NEJM Catal Innov Care Deliv. 2025 Feb;6(2). doi: 10.1056/CAT.24.0095. Epub 2025 Jan 15.

ABSTRACT

Cystic fibrosis (CF) affects more than 160,000 individuals globally and has seen improved survival rates due to multidisciplinary care models and pharmacotherapy innovations. However, the associated costs remain substantial, prompting the authors to study and evaluate the expense of CF ambulatory care to understand how care structure influences costs. People with CF (PwCF) at large pediatric CF centers in both the United States and Ireland were recruited for parallel observational, prospective studies. Based upon the process of care, the lead clinicians at both sites identified and agreed on three strata of patients (0-11 months, 1-5 years, and 6-17 years of age). Process maps were developed for each of the age cohorts at each site, and the costs of ambulatory care - with emphasis on routine CF clinic visits - were measured utilizing time-driven activity-based costing (TDABC). A dollar-per-minute capacity cost rate (CCR) was calculated for all resources used in the care cycle. The total direct cost was obtained by multiplying the CCR for each resource by the time the resource was used during the patient's care cycle. The cost was summed across all resource types to obtain the cost over the entire care cycle for each site. Service operations were benchmarked to one site and variance analysis was performed. In total, 58 PwCF were included in the analysis (49 in the United States and 9 in Ireland); 4 were 0-11 months, 17 were 1-5 years, and 37 were 6-17 years of age. Physicians (United States) and respiratory consultants (Ireland) had the highest CCRs. Physicians and registered dietitians spent the most time with patients in the United States, compared with the clinical nurse specialists and dietitians in Ireland. The total variance in cost for clinical visits was largest in the 6- to 17-year-old group (28% variance, with 100% in the United States vs. 128% in Ireland). In the 6- to 17-year-old group, the largest drivers in total variance were quantity variance (variance in duration of time spent with patients), which was 108% greater in Ireland); the skill mix variance (variance in clinician type performing service for a given time), which was 49% greater in the United States; and the rate variance (variance in compensation levels across sites), which was 31% greater in the United States. The authors' use of TDABC to characterize the cost of multidisciplinary care during ambulatory clinic visits for PwCF, in combination with variance analysis (the quantitative investigation of the difference between actual and expected costs), provides new and innovative ways to compare costs across similar health care service delivery sites, providing insights into the distinctive features of each. A granular understanding of cost and comparison of resource utilization between centers provides valuable, organizationally relevant insights.

PMID:40171477 | PMC:PMC11960789 | DOI:10.1056/CAT.24.0095

Front Pediatr. 2025 Mar 18;13:1564156. doi: 10.3389/fped.2025.1564156. eCollection 2025.

ABSTRACT

INTRODUCTION: Primary Ciliary Dyskinesia (PCD) is a rare genetic disorder affecting motile cilia, leading to impaired mucociliary clearance and increased susceptibility to respiratory infections. These infections contribute to long-term complications such as bronchiectasis and lung function decline.

OBJECTIVES: This review explores both the acute and long-term impact of respiratory infections in children with PCD, while highlighting the multiple contributors to infection susceptibility. The review also evaluates emerging personalized approaches such as gene and mRNA therapy that hold promise for restoring ciliary function and reducing the burden of acute infections in pediatric PCD.

KEY FINDINGS AND CONCLUSIONS: Acute respiratory infections have a significant impact on morbidity in pediatric PCD, driving progressive airway remodeling. While current treatment strategies focus on managing infections directly, emerging therapies targeting inflammation and genetic causes hold promise for reducing infection burden and improving long-term outcomes. Future advances in personalized medicine could further enhance therapeutic approaches in this population.

PMID:40171169 | PMC:PMC11958984 | DOI:10.3389/fped.2025.1564156

Front Cell Infect Microbiol. 2025 Mar 18;15:1445660. doi: 10.3389/fcimb.2025.1445660. eCollection 2025.

