Pediatr Pulmonol. 2025 Feb;60(2):e27504. doi: 10.1002/ppul.27504.

NO ABSTRACT

PMID:39898600 | DOI:10.1002/ppul.27504

AACE Clin Case Rep. 2024 Oct 4;11(1):32-35. doi: 10.1016/j.aace.2024.09.008. eCollection 2025 Jan-Feb.

ABSTRACT

BACKGROUND/OBJECTIVE: Men with cystic fibrosis (CF) have a high prevalence of low testosterone levels. A recent retrospective study demonstrated a quarter of a cohort of men with CF had serum testosterone levels below 300 ng/dL. The evaluation of hypogonadism is of increasing clinical importance in order to prevent unfavorable outcomes. Herein we present a 31-year-old man with CF and a relatively low serum testosterone value who was found to have an additional unsuspected cause of male hypogonadism.

CASE REPORT: The patient was a 31-year-old man with history of CF who was referred to endocrinology clinic for the evaluation of hypogonadism. Serum testing revealed a total testosterone of 175 ng/mL (296-1377), luteinizing hormone 2.8 mIU/mL (1.2-8.6), and a prolactin of 341 ng/mL (3-13). A brain magnetic resonance imaging was obtained, which revealed a 1 cm hypoenhancing left sellar lesion. He was started on cabergoline. His testosterone increased to 707 ng/dL after a year on cabergoline treatment. His prolactin decreased to 12 ng/mL after a year of treatment. The pituitary adenoma decreased 50% in size 2 years after cabergoline was initiated.

DISCUSSION: The most common etiologies of CF are recurrent infections, chronic inflammation, and glucocorticoid administration, which lead to both hypothalamic-pituitary dysregulation and primary hypogonadism. However, other less common causes of hypogonadism can also be found in CF.

CONCLUSION: We suggest that all men with cystic fibrosis found to have hypogonadism undergo additional evaluation for causes of hypogonadism prior to treatment with testosterone.

PMID:39896950 | PMC:PMC11784604 | DOI:10.1016/j.aace.2024.09.008

OTO Open. 2025 Jan 31;9(1):e70084. doi: 10.1002/oto2.70084. eCollection 2025 Jan-Mar.

ABSTRACT

OBJECTIVE: Individuals with primary ciliary dyskinesia (PCD) frequently report olfactory dysfunction, yet this deficit is poorly documented. The purpose of this study was to characterize the presence and degree of olfactory dysfunction in PCD compared to controls and determine whether certain PCD genes are associated with worse olfaction.

STUDY DESIGN: A prospective cohort study.

SETTING: Tertiary referral center.

METHODS: We administered the University of Pennsylvania Smell Identification Test (UPSIT) to individuals with PCD. Participants were divided into 3 age groups (15-29 years, 30-44 years, and 45+ years) and compared to age- and sex-matched normal controls (n = 2170).

RESULTS: Twenty-nine individuals with PCD (8 males and 21 females) met the criteria (median age: 38 years; interquartile range: 22-47). Only 27.6% of patients with PCD had UPSIT scores within the normosmia range. The UPSIT median scores of each PCD age group were significantly lower than the median scores of the controls (P < .0001 for each age group). UPSIT scores generally worsened with age: mean 33 (mild hyposmia) for 15 to 29 years, 26.8 (moderate hyposmia) for 30 to 44 years, and 20.9 (severe hyposmia) for 45+ years. The most common genes coded were absent inner dynein arm/microtubule disorientation (IDA/MTD) defect (11/24, 45.8%), followed by absent outer dynein arm defect (8/24, 33.3%). The CCDC39 gene (IDA/MTD) was associated with worse olfactory dysfunction.

CONCLUSION: Individuals with PCD have a substantially higher prevalence and degree of olfactory dysfunction compared to age-matched controls. Our study is the first to report greater olfactory dysfunction with age in PCD patients, highlighting an important area for research.

