Cancer Metastasis Rev. 2025 Feb 21;44(1):35. doi: 10.1007/s10555-025-10246-6.

ABSTRACT

The efficacy and off-target effects of immune checkpoint inhibitors (ICI) in cancer treatment vary among patients. Monocytes likely contribute to this heterogeneous response due to their crucial role in immune homeostasis. We conducted a systematic review and meta-analysis to evaluate the impact of monocytes on ICI efficacy and immune-related adverse events (irAEs) in patients with cancer. We systematically searched PubMed, Web of Science, and Embase for clinical studies from January 2000 to December 2023. Articles were included if they mentioned cancer, ICI, monocytes, or any monocyte-related terminology. Animal studies and studies where ICIs were combined with other biologics were excluded, except for studies where two ICIs were used. This systematic review was registered with PROSPERO (CRD42023396297) prior to data extraction and analysis. Monocyte-related markers, such as absolute monocyte count (AMC), monocyte/lymphocyte ratio (MLR), specific monocyte subpopulations, and m-MDSCs were assessed in relation to ICI efficacy and safety. Bayesian meta-analysis was conducted for AMC and MLR. The risk of bias assessment was done using the Cochrane-ROBINS-I tool. Out of 5787 studies identified in our search, 155 eligible studies report peripheral blood monocyte-related markers as predictors of response to ICI, and 32 of these studies describe irAEs. Overall, based on 63 studies, a high MLR was a prognostic biomarker for short progression-free survival (PFS) and overall survival (OS) hazard ratio (HR): 1.5 (95% CI: 1.21-1.88) and 1.52 (95% CI:1.13-2.08), respectively. The increased percentage of classical monocytes was an unfavorable predictor of survival, while low baseline rates of monocytic myeloid-derived suppressor cells (m-MDSCs) were favorable. Elevated intermediate monocyte frequencies were associated but not significantly correlated with the development of irAEs. Baseline monocyte phenotyping may serve as a composite biomarker of response to ICI; however, more data is needed regarding irAEs. Monocyte-related variables may aid in risk assessment and treatment decision strategies for patients receiving ICI in terms of both efficacy and safety.

PMID:39982537 | DOI:10.1007/s10555-025-10246-6

Front Pharmacol. 2025 Jan 24;16:1492517. doi: 10.3389/fphar.2025.1492517. eCollection 2025.

ABSTRACT

Polyphenols, naturally occurring phytonutrients found in plant-based foods, have attracted significant attention for their potential therapeutic effects in neurological diseases and neuroinflammation. These compounds possess diverse neuroprotective capabilities, including antioxidant, anti-inflammatory, and anti-amyloid properties, which contribute to mitigating the progression of neurodegenerative conditions such as Alzheimer's Disease (AD), Parkinson's Disease (PD), Dementia, Multiple Sclerosis (MS), Stroke, and Huntington's Disease (HD). Polyphenols have been extensively studied for their ability to regulate inflammatory responses by modulating the activity of pro-inflammatory genes and influencing signal transduction pathways, thereby reducing neuroinflammation and neuronal death. Additionally, polyphenols have shown promise in modulating various cellular signaling pathways associated with neuronal viability, synaptic plasticity, and cognitive function. Epidemiological and clinical studies highlight the potential of polyphenol-rich diets to decrease the risk and alleviate symptoms of neurodegenerative disorders and neuroinflammation. Furthermore, polyphenols have demonstrated their therapeutic potential through the regulation of key signaling pathways such as Akt, Nrf2, STAT, and MAPK, which play critical roles in neuroprotection and the body's immune response. This review emphasizes the growing body of evidence supporting the therapeutic potential of polyphenols in combating neurodegeneration and neuroinflammation, as well as enhancing brain health. Despite the substantial evidence and promising hypotheses, further research and clinical investigations are necessary to fully understand the role of polyphenols and establish them as advanced therapeutic targets for age-related neurodegenerative diseases and neuroinflammatory conditions.

PMID:39981183 | PMC:PMC11840759 | DOI:10.3389/fphar.2025.1492517

Pharmacogenomics. 2025 Feb 21:1-9. doi: 10.1080/14622416.2025.2466413. Online ahead of print.

