Cureus. 2025 Jul 7;17(7):e87461. doi: 10.7759/cureus.87461. eCollection 2025 Jul.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease (ILD) characterized by progressive fibrosis and poor survival outcomes. Accurate diagnosis and prognosis remain challenging due to overlapping features with other ILDs and variability in imaging interpretation. This systematic review evaluates the current evidence on artificial intelligence (AI) and machine learning (ML) applications for the diagnosis and prognosis of IPF using computed tomography (CT) imaging. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, eight studies published between 2017 and 2024 were included, demonstrating promising results across various methodologies, including deep learning (DL) models, support vector machines (SVMs), and ensemble approaches. AI-derived parameters, particularly measures of fibrotic burden and pulmonary vascular volume, consistently outperformed conventional visual CT scores for prognostication. Strong correlations between AI-quantified CT features and pulmonary function (PF) tests suggest potential surrogate markers for physiological parameters. Novel prognostic biomarkers identified through AI analysis expand understanding beyond traditional parenchymal assessment. Despite these advances, limitations include retrospective designs, sample size constraints, male-predominant cohorts, and limited external validation. Future research should prioritize large, prospective, multi-center studies with diverse populations, standardized protocols, explainable AI (XAI) techniques, and integration into clinical workflows to realize the transformative potential of AI for improving IPF management.

PMID:40772136 | PMC:PMC12327841 | DOI:10.7759/cureus.87461

Nat Mach Intell. 2024 Sep;6(9):1106-1118. doi: 10.1038/s42256-024-00892-w. Epub 2024 Sep 19.

ABSTRACT

Two-photon calcium imaging provides large-scale recordings of neuronal activities at cellular resolution. A robust, automated and high-speed pipeline to simultaneously segment the spatial footprints of neurons and extract their temporal activity traces while decontaminating them from background, noise and overlapping neurons is highly desirable to analyze calcium imaging data. In this paper, we demonstrate DeepCaImX, an end-to-end deep learning method based on an iterative shrinkage-thresholding algorithm and a long-short-term-memory neural network to achieve the above goals altogether at a very high speed and without any manually tuned hyper-parameter. DeepCaImX is a multi-task, multi-class and multi-label segmentation method composed of a compressed-sensing-inspired neural network with a recurrent layer and fully connected layers. It represents the first neural network that can simultaneously generate accurate neuronal footprints and extract clean neuronal activity traces from calcium imaging data. We trained the neural network with simulated datasets and benchmarked it against existing state-of-the-art methods with in vivo experimental data. DeepCaImX outperforms existing methods in the quality of segmentation and temporal trace extraction as well as processing speed. DeepCaImX is highly scalable and will benefit the analysis of mesoscale calcium imaging.

PMID:40771998 | PMC:PMC12327232 | DOI:10.1038/s42256-024-00892-w

Front Neurol. 2025 Jul 16;16:1607924. doi: 10.3389/fneur.2025.1607924. eCollection 2025.

ABSTRACT

This bibliometric review examines the evolving landscape of artificial intelligence (AI) in neurodegenerative diseases research from 2000 to March 16, 2025, utilizing data from 1,402 publications (1,159 articles, 243 reviews) indexed in the Web of Science Core Collection. Through advanced tools - VOSviewer, CiteSpace, and Bibliometrix R - the study maps collaboration networks, keyword trends, and knowledge trajectories. Results reveal exponential growth post-2017, driven by advancements in deep learning and multimodal data integration. The United States (25.96%) and China (24.11%) dominate publication volume, while the UK exhibits the highest collaboration centrality (0.24) and average citations per publication (31.68). Core journals like Scientific Reports and Frontiers in Aging Neuroscience published the most articles in this field. Highly cited publications and burst references highlight important milestones in the development history. High-frequency keywords include "alzheimer's disease," "parkinson's disease," "magnetic resonance imaging," "convolutional neural network," "biomarkers," "dementia," "classification," "mild cognitive impairment," "neuroimaging," and "feature extraction." Key hotspots include intelligent neuroimaging analysis, machine learning methodological iterations, molecular mechanisms and drug discovery, and clinical decision support systems for early diagnosis. Future priorities encompass advanced deep learning architectures, multi-omics integration, explainable AI systems, digital biomarker-based early detection, and transformative technologies including transformers and telemedicine. This analysis delineates AI's transformative role in optimizing diagnostics and accelerating therapeutic innovation, while advocating for enhanced interdisciplinary collaboration to bridge computational advances with clinical translation.