ABSTRACT

Mycobacterium abscessus is a nontuberculous mycobacterium emerging as a significant pathogen in individuals with chronic lung diseases, including cystic fibrosis and chronic obstructive pulmonary disease. Current therapeutics have poor efficacy. Strategies of bacterial control based on host defenses are appealing; however, antimycobacterial immunity remains poorly understood and is further complicated by the appearance of smooth and rough morphotypes, which elicit distinct host responses. We investigated the role of serum components in neutrophil-mediated clearance of M. abscessus morphotypes. M. abscessus opsonization with complement enhanced bacterial killing compared to complement-deficient opsonization. Killing of rough isolates was less reliant on complement. Complement C3 and mannose-binding lectin 2 (MBL2) were deposited on M. abscessus morphotypes in distinct patterns, with a greater association of MBL2 on rough M. abscessus. Killing was dependent on C3; however, depletion and competition experiments indicate that canonical complement activation pathways are not involved. Complement-mediated killing relied on natural IgG and IgM for smooth morphotypes and on IgG for rough morphotypes. Both morphotypes were recognized by complement receptor 3 in a carbohydrate- and calcium-dependent manner. These findings indicate a role for noncanonical C3 activation pathways for M. abscessus clearance by neutrophils and link smooth-to-rough adaptation to complement activation.

PMID:40171164 | PMC:PMC11959001 | DOI:10.3389/fcimb.2025.1445660

Pediatr Pulmonol. 2025 Apr;60(4):e71078. doi: 10.1002/ppul.71078.

ABSTRACT

BACKGROUND: As the life expectancy of people with cystic fibrosis (PwCF) increases, understanding long-term complications, including CF-related bone disease (CFBD), is crucial.

OBJECTIVE: This study aimed to longitudinally characterize CFBD and to compare the bone status of pancreatic sufficient (PS) and pancreatic insufficient (PI) PwCF.

METHODS: This longitudinal analysis included PwCF older than 8 years of age who had at least one dual-energy X-ray absorptiometry test between 2008 and 2021. Data were collected on serum parameters of bone metabolism, nutritional history, habitual activity, and fractures in addition to other demographic and clinical characteristics.

RESULTS: The study included 80 PwCF: 32 (40%) were PS and 48 (60%) PI. Normal dual-energy X-ray absorptiometry results were found in 42 (53%) patients: 16 (50%) in the PS group and 26 (54%) in the PI group (p = 0.72). Three (9%) of the PS group and seven (15%) of the PI group had at least one Z-score below -2 (p = 0.49). The longitudinal bone density decline over a mean of 4.8 years was similar in the two groups. In a logistic regression analysis, pancreatic insufficiency was not found to be a risk factor for CFBD. Female sex was the only significant risk factor for a pathological Z-score.

CONCLUSIONS: The prevalence and severity of CFBD were not found to correlate with pancreatic sufficiency. The similar prevalence of CFBD between patients with PS and PI suggests that screening, and eventually treatment, should be offered to all PwCF, irrespective of pancreatic status.

PMID:40170622 | DOI:10.1002/ppul.71078

Br J Clin Pharmacol. 2025 Apr 2. doi: 10.1002/bcp.70027. Online ahead of print.

ABSTRACT

AIM: Dipeptidyl peptidase-1 (DPP-1) inhibitors have been studied for the treatment of neutrophil-mediated inflammatory diseases including bronchiectasis, bronchial asthma and cystic fibrosis. This study evaluated the pharmacokinetics, pharmacodynamics, safety and tolerability of DPP-1 inhibitor HSK31858 in healthy Chinese volunteers.

METHODS: Volunteers in Part A randomly received single doses of HSK31858 (15, 40, 60 and 80 mg) or placebo in fasted states. The 40-mg cohort also received HSK31858 40 mg or placebo in fed states. In Part B, volunteers randomly received HSK31858 10, 20 and 40 mg or placebo once daily for 28 days in fasted states. The primary endpoints were safety and tolerability of HSK31858.

