Am J Cardiovasc Dis. 2025 Feb 15;15(1):1-12. doi: 10.62347/YQHQ1079. eCollection 2025.

ABSTRACT

Brugada Syndrome (BrS) is a hereditary cardiac condition associated with an elevated risk of lethal arrhythmias, making precise and prompt diagnosis vital to prevent life-threatening outcomes. The diagnosis of BrS is challenging due to the requirement of invasive drug challenge tests, limited human visual capacity to detect subtle electrocardiogram (ECG) patterns, and the transient nature of the disease. Artificial intelligence (AI) can detect almost all patterns of BrS in ECG, some of which are even beyond the capability of expert eyes. AI is subcategorized into several models, with deep learning being considered the most beneficial, boasting its highest accuracy among the other models. With the capability to discriminate subtle data and analyze extensive datasets, AI has achieved higher accuracy, sensitivity, and specificity compared to trained cardiologists. Meanwhile, AI proficiency in managing complex data enables us to discover unclassified genetic variants. AI can also analyze data extracted from induced pluripotent stem cell-derived cardiomyocytes to distinguish BrS from other inherited cardiac arrhythmias. The aim of this study is to present a synopsis of the evolution of various algorithms of artificial intelligence utilized in the diagnosis of BrS and compare their diagnostic abilities to trained cardiologists. In addition, the application of AI for classification of BrS gene variants is also briefly discussed.

PMID:40124093 | PMC:PMC11928888 | DOI:10.62347/YQHQ1079

ACS Omega. 2025 Mar 4;10(10):10468-10479. doi: 10.1021/acsomega.4c10763. eCollection 2025 Mar 18.

ABSTRACT

Accurate detection and segmentation of esophageal lesions are crucial for diagnosing and treating gastrointestinal diseases. However, early detection of esophageal cancer remains challenging, contributing to a reduced five-year survival rate among patients. This paper introduces a novel multitask deep learning model for automatic diagnosis that integrates classification and segmentation tasks to assist endoscopists effectively. Our approach leverages the MobileNetV2 deep learning architecture enhanced with a mutual attention module, significantly improving the model's performance in determining the locations of esophageal lesions. Unlike traditional models, the proposed model is designed not to replace endoscopists but to empower them to correct false predictions when provided with additional Supporting Information. We evaluated the proposed model on three well-known data sets: Early Esophageal Cancer (EEC), CVC-ClinicDB, and KVASIR. The experimental results demonstrate promising performance, achieving high classification accuracies of 98.72% (F1-score: 98.08%) on CVC-ClinicDB, 98.95% (F1-score: 98.32%) on KVASIR, and 99.12% (F1-score: 99.00%) on our generated EEC data set. Compared to state-of-the-art models, our classification results show significant improvement. For the segmentation task, the model attained a Dice coefficient of 92.73% and an Intersection over Union (IoU) of 91.54%. These findings suggest that the proposed multitask deep learning model can effectively assist endoscopists in evaluating esophageal lesions, thereby alleviating their workload and enhancing diagnostic precision.

PMID:40124037 | PMC:PMC11923690 | DOI:10.1021/acsomega.4c10763

Digit Health. 2025 Mar 21;11:20552076251324446. doi: 10.1177/20552076251324446. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: To conduct a bibliometric analysis using VOSviewer and Citespace to explore the current applications, trends, and future directions of artificial intelligence (AI) in obstructive sleep apnea (OSA).

METHODS: On 13 September 2024, a computer search was conducted on the Web of Science Core Collection dataset published between 1 January 2011, and 30 August 2024, to identify literature related to the application of AI in OSA. Visualization analysis was performed on countries, institutions, journal sources, authors, co-cited authors, citations, and keywords using Vosviewer and Citespace, and descriptive analysis tables were created by using Microsoft Excel 2021 software.

RESULTS: A total of 867 articles were included in this study. The number of publications was low and stable from 2011 to 2016, with a significant increase after 2017. China had the highest number of publications. Alvarez, Daniel, and Hornero, Roberto were the two most prolific authors. Universidad de Valladolid and the IEEE Journal of Biomedical and Health Informatics were the most productive institution and journal, respectively. The top three authors in terms of co-citation frequency are Hassan, Ar, Young, T, and Vicini, C. "Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis" was cited the most frequently. Keywords such as "OSA," "machine learning," "Electrocardiography," and "deep learning" were dominant.