PMID:40771972 | PMC:PMC12327369 | DOI:10.3389/fneur.2025.1607924

Front Artif Intell. 2025 Jul 23;8:1589358. doi: 10.3389/frai.2025.1589358. eCollection 2025.

ABSTRACT

INTRODUCTION: Knee injuries, especially Anterior Cruciate Ligament (ACL) tears and meniscus tears, are becoming increasingly common and can severely restrict mobility and quality of life. Early diagnosis is essential for effective treatment and for preventing long-term complications such as knee osteoarthritis. While deep learning approaches have shown promise in identifying knee injuries from MRI scans, they often require large amounts of labeled data, which can be both scarce and privacy-sensitive.

METHODS: This paper analyses a hybrid methodology that integrates few-shot learning with federated learning for the diagnosis of knee injuries using MRI scans. The proposed model used a 3DResNet50 architecture as the backbone to enhance both feature extraction and embedding representation. A combined Centralized and Federated Few-Shot Learning Framework is analysed to leverage episodic-intermittent training strategy based on Prototypical Networks. The model is trained incorporating Stochastic Gradient Descent (SGD), Cross-Entropy Loss, and a MultiStep Learning Rate scheduler to enhance few-shot classification. This model also addressed the challenge of limited annotated data ensuring patient data privacy through distributed learning across multiple regions.

RESULTS: The models performance was evaluated on the MRNet dataset for multi-label classification. In the centralized setting, the model achieved accuracies of 85.3% on axial views, 82.1% on sagittal views, and 71% on coronal views. The propose work attained accuracies as 83% (axial), 83.9% (sagittal), and 65% (coronal), demonstrating the framework's effectiveness across different learning configurations.

DISCUSSION: The proposed method outperforms in diagnostic accuracy, generalization across MRI planes, and patient privacy via federated learning. However, it faces limitations, including lower coronal view performance and high computational demands due to its complex architecture.

PMID:40771942 | PMC:PMC12326743 | DOI:10.3389/frai.2025.1589358

Front Artif Intell. 2025 Jul 23;8:1582960. doi: 10.3389/frai.2025.1582960. eCollection 2025.

ABSTRACT

BACKGROUND: Osteoporosis, a systemic skeletal disorder characterized by deteriorated bone microarchitecture and low bone mass, poses substantial fracture risks to aging populations globally. Early detection of reduced bone mineral density (BMD) through opportunistic screening is critical for preventing fragility fractures. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis, many patients have not undergone screening with this technique. Therefore, developing an automated tool that can diagnose bone density through routine chest and abdominal CT examinations is highly important. With advancements in technology and the accumulation of clinical data, the role of bone density artificial intelligence (AI) in the diagnosis and management of osteoporosis is becoming increasingly significant.

OBJECTIVE: First to validate the diagnostic equivalence of AI-based BMD prediction against quantitative CT (QCT) reference standards, second to assess inter-device measurement consistency across multi-vendor CT systems (Siemens, GE, Philips). Ultimately, the objective is to determine the clinical utility of AI-derived BMD for osteoporosis classification.

METHODS: In this retrospective multicenter study, paired CT/QCT datasets from 702 patients (2019-2022) were analyzed. The accuracy, sensitivity, and specificity of an Bone Density AI model were evaluated by comparing the predicted bone mineral density values from bone density AI with the measured values from QCT. Moreover, the consistency of lumbar spine BMD measurements between QCT and Bone Density AI on different devices was compared.