PMID:39896853 | PMC:PMC11783683 | DOI:10.1002/oto2.70084

bioRxiv [Preprint]. 2025 Jan 26:2025.01.24.634776. doi: 10.1101/2025.01.24.634776.

ABSTRACT

Modulator agents that restore cystic fibrosis transmembrane conductance regulator (CFTR) function have revolutionized outcomes in cystic fibrosis, an incurable multisystem disease. Barriers exist to modulator use, making local CFTR gene and cell therapies attractive, especially in the respiratory tract. We used CRISPR to gene-correct CFTR in upper airway basal stem cells (UABCs) and show durable local engraftment into recipient murine respiratory epithelium. Interestingly, the human cells recapitulate the in vivo organization and differentiation of human sinus epithelium, with little expansion or contraction of the engrafted population over time, while retaining expression of the CFTR transgene. Our results indicate that human airway stem cell transplantation with locoregional restoration of CFTR function is a feasible approach for treating CF and potentially other diseases of the respiratory tract.

PMID:39896581 | PMC:PMC11785248 | DOI:10.1101/2025.01.24.634776

J Comput Biol. 2025 Feb 3. doi: 10.1089/cmb.2024.0505. Online ahead of print.

ABSTRACT

The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent years, with the development of deep learning, several methods have been proposed to assist in the diagnosis of depression and promote its automatic identification. However, these methods still have some limitations. The current approaches overlook the importance of subgraphs in brain graphs, resulting in low accuracy. Using these methods with low accuracy for FC analysis may lead to unreliable results. To address these issues, we have designed a graph neural network-based model called AFMDD, specifically for analyzing FC features of depression and depression identification. Through experimental validation, our model has demonstrated excellent performance in depression diagnosis, achieving an accuracy of 73.15%, surpassing many state-of-the-art methods. In our study, we conducted visual analysis of nodes and edges in the FC networks of depression and identified several novel FC features. Those findings may provide valuable clues for the development of biomarkers for the clinical diagnosis of depression.

PMID:39899351 | DOI:10.1089/cmb.2024.0505

Cancer Control. 2025 Jan-Dec;32:10732748251318387. doi: 10.1177/10732748251318387.

ABSTRACT

INTRODUCTION: Precision radiotherapy relies on accurate segmentation of tumor targets and organs at risk (OARs). Clinicians manually review automatically delineated structures on a case-by-case basis, a time-consuming process dependent on reviewer experience and alertness. This study proposes a general process for automated threshold generation for structural evaluation indicators and patient-specific quality assurance (QA) for automated segmentation of nasopharyngeal carcinoma (NPC).

METHODS: The patient-specific QA process for automated segmentation involves determining the confidence limit and error structure highlight stage. Three expert physicians segmented 17 OARs using computed tomography images of NPC and compared them using the Dice similarity coefficient, the maximum Hausdorff distance, and the mean distance to agreement. For each OAR, the 95% confidence interval was calculated as the confidence limit for each indicator. If two or more evaluation indicators (N2) or one or more evaluation indicators (N1) exceeded the confidence limits, the structure segmentation result was considered abnormal. The quantitative performances of these two methods were compared with those obtained by artificially introducing small/medium and serious errors.

RESULTS: The sensitivity, specificity, balanced accuracy, and F-score values for N2 were 0.944 ± 0.052, 0.827 ± 0.149, 0.886 ± 0.076, and 0.936 ± 0.045, respectively, whereas those for N1 were 0.955 ± 0.045, 0.788 ± 0.189, 0.878 ± 0.096, and 0.948 ± 0.035, respectively. N2 and N1 had small/medium error detection rates of 97.67 ± 0.04% and 98.67 ± 0.04%, respectively, with a serious error detection rate of 100%.

CONCLUSION: The proposed automated patient-specific QA process effectively detected segmentation abnormalities, particularly serious errors. These are crucial for enhancing review efficiency and automated segmentation, and for improving physician confidence in automated segmentation.