CONCLUSION: AI's application in OSA research is expanding. This study indicates that AI, particularly deep learning, will continue to be a key research area, focusing on diagnosis, identification, personalized treatment, prognosis assessment, telemedicine, and management. Future efforts should enhance international cooperation and interdisciplinary communication to maximize the potential of AI in advancing OSA research, comprehensively empowering sleep health, bringing more precise, convenient, and personalized medical services to patients and ushering in a new era of sleep health.

PMID:40123882 | PMC:PMC11930495 | DOI:10.1177/20552076251324446

Comput Struct Biotechnol J. 2025 Feb 24;27:928-936. doi: 10.1016/j.csbj.2025.02.020. eCollection 2025.

ABSTRACT

High-content imaging (HCI) enables the characterization of cellular states through the extraction of quantitative features from fluorescence microscopy images. Despite the widespread availability of HCI data, the development of generalizable feature extraction models remains challenging due to the heterogeneity of microscopy images, as experiments often differ in channel count, cell type, and assay conditions. To address these challenges, we introduce uniDINO, a generalist feature extraction model capable of handling images with an arbitrary number of channels. We train uniDINO on a dataset of over 900,000 single-channel images from diverse experimental contexts and concatenate single-channel features to generate embeddings for multi-channel images. Our extensive validation across varied datasets demonstrates that uniDINO outperforms traditional computer vision methods and transfer learning from natural images, while also providing interpretability through channel attribution. uniDINO offers an out-of-the-box, computationally efficient solution for feature extraction in fluorescence microscopy, with the potential to significantly accelerate the analysis of HCI datasets.

PMID:40123801 | PMC:PMC11930362 | DOI:10.1016/j.csbj.2025.02.020

Phys Imaging Radiat Oncol. 2025 Feb 23;33:100734. doi: 10.1016/j.phro.2025.100734. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND PURPOSE: Accurate delineation of the gross tumor volume (GTV) is essential for radiotherapy of soft tissue sarcomas. However, manual GTV delineation from multi-modality images is time-consuming. Furthermore, GTV delineation is subject to inter- and intra-reader variability, which reduces the reproducibility of treatment planning. To address these issues, this work aims to develop a highly accurate automatic delineation technique modeling reader variability for soft tissue sarcomas using deep learning.

MATERIALS AND METHODS: We employed a publicly available soft tissue sarcoma dataset consisting of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET), X-ray Computed Tomography (CT), and pre-contrast T1-weighted Magnetic Resonance Imaging (MRI) scans for 51 patients, of which 49 were selected for analysis. The GTVs were delineated by six experienced readers, each reader performing GTV contouring multiple times for every patient. The confidence maps were calculated by averaging the labels provided by all readers, resulting in values ranging from 0 to 1. We developed and trained a diffusion model-based neural network to predict confidence maps of GTV for soft tissue sarcomas from multi-modality medical images.

RESULTS: Quantitative analysis showed that the proposed diffusion model performed competitively with U-Net-based models, frequently ranking first or second across five evaluation metrics: Dice Index, Hausdorff Distance, Recall, Precision, and Brier Score. Additionally, experiments evaluating the impact of different imaging modalities demonstrated that incorporating multi-modality image inputs provided improved performance compared to single-modality and dual-modality inputs.

CONCLUSION: The proposed diffusion model is capable of predicting accurate confidence maps of GTV for soft tissue sarcomas from multi-modality inputs.

PMID:40123775 | PMC:PMC11926426 | DOI:10.1016/j.phro.2025.100734

Shape Med Imaging (2024). 2025;15275:164-177. doi: 10.1007/978-3-031-75291-9_13. Epub 2024 Oct 26.