RESULTS: Among 38 volunteers in Part A and 36 in Part B, HSK31858 was well tolerated; no deaths, serious adverse events, or discontinuations due to adverse events occurred. The median Tmax was 0.75 to 1.0 h and the mean terminal t1/2 was 16.5 to 21.0 h in the fasted state with single doses of HSK31858. Both Cmax and AUC0-t exhibited a dose-dependent rise. Food had no effect on AUC. Multiple doses of HSK31858 demonstrated a similar pharmacokinetics profile, with about 2-fold accumulation in AUC. HSK31858 dose-dependently inhibited neutrophil count-normalized neutrophil elastase (NEnorm) activity. The maximal percentage decrease in NEnorm activity relative to baseline during 28 days of HSK31858 treatments was 13.6% and 76.4% with HSK31858 10 and 40 mg once-daily, respectively.

CONCLUSION: HSK31858 was safe and well tolerated. The pharmacokinetics and pharmacodynamics profile of HSK31858 supports further clinical development for the treatment of neutrophil-mediated inflammatory diseases.

TRIAL REGISTRATION: NCT05663593.

PMID:40170587 | DOI:10.1002/bcp.70027

Ther Adv Respir Dis. 2025 Jan-Dec;19:17534666251329792. doi: 10.1177/17534666251329792. Epub 2025 Apr 1.

NO ABSTRACT

PMID:40170358 | DOI:10.1177/17534666251329792

Front Plant Sci. 2025 Mar 18;16:1498913. doi: 10.3389/fpls.2025.1498913. eCollection 2025.

ABSTRACT

The decline of insect biomass, including pollinators, represents a significant ecological challenge, impacting both biodiversity and ecosystems. Effective monitoring of pollinator habitats, especially floral resources, is essential for addressing this issue. This study connects drone and deep learning technologies to their practical application in ecological research. It focuses on simplifying the application of these technologies. Updating an object detection toolbox to TensorFlow (TF) 2 enhanced performance and ensured compatibility with newer software packages, facilitating access to multiple object recognition models - Faster Region-based Convolutional Neural Network (Faster R-CNN), Single-Shot-Detector (SSD), and EfficientDet. The three object detection models were tested on two datasets of UAV images of flower-rich grasslands, to evaluate their application potential in practice. A practical guide for biologists to apply flower recognition to Unmanned Aerial Vehicle (UAV) imagery is also provided. The results showed that Faster RCNN had the best overall performance with a precision of 89.9% and a recall of 89%, followed by EfficientDet, which excelled in recall but at a lower precision. Notably, EfficientDet demonstrated the lowest model complexity, making it a suitable choice for applications requiring a balance between efficiency and detection performance. Challenges remain, such as detecting flowers in dense vegetation and accounting for environmental variability.

PMID:40171479 | PMC:PMC11959073 | DOI:10.3389/fpls.2025.1498913

Cureus. 2025 Mar 1;17(3):e79871. doi: 10.7759/cureus.79871. eCollection 2025 Mar.

ABSTRACT

Artificial intelligence (AI) has rapidly progressed in the last decade and will inevitably become incorporated into trauma and surgical systems. In such settings, surgeons often need to make high-stakes, time-sensitive, and complex decisions with limited or uncertain information. AI has great potential to augment the pre-operative, intra-operative, and post-operative phases of trauma care. Despite the expeditious advancement of AI, many surgeons lack a foundational understanding of AI terminology, its processes, and potential applications in clinical practice. This narrative review aims to educate general surgeons about the basics of AI, highlight its applications in thoraco-abdominal trauma, and discuss the implications of incorporating its use into the Australian health care system. This review found that studies of AI in trauma care have predominantly focused on machine learning and deep learning applied to diagnostics, risk prediction, and decision-making. Other subfields of AI include natural language processing and computer vision. While AI tools have many potential applications in trauma care, current clinical use is limited. Future prospective, locally validated research is required prior to incorporating AI into clinical practice.

PMID:40171361 | PMC:PMC11958818 | DOI:10.7759/cureus.79871

Front Psychiatry. 2025 Mar 18;16:1452557. doi: 10.3389/fpsyt.2025.1452557. eCollection 2025.

