ABSTRACT

Due to the diverse molecular structures of chemical compounds and their intricate biological pathways of toxicity, predicting their reproductive and developmental toxicity remains a challenge. Traditional Quantitative Structure-Activity Relationship models that rely on molecular descriptors have limitations in capturing the complexity of reproductive and developmental toxicity to achieve high predictive performance. In this study, we developed a descriptor-free deep learning model by constructing a Graph Convolutional Network designed with multi-head attention and gated skip-connections to predict reproductive and developmental toxicity. By integrating structural alerts directly related to toxicity into the model, we enabled more effective learning of toxicologically relevant substructures. We built a dataset of 4,514 diverse compounds, including both organic and inorganic substances. The model was trained and validated using stratified 5-fold cross-validation. It demonstrated excellent predictive performance, achieving an accuracy of 81.19% on the test set. To address the interpretability of the deep learning model, we identified subgraphs corresponding to known structural alerts, providing insights into the model's decision-making process. This study was conducted in accordance with the OECD principles for reliable Quantitative Structure-Activity Relationship modeling and contributes to the development of robust in silico models for toxicity prediction.

PMID:40415056 | DOI:10.1038/s41598-025-02590-y

Sci Rep. 2025 May 25;15(1):18256. doi: 10.1038/s41598-025-02833-y.

ABSTRACT

In response to the demand for efficient and accurate recognition of traffic police gestures by driverless vehicles, this paper introduces a novel traffic police gesture recognition framework (Novel Traffic Police Gesture Recognizer, NTPGR). Initially, keypoints related to traffic police gestures are extracted using the Efficient Progressive Feature Fusion Network (EPFFNet), followed by feature modeling and fusion to enable the recognition network to better learn the temporal characteristics of gestures. Additionally, a convolution network branch and a hybrid attention branch are incorporated to further extract skeleton information from the traffic police gesture data, assign different temporal weights to key frames, and enhance the focus on important channels. Finally, in conjunction with Long Short Term Memory (LSTM), a multi-branch gesture recognition network, termed the Multi-Sequence Gesture Recognition Network (MSNet), is proposed to facilitate the integration of three branches of gesture features, thereby enhancing the targeted extraction of temporal characteristics in traffic police gestures. Experimental results indicate that NTPGR achieves 97.56% and 96.76% accuracy on the Police Gesture Dataset and UTD-MHAD Dataset, respectively, as well as average response times of 0.76s and 0.74s. It not only recognizes traffic police gestures in real-time with high efficiency but also demonstrates strong robustness and Credibility in recognizing gestures in complex environments and dynamic scenarios.

PMID:40415045 | DOI:10.1038/s41598-025-02833-y

Commun Biol. 2025 May 25;8(1):798. doi: 10.1038/s42003-025-08202-9.

ABSTRACT

Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when many structures are obtained from datasets with conformational heterogeneity. Here we present a model refinement protocol that automatically generates series of molecular models from CryoEM datasets, which describe the dynamics of the macromolecular system and have near-perfect geometry scores. This method makes it easier to interpret the movement of the protein complex from heterogeneity analysis and to compare the structural dynamics observed from CryoEM data with results from other experimental and simulation techniques.

PMID:40415012 | DOI:10.1038/s42003-025-08202-9

Sci Rep. 2025 May 25;15(1):18148. doi: 10.1038/s41598-025-00642-x.

ABSTRACT

Sustainable agriculture is an approach that involves adopting and developing agricultural practices to increase efficiency and preserve resources, both environmentally and economically. Jute is one of the primary sources of income grown in many countries. At this stage, increasing efficiency in jute production and protecting it from pests is essential. Detecting jute pests at an early stage will not only improve crop yield but also provide more income. In this paper, an artificial intelligence-based model was suggested to detect jute pests at an early stage. In this developed model, two different pre-trained models were used for feature extraction. To improve the performance of the developed model, the features obtained using the DarkNet-53 and DenseNet-201 models were combined. After this stage, the metaheuristic Mountain Gazelle Optimizer (MGO) was used, allowing the developed model to work faster and achieve more successful results. Feature selection was carried out using MGO; thus, more successful results were obtained with fewer, more compelling features. The proposed model was compared with six different models and five different classifiers accepted in the literature. In the developed model, 17 different jute pests were detected with 96.779% accuracy. The accuracy value achieved in the developed model is promising in successfully detecting jute pests.

PMID:40414953 | DOI:10.1038/s41598-025-00642-x

Sci Rep. 2025 May 25;15(1):18229. doi: 10.1038/s41598-025-02599-3.