ABSTRACT

Motion and deformation analysis of cardiac magnetic resonance (CMR) imaging videos is crucial for assessing myocardial strain of patients with abnormal heart functions. Recent advances in deep learning-based image registration algorithms have shown promising results in predicting motion fields from routinely acquired CMR sequences. However, their accuracy often diminishes in regions with subtle appearance changes, with errors propagating over time. Advanced imaging techniques, such as displacement encoding with stimulated echoes (DENSE) CMR, offer highly accurate and reproducible motion data but require additional image acquisition, which poses challenges in busy clinical flows. In this paper, we introduce a novel Latent Motion Diffusion model (LaMoD) to predict highly accurate DENSE motions from standard CMR videos. More specifically, our method first employs an encoder from a pre-trained registration network that learns latent motion features (also considered as deformation-based shape features) from image sequences. Supervised by the ground-truth motion provided by DENSE, LaMoD then leverages a probabilistic latent diffusion model to reconstruct accurate motion from these extracted features. Experimental results demonstrate that our proposed method, LaMoD, significantly improves the accuracy of motion analysis in standard CMR images; hence improving myocardial strain analysis in clinical settings for cardiac patients. Our code is publicly available at https://github.com/jr-xing/LaMoD.

PMID:40123747 | PMC:PMC11929565 | DOI:10.1007/978-3-031-75291-9_13

J Exp Orthop. 2025 Mar 22;12(1):e70195. doi: 10.1002/jeo2.70195. eCollection 2025 Jan.

ABSTRACT

PURPOSE: There has been substantial growth in the literature describing the effectiveness of artificial intelligence (AI) and machine learning (ML) applications in total hip arthroplasty (THA); these models have shown the potential to predict post-operative outcomes using algorithmic analysis of acquired data and can ultimately optimize clinical decision-making while reducing time, cost and complexity. The aim of this review is to analyze the most updated articles on AI/ML applications in THA as well as present the potential of these tools in optimizing patient care and THA outcomes.

METHODS: A comprehensive search was completed through August 2024, according to the PRISMA guidelines. Publications were searched using the Scopus, Medline, EMBASE, CENTRAL and CINAHL databases. Pertinent findings and patterns in AI/ML methods utilization, as well as their applications, were quantitatively summarized and described using frequencies, averages and proportions. This study used a modified eight-item Methodological Index for Non-Randomized Studies (MINORS) checklist for quality assessment.

RESULTS: Nineteen articles were eligible for this study. The selected studies were published between 2016 and 2024. Out of the various ML algorithms, four models have proven to be particularly significant and were used in almost 20% of the studies, including elastic net penalized logistic regression, artificial neural network, convolutional neural network (CNN) and multiple linear regression. The highest area under the curve (=1) was reported in the preoperative planning outcome variable and utilized CNN. All 20 studies demonstrated a high level of quality and low risk of bias, with a modified MINORS score of at least 7/8 (88%).

CONCLUSIONS: Developments in AI/ML prediction models in THA are rapidly increasing. There is clear potential for these tools to assist in all stages of surgical care as well as in challenges at the broader hospital administrative level and patient-specific level.

LEVEL OF EVIDENCE: Level III.

PMID:40123682 | PMC:PMC11929018 | DOI:10.1002/jeo2.70195

Ther Adv Cardiovasc Dis. 2025 Jan-Dec;19:17539447251321589. doi: 10.1177/17539447251321589. Epub 2025 Mar 24.

ABSTRACT

OBJECTIVE: The goal of this study was to compare the computed tomography angiography scans of the segmentation results from the Cvpilot, 3mensio, and Volume Viewer systems to explore the practicability of the Cvpilot system in the automatic segmentation and technical evaluation of the aortic root before transcatheter aortic valve replacement (TAVR).

DESIGN: A total of 154 patients who underwent TAVR at our center from January 2022 to May 2023 were enrolled, and their computed tomography angiography images were analyzed using the Cvpilot, 3mensio, and Volume Viewer systems, respectively.

SETTING: Not applicable.

PARTICIPANTS: Not applicable.

MAIN OUTCOME MEASURES: The reconstructed computed tomography angiography images were evaluated by experts, and the measurements of the aortic roots were analyzed statistically.

RESULTS: Compared with the 3mensio system, 92.2% of patients (n = 142) evaluated with the Cvpilot system reached grade A, 5.2% of patients (n = 8) reached grade B, and 2.6% of patients (n = 4) reached grade C. Compared with the Volume Viewer system, 90.9% of patients (n = 140) evaluated with the Cvpilot system achieved grade A, 7.1% of patients (n = 11) achieved grade B, and 2.0% of patients (n = 3) achieved grade C. Furthermore, there was no significant difference among the measurement results of the Cvpilot, 3mensio, and Volume Viewer systems (all p > 0.05).