RESULTS: The AUC of Bone Density AI model in diagnosing osteoporosis was 0.822 (95% CI: 0.787-0.867, p < 0.001), with an accuracy of 0.9456, sensitivity of 0.9601, and specificity of 0.9270, indicating good performance in predicting bone density. The consistency study between Bone Density AI and QCT for the vertebral BMD measurements revealed no statistically significant difference in R 2 values, suggesting no significant difference in performance between the two methods in measuring BMD. The linear regression fit between the R 2 values of QCT and Bone Density AI for measuring lumbar spine BMD with different equipment ranged from 0.88 to 0.96, indicating a high degree of consistency between the two measurement methods across devices.

CONCLUSION: This multicenter study pioneers a dual-validation framework to establish the clinical validity of deep learning-based BMD prediction algorithms using routine thoracic/abdominal CT scans. Our data suggest that AI-driven BMD quantification demonstrates non-inferior diagnostic accuracy to QCT while overcoming DXA's accessibility limitations. This technology enables cost-effective, radiation-free osteoporosis screening through routine CT repurposing, particularly beneficial for resource-constrained settings.

PMID:40771941 | PMC:PMC12325224 | DOI:10.3389/frai.2025.1582960

Front Artif Intell. 2025 Jul 23;8:1618607. doi: 10.3389/frai.2025.1618607. eCollection 2025.

ABSTRACT

BACKGROUND: Abdominal ultrasonography is a primary diagnostic tool for evaluating medical conditions within the abdominal cavity. Accurate determination of the relative locations of intra-abdominal organs and lesions based on anatomical features in ultrasound images is essential in diagnostic sonography. Recognizing and extracting anatomical landmarks facilitates lesion evaluation and enhances diagnostic interpretation. Recent artificial intelligence (AI) segmentation methods employing deep neural networks (DNNs) and transformers encounter computational efficiency challenges to balance the preservation of feature dependencies information with model efficiency, limiting their clinical applicability.

METHODS: The anatomical structure recognition framework, MaskHybrid, was developed using a private dataset comprising 34,711 abdominal ultrasound images of 2,063 patients from CSMUH. The dataset included abdominal organs and vascular structures (hepatic vein, inferior vena cava, portal vein, gallbladder, kidney, pancreas, spleen) and liver lesions (hepatic cyst, tumor). MaskHybrid adopted a mamba-transformer hybrid architecture consisting of an evolved backbone network, encoder, and corresponding decoder to capture long-range spatial dependencies and contextual information effectively, demonstrating improved image segmentation capabilities in visual tasks while mitigating the computational burden associated with the transformer-based attention mechanism.

RESULTS: Experiments on the retrospective dataset achieved a mean average precision (mAP) score of 74.13% for anatomical landmarks segmentation in abdominal ultrasound images. Our proposed framework outperformed baselines across most organ and lesion types and effectively segmented challenging anatomical structures. Moreover, MaskHybrid exhibited a significantly shorter inference time (0.120 ± 0.013 s), achieving 2.5 times faster than large-sized AI models of similar size. Combining Mamba and transformer architectures, this hybrid design was well-suited for the timely analysis of complex anatomical structures segmentation in abdominal ultrasonography, where accuracy and efficiency are critical in clinical practice.

CONCLUSION: The proposed mamba-transformer hybrid recognition framework simultaneously detects and segments multiple abdominal organs and lesions in ultrasound images, achieving superior segmentation accuracy, visualization effect, and inference efficiency, thereby facilitating improved medical image interpretation and near real-time diagnostic sonography that meets clinical needs.

PMID:40771938 | PMC:PMC12325247 | DOI:10.3389/frai.2025.1618607

Front Pharmacol. 2025 Jul 23;16:1581820. doi: 10.3389/fphar.2025.1581820. eCollection 2025.

ABSTRACT

BACKGROUND: Sorafenib, a multi-kinase inhibitor, is a key therapeutic agent in the treatment of advanced hepatocellular carcinoma (HCC), metastatic renal cell carcinoma (RCC), and radioactive iodine-refractory differentiated thyroid cancer (DTC). However, its clinical efficacy is frequently hampered by the rising prevalence of sorafenib resistance, particularly in HCC. This reality underscores the urgent need for a comprehensive pan-cancer investigation to elucidate the underlying mechanisms of resistance.