PMID:39899269 | DOI:10.1177/10732748251318387

Interdiscip Sci. 2025 Feb 3. doi: 10.1007/s12539-025-00687-6. Online ahead of print.

ABSTRACT

Drug-drug interactions (DDIs) can result in deleterious consequences when patients take multiple medications simultaneously, emphasizing the critical need for accurate DDI prediction. Computational methods for DDI prediction have garnered recent attention. However, current approaches concentrate solely on single-view features, such as atomic-view or substructure-view features, limiting predictive capacity. The scarcity of research on interpretability studies based on multi-view features is crucial for tracing interactions. Addressing this gap, we present MI-DDI, a multi-view feature-based interpretable deep learning framework for DDI. To fully extract multi-view features, we employ a Message Passing Neural Network (MPNN) to learn atomic features from molecular graphs generated by RDkit, and transformer encoders are used to learn substructure-view embeddings from drug SMILES simultaneously. These atomic-view and substructure-view features are then amalgamated into a holistic drug embedding matrix. Subsequently, an intricately designed interaction module not only establishes a tractable path for understanding interactions but also directly informs the construction of weight matrices, enabling precise and interpretable interaction predictions. Validation on the BIOSNAP dataset and DrugBank dataset demonstrates MI-DDI's superiority. It surpasses the current benchmarks by a substantial average of 3% on BIOSNAP and 1% on DrugBank. Additional experiments underscore the significance of atomic-view information for DDI prediction and confirm that our interaction module indeed learns more effective information for DDI prediction. The source codes are available at https://github.com/ZihuiCheng/MI-DDI .

PMID:39899225 | DOI:10.1007/s12539-025-00687-6

Int J Comput Assist Radiol Surg. 2025 Feb 3. doi: 10.1007/s11548-025-03327-y. Online ahead of print.

ABSTRACT

Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance.Methods DICOM-structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration with interactive filtering capabilities, thereby simplifying the process of creation of patient cohorts over several sites with consistent multi-modal data.Results In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data include imaging and waveform data (i.e., computed tomography images, electrocardiography scans) as well as annotations (i.e., calcification segmentations, and pointsets), and metadata (i.e., prostheses and pacemaker dependency).Conclusion Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for multi-centric data analysis. The graphical interface as well as example structured report templates are available at https://github.com/Cardio-AI/fl-multi-modal-dataset-creation .

PMID:39899185 | DOI:10.1007/s11548-025-03327-y

Med Phys. 2025 Feb 3. doi: 10.1002/mp.17618. Online ahead of print.

ABSTRACT

BACKGROUND: Most attention-based networks fall short in effectively integrating spatial and channel-wise information across different scales, which results in suboptimal performance for segmenting coronary vessels in x-ray digital subtraction angiography (DSA) images. This limitation becomes particularly evident when attempting to identify tiny sub-branches.

PURPOSE: To address this limitation, a multi-scale dual attention embedded network (named MDA-Net) is proposed to consolidate contextual spatial and channel information across contiguous levels and scales.

METHODS: MDA-Net employs five cascaded double-convolution blocks within its encoder to adeptly extract multi-scale features. It incorporates skip connections that facilitate the retention of low-level feature details throughout the decoding phase, thereby enhancing the reconstruction of detailed image information. Furthermore, MDA modules, which take in features from neighboring scales and hierarchical levels, are tasked with discerning subtle distinctions between foreground elements, such as coronary vessels of diverse morphologies and dimensions, and the complex background, which includes structures like catheters or other tissues with analogous intensities. To sharpen the segmentation accuracy, the network utilizes a composite loss function that integrates intersection over union (IoU) loss with binary cross-entropy loss, ensuring the precision of the segmentation outcomes and maintaining an equilibrium between positive and negative classifications.

