ACS Nano. 2025 Jan 18. doi: 10.1021/acsnano.4c14927. Online ahead of print.

ABSTRACT

The abnormally viscous and thick mucus is a hallmark of cystic fibrosis (CF). How the mutated CF gene causes abnormal mucus remains an unanswered question of paramount interest. Mucus is produced by the hydration of gel-forming mucin macromolecules that are stored in intracellular granules prior to release. Current understanding of mucin/mucus structure before and after secretion remains limited, and contradictory models exist. Here, we used a molecular viscometer and fluorescence lifetime imaging of human bronchoepithelial cells (Normal and CF) to measure nanometer-scale viscosity. We found significantly elevated intraluminal nanoviscosity in a population of CF mucin granules, indicating an intrinsic, presecretory mucin defect. Nanoviscosity influences protein conformational dynamics and function. Its elevation along the protein secretory pathway could arise from molecular overcrowding, impacting mucin's post-translational processing, hydration, and mucus rheology after release. The nanoviscosity of secreted CF mucus was elevated compared to that of non-CF. Interestingly, it was higher after release than in granules. Validation experiments indicate that reduced mobility of water hydrating mucin macromolecules may contribute to the high nanoviscosity in mucus and mucin granules. This suggests that mucins have a weakly ordered state in granules but adopt a highly ordered, nematic crystalline structure when secreted. This challenges the traditional view of mucus as a porous agarose-like gel and suggests an alternative model for mucin organization before and after secretion. Our study also indicates that endoplasmic reticulum stress due to molecular overcrowding could contribute to mucus pathogenesis in CF cells. It encourages the development of therapeutics that target presecretory mechanisms in CF and other muco-obstructive lung diseases.

PMID:39825840 | DOI:10.1021/acsnano.4c14927

Hum Mol Genet. 2025 Jan 17:ddaf007. doi: 10.1093/hmg/ddaf007. Online ahead of print.

ABSTRACT

BACKGROUND: Individuals with cystic fibrosis (CF; a recessive disorder) have an increased risk of colorectal cancer (CRC). Evidence suggests individuals with a single CFTR variant may also have increased CRC risk.

METHODS: Using population-based studies (GECCO, CORECT, CCFR, and ARIC; 53 785 CRC cases and 58 010 controls), we tested for an association between the most common CFTR variant (Phe508del) and CRC risk. For replication, we used whole exome sequencing data from UK Biobank (UKB; 5126 cases and 20 504 controls matched 4:1 based on genetic distance, age, and sex), and extended our analyses to all other heterozygous CFTR variants annotated as CF-causing.

RESULTS: In our meta-analysis of GECCO-CORECT-CCFR-ARIC, the odds ratio (OR) for CRC risk associated with Phe508del was 1.11 (P = 0.010). In our UKB replication, the OR for CRC risk associated with Phe508del was 1.28 (P = 0.002). The sequencing data from UKB also revealed an association between the presence of any other single CF-causing variant (excluding Phe508del) and CRC risk (OR = 1.33; P = 0.030). When stratifying CFTR variants by functional class, class I variants (no protein produced) had a stronger association (OR = 1.77; p = 0.002), while class II variants (misfolding and retention of the protein in the endoplasmic reticulum) other than Phe508del (OR = 1.75; p = 0.107) had similar effect size as Phe508del, and variants in classes III-VI had non-significant ORs less than 1.0 and/or were not present in cases.

CONCLUSIONS: CF-causing heterozygous variants, especially class I variants, are associated with a modest but statistically significant increased CRC risk. More research is needed to explain the biology underlying these associations.

PMID:39825500 | DOI:10.1093/hmg/ddaf007

Brain Struct Funct. 2025 Jan 18;230(2):32. doi: 10.1007/s00429-024-02889-y.

ABSTRACT

The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks. The gray matter (GM) density maps obtained from T1 MRI data of 16,377 healthy participants aged 45 to 82 years from the UKB database were included in this study (mean age, 64.27 ± 7.52 , 7811 men). We propose an innovative deep learning architecture for predicting brain age based on GM density maps. The architecture combines a 3D dual-stream fully convolutional residual network (ds-FCRN) with a Transformer-based global-local feature learning paradigm to enhance prediction accuracy. Moreover, we employed Shapley values to elucidate the influence of various brain regions on prediction precision. On a test set of 3,276 healthy subjects (mean age, 64.15 ± 7.45 , 1561 men), our 3D ds-FCRN model achieved a mean absolute error of 2.2 years in brain age prediction, outperforming existing models on the same dataset. The posterior interpretation revealed that the temporal lobe plays the most significant role in the brain age prediction process, while frontal lobe aging is associated with the greatest number of lifestyle factors. Our designed 3D ds-FCRN model achieved high predictive accuracy and high decision transparency. The brain age vectors constructed using Shapley values provided brain region-level insights into life factors associated with abnormal brain aging.

PMID:39826018 | DOI:10.1007/s00429-024-02889-y

Pediatr Cardiol. 2025 Jan 18. doi: 10.1007/s00246-024-03762-9. Online ahead of print.