METHODS: We employed a systematic bibliometric approach utilizing the Web of Science Core Collection to conduct a structured literature search. Performance analysis and visualization were conducted using VOSviewer and CiteSpace. A triphasic screening protocol was implemented to identify publications focused on sorafenib resistance, covering a period from 2006 to 2025.

RESULTS: Our analysis identified 1,484 eligible publications, with a peak of 194 articles published in 2022. The majority of research (79.48%) centered on HCC, with significant contributions from institutions in China and the United States. Co-authorship and keyword network analyses revealed a robust interdisciplinary collaboration landscape. Key themes emerged, including dysregulation of drug transporters and clearance mechanisms, metabolic reprogramming, programmed cell death, interactions within the tumor microenvironment, and epigenetic regulatory mechanisms, highlighting critical areas contributing to resistance.

CONCLUSION: This study highlights the current research landscape concerning sorafenib resistance, facilitating the identification of emerging trends and research gaps. Future research priorities should include biomarker-driven clinical trials, the development of nanoparticle delivery systems, and the clinical translation of combination therapy strategies. Additionally, the integration of deep learning algorithms in the context of big data has the potential to enhance our understanding of resistance mechanisms in silico, ultimately aiming to overcome resistance challenges and improve patient survival outcomes.

PMID:40771926 | PMC:PMC12325432 | DOI:10.3389/fphar.2025.1581820

Front Pharmacol. 2025 Jul 23;16:1608832. doi: 10.3389/fphar.2025.1608832. eCollection 2025.

ABSTRACT

INTRODUCTION: Drug combination therapy represents a promising strategy for addressing complex diseases, offering the potential for improved efficacy while mitigating safety concerns. However, conventional wet-lab experimentation for identifying optimal drug combinations is resource-intensive due to the vast combinatorial search space. To address this challenge, computational methods leveraging machine learning and deep learning have emerged to effectively navigate this space.

METHODS: In this study, we introduce a Calibrated Deep Feature Aggregation (CDFA) framework for screening synergistic drug combinations. Concretely, CDFA utilizes a novel cell line representation based on the protein information and gene expression capturing complementary biological determinants of drug response. Besides, a novel feature aggregation network is proposed based on the Transformer to model the intricate interactions between drug pairs and cell lines through multi-head attention mechanisms, enabling discovery of non-linear synergy patterns. Furthermore, a method is introduced to quantify and calibrate the uncertainties associated with CDFA's predictions, enhancing the reliability of the identified synergistic drug combinations.

RESULTS: Experiments results have demonstrated that CDFA outperforms existing state-of-the-art deep learning models.

DISCUSSION: The superior performance of CDFA stems from its biologically informed cell line representation, its ability to capture complex non-linear drug-cell interactions via attention mechanisms, and its enhanced reliability through uncertainty calibration. This framework provides a robust computational tool for efficient and reliable drug combination screening.

PMID:40771923 | PMC:PMC12325400 | DOI:10.3389/fphar.2025.1608832

Cureus. 2025 Jul 7;17(7):e87461. doi: 10.7759/cureus.87461. eCollection 2025 Jul.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease (ILD) characterized by progressive fibrosis and poor survival outcomes. Accurate diagnosis and prognosis remain challenging due to overlapping features with other ILDs and variability in imaging interpretation. This systematic review evaluates the current evidence on artificial intelligence (AI) and machine learning (ML) applications for the diagnosis and prognosis of IPF using computed tomography (CT) imaging. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, eight studies published between 2017 and 2024 were included, demonstrating promising results across various methodologies, including deep learning (DL) models, support vector machines (SVMs), and ensemble approaches. AI-derived parameters, particularly measures of fibrotic burden and pulmonary vascular volume, consistently outperformed conventional visual CT scores for prognostication. Strong correlations between AI-quantified CT features and pulmonary function (PF) tests suggest potential surrogate markers for physiological parameters. Novel prognostic biomarkers identified through AI analysis expand understanding beyond traditional parenchymal assessment. Despite these advances, limitations include retrospective designs, sample size constraints, male-predominant cohorts, and limited external validation. Future research should prioritize large, prospective, multi-center studies with diverse populations, standardized protocols, explainable AI (XAI) techniques, and integration into clinical workflows to realize the transformative potential of AI for improving IPF management.