ABSTRACT

Neurological disorders (e.g., Alzheimer's disease and Parkinson's disease) and mental disorders (e.g., depression and anxiety), pose huge challenges to global public health. The pathogenesis of these diseases can usually be attributed to many factors, such as genetic, environmental and socioeconomic status, which make the diagnosis and treatment of the diseases difficult. As research on the diseases advances, so does the body of medical data. The accumulation of such data provides unique opportunities for the basic and clinical study of these diseases, but the vast and diverse nature of the data also make it difficult for physicians and researchers to precisely extract the information and utilize it in their work. A powerful tool to extract the necessary knowledge from large amounts of data is knowledge graph (KG). KG, as an organized form of information, has great potential for the study neurological and mental disorders when it is paired with big data and deep learning technologies. In this study, we reviewed the application of KGs in common neurological and mental disorders in recent years. We also discussed the current state of medical knowledge graphs, highlighting the obstacles and constraints that still need to be overcome.

PMID:40171303 | PMC:PMC11958944 | DOI:10.3389/fpsyt.2025.1452557

Front Oncol. 2025 Mar 18;15:1525650. doi: 10.3389/fonc.2025.1525650. eCollection 2025.

ABSTRACT

In recent years, with the rapid advancement of computer science, artificial intelligence has found extensive applications and has been the subject of significant research within the healthcare industry, particularly in areas such as medical imaging, diagnostics, biomedical engineering, and health data analytics. Artificial intelligence has also made considerable inroads in the diagnosis and treatment of thyroid cancer. This study aims to evaluate the progress, current hotspots, and potential future directions of research on artificial intelligence in the field of thyroid cancer through a bibliometric analysis. This study retrieved literature on the application of artificial intelligence in thyroid cancer from 2004 to 2024 from the Web of Science Core Collection (WoSCC) database. A retrospective bibliometric analysis and visualization study of the filtered data were conducted using VOSviewer, CiteSpace, and the Bibliometrix package in R software. A total of 956 articles from 70 countries/regions were included. China had the highest number of publications, with Shanghai Jiao Tong University (China) being the most prolific research institution. The most prolific author was Wei, X. (n=14), while Haugen, B. R. was the most co-cited author (n=297). The Frontiers in Oncology (35 articles, IF=3.5, Q1) was the most frequently publishing journal, and Thyroid (cited 1,705 times) was the most co-cited journal. Keywords such as 'ultrasound,' 'deep learning,' and 'diagnosis' indicate research hotspots in this field. This study provides a comprehensive exposition of the current advancements, emerging trends, and future directions of artificial intelligence in thyroid cancer research. It serves as a valuable resource for clinicians and researchers, offering a systematic understanding of key focal areas in the field, thereby assisting in the identification and determination of future research trajectories.

PMID:40171256 | PMC:PMC11958942 | DOI:10.3389/fonc.2025.1525650

Front Endocrinol (Lausanne). 2025 Mar 18;16:1485311. doi: 10.3389/fendo.2025.1485311. eCollection 2025.

ABSTRACT

OBJECTIVE: To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).

METHODS: We conducted a comprehensive literature search in multiple databases including PubMed, Cochrane library, Web of Science, Embase and IEEE Xplore up to July 2024. Studies that utilized deep learning techniques for the detection of DR using OCT and retinal images were included. Data extraction and quality assessment were performed independently by two reviewers. Meta-analysis was conducted to determine pooled sensitivity, specificity, and diagnostic odds ratios.

RESULTS: A total of 47 studies were included in the systematic review, 10 were meta-analyzed, encompassing a total of 188268 retinal images and OCT scans. The meta-analysis revealed a pooled sensitivity of 1.88 (95% CI: 1.45-2.44) and a pooled specificity of 1.33 (95% CI: 0.97-1.84) for the detection of DR using deep learning models. All of the outcome of deep learning-based optical coherence tomography ORs ≥0.785, indicating that all included studies with artificial intelligence assistance produced good boosting results.