CONCLUSION: Overall, the Cvpilot system is effective and reliable. It can accurately complete the segmentation and the measurement of aortic root structures, thereby effectively improving the measurement quality before TAVR.

TRIAL REGISTRATION: Not applicable.

PMID:40123453 | DOI:10.1177/17539447251321589

Clin EEG Neurosci. 2025 Mar 23:15500594251328068. doi: 10.1177/15500594251328068. Online ahead of print.

ABSTRACT

Objective: Schizophrenia is a chronic mental disorder marked by symptoms such as hallucinations, delusions, and cognitive impairments, which profoundly affect individuals' lives. Early detection is crucial for improving treatment outcomes, but the diagnostic process remains complex due to the disorder's multifaceted nature. In recent years, EEG data have been increasingly investigated to detect neural patterns linked to schizophrenia. Methods: This study presents a deep learning framework that integrates both raw multi-channel EEG signals and their spectrograms. Our two-branch model processes these complementary data views to capture both temporal dynamics and frequency-specific features while employing depth-wise convolution to efficiently combine spatial dependencies across EEG channels. Results: The model was evaluated on two datasets, consisting of 84 and 28 subjects, achieving classification accuracies of 0.985 and 0.994, respectively. These results highlight the effectiveness of combining raw EEG signals with their time-frequency representations for precise and automated schizophrenia detection. Additionally, an ablation study assessed the contributions of different architectural components. Conclusions: The approach outperformed existing methods in the literature, underscoring the value of utilizing multi-view EEG data in schizophrenia detection. These promising results suggest that our framework could contribute to more effective diagnostic tools in clinical practice.

PMID:40123224 | DOI:10.1177/15500594251328068

ACS Sens. 2025 Mar 23. doi: 10.1021/acssensors.5c00254. Online ahead of print.

ABSTRACT

Various sensing methods have been developed for food spoilage research, but in practical applications, the accuracy of these methods is frequently constrained by the limitation of single-source data and challenges in cross-validating multimodal data. To address these issues, a new method combining multidimensional sensing technology with deep learning-based dynamic fusion has been developed, which can precisely monitor the spoilage process of beef. This study designs a gas sensor based on surface-enhanced Raman scattering (SERS) to directly analyze volatile organic compounds (VOCs) adsorbed on MIL-101(Cr) with amine-specific adsorption for data collection while also evaluating the moisture distribution of beef through low-field nuclear magnetic resonance (LF-NMR), providing multidimensional recognition and readings. By introducing the self-attention mechanism and SENet scaling features into the multimodal deep learning model, the system is able to adaptively fuse and focus on the important features of the sensors. After training, the system can predict the storage time of beef under controlled storage conditions, with an R2 value greater than 0.98. Furthermore, it can provide accurate freshness assessments for beef samples under unknown storage conditions. Relative to single-modality methods, accuracy improves from 90 to over 97%. Overall, the newly developed dynamic fusion deep learning multimodal model effectively integrates multimodal information, enabling the fast and reliable monitoring of beef freshness.

PMID:40123082 | DOI:10.1021/acssensors.5c00254

Anal Chem. 2025 Mar 23. doi: 10.1021/acs.analchem.4c06786. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible fatal disease, the prevalence of which has been increasing in recent years. Nonradiographic and noninvasive early diagnosis of pulmonary fibrosis could improve prognosis but is a formidable challenge. As one of the fundamental microenvironmental parameters, viscosity is relevant to various pathological states, such as acute inflammation. Nevertheless, the potential biological roles of viscosity during the IPF process have been relatively underexplored. To address this issue, herein, we developed a new viscosity-responsive probe (JZ-2), which displayed high sensitivity and selectivity for viscosity, as well as excellent characteristics for targeting mitochondria. JZ-2 was successfully applied to map the changes in mitochondrial viscosity in cells caused by various stimuli, such as nystatin and lipopolysaccharide. Besides, JZ-2 was capable of differentiating cancer cells from normal cells and even tissues. More importantly, JZ-2 has been demonstrated to be sufficiently sensitive for tumor detection and early identification of IPF in vivo, revealing a significant increase in the viscosity of lung fibrosis tissues. Thus, JZ-2 is expected to be a swift and reliable diagnostic modality for the prediction of IPF progression in clinical settings.