PMID:40772136 | PMC:PMC12327841 | DOI:10.7759/cureus.87461

Geroscience. 2025 Aug 6. doi: 10.1007/s11357-025-01819-y. Online ahead of print.

ABSTRACT

Δ133p53α is a naturally occurring isoform of the tumor suppressor protein p53. Δ133p53α functions as a physiological dominant-negative inhibitor of the full-length p53 protein (commonly referred to as p53). Δ133p53α preferentially inhibits p53-mediated cellular senescence, while it does not inhibit, or may even promote, p53-mediated DNA repair. Owing to this selective inhibitory activity that preserves genome stability, Δ133p53α represents a promising target for enhancement in the prevention and treatment of diseases associated with increased senescence of normal cells. These diseases include Alzheimer's and other neurodegenerative diseases, premature aging diseases such as Hutchinson-Gilford progeria syndrome (HGPS), and idiopathic pulmonary fibrosis (IPF). Current cell-based therapies, which are limited by increased cellular senescence, may also benefit from Δ133p53α-mediated improvements. As an initial application of Δ133p53α in improving therapeutic cells, we here introduce Δ133p53α-armored chimeric antigen receptor (CAR)-T cells. Based on our previous and ongoing studies using various types of senescent human cells in vitro, we also discuss the importance of further exploring the therapeutic potentials of Δ133p53α, with particular focus on HGPS and IPF. The development of mouse models facilitates in vivo evaluation of the therapeutic effects of Δ133p53α, potentially leading to future clinical applications.

PMID:40770529 | DOI:10.1007/s11357-025-01819-y

Front Pharmacol. 2025 Jul 23;16:1542159. doi: 10.3389/fphar.2025.1542159. eCollection 2025.

ABSTRACT

BACKGROUD: Papillary thyroid carcinoma (PTC) represents a malignant epithelial tumor characterized with a preference for younger individuals. Despite its generally favorable prognosis, PTC still poses considerable challenges, particularly in regards to the propensity for distant metastasis. As a key enzyme in the glycolytic pathway, phosphoglycerate kinase 1 (PGK1) has been linked to the progression of various cancer types. However, its role in PTC remains to be elucidated. This study aimed to investigate the association between PGK1 expression in thyroid cancer tissues and clinicopathological features, postoperative recurrence, and prognosis to provide clinical assessment and intervention reference.

METHODS: We investigated the correlation between PGK1 expression and the clinicopathological characteristics, recurrence, and prognosis in 97 PTC patients who underwent surgical treatments between 1 January 2020, and 31 December 2020 in Zhengzhou University First Affiliated Hospital. Besides, we also analysed the correlation of PGK1 expression with the 10-year survival rate of patients with thyroid carcinoma (THCA) in UALCAN database.

RESULTS: PGK1 expression was higher in cancerous tissues than that in adjacent non-cancerous tissues. Further analysis of PGK1 expression across clinicopathological characteristics revealed that patients with poorly differentiated tumors, TNM stages III-IV, lymph node metastasis, and tumor diameter ≥1.0 cm exhibited higher PGK1 expression levels in cancerous tissues. A subsequent 3-year postoperative follow-up was conducted to evaluate the correlation between PGK1 expression and recurrence. During this period, 31.96% of patients experienced recurrence, with higher PGK1 expression correlating with increased recurrence rates. Moreover, patients with higher PGK1 expression in cancerous tissue exhibited a significantly lower survival rate of 79.20% compared to the PGK1-low/medium group. Lastly, age, lymph node metastasis, differentiation degree, TNM stage, and tumor diameter were identified as risk factors for poor prognosis in patients with PTC analyzed by Cox regression.

CONCLUSION: Our study demonstrated that PGK1 expression may serve as a potential prognostic biomarker of PTC.