RESULTS: Experimental results demonstrate that MDA-Net not only performs more robustly and effectively on DSA images under various image conditions, but also achieves significant advantages over state-of-the-art methods, achieving the optimal scores in terms of IoU, Dice, accuracy, and Hausdorff distance 95%.

CONCLUSIONS: MDA-Net has high robustness for coronary vessels segmentation, providing an active strategy for early diagnosis of cardiovascular diseases. The code is publicly available at https://github.com/30410B/MDA-Net.git.

PMID:39899182 | DOI:10.1002/mp.17618

Rev Esp Enferm Dig. 2025 Feb 3. doi: 10.17235/reed.2025.11081/2024. Online ahead of print.

ABSTRACT

Over the last decade, technological advances in deep learning (artificial neural networks, big data and computing power) have made possible to build digital solutions that imitate human cognitive process (language, vision, hearing, etc) and are able to generate new content when prompted. This generative AI is going to disrupt healthcare. Healthcare professionals must get prepared because there are ethical and legal challenges that must be identified and tackled.

PMID:39898717 | DOI:10.17235/reed.2025.11081/2024

Clin Implant Dent Relat Res. 2025 Feb;27(1):e13431. doi: 10.1111/cid.13431.

ABSTRACT

OBJECTIVE: Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipeline for the classification of maxillary sinus lesions in cone beam computed tomography (CBCT) images to provide auxiliary support for clinical diagnosis.

METHODS: This study utilized 45 136 maxillary sinus images from CBCT scans of 541 patients. A cascade network was designed, comprising a semi-supervised maxillary sinus area object detection module and a maxillary sinus lesions classification module. The object detection module employed a semi-supervised pseudo-labelling training strategy to expand the maxillary sinus annotation dataset. In the classification module, the performance of Convolutional Neural Network and Transformer architectures was compared for maxillary sinus mucosal lesion classification. The object detection and classification modules were evaluated using metrics including Accuracy, Precision, Recall, F1 score, and Average Precision, with the object detection module additionally assessed using Precision-Recall Curve.

RESULTS: The fully supervised pseudo-label generation model achieved an average accuracy of 0.9433, while the semi-supervised maxillary sinus detection model attained 0.9403. ResNet-50 outperformed in classification, with accuracies of 0.9836 (sagittal) and 0.9797 (coronal). Grad-CAM visualization confirmed accurate focus on clinically relevant lesion features.

CONCLUSION: The proposed pipeline achieves high-precision detection and classification of maxillary sinus mucosal lesions, reducing manual annotation while maintaining accuracy.

PMID:39898709 | DOI:10.1111/cid.13431

J Acoust Soc Am. 2025 Feb 1;157(2):681-698. doi: 10.1121/10.0035577.

ABSTRACT

Feature-aided tracking integrates supplementary features into traditional methods and improves the accuracy of data association methods that rely solely on kinematic measurements. However, previous applications of feature-aided data association methods in multi-target tracking of passive sonar arrays directly utilized raw features for likelihood calculations, causing performance degradation in complex marine scenarios with low signal-to-noise ratio and close-proximity trajectories. Inspired by the successful application of deep learning, this study proposes BiChannel-SiamDinoNet, an advanced network derived from the Siamese network and integrated into the joint probability data association framework to calculate feature measurement likelihood. This method forms an embedding space through the feature structure of acoustic targets, bringing similar targets closer together. This makes the system more robust to variations, capable of capturing complex relationships between measurements and targets and effectively discriminating discrepancies between them. Additionally, this study refines the network's feature extraction module to address underwater acoustic signals' unique line spectrum and implement the knowledge distillation training method to improve the network's capability to assess consistency between features through local representations. The performance of the proposed method is assessed through simulation analysis and marine experiments.

PMID:39898705 | DOI:10.1121/10.0035577

Phys Med Biol. 2025 Jan 31. doi: 10.1088/1361-6560/adb124. Online ahead of print.