ABSTRACT

INTRODUCTION: Pharmacogenetic (PGx) screening is intended to optimize drug efficacy and reduce adverse drug reactions. Current screening options include genotyping assays for preselected PGx variants and broader next-generation sequencing panels (NGS). Few studies have directly compared preemptive PGx screening methods.

MATERIALS AND METHODS: The two PGx methods were compared in a cross-sectional study of adult Military Health System (MHS) clinic beneficiaries. Participants had initial targeted CYP2C19/CYP2D6 genotyping at a Military Health System Laboratory. Genotyping was followed by multi-gene NGS testing. Current prescriptions were recorded and potential drug-drug interactions screened to evaluate prescribing risk.

RESULTS: All participants (100%) had at least one clinically actionable NGS panel result compared to 81% with targeted CYP2C19/CYP2D6 genotyping. Participants (n = 162) had an average of 6.6 (range 0-22) prescriptions and 2.7 (range 0-24) drug-drug interactions. Among those with at least one clinically actionable NGS result, 42% were currently taking medication with actionable CPIC guidelines (Level A/B), compared with 24% with CYP2C19/CYP2D6 genotyping. Sixteen participants (10%) had uncertain NGS panel results, with none for CYP2C19/CYP2D6 genotyping.

CONCLUSIONS: Preemptive multi-gene NGS detected more clinically actionable PGx results than targeted CYP2C19/CYP2D6 genotyping. Effective PGx screening in the MHS may decrease preventable adverse effects and improve military readiness.

PMID:39981562 | DOI:10.1080/14622416.2025.2466413

JHEP Rep. 2024 Nov 14;7(3):101271. doi: 10.1016/j.jhepr.2024.101271. eCollection 2025 Mar.

ABSTRACT

BACKGROUND & AIMS: This study used the Global Burden of Disease data (2010-2021) to analyze the rates and trends of point prevalence, annual incidence, and years lived with disability (YLDs) for metabolic dysfunction-associated steatotic liver disease (MASLD) in 204 countries.

METHODS: Total numbers and age-standardized rates per 100,000 population for MASLD prevalence, annual incidence, and YLDs were compared across regions and countries by age, sex, and sociodemographic index (SDI). Smoothing spline models were used to evaluate the relationship between the burden of MASLD and SDI. Estimates were reported with uncertainty intervals (UI).

RESULTS: Globally, in 2021, the age-standardized rates per 100,000 population of point prevalence of MASLD were 15,018.1 cases (95% UI 13,756.5-16,361.4), annual incidence rates were 608.5 cases (598.8-617.7), and YLDs were 0.5 (0.3-0.8) years. MASLD point prevalence was higher in men than women (15,731.4 vs. 14,310.6 cases per 100,000 population). Prevalence peaked at ages 45-49 for men and 50-54 for women. Kuwait (32,312.2 cases per 100,000 people; 95% UI: 29,947.1-34,839.0), Egypt (31,668.8 cases per 100,000 people; 95% UI: 29,272.5-34,224.7), and Qatar (31,327.5 cases per 100,000 people; 95% UI: 29,078.5-33,790.9) had the highest prevalence rates in 2021. The largest increases in age-standardized point prevalence estimates from 2010 to 2021 were in China (16.9%, 95% UI 14.7%-18.9%), Sudan (13.3%, 95% UI 9.8%-16.7%) and India (13.2%, 95% UI 12.0%-14.4%). MASLD incidence varied with SDI, peaking at moderate SDI levels.

CONCLUSIONS: MASLD is a global health concern, with the highest prevalence reported in Kuwait, Egypt, and Qatar. Raising awareness about risk factors and prevention is essential in every country, especially in China, Sudan and India, where disease incidence and prevalence are rapidly increasing.

IMPACT AND IMPLICATIONS: This research provides a comprehensive analysis of the global burden of MASLD, highlighting its rising prevalence and incidence, particularly in countries with varying sociodemographic indices. The findings are significant for both clinicians and policymakers, as they offer critical insights into the regional disparities in MASLD burden, which can inform targeted prevention and intervention strategies. However, the study's reliance on modeling and available data suggests cautious interpretation, and further research is needed to validate these findings in clinical and real-world settings.