ABSTRACT

Nanoparticles (NP) have been driving the rapid advancement of nanomedicines in recent decades. However, their wide application also raises safety concerns, particularly their neurotoxicity due to their ability to cross the blood-brain barrier and accumulate in the brain, which remains largely underexplored. Here, we used silica nanoparticles (SiO2 NP) as a model to study the neurotoxicity of nanomedicine, based on their general features and functionalities. Using the light/dark preference behavioral assays of larval zebrafish, we focused on the neurotoxic consequences of exposure to an array of low concentrations of SiO2 NP, which reflected real-world conditions compared to previous studies, and examined the effect of different exposure durations. We observed dose-dependent and temporally sensitive changes in locomotor activities and elevated anxiety-related behaviors after exposure. Strikingly, exposed animals exhibited biphasic alteration: hypo-locomotion after 24-hour exposure and hyper-locomotion after 48-hour exposure. Our work provided real-world relevant behavioral insights, and highlighted the biphasic response and the temporal sensitivity of the SiO2 NP neurotoxicity. These findings underscore the potential neurotoxic risks of nanomedicine applications and emphasize the urgent need for further research into NP-associated neurotoxicity and public awareness.

PMID:40414979 | DOI:10.1038/s41598-025-02599-3

Am J Clin Pathol. 2025 May 25:aqaf047. doi: 10.1093/ajcp/aqaf047. Online ahead of print.

ABSTRACT

OBJECTIVE: TP53 mutations, including missense and inactivating (frameshift, splice site, and nonsense) mutations, occur in approximately 10% of myeloid neoplasms and confer adverse outcomes. Classification of myeloid neoplasms by World Health Organization and International Consensus Classification standards recognizes the importance of early detection of TP53 mutations. p53 immunohistochemistry (IHC) is a widely accessible method used to detect mutations; however, previous studies have demonstrated variable accuracy, especially for inactivating TP53 mutations. Recently, sequencing using targeted panels has seen increased use. Although highly accurate, sequencing is resource intensive and not universally available.

METHODS: Using 134 bone marrow samples from patients with acute myeloid leukemia evaluated for TP53 mutation by sequencing, we assessed the concordance of p53 IHC with sequencing as well as the interrater-reliability for IHC intensity and percent positivity.

RESULTS: Consistent with previous studies, we found that p53 IHC was strongly specific and modestly sensitive for missense mutations and that overall performance improved with dedicated hematopathology training. We also found that IHC performed poorly for inactivating mutations and was even variable between cases harboring identical amino acid changes. Low predicted transcriptional activity of p53 missense proteins correlated with a mutant pattern of IHC staining. The status of the second allele and variant allele frequency also affected the accuracy of p53 IHC as a surrogate for TP53 allele status.

CONCLUSION: Cases of acute myeloid leukemia with TP53 mutations predicted to have low transcriptional activity showed reduced overall survival. Our results demonstrate limited practical utility of p53 IHC for accurate evaluation of TP53 mutation status because of multifactorial confounders.

PMID:40414698 | DOI:10.1093/ajcp/aqaf047

Neurosci Lett. 2025 May 23:138272. doi: 10.1016/j.neulet.2025.138272. Online ahead of print.

ABSTRACT

Meditation training has been shown to improve physical and mental health and promote neural plasticity, but more research is needed on the relationships between these effects. This study analyzed the Resting State Functional Connectivity (RSFC) of posterior cingulate cortex (PCC) among 94 chronically stressed but otherwise healthy adults randomized 1:1:1 to receive eight weeks of in-person one-on-one interventions focused either on meditation (n = 32), yoga (n = 31), or stress education (n = 31). We found only in the meditation arm, there was a significant reduction of PCC RSFC with the left hippocampus (p < 0.05, FWE corrected). Post-intervention changes of PCC-hippocampal RSFC were significantly (all p ≤ 0.01) correlated with changes of perceived stress (r = 0.54), allostatic load index (r = 0.58), and NF-κB anti-inflammatory gene expression (r = -0.55), suggesting the neural effects of meditation are closely associated with biomarkers of physical wellness. No significant changes with PCC RSFC were observed within the yoga or stress education arm, suggesting this neurobiological mechanism might be unique to meditation training.

PMID:40414454 | DOI:10.1016/j.neulet.2025.138272

Lancet Healthy Longev. 2025 May 21:100681. doi: 10.1016/j.lanhl.2024.100681. Online ahead of print.

ABSTRACT

BACKGROUND: Intrinsic capacity-the composite of all the physical and mental capacities of an individual-has been proposed by WHO as a marker of healthy ageing. However, the association of movement behaviours (physical activity and sedentary behaviour) with intrinsic capacity remains largely unexplored. We aimed to prospectively analyse the association of movement behaviours with intrinsic capacity in older adults.