ABSTRACT

OBJECTIVE: This study investigates the effects of various training protocols on enhancing the precision of MRI-only Pseudo-CT generation for radiation treatment planning and adaptation in head & neck cancer patients. It specifically tackles the challenge of differentiating bone from air, a limitation that frequently results in substantial deviations in the representation of bony structures on Pseudo-CT images.

APPROACH: The study included 25 patients, utilizing pre-treatment MRI-CT image pairs. Five cases were randomly selected for testing, with the remaining 20 used for model training and validation. A 3D U-Net deep learning model was employed, trained on patches of size 643with an overlap of 323. MRI scans were acquired using the Dixon gradient echo (GRE) technique, and various contrasts were explored to improve Pseudo-CT accuracy, including in-phase, water-only, and combined water-only and fat-only images. Additionally, bone extraction from the fat-only image was integrated as an additional channel to better capture bone structures on Pseudo-CTs. The evaluation involved both image quality and dosimetric metrics.

MAIN RESULTS: The generated Pseudo-CTs were compared with their corresponding registered target CTs. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the base model using combined water-only and fat-only images were 19.20 ± 5.30 HU and 57.24 ± 1.44 dB, respectively. Following the integration of an additional channel using a CT-guided bone segmentation, the model's performance improved, achieving MAE and PSNR of 18.32 ± 5.51 HU and 57.82 ± 1.31 dB, respectively. The dosimetric assessment confirmed that radiation treatment planning on Pseudo-CT achieved accuracy comparable to conventional CT. The measured results are statistically significant, with ap-value < 0.05.

SIGNIFICANCE: This study demonstrates improved accuracy in bone representation on Pseudo-CTs achieved through a combination of water-only, fat-only and extracted bone images; thus, enhancing feasibility of MRI-based simulation for radiation treatment planning.

PMID:39898433 | DOI:10.1088/1361-6560/adb124

Plant Cell Environ. 2025 Feb 3. doi: 10.1111/pce.15413. Online ahead of print.

ABSTRACT

Wheat crop production is under constant threat from leaf and stripe rust, an airborne fungal disease caused by the pathogen Puccinia triticina. Early detection and efficient crop phenotyping are crucial for managing and controlling the spread of this disease in susceptible wheat varieties. Current detection methods are predominantly manual and labour-intensive. Traditional strategies such as cultivating resistant varieties, applying fungicides and practicing good agricultural techniques often fall short in effectively identifying and responding to wheat rust outbreaks. To address these challenges, we propose an innovative computer vision-based disease severity prediction pipeline. Our approach utilizes a deep learning-based classifier to differentiate between healthy and rust-infected wheat leaves. Upon identifying an infected leaf, we apply Grabcut-based segmentation to isolate the foreground mask. This mask is then processed in the CIELAB color space to distinguish leaf rust stripes and spores. The disease severity ratio is calculated to measure the extent of infection on each test leaf. This paper introduces a ground-breaking disease severity prediction method, offering a low-cost, accessible and automated solution for wheat rust disease screening in field conditions using digital colour images. Our approach represents a significant advancement in crop disease management, promising timely interventions and better control measures for wheat rust.

PMID:39898421 | DOI:10.1111/pce.15413

OMICS. 2025 Feb 3. doi: 10.1089/omi.2024.0213. Online ahead of print.