ABSTRACT

Kawasaki disease (KD) is a febrile vasculitis disorder, with coronary artery lesions (CALs) being the most severe complication. Early detection of CALs is challenging due to limitations in echocardiographic equipment (UCG). This study aimed to develop and validate an artificial intelligence algorithm to distinguish CALs in KD patients and support diagnostic decision-making at admission. A deep learning algorithm named KCPREDICT was developed using 24 features, including basic patient information, five classic KD clinical signs, and 14 laboratory measurements. Data were collected from patients diagnosed with KD between February 2017 and May 2023 at Shanghai Children's Medical Center. Patients were split into training and internal validation cohorts at an 80:20 ratio, and fivefold cross-validation was employed to assess model performance. Among the 1474 KD cases, the decision tree model performed best during the full feature experiment, achieving an accuracy of 95.42%, a precision of 98.83%, a recall of 93.58%, an F1 score of 96.14%, and an area under the receiver operating characteristic curve (AUROC) of 96.00%. The KCPREDICT algorithm can aid frontline clinicians in distinguishing KD patients with and without CALs, facilitating timely treatment and prevention of severe complications. The use of the complete set of 24 diagnostic features is the optimal choice for predicting CALs in children with KD.

PMID:39825907 | DOI:10.1007/s00246-024-03762-9

Microsc Res Tech. 2025 Jan 17. doi: 10.1002/jemt.24809. Online ahead of print.

ABSTRACT

The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights. VGG16, known for its depth and high performance, is utilized for this purpose. The study demonstrates the model's potential for precise and effective diagnosis by examining how well it can differentiate between areas of normal brain tissue and cancerous regions, leveraging both MRI and microscopy data. We describe in full the pre-processing actions taken to improve the quality of input data and maximize model efficiency. A carefully selected dataset, incorporating diverse tumor sizes and types from both microscopy and MRI sources, is used during the training phase to ensure representativeness. The proposed modified VGG19 model achieved 98.81% validation accuracy. Despite good accuracy, interpretation of the result still questionable. The proposed methodology integrates explainable AI (XAI) for brain tumor detection to interpret system decisions. The proposed study uses a gradient explainer to interpret classification results. Comparative statistical analysis highlights the effectiveness of the proposed explainer model over other XAI techniques.

PMID:39825619 | DOI:10.1002/jemt.24809

Magn Reson Med. 2025 Jan 17. doi: 10.1002/mrm.30422. Online ahead of print.

ABSTRACT

PURPOSE: The aim of the work is to develop a cascaded diffusion-based super-resolution model for low-resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low-resolution images.

METHODS: We introduced TagGen, a diffusion-based conditional generative model that uses low-resolution MR tagging images as guidance to generate corresponding high-resolution tagging images. The model was developed on 50 patients with long-axis-view, high-resolution tagging acquisitions. During training, we retrospectively synthesized LR tagging images using an undersampling rate (R) of 3.3 with truncated outer phase-encoding lines. During inference, we evaluated the performance of TagGen and compared it with REGAIN, a generative adversarial network-based super-resolution model that was previously applied to MR tagging. In addition, we prospectively acquired data from 6 subjects with three heartbeats per slice using 10-fold acceleration achieved by combining low-resolution R = 3.3 with GRAPPA-3 (generalized autocalibrating partially parallel acquisitions 3).

RESULTS: For synthetic data (R = 3.3), TagGen outperformed REGAIN in terms of normalized root mean square error, peak signal-to-noise ratio, and structural similarity index (p < 0.05 for all). For prospectively 10-fold accelerated data, TagGen provided better tag grid quality, signal-to-noise ratio, and overall image quality than REGAIN, as scored by two (blinded) radiologists (p < 0.05 for all).

CONCLUSIONS: We developed a diffusion-based generative super-resolution model for MR tagging images and demonstrated its potential to integrate with parallel imaging to reconstruct highly accelerated cine MR tagging images acquired in three heartbeats with enhanced tag grid quality.

PMID:39825522 | DOI:10.1002/mrm.30422

Toxicol Pathol. 2025 Jan 17:1926233241309328. doi: 10.1177/01926233241309328. Online ahead of print.

ABSTRACT

Thyroid tissue is sensitive to the effects of endocrine disrupting substances, and this represents a significant health concern. Histopathological analysis of tissue sections of the rat thyroid gland remains the gold standard for the evaluation for agrochemical effects on the thyroid. However, there is a high degree of variability in the appearance of the rat thyroid gland, and toxicologic pathologists often struggle to decide on and consistently apply a threshold for recording low-grade thyroid follicular hypertrophy. This research project developed a deep learning image analysis solution that provides a quantitative score based on the morphological measurements of individual follicles that can be integrated into the standard pathology workflow. To achieve this, a U-Net convolutional deep learning neural network was used that not just identifies the various tissue components but also delineates individual follicles. Further steps to process the raw individual follicle data were developed using empirical models optimized to produce thyroid activity scores that were shown to be superior to the mean epithelial area approach when compared with pathologists' scores. These scores can be used for pathologist decision support using appropriate statistical methods to assess the presence or absence of low-grade thyroid hypertrophy at the group level.