METHODS: The Seniors-ENRICA-2 prospective, population-based study included a cohort of male and female community-dwelling older adults aged 65-94 years living in Spain. Accelerometer-based levels of sedentary, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) were assessed at baseline. An intrinsic capacity composite score (with higher scores indicating higher intrinsic capacity) was calculated at baseline and at two follow-up assessments across six domains: vitality (handgrip strength, appetite, and weight loss), cognition (Mini-Mental State Examination), psychological (Geriatric Depression Scale), locomotion (Short Physical Performance Battery), vision, and hearing.

FINDINGS: Between Dec 2, 2015, and Nov 23, 2017, 3273 participants were recruited to the Seniors-ENRICA-2 study. 2477 (75·7%) of 3273 participants had complete data for movement behaviours and intrinsic capacity at baseline and were therefore included in the analyses. 1314 (53·0%) of 2477 participants were female and 1163 (47·0%) were male. 1463 (59·1%) of 2477 participants provided follow-up data over a median of 2·3 years (IQR 2·1 to 2·5) and 940 over 5·5 years (5·2 to 5·8). When analysed as a continuous variable, higher levels of MVPA (mean percentage change [MPC] per 15 min 0·63%, 95% CI 0·06 to 1·21), but not LPA (-0·39%,-0·85 to 0·07), were associated with improvements in intrinsic capacity during follow-up, whereas higher levels of sedentary behaviour were associated with declines in intrinsic capacity (-0·29%, -0·57 to -0·01). Analyses by tertiles of physical activity confirmed that the highest (MPC 4·83%, 95% CI 1·98 to 7·75) and intermediate (5·44%, 2·52 to 8·45) tertiles of MVPA were associated with improvements in intrinsic capacity compared with the lowest tertile. By contrast, compared with the highest tertile, the lowest (MPC 5·48%, 95% CI 2·88 to 8·02) and intermediate (5·73%, 3·16 to 8·22) tertiles of sedentary behaviour were associated with improvements in intrinsic capacity.

INTERPRETATION: Sedentary behaviour was associated with a reduction of intrinsic capacity, and MVPA (but not LPA) was associated with an improvement in intrinsic capacity in older adults. Our findings support the importance of promoting physical activity and reducing sedentary behaviour for healthy ageing.

FUNDING: Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, French Agence Nationale de la Recherche, European Regional Development Fund/European Social Fund, Fondo de Investigaciones Sanitarias, the EU NextGenerationEU/Plan de Recuperación, and Transformación y Resiliencia.

PMID:40414228 | DOI:10.1016/j.lanhl.2024.100681

Sci Rep. 2025 May 25;15(1):18175. doi: 10.1038/s41598-025-03192-4.

ABSTRACT

The aim of this study is to evaluate the real-world safety of brodalumab by analyzing adverse events (AEs) associated with the drug. The AE reports related to brodalumab from the FAERS database from 2017 Q1 to 2023 Q4 were collected. Subsequently, we employed four disproportionality analysis methods to identify positive signals among AEs associated with brodalumab, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS). In 1480 AE reports with brodalumab as the primary suspected drug, 168 preferred terms (PTs) exhibiting positive signals were identified. This study confirmed several known positive AEs, such as injection site vesicles and injection site hemorrhage. In addition, the study identified several positive AEs not listed in the drug product information, including palmoplantar pustulosis and extranodal marginal zone b-cell lymphoma (malt type). This study evaluated the real-world safety profile of brodalumab and identified several unexpected AEs, such as palmoplantar pustulosis and extranodal marginal zone b-cell lymphoma (malt type). These findings provide new safety insights for clinicians and may contribute to the safer and more rational use of brodalumab in clinical practice.

PMID:40414978 | DOI:10.1038/s41598-025-03192-4

Can J Ophthalmol. 2025 May 22:S0008-4182(25)00243-1. doi: 10.1016/j.jcjo.2025.05.005. Online ahead of print.

ABSTRACT

OBJECTIVE: To identify potential teratogenic medication associated with optic nerve hypoplasia (ONH) and/or septo-optic dysplasia (SOD), by screening the Food and Drug Administration Adverse Events Reporting System (FAERS) database.

DESIGN: Retrospective pharmacovigilance study using disproportionality signal detection methods.

PARTICIPANTS: Adverse event reports submitted to FAERS between Q1 2004 and Q3 2024. Reports were included if ONH or SOD was listed as an adverse event and drug exposure occurred in utero.