PMID:40771921 | PMC:PMC12325314 | DOI:10.3389/fphar.2025.1542159

Front Nutr. 2025 Jul 23;12:1640407. doi: 10.3389/fnut.2025.1640407. eCollection 2025.

ABSTRACT

BACKGROUND AND AIMS: The bioelectrical impedance vector analysis (BIVA) requires population-specific references to correctly classify individuals based on body composition properties. The aim of this study was: (i) to develop new references specific to the older adult population; (ii) to evaluate vector patterns based on age and appendicular lean soft mass (ALMS); (iii) to compare the new references with others already existing in the literature.

METHODS: The present study included 835 older adults [472 women (mean age 73.9 ± 7.4 years, BMI 27.2 ± 5.4 kg/m2) and 363 men (mean age 73.1 ± 7.2 years, BMI 27.0 ± 4.4 kg/m2)]. Bioimpedance analysis was conducted using a phase-sensitive foot-to-hand technology at 50 kHz. Bioelectrical properties were analyzed among participants grouped by age categories and ALSM tertiles. New bivariate tolerance ellipses for resistance (R) and reactance (Xc), standardized by participants' height (H), were compared with data from adult populations and the original BIVA references proposed by Piccoli in 1995 (ages 15-85).

RESULTS: New reference values for older adults were established. Significant differences (p < 0.001) in R/H and phase angle were observed when older adults were grouped by age categories, while R/H, Xc/H, and phase angle showed significant differences among ALSM/H2 tertiles. The mean bioelectrical vector for older adults differed from the references in the literature, showing a moderate magnitude relative to Piccoli's original BIVA references (men: D2 = 0.6; women: D2 = 0.5) and a larger magnitude compared to the adult standards (men: D2 = 1.7; women: D2 = 1.8).

CONCLUSION: This study provides BIVA references for older adults. Aging was associated with increased R/H and decreased phase angle, whereas older individuals with higher ALSM exhibited a greater phase angle and lower R/H, and Xc/H. The original BIVA references proposed in 1995 lack specificity and are no longer recommended for future use, as age-specific bioelectrical references are now available.

PMID:40771207 | PMC:PMC12325078 | DOI:10.3389/fnut.2025.1640407

Front Aging Neurosci. 2025 Jul 23;17:1591796. doi: 10.3389/fnagi.2025.1591796. eCollection 2025.

ABSTRACT

Alzheimer's disease (AD) is a growing global challenge, representing the most common neurodegenerative disorder and affecting millions of lives. As life expectancy continues to rise and populations expand, the number of individuals coping with the cognitive declines caused by AD is projected to double in the coming years. By 2050, we may see over 115 million people diagnosed with this devastating condition. Unfortunately, while we currently lack effective cures, there are preventative measures that can slow disease progression in symptomatic patients. Thus, research has shifted toward early detection and intervention for AD in recent years. With technological advances, we are now harnessing large datasets and more efficient, minimally invasive methods for diagnosis and treatment. This review highlights critical demographic insights, health conditions that increase the risk of developing AD, and lifestyle factors in midlife that can potentially trigger its onset. Additionally, we delve into the promising role of plant-based metabolites and their sources, which may help delay the disease's progression. The innovative multi-omics research is transforming our understanding of AD. This approach enables comprehensive data analysis from diverse cell types and biological processes, offering possible biomarkers of this disease's mechanisms. We present the latest advancements in genomics, transcriptomics, Epigenomics, proteomics, and metabolomics, including significant progress in gene editing technologies. When combined with machine learning and artificial intelligence, multi-omics analysis becomes a powerful tool for uncovering the complexities of AD pathogenesis. We also explore current trends in the application of radiomics and machine learning, emphasizing how integrating multi-omics data can transform our approach to AD research and treatment. Together, these pioneering advancements promise to develop more effective preventive and therapeutic strategies soon.

PMID:40771197 | PMC:PMC12325291 | DOI:10.3389/fnagi.2025.1591796

J Cell Mol Med. 2025 Aug;29(15):e70725. doi: 10.1111/jcmm.70725.