PMID:40123047 | DOI:10.1021/acs.analchem.4c06786

Sci Rep. 2025 Mar 23;15(1):10009. doi: 10.1038/s41598-025-94801-9.

ABSTRACT

The swift transmission rate and unfavorable prognosis associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have prompted the pursuit of more effective therapeutic interventions. Azithromycin (AZM) has garnered significant attention for its distinctive pharmacological mechanisms in the treatment of SARS-CoV-2. This study aims to elucidate the biological rationale for employing AZM in patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) who are infected with SARS-CoV-2. Genetic data about COVID-19, COPD, and IPF were independently obtained from the GeneCards database. And 40 drug targets about AZM were retrieved from the STITCH database. The analysis revealed that 311 DEGs were common among COPD, IPF, and COVID-19, and we further found eight genes that interacted with AZM targets. We conducted an analysis of hub genes and their corresponding signaling pathways in these patient cohorts. Additionally, we explored the inhibitory effects of AZM on these hub genes. AZM demonstrated a significant inhibitory effect on eight key genes, except for AR and IL-17 A. These findings suggest that AZM may serve as a promising therapeutic agent for patients with COPD and IPF and SARS-CoV-2 infection.

PMID:40122903 | DOI:10.1038/s41598-025-94801-9

Front Cell Dev Biol. 2025 Mar 7;13:1561540. doi: 10.3389/fcell.2025.1561540. eCollection 2025.

ABSTRACT

Transcription factors (TFs) play a crucial role in the regulation of gene expression and the structural organization of chromatin. They interact with proteins, RNA, and chromatin DNA to exert their functions. Therefore, an efficient and straightforward experimental approach that simultaneously captures the interactions of transcription factors with DNA, RNA, and proteins is essential for studying these regulatory proteins. In this study, we developed a novel method, TF-chRDP (Transcription Factor binding Chromatin-associated RNA, DNA, and Protein), which allows for the concurrent capture of these biomolecules in a single experiment. We enriched chromatin complexes using specific antibodies and divided the chromatin into three fractions: one for DNA library preparation to analyze the genomic binding sites of transcription factors, another for RNA library preparation to investigate the RNA associated with transcription factor binding, and the third for proteomic analysis to identify protein cofactors interacting with transcription factors. We applied this method to study the transcription factor p53 and its associated chromatin complexes. The results demonstrated high specificity in the enrichment of DNA, RNA and proteins. This method provides an efficient tool for simultaneously capturing chromatin-associated RNA, DNA and protein bound to specific TF, making it particularly useful for analyzing the role of protein-DNA-RNA complexes in transcriptional regulation.

PMID:40123855 | PMC:PMC11925928 | DOI:10.3389/fcell.2025.1561540

IMA Fungus. 2025 Mar 13;16:e144994. doi: 10.3897/imafungus.16.144994. eCollection 2025.

ABSTRACT

Based on molecular and morphological evidence the new subfamily Pseudobaeosporoideae of the Tricholomataceae is established within the Tricholomatineae for accommodating the unique features of Pseudobaeospora such as gymnocarpic mycenoid/collybioid habit, small-sized spores with thick and dextrinoid wall, and presence of crassobasidia. Twenty-six Pseudobaeospora collections corresponding to eleven species (five types) were newly sequenced. Collections morphologically attributable to P.oligophylla (type of the genus) or to P.pillodii are here sequenced for the first time: accordingly, P.oligophylla is considered as a posterior synonym of P.pillodii. Quélet's original plate is selected as a lectotype for Collybiapillodii and a French collection as its epitype collection. Pseudobaeosporadeceptiva is described as a new species from Italy very close to P.pillodii from which it differs mainly by bigger spores and SSU and LSU rDNA sequences. The presence of P.pyrifera in Italy is documented for the first time and P.mutabilis is reduced to its later synonym. A neotype is established for P.jamonii which is here proved to be an independent species. Finally, a critical review of the characters used for interspecific distinctions in Pseudobaeospora was provided.