METHODS: A qualitative assessment evaluated patient demographics, and a disproportionality analysis covered pharmacovigilance signal detection and drug-event reporting frequencies. Pharmacovigilance algorithms that were applied to determine the statistical significance of signals included the proportional reporting ratio (PRR), chi-squared with Yates' correction (χ2), reporting odds ratio (ROR), empirical Bayes geometric mean (EBGM), and information component (IC).

RESULTS: A total of 103 adverse event reports for ONH and/or SOD were identified. The 75 cases reporting prenatal medication exposure were included. Twenty-three reports were of male patients, 13 reports of female patients, and 39 of unspecified gender. Thirty drugs were implicated as primary suspect drugs. Diazepam was the most reported primary suspect medication (n = 15; 20%) followed by methadone and citalopram (n = 8; 11%). The disproportionality analysis showed a positive signal with one medication: diazepam (n = 15; PRR = 82.24; χ2 = 1008.66, ROR 95% CI: 102.55 [56.75-185.33], EBGM [EBGM05]: 48.45 [28.16], IC [IC05]: 4.46 [3.67]).

CONCLUSIONS: A possible association was found between prenatal diazepam exposure and ONH/SOD. Further investigation is required to confirm this relationship and drug safety profiles.

PMID:40414255 | DOI:10.1016/j.jcjo.2025.05.005

Clin Pharmacokinet. 2025 May 25. doi: 10.1007/s40262-025-01528-x. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Nortriptyline, a tricyclic antidepressant, has an important role in the pharmacotherapy of major depressive disorder (MDD). Individualized dosing approaches, such as pharmacogenetics-based and phenotype-based dosing, may enhance early achievement of therapeutic plasma concentrations, but their comparative accuracy has not been investigated. Our objective was to compare the accuracy of three nortriptyline dosing strategies: pharmacogenetics-based, phenotype-based, and standard dosing.

METHODS: Using pharmacokinetic modeling based on data from a randomized controlled trial, we assessed and compared the following dosing strategies: pharmacogenetics-based dosing depending on the cytochrome P-450 (CYP) 2D6 genotype, phenotype-based dosing determined by the plasma concentration measured after a single nortriptyline administration, and standard dosing (125 mg/day). A population pharmacokinetic model was developed to assess phenotype-based dosing recommendations. We evaluated the dosing strategies by comparing the number of participants with predicted therapeutic, subtherapeutic, and supratherapeutic plasma concentrations using Chi-squared (χ2) tests. Variability in plasma concentrations was assessed using F-tests.

RESULTS: Both pharmacogenetics-based (χ2 (1) = 8.0, p = 0.01) and phenotype-based dosing (χ2 (1) = 5.3, p = 0.02) significantly increased the likelihood of achieving therapeutic plasma concentrations compared with standard dosing while reducing plasma concentration variability. No significant difference was found in the prediction of therapeutic concentrations between the two individualized dosing strategies (χ2 (1) = 0.33, p = 0.56).

CONCLUSIONS: Pharmacogenetics-based and phenotype-based dosing demonstrate greater accuracy in predicting therapeutic nortriptyline plasma concentrations than standard dosing. Further research is warranted to explore the clinical application of model-informed precision dosing for nortriptyline and other psychotropic medications.

PMID:40413686 | DOI:10.1007/s40262-025-01528-x

Ann Clin Transl Neurol. 2025 May 25. doi: 10.1002/acn3.70086. Online ahead of print.

ABSTRACT

OBJECTIVES: To investigate whether pulse pressure or mean arterial pressure mediates the relationship between age and white matter hyperintensity load and to examine the mediating effect of white matter hyperintensities on cognition.

METHODS: Demographic information, blood pressure, current medication lists, and Montreal Cognitive Assessment scores for 231 stroke- and dementia-free adults were retrospectively obtained from the Aging Brain Cohort study. Total WMH load was determined from T2-FLAIR magnetic resonance scans using the TrUE-Net deep learning tool for white matter segmentation. In separate models, we used mediation analysis to assess whether pulse pressure or MAP mediates the relationship between age and total white matter hyperintensity load, controlling for cardiovascular confounds. We also assessed whether white matter hyperintensity load mediated the relationship between age and cognitive scores.

RESULTS: Pulse pressure, but not mean arterial pressure, significantly mediated the relationship between age and white matter hyperintensity load. White matter hyperintensity load partially mediated the relationship between age and Montreal Cognitive Assessment score.

INTERPRETATION: Our results indicate that pulse pressure, but not mean arterial pressure, is mechanistically associated with age-related accumulation of white matter hyperintensities, independent of other cardiovascular risk factors. White matter hyperintensity load was a mediator of cognitive scores across the adult lifespan. Effective management of pulse pressure may be especially important for maintenance of brain health and cognition.

PMID:40413732 | DOI:10.1002/acn3.70086