PMID:39980749 | PMC:PMC11840544 | DOI:10.1016/j.jhepr.2024.101271

OTO Open. 2025 Feb 20;9(1):e70064. doi: 10.1002/oto2.70064. eCollection 2025 Jan-Mar.

ABSTRACT

OBJECTIVE: Given the recent dramatic changes in medical therapy for cystic fibrosis (CF), this study aims to describe temporal changes in chronic rhinosinusitis (CRS) and endoscopic sinus surgery (ESS) rates.

METHODS: National Inpatient Sample (2004-2019; weighted estimates for 119,067 pediatric and 202,407 adult patients) was used to analyze adult (age ≥18 years) and pediatric patients with CF with pulmonary manifestations. Comorbid CRS, ESS rates, and extended length of stay (LOS, ≥75th percentile) were analyzed.

RESULTS: The rate of CRS in both pediatric (14.1% vs 21.1%, P < .001) and adult (16.5% vs 40.9%, P < .001) patients increased. Rate of ESS in pediatric patients with CRS decreased from 25.3% to 3.4% (P < .001). A similar decline occurred in adults with CRS (12.3% vs 3.6%, P < .001). In multivariate analysis from 2015 to 2019, ESS and extended LOS were associated with admission in the Western United States (P < .001). CRS (OR 1.14, P = .002) and ESS (OR 1.78, P = .002) were independent predictors of extended LOS. Elective admission, primary insurance, race, and hospital teaching/location were significantly associated with ESS and extended LOS (P < .05).

CONCLUSION: Despite the increased prevalence of CRS in adults and pediatric patients with CF, rates of inpatient ESS have declined from 2004 to 2019. Patient and hospital factors affect undergoing ESS in 2015 to 2019. CRS and ESS are associated with extended LOS in recent years.

PMID:39981143 | PMC:PMC11840695 | DOI:10.1002/oto2.70064

Respir Med Case Rep. 2025 Jan 25;53:102171. doi: 10.1016/j.rmcr.2025.102171. eCollection 2025.

ABSTRACT

Lung cancer is uncommon among people with cystic fibrosis (pwCF). We describe the case of a 35-year-old man with mild, stable CF disease who presented with severe respiratory distress, systemic symptoms, elevated liver enzymes and hypereosinophilia along with a lung mass and pleural effusion. The patient was subsequently diagnosed with non-small cell lung carcinoma (NSCLC), featuring anaplastic lymphoma kinase (ALK) translocation. Following treatment with a targeted tyrosine kinase inhibitor (TKI) there was a rapid tumor regression, however, his dyspnea and hypoxemia subsequently worsened. A trial of Elexacaftor/Tezacaftor/Ivacaftor (ETI) led to significant clinical improvement and enhanced pulmonary function. In vitro testing using patient-derived intestinal organoids was performed in parallel and also demonstrated a significant response to ETI. The deterioration observed following the initiation of ALK inhibitor treatment and subsequent improvement with CFTR modulators suggest that ALK inhibitor therapy may potentially impair CFTR activity. A better understanding of the relationship between these pathways could provide valuable insights and contribute to the development of more effective and tailored treatment strategies for patients with coexisting conditions. To our knowledge, this is the first reported case of ALK-translocated lung cancer in a CF patient, underscoring the necessity for a high degree of clinical suspicion in atypical presentations of pulmonary exacerbation and potentially linking ALK-EML4 activation pathways, TKI therapy and CFTR. Care for pwCF with lung cancer requires a unique multi-disciplinary approach to optimize their complex multifactorial treatment.

PMID:39980610 | PMC:PMC11841201 | DOI:10.1016/j.rmcr.2025.102171

SAGE Open Med. 2025 Feb 19;13:20503121251320849. doi: 10.1177/20503121251320849. eCollection 2025.