CONCLUSION: Deep learning-based approaches show high accuracy in detecting diabetic retinopathy from OCT and retinal images, supporting their potential as reliable tools in clinical settings. Future research should focus on standardizing datasets, improving model interpretability, and validating performance across diverse populations.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024575847.

PMID:40171193 | PMC:PMC11958191 | DOI:10.3389/fendo.2025.1485311

Digit Health. 2025 Mar 31;11:20552076251332999. doi: 10.1177/20552076251332999. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: Gait analysis plays a pivotal role in evaluating walking abilities, with recent advancements in digital health stressing the importance of efficient data collection methods. This study aims to classify nine gait types including one normal and eight abnormal gaits, using sequential network-based models and diverse feature combinations obtained from insole sensors.

METHODS: The dataset was collected using insole sensors from subjects performing 15 m walking with designated gait types. The sensors incorporated pressure sensors and inertial measurement units (IMUs), along with the center of pressure engineered from the pressure readings. A number of deep learning architectures were evaluated for their ability to classify the gait types, focusing on feature sets including temporal parameters, statistical features of pressure signals, center of pressure data, and IMU data. Ablation studies were also conducted to assess the impact of combining features from different modalities.

RESULTS: Our results demonstrate that models incorporating IMU features outperform those using different combinations of modalities including individual feature sets, with the top-performing models achieving F1-scores of up to 90% in sample-wise classification and 92% in subject-wise classification. Additionally, an ablation study reveals the importance of considering diverse feature modalities, including temporal parameters, statistical features from pressure signals, center of pressure data, and IMU data, for comprehensive gait classification.

CONCLUSION: Overall, this study successfully developed deep sequential models that effectively classify nine different gait types, with the ablation study underscoring the potential for integrating features from diverse domains to enhance clinical applications, such as intervention for gait-related disorders.

PMID:40171146 | PMC:PMC11960168 | DOI:10.1177/20552076251332999

Front Physiol. 2025 Mar 18;16:1511716. doi: 10.3389/fphys.2025.1511716. eCollection 2025.

ABSTRACT

INTRODUCTION: Lung nodule detection is a crucial task for diagnosis and lung cancer prevention. However, it can be extremely difficult to identify tiny nodules in medical images since pulmonary nodules vary greatly in shape, size, and location. Further, the implemented methods have certain limitations including scalability, robustness, data availability, and false detection rate.

METHODS: To overcome the limitations in the existing techniques, this research proposes the Cnidaria Herd Optimization (CHO) algorithm-enabled Bi-directional Long Short-Term Memory (CHSTM) model for effective lung nodule detection. Furthermore, statistical and texture descriptors extract the significant features that aid in improving the detection accuracy. In addition, the FC2R segmentation model combines the optimized fuzzy C-means clustering algorithm and the Resnet -101 deep learning approach that effectively improves the performance of the model. Specifically, the CHO algorithm is modelled using the combination of the induced movement strategy of krill with the time control mechanism of the cnidaria to find the optimal solution and improve the CHSTM model's performance.

RESULTS: According to the experimental findings of a performance comparison between other established methods, the FC2R + CHSTM model achieves 98.09% sensitivity, 97.71% accuracy, and 97.03% specificity for TP 80 utilizing the LUNA-16 dataset. Utilizing the LIDC/IDRI dataset, the proposed approach attained a high accuracy of 97.59%, sensitivity of 96.77%, and specificity of 98.41% with k-fold validation outperforming the other existing techniques.

CONCLUSION: The proposed FC2R + CHSTM model effectively detects lung nodules with minimum loss and better accuracy.

PMID:40171113 | PMC:PMC11959082 | DOI:10.3389/fphys.2025.1511716

J Appl Crystallogr. 2025 Feb 28;58(Pt 2):513-522. doi: 10.1107/S1600576725000974. eCollection 2025 Apr 1.