PMID:40123765 | PMC:PMC11926610 | DOI:10.3897/imafungus.16.144994

Drug Discov Today. 2025 Apr;30(4):104343. doi: 10.1016/j.drudis.2025.104343. Epub 2025 Mar 22.

ABSTRACT

Drug-device combinations (DDCs) are therapeutic products that integrate drugs with medical devices to enhance treatment efficacy and/or safety. These combinations hold significant promise for rare diseases, which affect millions of patients globally, by improving drug delivery, targeting specific organs, and reducing side effects. However, the regulatory framework for DDCs remains complex and lacks specific incentives for rare diseases, unlike orphan drugs. This review examines regulatory approaches and case studies of DDCs in rare diseases, and highlights specific challenges and untapped opportunities. Moreover, the publication discusses recommendations to overcome these challenges through tailored policies and incentives to unlock the potential of DDCs in the context of rare diseases.

PMID:40122448 | DOI:10.1016/j.drudis.2025.104343

Chin J Nat Med. 2025 Mar;23(3):368-376. doi: 10.1016/S1875-5364(25)60837-5.

ABSTRACT

In this study, we employed a combination of genome mining and heteronuclear single quantum coherence (HSQC)-based small molecule accurate recognition technology (SMART) technology to search for fernane-type triterpenoids. Initially, potential endophytic fungi were identified through genome mining. Subsequently, fine fractions containing various fernane-type triterpenoids were selected using HSQC data collection and SMART prediction. These triterpenoids were then obtained through targeted isolation and identification. Finally, their antifungal activity was evaluated. As a result, three fernane-type triterpenoids, including two novel compounds, along with two new sesquiterpenes and four known compounds were isolated from one potential strain, Diaporthe discoidispora. Their structures were elucidated through analysis of high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) and nuclear magnetic resonance (NMR) spectroscopic data. The absolute configurations were determined using single-crystal X-ray diffraction analysis and electron capture detector (ECD) analysis. Compound 3 exhibited moderate antifungal activity against Candida albicans CMCC 98001 and Aspergillus niger.

PMID:40122666 | DOI:10.1016/S1875-5364(25)60837-5

Thorax. 2025 Mar 23:thorax-2024-221825. doi: 10.1136/thorax-2024-221825. Online ahead of print.

ABSTRACT

INTRODUCTION: Current bronchiectasis guidelines advise against the use of inhaled corticosteroids (ICS) except in patients with associated asthma, allergic bronchopulmonary aspergillosis (ABPA) and/or chronic obstructive pulmonary disease (COPD). This study aimed to describe the use of ICS in patients with bronchiectasis across Europe.

METHODS: Patients with bronchiectasis were enrolled into the European Bronchiectasis Registry from 2015 to 2022. Patients were grouped into ICS users and non-users at baseline and clinical characteristics associated with ICS use were investigated. Patients were followed up for clinical outcomes of exacerbation, hospitalisation and mortality for up to 5 years. We evaluated if elevated blood eosinophil counts (above the laboratory upper limit of normal) modified the effect of ICS on exacerbations.

RESULTS: 19 324 patients were included for analysis and 10 109 (52.3%) were recorded as being prescribed ICS at baseline. After exclusion of patients with a history of asthma, COPD and/or ABPA, 3174/9715 (32.7%) patients with bronchiectasis were prescribed ICS. Frequency of ICS use varied across countries, ranging from 17% to 85% of included patients. ICS users had more severe disease, with significantly worse lung function, higher Bronchiectasis Severity Index scores and more frequent exacerbations at baseline (p<0.0001). Overall, ICS users did not have a reduced risk of exacerbation or hospitalisation during follow-up, but a significant reduction in exacerbation frequency was observed in the subgroup of ICS users with elevated blood eosinophil counts (relative risk 0.70, 95% CI 0.59 to 0.84, p<0.001).

CONCLUSION: ICS use is common in bronchiectasis, including in those not currently recommended ICS according to bronchiectasis guidelines. ICS use may be associated with reduced exacerbation frequency in patients with elevated blood eosinophils.

PMID:40122611 | DOI:10.1136/thorax-2024-221825

Respir Med. 2025 Mar 21:108047. doi: 10.1016/j.rmed.2025.108047. Online ahead of print.