ABSTRACT

INTRODUCTION: Bronchiectasis is a chronic respiratory disease caused by various respiratory and systemic conditions. It is now considered a potentially reversible disease, particularly when diagnosed early and managed with appropriate respiratory care strategies. Although rare in children, it typically develops in patients with recurrent lower respiratory tract infections. The etiology of bronchiectasis in children differs from that in adults. This study aims to identify the clinical features, causes, and outcomes of non-cystic fibrosis bronchiectasis in children at a tertiary center.

METHODS: A retrospective review was conducted among children with non-cystic fibrosis bronchiectasis who attended a university-affiliated hospital between January 2007 and December 2021. Clinical outcomes were assessed based on pulmonary function tests, exacerbation, and mortality.

RESULTS: The study included 35 children with non-cystic fibrosis bronchiectasis. The median age at diagnosis was 36 months (IQR: 24-170 months). Bronchiectasis was linked to underlying conditions in 22 cases (62.9%), such as primary immunodeficiency, chronic aspiration, and primary ciliary dyskinesia. Thirteen children had infectious-associated bronchiectasis (37.1%), with four cases related to pulmonary tuberculosis. At diagnosis, cystic bronchiectasis was most common (n = 17, 48.6%), followed by varicose (n = 13, 37.1%) and cylindrical bronchiectasis (n = 5, 14.3%). Pulmonary exacerbation occurred in 28 (80%) children, with a higher rate in noninfectious bronchiectasis than postinfectious bronchiectasis (90.9% vs 61.5%, p = 0.036). Hospitalization was required for 26 (77.1%) children, with a higher rate of noninfectious bronchiectasis than postinfectious bronchiectasis (86.3% vs 53.8%, p = 0.033).

CONCLUSIONS: Primary immune deficiency and chronic aspiration are the most common non-infective causes of non-cystic fibrosis bronchiectasis. Noninfectious bronchiectasis leads to higher exacerbation and hospitalization rates.

PMID:39980590 | PMC:PMC11840848 | DOI:10.1177/20503121251320849

Phys Imaging Radiat Oncol. 2025 Jan 30;33:100716. doi: 10.1016/j.phro.2025.100716. eCollection 2025 Jan.

ABSTRACT

For 18 months following clinical introduction of deep-learning auto-segmentation (DLAS), an audit of organ at risk (OAR) contour editing was performed, including 1255 patients from a single institution and the majority of tumour sites. Mean surface-Dice similarity coefficient increased from 0.87 to 0.97, the number of unedited OARs increased from 21.5 % to 40 %. The audit identified changes in editing corresponding to vendor model changes, adaption of local contouring practice and reduced editing in areas of no clinical significance. The audit allowed assessment of the level and frequency of editing and identification of outlier cases.

PMID:39981522 | PMC:PMC11840498 | DOI:10.1016/j.phro.2025.100716

Cancer Innov. 2025 Feb 20;4(2):e159. doi: 10.1002/cai2.159. eCollection 2025 Apr.

ABSTRACT

Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement and refinement, artificial intelligence (AI) has demonstrated exceptional capabilities in processing intricate multidimensional BC-related data. AI has proven advantageous in various facets of BC management, encompassing efficient screening and diagnosis, precise prognosis assessment, and personalized treatment planning. However, the implementation of AI into precision medicine and clinical practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, and integration of multiple clinical pathways. In this review, we provide a comprehensive overview of the current research related to AI in BC, highlighting its extensive applications throughout the whole BC cycle management and its potential for innovative impact. Furthermore, this article emphasizes the significance of constructing patient-oriented AI algorithms. Additionally, we explore the opportunities and potential research directions within this burgeoning field.

PMID:39981497 | PMC:PMC11840326 | DOI:10.1002/cai2.159

Front Neurosci. 2025 Feb 5;19:1497881. doi: 10.3389/fnins.2025.1497881. eCollection 2025.