PMID:39825517 | DOI:10.1177/01926233241309328

Epigenetics. 2025 Dec;20(1):2453275. doi: 10.1080/15592294.2025.2453275. Epub 2025 Jan 18.

ABSTRACT

Menstrual effluent cell profiles have potential as noninvasive biomarkers of female reproductive and gynecological health and disease. We used DNA methylation-based cell type deconvolution (methylation cytometry) to identify cell type profiles in self-collected menstrual effluent. During the second day of their menstrual cycle, healthy participants collected menstrual effluent using a vaginal swab, menstrual cup, and pad. Immune cell proportions were highest in menstrual cup samples, and epithelial cells were highest in swab samples. Our work demonstrates the feasibility and utility of menstrual effluent cell profiling in population-level research using remotely collected samples and DNA methylation.

PMID:39825876 | DOI:10.1080/15592294.2025.2453275

Acta Physiol (Oxf). 2025 Feb;241(2):e14276. doi: 10.1111/apha.14276.

ABSTRACT

AIM: Long QT syndrome (LQTS) and catecholaminergic polymorphism ventricular tachycardia (CPVT) are inherited cardiac disorders often caused by mutations in ion channels. These arrhythmia syndromes have recently been associated with calmodulin (CaM) variants. Here, we investigate the impact of the arrhythmogenic variants D131E and Q135P on CaM's structure-function relationship. Our study focuses on the L-type calcium channel Cav1.2, a crucial component of the ventricular action potential and excitation-contraction coupling.

METHODS: We used circular dichroism (CD), 1H-15N HSQC NMR, and trypsin digestion to determine the structural and stability properties of CaM variants. The affinity of CaM for Ca2+ and interaction of Ca2+/CaM with Cav1.2 (IQ and NSCaTE domains) were investigated using intrinsic tyrosine fluorescence and isothermal titration calorimetry (ITC), respectively. The effect of CaM variants of Cav1.2 activity was determined using HEK293-Cav1.2 cells (B'SYS) and whole-cell patch-clamp electrophysiology.

RESULTS: Using a combination of protein biophysics and structural biology, we show that the disease-associated mutations D131E and Q135P mutations alter apo/CaM structure and stability. In the Ca2+-bound state, D131E and Q135P exhibited reduced Ca2+ binding affinity, significant structural changes, and altered interaction with Cav1.2 domains (increased affinity for Cav1.2-IQ and decreased affinity for Cav1.2-NSCaTE). We show that the mutations dramatically impair Ca2+-dependent inactivation (CDI) of Cav1.2, which would contribute to abnormal Ca2+ influx, leading to disrupted Ca2+ handling, characteristic of cardiac arrhythmia syndromes.

CONCLUSIONS: These findings provide insights into the molecular mechanisms behind arrhythmia caused by calmodulin mutations, contributing to our understanding of cardiac syndromes at a molecular and cellular level.

PMID:39825574 | DOI:10.1111/apha.14276

Funding Opportunity PAR-24-332 from the NIH Guide for Grants and Contracts. The purpose of this Notice for Funding Opportunity (NOFO) is to promote diversity in the translational research workforce for Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD). This postdoctoral fellowship will support research training of promising postdoctoral candidates from diverse backgrounds, and emphasize the development of translational research skills and their application to various aspects of AD/ADRD research (from populations studies to research that can lead to new treatments and diagnostics, including all aspects of behavioral and social research). The long-term goal of this program is to develop a diverse translational workforce that can effectively participate in and/or lead a team-science, interdisciplinary approach to studies of AD/ADRD treatment, prevention, early detection, and disease management and care. is designed for candidates proposing research that does not involve leading an independent clinical trial, a clinical trial feasibility study, or an ancillary clinical trial. Under this NOFO candidates are permitted to propose a research experience in a clinical trial led by a mentor or co-mentor.

Notice NOT-AG-24-092 from the NIH Guide for Grants and Contracts

Notice NOT-HL-25-005 from the NIH Guide for Grants and Contracts

Notice NOT-DA-25-036 from the NIH Guide for Grants and Contracts

Notice NOT-DA-25-035 from the NIH Guide for Grants and Contracts

Notice NOT-RM-25-003 from the NIH Guide for Grants and Contracts

Notice NOT-AG-24-029 from the NIH Guide for Grants and Contracts

Notice NOT-CA-25-035 from the NIH Guide for Grants and Contracts

Notice NOT-OD-25-065 from the NIH Guide for Grants and Contracts

Funding Opportunity PAR-24-333 from the NIH Guide for Grants and Contracts. The purpose of this Notice of Funding Opportunity (NOFO) is to promote diversity in the translational research workforce for Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD). This predoctoral fellowship program will support research training of promising predoctoral candidates from diverse backgrounds and emphasize the development of translational research skills and their application to various aspects of AD/ADRD research (from populations studies to research that can lead to new treatments and diagnostics, including all aspects of behavioral and social research). The long-term goal of this program is to develop a diverse translational workforce that can effectively participate in and/or lead a team-science, interdisciplinary approach to studies of AD/ADRD treatment, prevention, early detection, and disease management and care.

Notice NOT-NR-25-011 from the NIH Guide for Grants and Contracts