ABSTRACT

INTRODUCTION: Newborn screening (NBS) for cystic fibrosis (CF) facilitates early diagnosis and has been shown to significantly improve long-term clinical outcomes. In this study, we aimed to evaluate the 7-year results of the immunoreactive trypsinogen (IRT)/IRT NBS of Turkey.

METHODS: The study included all CF patients who were born after NBS implementation, and who were enrolled in the CF Registry of Turkey (CFRT) in 2022. Patients were divided into three groups according to NBS results: Group 1 with positive NBS, Group 2 with negative NBS, and Group 3 with no screening or unknown screening results. All clinical and demographic data were compared between the three groups.

RESULTS: A total of 853 patients were included in the study, 668 (78.3%) patients were in Group 1, 90 (10.5%) in Group 2, and 95 (11.2%) in Group 3. The age at diagnosis was 0.17 (0.08-0.33) years in Group 1, 0.50 (0.25-1.0) in Group 2, and 0.33 (0.17-0.75) in Group 3 (p<0.001). The first and second sweat test results and frequency of pancreatic insufficiency were lowest in Group 2 (p<0.05). Median FEV1 (%) was 88 (77-103) in Group 1, 90 (71.5-104) in Group 2, 89.5 (81.75-97.5) in Group 3 (p>0.05). 49% of the patients had a severe genotype and it was detected most frequently in Group 1 (p=0.021).

CONCLUSIONS: Patients with pancreatic sufficiency may be missed by IRT/IRT NBS and lower and negative sweat test results may contribute to delays in CF diagnosis. Approximately 22% of patients are not diagnosed through this screening method.

PMID:40122405 | DOI:10.1016/j.rmed.2025.108047

Fertil Steril. 2025 Mar 21:S0015-0282(25)00162-1. doi: 10.1016/j.fertnstert.2025.03.014. Online ahead of print.

ABSTRACT

Male-factor infertility is a multifactorial, complex, and increasingly common condition, of which genetic factors have more frequently been implicated in. Not only are the causal relationships between genetic variation and male infertility phenotypes understudied, but also the differences in frequency of disease-causing genetic alterations within different geographic and ethnic groups. Guidelines remain inconsistent as to recommended genomic testing during the male infertility workup. Our current fund of knowledge limits our diagnostic capability where the etiology of male infertility remains idiopathic in about 40% of patients, despite advances in genomic sequencing and testing.

PMID:40122225 | DOI:10.1016/j.fertnstert.2025.03.014

Z Med Phys. 2025 Mar 22:S0939-3889(25)00035-2. doi: 10.1016/j.zemedi.2025.02.005. Online ahead of print.

ABSTRACT

OBJECTIVES: High prevalence of visceral obesity and its associated complications underscore the importance of accurately quantifying visceral adipose tissue (VAT) depots. While whole-body MRI offers comprehensive insights into adipose tissue distribution, it is resource-intensive. Alternatively, evaluation of defined single slices provides an efficient approach for estimation of total VAT volume. This study investigates the influence of sex-, age-, and BMI on VAT distribution along the craniocaudal axis and total VAT volume obtained from single slice versus volumetric assessment in 3D MRI and aims to identify age-independent locations for accurate estimation of VAT volume from single slice assessment.

MATERIALS AND METHODS: This secondary analysis of the prospective population-based German National Cohort (NAKO) included 3D VIBE Dixon MRI from 11,191 participants (screened between May 2014 and December 2016). VAT and spine segmentations were automatically generated using fat-selective images. Standardized craniocaudal VAT profiles were generated. Axial percentage of total VAT was used for identification of reference locations for volume estimation of VAT from a single slice.

RESULTS: Data from 11,036 participants (mean age, 52 ± 11 years, 5681 men) were analyzed. Craniocaudal VAT distribution differed qualitatively between men/women and with respect to age/BMI. Age-independent single slice VAT estimates demonstrated strong correlations with reference VAT volumes. Anatomical locations for accurate VAT estimation varied with sex/BMI.

CONCLUSIONS: The selection of reference locations should be different depending on BMI groups, with a preference for caudal shifts in location with increasing BMI. For women with obesity (BMI >30 kg/m2), the L1 level emerges as the optimal reference location.

PMID:40122750 | DOI:10.1016/j.zemedi.2025.02.005