ABSTRACT

This study investigated the impact of brain atlas selection on the classification accuracy of Autism Spectrum Disorder (ASD) models using functional Magnetic Resonance Imaging (fMRI) data. Brain atlases, such as AAL, CC200, Harvard-Oxford, and Yeo 7/17, are used to define regions of interest (ROIs) for fMRI analysis and play a crucial role in enabling researchers to study connectivity patterns and neural dynamics in ASD patients. Through a systematic review, we examined the performance of different atlases in various machine-learning and deep-learning frameworks for ASD classification. The results reveal that atlas selection significantly affects classification accuracy, with denser atlases, such as CC400, providing higher granularity, whereas coarser atlases such as AAL, offer computational efficiency. Furthermore, we discuss the dynamics of combining multiple atlases to enhance feature extraction and explore the implications of atlas selection across diverse datasets. Our findings emphasize the need for standardized approaches to atlas selection and highlight future research directions, including the integration of novel atlases, advanced data augmentation techniques, and end-to-end deep-learning models. This study provides valuable insights into optimizing fMRI-based ASD diagnosis and underscores the importance of interpreting atlas-specific features for an improved understanding of brain connectivity in ASD.

PMID:39981402 | PMC:PMC11841380 | DOI:10.3389/fnins.2025.1497881

Heliyon. 2025 Feb 1;11(3):e42404. doi: 10.1016/j.heliyon.2025.e42404. eCollection 2025 Feb 15.

ABSTRACT

This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters-Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH-were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy. Comparative analyses showed that ML-based models effectively captured spatial and temporal variations, aligning closely with field measurements. This integration provides a more efficient alternative to traditional methods, which are resource-intensive and less practical for large-scale, remote areas. Our findings demonstrate that this semi-automated technique is a valuable tool for continuous water quality monitoring, particularly in ecologically sensitive areas with limited accessibility. The approach also offers significant applications for climate resilience and policy-making, as it enables timely identification of deteriorating water quality trends that may impact biodiversity and ecosystem health. However, the study acknowledges limitations, including the variability in data availability and the inherent uncertainties in ML predictions for dynamic water systems. Overall, this research contributes to the advancement of water quality monitoring techniques, supporting sustainable environmental management practices and the resilience of the Sundarbans against emerging climate challenges.

PMID:39981364 | PMC:PMC11840191 | DOI:10.1016/j.heliyon.2025.e42404

Front Artif Intell. 2025 Feb 6;8:1508664. doi: 10.3389/frai.2025.1508664. eCollection 2025.

ABSTRACT

In marine security and surveillance, accurately identifying ships and ship ports from satellite imagery remains a critical challenge due to the inefficiencies and inaccuracies of conventional approaches. The proposed method uses an enhanced YOLO (You Only Look Once) model, a robust real-time object detection method. The method involves training the YOLO model on an extensive collection of annotated satellite images to detect ships and ship ports accurately. The proposed system delivers a precision of 86% compared to existing methods; this approach is designed to allow for real-time deployment in the context of resource-constrained environments, especially with a Jetson Nano edge device. This deployment will ensure scalability, efficient processing, and reduced reliance on central computing resources, making it especially suitable for maritime settings in which real-time monitoring is vital. The findings of this study, therefore, point out the practical implications of this improved YOLO model for maritime surveillance: offering a scalable and efficient solution to strengthen maritime security.

PMID:39981193 | PMC:PMC11839658 | DOI:10.3389/frai.2025.1508664

Front Med (Lausanne). 2025 Feb 6;12:1506363. doi: 10.3389/fmed.2025.1506363. eCollection 2025.

ABSTRACT

BACKGROUND: Pulmonary embolism (PE) is a common and potentially fatal condition. Timely and accurate risk assessment in patients with acute deep vein thrombosis (DVT) is crucial. This study aims to develop a deep learning-based, precise, and efficient PE risk prediction model (PE-Mind) to overcome the limitations of current clinical tools and provide a more targeted risk evaluation solution.

METHODS: We analyzed clinical data from patients by first simplifying and organizing the collected features. From these, 37 key clinical features were selected based on their importance. These features were categorized and analyzed to identify potential relationships. Our prediction model uses a convolutional neural network (CNN), enhanced with three custom-designed modules for better performance. To validate its effectiveness, we compared this model with five commonly used prediction models.