ABSTRACT

Abdominal aortic aneurysm (AAA) is the most prevalent and lethal form of arterial aneurysm, frequently manifesting asymptomatically until a catastrophic rupture occurs. While various diagnostic imaging tools and several potential biomarkers have been explored for the purpose of early AAA screening, the usage of liquid biopsy such as extracellular vesicles (EVs)-carried protein for the early diagnosis of AAA is still being overlooked. In this study, we enrolled 18 AAA patients and nine healthy normal controls, including data from the National Drug Clinical Trial Organisation-Vascular Surgery (NDCTO) (in-house cohort) and the Second Clinical Medical College, Jinan University (Shenzhen People's Hospital) (external cohort). We employed Olink's proximity extension assay (PEA) technology based on the plasma EV proteins and first comprehensively characterised the proteomics landscape in circulating EV underlying AAA disease development. A complex profile of differential EV proteins and EV protein-protein interactions network in AAA patients was identified. The differentially expressed EV proteins in AAA patients were found to be significantly associated with several enriched pathways, including the cellular response to cytokine stimuli, inflammatory response, and the regulation of the glucocorticoid receptor (GR) pathway. Moreover, five hub proteins were identified as being of particular significance: these were Interleukin-4, Interleukin-6, MCP-1, Neurturin, and Oncostatin-M. The Olink proteomics technique was utilised in order to identify these proteins. The significance of these proteins was further validated through Western blotting and enzyme-linked immunosorbent assay (ELISA) in the external cohort. The five EV proteins displayed reliable performance and robustness for distinguishing AAA from healthy people, revealing high accuracy with AUC values of 0.760, 0.840, 0.800, 0.840, and 0.900, respectively. The present study has revealed the plasma EV proteins landscape within AAA and further uncovered their potential roles in the pathogenesis of the disease. This presents a new direction for clinical diagnosis and management of AAA. Consequently, these five EV proteins have the potential to serve as useful biomarkers for the diagnosis and prediction of AAA. Further research is warranted to explore their potential as therapeutic targets.

PMID:40770945 | DOI:10.1111/jcmm.70725

Nat Genet. 2025 Aug 6. doi: 10.1038/s41588-025-02284-1. Online ahead of print.

ABSTRACT

Cell atlas projects have revealed that common cell types often comprise distinct, recurrent transcriptional states, but the function and regulation of these states remain poorly understood. Here, we show that systematic activation of transcription factors can recreate such states in vitro, providing tractable models for mechanistic and functional studies. Using a scalable CRISPR activation (CRISPRa) Perturb-seq platform, we activated 1,836 transcription factors in two cell types. CRISPRa induced gene expression within physiological ranges, with chromatin features predicting responsiveness. Comparisons with atlas datasets showed that transcription factor perturbations recapitulated key fibroblast states and identified their regulators, including KLF2 and KLF4 for a universal state present in many tissues, and PLAGL1 for a disease-associated inflammatory state. Inducing the universal state suppressed the inflammatory state, suggesting therapeutic potential. These findings position CRISPRa as a nuanced tool for perturbing differentiated cells and establish a general strategy for studying clinically relevant transcriptional states ex vivo.

PMID:40770575 | DOI:10.1038/s41588-025-02284-1

Nat Neurosci. 2025 Aug 6. doi: 10.1038/s41593-025-02010-4. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is characterized by inclusions of α-synuclein (α-syn) and mitochondrial dysfunction in dopaminergic (DA) neurons of the substantia nigra pars compacta (SNpc). Patients with PD anecdotally experience symptom improvement at high altitude; chronic hypoxia prevents the development of Leigh-like brain disease in mice with mitochondrial complex I deficiency. Here we report that intrastriatal injection of α-syn preformed fibrils (PFFs) in mice resulted in neurodegeneration and movement disorder, which were prevented by continuous exposure to 11% oxygen. Specifically, PFF-induced α-syn aggregation resulted in brain tissue hyperoxia, lipid peroxidation and DA neurodegeneration in the SNpc of mice breathing 21% oxygen, but not in those breathing 11% oxygen. This neuroprotective effect of hypoxia was also observed in Caenorhabditis elegans. Moreover, initiating hypoxia 6 weeks after PFF injection reversed motor dysfunction and halted further DA neurodegeneration. These results suggest that hypoxia may have neuroprotective effects downstream of α-syn aggregation in PD, even after symptom onset and neuropathological changes.