ABSTRACT

Innovation in drug discovery for human diseases stands to benefit from systems science and next-generation phenomics approaches. An example is idiopathic pulmonary fibrosis (IPF) that is a chronic pulmonary disorder leading to respiratory failure and for which preventive and therapeutic medicines are sorely needed. Matrix metalloproteinases (MMPs), particularly MMP1 and MMP7, have been associated with IPF pathogenesis and are thus relevant to IPF drug discovery. This study evaluates the comparative therapeutic potentials of doxycycline, pirfenidone, and nintedanib in relation to MMP1 and MMP7 using molecular docking, molecular dynamics simulations, and a next-generation phenomics approach. Adsorption, distribution, metabolism, excretion, and toxicity analysis revealed that doxycycline and nintedanib adhered to Lipinski's rule of five, while pirfenidone exhibited no violations. The toxicity analysis revealed favorable safety profiles, with lethal dose 50 values of doxycycline, pirfenidone, and nintedanib being 2240kg, 580, and 500 mg/kg, respectively. Homology modeling validated the accuracy of the structures of the target proteins, that is, MMP1 and MMP7. The Protein Contacts Atlas tool, a next-generation phenomics platform that broadens the scope of phenomics research, was employed to visualize protein contacts at atomic levels, revealing interaction surfaces in MMP1 and MMP7. Docking studies revealed that nintedanib exhibited superior binding affinities with the candidate proteins (-6.9 kcal/mol for MMP1 and -7.9 kcal/mol for MMP7) compared with doxycycline and pirfenidone. Molecular dynamics simulations further demonstrated the stability of protein-ligand complexes. These findings highlight the notable potential of nintedanib in relation to future IPF therapeutics innovation. By integrating in silico and a next-generation phenomics approach, this study opens up new avenues for drug discovery and development for IPF and possibly, for precision/personalized medicines that consider the molecular signatures of therapeutic candidates for each patient.

PMID:39899320 | DOI:10.1089/omi.2024.0213

Front Immunol. 2025 Jan 17;15:1437984. doi: 10.3389/fimmu.2024.1437984. eCollection 2024.

ABSTRACT

BACKGROUND: Growing evidence indicates an association between circulating immune cell phenotypes and idiopathic pulmonary fibrosis (IPF). Although studies have attempted to elucidate the causal relationship between the two, further clarification of the specific mechanisms and causal linkages is warranted.

OBJECTIVE: We aimed to conduct a two-sample Mendelian randomization (MR) analysis with transcriptomics data analysis to elucidate the causal relationship between circulating immune cells and IPF and to explore potential biomarkers.

METHODS: We first explored the bidirectional causal association between IPF and immune cell phenotypes using two-sample MR analysis. Genome-wide association studies data for immune cell phenotype and IPF were obtained from publicly available databases. A standardized instrumental variable screening process was used to select single nucleotide polymorphisms (SNPs) for inclusion in the MR. Five methods represented by IVW were used to assess causal effects. Subsequently, SNP-nearest genes combined with the transcriptomics data of IPF were subjected to multiple bioinformatics analyses such as TIMER, WGCNA, functional enrichment analysis, protein-protein interaction analysis, and ROC to identify IPF biomarkers. Finally, the single-cell RNA sequencing (scRNA-seq) data was used to validate our findings by single-cell analysis.

RESULTS: The MR study identified 27 immune cell phenotypes causally associated with IPF, of which 20 were associated with a decreased risk of developing IPF and 7 were associated with an increased risk. CTSB (AUC=0.98), IL10 (AUC=0.83), and AGER (AUC=0.87) were identified as promising biomarkers of IPF. Single cell analysis showed differences in CD14+ CD16+ monocytes, CD16+ monocytes and Granulocyte-monocyte progenito between the IPF group and the healthy control group. The three hub genes were highly expressed in three immune cell subsets of IPF patients. It underscores the potential feasibility of three genes as biomarkers.

CONCLUSIONS: Our study demonstrates the causal associations of specific immune cell phenotypes with IPF through genetic methods and identifies CTSB, IL10, and AGER as biomarkers of IPF through bioinformatics analysis. These findings provide guidance for future clinical and basic research.

PMID:39896814 | PMC:PMC11782250 | DOI:10.3389/fimmu.2024.1437984

Elife. 2025 Feb 3;13:RP98934. doi: 10.7554/eLife.98934.