RESULTS: PE-Mind demonstrated the highest accuracy and reliability, achieving 0.7826 accuracy and an area under the receiver operating characteristic curve of 0.8641 on the prospective test set, surpassing other models. Based on this, we have also developed a Web server, PulmoRiskAI, for real-time clinician operation.

CONCLUSION: The PE-Mind model improves prediction accuracy and reliability for assessing PE risk in acute DVT patients. Its convolutional architecture and residual modules substantially enhance predictive performance.

PMID:39981086 | PMC:PMC11839595 | DOI:10.3389/fmed.2025.1506363

Front Med (Lausanne). 2025 Feb 6;12:1477351. doi: 10.3389/fmed.2025.1477351. eCollection 2025.

ABSTRACT

PURPOSE: This study analyzed the research trends in machine learning (ML) pertaining to myocardial infarction (MI) from 2008 to 2024, aiming to identify emerging trends and hotspots in the field, providing insights into the future directions of research and development in ML for MI. Additionally, it compared the contributions of various countries, authors, and agencies to the field of ML research focused on MI.

METHOD: A total of 1,036 publications were collected from the Web of Science Core Collection database. CiteSpace 6.3.R1, Bibliometrix, and VOSviewer were utilized to analyze bibliometric characteristics, determining the number of publications, countries, institutions, authors, keywords, and cited authors, documents, and journals in popular scientific fields. CiteSpace was used for temporal trend analysis, Bibliometrix for quantitative country and institutional analysis, and VOSviewer for visualization of collaboration networks.

RESULTS: Since the emergence of research literature on medical imaging and machine learning (ML) in 2008, interest in this field has grown rapidly, particularly since the pivotal moment in 2016. The ML and MI domains, represented by China and the United States, have experienced swift development in research after 2015, albeit with the United States significantly outperforming China in research quality (as evidenced by the higher impact factors of journals and citation counts of publications from the United States). Institutional collaborations have formed, notably between Harvard Medical School in the United States and Capital Medical University in China, highlighting the need for enhanced cooperation among domestic and international institutions. In the realm of MI and ML research, cooperative teams led by figures such as Dey, Damini, and Berman, Daniel S. in the United States have emerged, indicating that Chinese scholars should strengthen their collaborations and focus on both qualitative and quantitative development. The overall direction of MI and ML research trends toward Medicine, Medical Sciences, Molecular Biology, and Genetics. In particular, publications in "Circulation" and "Computers in Biology and Medicine" from the United States hold prominent positions in this study.

CONCLUSION: This paper presents a comprehensive exploration of the research hotspots, trends, and future directions in the field of MI and ML over the past two decades. The analysis reveals that deep learning is an emerging research direction in MI, with neural networks playing a crucial role in early diagnosis, risk assessment, and rehabilitation therapy.

PMID:39981082 | PMC:PMC11839716 | DOI:10.3389/fmed.2025.1477351

MethodsX. 2025 Jan 25;14:103185. doi: 10.1016/j.mex.2025.103185. eCollection 2025 Jun.

ABSTRACT

Diabetic Retinopathy (DR), a diabetes-related eye condition, damages retinal blood vessels and can lead to vision loss if undetected early. Precise diagnosis is challenging due to subtle, varied symptoms. While classical deep learning (DL) models like CNNs and ResNet's are widely used, they face resource and accuracy limitations. Quantum computing, leveraging quantum mechanics, offers revolutionary potential for faster problem-solving across fields like cryptography, optimization, and medicine. This research introduces QuantumNet, a hybrid model combining classical DL and quantum transfer learning to enhance DR detection. QuantumNet demonstrates high accuracy and resource efficiency, providing a transformative solution for DR detection and broader medical imaging applications. The method is as follows:•Evaluate three classical deep learning models-CNN, ResNet50, and MobileNetV2-using the APTOS 2019 blindness detection dataset on Kaggle to identify the best-performing model for integration.•QuantumNet combines the best-performing classical DL model for feature extraction with a variational quantum classifier, leveraging quantum transfer learning for enhanced diagnostics, validated statistically and on Google Cirq using standard metrics.•QuantumNet achieves 94.11 % accuracy, surpassing classical DL models and prior research by 11.93 percentage points, demonstrating its potential for accurate, efficient DR detection and broader medical imaging applications.