PMID:40770507 | DOI:10.1038/s41593-025-02010-4

Sci Rep. 2025 Aug 7;15(1):28852. doi: 10.1038/s41598-025-13635-7.

ABSTRACT

The gastrointestinal epithelium relies on activation of the hypoxia-inducible factor (HIF) to promote cell survival and maintain bioenergetic homeostasis during hypoxia. While many pathogens can activate HIF, the effects of enteric protozoa on HIF activation in gastrointestinal epithelial cells remain unclear. Giardia duodenalis, a prevalent protozoan enteropathogen, causes intestinal barrier dysfunction characterized by epithelial malabsorption, mucus depletion, altered mucin glycosylation, and microbiota dysbiosis. Findings from the present study reveal an epithelial hypoxic signature upon Giardia infection. Human intestinal epithelial cells were exposed to vehicle or Giardia duodenalis isolate GS/M under normoxic (21% O2) or hypoxic (1% O2) conditions. In normoxia, infected cells displayed a time-dependent increase in HIF-1α protein expression, the oxygen-dependent subunit of HIF-1. In normoxia, Giardia infection upregulated HIF-1 target genes involved in cellular stress (i.e., VEGFA, ANKRD37, GADD45A) and glycolysis (i.e., HK2, LDHA). This was accompanied by changes in the abundance of glycolytic intermediates (i.e., glucose-6-phosphate, pyruvate, lactate). Although infection in hypoxia failed to augment the hypoxia-induced HIF-1α stabilization, HIF-1 target genes were still upregulated, albeit to a lesser degree. These findings indicate that Giardia induces a transient epithelial hypoxic response in normoxic conditions, revealing a hitherto unrecognized epithelial rescue response to this intestinal parasite.

PMID:40770382 | DOI:10.1038/s41598-025-13635-7

Sr Care Pharm. 2025 Aug 1;40(8):306-314. doi: 10.4140/TCP.n.2025.306.

ABSTRACT

With the increasing prevalence of polypharmacy, age-related physiological changes, and the need for individualized pharmacotherapy in older patients, understanding new drug approvals is crucial to optimizing medication management. This paper synthesizes the latest evidence and offers insights into prescribing considerations, potential drug-drug interactions, and strategies to mitigate adverse effects. We believe this work will be of significant interest to health care professionals, including pharmacists, physicians, and geriatric specialists, as they navigate the evolving landscape of pharmacotherapy in older adults.

PMID:40770590 | DOI:10.4140/TCP.n.2025.306

bioRxiv [Preprint]. 2025 Aug 2:2025.07.31.667803. doi: 10.1101/2025.07.31.667803.

ABSTRACT

A major goal in agriculture is to engineer crops that can maintain yield with less nitrogen (N) fertilizer input. Major orchestrators of plant responses to N include members of the HRS1 HOMOLOG (HHO) family of transcription factors (TFs). However, HHO TFs have been difficult targets for functional studies in planta due to their redundancy. Here, we highlight a unique role for a phylogenetically diverged HHO TF, HHO5, whose expression is regulated in an N-dose dependent fashion and is specifically expressed in phloem. We found that an HHO5 single mutant displays significant misregulation of N-dose dependent genes and plant growth rates. HHO5 is also unique as it displays a dual activator/repressor activity on N-dose dependent gene regulation. HHO5 specifically acts as a direct gene repressor when binding DNA targets. In contrast, genes activated by HHO5 include indirect targets regulated by TFs downstream of HHO5 (TF2s). To validate the influence of HHO5 via its direct TF2s, we used validated TF2 data to build a gene regulatory network that links HHO5-TF2 targets to ~70% of the N-dose genes regulated by HHO5 in planta. By these means, we define HHO5 as a novel dual activator/repressor of plant N-dose signaling.

PMID:40766722 | PMC:PMC12324496 | DOI:10.1101/2025.07.31.667803