ABSTRACT

Recent advancements in X-ray sources and detectors have dramatically increased data generation, leading to a greater demand for automated data processing. This is particularly relevant for real-time grazing-incidence wide-angle X-ray scattering (GIWAXS) experiments which can produce hundreds of thousands of diffraction images in a single day at a synchrotron beamline. Deep learning (DL)-based peak-detection techniques are becoming prominent in this field, but rigorous benchmarking is essential to evaluate their reliability, identify potential problems, explore avenues for improvement and build confidence among researchers for seamless integration into their workflows. However, the systematic evaluation of these techniques has been hampered by the lack of annotated GIWAXS datasets, standardized metrics and baseline models. To address these challenges, we introduce a comprehensive framework comprising an annotated experimental dataset, physics-informed metrics adapted to the GIWAXS geometry and a competitive baseline - a classical, non-DL peak-detection algorithm optimized on our dataset. Furthermore, we apply our framework to benchmark a recent DL solution trained on simulated data and discover its superior performance compared with our baseline. This analysis not only highlights the effectiveness of DL methods for identifying diffraction peaks but also provides insights for further development of these solutions.

PMID:40170972 | PMC:PMC11957406 | DOI:10.1107/S1600576725000974

Stat Biosci. 2025 Apr;17(1):132-150. doi: 10.1007/s12561-023-09394-6. Epub 2023 Oct 28.

ABSTRACT

Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable for many biomedical researchers. In this paper, we first provide a theoretical analysis and derive an upper bound for the bias of average treatment effect (ATE) estimation under the strong ignorability assumption. Derived by leveraging appealing properties of the weighted energy distance, our upper bound is tighter than what has been reported in the literature. Motivated by the theoretical analysis, we propose a novel objective function for estimating the ATE that uses the energy distance balancing score and hence does not require the correct specification of the propensity score model. We also leverage recently developed neural additive models to improve interpretability of deep learning models used for potential outcome prediction. We further enhance our proposed model with an energy distance balancing score weighted regularization. The superiority of our proposed model over current state-of-the-art methods is demonstrated in semi-synthetic experiments using two benchmark datasets, namely, IHDP and ACIC, as well as is examined through the study of the effect of smoking on the blood level of cadmium using NHANES.

PMID:40170916 | PMC:PMC11957463 | DOI:10.1007/s12561-023-09394-6

Health Data Sci. 2025 Apr 1;5:0256. doi: 10.34133/hds.0256. eCollection 2025.

ABSTRACT

Background: Multimodal large language models (LLMs) have shown potential in various health-related fields. However, many healthcare studies have raised concerns about the reliability and biases of LLMs in healthcare applications. Methods: To explore the practical application of multimodal LLMs in skin disease identification, and to evaluate sex and age biases, we tested the performance of 2 popular multimodal LLMs, ChatGPT-4 and LLaVA-1.6, across diverse sex and age groups using a subset of a large dermatoscopic dataset containing around 10,000 images and 3 skin diseases (melanoma, melanocytic nevi, and benign keratosis-like lesions). Results: In comparison to 3 deep learning models (VGG16, ResNet50, and Model Derm) based on convolutional neural network (CNN), one vision transformer model (Swin-B), we found that ChatGPT-4 and LLaVA-1.6 demonstrated overall accuracies that were 3% and 23% higher (and F1-scores that were 4% and 34% higher), respectively, than the best performing CNN-based baseline while maintaining accuracies that were 38% and 26% lower (and F1-scores that were 38% and 19% lower), respectively, than Swin-B. Meanwhile, ChatGPT-4 is generally unbiased in identifying these skin diseases across sex and age groups, while LLaVA-1.6 is generally unbiased across age groups, in contrast to Swin-B, which is biased in identifying melanocytic nevi. Conclusions: This study suggests the usefulness and fairness of LLMs in dermatological applications, aiding physicians and practitioners with diagnostic recommendations and patient screening. To further verify and evaluate the reliability and fairness of LLMs in healthcare, experiments using larger and more diverse datasets need to be performed in the future.

PMID:40170800 | PMC:PMC11961048 | DOI:10.34133/hds.0256

Eur Rev Med Pharmacol Sci. 2025 Mar;29(3):123-134. doi: 10.26355/eurrev_202503_37125.