PMID:39981059 | PMC:PMC11840206 | DOI:10.1016/j.mex.2025.103185

Health Sci Rep. 2025 Feb 19;8(2):e70200. doi: 10.1002/hsr2.70200. eCollection 2025 Feb.

ABSTRACT

BACKGROUND AND AIMS: Artificial intelligence (AI) has become integral to medical research, impacting various aspects such as data analysis, writing assistance, and publishing. This paper explores the multifaceted influence of AI on the process of writing medical research papers, encompassing data analysis, ethical considerations, writing assistance, and publishing efficiency.

METHODS: The review was conducted following the PRISMA guidelines; a comprehensive search was performed in Scopus, PubMed, EMBASE, and MEDLINE databases for research publications on artificial intelligence in medical research published up to October 2023.

RESULTS: AI facilitates the writing process by generating drafts, offering grammar and style suggestions, and enhancing manuscript quality through advanced models like ChatGPT. Ethical concerns regarding content ownership and potential biases in AI-generated content underscore the need for collaborative efforts among researchers, publishers, and AI creators to establish ethical standards. Moreover, AI significantly influences data analysis in healthcare, optimizing outcomes and patient care, particularly in fields such as obstetrics and gynecology and pharmaceutical research. The application of AI in publishing, ranging from peer review to manuscript quality control and journal matching, underscores its potential to streamline and enhance the entire research and publication process. Overall, while AI presents substantial benefits, ongoing research, and ethical guidelines are essential for its responsible integration into the evolving landscape of medical research and publishing.

CONCLUSION: The integration of AI in medical research has revolutionized efficiency and innovation, impacting data analysis, writing assistance, publishing, and others. While AI tools offer significant benefits, ethical considerations such as biases and content ownership must be addressed. Ongoing research and collaborative efforts are crucial to ensure responsible and transparent AI implementation in the dynamic landscape of medical research and publishing.

PMID:39980823 | PMC:PMC11839394 | DOI:10.1002/hsr2.70200

Front Plant Sci. 2025 Feb 3;15:1496801. doi: 10.3389/fpls.2024.1496801. eCollection 2024.

ABSTRACT

Accurate counting of crop plants is essential for agricultural science, particularly for yield forecasting, field management, and experimental studies. Traditional methods are labor-intensive and prone to errors. Unmanned Aerial Vehicle (UAV) technology offers a promising alternative; however, varying UAV altitudes can impact image quality, leading to blurred features and reduced accuracy in early maize seedling counts. To address these challenges, we developed RC-Dino, a deep learning methodology based on DINO, specifically designed to enhance the precision of seedling counts from UAV-acquired images. RC-Dino introduces two innovative components: a novel self-calibrating convolutional layer named RSCconv and an adaptive spatial feature fusion module called ASCFF. The RSCconv layer improves the representation of early maize seedlings compared to non-seedling elements within feature maps by calibrating spatial domain features. The ASCFF module enhances the discriminability of early maize seedlings by adaptively fusing feature maps extracted from different layers of the backbone network. Additionally, transfer learning was employed to integrate pre-trained weights with RSCconv, facilitating faster convergence and improved accuracy. The efficacy of our approach was validated using the Early Maize Seedlings Dataset (EMSD), comprising 1,233 annotated images of early maize seedlings, totaling 83,404 individual annotations. Testing on this dataset demonstrated that RC-Dino outperformed existing models, including DINO, Faster R-CNN, RetinaNet, YOLOX, and Deformable DETR. Specifically, RC-Dino achieved improvements of 16.29% in Average Precision (AP) and 8.19% in Recall compared to the DINO model. Our method also exhibited superior coefficient of determination (R²) values across different datasets for seedling counting. By integrating RSCconv and ASCFF into other detection frameworks such as Faster R-CNN, RetinaNet, and Deformable DETR, we observed enhanced detection and counting accuracy, further validating the effectiveness of our proposed method. These advancements make RC-Dino particularly suitable for accurate early maize seedling counting in the field. The source code for RSCconv and ASCFF is publicly available at https://github.com/collapser-AI/RC-Dino, promoting further research and practical applications.