ABSTRACT

Copy number variants (CNVs) are an important source of genetic variation underlying rapid adaptation and genome evolution. Whereas point mutation rates vary with genomic location and local DNA features, the role of genome architecture in the formation and evolutionary dynamics of CNVs is poorly understood. Previously, we found the GAP1 gene in Saccharomyces cerevisiae undergoes frequent amplification and selection in glutamine-limitation. The gene is flanked by two long terminal repeats (LTRs) and proximate to an origin of DNA replication (autonomously replicating sequence, ARS), which likely promote rapid GAP1 CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we evolved engineered strains lacking either the adjacent LTRs, ARS, or all elements in glutamine-limited chemostats. Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. Removal of local DNA elements significantly impacts the fitness effect of GAP1 CNVs and the rate of adaptation. In 177 CNV lineages, across all four strains, between 26% and 80% of all GAP1 CNVs are mediated by Origin Dependent Inverted Repeat Amplification (ODIRA) which results from template switching between the leading and lagging strand during DNA synthesis. In the absence of the local ARS, distal ones mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination can mediate gene amplification following de novo retrotransposon events. Our study reveals that template switching during DNA replication is a prevalent source of adaptive CNVs.

PMID:39899365 | DOI:10.7554/eLife.98934

iScience. 2025 Jan 4;28(2):111745. doi: 10.1016/j.isci.2025.111745. eCollection 2025 Feb 21.

ABSTRACT

Talin-1 (TLN1) is best known to activate integrin receptors and transmit mechanical stimuli to the actin cytoskeleton at focal adhesions. However, the localization of TLN1 is not restricted to focal adhesions. By utilizing both subcellular fractionations and confocal microscopy analyses, we show that TLN1 localizes to the nucleus in several human cell lines, where it is tightly associated with the chromatin. Importantly, small interfering RNA (siRNA)-mediated depletion of endogenous TLN1 triggers extensive changes in the gene expression profile of human breast epithelial cells. To determine the functional impact of nuclear TLN1, we expressed a TLN1 fusion protein containing a nuclear localization signal. Our findings revealed that the accumulation of nuclear TLN1 alters the expression of a subset of genes and impairs the formation of cell-cell clusters. This study introduces an additional perspective on the canonical view of TLN1 subcellular localization and function.

PMID:39898029 | PMC:PMC11787672 | DOI:10.1016/j.isci.2025.111745

iScience. 2025 Jan 2;28(2):111730. doi: 10.1016/j.isci.2024.111730. eCollection 2025 Feb 21.

ABSTRACT

Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks and corresponding transcriptomic data are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.

PMID:39898023 | PMC:PMC11787609 | DOI:10.1016/j.isci.2024.111730

Heliyon. 2025 Jan 10;11(2):e41881. doi: 10.1016/j.heliyon.2025.e41881. eCollection 2025 Jan 30.

ABSTRACT

Anterior eye disorders including dry eye syndrome, keratitis, chemical burns, and trauma have varying prevalence rates in the world. Classical dosage forms based-topical ophthalmic drugs are popular treatments for managing corneal diseases. However, current dosage forms of ocular drugs can be associated with major challenges such as the short retention time in the presence of ocular barriers. Developing alternative therapeutic methods is required to overcome drug bioavailability from ocular barriers. Nanocarriers are major platforms and promising candidates for the administration of ophthalmic drugs in an adjustable manner. This paper briefly introduces the advantages, disadvantages, and characteristics of delivery systems for the treatment of corneal diseases. Additionally, advanced technologies such as 3D printing are being considered to fabricate ocular drug carriers and determine drug dosages for personalized treatment. This comprehensive review is gathered through multiple databases such as Google Scholar, PubMed, and Web of Science. It explores information around "ocular drug delivery systems'', "nano-based drug delivery systems'', "engineered nanocarriers'', and "advanced technologies to fabricate personalized drug delivery systems''.

PMID:39897787 | PMC:PMC11783021 | DOI:10.1016/j.heliyon.2025.e41881