ABSTRACT

The interstitial lung disease (ILD) field is rapidly expanding as new insights highlight novel mechanisms and procedures that influence epidemiology, diagnosis, and treatment. The aim of this review is to report on recent advancements and future perspectives in clinical management and research in ILD, particularly in idiopathic pulmonary fibrosis (IPF), for the most common ILD. Whilst high-resolution computed tomography (HRCT) remains the gold standard for diagnosis, we focus on newer diagnostic techniques, including IPF genome analysis and epigenetics, biomarkers, bronchoscope robotic navigation, and transbronchial lung cryo biopsy for improving diagnostic accuracy. Further, we report IPF associated with pulmonary hypertension Group 3 and scores for defining disease progression. Positron emission tomography/computed tomography, treatment with prostacyclin and antifibrotic drugs, and lung transplantation as potential treatments for end-stage IPF are discussed. Lastly, we discuss contemporary perspectives on interstitial lung abnormalities (ILA), IPF associated with lung cancer, and the use of artificial intelligence (AI) for ILD diagnosis and monitoring.

GRAPHICAL ABSTRACT: https://www.europeanreview.org/wp/wp-content/uploads/123-134.jpg.

PMID:40171787 | DOI:10.26355/eurrev_202503_37125

iScience. 2025 Mar 4;28(4):112146. doi: 10.1016/j.isci.2025.112146. eCollection 2025 Apr 18.

ABSTRACT

Epidermal growth factor receptor (Egfr)-driven signaling regulates fundamental homeostatic processes. Dysregulated signaling via Egfr is implicated in numerous disease pathologies and distinct Egfr-associated disease etiologies are known to be tissue-specific. The molecular basis of this tissue-specificity remains poorly understood. Most studies of Egfr signaling to date have been performed in vitro or in tissue-specific mouse models of disease, which has limited insight into Egfr signaling patterns in healthy tissues. Here, we carried out integrated phosphoproteomic, proteomic, and transcriptomic analyses of signaling changes across various mouse tissues in response to short-term stimulation with the Egfr ligand Egf. We show how both baseline and Egf-stimulated signaling dynamics differ between tissues. Moreover, we propose how baseline phosphorylation and total protein levels may be associated with clinically relevant tissue-specific Egfr-associated phenotypes. Altogether, our analyses illustrate tissue-specific effects of Egf stimulation and highlight potential links between underlying tissue biology and Egfr signaling output.

PMID:40171493 | PMC:PMC11960661 | DOI:10.1016/j.isci.2025.112146

RSC Adv. 2025 Apr 1;15(13):9884-9890. doi: 10.1039/d4ra07801b. eCollection 2025 Mar 28.

ABSTRACT

This study focuses on the creation and application of an advanced impedimetric immunosensor designed for the sensitive detection of lymphoma cancer cells. The sensor was developed by modifying a glassy carbon electrode (GCE) with gold nanoparticles (AuNPs) and 3,3'-dithiodipropionic acid di(N-hydroxysuccinimide ester) boronic acid (AuNPs@DTSP-BA), followed by the attachment of rituximab monoclonal antibody. Incorporating the boronic acid (BA) component enabled effective oriented immobilization of the antibody, thereby improving the performance of the biosensor. Various spectroscopic techniques were used to characterize the immunosensor. The developed immunosensor demonstrated the ability to detect lymphoma cancer cells across a wide linear range of 100 to 50 000 cells per mL, with a detection sensitivity of 64 cells per mL.

PMID:40171289 | PMC:PMC11959537 | DOI:10.1039/d4ra07801b

Cancer Discov. 2025 Apr 2;15(4):685-701. doi: 10.1158/2159-8290.CD-24-0861.

ABSTRACT

Viewing the hallmarks as a sequence of adaptations captures the "why" behind the "how" of the molecular changes driving cancer. This eco-evolutionary view distils the complexity of cancer progression into logical steps, providing a framework for understanding all existing and emerging hallmarks of cancer and developing therapeutic interventions.

PMID:40170539 | DOI:10.1158/2159-8290.CD-24-0861