PMID:39980762 | PMC:PMC11841422 | DOI:10.3389/fpls.2024.1496801

Compr Physiol. 2025 Feb;15(1):e70004. doi: 10.1002/cph4.70004.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic respiratory disease characterized by progressive scarring of the lung parenchyma. While two drugs have been approved by the US Food and Drug Administration (FDA) for IPF, median survival remains limited at 3 years, and the discovery of novel therapeutic targets is urgently needed. Recent studies indicate that immune cells play a critical role in regulating fibrosis. In this Mini Review, we discuss the recent literature focused on cells of the myeloid lineage that serve as key agents of pathologic interorgan communication in fibrosis. These cells are recruited from the bone marrow and have been found to be key drivers of the fibrotic process in the lung.

PMID:39980172 | DOI:10.1002/cph4.70004

Digit Health. 2025 Feb 19;11:20552076241288724. doi: 10.1177/20552076241288724. eCollection 2025 Jan-Dec.

ABSTRACT

Digital biomarkers are quantifiable and objective indicators of a person's physiological function, behavioral state or treatment response, that can be captured using connected sensor technologies such as wearable devices and mobile apps. We envision that continuous and 24-h monitoring of the underlying physiological and behavioral processes through digital biomarkers can enhance early diagnostics, disease management, and self-care of cardiometabolic diseases. Cardiometabolic diseases, which include a combination of cardiovascular and metabolic disorders, represent an emerging global health threat. The prevention potential of cardiometabolic diseases is around 80%, indicating a promising role for interventions in the lifestyle and/or the environmental context. Disruption of sleep and circadian rhythms are increasingly recognized as risk factors for cardiometabolic disease. Digital biomarkers can be used to measure around the clock, that is, day and night, to quantify not only sleep patterns but also diurnal fluctuations of certain biomarkers and processes. In this way, digital biomarkers can support the delivery of optimal timed medical care. Night-time cardiometabolic patterns, such as blood pressure dipping, are predictive of cardiometabolic health outcomes. In addition, the sleep period provides an opportunity for digital cardiometabolic health monitoring with relatively low influence of artifacts, such as physical activity and eating. Digital biomarkers that utilize sleep as a window of health can be used during daily life to enable early diagnosis of cardiometabolic diseases, facilitate remote patient monitoring, and support self-management in people with cardiometabolic diseases. This review describes the influence of sleep and circadian rhythms on cardiometabolic disease and highlights the state-of-the-art sleep and circadian digital biomarkers which could be of benefit in the prevention of cardiometabolic disease.

PMID:39980570 | PMC:PMC11840856 | DOI:10.1177/20552076241288724

Rev Panam Salud Publica. 2025 Feb 20;49:e18. doi: 10.26633/RPSP.2025.18. eCollection 2025.

ABSTRACT

The Manual for Surveillance of Events Supposedly Attributable to Vaccination or Immunization in the Region of the Americas represented one of the first steps toward building the regional system for surveillance of events supposedly attributable to vaccination or immunization (ESAVIs) and adverse events of special interest (AESIs). This manual establishes that, after notification and investigation of an event, a national committee of experts should classify the event in accordance with the World Health Organization (WHO) causality classification. The Pan American Committee for Safe Vaccination (COPAVASE) was created in response to the introduction of the new COVID-19 vaccines to support causality analysis of complex regional ESAVI cases and to advise the Pan American Health Organization (PAHO) on strategies for developing safety information and implementing risk mitigation measures. As part of this work, two strategic planning exercises were carried out, one with Committee members and PAHO staff and another that included national authorities and committee members, who contributed ideas on how to strengthen the work both at the regional level and in countries' surveillance systems.Suggested areas of work included definition of clear guidelines, development of model terms of reference and case presentation guidelines, training, and strategies to ensure committee sustainability.With the strategies identified, PAHO expects to be able to continue strengthening national safe vaccination committees as key institutions for maintaining public trust.

PMID:39980595 | PMC:PMC11836906 | DOI:10.26633/RPSP.2025.18